Change detection (CD) in heterogeneous remote sensing images plays a crucial role in earth observation tasks, such as disaster monitoring and destruction assessment. Recent advancements in heterogeneous CD studies have substantially enhanced the capability to detect changes, but existing methodologies frequently lack effective control mechanisms for increasing false alarms when facing different heterogeneous scenes. Consequently, even with a high detection rate for changes, the real changes co-exist with lots of false alarms, thereby reducing the reliability and practical utility of the CD results. To address this issue, inspired by the insight of adaptive thresholding for false alarm control in constant false alarm rate (CFAR) detection, we propose a copula theory-based CD framework, named FAR-Aware-Copula-CD, to control false alarm rate (FAR) in heterogeneous CD. In the proposed FAR-Aware-Copula-CD, the heterogeneous CD problem is represented as a binary hypothesis testing problem. Then, the binary hypothesis testing problem is solved by a generalized likelihood ratio test based on copula theory, which effectively characterizes change statistics based on superpixel-level dependence within various heterogeneous image pairs. Finally, the decision thresholds of the copula-based change statistics are determined so as to satisfy the FAR constraint and ensure that the final CD result approaches a prespecified false alarm rate. Our FAR-Aware-Copula-CD provides a new approach for implementing controllable false alarms in heterogeneous CD tasks. Experimental results on four real-world datasets demonstrate the effectiveness of our proposed method.

The problem of scalar parameter estimation in a distributed wireless sensor network (WSN), in the presence of communication failures, is considered in this work. When sensors obtain measurements and attempt to transmit their measurements to the fusion center (FC) for parameter estimation, the transmissions to the FC may be unsuccessful due to various reasons such as poor communication channels, large distance between the sensors and FC or insufficient transmit power. To overcome the degradation of estimation performance due to missing data, we consider linear inter-sensor collaboration, where sensors exchange measurements with neighboring sensors, before transmitting to the FC. We consider two objectives: 1) maximize the estimation accuracy subject to collaboration power constraints and 2) minimize the collaboration power subject to the required estimation accuracy. We consider linear estimators for the parameter inference task, and propose methods for designing the collaboration scheme (collaboration weights). The performances of the estimators and collaboration design are compared using numerical results and simulations.

1-bit compressed sensing (1bCS) is a quantized signal acquisition technique to compress high-dimensional sparse signals. The goal is to design sensing matrices A ∈ ℝm×nwith the fewest possible rows that enable efficient and accurate recovery of sparse signals x ∈ ℝnfrom 1-bit measurements of the form sign(Ax). This work focuses on recovering the support of sparse signals from noisy 1-bit measurements, specifically in presence of adversarial noise. Existing methods handle random noise, or small number of adversarial sign flips. We demonstrate that exact support recovery is impossible when a constant fraction of measurements are affected by adversarial noise. Hence, we design sensing matrices that can reliably recover support in presence of (b, c)-burst noise, tolerating a constant fraction of errors concentrated in O(log m) blocks.

The time allocation problem in multi-function cognitive radar systems focuses on the trade-off between scanning for newly emerging targets and tracking the previously detected targets. We formulate this as a multi-objective optimization problem and employ deep reinforcement learning to find Pareto-optimal solutions and compare deep deterministic policy gradient (DDPG) and soft actor-critic (SAC) algorithms. Our results demonstrate the effectiveness of both algorithms in adapting to various scenarios, with SAC showing improved stability and sample efficiency compared to DDPG. We further employ the NSGA-II algorithm to estimate an upper bound on the Pareto front of the considered problem. This work contributes to the development of more efficient and adaptive cognitive radar systems capable of balancing multiple competing objectives in dynamic environments.

This paper investigates the integration of Internet of Things (IoT) networks with modern massive multiple-input multiple-output (MIMO) wireless systems to enable various new use cases. Given the dynamic nature of parameters monitored by IoT nodes, efficient techniques for tracking these time-varying parameters are required. In a typical IoT networks, each IoT node linearly precodes its observations and transmits them over a coherent multiple access channel to a fusion center (FC). These IoT networks are power and bandwidth constrained in nature. Therefore, designing transmit precoders for the IoT nodes and a combiner for the FC is essential. This work proposes online linear receive combiner and transmit precoder designs that minimize the mean square error (MSE) under transmit power constraints. Using an alternating optimization technique, we derive closed-form solutions for the combiners and transmit precoders. Our numerical results validate the effectiveness of the proposed algorithms.

Phased array radar (PAR) is shown to provide an enhanced performance for target tracking due to its beam agility and ability for time resource allocation. Existing algorithms for PAR resource allocation often consider standard and simplified dynamic models for target motion, which are not suitable for complex scenarios such as for ballistic target tracking. To expand the applications of resource allocation algorithms in this context, this paper proposes a robust dwell time allocation algorithm for multiple ballistic reentry target (BRT) tracking in PAR. We aim to achieve an online dwell time allocation by establishing and solving optimization problems at each tracking interval. The motion of BRT is characterized by using the flat earth model, and the predicted Cramér-Rao lower bound (PCRLB) for BRT is derived to quantify the performance of tracking. By minimizing the weighted sum of time consumption of the PAR and cost of attaining the desired performance, a robust dwell time allocation approach is established. Finally, the superiority of the proposed algorithm is demonstrated through simulation.

Change detection (CD) in heterogeneous remote sensing images has been widely used for disaster monitoring and land-use management. In the past decade, the heterogeneous CD problem has significantly benefited from the development of deep neural networks (DNNs). However, the purely data-driven DNNs perform like a black box where the lack of interpretability limits the trustworthiness and controllability of DNNs in most practical CD applications. As a powerful knowledge-driven tool, copula theory performs well in modeling dependence among random variables. To enhance the interpretability of existing neural networks for heterogeneous CD, we propose a knowledge-data-driven heterogeneous CD method based on a copula-guided neural network, named NN-Copula-CD. In our NN-Copula-CD, the mathematical characteristics of copula are employed as the loss functions to supervise a neural network to learn the dependence between bi-temporal heterogeneous superpixel pairs, and then the changed regions are identified via binary classification based on the degrees of dependence of all the superpixel pairs in the bi-temporal images. We conduct in-depth experiments on four datasets with heterogeneous images, including synthetic aperture radar (SAR), multispectral, and near-infrared images, where quantitative and visual results demonstrate the effectiveness and interpretability of our proposed NN-Copula-CD method.

In this work, we present distributed clustering algorithms that can handle large-scale data across multiple machines in the presence of faulty machines. These faulty machines can either be straggling machines that fail to respond within a stipulated time or Byzantines that send arbitrary responses. We propose redundant data assignment schemes that enable us to obtain clustering solutions based on the entire dataset, even when some machines are stragglers or adversarial in nature. Our proposed robust clustering algorithms generate a constant factor approximate solution in the presence of stragglers or Byzantines. We also provide various constructions of the data assignment scheme that provide resilience against a large fraction of faulty machines. Simulation results show that the distributed algorithms based on the proposed assignment scheme provide good-quality solutions for a variety of clustering problems.

This paper introduces a representative-based approach for distributed learning that transforms multiple raw data points into a virtual representation. Unlike traditional distributed learning methods such as Federated Learning, which do not offer human interpretability, our method makes complex machine learning processes accessible and comprehensible. It achieves this by condensing extensive datasets into digestible formats, thus fostering intuitive human-machine interactions. Additionally, this approach maintains privacy and communication efficiency, and it matches the training performance of models using raw data. Simulation results show that our approach is competitive with or outperforms traditional Federated Learning in accuracy and convergence, especially in scenarios with complex models and a higher number of clients. This framework marks a step forward in integrating human intuition with machine intelligence, which potentially enhances human-machine learning interfaces and collaborative efforts.

This article considers the Wasserstein barycentric coordinates problem for Gaussian distributions which is the inverse problem of the Wasserstein barycenter problem. These coordinates take into account the underlying geometry of the measure space of Gaussian distributions and are thus meaningful for applications such as diffusion analysis and distributed information fusion. When the probability supports are discrete and identical, the theory of Wasserstein barycentric coordinates is well developed. However, for general probability distributions, the computation of Wasserstein barycentric coordinates is intractable since the technical hurdles involve solving a non-convex and non-concave optimization problem. For Gaussian distributions, we derive the closed-form expression of the derivatives for the objective function and propose a projected gradient descent method to solve the problem. Finally, we illustrate its application in diffusion tensor image (DTI) denoising including simulated DTI with different noise levels and DTI of the human brain.

Traditional centralized direct localization methods require the transmission of the complete baseband signal to the fusion center (FC) for target localization. Due to the limited communication bandwidth as well as energy required in transmission, this centralized framework is not suitable for largescale sensor networks. This paper proposes an information-driven decentralized direct localization framework. Firstly, a maximum-likelihood position estimator, based on the Gauss-Newton method, is derived. Then, a decentralized implementation framework is constructed. At its core, there is no dedicated FC while the sensors transmit information to their neighboring nodes only through single hops, achieving target localization through iterative processes based on the concept of consensus. Simulation results confirm the stability and robustness of the proposed method in different scenarios.

This paper addresses the localization challenge in cooperative multi-agent wireless sensor networks, specifically focusing on range-based localization. To enhance robustness against outliers in range measurements, we employ the Huber function, leading to the formulation of a robust yet nonconvex optimization problem with coupled agent variables. Confronted with this nonconvex optimization challenge, particularly in largescale networks, we reformulate the problem using Lagrange duality and conjugate theory. This restructuring yields subproblems characterized by smooth strong convexity for dual variables and a simplified form for primal variables, thereby facilitating an efficient solution. Building upon this reformulation, we introduce a novel distributed primal-dual algorithm that employs coordinate descent and proximal minimization techniques within an iterative framework. This approach furnishes closed-form solutions for both primal and dual variables. Theoretically, our method ensures not only the convergence of the sequence of objective function values but also, by leveraging the KurdykaŁojasiewicz property, we establish the guaranteed global convergence of the location estimates sequence to a critical point of the original objective function. Notably, our proposed approach exhibits lower computational complexity, communication cost, and storage space compared to existing methods. Numerical experiments underscore the superiority of the proposed method in terms of robustness and localization accuracy when compared to the other methods in the literature.

This work considers an IoT network comprising of several IoT sensor nodes (SNs), a passive intelligent reflecting surface (IRS), and a fusion center (FC). Each IoT SN observes multiple physical phenomena, and transmits its observations to the FC for post processing. This necessitates the need for efficient preprocessing of each SN’s observations to combat wireless fading effects and optimize transmit power utilization. In this context, this paper presents a novel approach that jointly designs the transmit precoding matrix (TPM) for IoT SNs and optimizes the phase reflection matrix (PRM) for the IRS. The resulting non-convex optimization problem is tackled through an alternating optimization framework, where the individual TPM and PRM design subproblems are further addressed using the majorization minimization (MM) framework. Notably, the proposed solution yields closed-form expressions for TPM and PRM in each MM iteration, making it particularly suitable for low-cost IoT SNs. Numerical results demonstrate the efficacy of the proposed approach by showcasing significant enhancements in estimation performance compared to IoT networks lacking an IRS component.

In this paper, scanning and multi-target tracking in a radar system are considered, and adaptive radar resource management is analyzed. In particular, time management in radar scanning and tracking of multiple maneuvering targets subject to budget constraints is studied with the goal to jointly maximize the tracking and scanning performances of a cognitive radar. The constrained optimization of the dwell time allocation to each target is addressed via deep Q-network (DQN) based reinforcement learning. In the proposed constrained deep reinforcement learning (CDRL) algorithm, both the parameters of the DQN and the dual variable are learned simultaneously. Numerical results show that radar can autonomously allocate more time to the tracking task that requires greater attention while providing time for scanning and also constraining the total time budget below the predefined threshold.

This article presents a star-of-star topology for Internet of Things (IoT) networks using mega low-Earth-orbit constellations. The proposed topology enables IoT users to broadcast their sensed data to multiple satellites simultaneously over a shared channel, which is then relayed to the ground station (GS) using amplify-and-forward relaying. The GS coherently combines the signals from multiple satellites using maximal ratio combining. To analyze the performance of the proposed topology in the presence of interference, a comprehensive outage probability (OP) analysis is performed, assuming imperfect channel state information at the GS. This article employs stochastic geometry to model the random locations of satellites, making the analysis general and independent of any specific constellation. Furthermore, this article examines successive interference cancellation (SIC) and capture model (CM)-based decoding schemes at the GS to mitigate interference. The average OP for the CM-based scheme and the OP of the best user for the SIC scheme are derived analytically. This article also presents simplified expressions for the OP under a high signal-to-noise ratio (SNR) assumption, which are utilized to optimize the system parameters for achieving a target OP. The simulation results are consistent with the analytical expressions and provide insights into the impact of various system parameters, such as mask angle, altitude, number of satellites, and decoding order. The findings of this study demonstrate that the proposed topology can effectively leverage the benefits of multiple satellites to achieve the desired OP and enable burst transmissions without coordination among IoT users, making it an attractive choice for satellite-based IoT networks.

In this work, we consider a binary hypothesis testing problem involving human decision-makers. Due to the nature of human behavior, human decision-makers observe the phenomenon of interest sequentially up to a random length of time. The humans use a belief model to accumulate the log-likelihood ratios until they cease observing the phenomenon. The belief model is used to characterize the perception of the human decision-maker towards observations at different instants of time, i.e., some decision-makers may assign greater importance to observations that were observed earlier, rather than later and vice-versa. We further consider the performance of a group of humans using a global decision-maker that fuses human decisions using the Chair-Varshney rule. When the number of observations that were used by the humans to arrive at their respective decisions are available to the fusion center (FC), the weights in the Chair-Varshney rule are modified to include this information in the decision fusion rule. Numerical and simulation results are presented to corroborate and validate theoretical results.

Gibbs sampling is one of the most popular Markov chain Monte Carlo algorithms because of its simplicity, scalability, and wide applicability within many fields of statistics, science, and engineering. In the labeled random finite sets literature, Gibbs sampling procedures have recently been applied to efficiently truncate the single-sensor and multi-sensor $\delta$-generalized labeled multi-Bernoulli posterior density as well as the multi-sensor adaptive labeled multi-Bernoulli birth distribution. However, only a limited discussion has been provided regarding key Gibbs sampler architecture details including the Markov chain Monte Carlo sample generation technique and early termination criteria. This paper begins with a brief background on Markov chain Monte Carlo methods and a review of the Gibbs sampler implementations proposed for labeled random finite sets filters. Next, we propose a short chain, multi-simulation sample generation technique that is well suited for these applications and enables a parallel processing implementation. Additionally, we present two heuristic early termination criteria that achieve similar sampling performance with substantially fewer Markov chain observations. Finally, the benefits of the proposed Gibbs samplers are demonstrated via two Monte Carlo simulations.

1-bit compressed sensing (1bCS) is a quantized signal acquisition technique to compress high-dimensional sparse signals. The goal in 1bCS is to design sensing matrices A ∈ ℝm×nwith the fewest possible rows that enable efficient and accurate recovery of sparse signals x ∈ ℝnfrom 1-bit measurements of the form sign(Ax). In this work, we leverage the locality in sparsity patterns observed in many real-world datasets to recover the support of signals exhibiting this sparsity pattern. Our results improve the existing bounds on the number of measurements sufficient for support recovery when the non-zero entries of a signal occur within small local neighborhoods.

In distributed detection systems, energy-efficient ordered transmission (EEOT) schemes are able to reduce the number of transmissions required to make a final decision. In this work, we investigate the effect of data falsification attacks on the performance of EEOT-based systems. We derive the probability of error for an EEOT-based system under attack and find an upper bound (UB) on the expected number of transmissions required to make the final decision. Moreover, we tighten this UB by solving an optimization problem via integer programming (IP). We also obtain the FC's optimal threshold which guarantees the optimal detection performance of the EEOT-based system. Numerical and simulation results indicate that it is possible to reduce transmissions while still ensuring the quality of the decision with an appropriately designed threshold.

This paper considers the problem of change detection (CD) with multi-sensor optical remote sensing (RS) images. Copulas are adopted to characterize the dependence structure between the image pair. For this problem, a conditional copula-based CD technique has been proposed in the literature. However, in this technique, it is difficult to select the best copula function in an analytical framework. Resulting copula misspecification may lead to performance degradation. To deal with this problem, we model the CD problem as a binary hypothesis testing problem and propose a new superpixel-level copula-based statistical method (SCOPS) for CD, where an explicit strategy for copula selection is provided for the proposed method. The effectiveness of the copula selection strategy is verified on CD tasks with simulated multi-sensor optical RS images. Experiments on real RS datasets demonstrate the superiority of SCOPS over the state-of-the-art methods.

In this paper, a direct target localization algorithm with quantized measurements for non-coherent MIMO systems is proposed. In this system, each receiver transmits a low-bit quantized version of the echo rather than the full raw data to the fusion center. We construct a joint likelihood function based on the low-bit data of each receiver, from which the unknown target position can be directly determined. The Cramer-Rao lower bound (CRLB) is derived to analyze the localization performance of our proposed algorithm. To maximize the localization performance, a CRLB-based objective function is designed to obtain the optimum quantization thresholds. The formulated problem is a high-dimensional and non-convex optimization problem that when solved, determines two types of coupled parameters for the quantization thresholds and the complex-valued scaling coefficients of the signal. We propose a batch gradient descent embedded particle swarm optimization algorithm to solve this problem effectively. Numerical results show that the proposed algorithm delivers superior performance in terms of maximizing the overall localization performance, and the 3-bit quantized algorithm is able to provide performance that is very close to the unquantized algorithm. Experimental data recorded by three small radars are also provided to demonstrate the effectiveness of the proposed algorithm.

This paper considers the problem of distributed detection in the presence of a Sybil attack where a malicious sensor node can send multiple falsified decisions using multiple fake identities to a Fusion Center (FC) to degrade its decision-making performance. We study the problem under the Neyman-Pearson (NP) setup. We find that, due to the Sybil attack, the decisions received at the FC become correlated and that the degree of correlation is dependent on the number of fake identities used. The paper characterizes the optimal Sybil attack that blinds the FC, i.e., makes the FC incapable of making an informed decision. We find that if the sum of the local detection and false alarm probabilities of the sensor nodes is 1, the FC can be made blind when at least 50% of the decisions are sent using fake identities. However, if this condition is not met, then all decisions would have to be sent using fake identities in order to blind the FC. The paper also investigates strategic interactions between the FC and the Sybil attacker using Game Theory and proves the existence of a Nash Equilibrium (NE). Numerical results are presented to gain important insights.

In critical environments that require a high accuracy of decisions, utilizing human cognitive strengths and expertise in addition to machine observations is advantageous to improve decision quality and enhance situational awareness. While the current literature on human decision making is primarily based on the paradigm of perfect rationality, humans are subject to decision noise and employ stochastic choice rules. Human decision making under such realistic environments needs to be further studied. In this paper, instead of assuming that a human selects the optimal action with probability one, we employ a bounded rationality choice model where all the actions are candidates for selection, but better options are chosen with higher probabilities. In a Bayesian hypothesis testing framework, we evaluate the individual decision making performance when humans have different degrees of bounded rationality. Furthermore, we analyze the decision fusion rule for a team of two human agents and characterize the asymptotic performance of collaborative decision making as the number of human participants becomes large.

In this paper, we consider nonconvex minimax optimization, which is gaining prominence in many modern machine learning applications, such as GANs. Large-scale edge-based collection of training data in these applications calls for communication-efficient distributed optimization algorithms, such as those used in federated learning, to process the data. In this paper, we analyze local stochastic gradient descent ascent (SGDA), the local-update version of the SGDA algorithm. SGDA is the core algorithm used in minimax optimization, but it is not well-understood in a distributed setting. We prove that Local SGDA has order-optimal sample complexity for several classes of nonconvex-concave and nonconvex-nonconcave minimax problems, and also enjoys linear speedup with respect to the number of clients. We provide a novel and tighter analysis, which improves the convergence and communication guarantees in the existing literature. For nonconvex-PL and nonconvex-one-point-concave functions, we improve the existing complexity results for centralized minimax problems. Furthermore, we propose a momentum-based local-update algorithm, which has the same convergence guarantees, but outperforms Local SGDA as demonstrated in our experiments.

We consider a set of data samples such that a fraction of the samples are arbitrary outliers, and the rest are the output samples of a single-layer neural network with rectified linear unit (ReLU) activation. Our goal is to estimate the parameters (weight matrix and bias vector) of the neural network, assuming the bias vector to be non-negative. We estimate the network parameters using the gradient descent algorithm combined with either the median- or trimmed mean-based filters to mitigate the effect of the arbitrary outliers. We then prove that $\tilde{O}\left( \frac{1}{p^2}+\frac{1}{\epsilon^2p}\right)$ samples and $\tilde{O}\left( \frac{d^2}{p^2}+ \frac{d^2}{\epsilon^2p}\right)$ time are sufficient for our algorithm to estimate the neural network parameters within an error of $\epsilon$ when the outlier probability is $1-p$, where $2/3 DOI 10.52202/068431-0796

This article proposes an optimal scheduling policy for a system where spatiotemporally dependent sensor observations are broadcast to remote estimators over a resource-limited broadcast channel. We consider a system with a measurement-blind network scheduler that transmits observations, and design scheduling schemes that minimize mean squared error (MSE) by determining a subset of sensor observations to be broadcast based on their information freshness, as measured by their Age of Information (AoI). By modeling the problem as a finite state-space Markov decision process (MDP), we derive an optimal scheduling policy, with AoI as a state variable, minimizing the average MSE for an infinite time horizon. The resulting policy has a periodic pattern that renders an efficient implementation with low data storage. We further show that for any policy that minimizes the overall AoI, the estimation accuracy depends on how the scheduling order relates to the sensor’s intrinsic spatial correlation. Consequently, the estimation accuracy varies from worse than a randomized scheduling approach to near optimal. Thus, we present an additional age-minimizing policy with optimal scheduling order. We also present alternative policies for large state spaces that are attainable with less computational effort. Numerical results validate the presented theory.

In this article, we investigate the problem of decentralized federated learning (DFL) in Internet of Things (IoT) systems, where a number of IoT clients train models collectively for a common task without sharing their private training data in the absence of a central server. Most of the existing DFL schemes are composed of two alternating steps, i.e., model updating and model averaging. However, averaging model parameters directly to fuse different models at the local clients suffers from client-drift, especially when the training data are heterogeneous across different clients. This leads to slow convergence and degraded learning performance. As a possible solution, we propose the DFL via a mutual knowledge transfer (Def-KT) algorithm, where local clients fuse models by transferring their learned knowledge to each other. Our experiments on the MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100 data sets reveal that the proposed Def-KT algorithm significantly outperforms the baseline DFL methods with model averaging, i.e., Combo and FullAvg, especially when the training data are not independent and identically distributed (non-IID) across different clients.

