Advancements in Biological Signal Processing (BSP) and Machine-Learning (ML) models have paved the path for development of novel immersive Human-Machine Interfaces (HMI). In this context, there has been a surge of significant interest in Hand Gesture Recognition (HGR) utilizing Surface-Electromyogram (sEMG) signals. This is due to its unique potential for decoding wearable data to interpret human intent for immersion in Mixed Reality (MR) environments. To achieve the highest possible accuracy, complicated and heavy-weighted Deep Neural Networks (DNNs) are typically developed, which restricts their practical application in low-power and resource-constrained wearable systems. In this work, we propose a light-weight hybrid architecture (HDCAM) based on Convolutional Neural Network (CNN) and attention mechanism to effectively extract local and global representations of the input. The proposed HDCAM model with 58, 441 parameters reached a new state-of-the-art (SOTA) performance with 82.91% and 81.28% accuracy on window sizes of 300 ms and 200 ms for classifying 17 hand gestures. The number of parameters to train the proposed HDCAM architecture is 18.87× less than its previous SOTA counterpart.

Advances in biosignal signal processing and machine learning, in particular Deep Neural Networks (DNNs), have paved the way for the development of innovative Human-Machine Interfaces for decoding the human intent and controlling artificial limbs. DNN models have shown promising results with respect to other algorithms for decoding muscle electrical activity, especially for recognition of hand gestures. Such data-driven models, however, have been challenged by their need for a large number of trainable parameters and their structural complexity. Here we propose the novel Temporal Convolutions-based Hand Gesture Recognition architecture (TC-HGR) to reduce this computational burden. With this approach, we classified 17 hand gestures via surface Electromyogram (sEMG) signals by the adoption of attention mechanisms and temporal convolutions. The proposed method led to 81.65% and 80.72% classification accuracy for window sizes of 300 ms and 200 ms, respectively. The number of parameters to train the proposed TC-HGR architecture is 11.9 times less than that of its state-of-the-art counterpart.

This article proposes a resilient framework for optimized consensus using a dynamic event-triggering (DET) scheme, where the multiagent system (MAS) is subject to denial-of-service (DoS) attacks. When initiated by an adversary, DoS blocks the local and neighboring communication channels in the network. A distributed DET scheme is utilized to limit transmissions between the neighboring agents. A novel convex optimization approach is proposed that simultaneously co-designs all unknown control and DET parameters. The optimization is based on the weighted sum approach and increases the interevent interval for a predefined consensus convergence rate. In the presence of DoS, the proposed co-design framework is beneficial in two ways: 1) the desired level of resilience to DoS is included as a given (desired) input and 2) the upper bound for guaranteed resilience associated with the proposed co-design approach is less conservative (larger) compared to those obtained from other analytical solutions. A structured tradeoff between relevant features of the MAS, namely, the consensus convergence rate, frequency of event triggerings, and level of resilience to DoS attacks, is established. Simulations based on nonholonomic mobile robots quantify the effectiveness of the proposed implementation.

This work is motivated by the recent advancements of Deep Neural Networks (DNNs) for myoelectric prosthesis control. In this regard, hand gesture recognition via surface Electromyogram (sEMG) signals has shown a high potential for improving the performance of myoelectric control prostheses. Although the recent researches in hand gesture recognition with DNNs have achieved promising results, they are still in their infancy. The recent literature uses traditional supervised learning methods that usually have poor performance if a small amount of data is available or requires adaptation to a changing task. Therefore, in this work, we develop a novel hand gesture recognition framework based on the formulation of FewShot Learning (FSL) to infer the required output given only one or a few numbers of training examples. Thus in this paper, we proposed a new architecture (named as FHGR which refers to "Few-shot Hand Gesture Recognition") that learns the mapping using a small number of data and quickly adapts to a new user/gesture by combing its prior experience. The proposed approach led to 83.99% classification accuracy on new repetitions with few-shot observations, 76.39% accuracy on new subjects with few-shot observations, and 72.19% accuracy on new gestures with few-shot observations.

Accelerating Magnetic Resonance Imaging (MRI) acquisition process is a critical and challenging medical imaging problem as basic reconstructions obtained from the undersampled k-space often exhibit blur or aliasing effects. Despite its significance and recent advancements in the field of deep neural networks (DNNs), development of deep learning-based MRI reconstruction algorithms is not yet flourished due to unavailability of public and large datasets. The recently introduced large-scale fastMRI dataset is posed to change this state of affairs, however, existing DNN solutions developed based on fastMRI require learning a large number of parameters rendering their practical application limited due to the strict low-latency requirements of real-time MRI acquisition. In this paper, we aim to address this drawback and target reducing the computational cost associated with single-coil reconstruction task. More specifically, the paper proposes a novel deep model referred to as the SepUnet architecture achieving significant reduction in the required number of parameters while maintaining high accuracy. Performance of the proposed SepUnet architecture is evaluated based on the official test dataset from fastMRI illustrating accuracy improvement in comparison to its published counterparts while requiring significantly reduced number of trainable parameters (i.e., the SepUnet architecture is much faster and lighter than its counterparts).

