Achieving physical-world attacks on object detectors often relies on adversarial camouflage, which applies textures to target surfaces. However, existing methods typically simulate environmental variations through post-rendering image transformations, which do not fully account for the 3D object’s geometry and its interaction with lighting. To address this, we propose the Flexible Physical-camouflage Attack (FPA), a framework that integrates a differentiable 3D renderer with comprehensive, multi-parameter environmental randomization such as lighting, material, and viewpoint directly into the optimization loop. This ensures that the generated textures are robust to real-world variations. For texture generation, FPA leverages a denoising diffusion probabilistic model whose generative prior helps produce structurally coherent and visually realistic patterns. These are jointly optimized with a set of task-oriented loss functions including adversarial, smoothness, non-printability, and concealment constraints within the unified framework. Through systematic ablation and extensive physical experiments on a 1:24 scale model, we demonstrate that FPA achieves a high attack success rate (ASR) and strong transferability to black-box detectors. Crucially, our analysis reveals a controllable trade-off between adversarial effectiveness and visual stealth, validated by perceptual metrics and human evaluation. Our findings highlight the importance of integrated physical modeling and systematic evaluation for advancing physically realizable adversarial camouflage.

Black-box adversarial attacks remain challenging due to limited access to model internals. Existing methods often depend on specific network architectures or require numerous queries, resulting in limited cross-architecture transferability and high query costs. To address these limitations, we propose JAD, a latent diffusion model framework for black-box adversarial attacks. JAD generates adversarial examples by leveraging a latent diffusion model guided by attention maps distilled from both a convolutional neural network (CNN) and a Vision Transformer (ViT) models. By focusing on image regions that are commonly sensitive across architectures, this approach crafts adversarial perturbations that transfer effectively between different model types. This joint attention distillation strategy enables JAD to be architecture-agnostic, achieving superior attack generalization across diverse models. Moreover, the generative nature of the diffusion framework yields high adversarial sample generation efficiency by reducing reliance on iterative queries. Experiments demonstrate that JAD attack offers improved attack generalization, generation efficiency, and cross-architecture transferability compared to existing methods, providing a promising and effective paradigm for black-box adversarial attacks.

Unmanned aerial vehicles (UAVs) are increasingly employed to perform high-risk tasks that require minimal human intervention. However, they face escalating cybersecurity threats, particularly from GNSS spoofing attacks. While previous studies have extensively investigated the impacts of GNSS spoofing on UAVs, few have focused on its effects on specific tasks. Moreover, the influence of UAV motion states on the assessment of cybersecurity risks is often overlooked. To address these gaps, we first provide a detailed evaluation of how motion states affect the effectiveness of network attacks. We demonstrate that nonlinear motion states not only enhance the effectiveness of position spoofing in GNSS spoofing attacks but also reduce the probability of detecting speed-related attacks. Building upon this, we propose a state-triggered backdoor attack method (SSD) to deceive GNSS systems and assess its risk to trajectory planning tasks. Extensive validation of SSD’s effectiveness and stealthiness is conducted. Experimental results show that, with appropriately tuned hyperparameters, SSD significantly increases positioning errors and the risk of task failure, while maintaining high stealthy rates across three state-of-the-art detectors.

In this paper, a cloud-edge model predictive control (MPC) framework is proposed for cyber-physical systems in the presence of deception attacks and Denial-of-Service (DoS) attacks. In the proposed framework, the original MPC optimization problem is decomposed into cloud and edge layers by using an efficient parameterized control input sequence. Then, a novel controller updating mechanism is developed by discontinuously comparing the optimal value functions of the modified optimization problem and the original optimization problem, which saves the communicational and computational resources. Specifically, the control performance is optimized over all possible uncertainties and deception attack realizations using a min-max optimization technique, while the DoS attacks can be tackled with the parameterization feature of the control input sequence. Besides, the closed-loop system is guaranteed to be input-to-state practical stable (ISpS) under the proposed MPC strategy. Simulation studies and comparisons are performed to verify effectiveness of the proposed method.

A key problem in multiagent multitask systems is optimizing conflict-free strategies, especially when task-assignment is coupled with path-planning. Incomplete information exacerbates this complexity, leading to frequent conflicts, such as redundant agents performing the same task. Different from the existing single-type game model, this article introduces a distributed mixed cooperative-noncooperative (DMCN) model that considers nondifferentiable constraints. In order to deal with nondifferentiable task layer constraints, we use approximation operators and splitting schemes to transform the original optimization function into the primal-dual differentiable function. In order to obtain more stable solutions, a distributed approximate gradient descent optimization algorithm and conflict resolution mechanism are proposed, which enhances the convergence of our method. We use Lyapunov theory to verify the exponential convergence of the algorithm in the time range. Simulation and experiments demonstrate the superiority of this method and its applicability in engineering applications.

A recursive minimum upper bound estimator (UBE) is proposed in this article for stochastic systems with colored measurement noise (CMN) in the presence of generalized unknown disturbance (UD), which is motivated by noncooperative target tracking in the environment of continuous external interference. The CMN causes system noises to be correlated in time dimension, and the UD makes online calculation of estimate error covariance intractable, both of which give rise to deterioration of classical Kalman-like filtering and smoothing. By considering that constructing the upper bound of estimate error covariance requires looser conditions than directly calculating the theoretical covariance, an UBE is first defined, to obtain the filtered estimate and smoothed estimate together. Then, based on the reconstructed measurement model containing multiple state vectors due to measurement differencing to whiten system noises, the recursive structure of the defined UBE is derived in the case of CMN (CUBE) by introducing a free parameter to be optimized, and the existence condition of CUBE is also discussed. Finally, the minimum UBE with CMN, i.e., CMUBE, is presented by pursuing the minimum upper bound of estimate error covariance online through parameter optimization, in order to further suppress the peak of estimate errors. The advantages of estimation accuracy of the proposed CMUBE over Kalman filter (KF)/smoother, KF with CMN and minimum upper bound filter (MUBF) are demonstrated by an example of noncooperative target tracking in persistent interference environment, in terms of filtering versus smoothing, different values of the initial estimate error covariance and the positive-definite matrix setting a priori, sensor accuracies and different levels of UD.