In this article, we consider the problem of federated learning (FL) with training data that are non independent and identically distributed (non-IID) across the clients. To cope with data heterogeneity, an iterative federated clustering algorithm (IFCA) has been proposed. IFCA partitions the clients into a number of clusters and lets the clients in the same cluster optimize a shared model. However, in IFCA, the clusters are nonoverlapping, which leads to an inefficient utilization of the local information since the knowledge of a client is used by only one cluster during each round. To capture the complex nature of real-world data, soft clustering methods with overlapping clusters have been proposed that attain superior performance over the hard ones. Motivated by this, we propose a new algorithm named FL with soft clustering (FLSC) by combining the strengths of soft clustering and IFCA, where the clients are partitioned into overlapping clusters and the information of each participating client is used by multiple clusters simultaneously during each round. The experimental results show that FLSC achieves better learning performance on the classification tasks on the MNIST and Fashion-MNIST data sets, compared with the state-of-the-art baseline methods, i.e., the global model method and IFCA.

In this article, the problem of federated learning (FL) in the presence of label quality disparity is considered. To address this problem, the federated opportunistic computing for ubiquitous system (FOCUS) has been proposed very recently. In FOCUS, the central server utilizes its accurately labeled benchmark samples to quantify the credibility of different clients by computing the cross-entropy (CE) loss of the locally updated models on the benchmark data set and the CE loss of the global model on the local data sets. However, FOCUS assumes the availability of the accurate labels of the benchmark data set, which is difficult to guarantee under many practical scenarios. To overcome this limitation of FOCUS, we propose a new algorithm named robust federated opportunistic learning (RFOL), which does not require the benchmark samples at the central server to be labeled. In RFOL, the client credibility is evaluated by computing the Kullback–Leibler (KL) divergence among the soft predictions on the benchmark samples of different locally updated models and the CE loss of the global model on the local data sets. The experimental results on several popular data sets reveal that: 1) with an unlabeled benchmark data set at the server, the proposed RFOL algorithm attains almost the same learning performance as FOCUS, which requires an accurately labeled benchmark data set at the server; 2) with an inaccurately labeled benchmark data set, RFOL outperforms FOCUS, which shows that the former is more robust to the inaccurate labels of the benchmark samples; and 3) RFOL outperforms FedAvg, which assigns equal credibility to all the clients.

While there has been extensive work on modeling of human decision-making both for individuals and groups from a cognitive psychology point of view, research on this topic from a signal processing and information fusion perspective is relatively recent. In this work, we consider a distributed detection problem consisting of a number of human local decision makers and a fusion center (FC). Signal detection theory is exploited to answer why promoting heterogeneity could improve the performance of collaborative human decision-making. We consider the following two scenarios: 1) the local decision makers are independent and the level of heterogeneity is measured in terms of the variability of human expertise and 2) humans make correlated local decisions due to their perceptual and behavioral similarities and heterogeneity is measured by the amount of correlation. In both cases, we show that the detection performance of the FC can be improved with the increase of heterogeneity. In particular, in the second scenario, we develop a portfolio theory-based framework to select participants from correlated human agents so that heterogeneity is enhanced resulting in improved decision-making performance. Simulations are provided for illustration and performance comparison.

We address the problem of monitoring a set of binary stochastic processes and generating an alert when the number of anomalies among them exceeds a threshold. For this, the decision-maker selects and probes a subset of the processes to obtain noisy estimates of their states (normal or anomalous). Based on the received observations, the decision-maker first determines whether to declare that the number of anomalies has exceeded the threshold or to continue taking observations. When the decision is to continue, it then decides whether to collect observations at the next time instant or defer it to a later time. If it chooses to collect observations, it further determines the subset of processes to be probed. To devise this three-step sequential decision-making process, we use a Bayesian formulation wherein we learn the posterior probability on the states of the processes. Using the posterior probability, we construct a Markov decision process and solve it using deep actor-critic reinforcement learning. Via numerical experiments, we demonstrate the superior performance of our algorithm compared to the traditional model-based algorithms.

Unlike decision making systems made up of physical sensors where the system parameters are known a priori and can be controlled at will, human behavior in decision making is complex and uncertain. The objective of this work is to study how humans make decisions based on internal and external sources of information under cognitive memory limitations. Due to constrained capacity of working memory, humans are known to perform cognitive tasks and update their beliefs in a sequential manner rather than in parallel. In a Bayesian hypothesis testing framework, we derive the metrics for performance evaluation and comparison when the humans use different ordering of information for processing and to update their beliefs. We show that an appropriate order of information sources can help a cognitive memory limited human make better decisions. Simulations are presented to corroborate the theoretical results.

In this paper, we address the problem of one-bit compressed sensing using the data-driven deep learning approach. Our approach uses an untrained neural network to reconstruct sparse vectors from their one-bit measurements. We define a new cost function using the untrained network, which maximizes the consistency between one-bit measurements and the corresponding linear measurements. The resulting optimization problem is solved using the projected gradient descent scheme and the backpropagation method. Our algorithm offers superior empirical performance compared to the existing model-based algorithms. Also, unlike the other deep learning-based algorithms that use learned generative priors, our algorithm does not require a large training set. Further, we empirically show that the proposed algorithm exhibits performance that is comparable to the learned generative network-based method.

In this paper, the optimal jamming strategy by an adversary in distributed detection networks is investigated. By utilizing the game-theoretical framework to characterize the interaction between the fusion center (FC) and the jammer as a repeated game, we examine how the behavior of the FC changes with jammer’s strategy. Based on simulation results, we find that the ‘Evenly distributed’ strategy is not always optimal for the jammer. Instead, under certain conditions, the ‘Betting on one channel’ jamming strategy is the best strategy for the jammer.

In this paper, we present robust variants of distributed clustering algorithms for large datasets distributed across multiple machines in the presence of Byzantines. We propose a redundant data assignment scheme that enables us to obtain global information about the entire dataset for clustering purposes even when some machines are adversarial in nature. Simulation results show that the distributed algorithms based on the proposed assignment scheme provide good-quality solutions for a variety of clustering problems.

Federated Learning (FL) refers to the paradigm where multiple worker nodes (WNs) build a joint model by using local data. Despite extensive research, for a generic non-convex FL problem, it is not clear, how to choose the WNs' and the server's update directions, the minibatch sizes, and the local update frequency, so that the WNs use the minimum number of samples and communication rounds to achieve the desired solution. This work addresses the above question and considers a class of stochastic algorithms where the WNs perform a few local updates before communication. We show that when both the WN's and the server's directions are chosen based on certain stochastic momentum estimator, the algorithm requires $\tilde{\mathcal{O}}(\epsilon^{-3/2})$ samples and $\tilde{\mathcal{O}}(\epsilon^{-1})$ communication rounds to compute an $\epsilon$-stationary solution. To the best of our knowledge, this is the first FL algorithm that achieves such {\it near-optimal} sample and communication complexities simultaneously. Further, we show that there is a trade-off curve between local update frequencies and local minibatch sizes, on which the above sample and communication complexities can be maintained. {Finally, we show that for the classical FedAvg (a.k.a. Local SGD, which is a momentum-less special case of the STEM), a similar trade-off curve exists, albeit with worse sample and communication complexities. Our insights on this trade-off provides guidelines for choosing the four important design elements for FL algorithms, the update frequency, directions, and minibatch sizes to achieve the best performance.}

In this article, we address the problem of distributed detection of a noncooperative (unknown emitted signal) target with a wireless sensor network. When the target is present, sensors observe a (unknown) deterministic signal with attenuation depending on the unknown distance between the sensor and the target, multiplicative fading, and additive Gaussian noise. To model energy-constrained operations within Internet of Things, one-bit sensor measurement quantization is employed and two strategies for quantization are investigated. The fusion center receives sensor bits via noisy binary symmetric channels and provides a more accurate global inference. Such a model leads to a test with nuisances (i.e., the target positionxT) observable only underH1hypothesis. Davies' framework is exploited herein to design the generalized forms of Rao and locally optimum detection (LOD) tests. For our generalized Rao and LOD approaches, a heuristic approach for threshold optimization is also proposed. The simulation results confirm the promising performance of our proposed approaches.

We study the problem of a sensor performing inference tasks based on the utility theory, where the objective is to derive the optimal resource usage amount that maximizes a profit-cost-based utility function. Furthermore, to enable the concept ofsensing as a servicein the context of IoT systems, we present a market-based paradigm, where there is a “buyer” interested in buying the inference result from the sensor. We jointly optimize the resource usage policy and payment negotiation strategy for the sensor so as to maximize the expected profit. Optimal payment negotiation is analyzed in two situations, namely, when the sensor spends a fixed amount of resource, as well as when the sensor could vary the amount of resource consumption to maximize profit. It is shown that in the presence of the buyer, the optimal amount of resource consumption increases and, hence, the inference accuracy improves. Finally, we present some discussions on how energy efficiency affects the behavior of energy consumption in realistic environments. Simulation results are provided to illustrate the performance of our approach.

In this article, we investigate the problem of federated learning (FL) in a communication-constrained environment of the Internet of Things (IoT), where multiple IoT clients train a global model collectively by communicating model updates with a central server instead of sending raw data sets. To ease the communication burden in IoT systems, several approaches have been proposed for the FL tasks, including sparsification methods and data quantization strategies. To overcome the shortcomings of the existing methods, we propose two new FL algorithms based on compressed sensing (CS) referred to as the CS-FL algorithm and the 1-bit CS-FL algorithm, both of which compress the upstream and downstream data while communicating between the clients and the central server. The proposed algorithms improve upon the existing algorithms by letting the clients send analog and 1-bit data, respectively, to the server after compression with a random measurement matrix. Based on that, in CS-FL and 1-bit CS-FL, the clients update the model locally utilizing the result of sparse reconstruction obtained by iterative hard thresholding (IHT) and binary IHT (BIHT), respectively. Experiments conducted on the MNIST and the Fashion-MNIST data sets reveal the superiority of the proposed algorithm over the baseline algorithms, SignSGD with a majority vote, FL based on sparse ternary compression, and FedAvg.

In this work, we propose a joint collaboration-compression framework for the random signal detection problem in a resource constrained wireless sensor network (WSN). Specifically, we propose a framework where the local sensors first collaborate (via a linear collaboration matrix) with each other. Then a subset of sensors linearly compress their aggregated information before communicating with the fusion center (FC). We propose a novel metric called generalized deflection coefficient (GDC) for evaluating the detection performance which is shown to be tightly upper bounded by the Kullback-Leibler divergence for Gaussian observations. We jointly design the linear collaboration and compression strategies under power constraints via alternating maximization of the proposed GDC metric. Finally, numerical results are provided to demonstrate the effectiveness of the proposed framework.

In this paper, we address the anomaly detection problem where the objective is to find the anomalous processes among a given set of processes. To this end, the decision-making agent probes a subset of processes at every time instant and obtains a potentially erroneous estimate of the binary variable which indicates whether or not the corresponding process is anomalous. The agent continues to probe the processes until it obtains a sufficient number of measurements to reliably identify the anomalous processes. In this context, we develop a sequential selection algorithm that decides which processes to be probed at every instant to detect the anomalies with an accuracy exceeding a desired value while minimizing the delay in making the decision and the total number of measurements taken. Our algorithm is based on active inference which is a general framework to make sequential decisions in order to maximize the notion of free energy. We define the free energy using the objectives of the selection policy and implement the active inference framework using a deep neural network approximation. Using numerical experiments, we compare our algorithm with the state-of-the-art method based on deep actor-critic reinforcement learning and demonstrate the superior performance of our algorithm.

This work considers the design of linear minimum mean square error (MMSE) precoders and combiners for the estimation of an unknown vector parameter in a coherent multiple access channel (MAC)-based multiple-input multiple-output (MIMO) wireless sensor network. The proposed designs that minimize the mean squared error (MSE) of the parameter estimate at the fusion center are based on majorization theory, which leads to non-iterative closed-form solutions for the precoders and combiners. Various scenarios are considered for parameter estimation such as networks with ideal high precision sensors as well as noisy non-ideal sensors. Moreover, inter parameter correlation is also incorporated, which makes the analysis comprehensive. The Bayesian Cramer-Rao bound (BCRB) and centralized MMSE bound are determined to characterize the estimation performance. Simulation results demonstrate the improved performance and also corroborate our analytical formulations.

In this paper, we present a new detector for the detection of sparse stochastic signals using 1-bit data in two-level two- degree tree-structured sensor networks (2L-2D TSNs). Related prior work mostly concentrates on parallel sensor networks (PSNs). However, PSNs may sometime become impractical in many applications including the case where some sensors are beyond the communication range of the fusion center (FC). Therefore, we design the proposed detector for 2L-2D TSNs where information is transmitted hierarchically. To satisfy severe resource constraints, each local sensor performs 1-bit quantization before transmission to the FC. The FC fuses the received 1-bit data employing the locally most powerful test (LMPT). It is shown theoretically and numerically that, compared with the LMPT detector with Q sensors that transmit analog measurements in 2L-2D TSNs, the proposed 1-bit LMPT detector that uses quantization thresholds derived in this paper asymptotically requires 1.74Q sensors to compensate for the performance loss induced by local quantization.

We consider the distributed stochastic optimization problem of minimizing a nonconvex function f in an adversarial setting. All the w worker nodes in the network are expected to send their stochastic gradient vectors to the fusion center (or server). However, some (at most α-fraction) of the nodes may be Byzantines, which may send arbitrary vectors instead. Vanilla implementation of distributed stochastic gradient descent (SGD) cannot handle such misbehavior from the nodes. We propose a robust variant of distributed SGD which is resilient to the presence of Byzantines. The fusion center employs a novel filtering rule that identifies and removes the Byzantine nodes. We show that T = Õ (1/wϵ2+ α2/ϵ2) iterations are needed to achieve an ϵ-approximate stationary point (x such that ∥∇f(x)∥2≤ ϵ) for the nonconvex learning problem. Unlike other existing approaches, the proposed algorithm is independent of the problem dimension.

In this paper, we address the classical problem of one-bit compressed sensing. We present a deep learning based reconstruction algorithm that relies on a generative model. The generator which is a neural network, learns a mapping from a low dimensional space to a higher dimensional set comprising of sparse vectors. This pre-trained generator is used to reconstruct sparse vectors from their one-bit measurements by searching over the range of the generator. Hence, the algorithm presented in this paper provides excellent reconstruction accuracy by accounting for any other possible structure in the signal apart from sparsity. Further, we provide theoretical guarantees on the reconstruction accuracy of the presented algorithm. Using numerical results, we also demonstrate the efficacy of our algorithm compared to other existing algorithms.

We investigate the sparse activity detection problem in cell-free massive multiple-input multiple-output (MIMO) systems in this paper. With the approximate message passing (AMP) algorithm, the received pilot signals at the access points (APs) are decomposed into independent circularly symmetric complex Gaussian noise corrupted components. By using the minimum mean-squared error (MMSE) denoiser during the AMP procedure, we obtain a threshold detection rule, and analytically describe the noise covariance matrix of the corrupted components via the state evolution equations, which is helpful for the performance analysis of the detection rule. Using the law of large numbers, it can be shown that the error probability of this threshold detection rule tends to zero when the number of APs, pilots and users tend to infinity while the ratio of the number of pilots and users is kept constant. Numerical results show that the error probability decreases while the number of APs increases, corroborating our theoretical analysis. In addition, we investigate the relationship between the error probability of the threshold detection rule and the number of symbols used for pilot transmissions during each channel coherence interval via numerical results.

In this letter, we consider the problem of distributed detection of stochastic sparse signals in battery-powered sensor networks (SNs). For this problem, an original locally most powerful test (oLMPT) detector has previously been developed, where compressed measurements are collected from all local sensors and then fused at the fusion center (FC) for making the global decision. However, since the sensors always operate on limited energy resources, allowing all the nodes to send their observations to the FC all the time exerts tremendous pressure on their energy consumption and hinders the longevity of the sensors. To solve this problem, we propose a new censoring LMPT (cen-LMPT) detector by combining the strengths of censoring strategy and the oLMPT detector, where sensors are designated to merely send observations deemed informative enough so as to utilize the local energy more efficiently, and the FC still makes the global decision based on LMPT. We analytically derive the relationship between the detection performance and the communication rate for the proposed detector. It is shown that, compared with the oLMPT detector, the proposed cen-LMPT detector with the same number of nodes can achieve almost the same detection performance with significantly lower communication rate and, therefore, much lower local energy consumption. The simulation results verify our theoretical findings.

In this paper, we focus on near-optimal designs for variable-length error-correcting (VLEC) codes, while considering the performance in terms of both the average codeword length (ACL) and the symbol-error rate (SER). The criteria for narrowing the search space and enhancing the SER performance are investigated. An efficient code construction algorithm is then devised based on a random search process. Taking advantage of the significantly reduced search complexity, we are able to construct near-optimal VLEC codes for large source alphabets. Numerical results obtained for various free distance values and alphabet sizes show that constructed VLEC codes have both reduced ACL values and enhanced SER performances. Performance improvement is more notable for large source alphabets.

Due to hardware limitations of the imaging sensors, it is challenging to acquire images of high resolution in both spatial and spectral domains. Fusing a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to obtain an HR-HSI in an unsupervised manner has drawn considerable attention. Though effective, most existing fusion methods are limited due to the use of linear parametric modeling for the spectral mixture process, and even the deep learning-based methods only focus on deterministic fully-connected networks without exploiting the spatial correlation and local spectral structures of the images. In this paper, we propose a novel variational probabilistic autoencoder framework implemented by convolutional neural networks, in order to fuse the spatial and spectral information contained in the LR-HSI and HR-MSI, called FusionNet. The FusionNet consists of a spectral generative network, a spatial-dependent prior network, and a spatial-spectral variational inference network, which are jointly optimized in an unsupervised manner, leading to an end-to-end fusion system. Further, for fast adaptation to different observation scenes, we give a meta-learning explanation to the fusion problem, and combine the FusionNet with meta-learning in a synergistic manner. Effectiveness and efficiency of the proposed method are evaluated based on several publicly available datasets, demonstrating that the proposed FusionNet outperforms the state-of-the-art fusion methods.

We consider the problem of sparse signal reconstruction from noisy 1-bit compressed measurements using a statistically dependent signal, as an aid. We assume that this signal does not share joint sparse representation with the sparse signal and call it a heterogeneous side-information. We assume that compressed measurements are corrupted by additive white Gaussian noise before quantization and sign-flip errors after quantization. We propose a generalized approximate message passing-based algorithm for signal reconstruction from noisy 1-bit compressed measurements which leverages the dependence between the signal and the heterogeneous side-information. We model the dependence between signal and heterogeneous side-information using copula functions and show, through numerical experiments, that the proposed algorithm yields a better reconstruction performance than 1-bit CS-based recovery algorithms that do not exploit the side-information.

Human behavior while decision making is quite complex and uncertain. There are fundamental differences between traditional decision making systems based on sensor data and systems where the agents in the decision making process include humans. The modeling and analysis of human-machine collaborative decision making has become an important research area due to the potential applications in a variety of complex autonomous systems. Incorporating human inputs with physical sensors can be advantageous in enhancing situational assessment for certain situations, and at the same time, brings in technical challenges such as how to characterize the human decision making behavior. In this paper, we discuss some aspects of human-machine networks by focusing on three schemes that include collaborative human decision making with random local thresholds, decision fusion in integrated human-machine networks and binary decision making under cognitive biases. In each case, we aim to optimize the system performance based on appropriate modeling of the human behavior. We also provide a summary of current challenges and research directions related to this problem domain.

In this letter, we consider the detection of sparse stochastic signals with sensor networks (SNs), where the fusion center (FC) collects 1-bit data from the local sensors and then performs global detection. For this problem, a newly developed 1-bit locally most powerful test (LMPT) detector requires 3.3Q sensors to asymptotically achieve the same detection performance as the centralized LMPT (cLMPT) detector with Q sensors. This 1-bit LMPT detector is based on 1-bit quantized observations without any additional processing at the local sensors. However, direct quantization of observations is not the most efficient processing strategy at the sensors since it incurs unnecessary information loss. In this letter, we propose an improved-1-bit LMPT (Im-1-bit LMPT) detector that fuses local 1-bit quantized likelihood ratios (LRs) instead of directly quantized local observations. In addition, we design the quantization thresholds at the local sensors to ensure asymptotically optimal detection performance of the proposed detector. It is shown theoretically and numerically that, with the designed quantization thresholds, the proposed Im-1-bit LMPT detector for the detection of sparse signals requires less number of sensor nodes to compensate for the performance loss caused by 1-bit quantization.

We consider the distributed detection of weak signals from one-bit measurements collected by a sensor network where observation model uncertainties exist at all the sensor nodes. To solve this problem, a one-bit locally most powerful test (LMPT) detector is proposed in this letter. Moreover, asymptotically optimal one-bit quantizers at all the sensor nodes are designed for the proposed one-bit LMPT detector. In this letter, model uncertainties are interpreted as multiplicative noise and its variance represents the strength of model uncertainties. Theoretical analysis indicates that, when the strength of model uncertainties is finite, the proposed detector using one-bit data with πN/2 sensors approximately achieves the same detection performance as the clairvoyant detector that directly uses analog measurements with N sensors. Simulation results corroborate our theoretical analysis and show that, compared to the one-bit generalized likelihood ratio test detector, the proposed one-bit LMPT detector provides better detection performance in the presence of model uncertainties.

Motivated by the numerous healthcare applications of molecular communication inside blood vessels of the human body, this paper considers multiple relay/cooperative nanomachine (CN)-assisted molecular communication between a source nanomachine (SN) and a destination nanomachine (DN) where each nanomachine is mobile in a diffusion-advection flow channel. Using the first hitting time model, the impact of the intermediate CNs on the performance of the aforementioned system with fully absorbing receivers is comprehensively analyzed taking into account the presence of various degrading factors, such as inter-symbol interference, multi-source interference, and counting errors. For this purpose, the optimal decision rules are derived for symbol detection at each of the CNs and the DN. Furthermore, closed-form expressions are derived for the probabilities of detection and false alarm at each CN and DN, along with the overall end-to-end probability of error and channel achievable rate for communication between the SN and DN. Simulation results are presented to corroborate the theoretical results derived and also to yield insights into the system performance under various mobility conditions.