Referred to as the RQ-CEASE, this article proposes a resilient framework for quantized, event-triggered (ET), sampled-data, average consensus in multiagent systems subject to denial of service (DoS) attacks. The DoS attacks typically attempt to block the measurement and communication channels in the network. Two different ET approaches are considered in RQ-CEASE based on whether the ET threshold is dependent or independent of the state dynamics. For each approach, we analytically derive operating conditions (bounds) for the sampling period and ET design parameter guaranteeing the input-to-state stability (ISS) of the network under DoS attacks. In addition, upper bounds for duration and frequency of DoS attacks are derived within which the network remains operational. For each approach, the maximum possible error from the average consensus value is derived. The resilience of the two RQ-CEASE approaches to DoS attacks, as well as their steady-state consensus error, and transmission savings are compared both analytically and using simulations.

The widespread adoption of wearable devices and social media is generating population-scale data about people's behavior as situated in their everyday lives. In this paper, an embedded Cyber-Human System (CHS) is used to monitor a person's mental health via physiological, behavioral, and social data. These three data streams will then be relayed to an application used by a professional within the medical industry, to allow remote monitoring of the user's mental health without the uncertainty that face-to-face interviews can introduce. Experimentation and verification have been conducted on a group of test subjects with different test scenarios including a happy, sad, angry, and neutral state of being. We propose to use physiological data to predict a person's emotional state using a machine learning classification algorithm. The proposed system can distinguish between the different emotional states with an accuracy of 92.9%.

Recently, the security of heterogeneous Internet of Things (IoT) devices has become a major concern of every Wireless Sensor Network (WSN). Efficient power measurement in smart IoT devices has become one of the key research topics. Power optimization of current IoT technology allows the development of many IoT-based applications. In this paper, we design and develop a Cyber-Physical System (CPS) to detect IoT security threats via behavioral power profiling of a heterogeneous wireless sensor device using a smartphone. Experimentation and verification have been conducted on a sample of smart IoT devices with different test scenarios including the device in an Idle state, Active state, Idle, and Active with distributed denial-of-service (DDoS) attack, Idle, and Active with a man-in-the-middle (MitM) attack. We propose to use the device power consumption rate with and without attack to predict and detect a security threat using logistic regression techniques. The proposed system can detect a potential security threat with an average accuracy of 74% and a device high of 85%.

Capitalizing on the goal of addressing identified shortcomings of recent solutions developed for recognition tasks via sparse multichannel surface Electromyography (sEMG) signals, the paper proposes a novel deep learning model, referred to as the XceptionTime architecture. The proposed innovative XceptionTime architecture is designed by integration of depthwise separable convolutions, adaptive average pooling, and a novel no-linear normalization technique. At the hearth of the proposed architecture is several XceptionTime modules concatenated in series fashion designed to captures both temporal and spatial information-bearing contents of the sparse multichannel sEMG signals without the need for data augmentation and manual design of feature extraction. In addition to instruction of the new XceptionTime module, by integration of adaptive average pooling, instead of fully connected layers, and utilization of a novel non-linear normalization approach, the proposed architecture is less prone to overfitting, more robust to temporal translation of the input, and more importantly is independent from the input window size, i.e., there is no need to change/reconfigure the architecture by changing the size of the input sequence. Finally, by utilizing the depthwise separable convolutions, the XceptionTime network has far less parameters resulting in less complex network.

The paper investigates resilient conditions for the event-triggered average consensus problem under denial of service (DoS) attack and uncertainty in the network. To reach average consensus in the multiagent system, each node communicates with its neighbouring nodes only if an event-triggering condition is satisfied. In the presence of the DoS attack, no information can be communicated within the network. In addition to DoS, the information being transmitted through the communication channels is perturbed due to uncertainty in the nominally designed edge weights of the network. Using the Lyapunov theorem, we analytically determine the maximum allowable duration and frequency for the DoS attack and maximum network uncertainty for which the exponential event-triggered consensus convergence stays preserved. The practicability of the proposed event-triggering scheme is studied by proving the Zeno-behaviour exclusion. The performance of the implementation is quantified through simulations in different scenarios.

The paper proposes a Hybrid Deep Neural Network (HDNN) framework for remaining useful life (RUL) estimation for prognostic health management applications. The proposed HDNN framework is the first hybrid model designed for RUL estimation that integrates two deep learning architectures simultaneously and in a parallel fashion. More specifically, in contrary to the majority of existing data-driven prognostic approaches for RUL estimation, which are developed based on a single deep model and can hardly maintain satisfactory generalization performance across various prognostic scenarios, the proposed HDNN framework consists of two parallel paths (one based on Long Short Term Memory (LSTM) and one based on convolutional neural networks (CNN)) followed by a fully connected multilayer fusion neural network, which acts as the fusion center combining the outputs of the two paths to form the target RUL. The proposed HDNN framework is tested on the NASA commercial modular aero-propulsion system simulation (C-MAPSS) dataset. Our comprehensive experiments and comparisons with several recently proposed RUL estimation methodologies developed based on the same data-sets show that the proposed HDNN framework significantly outperforms all its counterparts in the complicated prognostic scenarios with increased number of operating conditions and fault modes.