In this paper, a non-myopic beam scheduling policy is proposed for multi-target tracking (MTT) in a phased-array radar network, seeking to minimize the discounted sum of tracking error of targets and improve the long-term tracking performance. The Whittle index policy based on the restless multiarmed bandit (RMAB) model can decompose the state space of the underlying optimization problem into independent spaces with reduced sizes. We consider the tracking error covariance (TEC) matrix as the state of each target (arm), which evolves based on the Kalman filter. However, for a real-world MTT, the exact calculation of the Whittle index in multiple dimensions is challenging. The neural network is established to achieve the feature extraction of TEC states and learn the corresponding Whittle index. The deep reinforcement learning (DRL) method is exploited to train the neural network by leveraging the threshold property of the Whittle index policy and engaging in interactions with a single target tracking environment. We propose the DRL-based Whittle index policy, namely DRLWI, aiming to solve the beam allocation problem for MTT with multi-dimensional TEC states. This approach effectively mitigates the exponential computational complexity of classical dynamic programming approaches and the low convergence rate caused by large joint state and action spaces in the simple application of DRL algorithms. Numerical results demonstrate the performance of the proposed DRLWI policy surpasses that of DRL algorithms and myopic policies.

Deep learning has been widely used in the field of radar image classification because of its powerful feature extraction capabilities. In the land-sea clutter classification of sky-wave over-the-horizon radar (OTHR), deep learning methods perform poorly due to the radar receiver noise and the ionosphere. Addressing this challenge, a dual attention aided residual neural networks (DAAResNet) is proposed for OTHR land-sea classification. Leveraging prior knowledge that landsea clutter features predominantly cluster around the 0 Hz frequency, two attention mechanisms are introduced. Firstly, a channel attention module (CAM) is proposed, which directs the network’s focus towards critical channels. Secondly, a frequency attention module (FAM) is proposed, which directs attention towards pivotal frequencies. The classification performance of DAAResNet is validated on the original dataset and the scarce dataset. Experimental results show that DAAResNet outperforms state-of-the-art methods.

Abstract Random numbers are very important for the security of computer system. However, generating qualified random numbers is difficult because we cannot always successfully introduce dedicated random number hardware into computer system. Although most operating systems provide random number generation capabilities, the effective entropy supply is still dependent on the hardware platform including memory and clocks etc. However, obtaining hardware events such as clocks requires system privileges, which is not conducive for entropy estimation at the application layer. In contrast, data related to the sensor hardware can be extracted directly at the application layer. These sensor data contain some randomness and may be used as a noise source. In this way, applications can use these sensors to implement their own proprietary random number generators. Before taking these sensors as the noise source, it is necessary to fully evaluate their entropy supply capability. In this paper, 300 Android smartphones and 30 iOS smartphones are selected as samples and their sensor entropy supply capabilities are comprehensively evaluated. Based on the entropy evaluation results, we give some suggestions on how to generate random numbers using these sensor data. We first design a framework for evaluating the entropy supply capability for smartphone sensors, based on the min-entropy estimation method proposed in NIST SP 800-90B. According to this framework, we simulate stationary and mobile working states for each smartphone, and collect sufficient sensor data as the min-entropy estimation dataset. The min-entropy estimation results show that in the stationary working state, each ACCELEROMETER sensor data collection can obtain at least 1.5 bits of entropy in Android, while each GYROSCOPE sensor data collection can obtain at least 20 bits of entropy in iOS. In the mobile working state, each ACCELEROMETER sensor data collection can obtain at least 1.9 bits of entropy, while each GYROSCOPE sensor data acquisition in iOS system can obtain at least 27 bits of entropy. This means that we can still get a stable entropy output from the sensor even when the smartphone is in stationary working state. Statistical analysis of the data using cross correlation methods suggests it is hard for an attacker to guess or predict the random numbers generated by a smartphone through another smartphone put in the similar external environment.

Evidential clustering is a promising clustering framework using Dempster–Shafer belief function theory to model uncertain data. However, evidential clustering needs to estimate more parameters compared with other clustering algorithms, and thus the clustering performance of evidential clustering will be greatly affected if data is insufficient or contaminated. In addition, the existing evidential clustering algorithms can not well deal with high-dimensional data such as texts and images. To solve the above problems, an evidential clustering algorithm based on transfer learning and deep autoencoder (TDEC) is proposed. The TDEC utilizes deep autoencoder to obtain evidential clustering-friendly representations of the original data, and applies the maximum mean discrepancy (MMD) constraint between the source network and the target network, so that the network can learn domain-invariant features. The algorithm jointly trains the deep evidential clustering networks in the source domain and the target domain, and realizes the deep feature representations of high-dimensional data in the target domain for evidential clustering by minimizing reconstruction loss, entropy-based evidential clustering loss, MMD loss and the regular penalty term of the network parameters. In addition, an iterative optimization method to solve the TDEC objective function is proposed. Extensive experiments were conducted to evaluate the clustering performance of the proposed TDEC algorithm compared with the existing shallow transfer clustering algorithms and deep clustering algorithms. For both image and text clustering tasks, the proposed TDEC achieved approximately 5% performance improvement over the comparison algorithms on average. In addition, the practical application value of the proposed TDEC algorithm was demonstrated in unsupervised remote sensing image scene classification.

Object detection is a fundamental task in various applications ranging from autonomous driving to intelligent security systems. However, recognition of a person can be hindered when their clothing is decorated with carefully designed graffiti patterns, leading to the failure of object detection. To achieve greater attack potential against unknown black-box models, adversarial patches capable of affecting the outputs of multiple-object detection models are required. While ensemble models have proven effective, current research in the field of object detection typically focuses on the simple fusion of the outputs of all models, with limited attention being given to developing general adversarial patches that can function effectively in the physical world. In this paper, we introduce the concept of energy and treat the adversarial patches generation process as an optimization of the adversarial patches to minimize the total energy of the “person” category. Additionally, by adopting adversarial training, we construct a dynamically optimized ensemble model. During training, the weight parameters of the attacked target models are adjusted to find the balance point at which the generated adversarial patches can effectively attack all target models. We carried out six sets of comparative experiments and tested our algorithm on five mainstream object detection models. The adversarial patches generated by our algorithm can reduce the recognition accuracy of YOLOv2 and YOLOv3 to 13.19% and 29.20%, respectively. In addition, we conducted experiments to test the effectiveness of T-shirts covered with our adversarial patches in the physical world and could achieve that people are not recognized by the object detection model. Finally, leveraging the Grad-CAM tool, we explored the attack mechanism of adversarial patches from an energetic perspective.