This paper considers the design of an auction mechanism for target tracking via crowdsensing. We consider that the crowdsourcing framework consists of a set of sensors, which are embedded in devices belonging to crowd participants, and a fusion center (FC) that uses the quantized measurements from the sensors to track a target. The auction mechanism we develop addresses participatory concerns of the sensors that arise due to energy consumption associated with sensor participation while maximizing the utility of the FC to achieve desired sensing objectives and preventing market manipulations. Moreover, since a crowdsensing environment is typically resource-constrained, in our auction model, we consider that the sensors in the network quantize their private value estimates regarding their energy costs prior to communicating them to the FC. Furthermore, the paper also proposes the concept of selecting a subset of sensors (bidders) to bid (from a set of available sensors) to satisfy resource constraints during the bidding process. Extensive numerical results are provided to gain insights into the proposed mechanism.

In this paper, we present an efficient methodology to design precoders for distributed detection of unknown high dimensional signals. We consider a wireless sensor network, where several distributed sensors collaborate to perform binary hypothesis testing based on observations of an unknown high dimensional signal corrupted by noise. The sensors collect data over both temporal and spatial domains. Due to network resource constraints, each sensor performs a linear compression (through precoding) of the observed high dimensional signal at each time instant and forwards the compressed signal to the fusion center (FC). The FC then employs the generalized likelihood ratio test (GLRT) to make a decision on the presence or absence of the signal. We propose online linear precoding/compression strategies for such sensors that collect data over spatio-temporal domain, so that the detection performance at the FC is maximized under certain network resource constraints. Through the measure of non-centrality parameter and receiver operating characteristics (ROC), we show that our proposed precoder design achieves very good detection performance.

The multi-dimensional assignment problem, and by extension the problem of finding the T-best (i.e., the T most likely) multi-sensor assignments, represent the main challenges of centralized and especially distributed multi-sensor tracking. In this paper, we propose a distributed multi-target tracking filter based on the δ-Generalized Labeled Multi-Bernoulli (δ-GLMB) family of labeled random finite set densities. Consensus is reached for high-scoring multi-sensor assignments jointly across the network by employing the cross-entropy method in conjunction with average consensus. This ensures that multi-sensor information is jointly used to select high-scoring multi-assignments without exchanging the measurements across the network and without exploring all possible single-target multi-assignments. In contrast, tracking algorithms that rely on posterior fusion, i.e., merging local posteriors of neighboring nodes until convergence, are suboptimal due to the use of only local information to select the T-best local assignments in the construction of local posteriors. Numerical simulations showcase this performance improvement of the proposed method with respect to a posterior-fusion δ-GLMB filter.

This paper proposes an energy-efficient counting rule for distributed detection by ordering sensor transmissions in wireless sensor networks. In the counting rule-based detection in an N-sensor network, the local sensors transmit binary decisions to the fusion center, where the number of all N local-sensor detections are counted and compared to a threshold. In the ordering scheme, sensors transmit their unquantized statistics to the fusion center in a sequential manner; highly informative sensors enjoy higher priority for transmission. When sufficient evidence is collected at the fusion center for decision making, the transmissions from the sensors are stopped. The ordering scheme achieves the same error probability as the optimum unconstrained energy approach (which requires observations from all the N sensors) with far fewer sensor transmissions. The scheme proposed in this paper improves the energy efficiency of the counting rule detector by ordering the sensor transmissions: each sensor transmits at a time inversely proportional to a function of its observation. The resulting scheme combines the advantages offered by the counting rule (efficient utilization of the network's communication bandwidth, since the local decisions are transmitted in binary form to the fusion center) and ordering sensor transmissions (bandwidth efficiency, since the fusion center need not wait for all the N sensors to transmit their local decisions), thereby leading to significant energy savings. As a concrete example, the problem of target detection in large-scale wireless sensor networks is considered. Under certain conditions the ordering-based counting rule scheme achieves the same detection performance as that of the original counting rule detector with fewer than N/2 sensor transmissions; in some cases, the savings in transmission approaches (N-1).

Allowing humans to act as soft sensors is increasingly becoming an attractive solution to enhance decision making performance when the available physical (hard) sensors are limited. While the fusion problem with hard data has a rich history, fusion of hard and soft data requires further understanding due to human related factors associated with human sensor data. In this work, we investigate how the presence of human sensors can be modeled in the statistical signal processing framework and the factors that need to be taken into account when integrating soft human sensor data with hard data in a signal detection framework. We consider two cases. In the first case, both types of sensors are assumed to make threshold based individual decisions using identical observations. While physical sensors use a fixed threshold, the thresholds used by human sensors are assumed to be random variables. With a given distribution for the random thresholds used at the human sensors, by properly designing the thresholds at the physical sensors, an enhanced detection performance can be observed in the integrated system compared to performing fusion with only physical sensors. In the second case, we evaluate the fusion performance when human sensors possess some side information regarding the phenomenon in addition to the common observations available at the two types of sensors.

This paper presents a novel parallel platform, C-Storm (Copula-based Storm), for the computationally complex problem of fusion of heterogeneous data streams for inference. C-Storm is designed by marrying copula-based dependence modeling for highly accurate inference and a highly-regarded parallel computing platform Storm for fast stream data processing. C-Storm has the following desirable features: 1) C-Storm offers fast inference responses. 2) C-Storm provides high inference accuracies. 3) C-Storm is a general-purpose inference platform that can support data fusion applications. 4) C-Storm is easy to use and its users do not need to know deep knowledge of Storm or copula theory. We implemented C-Storm based on Apache Storm 1.0.2 and conducted extensive experiments using a typical data fusion application. Experimental results show that C-Storm offers a significant 4.7× speedup over a commonly used sequential baseline and higher degree of parallelism leads to better performance.

In this paper, we consider the problem of sparse signal detection with compressed measurements in a Bayesian framework. Multiple nodes in the network are assumed to observe sparse signals. Observations at each node are compressed via random projections and sent to a centralized fusion center. Motivated by the fact that reliable detection of the sparse signals does not require complete signal reconstruction, we propose two computationally efficient methods for constructing decision statistics for detection. First, using the Laplace prior directly to impose sparsity as widely considered in Bayesian Compressive Sensing (BCS), we develop an average likelihood ratio based detection method where the average is taken over the Laplace probability density function. Second, we exploit a three-stage hierarchical prior on the signal and construct decision statistics based on the noisy reconstruction (partial estimates) of the signals. Experimental results show that both average likelihood-based detection method and noisy-reconstruction based methods outperform most of the state-of-the-art algorithms.

Random distortion testing (RDT) addresses the problem of testing whether or not a random signal, Ξ, deviates by more than a specified tolerance, τ, from a fixed value, ξ0[1]. The test is nonparametric in the sense that the distribution of the signal under each hypothesis is assumed to be unknown. The signal is observed in independent and identically distributed (i.i.d) additive noise. The need to control the probabilities of false alarm and missed detection while reducing the number of samples required to make a decision leads to the SeqRDT approach. We show that under mild assumptions on the signal, SeqRDT will follow the properties desired by a sequential test. Simulations show that the SeqRDT approach leads to faster decision making compared to its fixed sam-ple counterpart Block-RDT [2] and is robust to model mismatches compared to the Sequential Probability Ratio Test (SPRT) [3] when the actual signal is a distorted version of the assumed signal especially at low Signal-to-Noise Ratios (SNRs).

We explore the design of an effective crowdsourcing system for an M -ary classification task. Crowd workers complete simple binary microtasks whose results are aggregated to give the final decision. We consider the scenario where the workers have a reject option so that they are allowed to skip microtasks when they are unable to or choose not to respond to binary microtasks. We present an aggregation approach using a weighted majority voting rule, where each worker's response is assigned an optimized weight to maximize crowd's classification performance.

The emerging paradigm of Human-Machine Inference Networks (HuMaINs) combines complementary cognitive strengths of humans and machines in an intelligent manner to tackle various inference tasks and achieves higher performance than either humans or machines by themselves. While inference performance optimization techniques for human-only or sensor-only networks are quite mature, HuMaINs require novel signal processing and machine learning solutions. In this paper, we present an overview of the HuMaINs architecture with a focus on three main issues that include architecture design, inference algorithms including security/privacy challenges, and application areas/use cases.

This paper studies clustering using a Kolmogorov-Smirnov based K-means algorithm. All data sequences are assumed to be generated by unknown continuous distributions. The pairwise KS distances of the distributions are assumed to be lower bounded by a certain positive constant. The convergence analysis of the proposed algorithms and upper bounds on the error probability are provided for both known and unknown number of clusters. More importantly, it is shown that the probability of error decays exponentially as the sample size of each data sequence goes to infinity, and the error exponent is only a function of the pairwise KS distances of the distributions. the analysis is validated by simulation results.

In this paper, we design and evaluate a practical AMC system that can be readily deployed to provide robust performance in various real-time commercial scenarios. Thus, our main goal is to develop a robust AMC algorithm with low computational complexity for easy implementation and practical deployment. To this end, we utilize recently revitalized machine learning based approaches used for various classification purposes. In our proposed AMC architecture, we first propose various statistics that serve as features of the AMC signals; next, we design an artificial neural network (ANN) based classifier that performs AMC over a wide range of SNRs. We employ Nesterov accelerated adaptive moment (NADAM) estimation technique to improve the classification performance of our ANN. Further, to establish the practical feasibility of our proposed architecture, we implement it on a SDR testbed. The proposed ANN-based classifier is shown to outperforms the hybrid hierarchical AMC (HH-AMC) system and is flexible enough to easily expand the dictionary of modulation formats for other applications.

This paper presents a Neyman-Pearson (NP) criterion based optimal distributed detection framework for a massive multiple-input multiple-output (MIMO) wireless sensor network (WSN). Robust fusion rules are determined for the local decisions transmitted by the sensor nodes, considering the availability of both perfect as well as imperfect channel state information (CSI) at the fusion center. Further, the probability of error of the individual sensor decisions, which arises in practical scenarios, is also incorporated in the decision framework. Closed form expressions are derived to characterize the resulting probabilities of detection and false alarm for the system. Simulation results are presented to demonstrate the improved performance of the proposed detectors in comparison to the existing detectors and to validate the theoretical findings.

This paper proposes a new approach to construct high quality space-filling sample designs. First, we propose a novel technique to quantify the space-filling property and optimally trade-off uniformity and randomness in sample designs in arbitrary dimensions. Second, we connect the proposed metric (defined in the spatial domain) to the quality metric of the design performance (defined in the spectral domain). This connection serves as an analytic framework for evaluating the qualitative properties of space-filling designs in general. Using the theoretical insights provided by this spatial-spectral analysis, we derive the notion of optimal space-filling designs, which we refer to as space-filling spectral designs. Third, we propose an efficient estimator to evaluate the space-filling properties of sample designs in arbitrary dimensions and use it to develop an optimization framework for generating high quality space-filling designs. Finally, we carry out a detailed performance comparison on two different applications in varying dimensions: a) image reconstruction and b) surrogate modeling for several benchmark optimization functions and a physics simulation code for inertial confinement fusion (ICF). Our results clearly evidence the superiority of the proposed space-filling designs over existing approaches, particularly in high dimensions.

We consider the problem of fusing probability scores from a set of classifiers to estimate a final fused probability score. Our interest is in scenarios where the classifiers are statistically dependent. To that end, we propose a new classifier fusion approach that is data driven and founded on the statistical theory of copulas. Numerical results with both simulated and real data show that our copula based classifier fusion approach produces better probability scores than individual classifiers and outperforms existing probability score fusion approaches.

In this paper, an overview of recent developments in received-signal-strength (RSS)-based localization in wireless sensor networks is presented. Several important practical issues and their solutions are discussed. To save communication bandwidth and sensor energy, a maximum-likelihood estimator based on quantized data is presented along with its corresponding Cramér-Rao lower bound (CRLB) and optimal quantizer design schemes. For further system resource savings, an iterative sensor selection approach is presented to activate only the most informative sensors, by maximizing the mutual information or minimizing the posterior CRLB at each iteration. For a resource constrained WSN with imperfect wireless channels, channel-aware target localization is described, where the channel model is incorporated into the localization scheme itself, thereby improving performance without increasing communication overhead. Another practical issue involving the presence of malicious sensors called Byzantines is discussed and mitigation schemes are provided. A recent coding-theorybased approach which is both computationally inexpensive and robust to such malicious attacks is also discussed.

In this letter, the optimality of the likelihood ratio test (LRT) is investigated for binary hypothesis testing problems in the presence of a behavioral decision-maker. By utilizing prospect theory, a behavioral decision-maker is modeled to cognitively distort probabilities and costs based on some weight and value functions, respectively. It is proved that the LRT may or may not be an optimal decision rule for prospect theory-based binary hypothesis testing, and conditions are derived to specify different scenarios. In addition, it is shown that when the LRT is an optimal decision rule, it corresponds to a randomized decision rule in some cases; i.e., nonrandomized LRTs may not be optimal. This is unlike Bayesian binary hypothesis testing, in which the optimal decision rule can always be expressed in the form of a nonrandomized LRT. Finally, it is proved that the optimal decision rule for prospect theory-based binary hypothesis testing can always be represented by a decision rule that randomizes at most two LRTs. Two examples are presented to corroborate the theoretical results.

We consider the problem of energy-efficient distributed detection to infer the presence of a target in a wireless sensor network and analyze its robustness to modeling uncertainties. The sensors make noisy observations of the target's signal power, which follows the isotropic power-attenuation model. Binary local decisions of the sensors are transmitted to a fusion center, where a global inference regarding the target's presence is made, based on the counting rule. We consider uncertain knowledge of: 1) the signal decay exponent of the wireless medium; 2) the power attenuation constant; and 3) the distance between the target and the sensors. For a given degree of uncertainty, we show that there exists a limit on the target's signal power below which the distributed detector fails to achieve the desired performance regardless of the number of sensors deployed. Simulation results are presented to determine the level of sensitivity of the detector to uncertainty in these parameters. The results throw light on the limits of robustness for distributed detection, akin to “SNR walls” for classical detection.

The problem of weak signal detection in Gaussian noise is addressed in the Neyman-Pearson framework with compressive measurements. A locally optimum detector is first devised assuming that the signal is nonsparse by approximating the test statistic around zero using a Taylor series, which is a good estimate only in a small radius around zero. When the signal is sparse, it is shown that the performance of this test degrades. To improve its performance, a new test is devised by deriving the Padé approximation of the test statistic around zero. Padé approximants estimate functions as the rational quotient of two lower degree polynomials and consistently have a wider radius of convergence than the Taylor series. The performance of the Padé-approximated test is better than its Taylor series counterpart and is comparable to the conventional locally optimum test with uncompressed measurements. Simulation results are presented to support the analytical findings of the work.

We consider the problem of detection of sparse stochastic signals with a distributed sensor network. Multiple sensors in the network are assumed to observe sparse signals, which share the joint sparsity pattern. The Bernoulli-Gaussian (BG) distribution with sparsity-enforcing capability is imposed on the sparse signals. The sparsity degree in the BG model is positive and close to zero in the presence of the sparse signals and is zero in the absence of the signals. Motivated by this, the problem of detection of the sparse signals with a distributed sensor network is formulated as the problem of close and one-sided hypothesis testing on the sparsity degree. For this problem, we propose a detector based on the locally most powerful test (LMPT) to decide on the presence or absence of sparse signals with sensor networks. The proposed LMPT detector does not require signal recovery, which alleviates the complexity of the detection system in sensor networks. Simulation results illustrate the performance of the proposed LMPT detector and corroborate our theoretical analysis. Simulation results also show that, compared to the detector based on matching pursuit, the proposed LMPT detector significantly reduces the computational burden without noticeable performance loss.

In this letter, we investigate the problem of classification with high-dimensional data using low-dimensional random projections in the presence of inter- and intra-signal correlations. Each sensor is assumed to compress its high-dimensional (Gaussian) signal vector using random projections in a multisensor setting. In order to quantify the classification performance with compressed data, we consider the Bhattacharya distance as the performance metric. In the presence of intra-signal correlation at a given sensor, the degradation in the Bhattacharya distance with compressed data is shown to be nonlinear with the compression ratio in contrast to the case when there is no intra-signal correlation. In the presence of inter-signal correlation, the degradation in the Bhattacharya distance with compressed data depends on whether or not an identical projection matrix is used to compress data at multiple sensors.

Due to the growing interest in applying tail-biting convolutional coding techniques in real-time communication systems, fast decoding of tail-biting convolutional codes has become an important research direction. In this paper, a new maximum-likelihood decoder for tail-biting convolutional codes is proposed. It is named bidirectional priority-first search algorithm (BiPFSA) because priority-first search algorithm has been used both in forward and backward directions during decoding. Simulations involving the antipodal transmission of (2, 1, 6) and (2, 1, 12) tail-biting convolutional codes over additive white Gaussian noise channels shows that BiPFSA not only has the least average decoding complexity among the state-of-the-art decoding algorithms for tail-biting convolutional codes but can also provide a highly stable decoding complexity with respect to growing information length and code constraint length. More strikingly, at high SNR, its average decoding complexity can even approach the ideal benchmark complexity, obtained under a perfect noise-free scenario by any sequential-type decoding. This demonstrates the superiority of BiPFSA in terms of decoding efficiency.

A memristor crossbar, which is constructed with memristor devices, has the unique ability to change and memorize the state of each of its memristor elements. It also has other highly desirable features such as high density, low power operation and excellent scalability. Hence the memristor crossbar technology can potentially be utilized for developing low-complexity and high-scalability solution frameworks for solving a large class of convex optimization problems, which involve extensive matrix operations and have critical applications in multiple disciplines. This paper, as the first attempt towards this direction, proposes a novel memristor crossbar-based framework for solving two important convex optimization problems, i.e., second-order cone programming (SOCP) and homogeneous quadratically constrained quadratic programming (QCQP) problems. In this paper, the alternating direction method of multipliers (ADMM) is adopted. It splits the SOCP and homogeneous QCQP problems into sub-problems that involve the solution of linear systems, which could be effectively solved using the memristor crossbar in O(1) time complexity. The proposed algorithm is an iterative procedure that iterates a constant number of times. Therefore, algorithms to solve SOCP and homogeneous QCQP problems have pseudo-O(N) complexity, which is a significant reduction compared to the state-of-the-art software solvers (O(N3.5)-O(N4)).

Present day distributed inference systems consist of sensors with different modalities working as a system to perform specific tasks. With multiple sensors sensing heterogeneous data over multiple time instants, diversity is an inherent aspect of such systems. In this work, we take the first step to characterize the diversity of a general heterogeneous sensing system performing inference tasks. We provide a unified definition for diversity which can be customized for the system in use. The use of the definition is illustrated by applying it to a specific detection system where the sensors collect data over heterogeneous sensing channels. We assume the data to be both temporally and spatially correlated and analyze the effect of dependence on the diversity of the detection system.

In this paper, we propose a new approach for robust compressive sensing (CS) using memristor crossbars that are constructed by recently invented memristor devices. The exciting features of a memristor crossbar, such as high density, low power and great scalability, make it a promising candidate to perform large-scale matrix operations. To apply memristor crossbars to solve a robust CS problem, the alternating directions method of multipliers (ADMM) is employed to split the original problem into subproblems that involve the solution of systems of linear equations. A system of linear equations can then be solved using memristor crossbars with astonishing O(1) time complexity. We also study the impact of hardware variations on the memristor crossbar based CS solver from both theoretical and practical points of view. The resulting overall complexity is given by O(n), which achieves O(n2.5) speed-up compared to the state-of-the-art software approach. Numerical results are provided to illustrate the effectiveness of the proposed CS solver.

Performing likelihood ratio based detection with high dimensional multimodal data is a challenging problem since the computation of the joint probability density functions (pdfs) in the presence of intermodal dependence is difficult. While some computationally expensive approaches have been proposed for dependent multimodal data fusion (e.g., based on copula theory), a commonly used tractable approach is to compute the joint pdf as the product of marginal pdfs ignoring dependence. However, this method leads to poor performance when the data is strongly dependent. In this paper, we consider the problem of detection when dependence among multimodal data is modeled in a compressed domain where compression is obtained using low dimensional random projections. We employ a Gaussian approximation while modeling inter-modal dependence in the compressed domain which is computationally more efficient. We show that, under certain conditions, detection with multimodal dependent data in the compressed domain with a small number of compressed measurements yields enhanced performance compared to detection with high dimensional data via either the product approach or other suboptimal fusion approaches proposed in the literature.

In this work, we use crowds for acoustic classification of animal species in supervised and unsupervised manners. We demonstrate the effectiveness of the proposed triplet based crowdsourcing systems via actual experiments. Moreover, we propose a generalized 1-bit RPCA algorithm to further improve classification performance. The unique marriage of crowdsourcing and generalized 1-bit RPCA algorithm is shown to yield excellent performance for acoustic data classification.

In this work, we investigate the accelerated proximal gradient method for nonconvex programming (APGnc). The method compares between a usual proximal gradient step and a linear extrapolation step, and accepts the one that has a lower function value to achieve a monotonic decrease. In specific, under a general nonsmooth and nonconvex setting, we provide a rigorous argument to show that the limit points of the sequence generated by APGnc are critical points of the objective function. Then, by exploiting the Kurdyka-Lojasiewicz (KL) property for a broad class of functions, we establish the linear and sub-linear convergence rates of the function value sequence generated by APGnc. We further propose a stochastic variance reduced APGnc (SVRG-APGnc), and establish its linear convergence under a special case of the KL property. We also extend the analysis to the inexact version of these methods and develop an adaptive momentum strategy that improves the numerical performance.

Automatic modulation classification (AMC) with multiple sensors is a challenging problem when the channel conditions are unknown at the receiver. In this paper, using the Markov chain Monte Carlo (MCMC) approach, we develop a novel algorithm for AMC when the amplitude and phase of the channel gains are unknown. Using sampling techniques, we marginalize over the unknown channel parameters that follow a certain probability distribution. This improves the estimate of the a posteriori distribution of the modulation formats, thereby improving the overall classification performance. Further, to overcome the problem of local extrema traps encountered in sampling algorithms, we introduce the idea of adding artificial noise beyond a certain threshold of signal-to-noise (SNR). This improves the performance of the sampling based AMC algorithm in the high SNR regime. Simulation results and comparisons are provided to show the efficiency of the proposed algorithm over the most related works in the literature.