The paper proposes a novel distributed, sampled-data, event-triggered algorithm with quantized information exchange for average consensus (Q-CEASE) in multi-agent/multi-sensor networks. Q-CEASE communicates quantized information with its neighbouring nodes only if a discretized event-triggering condition is satisfied. Both design and implementation of Q-CEASE are distributed and do not require a fusion center. The design stage determines its operating region in terms of the sampling period and transmission thresholds for the constituent nodes. A minimum exponential rate for consensus convergence is guaranteed using the Lyapunov stability theorem. The performance of the Q-CEASE algorithm is quantified through Monte-Carlo simulations on randomized networks.

A major challenge of free-hand palpation ultrasound elastography (USE) is estimating the displacement of RF samples between pre- and post-compressed RF data. The problem of displacement estimation is ill-posed since the displacement of one sample by itself cannot be uniquely calculated. To resolve this problem, two categories of methods have emerged. The first category assumes that the displacement of samples within a small window surrounding the reference sample is constant. The second class imposes smoothness regularization and optimizes an energy function. Herein, we propose a novel method that combines both approaches, and as such, is more robust to noise. The second contribution of this work is the introduction of the L1 norm as the regularization term in our cost function, which is often referred to as the total variation (TV) regularization. Compared to previous work that used the L2 norm regularization, optimization of the new cost function is more challenging. However, the advantages of using the L1 norm are twofold. First, it leads to substantial improvement in the sharpness of displacement estimates. Second, to optimize the cost function with the L1 norm regularization, we use an iterative method that further increases the robustness. We name our proposed method tOtal Variation Regularization and WINDow-based time delay estimation (OVERWIND) and show that it is robust to signal decorrelation and generates sharp displacement and strain maps for simulated, experimental phantom and in-vivo data. In particular, OVERWIND improves strain contrast-to-noise ratio (CNR) by 27.26%, 144.05%, and 49.90% on average in simulation, phantom, and in-vivo data, respectively, compared to our recent Global Ultrasound Elastography (GLUE) method.

This paper proposes a distributed guaranteed-performance event-triggered average consensus (GP-ETAC) algorithm for multi-agent/sensor networks. The proposed GP-ETAC approach is distributed and event-triggered in the sense that the agents selectively limit their transmissions to local neighbourhoods when certain triggering conditions are satisfied. Using the Lyapunov stability theorem, a novel cost function is optimized to compute consensus design parameters (namely, the overall control gain and local event-triggering thresholds). The proposed cost function provides a structured trade-off between the number of local transmissions and the rate of consensus convergence. The performance of the GP-ETAC approach is evaluated through Monte-Carlo simulations.

This paper proposes a robust distributed event-triggered approach for consensus in linear multi-agent systems (MAS) with uncertain network topologies. To achieve consensus, each agent transmits its information only when a certain event-triggering condition is fulfilled. The connection weights in the network are uncertain and hence the information received by each agent is unreliable. In such an uncertain topology, the objective is to co-design robust consensus parameters (namely, the state transmission threshold and local control gains) that collectively ensure an exponential rate for consensus convergence. An objective function incorporating the transmission load and control effort is minimized to compute the design parameters. Numerical simulations quantify the effectiveness of the proposed event-triggered consensus approach in a second-order MAS.

The pedestrian detection algorithms form a key component in the multiple pedestrian tracking (MPT) systems. Despite efforts to detect a pedestrian accurately, it is still a challenging task. We propose a novel and efficient online method to improve the performance of the multiple person/pedestrian detector by introducing novel post-processing steps. These steps use an adaptive approach to determine both area and confidence score constraints for the output of any given multiple pedestrian detector. In this paper, we focus on pedestrian detection in video surveillance applications that require an automated, accurate and precise pedestrian detection algorithm. We demonstrate that the new steps make the multiple pedestrian detector more accurate, precise and tolerant to false positive detections. This is illustrated by evaluating the performance of the proposed method in test video sequences taken from the Pedestrian Detection Challenge, Multiple Object Tracking Benchmark (MOT Challenge 2017).

The resolution of ultrasound (US) images is limited by physical constraints and hardware restrictions, such as the frequency, width and focal zone of the US beam. Different interpolation methods are often used to increase the sampling rate of ultrasound images. However, interpolation methods generally introduce blur in images. Herein, we present a super resolution (SR) algorithm for reconstruction of the B-mode images using the information from the envelope of radio frequency (RF) data. Our method is based on utilizing repetitive data in the nonlocal neighborhood of samples. The performance of the proposed approach is determined both qualitatively and quantitatively using phantom and in-vivo data.

Based on the theory of linear matrix inequalities (LMI), this paper proposes an event-based distributed consensus algorithm for linear multi-agent/sensor networks that are heterogeneous. The proposed scheme is event-based in the sense that each agent transmits its information to its neighbouring nodes only under predefined circumstances. Ensuring the stability of the closed-loop system, the Lyapunov theorem is utilized to compute design parameters (heterogeneous controller gains and transmission threshold) used in the proposed consensus algorithm. Numerical simulations demonstrate a performance gain in the convergence time and a reduction in the number of data transmissions with the proposed approach.