This article presents two-filter smoothing (TFS) by maximizing the correntropy rather than minimizing the mean square error, for nonlinear systems with non-Gaussian noises, such as heavy-tailed distributed noises or outliers, motivated by high-precision noncooperative target backtracking. The maximum correntropy (MC) recursive TFS, abbreviated as MRTFS, is first derived, where the smoothed estimate is obtained by fusing forward and backward filtering results step by step through maximizing the correntropy. Then, the information filtering form of the above MRTFS is proposed in order to loose the initial conditions of forward and backward filtering and enhance the structural conciseness of MRTFS. Considering that the fixed-point iteration is adopted to realize MC-based forward-time filtering, backward-time filtering, and two-filter fusion, its convergence is shown in the premise that the interested state vector is bounded and the kernel bandwidth of the correntropy is larger than a special threshold. Meanwhile, the computational complexity of MRTFS is analyzed, which is similar to that of extended Kalman-like TFS. An experiment of noncooperative target backtracking shows that estimation accuracy of the proposed method is superior to that of extended Kalman filter (EKF), TFS, Rauch–Tung–Striebel smoother (RTS) and MC-based EKF/RTS, in terms of estimation confidence ellipses, different levels of kernel bandwidths and iterations.

This paper presents recursive two-filter smoothing (TFS) in the criterion of maximizing the correntropy (MC) instead of minimizing the mean square error, to pursue robustness for outlier rejections caused by non-Gaussian noises and obtain high-precision state estimate, which is motivated by non-cooperative target backtracking. Here, non-cooperative target tracking often needs to consider non-Gaussian noises. The MC-based recursive TFS (abbreviated as MRTFS) is put forward, where both the forward and backward filters are performed independently and recursively in the criterion of MC. Meanwhile, an MC-based fusion rule is further designed to obtain the final smoothed estimate by fusing the forward filtered estimate and backward predicted estimate step by step, in order to improve estimation accuracy. A target backtracking example with non-Gaussian noises is simulated to show the advantage of estimation accuracy of the proposed MRTFS over Kalman filter/smoothers, MC-based Kalman filter/Rauch-Tung-Striebel smoother, in terms of different kernel bandwidths and levels of process noises.

Deep reinforcement learning has advanced greatly and applied in many areas. In this paper, we explore the vulnerability of deep reinforcement learning by proposing a novel generative model for creating effective adversarial examples to attack the agent. Our proposed model can achieve both targeted attacks and untargeted attacks. Considering the specificity of deep reinforcement learning, we propose the action consistency ratio as a measure of stealthiness, and a new measurement index of effectiveness and stealthiness. Experiment results show that our method can ensure the effectiveness and stealthiness of attack compared with other algorithms. Moreover, our methods are considerably faster and thus can achieve rapid and efficient verification of the vulnerability of deep reinforcement learning.

Open set recognition (OSR) aims to correctly recognize the known classes and reject the unknown classes for increasing the reliability of the recognition system. The distance-based loss is often employed in deep neural networks-based OSR methods to constrain the latent representation of known classes. However, the optimization is usually conducted using the nondirectional euclidean distance in a single feature space without considering the potential impact of spatial distribution. To address this problem, we propose orientational distribution learning (ODL) with hierarchical spatial attention for OSR. In ODL, the spatial distribution of feature representation is optimized orientationally to increase the discriminability of decision boundaries for open set recognition. Then, a hierarchical spatial attention mechanism is proposed to assist ODL to capture the global distribution dependencies in the feature space based on spatial relationships. Moreover, a composite feature space is constructed to integrate the features from different layers and different mapping approaches, and it can well enrich the representation information. Finally, a decision-level fusion method is developed to combine the composite feature space and the naive feature space for producing a more comprehensive classification result. The effectiveness of ODL has been demonstrated on various benchmark datasets, and ODL achieves state-of-the-art performance.

Deep learning algorithms have achieved state-of-the-art progress in synthetic aperture radar (SAR) automatic target recognition (ATR) tasks. They theoretically assume that training and test samples are independent and identically distributed (i.i.d.) for generalization, but it is intractable for practical ATR scenarios. In this paper, we propose a novel contrastive feature disentangling framework termed ConFeDent to learn features with improved generalization performance under a condition of a weaker distribution consistency. More specifically, ConFeDent aims to describe the semantic interactions between two arbitrary SAR training samples instead of treating them independently. It can implicitly disentangle features encoding the pose and identity knowledge from the whole samples with a semi-parametric geometric transformation model and a second-order energy model. In particular, except for the identity label, we use deductive-based geometry knowledge as supervision to teach the model to learn the concept of aspect angle variation. A progressively amortized inference scheme is constructed for efficient feature learning and recognition in an end-to-end manner. Finally, we further release a strengthened version, called ConFeDent+, which can explicitly utilize and learn more information from cross-category samples. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) benchmark demonstrate the effectiveness of our proposed models in the SAR ATR. In particular, we validate the algorithms in a more challenging scenario where the range of aspect angles for training and testing samples is permitted to be disparate. Our model can achieve much higher recognition accuracy than other SAR ATR algorithms.

The existing sea–land clutter classification task of sky-wave over-the-horizon-radar (OTHR) assumes that the training data and test data are drawn from the same probability distribution. However, there is a distribution discrepancy/domain shift of the collected sea–land clutter under various working conditions of OTHR, which leads to the advanced sea–land clutter classification methods being difficult to achieve effective cross-domain classification. To solve this problem, this article proposes an improved maximum classifier discrepancy (MCD) framework, namely, triple loss adversarial domain adaptation network (TLADAN) for cross-domain sea–land clutter classification, which includes a metric-based feature-level loss, an adversarial-based instance-level loss, and an adversarial-based class-level loss. The proposed TLADAN performs feature-, instance-, and class-level alignments of the sea–land clutter from different domains, so as to learn the domain-invariant features to improve the classification performance in the cross-domain scenario. Our method is evaluated in six sea–land clutter domain adaptation (DA) scenarios. Meanwhile, state-of-the-art DA methods are selected for comparison. The experimental results validate the effectiveness and superiority of TLADAN.

In the sea-land clutter classification of sky-wave over-the-horizon-radar (OTHR), the imbalanced and scarce data leads to a poor performance of the deep learning-based classification model. To solve this problem, this paper proposes an improved auxiliary classifier generative adversarial network (AC-GAN) architecture, namely auxiliary classifier variational autoencoder generative adversarial network (AC-VAEGAN). AC-VAEGAN can synthesize higher quality sea-land clutter samples than AC-GAN and serve as an effective tool for data augmentation. Specifically, a one-dimensional convolutional AC-VAEGAN architecture is designed to synthesize sea-land clutter samples. Additionally, an evaluation method combining both traditional evaluation of GAN domain and statistical evaluation of signal domain is proposed to evaluate the quality of synthetic samples. Using a dataset of OTHR sea-land clutter, both the quality of the synthetic samples and the performance of data augmentation of AC-VAEGAN are verified. Further, the effect of AC-VAEGAN as a data augmentation method on the classification performance of imbalanced and scarce sea-land clutter samples is validated. The experiment results show that the quality of samples synthesized by AC-VAEGAN is better than those synthesized by the state-of-the-art GAN-based methods, and the data augmentation method with AC-VAEGAN is able to improve the classification performance in the case of imbalanced and scarce sea-land clutter samples.