In this letter, we model a complete-information zero-sum game between a centralized detection network with a multiple access channel between the sensors and the fusion center (FC), and a jammer with multiple transmitting antennas. We choose error probability at the FC as the performance metric, and investigate pure strategy equilibria for this game, and show that the jammer has no impact on the FC's error probability by employing pure strategies at the Nash equilibrium. Furthermore, we also show that the jammer has an impact on the expected utility if it employs mixed strategies.

The detection of a weak signal in additive Cauchy noise is of great importance in many applications. A locally optimum detector (LOD) exists for such a scenario; however, it is non-linear in nature. In general, implementation of non-linear detectors is difficult in practice, and linear detectors with good properties, such as high asymptotic relative efficiency (ARE) with respect to the LOD, are often desirable. In this paper, we propose a linear detector for a weak signal in additive Cauchy noise. The proposed test statistic is a linear combination of order statistics. For the special case of a constant signal in additive Cauchy noise, we prove the asymptotic normality of the trimmed linear detector, and show that the ARE of the trimmed linear detector with respect to the LOD is unity. Extensive simulation results are provided to demonstrate that the loss in the performance of the linear detector is very small compared with the non-linear LOD. We also discuss the hardware complexities of the LOD and the linear detector, and demonstrate the advantages of the linear detector over the LOD, in terms of hardware implementation.

Timely, precise, and reliable detection of fall events is very important for systems monitoring activities of elderly people, especially the ones living independently. In this paper, we propose an autonomous fall detection system by taking a completely different view compared with existing vision-based activity monitoring systems and applying a reverse approach. In our system, in contrast with static sensors installed at fixed locations, the camera is worn by the subject, and thus, monitoring is not limited only to areas where the sensors are located and extends to wherever the subject may travel. Moreover, the camera provides a richer set of data and helps lower the false positive rates compared with accelerometer-only systems. We employ a modified version of the histograms of oriented gradients (HOG) approach together with the gradient local binary patterns (GLBP). It is shown that, with the same training set, the GLBP feature is more descriptive and discriminative than HOG, histograms of template, and semantic local binary patterns. Moreover, we autonomously compute a threshold, for the detection of fall events, from the training data based on relative entropy, which is a member of Ali-Silvey distance measures. Experiments are performed with ten different people and a total of around 300 associated fall events indoors and outdoors. Experimental results show that, with the autonomously computed threshold, the proposed method provides 93.78% and 89.8% accuracy for detecting falls with indoor and outdoor experiments, respectively.

In this paper, we revisit the received signal strength (RSS)-based target localization technique presented in Vempaty et al., where a simple threshold quantizer was employed to quantize the RSS values prior to sending them to the fusion center. It was shown that the probability of misclassification of the distributed classification fusion using error correcting codes scheme vanishes as the number of sensors tends to infinity. This result was obtained based on an intuitive threshold design at the local sensors, and the question of how much a careful design of local thresholds can help improve the overall performance was not addressed. In this paper, we demonstrate the significance of threshold design for accurate and robust target localization in wireless sensor networks, particularly, when the number of sensors is finite. With this objective, we derive an upper bound on the probability of misclassification as a function of RSS thresholds by using the union inequality. The RSS thresholds that algorithmically minimize the derived misclassification error bound are then numerically obtained over a mirror-based homomorphic sensor deployment structure. Simulations over fading wireless links show that the scheme based on newly found optimized RSS thresholds considerably outperforms the previous scheme using the thresholds that are intuitively selected, especially in the presence of Byzantine attacks that severely impact information security.

In this paper, we propose a novel sealed-bid auction framework to address the problem of dynamic spectrum allocation in cognitive radio (CR) networks. We design an optimal auction mechanism that maximizes the moderator's expected utility, when the spectrum is not available with certainty. We assume that the moderator employs collaborative spectrum sensing in order to make a reliable inference about spectrum availability. Due to the presence of a collision cost whenever the moderator makes an erroneous inference, and a sensing cost at each CR, we investigate feasibility conditions that guarantee a non-negative utility at the moderator. Since the moderator fuses CRs' sensing decisions to obtain a global inference regarding spectrum availability, we propose a novel strategy-proof fusion rule that encourages the CRs to simultaneously reveal truthful sensing decisions, along with truthful valuations to the moderator. We also present tight theoretical bounds on instantaneous network throughput achieved by our auction mechanism. Numerical examples are presented to provide insights into the performance of the proposed auction under different scenarios.

In this paper, an adaptive two-way relay cooperation scheme is studied for multiple-relay cognitive radio networks to improve the performance of secondary transmissions. The power allocation and relay selection schemes are derived to minimize the secondary outage probability where only statistical channel information is needed. Exact closed-form expressions for secondary outage probability are derived under a constraint on the quality of service of primary transmissions in terms of the required primary outage probability. To better understand the impact of primary user interference on secondary transmissions, we further investigate the asymptotic behaviors of the secondary relay network, including power allocation and outage probability, when the primary signal-to-noise ratio goes to infinity. Simulation results are provided to illustrate the performance of the proposed schemes.

In this paper, we study the problem of generating uniform random point samples on a domain of d dimensional space based on a minimum distance criterion between point samples (Poisson-disk sampling or PDS). First, we formally define PDS via the pair correlation function (PCF) to quantitatively evaluate properties of the sampling process. Surprisingly, none of the existing PDS techniques satisfy both uniformity and minimum distance criterion, simultaneously. These approaches typically create an approximate PDS with high regularity, and inherently present high risk for sample aliasing. Our new formulation based on PCF introduces a new approach to evaluate PDS properties which leads to theoretical bounds on the size of a PDS in arbitrary dimensions as well as a faster algorithm to create better quality samplings than the current PDS approaches.

In many applications that employ wireless sensor networks (WSNs), robustness of distributed inference against Byzantine attacks is important. In this work, distributed inference is considered when local sensors send M-ary data to the fusion center. The optimal Byzantine attack policy is then derived under the assumption that the Byzantine adversary has the knowledge of the statistics of local quantization outputs. Our analysis indicates that the fusion center can be blinded such that the detection error is as poor as a random guess when an adequate fraction of sensors are compromised.

This letter considers the design of an auction mechanism to sell the object of a seller when the buyers quantize their private value estimates regarding the object into binary values prior to communicating them to the seller. The designed auction mechanism maximizes the utility of the seller (i.e., the auction is optimal), prevents buyers from communicating falsified quantized bids (i.e., the auction is incentive compatible), and ensures that buyers will participate in the auction (i.e., the auction is individually rational). The letter also investigates the design of the optimal quantization thresholds using which buyers quantize their private value estimates. Numerical results provide insights regarding the influence of the quantization thresholds on the auction mechanism.

This paper considers the problem of high-dimensional signal detection in a large distributed network whose nodes can collaborate with their one-hop neighboring nodes (spatial collaboration). We assume that only a small subset of nodes communicate with the fusion center (FC). We design optimal collaboration strategies which are universal for a class of deterministic signals. By establishing the equivalence between the collaboration strategy design problem and sparse principal component analysis (PCA), we solve the problem efficiently and evaluate the impact of collaboration on detection performance.

In this paper, distributed competitive interactions between a secondary user (SU) transmitter-receiver pair and a jammer are investigated using a game-theoretic framework under physical interference restrictions, power budget constraints, and incomplete knowledge of channel gains. In this game, the SU transmitter is expected to choose its power strategy with the objective of satisfying a minimum signal-to-interference plus noise ratio (SINR) at the corresponding receiver. Similarly, the jammer's objective is to strategically allocate its power so that the SINR constraint of the SU is not satisfied. Due to a lack of complete information, this strategic power allocation problem between the two players is modeled as a Bayesian game for which the self-enforcing strategies of the SU transmitter-receiver pair and the jammer are analyzed. Furthermore, probability distributions are employed by the corresponding players to model the incomplete nature of the game. The solution of the game corresponds to Nash equilibria points. Equilibrium analysis is carried out by considering the mixed strategy solution space and numerical examples are presented for illustration.

We employ Permutation Trellis Code (PTC) based multi-level Frequency Shift Keying signaling to mitigate the impact of Primary Users (PUs) on the performance of Secondary Users (SUs) in Cognitive Radio Networks (CRNs). The PUs are assumed to be dynamic in that they appear intermittently and stay active for an unknown duration. Our approach is based on the use of PTC combined with multi-level FSK modulation so that an SU can improve its data rate by increasing its transmission bandwidth while operating at low power and not creating destructive interference for PUs. We evaluate system performance by obtaining an approximation for the actual Bit Error Rate (BER) using properties of the Viterbi decoder and carry out a thorough performance analysis in terms of BER and throughput. The results show that the proposed coded system achieves i) robustness by ensuring that SUs have stable throughput in the presence of heavy PU interference and ii) improved resiliency of SU links to interference in the presence of multiple dynamic PUs.

A common solution to reducing visible aliasing artifacts in image reconstruction is to employ sampling patterns with a blue noise power spectrum. These sampling patterns can prevent discernible artifacts by replacing them with incoherent noise. Here, we propose a new family of blue noise distributions, Stair blue noise , which is mathematically tractable and enables parameter optimization to obtain the optimal sampling distribution. Furthermore, for a given sample budget, the proposed blue noise distribution achieves a significantly larger alias-free low-frequency region compared to existing approaches, without introducing visible artifacts in the mid-frequencies. We also develop a new sample synthesis algorithm that benefits from the use of an unbiased spatial statistics estimator and efficient optimization strategies.

In this paper, we address the constrained resource allocation problems arising in the context of spectrum sharing in cognitive radio networks utilizing a multi-dimensional formulation. Given the activity of the primary users (PUs), we consider multiple objectives and constraints, viz., sum rate, fairness, number of active secondary users (SUs), power consumption, and quality of service requirements (of both PUs and SUs). The three dimensions for the optimization task are the assignment of power, frequency, and antenna directionality to various SUs. Efficient heuristic algorithms are developed for five variations of the NP-hard optimization problems. Solution quality tradeoffs are shown for three algorithms, viz., convex relaxation with tree pruning, convex relaxation with gradual removal, and a genetic algorithm (GA); results show that the GA provides a reasonable balance between solution quality and computational effort. The multi-objective problems are solved using a modification of the NSGA-II evolutionary algorithm, obtaining a set of Pareto-optimal solutions under computational constraints.

A novel spectrum sharing strategy based on coupled detection and estimation is proposed for cognitive radio networks. The proposed approach is able to tradeoff throughput for reduced interference at the primary user (PU) via censored transmissions. We derive the optimum censoring strategy that maximizes the throughput of the cognitive radio system under an average interference power constraint at the PU. We then extend the proposed framework to jointly optimize the censoring and the power allocation strategies of the secondary user (SU) that maximize the throughput of the secondary network under average transmit power and average interference power constraints. Finally, we provide extensive simulation results to demonstrate the enhanced performance of the proposed censoring based spectrum sharing approach.

We study the problem of adaptive sensor management for target tracking, where at every instant we search for the best sensors to be activated at the next time step. In our problem formulation, the measurements may be corrupted by correlated noises, and the impact of correlated measurements on sensor selection is studied. Specifically, we adopt an alternative conditional posterior Cramér-Rao lower bound (C-PCRLB) as the optimization criterion for sensor selection, where the trace of the conditional Fisher information matrix is maximized subject to an energy constraint. We demonstrate that the proposed sensor selection problem can be transformed into the problem of maximizing a convex quadratic function over a bounded polyhedron. This optimization problem is NP-hard in nature, and thus we employ a linearization method and a bilinear programming approach to obtain locally optimal sensor schedules in a computationally efficient manner.

Rabin's information dispersal algorithm (IDA) simultaneously addresses secrecy and fault-tolerance by encoding a data file and parsing it into unrecognizable data-packets before transmitting or storing them in a network. In this paper, we redesign Rabin's IDA for cognitive-radio networks where the routing paths are available with uncertainty. In addition, we also assume the presence of an attacker in the network which attempts to simultaneously compromise the confidentiality and data-integrity of the source message. Due to the presence of two rational entities with conflicting motives, we model the problem as a zero-sum game between the source and the attacker and investigate the mixed-strategy Nash Equilibrium by decoupling the game into two linear programs which have a primal-dual relationship.

In this letter, we investigate distributed detection over channels with memory. We model the wireless channel between each sensor and the fusion center by a two state Markov chain and derive the optimal fusion rule. We demonstrate the improved detection performance of the derived fusion rule for burst error channels, over a channel aware fusion rule for a memoryless channel and the Chair-Varshney fusion rule. We further show that when the number of sensor decisions is large, as well as when the channel is memoryless, the derived fusion rule reduces to the well known counting rule.

In this letter, we consider the problem of distributed Bayesian detection in the presence of Byzantine data. The problem of distributed detection is formulated as a binary hypothesis test at the fusion center (FC) based on 1-bit data sent by the sensors. Adopting Chernoff information as our performance metric, we study the detection performance of the system under Byzantine attack in the asymptotic regime. The expression for minimum attacking power required by the Byzantines to blind the FC is obtained. More specifically, we show that above a certain fraction of Byzantine attackers in the network, the detection scheme becomes completely incapable of utilizing the sensor data for detection. When the fraction of Byzantines is not sufficient to blind the FC, we also provide closed form expressions for the optimal attacking strategies for the Byzantines that most degrade the detection performance.

Regenerating codes provide an efficient way to recover data at failed nodes in distributed storage systems. It has been shown that regenerating codes can be designed to minimize the per-node storage (called MSR) or minimize the communication overhead for regeneration (called MBR). In this work, we propose new encoding schemes for error-correcting MSR and MBR codes that generalize our earlier results on error-correcting regenerating codes. General encoding schemes for product-matrix MSR and MBR codes are derived such that the encoder based on Reed-Solomon (RS) codes is no longer limited to the Vandermonde matrix proposed earlier. Furthermore, MSR codes and MBR codes with the least update complexity can be found. A decoding scheme is proposed that utilizes RS codes to perform data reconstruction for MSR codes. The proposed decoding scheme has better error correction capability and incurs least number of node accesses when errors are present. A new decoding scheme is also proposed for MBR codes that is more capable and can correct more error-patterns. Simulation results are presented that exhibit the superior performance of the proposed schemes.

Lower dimensional signal representation schemes frequently assume that the signal of interest lies in a single vector space. In the context of the recently developed theory of compressive sensing, it is often assumed that the signal of interest is sparse in an orthonormal basis. However, in many practical applications, this requirement may be too restrictive. A generalization of the standard sparsity assumption is that the signal lies in a union of subspaces. Recovery of such signals from a small number of samples has been studied recently in several works. Here, we consider the problem of only subspace recovery in which our goal is to identify the subspace (from the union) in which the signal lies using a small number of samples, in the presence of noise. More specifically, we derive performance bounds and conditions under which reliable subspace recovery is guaranteed using maximum likelihood (ML) estimation. We begin by treating general unions and then obtain the results for the special case in which the subspaces have structure leading to block sparsity. In our analysis, we treat both general sampling operators and random sampling matrices. With general unions, we show that under certain conditions, the number of measurements required for reliable subspace recovery in the presence of noise via ML is less than that implied using the restricted isometry property, which guarantees complete signal recovery. In the special case of block sparse signals, we quantify the gain achievable over standard sparsity in subspace recovery. Our results also strengthen existing results on sparse support recovery in the presence of noise under the standard sparsity model.

In this paper, we consider distributed maximum likelihood (ML) classification of digital amplitude-phase modulated signals using multiple sensors that observe the same sequence of unknown symbol transmissions over nonidentical flat blockfading Gaussian noise channels. A variant of the expectation-maximization (EM) algorithm is employed to obtain the ML estimates of the unknown channel parameters and compute the global log-likelihood of the observations received by all the sensors in a distributed manner by means of an average consensus filter. This procedure is repeated for all candidate modulation formats in the reference library, and a classification decision, which is available at any of the sensors in the network, is declared in favor of the modulation with the highest log-likelihood score. The proposed scheme improves the classification accuracy by exploiting the signal-to-noise ratio (SNR) diversity in the network while restricting the communication to a small neighborhood of each sensor. Numerical examples show that the proposed distributed EM-based classifier can achieve the same classification performance as that of a centralized classifier, which has all the sensor measurements, for a wide range of SNR values.

In this paper, we consider the problem of automatic modulation classification with multiple sensors in the presence of unknown time offset, phase offset and received signal amplitude. We develop a novel hybrid maximum likelihood (HML) classification scheme based on a generalized expectation maximization (GEM) algorithm. GEM is capable of finding ML estimates numerically that are extremely hard to obtain otherwise. Assuming a good initialization technique is available for GEM, we show that the classification performance (in terms of the probability of error) can be greatly improved with multiple sensors compared to that with a single sensor, especially when the signal-to-noise ratio (SNR) is low. We further demonstrate the superior performance of our approach when simulated annealing (SA) with uniform as well as nonuniform grids is employed for initialization of GEM in low SNR regions. The proposed GEM based approach employs only a small number of samples (in the order of hundreds) at a given sensor node to perform both time and phase synchronization, signal power estimation, followed by modulation classification. We provide simulation results to show the efficiency and effectiveness of the proposed algorithm.

Distributed detection in censoring sensor networks, where each sensor transmits “informative” observations to the Fusion Center (FC), and censors those deemed “uninformative”, has been investigated by many researchers, but under the assumption of conditionally independent observations. In this paper, we consider a more realistic situation in a censoring sensor network where observations may not be independent. We derive optimal fusion rules at the FC under both Neyman-Perason (NP) and Bayesian frameworks, assuming that each sensor sends complete observations to the FC only when its observation falls out of a certain no-send region. Simulation results are provided to demonstrate the superior performance of our fusion rule compared with several other fusion rules derived in earlier work.

This paper considers the problem of performance analysis of data fusion schemes in the presence of Byzantine attacks. First, we analyze the security performance of data fusion schemes with Byzantines. We show that when more than a certain fraction of Byzantines are present in the network, the raw data fusion schemes become completely incapable (blind). More specifically, we obtain a closed form expression for the lower bound on the fraction of Byzantines needed to blind the fusion center as a function of attacker's strength. Next, we investigate the global detection performance in the presence of Byzantine attacks, and analytically characterize the effect of Byzantines on detection performance. Numerical results provide insights into our analysis.

We study an estimation problem where a fusion center estimates a random parameter by using a partially connected network of sensor nodes. The process involves two stages. In the collaboration stage, the sensor nodes share their observations with their neighbors. In the estimation stage, all the sensor nodes form a coherent beam to the fusion center using the analog amplify-and-forward procedure. In the previous work of Kar and Varshney (2013) on this topic, a control center determines the optimum collaboration strategy that is sent to the sensor nodes prior to starting the two-stage procedure. In this paper, we develop a new framework where the collaboration strategies are computed in a decentralized manner using minimal communication with the control center. This makes the sensor network more energy efficient and reduces control channel communication requirements.

To solve the problem of joint sparsity pattern recovery in a decentralized network, we propose an algorithm named decentralized and collaborative subspace pursuit (DCSP). The basic idea of DCSP is to embed collaboration among nodes and fusion strategy into each iteration of the standard subspace pursuit (SP) algorithm. In DCSP, each node collaborates with several of its neighbors by sharing high-dimensional coefficient estimates and communicates with other remote nodes by exchanging low-dimensional support set estimates. Experimental evaluations show that, compared with several existing algorithms for sparsity pattern recovery, DCSP produces satisfactory results in terms of accuracy of sparsity pattern recovery with much less communication cost.

In this paper, we consider the problem of estimating a spatially varying field in a wireless sensor network, where resource constraints limit the number of sensors selected in the network that provide their measurements for field estimation. Based on a one-to-one correspondence between the selected sensors and the nonzero elements of Kriging weights, we propose a sparsity-promoting ordinary Kriging approach where we minimize the Kriging error variance while penalizing the number of nonzero Kriging weights. This yields a combinatorial optimization problem, which is intractable in general. To solve the proposed non-convex optimization problem, we employ the alternating direction method of multipliers (ADMM) and the reweighted ℓ1minimization method, respectively. Numerical results are provided to illustrate the effectiveness of our proposed approaches that provide a balance between the estimation accuracy and the number of selected sensors.

In this paper, we consider the problem of sensor management for target tracking in a wireless sensor network (WSN). To determine the set of sensors that have the most information, we develop a probabilistic sensor management scheme based on the concepts developed in compressive sensing. In the proposed scheme, each senor node decides whether it should transmit its observation via multiple access channels to the fusion center with a certain probability. With this probabilistic transmission scheme, the observation vector received at the fusion center becomes a compressed version of the original observations. Our goal is to determine the optimal values of the probability using which each node should transmit so that the determinant of the Fisher information matrix (FIM) is maximized at any given time instant with a constraint on the available energy. Numerical examples are provided to show the performance of the proposed scheme.

In the context of distributed estimation we consider the problem of sensor collaboration, which refers to the act of sharing measurements with neighboring sensors prior to transmission to a fusion center. While incorporating the cost of sensor collaboration, we aim to find optimal sparse collaboration topologies subject to a certain information or energy constraint. To achieve this goal, we present a tractable optimization framework and propose efficient methods to solve the formulated sensor collaboration problems. The effectiveness of our approach is demonstrated by numerical examples.

This paper considers the problem of distributed compressive detection under a perfect secrecy constraint. More specifically, we consider the problem where the distributed inference network operates in the presence of an eavesdropper who wants to discover the state of the nature being monitored by the system. It is shown that perfect secrecy can be achieved by using cooperating trustworthy nodes that assist the Fusion Center (FC) by providing falsified data to the eavesdroppers. We also consider the problem of determining optimal system parameters which maximize the detection performance at the FC, while ensuring perfect secrecy at the eavesdropper.

Noise, traditionally defined as an unwanted signal or disturbance, has been shown to play an important constructive role in many information processing systems and algorithms. This noise enhancement has been observed and employed in many physical, biological, and engineered systems. Indeed stochastic facilitation (SF) has been found critical for certain biological information functions such as detection of weak, subthreshold stimuli or suprathreshold signals through both experimental verification and analytical model simulations. In this paper, we present a systematic noise-enhanced information processing framework to analyze and optimize the performance of engineered systems. System performance is evaluated not only in terms of signal-to-noise ratio but also in terms of other more relevant metrics such as probability of error for signal detection or mean square error for parameter estimation. As an important new instance of SF, we also discuss the constructive effect of noise in associative memory recall. Potential enhancement of image processing systems via the addition of noise is discussed with important applications in biomedical image enhancement, image denoising, and classification.