Ultrasound elastography involves imaging tissue while it undergoes deformation and inferring its mechanical properties from the deformation pattern. The initial deformation in the tissue is typically induced through an external mechanical force, for example, by exerting a slight pressure using an ultrasound probe or by applying an acoustic radiation force (ARF) against the tissue. The ARF excites the tissue locally, which leads to the propagation of a shear-wave. The goal of the shear-wave elastography is to estimate the speed of the shear-wave that is explicitly related to the elasticity of tissue. We formulate tissue displacement estimation as an optimization problem and propose a computationally efficient approach to estimate the displacement field. A novel algorithm based on the minimization of a regularized cost function using higher-order analytical minimization (HAM) coupled with the second-order Taylor series approximation is proposed. Our algorithm first computes an integer displacement field based on dynamic programming (DP) that provides the global optima, which is then refined iteratively to obtain the subpixel displacement estimate. We test the proposed algorithm on real experimental data obtained from a tissue-mimicking phantom and illustrate the superiority of our approach over some commonly used elastography techniques using signal to noise ratio (SNR) comparisons.

Image denoising methods have been rapidly advanced in past few years. Image denoising is a challenging process of suppressing unwanted noise components from an image while retaining image details as mush as possible. Motivated by the recent advances in graph signal processing, in this paper, we address image denoising and enhancement problems from a new graph-based viewpoint. In particular, non-local similar patches of each color channel are grouped into a block for which a graph-based framework is proposed to construct a novel dictionary. The proposed graph-based sparse coding results in removing unwanted high frequency noise from the image. In addition and to further improve the contrast level of the image, a novel enhancement method is proposed based on iterative graph filtering. Simulations are conducted to evaluate the performance of the proposed color image denoising and enhancement method and to compare it with that of the other existing methods. The proposed method is shown to provide significantly improved visual quality for denoised images as well as higher peak signal-to-noise-ratio values as compared to other existing methods.

Due to recent demand for playback of high quality video on mobile devices, there is the need for a scalable, error resilient framework with the ability to adjust to the network and receiver's specification. To cope with the bandwidth fluctuations in the network, the framework's scalability allows for high bit-rate video to be transcoded to a low bit-rate format while preserving quality as much as possible. The new High Efficiency Video Coding (HEVC/H.265) standard allows for high compression rates, however, it is computationally intensive. We propose a novel Development Infrastructure for Video coding/transcoding Applications (DIVA). This framework is capable of providing different quality of services (e.g., Bronze, Silver, Gold), and has some error-resilient capability. Taking advantage of IBM Platform Symphony, the computationally intensive task of HEVC encoding can be distributed on available local or cloud resources. Our experiments illustrate the feasibility of this approach.

The paper proposes an on-line distributed implementation of the particle filter (DPF) for applications, where the sensing and consensus time scales are the same. We are motivated by state estimation problems in large, geographically-distributed agent/sensor networks, where bandwidth constraints limit the number of information transfers between neighbouring nodes. As an alternative to consensus strategies often used by the DPF, we propose a diffusive framework to eliminate the need of running the consensus step. In our Monte Carlo simulations, the proposed diffusion based DPF (D/DPF) outperforms the state-of-the-art consensus based DPF approaches in environments with limited bandwidth or/and intermittent connectivity.

The source localization problem for multiple-input, multiple-output (MIMO) radars was recently formulated by Yu et al. [7] in the compressive sensing (CS) framework. The resulting CS/MIMO radar achieves high resolution in joint direction and Doppler estimation, which is more pronounced with sparse data. However, the CS/MIMO technology does not operate well in environments with multipath, as is the case for low-angle targets located over flat surfaces. The paper applies the principle of time reversal (TR) to the CS/MIMO radar to converge the TR probing signal on the target leading to a stronger backscatter from the target as compared to the ones received from surrounding clutter. Monte Carlo simulations verify the superiority of the CSTR/MIMO radar over its CS/MIMO version without TR.

Recent developments in microgrids place strict constraints on the underlying state estimation technology, including the need for a dynamic and distributed approach. Since the problem is reminiscent of classical information fusion [2], the paper explores the application of a fusion-based reduced order, distributed unscented particle filter (FR/DUPF) for dynamic state estimation in microgrids. By partitioning the nonlinear microgrid into a network of nsublocalized and dynamically coupled systems, the FR/DUPF provides computational savings of a factor of nsubover its centralized version. Monte Carlo simulations verify its accuracy by confirming that estimates from the FR/DUPF and centralized filter evolve close to the ground truth.

This letter applies compressive sensing (CS) and time reversal (TR) to multiple-input and multiple-output (MIMO) radars to derive a novel algorithm for joint estimation of the direction of arrival, direction of departure, and Doppler information on potential targets embedded in an environment with rich clutter. The CSTR/MIMO radar uses multipath constructively and offers significant performance gains over its CS/MIMO counterpart without TR in our Monte Carlo simulations.

The paper develops a fusion-based, reduced order, distributed implementation of the unscented particle filter (FR/DUPF) for state estimation in complex nonlinear electric power grids (EPG). Based on partitioning the overall EPG system into nsublocalized but dynamically coupled subsystems, the near-optimal FR/DUPF provides a computational saving of up to a factor of nsubover the centralized particle filter. In our Monte Carlo simulations of the IEEE 14-bus test system, the FR/DUPF state estimates are close to the actual values and virtually indistinguishable from the centralized particle filter.