This article considers the path-following problem for an unmanned aerial system (UAS), in which an online remote control station computes and sends control input signals to the vehicle over an adverse communication network. In that network configuration, the cyberattackers and malicious eavesdroppers are prone to erode the UAS's safety properties such as operational security and information privacy. To guarantee these properties, we introduce a secure model-predictive control (MPC) framework for achieving both optimal and safe path-following performance. The unique feature of this framework is that it can simultaneously address all the adversaries occurring in both remote station and network transmission links. Then, an encrypted MPC law is designed using an effective encoding scheme and the Paillier cryptography scheme. It is shown that the closed-loop stability can be guaranteed under the proposed MPC law. Simulation studies of UAS path following are conducted to validate the effectiveness of the proposed framework.

Denoising and demosaicking long-wave infrared (LWIR) division-of-focal-plane (DoFP) polarization images are crucial for various vision applications. However, existing methods rely on the sequential application of individual denoising and demosaicking processes, which may result in the accumulation of errors produced by each process. To address this issue, we propose a joint denoising and demosaicking method for LWIR DoFP images based on a three-stage progressive deep convolutional neural network. To ensure the generalization ability of this network, it is essential to have adequate training data that closely resembles real data. Therefore, we model the complex noise sources that affect LWIR DoFP images as mixed Poisson-Additive-Stripe noise and construct a least-squares problem based on the polarization measurement redundancy error to estimate the parameters of this model on real images. Subsequently, the estimated noise parameters are used to generate training data that enables the network to learn accurate polarization image statistics and improve its generalization ability. The experimental results demonstrate the effectiveness of the proposed method in enhancing the image restoration performance on real LWIR DoFP polarization data.

We present an efficient two-phase approach to motion planning for fixed-wing Unmanned Aerial Vehicles (UAV) navigating in complex 3D air slalom environments. Firstly, in discrete 3D workspace, a global planner computer a obstacle-free path roughly which satisfies the kinematic constraints of the UAV. Given a coarse global path, a local planner generate a Dubins curve with collision avoidance based on the UAS's perception constraints, dynamic constraints and the collision perception information received. We also introduce a method of decoupling the horizontal and vertical motion directions of the fixed-wing UAV, realizing the 2D Dubins curve planning in 3D workspace, along with precomputed sets of motion primitives derived from the vehicle dynamics model in order to achieve high efficiency. Finally, the feasibility of two-phase 3D motion planning in appropriate FOV is experimentally demonstrated.

In clustering process, fuzzy partition performs better than hard partition when the boundaries between clusters are vague. Whereas, traditional fuzzy clustering algorithms produce less interpretable results, limiting their application in security, privacy, and ethics fields. To that end, this paper proposes an interpretable fuzzy clustering algorithm—fuzzy decision tree-based clustering which combines the flexibility of fuzzy partition with the interpretability of the decision tree. We constructed an unsupervised multi-way fuzzy decision tree to achieve the interpretability of clustering, in which each cluster is determined by one or several paths from the root to leaf nodes. The proposed algorithm comprises three main modules: feature and cutting point-selection, node fuzzy splitting, and cluster merging. The first two modules are repeated to generate an initial unsupervised decision tree, and the final module is designed to combine similar leaf nodes to form the final compact clustering model. Our algorithm optimizes an internal clustering validation metric to automatically determine the number of clusters without their initial positions. The synthetic and benchmark datasets were used to test the performance of the proposed algorithm. Furthermore, we provided two examples demonstrating its interest in solving practical problems.

The vulnerabilities of multi-agent-system (MAS) become a critical issue for cybersecurity. The article investigates the formation control problem for MASs under multi-channel denial-of-service (DoS) attacks. In this article, the attacks on each channel are independent, while most of the existing results show that DoS attacks are the same on all channels. Without loss of generality, we consider multi-channel DoS attacks are imposed on a leader-follower MAS. Firstly, we propose a distributed formation control protocol to achieve the desired formation in the presence of DoS attacks. A translation-adaptive method is considered to adjust the interaction weights among neighboring agents online. Furthermore, a performance guarantee is derived based on the state information, and hereafter state errors among all agents can be regulated. Moreover, we derive the sufficient conditions for system stability w.r.t the controller gain and the allowable attack duration in the form of linear matrix inequalities (LMIs). Finally, simulation results are given to illustrate the effectiveness of the proposed method.

In pattern classification, we may have a few labeled data points in the target domain, but a number of labeled samples are available in another related domain (called the source domain). Transfer learning can solve such classification problems via the knowledge transfer from source to target domains. The source and target domains can be represented by heterogeneous features. There may exist uncertainty in domain transformation, and such uncertainty is not good for classification. The effective management of uncertainty is important for improving classification accuracy. So, a new belief-based bidirectional transfer classification (BDTC) method is proposed. In BDTC, the intraclass transformation matrix is estimated at first for mapping the patterns from source to target domains, and this matrix can be learned using the labeled patterns of the same class represented by heterogeneous domains (features). The labeled patterns in the source domain are transferred to the target domain by the corresponding transformation matrix. Then, we learn a classifier using all the labeled patterns in the target domain to classify the objects. In order to take full advantage of the complementary knowledge of different domains, we transfer the query patterns from target to source domains using the K-NN technique and do the classification task in the source domain. Thus, two pieces of classification results can be obtained for each query pattern in the source and target domains, but the classification results may have different reliabilities/weights. A weighted combination rule is developed to combine the two classification results based on the belief functions theory, which is an expert at dealing with uncertain information. We can efficiently reduce the uncertainty of transfer classification via the combination strategy. Experiments on some domain adaptation benchmarks show that our method can effectively improve classification accuracy compared with other related methods.

This article investigates the problem of fixed-time event-triggered output consensus tracking for high-order multiagent systems (MASs) under directed interaction graphs. First, a fixed-time event-triggered distributed observer and triggering functions are proposed. Next, fixed-time convergence of the presented distributed observer is proved by the Lyapunov function approach, and an analysis is conducted to show the proposed distributed observer excludes zeno behavior. Then, an event-triggered adaptive dynamic surface fixed-time controller is designed to stabilize the tracking error system. Finally, simulation results are given to show the effectiveness and superiority of the consensus scheme developed. The contribution of this article is to present a novel event-triggered fixed-time distributed observer and a novel fixed-time controller, which can reduce frequency of communication and control update, avoid continuous monitor, exclude zeno behavior, eliminate the effect of mismatched disturbance caused by observation error, and achieve practical fixed-time output consensus tracking of high-order MAS under directed interaction graphs.