We address the problem of discovering unknown digital amplitude-phase modulations over block-fading additive noise channels. The proposed method uses the iterative Richardson-Lucy algorithm to determine the distribution of the transmitted symbols, which completely characterizes the underlying signal constellation. The decoding of the received signals can then be carried out based on the estimate of the signal constellation. An important application of the proposed method is to construct a modulation dictionary in an offline manner prior to performing any type of real time classification, thereby improving the performance of the automatic modulation classification algorithms proposed in the literature.

In this letter, a new sparsity-promoting penalty function is introduced for sensor selection problems in field reconstruction, which has the property of avoiding scenarios where the same sensors are successively selected. Using a reweighted ℓ1relaxation of the ℓ0norm, the sensor selection problem is reformulated as a convex quadratic program. In order to handle large-scale problems, we also present two fast algorithms: accelerated proximal gradient method and alternating direction method of multipliers. Numerical results are provided to demonstrate the effectiveness of our approaches.

In this letter, we investigate the asymptotic performance of categorical decision fusion in a human decision making framework. We assume that multiple human agents send categorized information to a moderator for final decision making. The local categorization is performed via a threshold based scheme where thresholds are assumed to be random variables. Considering the cases where the moderator has the knowledge of exact threshold values as well as when it has only probabilistic information of the individual thresholds, we analyze the asymptotic performance of likelihood ratio based decision fusion at the moderator in terms of the Chernoff information. Numerical results are presented for illustration.

In this paper, we consider the task of target localization using quantized data in wireless sensor networks. We propose a computationally efficient localization scheme by modeling it as an iterative classification problem. We design coding theory based iterative approaches for target localization where at every iteration, the fusion center (FC) solves an M-ary hypothesis testing problem and decides the region of interest for the next iteration. The coding theory based iterative approach works well even in the presence of Byzantine (malicious) sensors in the network. We further consider the effect of non-ideal channels. We suggest the use of soft-decision decoding to compensate for the loss due to the presence of fading channels between the local sensors and FC. We evaluate the performance of the proposed schemes in terms of the Byzantine fault tolerance capability and probability of detection of the target region. We also present performance bounds, which help us in designing the system. We provide asymptotic analysis of the proposed schemes and show that the schemes achieve perfect region detection irrespective of the noise variance when the number of sensors tends to infinity. Our numerical results show that the proposed schemes provide a similar performance in terms of mean square error as compared with the traditional maximum likelihood estimation but are computationally much more efficient and are resilient to errors due to Byzantines and non-ideal channels.

In this paper, we consider the problem of linear modulation classification in the presence of unknown time offset, phase offset and received signal amplitude. We develop a novel hybrid maximum likelihood (HML) approach based on a Generalized Expectation Maximization (GEM) algorithm [1]. Our approach is applicable to all QAM and PSK modulations, and it does not require any assumptions on the received signal-to-noise ratio (SNR). The GEM algorithm provides a tractable procedure to obtain maximum likelihood (ML) estimates which are extremely hard to obtain otherwise. Moreover, our approach employs only a small number of samples (in the order of hundreds) to perform both time and phase synchronization, signal power estimation, followed by modulation classification. The proposed approach also enables maximum a posteriori (MAP) decoding of the unknown constellation symbol sequence as a by-product of the GEM algorithm. We provide simulation results that show that the proposed approach provides excellent classification performance.

We consider a collaborative estimation problem using dependent observations in a wireless sensor network, where each sensor aims to maximize its estimation performance in terms of Fisher information (FI) by forming coalitions with other sensors and collaborating within a coalition. The energy consumed by the sensors increases with the size of the coalition and hence we prove that grand coalition will not form. We investigate the formation of non-overlapping coalitions such that each sensor's performance is maximized under a specific energy constraint. We decouple marginal and dependent components of FI obtained from the joint distribution by using copula theory. We introduce the concept of diversity gain and redundancy loss and demonstrate how a copula based formulation allows us to characterize these concepts. Distributed estimation problem is formulated as a coalitional game. A merge-and-split algorithm is used for finding an optimal partition. Stability of the proposed algorithm for this game is discussed. Finally, numerical results are discussed.

This paper considers the problem of optimal distributed detection with independent identical sensors in the presence of Byzantine attacks. By considering the attacker to be strategic in nature, we address the issue of designing the optimal fusion rule and the local sensor thresholds that minimize the probability of error at the fusion center (FC).We first consider the problem of finding the optimal fusion rule under the constraint of fixed local sensor thresholds and fixed Byzantine strategy. Next, we consider the problem of joint optimization of the fusion rule and local sensor thresholds for a fixed Byzantine strategy. Then we extend these results to the scenario where both the FC and the Byzantine attacker act in a strategic manner to optimize their own utilities. We model the strategic behavior of the FC and the attacker using game theory and show the existence of Nash Equilibrium. We also provide numerical results to gain insights into the solution.

In this paper, we propose a market based dynamic bit allocation scheme for target tracking in energy constrained wireless sensor networks using quantized data. We model the dynamic bit allocation problem as a market based policy where the fusion center is the customer and sensors are the producers of the market. The fusion center releases the energy to purchase m-bit measurements from sensors in such a way that the trace of the posterior Cramér-Rao lower bound (PCRLB) on the mean squared error (MSE) is minimized. Sensors then compete to purchase the energy released from the fusion center and produce their m-bit quantized measurements which maximize their profit. Simulation results show that the market based dynamic bit allocation scheme achieves tracking performance close to the case where all the sensors report their most accurate information to the fusion center while the market based dynamic bit allocation scheme releases energy which is significantly less than the energy required to transmit all sensor data to the fusion center.

In this paper, we consider the problem of quantizer design for distributed estimation under the Bayesian criterion. We derive general optimality conditions under the assumption of conditionally independent observations at the local sensors and show that for a conditionally unbiased and efficient estimator at the Fusion Center, identical quantizers are optimal when local observations have identical distributions. This results in an N-fold reduction in complexity where N is the number of sensors. We illustrate our approach by applying it to the location parameter estimation problem.

We consider the problem of target localization using quantized data in Wireless Sensor Networks in the presence of Byzantines (malicious sensors). Since the effect of Byzantines can be treated as errors in the transmitted data, we propose the use of error correcting codes for the task of target localization. We design coding based iterative schemes for target localization where, at every iteration, the Fusion Center performs an M-ary hypothesis test and decides the Region of Interest for the next iteration. Simulation results show that our proposed schemes provide a better performance as compared to the traditional Maximum Likelihood Estimation and are also computationally much more efficient.

We consider the use of error-control codes and decoding algorithms to perform reliable classification using unreliable and anonymous human crowd workers by adapting coding-theoretic techniques for the specific crowdsourcing application. We develop an ordering principle for the quality of crowds and describe how system perfor-mance changes with the quality of the crowd. We demonstrate the effectiveness of the proposed coding scheme using both simulated data and real datasets from Amazon Mechanical Turk, a crowd-sourcing microtask platform. Results suggest that good codes may improve the performance of the crowdsourcing task over typical majority-vote approaches. Index Terms — crowdsourcing, classification, error-control codes

In this paper, we address the problem of sparsity pattern recovery of a sparse signal with multiple measurement data in a distributed network. We consider that each node in the network makes measurements via random projections regarding the same sparse signal. We propose a distributed greedy algorithm based on Orthogonal Matching Pursuit (OMP) in which the locations of non zero coefficients of the sparse signal are estimated iteratively while performing fusion of estimates at distributed nodes. In the proposed distributed framework, each node has to perform less number of iterations of OMP compared to the sparsity index of the sparse signal. With each node having a very small number of compressive measurements, a significant performance gain in sparsity pattern detection is achieved via the proposed collaborative scheme compared to the case where each node estimates the sparsity pattern independently and then fusion is performed to get a global estimate. We further extend the algorithm to a binary hypothesis testing framework, where the algorithm first detects the presence of a sparse signal collaborating among nodes with a fewer number of iterations of OMP and then increases the number of iterations to estimate the sparsity pattern only if the signal is detected.

In this paper, we consider a Bayesian estimation problem in a sensor network where the local sensor observations are quantized before their transmission to the fusion center (FC). Inspired by Widrow's statistical theory on quantization, at the FC, instead of fusing the quantized data directly, we propose to fuse the post-processed data obtained by adding independent controlled noise to the received quantized data. The injected noise acts like a low-pass filter in the characteristic function (CF) domain such that the output is an approximation of the original raw observation. The optimal minimum mean squared error (MMSE) estimator and the posterior Cramér-Rao lower bound for this estimation problem are derived. Based on the Fisher information, the optimal controlled Gaussian noise and the optimal bit allocation are obtained. In addition, a near-optimal linear MMSE estimator is derived to reduce the computational complexity significantly.

Optimal channel switching and detector design is studied for M-ary communication systems in the presence of stochastic signaling, which facilitates randomization of signal values transmitted for each information symbol. Considering the presence of multiple additive noise channels (which can have non-Gaussian distributions in general) between a transmitter and a receiver, the joint optimization of the channel switching (timesharing) strategy, stochastic signals, and detectors is performed in order to achieve the minimum average probability of error. It is proved that the optimal solution to this problem corresponds to either (i) switching between at most two channels with deterministic signaling over each channel, or (ii) time-sharing between at most two different signals over a single channel (i.e., stochastic signaling over a single channel). For both cases, the optimal solutions are shown to employ corresponding maximum a posteriori probability (MAP) detectors at the receiver. Numerical results are presented to investigate the proposed approach.

Regenerating codes provide an efficient way to recover data at failed nodes in distributed storage systems. It has been shown that regenerating codes can be designed to minimize the per-node storage (called MSR) or minimize the communication overhead for regeneration (called MBR). In this work, we propose a new encoding scheme for [n, d] error-correcting MSR codes that generalizes our earlier work on error-correcting regenerating codes. We show that by choosing a suitable diagonal matrix, any generator matrix of the [n, α] Reed-Solomon (RS) code can be integrated into the encoding matrix. Hence, MSR codes with the least update complexity can be found. An efficient decoding scheme is also proposed that utilizes the [n, α] RS code to perform data reconstruction. The proposed decoding scheme has better error correction capability and incurs the least number of node accesses when errors are present.

This study presents a novel scheme for the enhancement of images using stochastic resonance (SR) noise. In this scheme, a suitable dose of noise is added to the lower quality images such that the performance of a sub‐optimal image enhancer is improved without altering its parameters. Image enhancement is modelled as a constrained multi‐objective optimisation (MOO) problem, with similarity and some desired image‐enhancement characteristic being the two objective functions. The principle of SR noise‐refined image enhancement is analysed, and an image‐enhancement system is developed. A genetic algorithm‐based MOO technique is employed to find the optimum parameters of the SR noise distribution. Several image‐enhancement examples are provided, where the efficiency of the presented method in several real‐world applications is shown.

Registration of two high-dimensional data sets often involves dimensionality reduction to yield a single-band image from each data set followed by pairwise image registration. We develop a new application-specific algorithm for dimensionality reduction of high-dimensional data sets such that the weighted harmonic mean of Cramér-Rao lower bounds for the estimation of the transformation parameters for registration is minimized. The performance of the proposed dimensionality reduction algorithm is evaluated using three remotes sensing data sets. The experimental results using mutual information-based pairwise registration technique demonstrate that our proposed dimensionality reduction algorithm combines the original data sets to obtain the image pair with more texture, resulting in improved image registration.

A power-constrained sensor network that consists of multiple sensor nodes and a fusion center (FC) is considered, where the goal is to estimate a random parameter of interest. In contrast to the distributed framework, the sensor nodes may be partially connected, where individual nodes can update their observations by (linearly) combining observations from other adjacent nodes. The updated observations are communicated to the FC by transmitting through a coherent multiple access channel. The optimal collaborative strategy is obtained by minimizing the expected mean-square error subject to power constraints at the sensor nodes. Each sensor can utilize its available power for both collaboration with other nodes and transmission to the FC. Two kinds of constraints, namely the cumulative and individual power constraints, are considered. The effects due to imperfect information about observation and channel gains are also investigated. The resulting performance improvement is illustrated analytically through the example of a homogeneous network with equicorrelated parameters. Assuming random geometric graph topology for collaboration, numerical results demonstrate a significant reduction in distortion even for a moderately connected network, particularly in the low local signal-to-noise ratio regime.

The optimum power randomization problem is studied to minimize outage probability in flat block-fading Gaussian channels under an average transmit power constraint and in the presence of channel distribution information at the transmitter. When the probability density function of the channel power gain is continuously differentiable with a finite second moment, it is shown that the outage probability curve is a nonincreasing function of the normalized transmit power with at least one inflection point and the total number of inflection points is odd. Based on this result, it is proved that the optimum power transmission strategy involves randomization between at most two power levels. In the case of a single inflection point, the optimum strategy simplifies to on-off signaling for weak transmitters. Through analytical and numerical discussions, it is shown that the proposed framework can be adapted to a wide variety of scenarios including log-normal shadowing, diversity combining over Rayleigh fading channels, Nakagami-m fading, spectrum sharing, and jamming applications. We also show that power randomization does not necessarily improve the outage performance when the finite second moment assumption is violated by the power distribution of the fading.

In this paper, we address the problem of Optimal Content Delivery (OCD) in Dynamic Spectrum Access (DSA) networks, where the source of a flow sends data traffic to the destination in exchange for some monetary benefit, such as a subscription fee. Also, each intermediate secondary node incurs a cost for routing traffic. We propose a path auction based content delivery mechanism in which each secondary node announces its cost (considered as private information) to the auction mechanism. Based on the announced costs, the optimal flow rate between the end nodes is determined, a multi-path route is chosen and payments are made to the nodes that route traffic, such that the profit of the source node is maximized in sending data to the destination. Furthermore, the auction mechanism is strategy-proof, i.e., it can induce the intermediate nodes to truthfully declare their costs. We provide polynomial time algorithms for implementing our auction based content delivery mechanism in DSA networks.

We consider a power-constrained sensor network, consisting of multiple sensor nodes and a fusion center (FC), that is deployed for the purpose of estimating a common random parameter of interest. In contrast to the distributed framework, the sensor nodes are allowed to update their individual observations by (linearly) combining observations from neighboring nodes. The updated observations are communicated to the FC using an analog amplify-and-forward modulation scheme and through a coherent multiple access channel. The optimal collaborative strategy is obtained by minimizing the cumulative transmission power subject to a maximum distortion constraint. For the distributed scenario (i.e., with no observation sharing), the solution reduces to the power-allocation problem considered by Xiao et. al.. Collaboration among neighbors significantly improves power efficiency of the network in the low local-SNR regime, as demonstrated through an insightful example and numerical simulations.

In this letter, we study the problem of target tracking based on energy readings of sensors. We minimize the estimation error by using an extended Kalman filter (EKF). The Kalman gain matrix is obtained as the solution to an optimization problem in which a sparsity-promoting penalty function is added to the objective. The added term penalizes the number of nonzero columns of the Kalman gain matrix, which corresponds to the number of active sensors. By using a sparse Kalman gain matrix only a few sensors send their measurements to the fusion center, thereby saving energy. Simulation results show that an EKF with a sparse Kalman gain matrix can achieve tracking performance that is very close to that of the classical EKF, where all sensors transmit to the fusion center.

Abstract Wireless sensor networks can be employed to provide distributed real‐time navigation instructions to users attempting to travel in hazardous environments. In this work, we propose a distributed path planning algorithm for sensor network navigation in dynamic hazardous environments. Using geographic or virtual coordinates of sensors and based on a partial reversal method for directed acyclic graphs (DAG), our algorithm constructs a distributed in‐network directed navigation graph, where each source sensor is guaranteed to have at least one desired directed path to one destination sensor. When the hazardous environment changes due to its dynamic nature, path replanning does not need to reconfigure most of the directed links in the graph unaffected by the changes. Correctness of our algorithm is proved and extensive simulation results demonstrate that the constructed navigation graph provides near‐optimal navigation paths for users, successfully adapts to dynamic hazardous environments, and requires very low communication overhead for maintenance, when compared to other navigation graphs constructed by existing algorithms that use frequent or periodic flooding. Copyright © 2010 John Wiley & Sons, Ltd.

We analyze the potential for using multiple transmission dimensions in a cognitive radio network (CRN) in terms of the probability of successful communications. We consider a random CRN where the users are randomly distributed in an area of interest. The users can be mobile. We derive the successful communication probability (SCP) with respect to transmit power for different density of primary users and secondary users by including different transmission dimensionalities such as time, frequency and antenna directionality. It is shown that using multiple transmission dimensions improves the SCP significantly. The advantage of using directional antennas for spatial reuse and increased range is also shown in terms of improved SCP.

We propose a modified Bayesian Cramér-Rao lower bound (BCRLB) for nonlinear tracking applications where the prediction distribution conditioned on past measurements is used as the prior. The novelty of the proposed modified BCRLB comes from the fact that it utilizes past measurements, therefore it is specific to the current realization of the track which makes it a useful online tool that can be used for real-time sensor management. The computation of our proposed modified BCRLB is not analytically tractable except under very restricted conditions. Therefore, we also develop a particle based numerical computation method for our modified BCRLB so that this new bound can be easily calculated in real-time using the particles already available from the underlying particle filter which is used to track the target. We show by simulations that our developed numerical computation method approaches to its true analytical value as the number of particles in the particle filter increases.

This paper considers the problem of Byzantine attacks on cooperative spectrum sensing in cognitive radio networks. Our major contribution is a technique to learn about the cognitive radio (CR) potential malicious behavior over time and thereby identifies the Byzantines and then estimates their probabilities of false alarm (Pfa) and detection (PD). We show that for a given set of data over time, the Byzantines can be identified for any a (percentage of Byzantines). It has also been shown that these estimates of Pfaand Pn of the Byzantines are asymptotically unbiased and converge to their true values at the rate of O(T-1/2). We then use these probabilities to adaptively design the fusion rule. We calculate the Probability of error (Qe) and compare it with the minimum probability of error possible.

In cognitive radio (CR) networks, spectrum sensing has to be performed in a reliable manner in challenging environments that arise due to propagation channels which undergo multi-path fading and non-Gaussian noise at CRs. Most existing literature on spectrum sensing has focused on impairments introduced by additive white Gaussian noise (AWGN). However, this assumption fails to model the behavior of certain noise types in practice, such as impulsive noise. In this paper, the use of a non-parametric, easily implementable detection device, polarity-coincidence-array (PCA) detector, is proposed for weak primary signal detection with a cognitive radio equipped with multiple antennas. The detector performance in terms of the probabilities of detection and false alarm is derived when the communication channels between the primary user transmitter and the multiple antennas at the cognitive radio undergo Rayleigh fading. From the numerical results, it is observed that a significant performance enhancement is achieved by the PCA detector compared to that of the simple energy detector as the heaviness of the tail of the non-Gaussian noise increases.

This paper introduces a new subpixel classification algorithm that incorporates prior information from known class proportions in the linear mixture model. The prior information is expressed in terms of the occurrence probabilities of each land-cover class in a pixel. The use of different error cost functions that measure the similarity between the model-derived mixed spectra and the observed spectra is also investigated. Under these assumptions, the maximum a posteriori (MAP) methodology is employed for optimization. Finally, optimization problems under the MAP criteria for different error cost functions are formulated and solved. Our numerical results illustrate that the performance of the subpixel classification algorithm can be significantly improved by incorporating prior information from the known class proportions. Furthermore, there are marginal differences in accuracy when different error cost functions are used.

Markov random field (MRF) models are powerful tools to model image characteristics accurately and have been successfully applied to a large number of image processing applications. This paper investigates the problem of fusion of remote sensing images, e.g., multispectral image fusion, based on MRF models and incorporates the contextual constraints via MRF models into the fusion model. Fusion algorithms under the maximum a posteriori criterion are developed to search for solutions. Our algorithm is applicable to both multiscale decomposition (MD)-based image fusion and non-MD-based image fusion. Experimental results are provided to demonstrate the improvement of fusion performance by our algorithms.

One of the main requirements of cognitive radio (CR) systems is the ability to perform spectrum sensing in a reliable manner in challenging environments that arise due to propagation channels which undergo multipath fading and non-Gaussian noise. While most existing literature on spectrum sensing has focused on impairments introduced by additive white Gaussian noise (AWGN), this assumption fails to model the behavior of certain types of noise found in practice. In this paper, the use of a non-parametric and easily implementable detection device, namely the polarity-coincidence-array (PCA) detector, is proposed for the detection of weak primary signals with a cognitive radio equipped with multiple antennas. Its performance is evaluated in the presence of heavy-tailed noise. The detector performance in terms of the probabilities of detection and false alarm is derived when the communication channels between the primary user transmitter and the multiple antennas at the cognitive radio are AWGN as well as when they undergo Rayleigh fading. From the numerical results, it is observed that a significant performance enhancement is achieved by the PCA detector compared to that of the energy detector with AWGN as well as fading channels as the heaviness of the tail of the non-Gaussian noise increases.

In this paper, we present a unifying framework for distributed detection with dependent or independent observations. This novel framework utilizes an expanded hierarchical model by introducing a hidden variable. Facilitated by this new framework, we identify several classes of distributed detection problems with conditionally dependent observations whose optimal sensor signaling structure resembles that of the independent case. These classes of problems exhibit a decoupling effect on the form of the optimal local decision rules, much in the same way as the conditionally independent case using both the Bayesian and the Neyman-Pearson criteria.

In this paper, we consider the problem of quantifying synchrony between multiple simultaneously recorded electroencephalographic signals. These signals exhibit nonlinear dependencies and non-Gaussian statistics. A copula based approach is presented to model the joint statistics. We then consider the application of copula derived synchrony measures for early diagnosis of Alzheimer's disease. Results on real data are presented.

The problem of dynamic bit allocation for target tracking is investigated in this paper under a total sum rate constraint in sensor networks. Bits are dynamically allocated to sensors in such a way that a cost function, which is based on the Cramér-Rao lower bound evaluated at the predicted target state, is minimized. The optimal solution to this problem, namely joint bit allocation and local quantizer design, is computationally prohibitive and not realistic for real-time online implementation. Instead, a two-step optimization procedure is proposed. First, the best time independent quantizers are obtained offline by maximizing the average Fisher information about the signal amplitude, for different number of bits. With the time independent quantizers, the generalized Breiman, Friedman, Olshen, and Stone (BFOS) algorithm is employed to dynamically assign bits to sensors. Simulation results show that with the same or even less sum bit rate, the proposed dynamic bit allocation approach leads to significantly improved tracking performance, compared with the static bit allocation approach where each sensor is allocated with equal number of bits.