Motivated by the problem of distributed signal processing in sensor networks, the paper considers the general problem of state estimation in geographically dispersed systems with nonlinear dynamics operating in an uncertain environment with communication constraints. Distributed particle filter implementations used as nonlinear state estimators introduce an additional consensus step, which must converge to achieve consistent values for local estimators' statistics in between two consecutive filter iterations. The number of consensus iterations per consensus run is high such that the consensus step may not converge in between two filter iterations especially in networks with intermittent connectivity. To reduce the consensus liability, we propose a consensus plus innovation based distributed implementation of the unscented particle filter (CI/DUPF), which extends the linear consensus and innovation framework to nonlinear distributed estimation. The CI/DUPF does not require the consensus step to converge and is suited for environments with intermittent connectivity. In our Monte Carlo simulations, the performance of the CI/DUPF follows that of its centralized counterpart even with a limited number of consensus iterations per consensus run.

The posterior Cramέr Rao lower bound (PCRLB) has recently been proposed as an effective selection criteria for sensor resource management in large, geographically distributed sensor networks. Existing algorithms (in particular the decentralized approaches with no central fusion centre) designed for computing the PCRLB are based on raw observations resulting in significant communication overhead from the sensor nodes to the associated local processing nodes. The paper derives distributive computational techniques for determining the PCRLB for quantized sensor networks configured using decentralized architectures. We refer to the distributed computation of the PCRLB as dPCRLB. The main contribution of the paper is extending the dPCRLB algorithm [1] to quantized observations that leads to significant savings in the communication overhead over its counterparts that use raw observations. In our Monte Carlo simulations, we show that the proposed dPCRLB closely follows the centralized bound based on quantized observations. As expected, there is potential performance loss with quantization as is illustrated by the difference between the dPCRLBs computed using raw and quantized observations. The drop in the estimator's performance is, however, compensated for with an increase in the number of quantization levels associated with the observation quantizer.

The paper derives closed form (nonmatrix) expression for the deterministic Cramér-Rao bound (CRB) for the direction-of-arrival associated with a target embedded in a noisy, multipath channel using the time reversal (TR) MIMO system. By incorporating the TR built-in adaptive waveform processing feature to reshape the MIMO probing signals, we prove that the CRBs for the direction of arrival can be improved in ways not foreseen with the conventional MIMO radars. A second contribution of the paper is the analytical derivation of the Angular Resolution Limit (ARL) defined as the minimal separation between two targets to be separately resolved by the MIMO radar. At high signal-to-noise ratio, we show that the TR ARL inherits the properties of the TR CRB and is superior to its conventional counterpart by a factor proportional to the order of the channel multipath.

Motivated by the problem of adaptive resource management in decentralized sensor networks, the paper derives an algorithm for the distributed computation of the conditional posterior Cramér-Rao lower bound (PCRLB) for nonlinear tracking applications as an alternative to the non-conditional (conventional) PCRLB. Using the proposed conditional bound, a decentralized adaptive sensor-selection algorithm is then developed with the objective of dynamically activating a subset of observation nodes to optimize the network's performance. Our Monte Carlo simulations verify the superiority of the proposed decentralized PCRLB based sensor selection approach in bearing only tracking applications over its conventional counterparts.

The paper considers the problem of performing distributed particle filtering in intermittently connected networks with nonlinear state dynamics. In the context of large, geographically-distributed sensor networks, communication delays affect the convergence of the consensus algorithms used to derive the global state estimate from local estimates. We propose a non-linear fusion rule that relaxes the condition of requiring convergence of the consensus step between two successive iterations of the localized particle filters, thereby, allowing the consensus step to catch up with the localized filters in case of communication delays. Our Monte Carlo simulations illustrate the ability of the modified consensus/fusion based distributed implementation of the particle filter (MCF/DPF) to successfully handle intermittence in the network connectivity.

Motivated by the decentralized adaptive resource management problems, the letter derives recursive expressions for online computation of the conditional decentralized posterior Cramér-Rao lower bound (PCRLB). Compared to the non-conditional PCRLB, the conditional PCRLB is a function of the past history of observations made and, therefore, a more accurate representation of the estimator's performance and, consequently, a better criteria for sensor selection. Previous algorithms to compute the conditional PCRLB are limited to centralized architectures. The letter addresses this gap. Our simulations verify the optimality of the conditional dPCRLB by comparing it with the centralized conditional PCRLB in bearing-only tracking applications.

The paper derives analytical Cramér-Rao bound expressions for the Doppler-Angle estimators used in multiple-input multiple-output (MIMO) communication systems. Our motivation is two folds. First, these analytical expressions are used as a measure of the optimal performance that an Angle-Doppler MIMO estimator can potentially achieve. Second, we illustrate the potential of improvement with the time reversal (TR) coupled MIMO Angle-Doppler estimator over its conventional counterpart. In our Monte Carlo simulations, the proposed TR/MIMO communication system provide gains of up to 5dB.