In synthetic aperture radar (SAR) automatic target recognition (ATR), there are mainly two types of methods: physics-driven model and data-driven network. The physics-driven model can exploit electromagnetic theory to obtain physical properties, while the data-driven network will extract deep discriminant feature of targets. These two types of features represent the target characteristics in scattering domain and image domain, respectively. However, the representation discrepancy caused by the different modalities between them hinders the further comprehensive utilization and fusion of both features. In order to take full advantage of physical knowledge and deep discriminant feature for SAR ATR, we propose a new feature fusion learning framework SDF-Net to combine scattering and deep image features. In this work, we treat the attributed scattering centers (ASC) as set-data instead of multiple individual points, which can well mine the topological interaction among scatterers. Then multi-region multi-scale sub-sets are constructed at both component and target levels. To be specific, the most significant scattering intensity and overall representation in these sub-sets are exploited successively to learn permutation-invariant scattering features according to a set-oriented deep network. The scattering representations can provide mid-level semantic and structural features that are subsequently fused with the complementary deep image features to yield an end-to-end high-level feature learning framework, which helps enhance the generalization ability of networks especially under complex observation conditions. Extensive experiments on Moving and Stationary Target Acquisition and Recognition database verify the effectiveness and robustness of the SDF-Net compared against both typical SAR ATR networks and ASC-based models.

It is quite an important and challenging problem for change detection (CD) from heterogeneous remote sensing images. The images obtained from different sensors (i.e., synthetic aperture radar (SAR) & optical camera) characterize the distinct properties of objects. Thus, it is impossible to detect changes by direct comparison of heterogeneous images. In this article, a new unsupervised change detection (USCD) method is proposed based on image translation. The cycle-consistent adversarial networks (CycleGANs) are employed to learn the subimage to subimage mapping relation using the given pair (i.e., before and after the event) of heterogeneous images from which the changes will be detected. Then, we can translate one image (e.g., SAR) from its original feature space (e.g., SAR) to another space (e.g., optical). By doing this, the pair of images can be represented in a common feature space (e.g., optical). The pixels with close pattern values in the before-event image may have quite different values in the after-event image if the change happens on some ones. Thus, we can generate the difference map between the translated before-event image and the original after-event image. Then, the difference map is divided into changed and unchanged parts. However, these detection results are not very reliable. We will select some significantly changed and unchanged pixel pairs from the two parts with the clustering technique (i.e.,$K$-means). These selected pixel pairs are used to learn a binary classifier, and the other pixel pairs will be classified by this classifier to obtain the final CD results. Experimental results on different real datasets demonstrate the effectiveness of the proposed USCD method compared with several other related methods.

Flocking control has been studied extensively along with the wide applications of multivehicle systems. In this article, the distributed flocking control strategy is studied for a network of autonomous vehicles with limited communication range. The main difference from the existing methods lies in that collision avoidance is considered a necessary condition while the vehicles are driven to follow a common desired trajectory under the proximity network. The sufficient conditions for system feasibility and stability are given by the proposed strategy. First, a centralized standard model predictive control (MPC) scheme is adopted to formulate the multivehicle flocking control problem by setting collision avoidance as an optimization constraint under the proximity network. Further, an equivalent distributed MPC (DMPC) is developed based on the consensus of local controllers under the existing framework of the alternating direction method of multiplier (ADMM). However, it may require infinite time to achieve consensus for all vehicles and, thus, the local controllers resulting in a limited number of ADMM iterations may not satisfy the given constraints. The constraints for each local controller are then modified so that the collision between vehicles is avoided all of the time. The feasibility and stability of the proposed method are analyzed under practical conditions. Simulation and experimental results show that the flocking of vehicles can track the common desired trajectory stably with no collisions by the proposed method.

This article studies an efficient nonlinear model-predictive control (NMPC) scheme for trajectory tracking control of a quadrotor unmanned aerial vehicle (UAV). By augmenting the desired trajectory to a reference dynamical system, we can make the tracking task fit into the standard NMPC framework. In order to alleviate the heavy computational burden caused by solving the corresponding NMPC optimization problem online, we develop an improved continuation/generalized minimal residual ( [Formula: see text]/GMRES) algorithm. Compared with the standard C/GMRES method, the inequality constraint is relaxed by imposing the penalty term on the cost function. To guarantee the closed-loop system stability, we introduce a contraction constraint. Based on the proposed numerical algorithm and the stability constraint, we develop a novel efficient-NMPC algorithm to achieve acceptable control performance with reduced computational complexity. The numerical convergence of [Formula: see text]/GMRES solutions and the closed-loop stability of efficient-NMPC are theoretically analyzed in the presence of the input constraint. Finally, the numerical simulations, software-in-the-loop (SIL) simulations, and the real-time experiment are given to demonstrate the effectiveness of the proposed [Formula: see text]/GMRES algorithm and efficient-NMPC scheme.

Assessing the quality of polarization images is of significance for recovering reliable polarization information. Widely used quality assessment methods including peak signal-to-noise ratio and structural similarity index require reference data that is usually not available in practice. We introduce a simple and effective physics-based quality assessment method for polarization images that does not require any reference. This metric, based on the self-consistency of redundant linear polarization measurements, can thus be used to evaluate the quality of polarization images degraded by noise, misalignment, or demosaicking errors even in the absence of ground-truth. Based on this new metric, we propose a novel processing algorithm that significantly improves demosaicking of division-of-focal-plane polarization images by enabling efficient fusion between demosaicking algorithms and edge-preserving image filtering. Experimental results obtained on public databases and homemade polarization images show the effectiveness of the proposed method.

The scattering signatures of a synthetic aperture radar (SAR) target image will be highly sensitive to different azimuth angles/poses, which aggravates the demand for training samples in learning-based SAR image automatic target recognition (ATR) algorithms, and makes SAR ATR a more challenging task. This paper develops a novel rotation awareness-based learning framework termed RotANet for SAR ATR under the condition of limited training samples. First, we propose an encoding scheme to characterize the rotational pattern of pose variations among intra-class targets. These targets will constitute several ordered sequences with different rotational patterns via permutations. By further exploiting the intrinsic relation constraints among these sequences as the supervision, we develop a novel self-supervised task which makes RotANet learn to predict the rotational pattern of a baseline sequence and then autonomously generalize this ability to the others without external supervision. Therefore, this task essentially contains a learning and self-validation process to achieve human-like rotation awareness, and it serves as a task-induced prior to regularize the learned feature domain of RotANet in conjunction with an individual target recognition task to improve the generalization ability of the features. Extensive experiments on moving and stationary target acquisition and recognition benchmark database demonstrate the effectiveness of our proposed framework. Compared with other state-of-the-art SAR ATR algorithms, RotANet will remarkably improve the recognition accuracy especially in the case of very limited training samples without performing any other data augmentation strategy.