Collaborative (or distributed) spectrum sensing has been shown to have various advantages in terms of spectrum utilization and robustness in cognitive radio networks (CRNs). The data fusion scheme is a key component of collaborative spectrum sensing. We have recently analyzed the problem of Byzantine attacks in CRNs, where malicious users send false sensing data to the fusion center (FC) leading to an increased probability of spectrum sensing error. In this paper, we propose a novel and easy to implement technique to counter Byzantine attacks in CRNs. In this approach, the FC identifies the attackers and removes them from the data fusion process. Our analysis indicates that the proposed scheme is robust against Byzantine attacks and can successfully remove the Byzantines in a short time span.

For distributed detection in a wireless sensor network, sensors arrive at decisions about a specific event that are then sent to a central fusion center that makes global inference about the event. For such systems, the determination of the decision thresholds for local sensors is an essential task. In this paper, we study the distributed detection problem and evaluate the sensor thresholds by formulating and solving a multiobjective optimization problem, where the objectives are to minimize the probability of error and the total energy consumption of the network. The problem is investigated and solved for two types of fusion schemes: 1) parallel decision fusion and 2) serial decision fusion. The Pareto optimal solutions are obtained using two different multiobjective optimization techniques. The normal boundary intersection (NBI) method converts the multiobjective problem into a number of single objective-constrained subproblems, where each subproblem can be solved with appropriate optimization methods and nondominating sorting genetic algorithm-II (NSGA-II), which is a multiobjective evolutionary algorithm. In our simulations, NBI yielded better and evenly distributed Pareto optimal solutions in a shorter time as compared with NSGA-II. The simulation results show that, instead of only minimizing the probability of error, multiobjective optimization provides a number of design alternatives, which achieve significant energy savings at the cost of slightly increasing the best achievable decision error probability. The simulation results also show that the parallel fusion model achieves better error probability, but the serial fusion model is more efficient in terms of energy consumption.

We present a framework for the joint processing of multimodal data such as audio-video data streams. We first consider the problem of estimating the joint distribution of statistically dependent multimodal random variables. We discuss the issues involved and provide a copula based solution. Application of this approach to solve a multisensor fusion problem for the detection of a random event is also discussed.

We study the parametric distributed estimation problem using a wireless sensor network (WSN) where each sensor observes an unknown scalar parameter, quantizes its observation and sends its quantized observation to a fusion center via fading and noisy communication channels. We propose to incorporate channel statistics rather than the instantaneous channel state information (CSI) into the maximum likelihood (ML) formulation and show that the resulting likelihood function is strictly log-concave almost surely with a change of variable provided that at least one of the communication channels between the sensors and the fusion center has nonzero capacity. We also investigate the effects of channel layer on the sensor threshold design and show that the threshold design problem is coupled with the channel layer and the sensor signal-to-noise ratio (SNR) only for nonsymmetric channels. Our formulation is very general in the sense that no assumptions are made about the physical layer in terms of the modulation schemes and the reception techniques.

Detection of a nuclear radioactive source is considered using a parallel sensor network architecture and a fusion center. A Poisson-Gamma hierarchical model is used to represent the distribution of the count data received by the sensors. Local sensors are assumed to be single threshold binary quantizers that send a vector of sensor decisions over time to the fusion center for global decision-making. Using the developed count model, a generalized likelihood ratio test (GLRT) using a restricted range MLE (RMLE) is proposed to declare the global decision. The performance improvement resulting from using the restricted range MLE over the unrestricted MLE while implementing the GLRT is depicted using simulated as well as real data collected from a test-bed using radiation sensors. Using bootstrap, 95% confidence bounds on the ROC curves, evaluated using real data, are obtained.

Registration of remote sensing data often involves dimensionality reduction of high-dimensional data to yield an image from each data set followed by pairwise image registration. We develop a new rule for dimensionality reduction such that the the Crame¿r-Rao lower bound (CRLB) for the estimation of the transformation parameters is minimized. A hyperspectral data set and a multispectral data set are used to evaluate our proposed rule. The experimental results using Mutual Information (MI) based pairwise registration technique demonstrate that our proposed rule can select the image pair with more texture, resulting in improved image registration results.

Distributed detection with conditionally independent observations at local sensors is well understood. The problem becomes significantly more complicated when dependence is present among sensor observations. In this paper, we attempt to make progress in our understanding of the dependent observation case. Toward this end, we present a new hierarchical model by introducing a hidden or latent variable; this model attempts to present a unified framework for distributed detection with conditionally dependent or independent observations. By a close examination of this model, we identify a class of distributed detection problems with conditionally dependent observations whose optimal sensor signaling structure resembles that of the independent case. This class of problems exhibits a decoupling effect on the form of the optimal local decision rules, much in the same way as the conditionally independent case. Important cases of this class of problems include both the previously known Gaussian case under certain parameter regimes as well as several problems first introduced in this paper. An example is given to illustrate the proposed design approach.

Image registration is the process of estimating the misalignment between two images. Automatic image registration procedures are important in many remote-sensing applications. A popular approach for coarse registration is Fourier based. Fourier-based image registration is essentially a frequency estimation problem in the frequency domain of the image data. This letter studies the application of a subspace-based frequency estimation approach for the Fourier-based image registration problem. By employing the Multiple Signal Classifier algorithm, a more robust and accurate registration result is achieved at the expense of moderate computational complexity. In the examples considered, performance for registration of remote-sensing images using this method is comparable to a mutual information-based and a correlation-based registration algorithm but requires less computation.

In this paper, we consider the design of local decision rules for distributed detection systems where decisions from peripheral detectors are transmitted over dependent nonideal channels. Under the conditional independence assumption among multiple sensor observations, we show that the optimal detection performance can be achieved by employing likelihood-ratio quantizers (LRQ) as local decision rules under both the Bayesian criterion and Neyman-Pearson (NP) criterion even for the cases where the channels between the fusion center and local sensors are dependent and noisy. This work generalizes the previous work where independence among such channels was assumed. A person-by-person optimization (PBPO) procedure to obtain the solution is presented along with an illustrative example.

This paper investigates potential improvement of nonparametric detection performance via addition of noise and evaluates the performance of noise modified nonparametric detectors. Detection performance comparisons are made between the original detectors and noise modified detectors. Conditions for improvability as well as the optimum additive noise distributions of the widely used sign detector, the Wilcoxon detector, and the dead-zone limiter detector are derived. Finally, a simple and fast learning algorithm to find the optimal noise distribution solely based on received data is presented. A near-optimal solution can be found quickly based on a relatively small dataset.

This paper investigates the probabilistic connectivity of wireless sensor networks in the presence of channel fading. Due to the stochastic nature of wireless channels in sensor networks, the geometric disk shaped model widely used for connectivity analysis of sensor networks can be misleading. In this paper, we develop a mathematical model to evaluate the probabilistic connectivity of sensor networks which incorporates the characteristics of wireless channels, multi-access interference, the network topology and the propagation environment. We present an analytical framework for the computation of node isolation probability and network connectivity under different channel fading models. We also analyze the connectivity of sensor networks in the presence of unreliable sensors. We present numerical and simulation results that compare different regular topologies in terms of several metrics such as node isolation probability, end-to-end connectivity, and network connectivity.

This paper studies the effect of wireless channel imperfections on the transport and estimation of spatially distributed events using wireless sensor networks (WSNs). It is observed that the quality of event estimation at the sink (fusion center) degrades considerably with correlated packet losses during transmission from the sensors. A novel diversity technique based on field estimation is proposed to mitigate the effects of packet losses on the quality of estimation at the sink. Dense deployment of sensor nodes and the spatial nature of the observed physical phenomenon result in the sensor observations being noisy spatial samples of an unknown underlying function. The proposed algorithm exploits this feature, using supervised learning to achieve diversity. A new information fusion methodology based on approximate likelihood is proposed to integrate the information obtained from the learning algorithm into the classical estimation framework. Simulation results are provided to demonstrate the performance of the proposed approach.

We consider decentralized detection for resource-constrained wireless sensor networks where local sensor decisions need to go through a multi-hop relay network before reaching the fusion center. Our objective is to collectively design sensor decision rules and relay rules for optimum detection performance. Under the Bayesian criterion, we establish the necessary conditions for an optimal system and derive the form of the optimal fusion rule at the fusion center. Under some conditional independence assumptions, we derive the forms of the optimal local decision rules and the optimal relay rules and show that the optimal set of decision rules for the entire system can be specified by a set of parameters. We demonstrate the advantages of our proposed systematic approach against more conventional design approaches through a numerical example.

This paper extends our earlier work on sensor selection [1]. We are now focusing on a more challenging problem of how to effectively utilize quantized sensor data for target tracking in sensor networks by considering sensor selection problems with quantized data. A subset of sensors are dynamically selected to optimize the tracking performance. The one-step- look-ahead posterior Cramer-Rao Lower Bound (CRLB) on the state estimation error is proposed as the sensor selection criterion. Particle filtering method is employed to compute the posterior CRLB, as well as to estimate the target state. Simulation results show that the proposed posterior CRLB based method outperforms the one based on information theoretic measures.

In this paper, we consider the distributed parameter estimation problem using one-bit quantized data from local sensors. Nonparametric distributed estimators are proposed based on knowledge of the moments of sensor noise. These estimators are shown to be either unbiased or asymptotically unbiased with bounded estimation variance for all possible parameter values. Relationship between the proposed approaches and dithering in quantization is investigated. Performance comparison is made between the proposed estimators and the Sign quantizer via an illustrative example.

We propose a set of distributed algorithms for in-network path planning that enables a distributed sensor network navigation service in dynamic environments. Different from existing algorithms that use frequent or periodic flooding, our algorithms exploit geographic information of sensors to construct and maintain navigation links. Based on a partial reversal method of directed acyclic graphs, our algorithms ensure that each source sensor has at least one safe navigation path to one of the multiple destination sensors.

In this letter, we present a novel instance of the stochastic resonance effect in sequential detection. For a general binary hypotheses sequential detection problem, the detection performance is evaluated in terms of the expected sample size under both hypotheses. Improvability conditions are established for an injected noise to reduce at least one of the expected sample sizes for a sequential detection system using stochastic resonance. The optimal noise is also determined under such criteria. An illustrative example is presented where performance comparisons are made between the original detector and different noise modified detectors.

This paper considers the decentralized fusion problem involving local sensor detection as well as the fusion of decisions transmitted over non-ideal transmission channels in a wireless sensor network. Prime emphasis is given to the enhancement of several fusion rules using a recently developed stochastic resonance methodology applied at the local sensors. Further, it is shown that the optimal form of the stochastic resonance probability mass density for the decentralized sensor fusion problem retains the same form as that previously developed for the single sensor case.

In this paper, we propose a new maximumlikelihood (ML) target location estimator which uses quantized sensor data and wireless channel statistics in a wireless sensor network. The novelty of our approach comes from the fact that imperfect channel statistics between wireless sensors and the fusion center are incorporated in the localization algorithm. We call this approach “channel-aware target localization”. Furthermore, we derive the Cramer-Rao lower bound as a performance bound for our channel-aware ML estimator. Simulation results are presented to show that the performance of the channel-aware ML location estimator is quite close to its theoretical performance bound even with relatively small number of sensors and it has superior performance compared to that of the channel-unaware ML estimator.

This paper presents a new framework for distributed target detection in wireless sensor networks (WSNs). In our previous work, for multiple networked sensors collaboratively detecting the presence or absence of a target in the sensor field, every sensor uses an identical threshold for local decision-making. In this paper, we propose a framework where the sensors in the network collaboratively decide and select non-identical thresholds to improve network-wide detection performance in a dynamic manner. This threshold selection scheme is based on a new statistical metric called False Discovery Rate (FDR). Assuming a signal attenuation model, where the received signal power decays as the distance from the target increases, various performance indices like system level probability of detection and probability of false alarm are studied. Analytical and simulation results are provided for system level probability of false alarm and probability of detection. Performance comparison between the proposed approach and the classical identical local sensor threshold approach is provided to demonstrate the effectiveness of this scheme.

A distributed detection method is developed for the detection of a nuclear radioactive source using a small number of radiation counters. Local one bit decisions are made at each sensor over a period of time and a fusion center makes the global decision. A novel test for the fusion of correlated decisions is derived using the theory of copulas and optimal sensor thresholds are obtained using the Normal copula function. The performance of the derived fusion rule is compared with that of the Chair-Varshney rule. An increase in detection performance is observed. A method to estimate the correlation between the sensor observations using only the vector of sensor decisions is also proposed.

Hyperspectral imaging is becoming increasingly important in a variety of applications. These images contain a large number of contiguous bands to provide information at a fine spectral resolution and, therefore, cannot be displayed directly using an RGB color display. There has been some recent work on the problem of fusing hyperspectral images to three-band images for color display purposes. In this paper, we evaluate the performance of our recently proposed approach based on independent component analysis, correlation coefficient and mutual information (ICA- CCMI) to fuse the information from a large number of bands to three images suitable for color display. Depending on whether the reference images are available or not, several image quality metrics such as entropy and edge correlation have been proposed and employed to evaluate the fusion performance via three widely used hyperspectral image datasets.

Source localization is investigated for a sensor network with passive sensors. The signal emitted by the source endures Rayleigh fading during its propagation, and its average intensity is a function of the distance from the source. Maximum likelihood (ML) source location estimators that use the output, or its quantized version, of the non-coherent receiver is proposed. The ML estimators' Cramer-Rao lower bounds (CRLBs) are derived. Due to the fading effect, the proposed estimator's performance is degraded, compared to the ideal case without fading. However, it can still accurately estimate the source's position and intensity, and achieve its CRLB with relatively small amount of resources, namely small number of observations, sensors and quantization bits.

The objective in sensor collaboration for target tracking is to dynamically select a subset of sensors over time to optimize tracking performance in terms of mean square error (MSE). In this paper, we apply the Monte Carlo method to compute the expected posterior Cramer-Rao lower bound (CRLB) in a nonlinear, possibly non-Gaussian, dynamic system. The joint recursive one-step-ahead CRLB on the state vector is introduced as the criterion for sensor selection. The proposed approach is validated by simulation results. In the experiments, a particle filter is used to track a single target moving according to a white noise acceleration model through a two-dimensional field where bearing-only sensors are randomly distributed. Simulation results demonstrate the improved tracking performance of the proposed method compared to other existing methods in terms of tracking accuracy.

The problem of dimensionality reduction for color representation of hyperspectral images has received recent attention. In this paper, several independent component analysis (ICA) based approaches are proposed to reduce the dimensionality of hyperspectral images for visualization. We also develop a simple but effective method, based on correlation coefficient and mutual information (CCMI), to select the suitable independent components for RGB color representation. Experimental results are presented to illustrate the performance of our approaches.

Efficient localization of a ballistic missile is an important task in missile defense problems. This paper formulates and solves the sensor placement problem for efficient estimation of the missile location. The first part of this paper develops a mathematical framework for the localization of the missile using multiple sensors based on Cramer-Rao lower bound (CRLB) analysis. We derive the Fisher information matrix to facilitate the evaluation of estimation accuracy. The second part of the paper presents an evolutionary algorithm for obtaining the sensor placements. Simulation results show that the evolutionary algorithm outperforms a greedy sensor placement algorithm and obtains sensor placements with very low estimation error.

The transmission range that achieves the most economical use of energy in wirelessad hocnetworks is studied for uniformly distributed network nodes. By assuming the existence of forwarding neighbors and the knowledge of their locations, the average per-hop packet progress for a transmission range that is universal for all nodes is derived. This progress is then used to identify the optimal per-hop transmission range that gives the maximal energy efficiency. Equipped with this analytical result, the relation between the most energy-economical transmission range and the node density, as well as the path loss exponent, is numerically investigated. It is observed that when the path loss exponent is high (such as four), the optimal transmission ranges are almost identical over the range of node densities that we studied. However, when the path loss exponent is only two, the optimal transmission range decreases noticeably as the node density increases. Simulation results also confirm the optimality of the per-hop transmission range, which we found analytically.

The transmission range that achieves the most economical use of energy in wireless ad hoc networks is studied for uniformly distributed network nodes. By assuming the existence of forwarding neighbors and the knowledge of their locations, the average per-hop packet progress for a transmission range that is universal for all nodes is derived. This progress is then used to identify the optimal per-hop transmission range that gives the maximal energy efficiency. Equipped with this analytical result, the relation between the most energy-economical transmission range and the node density, as well as the path-loss exponent, is numerically investigated. It is observed that when the path-loss exponent is high (such as four), the optimal transmission ranges are almost identical over the range of node densities that we studied. However, when the path-loss exponent is only two, the optimal transmission range decreases noticeably as the node density increases. Simulation results also confirm the optimality of the per-hop transmission range that we found analytically.

Distributed classification fusion using error-correcting codes (DCFECC) has recently been proposed for wireless sensor networks operating in a harsh environment. It has been shown to have a considerably better capability against unexpected sensor faults than the optimal likelihood fusion. In this paper, we analyze the performance of a DCFECC code with minimum Hamming distance fusion. No assumption on identical distribution for local observations, as well as common marginal distribution for the additive noises of the wireless links, is made. In addition, sensors are allowed to employ their own local classification rules. Upper bounds on the probability of error that are valid for any finite number of sensors are derived based on large deviations technique. A necessary and sufficient condition under which the minimum Hamming distance fusion error vanishes as the number of sensors tends to infinity is also established. With the necessary and sufficient condition and the upper error bounds, the relation between the fault-tolerance capability of a DCFECC code and its pair-wise Hamming distances is characterized, and can be used together with any code search criterion in finding the code with the desired fault-tolerance capability. Based on the above results, we further propose a code search criterion of much less complexity than the minimum Hamming distance fusion error criterion adopted earlier by the authors. This makes the code construction with acceptable fault-tolerance capability for a network with over a hundred of sensors practical. Simulation results show that the code determined based on the new criterion of much less complexity performs almost identically to the best code that minimizes the minimum Hamming distance fusion error. Also simulated and discussed are the performance trends of the codes searched based on the new simpler criterion with respect to the network size and the number of hypotheses.

We study routing misbehavior in MANETs (mobile ad hoc networks) in this paper. In general, routing protocols for MANETs are designed based on the assumption that all participating nodes are fully cooperative. However, due to the open structure and scarcely available battery-based energy, node misbehaviors may exist. One such routing misbehavior is that some selfish nodes will participate in the route discovery and maintenance processes but refuse to forward data packets. In this paper, we propose the 2ACK scheme that serves as an add-on technique for routing schemes to detect routing misbehavior and to mitigate their adverse effect. The main idea of the 2ACK scheme is to send two-hop acknowledgment packets in the opposite direction of the routing path. In order to reduce additional routing overhead, only a fraction of the received data packets are acknowledged in the 2ACK scheme. Analytical and simulation results are presented to evaluate the performance of the proposed scheme

In this correspondence, we address the problem of fusing data for object tracking for video surveillance. The fusion process is dynamically regulated to take into account the performance of the sensors in detecting and tracking the targets. This is performed through a function that adjusts the measurement error covariance associated with the position information of each target according to the quality of its segmentation. In this manner, localization errors due to incorrect segmentation of the blobs are reduced thus improving tracking accuracy. Experimental results on video sequences of outdoor environments show the effectiveness of the proposed approach.

Stochastic-resonance (SR), a nonlinear physical phenomenon in which the performance of some nonlinear systems can be enhanced by adding suitable noise, has been observed and applied in many areas. However, it has not been shown whether or not this phenomenon plays a role in distributed detection. It seems counterintuitive that adding additional noise to the received decisions at the fusion center can improve detection performance. However, in this paper, we demonstrate the existence of the SR phenomenon in decision fusion by examples. An explanation for its existence is provided

This paper considers the problem tracking a moving target in a multisensor environment using distributed particle filters (DPFs). Particle filters have a great potential for solving highly nonlinear and non-Gaussian estimation problems, in which the traditional Kalman filter (KF) and extended Kalman filter (EKF) generally fail. How ever, in a sensor network, the implementation of distributed particle filters requires huge communications between local sensor nodes and the fusion center. To make the DPF approach feasible for real time processing and to reduce communication requirements, we approximate a posteriori distribution obtained from the local particle filters by a Gaussian mixture model (GMM). We propose a modified EM algorithm to estimate the parameters of GMMs obtained locally. These parameters are transmitted to the fusion center where the best linear unbiased estimator (BLUE) is used for fusion. Simulation results are presented to illustrate the performance of the proposed algorithm

This paper considers the stochastic resonance (SR) effect in the two hypotheses signal detection problem. Performance of a SR enhanced detector is derived in terms of the probability of detection PDand the probability of false alarm PFA. Furthermore, the conditions required for potential performance improvement using SR are developed. Expression for the optimal stochastic resonance noise pdf which renders the maximum po without increasing PFAis derived. By further strengthening the conditions, this approach yields the constant false alarm rate (CFAR) receiver. Finally, detector performance comparisons are made between the optimal SR noise, Gaussian, uniform and optimal symmetric pdf noises

Hyperspectral data form a data-cube consisting of images of an object collected at several hundred, closely spaced wavelengths. They have been found to be of significant potential benefit in areas such as remote sensing of the Earth, medicine, and non-destructive evaluation. Effective extraction of information from the hyperspectral data cube presents several signal processing challenges, some of them unique to hyperspectral data. The problems involved range from registration and enhancement to development of statistical signal processing algorithms and models for object detection and classification. The focus of this paper is to provide an overview of select processing and modeling techniques for hyperspectral data

Image registration is a fundamental and important task in image processing. It essentially estimates a transformation that aligns two images. Cramer Rao Lower bound has recently been used to establish the performance limit of image registration algorithms. However, it is known to be a weak lower bound for some problems. In this paper, we analyze the mean square error performance of transformation estimation in image registration problems We focus on rigid body transformations, and derive a set of tighter alternatives, namely the Bhattacharya bound and the Ziv-Zakai bound. Experimental results demonstate the validity of our performance bounds.

Occurrence of shadowy pixels in remote sensing images is a common phenomenon particularly with passive sensors. In these cases, analysts may treat these pixels as a separate land cover class. This may result in the loss of information present in the shadowy pixels A better approach may be to correct light intensity values in shadowy pixels and use the light-corrected image to produce a land cover map. Most light intensity correction algorithms are not designed to optimize the classification performance. Consequently, the accuracy of a resulting land cover map may be degraded. As a result, this paper proposes a new approach to simultaneously determine the land cover map and determine the light intensity value of shadowy pixels based on a Markov random field model. With this approach, the light intensity correction is performed such that the classification accuracy is maximized. The outputs of the proposed algorithm are a land cover map and shadow-free remote sensing image.