In sensor networks deployed over large-scale, multidimensional physical systems with limited spatial observability, reduced-order, distributed estimation is a practical alternative to centralized estimation. For such reduced-order systems, centralized computation of the posterior Cramér Rao lower bound (CRLB) is not possible as the global estimate of the entire state vector is not accessible at a single processing node. We derive the distributed PCRLB (dPCRLB) implementations encompassing both linear and nonlinear reduced-order dynamical systems and verify their optimality through Monte Carlo simulations.

A constrained sufficient statistic based distributed implementation of the particle filter (CSS/DPF) is proposed for angle/bearing-only tracking (BOT) applications. The CSS/DPF runs localized particle filters at each sensor node and computes the global sufficient statistics (GSS) of the overall system as a function (summation) of the local sufficient statistics (LSS). The CSS/DPF is, therefore, a two stage procedure: (i) First, the means of LSS at local nodes are computed by running average consensus algorithms to derive the GSS, and; (ii) Each node then updates its localized particle filter using the modified GSS. Simulation results show that the CSS/DPF is near-optimal with its performance almost identical to that of the centralized particle filter. The number of average consensus runs in the CSS/DPF are reduced by an order of magnitude of the dimension of the state vector, thereby, reducing the communication complexity and bandwidth requirement of the distributed implementation.

In this paper, the effect of coupling time reversal (TR) to direction of arrival (DOA) estimation is studied through theoretical Cramér-Rao bound (CRB) analysis and numerical simulations. The proposed TR/DOA estimator adds an additional stage and retransmits the time-reversed versions of the observations made during the original forward probing stage. The backscatters of the time reversed probing signals obtained from this second TR stage is used for DOA estimation based on the Capon algorithm. Simulations results and CRBs comparing the performance of the proposed TR/DOA estimator with that of the conventional approach based only on observations from the forward probing stage are presented.

In this paper, we study the Cramér-Rao bound (CRB) for time reversal (TR) based direction of arrival (DOA) estimators operating in a rich multipath environment. Our setup is based on an array of active antennas capable of estimating the range and DOA of a passive target. We derive an analytical expression for the CRB of the TR/DOA estimator and compare it with that of the conventional DOA estimator by expressing the two CRBs in terms of the multipath parameters (multipath's attenuations and delays). Our analytical results are verified by running Ground Penetrating Radar (GPR) simulations using the electromagnetic Finite Difference Time Domain (FDTD) models. Our simulations illustrate the potential of superior performance with gains of up to 15 dB possible with the TR/DOA estimator over the conventional approach.

As an improvement to the conventional radar range estimation based on a single forward propagation of the probing signal, the paper presents a time reversal (TR) based range estimation technique for radar tracking applications and develops the Cramér-Rao lower bounds (CRLB) for the conventional and TR range estimators in an N-multipath scattering environment, where N may assume any value. The CRLBs quantify possible improvement offered by the TR range estimator over the conventional approach. Stronger secondary multipath improves the performance of the TR range estimator as its CRLB decreases with a higher number of multipaths.

In this paper, first, a new range-estimation technique using time reversal (TR) for ground-penetrating-radar (GPR) applications is presented. The estimator is referred to as the TR/GPR range estimator. The motivation for this paper comes from the need of accurately estimating the location of underground objects such as landmines or unexploded ordinance for safe clearance. Second, the Cramér-Rao lower bound (CRLB) for the performance of the TR/GPR range estimator is derived and compared with the CRLB for the conventional matched filter (MF). The CRLB analysis shows that the TR/GPR range estimator has the potential to achieve higher accuracy in estimating the location of the target than that of the conventional MF estimator. Third, the proposed TR/GPR estimator is tested using finite-difference time-domain simulations, where the surface-based reflection GPR is modeled using an electromagnetic transverse-magnetic (TM) mode formulation. In our simulations, the TR/GPR estimator outperforms the conventional MF approach by up to 5-dB reduction in mean square error at signal-to-noise ratios ranging from -20 to 20 dB for dry-soil environments.

Atimereversal(TR)devicehasthecapabilityofreceivingatime varying signal, saving it in memory, and retransmitting the stored signal back into the medium in the reversed direction of time. The paper designs an N-ary maximum likelihood TR detector capable of detecting the presence of a target and classifying the detected target as an unknown enemy target or one of the (N − 2) known friendly targets. In our experiments, the proposed N-ary TR detector provides a gain of over 5dB over conventional detectors at low signal to noise ratios (SNR).

Sequencing technologies used to generate long strands of DNA are susceptible to laboratory errors that may result in several DNA nucleotides being deleted from the genome. Detecting such deletions in the protein coding regions is of utmost importance. Missing even a single nucleotide may lead to frame shifts with all the following codons (and consequently the encoded amino acids) being identified incorrectly. In addition to the deletion of nucleotides during sequencing, frame shifts can occur because of a variety of other reasons including mutations. In this paper, we present a fast computational technique to identify frame shifts in protein coding regions in DNA sequences. Our technique is based on Fourier spectral characteristics of coding regions in DNA sequences. We provide two applications of our technique - detecting deletions in DNA sequences in coding regions and also detecting frame shifts in viral DNA