The increasing aviation activities pose a challenge to ensure a safe and orderly flight. Trajectory prediction is one of the most important forecasting tasks in Air Traffic Management. Accurate prediction is reasonable for safe and orderly flight tasks in civil aviation monitoring. Points of interests play an important role in most land traffic prediction algorithms due to their abilities in positioning and marking. Compared with land traffic, the sparse way-points and shared airways make it difficult for flight trajectory prediction. A constrained Long Short-Term Memory network for flight trajectory prediction is proposed in this paper. According to the dynamic characteristics of the aircraft, we propose three kinds of constraints to climbing, cruising, and descending/approaching phases, in particular, they are Top of climb, Way-points, and Runway direction, correspondingly. Our model is able to keep long-term dependencies with dynamic physical constraints. Density-Based Spatial Clustering of Applications with Noise and Linear Least Squares are used in data segmentation and preprocessing. Sliding windows help maintain the continuity of trajectory. Four-dimensional spatial-temporal trajectory set consisting of spatial position and timestamps is used to prove the efficiency of our approach. Multiple ADS-B ground stations contribute to our experimental dataset. The widely used Long Short-Term Memory network, Markov Model, weighted Markov Model, Support Vector Machine, and Kalman Filter are used for comparison. Quantitative analysis demonstrates that our model outperforms the above-mentioned state-of-the-art models, and lays a good foundation for decision-making in different scenarios.

It remains a challenging problem for data classification without training patterns. In many applications, there may exist some labeled data in other related domains (called source domain), and such labeled data can be helpful to solve the classification problem in the target domain. It is considered that the source domain and target domain are heterogeneous here and they represent the distinct feature spaces. A new transfer classification method for heterogeneous data is proposed based on the evidence theory. Some pattern pairs in the source domain and target domain are given to predict the link of these two domains. For each pattern in the target domain, we estimate its possible mapping value in the source domain using these pattern pairs with a self-organizing map (SOM) technique, and then the mapping value is classified using the labeled data in the source domain. However, the patterns with close values in the target domain may have more or less different values in the source domain due to the distinct characteristics of these two domains. So the mapping value can be very uncertain sometimes. In such a case, the target pattern is allowed to have multiple mapping values with different weights/reliabilities in the source domain. These mapping values can produce different classification results. The evidence theory is good at characterizing and combining uncertain information. In order to improve the classification accuracy, a new evidence-based weighted fusion method is developed for combining these classification results, which are discounted by the corresponding weights under the belief functions framework, and the final class decision is made according to the combination result. In experimental applications, some heterogeneous remote sensing data and UCI data are used to test the performance of new method with respect to several other methods, and it shows that the new method can efficiently improve the classification accuracy.

Most digital cameras use a single sensor covered with a Color Filter Array (CFA) in order to reduce the size, complexity and cost. Now, more and more researchers have paid attentions to the representation of CFA since its crucial role in the process of reconstructing the full color image. The representation of CFA in frequency domain records all the frequency information of image mosaicked with the CFA, thus provides a theoretical approach to design the CFA. However, almost all the existing CFA design methods in frequency domain have limitations. In this paper, we propose a new automatic CFA design method in frequency domain. We choose the optimal frequency structures which satisfy all the design principles through a novel coded multi-objective evolutionary algorithm (MOEA) at first. Then we convert the parameters optimization of the given frequency structures into a constraint single optimization problem, and we solve it by converting it into a multi-objective optimization problem without constraints which is hard to handle. So a MOEA with novel selection and crossover-mutate strategies is proposed to solve the model. The experimental results show that the proposed CFA design method has more advantage when compared with the other existing method.

In the personal house renting scenario, customers usually make quick assessments based on previous customers' reviews, which makes such reviews essential for the business. If the house is assessed as popular, a Matthew effect will be observed as more people will be willing to book it. Due to the lack of definition and quantity assessment measures, however, it is difficult to make a popularity evaluation and prediction. To solve this problem, the concept of house popularity is well defined in this paper. Specifically, the house popularity is decided by inter-event timeand rating score at the same time. To make a more effective prediction over these two correlated variables, a dual-gated recurrent unit (DGRU) is employed. Furthermore, an encoder-decoder framework with DGRU is proposed to perform popularity prediction. Empirical results show the effectiveness of the proposed DGRU and the encoder-decoder framework in two-correlated sequences prediction and popularity prediction, respectively.

With the development of sensor fusion technologies, there has been a lot of research on intelligent ground vehicles, where obstacle detection is one of the key aspects of vehicle driving. Obstacle detection is a complicated task, which involves the diversity of obstacles, sensor characteristics, and environmental conditions. While the on-road driver assistance system or autonomous driving system has been well researched, the methods developed for the structured road of city scenes may fail in an off-road environment because of its uncertainty and diversity. A single type of sensor finds it hard to satisfy the needs of obstacle detection because of the sensing limitations in range, signal features, and working conditions of detection, and this motivates researchers and engineers to develop multi–sensor fusion and system integration methodology. This survey aims at summarizing the main considerations for the onboard multi-sensor configuration of intelligent ground vehicles in the off-road environments and providing users with a guideline for selecting sensors based on their performance requirements and application environments. State-of-the-art multi-sensor fusion methods and system prototypes are reviewed and associated to the corresponding heterogeneous sensor configurations. Finally, emerging technologies and challenges are discussed for future study.

Stereo videos for the dynamic scenes often show unpleasant blurred effects due to the camera motion and the multiple moving objects with large depth variations. Given consecutive blurred stereo video frames, we aim to recover the latent clean images, estimate the 3D scene flow and segment the multiple moving objects. These three tasks have been previously addressed separately, which fail to exploit the internal connections among these tasks and cannot achieve optimality. In this paper, we propose to jointly solve these three tasks in a unified framework by exploiting their intrinsic connections. To this end, we represent the dynamic scenes with the piece-wise planar model, which exploits the local structure of the scene and expresses various dynamic scenes. Under our model, these three tasks are naturally connected and expressed as the parameter estimation of 3D scene structure and camera motion (structure and motion for the dynamic scenes). By exploiting the blur model constraint, the moving objects and the 3D scene structure, we reach an energy minimization formulation for joint deblurring, scene flow and segmentation. We evaluate our approach extensively on both synthetic datasets and publicly available real datasets with fast-moving objects, camera motion, uncontrolled lighting conditions and shadows. Experimental results demonstrate that our method can achieve significant improvement in stereo video deblurring, scene flow estimation and moving object segmentation, over state-of-the-art methods.