In this work, we analyze the performance of a wireless sensor network with distributed classification codes, where independence across sensors, including local observations, local classifications and sensor-fusion link noises, is assumed. In terms of large deviations technique, we establish the necessary and sufficient condition under which the minimum Hamming distance fusion error vanishes as the number of sensors tends to infinity. With the necessary and sufficient condition and the upper performance bounds, the relation between the fault-tolerance capability of a distributed classification code and its pair-wise Hamming distances is characterized

Feature selection is a key task in remote sensing data processing, particularly in case of classification from hyperspectral images. A logistic regression (LR) model may be used to predict the probabilities of the classes on the basis of the input features, after ranking them according to their relative importance. In this letter, the LR model is applied for both the feature selection and the classification of remotely sensed images, where more informative soft classifications are produced naturally. The results indicate that, with fewer restrictive assumptions, the LR model is able to reduce the features substantially without any significant decrease in the classification accuracy of both the soft and hard classifications.

The problem of reducing the probability of decision error of an existing binary receiver that is suboptimal using the ideas of stochastic resonance is solved. The optimal probability density function of the random variable that should be added to the input is found to be a Dirac delta function, and hence, the optimal random variable is a constant. The constant to be added depends upon the decision regions and the probability density functions under the two hypotheses and is illustrated with an example. Also, an approximate procedure for the constant determination is derived for the mean-shifted binary hypothesis testing problem

To achieve security in wireless sensor networks, it is important to be able to encrypt messages sent among sensor nodes. Keys for encryption purposes must be agreed upon by communicating nodes. Due to resource constraints, achieving such key agreement in wireless sensor networks is nontrivial. Many key agreement schemes used in general networks, such as Diffie-Hellman and public-key-based schemes, are not suitable for wireless sensor networks. Predistribution of secret keys for all pairs of nodes is not viable due to the large amount of memory used when the network size is large. Recently, a random key predistribution scheme and its improvements have been proposed. A common assumption made by these random key predistribution schemes is that no deployment knowledge is available. Noticing that, in many practical scenarios, certain deployment knowledge may be available a priori, we propose a novel random key predistribution scheme that exploits deployment knowledge and avoids unnecessary key assignments. We show that the performance (including connectivity, memory usage, and network resilience against node capture) of sensor networks can be substantially improved with the use of our proposed scheme. The scheme and its detailed performance evaluation are presented in this paper.

Existing studies on the classical distributed detection problem typically assume idealized transmissions between local sensors and a fusion center. This is not guaranteed in the emerging wireless sensor networks with low-cost sensors and stringent power/delay constraints. By focusing on discrete transmission channels, we study the performance limits, in both asymptotic and non-asymptotic regimes, of a distributed detection system as a function of channel characteristics. For asymptotic analysis, we compute the error exponents of the underlying hypothesis testing problem; while for cases with a finite number of sensors, we determine channel conditions under which the distributed detection systems become useless - observing the channel outputs cannot help reduce the error probability at the fusion center. We demonstrate that as the number of sensors or the quantization levels at local sensors increase, the requirements on channel quality can be relaxed

In this paper, we consider the distributed classification problem in wireless sensor networks. Local decisions made by local sensors, possibly in the presence of faults, are transmitted to a fusion center through fading channels. Classification performance could be degraded due to the errors caused by both sensor faults and fading channels. Integrating channel decoding into the distributed fault-tolerant classification fusion algorithm, we obtain a new fusion rule that combines both soft-decision decoding and local decision rules without introducing any redundancy. The soft decoding scheme is utilized to combat channel fading, while the distributed classification fusion structure using error correcting codes provides good sensor fault-tolerance capability. Asymptotic performance of the proposed approach is also investigated. Performance evaluation of the proposed approach with both sensor faults and fading channel impairments is carried out. These results show that the proposed approach outperforms the system employing the MAP fusion rule designed without regard to sensor faults and the multiclass equal gain combining fusion rule

An approach for data fusion in wireless sensor networks involves the use of mobile agents that selectively visit the sensors and incrementally fuse the data, thereby eliminating the unnecessary transmission of irrelevant or non-critical data. The order of sensors visited along the route determines the quality of the fused data and the communication cost. We model the mobile agent routing problem as a multi-objective optimization problem, maximizing the total detected signal energy while minimizing the energy consumption and path loss. Simulation results show that this problem can be solved successfully using evolutionary multi-objective algorithms such as EMOCA and NSGA-II. This approach also enables choosing between two alternative routing algorithms, to determine which one results in higher detection accuracy.

Feature subset selection is very important in high dimensional datasets such as hyperspectral images. In this paper, we define a new feature redundancy measure. Two different feature selection algorithms are proposed based on this measure. Experimental results on a real hyperspectral dataset are presented to demonstrate the effectiveness of our methodology.

Distributed (M-ary) detection and fault-tolerance have been considered as two fundamental functions in the context of large-scale sensor networks. Distributed multiclass classification fusion using error correcting codes (DCFECC) has been proposed to provide good fault-tolerance capability in wireless sensor networks. Minimum Hamming distance fusion is an essential part of the DCFECC approach. In this paper, we study the asymptotic performance of minimum Hamming distance fusion for both fault-free and faulty situations when the number of sensors tends to infinity. We conclude that the error probability vanishes asymptotically as long as the minimum Hamming distance d/sub min/ of the DCFECC code approaches infinity, and the probabilities of correct local classification for all hypotheses are greater than one half. In case d/sub min//2, normalized by the number of sensors, can be made larger than the largest local classification error, an explicit expression for the error exponent of the DCFECC system in terms of the Kullback-Leibler divergence can be established. A converse where the DCFECC decoding error is bounded away from zero is also addressed.

For a wireless sensor network (WSN) with a random number of sensors, a decision fusion rule that uses the total number of detections reported by local sensors for hypothesis testing, is proposed. It is assumed that the number of sensors follows a Poisson distribution and the locations of sensors follow a uniform distribution within the region of interest (ROI). Both analytical and simulation results for the system level detection performance are provided. This fusion rule can achieve a very good system level detection performance even at very low signal to noise ratio (SNR), if the average number of sensors is sufficiently large. In addition, the problem of choosing an optimum local sensor level threshold is investigated for various system parameters.

Abstract — Contention-based Geographic Forwarding (CGF) is a state-free communication paradigm for information delivery in multihop ad hoc and sensor networks. A priori selection of the forwarding area impacts its overall network performance and the design of the CGF protocol as well. In this work, we study the fundamental problem of defining the forwarding area apriorifor CGF and determine its impact on the performance. We model CGF without void (i.e., absence of a next-hop node in the forwarding area) handling as a 3-step forwarding strategy. Based on this model and given a random distribution of network nodes, we develop a general mathematical analysis technique to evaluate the performance of CGF with different forwarding areas, in terms of the performance metric average single-hop packet progress. Further, we introduce two state-free void handling schemes, i.e., active exploration and passive participation, for CGF and study their performance in depth. Our theoretical analysis and numerically evaluated results, validated by extensive simulations, provide a guideline regarding the selection of specific forwarding areas for the design of a practical CGF protocol. It also serves as a general performance evaluation framework for the existing CGF protocols. I.

Mobile ad hoc networks (MANETs) operate on the basic underlying assumption that all participating nodes fully collaborate in self-organizing functions. However, performing network functions consumes energy and other resources. Therefore, some network nodes may decide against cooperating with others. Providing these selfish nodes, also termed misbehaving nodes, with an incentive to cooperate has been an active research area recently. In this paper, we propose two network-layer acknowledgment-based schemes, termed the TWOACK and the S-TWOACK schemes, which can be simply added-on to any source routing protocol. The TWOACK scheme detects such misbehaving nodes, and then seeks to alleviate the problem by notifying the routing protocol to avoid them in future routes. Details of the two schemes and our evaluation results based on simulations are presented in this paper. We have found that, in a network where up to 40% of the nodes may be misbehaving, the TWOACK scheme results in 20% improvement in packet delivery ratio, with a reasonable additional routing overhead.

A novel, state-free, and competition-based data delivery protocol, called state-free implicit forwarding (SIF), is proposed for multihop wireless sensor networks. The SIF protocol assumes moderate node density and distance-to-sink awareness. The state-free feature of SIF makes it robust to high network dynamics. SIF also combines the tasks of routing and MAC, via cross-layer design, to simplify the complexity of the protocol stack in sensors and to save precious network resources. Simulation results are presented to show that SIF performs better than some previously proposed protocols for data delivery in terms of communication overhead, packet delivery ratio, and average packet delay.

Fault-tolerance and data fusion have been considered as two fundamental functions in wireless sensor networks. In this paper, we propose a novel approach for distributed multiclass classification using a fault-tolerant fusion rule for wireless sensor networks. Binary decisions from local sensors, possibly in the presence of faults, are forwarded to the fusion center that determines the final classification result. Classification fusion in our approach is implemented via error correcting codes to incorporate fault-tolerance capability. This new approach not only provides an improved fault-tolerance capability but also reduces computation time and memory requirements at the fusion center. Code matrix design is essential for the design of such systems. Two efficient code matrix design algorithms are proposed in this paper. The relative merits of both algorithms are also studied. We also develop sufficient conditions for asymptotic detection of the correct hypothesis by the proposed approach. Performance evaluation of the proposed approach in the presence of faults is provided. These results show significant improvement in fault-tolerance capability as compared with conventional parallel fusion networks.

To achieve security in wireless sensor networks, it is important to be able to encrypt and authenticate messages sent between sensor nodes. Before doing so, keys for performing encryption and authentication must be agreed upon by the communicating parties. Due to resource constraints, however, achieving key agreement in wireless sensor networks is nontrivial. Many key agreement schemes used in general networks, such as Diffie-Hellman and other public-key based schemes, are not suitable for wireless sensor networks due to the limited computational abilities of the sensor nodes. Predistribution of secret keys for all pairs of nodes is not viable due to the large amount of memory this requires when the network size is large.In this paper, we provide a framework in which to study the security of key predistribution schemes, propose a new key predistribution scheme which substantially improves the resilience of the network compared to previous schemes, and give an in-depth analysis of our scheme in terms of network resilience and associated overhead. Our scheme exhibits a nice threshold property: when the number of compromised nodes is less than the threshold, the probability that communications between any additional nodes are compromised is close to zero. This desirable property lowers the initial payoff of smaller-scale network breaches to an adversary, and makes it necessary for the adversary to attack a large fraction of the network before it can achieve any significant gain.

The problem of distributed sequential detection in the presence of communication constraints is considered. The observations available at each sensor are first compressed to multibit sensor decisions and sent to the fusion center. At the fusion center, a sequential data fusion scheme is implemented in order to reach a global decision. An algorithm is developed for optimal bandwidth distribution among sensors under a fixed bandwidth constraint. Under symmetry and conditional independence assumptions, the algorithm can be simplified substantially: the cooperative quantizer design algorithm reduces to independent quantizer design. The case when communication bandwidth is the only constraint is also considered.

Many visions of the future include people immersed in an environment surrounded by sensors and intelligent devices, which use smart infrastructures to improve the quality of life and safety in emergency situations. Ubiquitous communication enables these sensors or intelligent devices to communicate with each other and the user or a decision maker by means of ad hoc wireless networking. Organization and optimization of network resources are essential to provide ubiquitous communication for a longer duration in large-scale networks and are helpful to migrate intelligence from higher and remote levels to lower and local levels. In this paper, distributed energy-efficient deployment algorithms for mobile sensors and intelligent devices that form an Ambient Intelligent network are proposed. These algorithms employ a synergistic combination of cluster structuring and a peer-to-peer deployment scheme. An energy-efficient deployment algorithm based on Voronoi diagrams is also proposed here. Performance of our algorithms is evaluated in terms of coverage, uniformity, and time and distance traveled until the algorithm converges. Our algorithms are shown to exhibit excellent performance.

This paper presents an evolutionary approach to the sensor management of a biometric security system that improves robustness. Multiple biometrics are fused at the decision level to support a system that can meet more challenging and varying accuracy requirements as well as address user needs such as ease of use and universality better than a single biometric system or static multimodal biometric system. The decision fusion rules are adapted to meet the varying system needs by particle swarm optimization, which is an evolutionary algorithm. This paper focuses on the details of this new sensor management algorithm and demonstrates its effectiveness. The evolutionary nature of adaptive, multimodal biometric management (AMBM) allows it to react in pseudoreal time to changing security needs as well as user needs. Error weights are modified to reflect the security and user needs of the system. The AMBM algorithm selects the fusion rule and sensor operating points to optimize system performance in terms of accuracy.

We investigate the effects of the carrier sensing range of the IEEE 802.11 multiple access control (MAC) scheme in this paper. Contrary to the simple and inaccurate cut-off circular collision model that is commonly used, we employ a more accurate collision model to realistically simulate MAC schemes in ad hoc networks. We argue that the carrier sensing range is a tunable parameter that can significantly affect the MAC performance in multihop ad hoc networks. An optimal carrier sensing range should balance the trade-off between the amount of spatial frequency reuse and the possibility of packet collisions. A reward formulation for the optimization of the carrier sensing range is presented. Extensive simulation results are provided to substantiate our study.

In this paper, four sampling schemes for sequential detection in colored noise are introduced. Two of them use uniform sampling procedures with high and low sampling rates, respectively. The other two employ groups of samples, which are separated by long intergroup gaps such that the intergroup correlations are negligible. Their performance, in terms of average termination time, is derived analytically. Under the assumption that all the schemes have the same power and sampling interval (x), their efficiencies are compared through analytical and numerical methods. Our results show that the scheme using group sampling with an optimal signal is the most efficient.

In this paper, we consider the distributed classification problem in wireless sensor networks. Local decisions made by local sensors, possibly in the presence of faults, are transmitted to the fusion center through fading channels. We integrate channel coding with the distributed fault-tolerant classification fusion approach, i.e., the DCFECC approach. We obtain a new fusion rule that combines both soft-decision decoding and local decision rules without introducing any additional redundancy. The soft decoding scheme is utilized to combat channel fading, while the DCFECC fusion structure provides excellent fault-tolerance capability.

Change detection is known to be a significant and difficult research problem in automated surveillance systems. In this paper, we propose a new change detection approach based on the least squares method, which is robust to changes in illumination and shadow conditions. This new approach is employed to design our detection and tracking system that is shown to successfully detect a moving object in a complex outdoor environment.

Registration consistency has been used as a performance evaluation criterion for mutual information based image registration techniques when the ground truth is not known. In practice, when the spatial resolutions of the two images to be registered are different, the low resolution image is often chosen as the floating image to expedite the registration process because it involves fewer pixels. However, we have found that this choice introduces problems when the difference in spatial resolution is large. This is because the resulting mutual information registration function calculated through linear interpolation or partial volume interpolation can be extremely rough that makes the optimization hard to perform and the registration result unreliable. The main contribution of this paper is the development of a size-dependent kernel to resample the high resolution reference image for joint histogram estimation. Since the size of the support of the kernel can be very large, the computational load of this approach is high and loses the advantage of using the low resolution image as the floating image. As an alternate approach, an offline preprocessing of the high resolution image is proposed in this paper. After preprocessing the high resolution reference image, conventional linear and partial volume interpolations can be employed to estimate the joint histogram efficiently. A HyMap image (6.8m/pixel) and a digital aerial photograph (0.15m/pixel) are used in our experiments to demonstrate the effectiveness of the proposed approach.

In this paper, a novel approach for unsupervised spectral unmixing in remote sensing imagery is presented. This approach is derived from independent component analysis (ICA). First, we present the limitations of Gaussian mixture model (GMM) and ICA for spectral unmixing. To overcome these limitations we have developed an approach that employs the ICA model to characterize the data generation process and have proposed an ICA mixture model (ICAMM) based approach for unsupervised spectral unmixing. This approach estimates the endmember probability density function by modeling it with a nonGaussian probability distribution. Thus, the ability to model higher order statistical properties of remote sensing imagery increases the practical applicability of ICAMM for spectral unmixing. The results from our experimental study have demonstrated the efficacy of the proposed algorithm for unsupervised spectral unmixing.

To achieve security in wireless sensor networks, it is important to he able to encrypt messages sent among sensor nodes. Keys for encryption purposes must he agreed upon by communicating nodes. Due to resource constraints, achieving such key agreement in wireless sensor networks is nontrivial. Many key agreement schemes used in general networks, such as Diffie-Hellman and public-key based schemes, are not suitable for wireless sensor networks. Pre-distribution of secret keys for all pairs of nodes is not viable due to the large amount of memory used when the network size is large. Recently, a random key pre-distribution scheme and its improvements have been proposed. A common assumption made by these random key pre-distribution schemes is that no deployment knowledge is available. Noticing that in many practical scenarios, certain deployment knowledge may be available a priori, we propose a novel random key pre-distribution scheme that exploits deployment knowledge and avoids unnecessary key assignments. We show that the performance (including connectivity, memory usage, and network resilience against node capture) of sensor networks can he substantially improved with the use of our proposed scheme. The scheme and its detailed performance evaluation are presented in this paper.

The transmission range that achieves the most economical use of energy in wireless ad hoc networks is studied under homogeneous node distribution. By assuming the knowledge of node location, we first proposed a transmission strategy to ensure the progress of data packets toward their final destinations. Then the average packet progress for a transmission range universal for all nodes is derived, which is accordingly used to determine the optimal transmission range that gives the maximum efficiency of energy consumption. Different from some previous work, our analysis does not make the assumption of large nodal density in the wireless ad hoc networks studied. Numerical and simulation results are presented to examine our analysis for wireless ad hoc networks.

This paper presents a novel error detection and correction methodology for corrupted discrete cosine transform (DCT) coefficients caused by the transmission of DCT coded images over a noisy wireless channel. This method is based on the properties of DCTs and the transmission of exact intensity values of a few reference pixels. These allow us to identify the error pattern and correct the corrupted DCT coefficients. It is possible for the receiver to correct up to t corrupted DCT coefficients in an image block with the knowledge of 2t+1 reference pixel intensity values for each image block. When the number of corrupted DCT coefficients exceeds the correction capability of the proposed algorithm, concealment techniques are employed to recover the corrupted image block by using the reference pixel intensity values. Simulation results are presented to demonstrate the performance of our algorithm.

The paper addresses the problem of registering regions of interest in two video sequences. Potential applications include blob fusion and target tracking in blurry sequences. It is assumed that the moving target is tracked successfully in one of the two sequences and is represented by a bounding box in each frame of the first sequence. The goal is to find the corresponding bounding box for each frame of the second video sequence. The registration algorithm developed is based on mutual information. To facilitate the registration process, the two cameras are assumed to be calibrated such that the geometrical transformation required to register the corresponding bounding boxes is a 2D rigid body transformation without rotation. Visual and IR video sequences are used to test the proposed approach.

An outdoor multi-camera video surveillance system operating under changing weather conditions is presented. A new confidence measure, appearance ratio (AR), is defined to evaluate automatically the sensors' performance for each time instant. By comparing their ARs, the system can select the most appropriate cameras to perform specific tasks. When redundant measurements are available for a target, the AR measures are used to perform a weighted fusion of them. Experimental results are presented on outdoor scenes under different weather conditions.

To achieve security in wireless sensor networks, it is important to be able to encrypt and authenticate messages sent among sensor nodes. Keys for encryption and authentication purposes must be agreed upon by communicating nodes. Due to resource constraints, achieving such key agreement in wireless sensor networks is non-trivial. Many key agreement schemes used in general networks, such as Diffie-Hellman and public-key based schemes, are not suitable for wireless sensor networks. Pre-distribution of secret keys for all pairs of nodes is not viable due to the large amount of memory used when the network size is large. To solve the key pre-distribution problem, two elegant key pre-distribution approaches have been proposed recently [11, 7].In this paper, we propose a new key pre-distribution scheme, which substantially improves the resilience of the network compared to the existing schemes. Our scheme exhibits a nice threshold property: when the number of compromised nodes is less than the threshold, the probability that any nodes other than these compromised nodes is affected is close to zero. This desirable property lowers the initial payoff of smaller scale network breaches to an adversary, and makes it necessary for the adversary to attack a significant proportion of the network. We also present an in depth analysis of our scheme in terms of network resilience and associated overhead.

In wireless sensor networks, sensor nodes are spread randomly over the coverage area to collect information of interest. Data fusion is used to process these collected information before they are sent to the base station, the observer of the sensor network. We study the security of the data fusion process in this work. In particular, we propose a witness-based solution to assure the validation of the data sent from data fusion nodes to the base station. We also present the theoretical analysis for the overhead associated with the mechanism, which indicates that even in an extremely harsh environment the overhead is low for the proposed mechanism.

Registration is the basic image processing operation in a variety of tasks such as multi-source classification, image fusion and change detection. Automatic intensity based registration techniques are gaining importance. In this paper, we investigate an intensity based technique that utilizes mutual information as the similarity measure. We apply this technique to perform multi-sensor image registration. The performance of a number of joint histogram estimation methods for the determination of mutual information has been evaluated using a measure called registration consistency. These methods include partial volume interpolation, cubic convolution interpolation, linear interpolation, and nearest neighborhood interpolation. The results show that partial volume interpolation produces the most reliable registration consistency. Nearest neighbor interpolation outperforms linear and cubic convolution interpolation.

Occurrence of mixed pixels in remote sensing images is a common phenomenon particularly in coarse spatial resolution images. In these cases, sub-pixel or soft classification may be preferred over conventional hard classification. However, sub-pixel classification fails to account for the spatial distribution of class proportions within the pixel. A better approach may be to generate a land cover map at a finer resolution from the coarse resolution images based on image models that accurately characterize the spatial distribution of the classes. The resulting fine resolution map may be called a sub-pixel or super resolution map. In this paper, an approach based on Markov random fields is introduced to generate sub-pixel land cover maps from remote sensing images dominated by mixed pixels.

In this paper, we propose an image quality measure that closely resembles the image quality measure used by the human visual system. In our application, we are more concerned with localized errors due to transmission than the global errors due to compression, and we develop an approach that is appropriate for this purpose. We use psychophysical experiments to determine the visibility threshold level for each frequency component of each image block. The visibility threshold levels are adjusted according to the background variation and intensity of the image block. Only those frequency components of an error that exceed the corresponding threshold level are included in the computation of the proposed image quality measure. The proposed image quality measure gives the PSNR of the errors that are visible to the eye and the percentage area of the extent of the visible errors in the image.