We propose a rate-adaptive multicast (RAM) routing protocol for mobile ad-hoc networks (MANETs) that is multirate-aware. During the process of path discovery, the quality of wireless links is estimated to suggest optimal transmission rates, which are then used to calculate the total transmission time incurred by the mobile nodes on a path. Among several considered paths from a source to a destination, RAM selects the path with the lowest total transmission time. Simulation results have shown that RAM outperforms single-rate multicast in terms of packet delivery ratio, packet end-to-end delay, and throughput of the multicast group. In this paper, we apply RAM to effectively support video multicast in MANETs

The paper presents a video multicasting architecture for mobile ad-hoc networks (MANETs). The proposed framework uses the scalable, noncausal predictive codec with vector quantization and conditional replenishment (SNP/VQR) for encoding video into multiple layers of data streams at bit rates between 10 Kbps to 500 Kbps. A rate adaptive multicast (RAM) routing algorithm is proposed, which dynamically adjusts the transmission rate based on the channel quality to obtain the optimum throughput. The scalability of SNP/VQR, coupled with RAM, enables strong resilience against bandwidth fluctuations as well as the capability to support a range of heterogeneous mobile receivers. Experimental results demonstrate that uninterrupted video of reasonable quality is multicasted with the proposed architecture even under heavy traffic conditions

In this paper, we describe a bandwidth adaptable, low bit-rate video coding scheme, referred to as scalable, noncausal prediction with vector quantization and conditional replenishment (SNP/VQR). Practical implementations of SNP/VQR are derived by exploiting the convergence and block banded properties of the state matrices. The resulting subblock SNP/VQR reduces the computational complexity and the storage requirements of the direct SNP/VQR by two orders of the linear magnitude of the frame dimensions. The subblock SNP/VQR is also capable of offering different quality of services (QoS) in the spatial and temporal domains. In the experiments, the block SNP/VQR compares favorably with the standard video codecs including the International Standards Organization (ISO) proposed MPEG4 and the International Telecommunication Union (ITU) proposed H.263, especially at low bit rates.

We propose a real-time video transmission scheme, referred to as SNP/VQR (scalable non-causal predictive codec with vector quantization and conditional replenishment), which is capable of providing spatial and temporal scalabilities. SNP/VQR eliminates error propagation by combining bandwidth adaptability with error concealment. A software implementation of SNP/VQR is tested over a wireless network with 5% to 20% of simulated packet losses. SNP/VQR produces a relatively constant visual quality in our packet loss studies.

The paper provides theoretical justification for the "3-periodicity property" observed in protein coding regions within genomic DNA sequences. We propose a new classification criteria improving upon traditional frequency based approaches for identification of coding regions. Experimental studies indicate superior performance compared with other algorithms that use the 3-periodicity property.

Computationally efficient implementations of the Kalman Bucy filter (KBf) for data assimilation are presented. We apply our algorithms to assimilate satellite altimetry data into the Navy layered ocean model (NLOM). Compared to direct KBf, our implementations provide computational savings of up to two orders of magnitude of the linear dimension of the ocean field. Results from a 1/4 /spl deg/ equatorial channel simulation of the Pacific Ocean are included.

We present a scalable video coding scheme based on a three dimensional (3D) Gauss Markov random process (GMrp) and replenishment extended vector quantization (VQ). The proposed video codec is capable of offering different quality of service (QoS) in the temporal and spatial domains. Compression results illustrate the superiority of the proposed scheme over the ITU standard, H.263, at high compression ratios.

We present practical data assimilation algorithms based on the Kalman-Bucy filter (KBf) for combining satellite altimetry data with the nonlinear ocean circulation models. Data assimilation in such applications is computationally challenging because of the large dimensions of the state fields. Compared with the direct KBf, our KBf implementations provide computational savings of two orders of the magnitude of the linear dimension of the state field. We run twin experiments by interfacing our data assimilation algorithms with the NLOM, a nonlinear ocean circulation model developed at the Naval Research Laboratory.

The letter describes how multimedia technology and cooperative learning were used to teach an online course, Signal Processing techniques in Communication Systems (SP/CS). Students study online presentations enhanced by multimedia animations and perception quizzes, discuss problems using an asynchronous conferencing system, and complete cooperative laboratory simulations based on MATLAB. A locally designed course management system provides access to the online features of the course. A survey that assesses the benefits of multimedia and cooperative support materials is also included.

We describe a technique for restoration of blurred images corrupted with additive noise. Our algorithm uses a practical implementation of the Rauch-Tung-Striebel (RTS) smoother-based on noncausal prediction that models the blurred image as a finite-lattice Gauss-Markov random process (GMRP). The one-sided regressors of the GMRP converge at a geometric rate to shift-invariant values along the rows of the image. This leads to a steady-state solution for the RTS filter. Experimental results illustrate the superiority of our RTS-based algorithm over Wiener filter, deterministic filters, and filters that use the one-sided causal state model.

Advances in multimedia technology provide an opportunity to enhance learning in university education. The paper presents a sample of multimedia learning objects and cooperative activities based on Macromedia Flash, Questionmark Perception, and Mathworks Matlab, used to teach the theoretical concepts in a senior level course, "Digital signal processing in communication systems" (DSP/CS). A study that compares the performance of students with and without multimedia access is also included.