A key problem in the formation control of homogeneous multiagent systems is the collision-free convergence of the agent positions into a desired formation. It is a typical NP-hard problem by considering the problem as optimizing the assignment of multiple destinations to the same number of agents deployed in an open space. It becomes even harder if the collision avoidance is required during the motion of agents, and thus, a suboptimal but efficient solution is adequate. The traditional methods make it by accurate preplanning of the motion trajectory of each single agent, or simply letting them reach an equilibrium as a tradeoff between the collision avoidance and the desired formation. In this article, a distributed control algorithm embedded with an assignment switch scheme is proposed to guarantee that the asymptotic convergence to the desired formation is achieved with no collisions between agents. By the proposed algorithm, the agents keep moving in straight lines toward their respective destinations until they are going to collide if they do not stop, at which moment the agents will communicate their information locally to switch their destination assignments so that they will continue to move in different directions and avoid potential collisions. Distributed control rules are also defined to confine the motion space of each agent for collision avoidance. It has been rigorously proven that the positions of all agents converge to the desired formation with no collision under random initial deployment. In addition, a detailed parameter design procedure is provided for both setting and controlling of the formation. Finally, Monte Carlo simulations and actual experiments in the outdoor environment are implemented and the results verify the effectiveness of the proposed algorithm.

In this paper, we newly suggest that more attention should be paid on learning rotation-equivariant and label-invariant features for each target instead of the conventional rotation-invariant ones. To achieve this goal, we present a novel rotation awareness based self-supervised learning (RR-SSL) deep model to recognize the behavior of target rotation, which is also benefit from the discriminative training scheme without manual labeling. Then this model is incorporated into another deep discriminative model of target recognition to form a dual-task learning framework, where their bottom layers are shared to capture the expected features. Sufficient experimental results on moving and stationary target acquisition and recognition (MSTAR) database demonstrate the effectiveness of our proposed model. The overall framework can achieve a better or comparative recognition accuracy compared with other state-of-the-art SAR-ATR algorithms.

Industrial control system (ICS) security is an important topic in context of Industry 4.0. Due to limited industrial network data and insufficient data analysis, anomaly detection for ICS is hardly to implement to enforce ICS security. The paper analyzes the traditional IT network data and ICS network data, bridges the common knowledge of network flow data feature of the both networks, and transfers the anomaly detection knowledge of traditional IT network into ICS network by means of a designed convolutional neural network (CNN) mechanism. Specific experiments validate the accuracy and reliability of the proposed CNN mechanism.

We present a distributed Bernoulli filter for tracking a target that may be present or absent in the cluttered surveillance area in unknown time intervals by using a decentralized sensor network. As a key feature of the Bernoulli filter, a parameter referring to the target existence probability is online updated jointly with the target state probability density function. We propose to fuse them in parallel, both in an arithmetic average fusion manner via the standard consensus or flooding scheme. Alternatively, one may communicate and fuse merely target existence probabilities, leading to a communication-inexpensive protocol. We experimentally compare the proposed approaches, based on the Gaussian mixture implementation of the Bernoulli filter, with the cutting-edge geometric average fusion approach based on a Doppler shift sensor network. Advantages are observed in computing efficiency and in dealing with local missed detection.

Feature learning-based polarimetric synthetic aperture radar (PolSAR) classification model will generally suffer from the challenge of deficient labeled pixels. In this paper, we propose a novel generative-discriminative network for PolSAR polar-spatial feature fusion learning and classification, which comprises of a deep generative network and a discriminative network with their bottom layers shared. With this architecture, it enables to make use of both labeled and unlabeled pixels in a PolSAR image for model learning in a semisupervised way. Moreover, the proposed network imposes a Gaussian random field prior and a conditional random field posterior on the learned fusion features and the output label configuration, respectively. Without the need of the complicated recurrent iterations, our network can still efficiently produce the structured fusion feature as well as a smoothed classification map by involving some auxiliary variables, and it is specifically optimized via variational inference within an alternating direction method of multipliers iteration scheme. Extensive experiments on different benchmark PolSAR imageries demonstrate the effectiveness and superiority of the proposed network. Compared with other state-of-the-art algorithms of PolSAR feature learning and classification, our model can achieve a much better performance in terms of the visual quality of the label map and overall classification accuracy, facilitating the much less labeling pixels.

This paper proposes a new virtual ionospheric height model for over-the-horizon radar (OTHR) target tracking. Considering the spatial correlation of different ionosphere site, the virtual ionospheric heights are modeled by a Gaussian Markov random field (GMRF). The priors of the GMRF model can be learned from the historical measurements from ionosondes. Given the acquired measurements of the ionosphere subregions, the virtual ionospheric heights of the unmeasured subregions are inferred based on the GMRF model. Then we present the multipath probabilistic data association for uncertain coordinate registration (MPCR) with the new virtual ionospheric height model. Numerical simulation shows that the accuracy of OTHR target tracking is improved.

In this paper, a rule learning method based on interval-constrained clustering is proposed to efficiently design a compact belief rule-based classifier. The main idea of this method is to learn a compact belief rule base based on a set of prototypes generated from the original training set. First, an interval-constrained clustering algorithm is used to divide the training data for each class into several clusters, with which the number of data belonging to each cluster can be constrained within a given interval. Then, we define a belief rule based on the centroid of each cluster. Finally, a two-objective optimization procedure is designed to get a compact belief rule base with a better trade-off between accuracy and interpretability. Two experiments based on synthetic and benchmark data sets have been carried out to evaluate the performance of the proposed classifier.

It is a challenging problem for pattern classification with few labeled instances. Transfer learning provides an efficient solution to improve the classification accuracy using some training knowledge in the related domain (called source domain). Nevertheless, the single transformation in one direction may be uncertain in some cases, and this is harmful for classification. So we propose a new classification method based on the fusion of data transformations in different directions between source domain and target domain. At first, the mapping of target in the source domain is estimated by K-nearest neighbor technique using some one-to-one instance pairs, and the estimated mapping instance (pattern) can be classified in the source domain according to the available training data. Then, the credibility of classification result is evaluated. If the credibility achieves the expected threshold, the classification result is directly output. Otherwise, it indicates that the transformation may be not very reliable, and the labeled instances in source domain will be transferred to target domain for the classification of target. The two versions of classification results will be fused with different weights based on evidential reasoning, and the weighting factors are optimized using the available training instances. By doing this, we can efficiently reduce the uncertainty of transformation and improve the classification accuracy. Some real data sets from UCI have been employed to validate the effectiveness of the proposed by comparing with other related methods.