Energy in a wireless sensor network (WSN) is a precious resource. Deployment of mobile sensors in a WSN is an energy consuming process and it should be carefully designed. In this paper, we propose an intelligent energy-efficient deployment algorithm for cluster-based WSN by a synergistic combination of cluster structuring and a peer-to-peer deployment scheme. Performance of our algorithm is evaluated in terms of coverage, uniformity, and time and distance traveled till the algorithm converges. Our algorithm is shown to exhibit excellent performance.

Sensor deployment is an important problem in mobile wireless sensor networks. This paper presents a distributed self deployment algorithm for mobile sensors. Performance metrics to evaluate algorithm performance are coverage, uniformity, time and distance traveled till the algorithm converges. Our algorithm is compared with a simulated annealing based algorithm for deployment and is shown to exhibit excellent performance.

Efficient bandwidth management is necessary in order to provide high quality service to users in a multimedia wireless/mobile network. In this paper, we propose an on-line load balancing algorithm with preemption. This technique is able to balance the traffic load among cells accommodating heterogeneous multimedia services while ensuring efficient bandwidth utilization. The most important features of our algorithm are its adaptability, flexibility and responsiveness to current network conditions. In addition, our online scheme to control bandwidth adaptively is a cell-oriented approach. This approach has low complexity making it practical for real cellular networks. Simulation results indicate the superior performance of our algorithm.

An equicorrelation-based multiuser communication (ECBMC) scheme for direct-sequence code-division multiple-access (DS-CDMA) systems is presented. The ECBMC receiver has low computational complexity that is comparable to that of the conventional detector. By using the equality of cross correlations, the ECBMC scheme can completely eliminate multiple-access interference (MAI) in a synchronous single-path DS-CDMA network. The system performance is independent of the number of active users. The scheme is extended to include the effects of multipath fading. It is able to suppress a major portion of the MAI. This proposed ECBMC scheme is quite attractive for an MAI-dominant environment.

Accurate registration of multitemporal remote sensing images is essential for various change detection applications. Mutual information has recently been used as a similarity measure for registration of medical images because of its generality and high accuracy. Its application in remote sensing is relatively new. There are a number of algorithms for the estimation of joint histograms to compute mutual information, but they may suffer from interpolation-induced artifacts under certain conditions. In this paper, we investigate the use of a new joint histogram estimation algorithm called generalized partial volume estimation (GPVE) for computing mutual information to register multitemporal remote sensing images. The experimental results show that higher order GPVE algorithms have the ability to significantly reduce interpolation-induced artifacts. In addition, mutual-information-based image registration performed using the GPVE algorithm produces better registration consistency than the other two popular similarity measures, namely, mean squared difference (MSD) and normalized cross correlation (NCC), used for the registration of multitemporal remote sensing images.

Mutual information (MI)-based image registration has been found to be quite effective in many medical imaging applications. To determine the MI between two images, the joint histogram of the two images is required. In the literature, linear interpolation and partial volume interpolation (PVI) are often used while estimating the joint histogram for registration purposes. It has been shown that joint histogram estimation through these two interpolation methods may introduce artifacts in the MI registration function that hamper the optimization process and influence the registration accuracy. In this paper, we present a new joint histogram estimation scheme called generalized partial volume estimation (GPVE). It turns out that the PVI method is a special case of the GPVE procedure. We have implemented our algorithm on the clinically obtained brain computed tomography and magnetic resonance image data furnished by Vanderbilt University. Our experimental results show that, by properly choosing the kernel functions, the GPVE algorithm significantly reduces the interpolation-induced artifacts and, in cases that the artifacts clearly affect registration accuracy, the registration accuracy is improved.

The paper presents a novel error detection and correction methodology for corrupted coefficients caused by the transmission over a noisy channel of images coded using orthogonal transforms. The method is based on the orthogonal property of image transforms, such as the discrete cosine transform. A few reference pixel intensities of each image block are replaced by a predetermined intensity level prior to transmission. This allows the receiver to identify and correct the error pattern generated by the corruption of DCT coefficients. It is possible to correct t corrupted DCT coefficients in an image block by altering the value of 2t+1 pixel intensity levels to a predetermined value in each image block. After recovering the corrupted DCT coefficients, if necessary, and reconstructing the image, the original intensity level of each reference pixel is estimated by averaging the intensity levels of the adjacent pixels. The resulting image is indistinguishable from the original image when examined by a human. The algorithm does not require any channel overhead. An illustrative example is presented to demonstrate the performance of our algorithm.

In this paper, a brief state of the art in the field of advanced surveillance systems is given. The goal is to provide a framework under which to describe the different contributions to research presented in the special session on multisensor surveillance systems based on image and video data. Some problems of interest considered by current research are highlighted starting from the necessity of augmented perceptual capabilities, to the use of video object based coding techniques, going to the capability of extending current video representation techniques in order to make it possible to easily correlate, recognize, index and retrieve multisensor video observations.

We have developed a tool that facilitates dynamically updating beliefs with time. This tool addresses directed probabilistic inference networks that may contain cycles, and takes into account the time delays associated with observations and decisions. Relevance of different observers may decay at different rates in the same application, and the belief in a hypothesis decays towards the associated prior probability. Simple models with few parameters have been implemented, with a user interface that facilitates changes to the structure and parameters of the graphical model, and associated conditional probabilities.

This paper describes a new algorithm for feature extraction in hyperspectral images based on independent component analysis (ICA). The improvement introduced aims at reducing the computation times without decreasing the accuracy. Instead of using the entire image, we perform ICA processing on a subset of representative pixel vectors obtained through spectral screening. Spectral screening is a technique that measures the similarity between pixel vectors by calculating the angle between them. In multispectral/hyperspectral imagery, the independent components can be associated with features present in the image. ICA projects them in different image frames. The features are separated using an algorithm involving gradient descent minimization of the mutual information between frames. The effectiveness of the proposed algorithm (SSICA) has been tested by performing target detection on data from the Hyperspectral Digital Imagery Collection Experiment (HYDICE). Small targets present in the image are separated from the background in different frames and the information pertaining to them is concentrated in these frames. Further selection using kurtosis, skewness and histogram thresholding lead to automated detection of the targets allowing a quantitative assessment of the results. When compared with a target detection ICA algorithm previously introduced by the authors, SSICA achieves similar accuracy, and, at the same time, considerable speedup is obtained.

This paper addresses the problem of image change detection (ICD) based on Markov random field (MRF) models. MRF has long been recognized as an accurate model to describe a variety of image characteristics. Here, we use the MRF to model both noiseless images obtained from the actual scene and change images (CIs), the sites of which indicate changes between a pair of observed images. The optimum ICD algorithm under the maximum a posteriori (MAP) criterion is developed under this model. Examples are presented for illustration and performance evaluation.

We consider the problem of binary hypothesis testing using binary decisions from independent and identically distributed (i.i.d). sensors. Identical likelihood-ratio quantizers with threshold /spl lambda/ are used at the sensors to obtain sensor decisions. Under this condition, the optimal fusion rule is known to be a k-out-of-n rule with threshold k. For the Bayesian detection problem, we show that given k, the probability of error is a quasi-convex function of /spl lambda/ and has a single minimum that is achieved by the unique optimal /spl lambda//sub opt/. Except for the trivial situation where one hypothesis is always decided, we obtain a sufficient and necessary condition on /spl lambda//sub opt/, and show that /spl lambda//sub opt/ can be efficiently obtained via the SECANT algorithm. The overall optimal solution is obtained by optimizing every pair of (k, /spl lambda/). For the Neyman-Pearson detection problem, we show that the use of the Lagrange multiplier method is justified for a given fixed k since the objective function is a quasi-convex function of /spl lambda/. We further show that the receiver operating characteristic (ROC) for a fixed k is concave downward.

The problem of diversity signal reception using quantized data is considered. This problem arises in situations where multiple receivers are employed over a geographically broad area. We propose a method for the design of the quantizers that are used by the receiver to make soft decisions. This quantizer minimizes the mean-squared error between the quantizer output and the log-likelihood ratio associated with the receiver observation. The soft decisions are then combined at a central location to yield the final decision. Our quantizers are then applied to the coherent detection of a BPSK signal over Rayleigh fading channels. Simulation results show that using coarse quantizers yields close to optimum performance.

This paper presents experimental results for a parallel pipeline STAP system with I/O task implementation using the parallel file systems on the Intel Paragon and the IBM SP. In our previous work, a parallel pipeline model was designed for radar signal processing applications on parallel computers. Based on this model, we implemented a real STAP application which demonstrated the performance scalability of this model in terms of throughput and latency. In this paper we study the effect on system performance when the I/O task is incorporated in the parallel pipeline model. There are two alternative for I/O implementation: embedding I/O in the pipeline or having a separate I/O task. From these two I/O implementations, we discovered that the latency may be improved when the structure of the pipeline is reorganized by merging multiple tasks into a single task. All the performance results shown in this paper demonstrated the scalability of parallel I/O implementation on the parallel pipeline STAP system.

This paper presents a multiuser detection scheme based on cell diversity for DS/CDMA systems. This scheme may be employed when multiple base stations can receive the signal from a mobile. Each participating base station uses a decorrelator to remove multiple access interference and obtains the decision statistic for each user. It then quantizes the decision statistic and transmits it to a central processor. Based on these received quantized cell decision statistics, the central processor determines the received bit for each user. The quantizer proposed here is a MMSE log likelihood ratio quantizer, and is obtained via a Lloyd-Max type of algorithm. The decision-making rule at the central processor is derived based on multisensor signal detection theory. Numerical results show that significant performance improvement over single cell multiuser detection can be obtained at a moderate cost in terms of additional signal processing and communication bandwidth.

A multi-sensor decision fusion scheme is presented in which the probabilities associated with the local sensor decisions are known to vary in a nonrandom fashion around their design values. The uncertainties associated with the local decisions are modeled by means of fuzzy sets. A Bayesian approach is used to design the optimum fusion rule for the case where the local sensor decisions are statistically independent across the sensors. In order to reach a crisp decision, the global Bayesian risk is defuzzified using a criterion for mapping fuzzy sets on to the real line. The performance of the optimum fusion rule obtained is illustrated by means of a numerical example.

We propose a wavelet domain diversity combining method to combat errors during image transmission on wireless channels. For images represented in the wavelet domain, diversity is used to obtain multiple data streams corresponding to the transmitted image at the receiver. These individual image data streams are combined to form a composite image with higher perceptual quality. Both uncompressed and compressed images are considered. The SPIHT algorithm is used for image compression. Diversity combining methods for both uncompressed and compressed images exploit the characteristics of the wavelet transform. For compressed images, unequal error protection is employed in conjunction with diversity combining. Simulation results demonstrate that the quality of the received image can be significantly improved.

A number of technologies are being developed for Concealed Weapon Detection (CWD). Use of appropriate processing techniques will be very important to the success of such technologies. This article describes digital image processing procedures currently being investigated to enhance the detection of weapons concealed underneath clothing.

We present an approach to automatically register and fuse IR and MMW images for concealed weapon detection. The distortion between the two images is assumed to be a rigid body transformation without rotation and we assume that the scale factor can be found from both the sensor parameters and the distance ratio of the object to the two sensors. Our registration procedure involves image segmentation, binary correlation and other image processing algorithms. Our fusion method involves a pyramidal image decomposition scheme based on the wavelet transform. Performance of the image registration and image fusion algorithm is illustrated through an example.

Busy tone multiple access protocols have been used in multihop networks to reduce the effect of the hidden terminal problem. Due to complexity, the performance of these protocols for large networks has not been analyzed. In this paper, using a Markov chain model and an approximation, we are able to analyze and evaluate the throughput performance of the non-persistent CSMA protocol, the conservative busy tone multiple access (C-BTMA) protocol and the ideal destination-based busy tone multiple access (ID-BTMA) protocol for large networks. The throughput comparison of the protocols is given. The results show that in a large multihop network, the BTMA protocols have a better performance than the non-persistent CSMA protocol and the ID-BTMA protocol has a better performance than the C-BTMA protocol at light channel loads.

Busy tone multiple access protocols have been used in multihop networks to reduce the effect of the hidden terminal problem. This paper demonstrates another approach to reduce the effect of the hidden terminal problem namely the use of multiple channel schemes. A protocol that uses both the busy tone and the multiple channel techniques achieves the best performance. Using a Markov chain model and an approximation, the throughput performance of the multiple channel non-persistent CSMA protocol and the multiple channel conservative BTMA protocol in a large network is evaluated and compared. The results show that the multichannel CSMA and BTMA schemes exhibit a better performance over their single channel counterparts in a multihop network.

This paper considers a system in which observations at a remote sensor are sampled and quantized, and then transmitted to a processor for signal detection. A constraint on the transmission rate is assumed. The problem of bit allocation among different samples is studied and illustrated by means of an example.

In this paper basic results on distributed detection are reviewed. In particular we consider the parallel and the serial architectures in some detail and discuss the decision rules obtained from their optimization based an the Neyman-Pearson (NP) criterion and the Bayes formulation. For conditionally independent sensor observations, the optimality of the likelihood ratio test (LRT) at the sensors is established. General comments on several important issues are made including the computational complexity of obtaining the optimal solutions the design of detection networks with more general topologies, and applications to different areas.

A relative entropy based approach to image thresholding was proposed recently. It was demonstrated that this method was successful for image thresholding. Relative entropy is a member of the class of Ali-Silvey distance measures. In this paper we generalize the relative entropy based approach and present image thresholding algorithms based on the class of Ali-Silvey distance measures. A number of members of this class are selected and used for implementation in image thresholding algorithms. Performance is evaluated by applying these algorithms to several images and comparing them to a few histogram based thresholding methods.

A heuristic approach to design quantizers for signal detection is presented. Quantizer parameters are obtained that minimize a tight upper bound on the probability of error. This approach is applied to the design of distributed detection systems. Numerical examples are presented to illustrate its near-optimum performance.

A decentralized detection system with feedback and memory using the Bayesian formulation is investigated. The optimization of this system results in a likelihood ratio test at the local detectors for statistically independent observations. In addition, local detector thresholds and the system probability of error are shown to be a function of the fed back global decision. The issue of data transmission between local detectors and the fusion center is addressed. Two protocols are proposed and studied to reduce data transmissions. Numerical examples are also presented for illustration.

This paper integrates the evidential reasoning methodology with the parallel distributed learning paradigm of artificial neural networks (ANN). As such, this work presents an algorithm for the detection and, if possible, subsequent correction of the errors in the neuron responses in the output layer of the multiple adaptive linear element (MADALINE) ANN. A geometrical perspective of the MADALINE ANN processing methodology is provided. This perspective is then used to formulate a statistical specification to identify and quantify the sources of uncertainties in the MADALINE processing methodology. A new algorithm, EMRII, is then developed as an extension to the original MRII (MADELINE rule II) algorithm, to formulate support and plausibility measures based on the statistical specification. The support and plausibility measures, thus formulated, are indicative of the degree of confidence of the ANN, in regards to the correctness of its outputs. Based on the support measure, a scheme utilizing two thresholds is proposed to facilitate the interpretation of the support values for error prediction in the ANN responses. Finally, simulation results for the application of the EMRII algorithm in the prediction of erroneous responses in an example problem is presented. These simulation results highlight the error detection capabilities of the EMRII algorithm.

In this paper, we present a new upper bound on the minimum probability of error of Bayesian decision systems for statistical pattern recognition. This new bound is continuous everywhere and is shown to be tighter than several existing bounds such as the Bhattacharyya and the Bayesian bounds. Numerical results are also presented.>

The problem of distributed Bayesian signal detection is addressed. The problem is reformulated and a new design approach is presented that allows the use of efficient optimization algorithms.>

A flow control procedure is presented for virtual circuit computer networks with multimedia data traffic. It is assumed that the data packets belong to different priority groups. Power is used as the performance measure for a virtual circuit (VC). The input rate controller of every virtual circuit selects a throughput which maximizes its own objective function. A distributed algorithm is given which can be executed by the input rate controllers of the VC's without the global knowledge of the network. It is shown that the algorithm coverages to the optimum of the objective function of every virtual circuit and the solution is shown to be independent of the initial throughputs. Some network examples are solved by analytical and numerical methods.>

The Gaussian multichannel binary detection problem is considered. A multichannel generalized likelihood ratio is implemented using a model-based approach where the signal is assumed to be characterized by an autoregressive vector process. Detection performance is obtained for the special case where the underlying processes are assumed to have known autoregressive process parameters. Specifically, results for two-channel signal vectors containing various temporal and cross-channel correlation are obtained using a Monte Carlo procedure. These results are plotted versus signal-to-noise ratio and are shown to be bounded by available optimal detection curves. The two-channel detection results are shown to decrease as (S/N)/sub 2/ decreases and approach the superior angle channel performance asymptotically. A likelihood ratio for a more general class of processes with correlated Gaussian quadrature components is noted.>

Four random test generation strategies are compared to determine their relative effectiveness: equiprobable 0s and 1s; two weighted random pattern generation algorithms; and the maximum output information entropy principle. The test generation strategies are compared at a variety of target fault coverages. Two statistically based metrics are used to evaluate the techniques: a large-sample test of the difference of means and an upper confidence limit. The two weighted random test pattern generation strategies are found to be generally superior to equiprobable 0s and 1s and maximum output entropy. For a given logic circuit, the same technique is not necessarily optimal at every fault coverage.>

A computationally efficient approach to the design of decentralized Bayesian detection systems is presented. This procedure is based on an alternate representation of the minimum average cost in terms of a modified form of the Kolmogorov variational distance. The utility of the approach is demonstrated by applying it to the design and performance evaluation of three decentralized detection structures. In all these structures, the design of the optimum systems reduces to the optimization of a single function of a certain number of variables. Numerical examples are presented for illustration.>

Performance aspects of minimum probability of error decentralized detection systems are considered. The relationship between the probability of error of a maximum a posteriori probability receiver and the Kolmogorov variational distance is derived. This relationship is used to derive the minimum probability of error for an n-sensor decentralized system in terms of the sensor decisions. A design procedure is presented that results in the optimum global minimum probability of error. The design of three suboptimum systems based on the Bharttacharyya distance, Kullback discrimination, and optimization of the probability of error at the local level is described. A numerical example comparing the performance of the optimum system to the performance of the suboptimum systems is also included.>

The distributed detection problem is considered from an information-theoretic point of view. An entropy-based cost function is used for system optimization. This cost function maximizes the amount of information transfer between the input and the output. Distributed detection system topologies with and without a fusion center are considered, and an optimal fusion rule and optimal decision rules are derived.>

The signal detection problem is considered for the case in which distributed sensors are used and a global decision is desired. Local decisions from the sensors are fed to a data fusion center, which yields a global decision based on a fusion rule. A Bayesian formulation of the problem is considered, and a person-by-person optimization of the overall system is carried out. The special case of identical detectors with independent observations is considered as well. An illustrative example is presented.>

The problem of distributed Bayesian hypothesis testing with distributed data fusion is examined. In the distributed data fusion configuration, some signal processing is done locally at the sensors and the partial results are transmitted to the other sensors for further processing and fusion. Global results are obtained at each of the sensors. This system configuration is attractive for many applications from the survivability point of view. The problem is formulated, and optimum decision rules and fusion schemes are obtained that minimize the Bayesian risk at each sensor. An example is presented for illustration.>

In this correspondence, we present an algorithm based on information theoretic concepts for the design of efficient sequential fault diagnosis experiments for permanent faults in modular systems.

Discrete-time analysis of two schemes for multiplexing voice and data is presented. In each scheme voice and data are multiplexed using the movable boundary frame allocation scheme. In the first scheme, speech activity detectors (SAD's) are not used, and hence, the variations in the voice traffic are only due to the on/off characteristics of voice. In the second scheme, SAD's are employed so that talker silences can he utilized for transmission of additional voice and/or data. In this scheme, the multiplexer performs digital speech interpolation as well as movable boundary frame allocation. The performance measures considered are probability of loss for voice calls, probability of speech clipping, speech packet rejection ratio, and the expected data message delay. In the case of the multiplexer with SAD, a tradeoff exists between data message delay and speech interpolation advantage. Some numerical examples are presented which illustrate the performance of the two multiplexers.

This paper considers the modeling of oscillator phase instability and the resulting spectral dispersion. A phase covariance matrix method is developed for determining the autocorrelation function and the power spectral density of the oscillator sinusoidal RF signal when corrupted by a superposition of a white phase random process and a random walk phase random process. By limiting the discussion to phase covariance matrices, it is shown that the direct use of a certain class of nonstationary phase random processes leads to stationary RF signal autocorrelation functions and associated power spectral densities. This is so despite the nonstationary phase driving force. The procedures provided here are also applied towards the determination of the average autocorrelation function and the average spectrum when the cisoidal oscillator signal undergoes modulation. Modulating waveforms used as examples include the CW, the infinite pulse train, and the finite pulse train.

In this correspondence we consider the problem of the construction of efficient sequential fault location experiments for permanent faults. The construction of optimum sequential experiments is an NP-complete problem and, therefore, a heuristic approach for the design of near-optimum sequential experiments is considered. The approach is based on information theoretic concepts and the suggested algorithm for the construction of near-optimum sequential fault location experiments is systematic, has a sound theoretical justification, and yet has low design complexity.

The problem of conversion of decision tables to decision trees is treated. In most cases, the construction of optimal decision trees is an NP-complete problem and, therefore, a heuristic approach to this problem is necessary. In this heuristic approach, an application of information theoretic concepts to construct efficient decision trees for decision tables which may include "don't care" entries is made. In contrast to most of the existing heuristic algorithms, this algorithm is systematic and is intuitively appealing from an information theoretic standpoint. The algorithm has low design complexity and yet provides near-optimal decision trees.

Combined quantization-detection of uncertain signals is considered. The distribution of the uncertain signal is not completely known, although it is known to belong to a finite set of possibilities. A simultaneous quantization-detection system consisting of a detector and a bank of quantizers is suggested. An example is presented for illustration.

This correspondence discusses three analytical models for intermittent faults in digital systems. These models attempt to represent the stochastic behavior of intermittent faults accurately. The models find applicatiońs in predicting the performance of fault detection algorithms. A fault detection procedure is described and its performance is examined based on the analytical models. A numerical example is presented which illustrates the performance prediction of the fault detection algorithm.

In this paper, an adaptive receiver with memory for fading communication channels is considered. The receiver consists of an estimator and a detector. The estimator is a finite-memory MMSE estimator with decision-feedback which minimizes the probability of error of the receiver. Asymptotic approximations are employed to derive an adaptive decision role based on the estimate. An example is presented where the receiver with memory is shown to perform better than an optimum receiver without memory.