Advances in the multimedia technology provide an opportunity to enhance learning in the university education. The paper presents a sample of multimedia learning objects and cooperative activities based on macromedia flash, questionmark perception, and mathworks Matlab, used to teach the theoretical concepts in a senior level course, digital signal processing in communication systems (DSP/CS). A study that compares the performance of students with and without multimedia access is also included.

Block banded matrices generalize banded matrices. In this paper, we exploit properties of full matrices whose inverses are L-block banded to derive fast inverses for such matrices, and inverses for matrices that are themselves block banded. We apply these results to design fast implementations for the Kalman-Bucy filter in applications arising often in the physical sciences where the underlying models derive from discretizations of partial differential equations and the observations are sparse.

The paper describes how the Technical University of British Columbia (TechBC) is using online and cooperative learning to improve the quality of education offered to its students. To explain how Web technology is being used to enhance learning, we focus on a sophomore level course in signal processing. Students study presentations delivered on the Web, discuss problems using an asynchronous conferencing system, and meet on campus once per week for a cooperative laboratory session. Comparative research indicates that introducing cooperative learning in university courses results in higher student achievement and more positive attitudes toward the subject and teaching methodology. Delivery of materials and learning activities via the Web provides greater access and flexibility for students. The students used a course management system (CMS) to study the online portion of the course.

We investigate the properties of block matrices with block banded inverses to derive efficient matrix inversion algorithms for such matrices. In particular, we derive the following: (1) a recursive algorithm to invert a full matrix whose inverse is structured as a block tridiagonal matrix; (2) a recursive algorithm to compute the inverse of a structured block tridiagonal matrix. These algorithms are exact. They reduce the computational complexity respectively by two and one orders of magnitude over the direct inversion of the associated matrices. We apply these algorithms to develop a computationally efficient approximate implementation of the Kalman-Bucy filter (KBf) that we refer to as the local KBf. The computational effort of the local KBf is reduced by a factor of I/sup 2/ over the exact KBf while exhibiting near-optimal performance.

In the physical sciences, e.g., meteorology and oceanography, combining measurements with the dynamics of the underlying models is usually referred to as data assimilation. Data assimilation improves the reconstruction of the image fields of interest. Assimilating data with algorithms like the Kalman-Bucy filter (KBf) is challenging due to their computational cost which for two-dimensional (2-D) fields is of O(I(6)) where I is the linear dimension of the domain. In this paper, we combine the block structure of the underlying dynamical models and the sparseness of the measurements (e.g., satellite scans) to develop four efficient implementations of the KBf that reduce its computational cost to O(I(5)) in the case of the block KBf and the scalar KBf, and to O(I(4)) in the case of the local block KBf (lbKBf) and the local scalar KBf (lsKBf). We illustrate the application of the IbKBf to assimilate altimetry satellite data in a Pacific equatorial basin.

We develop computationally fast and storage efficient implementations for the Kalman-Bucy filter (KBf) for data assimilation problems with large time varying multidimensional fields. We refer to them as the block KBf (bKBf) and the localized block KBf (lbKBf). For fields defined on a 2D lattice of linear dimension I, the bKBf reduces the computational complexity of the KBf by O(I). The lbKBf saves further on computations by a factor of I and decreases the storage requirements by O(I). We illustrate the IbKBf in assimilating satellite measurements in physical oceanography, presenting simulations for an equatorial beta plane.

We describe a technique for still image compression that combines (i) noncausal optimal recursive prediction, (ii) residual quadtree mean removal, and (iii) a modification of cascaded vector quantization. We refer to this image codec as noncausal prediction with residual mean removal, and cascaded vector quantization (NRQ/CVQ). We provide examples that illustrate the performance of NRQ/CVQ up to compression ratios of 42.5:1. We show that NRQ/CVQ outperforms alternative algorithms that we tested including the conventional causal prediction differential pulse code modulation (DPCM) with quadtree mean removal cascaded vector quantization and the Joint Photographic Experts Group (JPEG) baseline standard algorithm.

The paper discusses a scheme based on Kalman-Bucy filters for the assimilation of satellite data in equatorial beta plane ocean circulation models. The state equation of the Kalman-Bucy filter is obtained by decoupling the nonlinearities from the Navier-Stokes equations by assuming an inviscid isentropic shallow water motion. Direct application of the Kalman-Bucy filter leads to a computationally intensive algorithm which precludes its application to meaningful sized domains. By imposing a Gauss Markov random field (GMRF) structure on the error covariance matrix, the authors obtain an efficient recursive algorithm, capable of estimating the velocity fields and the sea surface height.

Current video compression standards compress video sequences at NTSC quality with factors in the range of 10-100, like in MPEG-1 and MPEC-2. To operate beyond this range, that is, MPEG-4, radically new techniques are needed. We discuss one such technique called generative video (GV). Video compression is realized in GV in two steps. First, the video sequence is reduced to constructs. Constructs are world images, corresponding to augmented images containing the non-redundant information on the sequence, and window, figure, motion, and signal processing operators, representing video sequence properties. Second, the world images are spatially compressed. The video sequence is reconstructed by applying the various operators to the decompressed world images. We apply GV to a 10 sec video sequence of a real 3-D scene and obtain compression ratios of about 2260 and 4520 for two experiments done with different quantization codebooks. The reconstructed video sequence exhibits very good perceptual quality.