This paper considers the joint recursive estimation of the dynamic state and the time-varying process noise covariance for a linear state space model. The conjugate prior on the process noise covariance, the inverse Wishart distribution, provides a latent variable. A variational Bayesian inference framework is then adopted to iteratively estimate the posterior density functions of the dynamic state, process noise covariance and the introduced latent variable. The performance of the algorithm is demonstrated with simulated data in a target tracking application.

In this paper, a novel nonlinear filter method named linear Gaussian regression filter (LG RF) is proposed. The LG RF utilizes the Variational Bayes (VB) to indirectly approximate the posterior probability density function (PDF) for state estimation. The core of the LG RF is to use a linear Gaussian distribution with a set of compensating parameters (CPs) to characterize the likelihood probability (LP) for maximizing the lower bound. Through iteratively and alternatively achieving the state estimation and CPs identification, the estimation accuracy can be improved gradually. In addition, compared with point-based filters, there is no decomposition of the covariance matrix in the LG RF so that the inborn defect of numerical instability is avoided. The superior performance of the LGRF is demonstrated in the simulation of maneuvering target tracking.

This paper considers the state smoothing problem for Markovian jump linear systems with non-Gaussian noises which obey Gaussian mixture distributions. On the basis of decomposing the total probability at the point of two adjacent Markov jumping parameters at the current and the next epochs, the posterior probability density of the state for smoothing is derived recursively. Then, through transforming the quotient of two Gaussian mixtures into the corresponding multiplication under the possible two adjacent Markov modes, a recursive Gaussian mixture smoother is designed with the conditional posterior probability density under each hypothesis being approximated by the Gaussian mixture. A maneuvering target tracking example with non-Gaussian noises validates the proposed method.

The traditional machine learning algorithms are easily affected by datasets in short text classification tasks, so they have weak generalization ability when confronted with new situations. This paper presents a new method SVMCNN by combining Convolutional Neural Networks and Support Vector Machine. Training the SVMCNN model with labeled datasets, and using the collected Twitter data for classification test. The results show that the SVMCNN, especially pre-trained SVMCNN has good performance in short text classification, which gets the high Precision rate, Recall rate and F1-measure.

The problem of collaborative self-localization and target tracking method under challenge environment is studied in this paper. Specifically, the scenario with general nonlinear process and sensing model as well as sparse communication is considered by combining the distributed tracking (DT) and the collaborative localization (CL) techniques. To better characterize the statistics after nonlinear transformations, the unscented transformation (UT) approach is adopted. Simulations are extensively studied to show that the proposed method have better performance on both self-localization and target tracking than the solo CL or DT method.

Safety ranks the first in Air Traffic Management (ATM). Accurate trajectory prediction can help ATM to forecast potential dangers and effectively provide instructions for safely traveling. Most trajectory prediction algorithms work for land traffic, which rely on points of interest (POIs) and are only suitable for stationary road condition. Compared with land traffic prediction, flight trajectory prediction is very difficult because way-points are sparse and the flight envelopes are heavily affected by external factors. In this paper, we propose a flight trajectory prediction model based on a Long Short-Term Memory (LSTM) network. The four interacting layers of a repeating module in an LSTM enables it to connect the long-term dependencies to present predicting task. Applying sliding windows in LSTM maintains the continuity and avoids compromising the dynamic dependencies of adjacent states in the long-term sequences, which helps to improve accuracy of trajectory prediction. Taking time dimension into consideration, both 3-D (time stamp, latitude and longitude) and 4-D (time stamp, latitude, longitude and altitude) trajectories are predicted to prove the efficiency of our approach. The dataset we use was collected by ADS-B ground stations. We evaluate our model by widely used measurements, such as the mean absolute error (MAE), the mean relative error (MRE), the root mean square error (RMSE) and the dynamic warping time (DWT) methods. As Markov Model is the most popular in time series processing, comparisons among Markov Model (MM), weighted Markov Model (wMM) and our model are presented. Our model outperforms the existing models (MM and wMM) and provides a strong basis for abnormal detection and decision-making.

Classifier fusion is an efficient strategy to improve the classification performance for the complex pattern recognition problem. In practice, the multiple classifiers to combine can have different reliabilities and the proper reliability evaluation plays an important role in the fusion process for getting the best classification performance. We propose a new method for classifier fusion with contextual reliability evaluation (CF-CRE) based on inner reliability and relative reliability concepts. The inner reliability, represented by a matrix, characterizes the probability of the object belonging to one class when it is classified to another class. The elements of this matrix are estimated from the -nearest neighbors of the object. A cautious discounting rule is developed under belief functions framework to revise the classification result according to the inner reliability. The relative reliability is evaluated based on a new incompatibility measure which allows to reduce the level of conflict between the classifiers by applying the classical evidence discounting rule to each classifier before their combination. The inner reliability and relative reliability capture different aspects of the classification reliability. The discounted classification results are combined with Dempster-Shafer's rule for the final class decision making support. The performance of CF-CRE have been evaluated and compared with those of main classical fusion methods using real data sets. The experimental results show that CF-CRE can produce substantially higher accuracy than other fusion methods in general. Moreover, CF-CRE is robust to the changes of the number of nearest neighbors chosen for estimating the reliability matrix, which is appealing for the applications.

In pattern classification problem, different classifiers learnt using different training data can provide more or less complementary knowledge, and the combination of classifiers is expected to improve the classification accuracy. Evidential reasoning (ER) provides an efficient framework to represent and combine the imprecise and uncertain informations. In this paper, we want to focus on the weighted combination of classifiers based on ER. Because each classifier may have different performance on the given dataset, the classifiers to combine are considered with different weights. A new weighted classifier combination method is proposed based on ER to enhance the classification accuracy. The optimal weighting factors of classifiers are obtained by minimizing the distances between fusion results obtained by Dempster's rule and the target output in training data space to fully take advantage of the complementarity of the classifiers. A confusion matrix is additionally introduced to characterize the probability of the object belonging to one class but classified to another class by the fusion result. This matrix is also optimized using training data jointly with classifier weight, and it is used to modify the fusion result to make it as close as possible to truth. Moreover, the training patterns are considered with different weights for the parameter optimization in classifier fusion, and the patterns hard to classify are committed with bigger weight than the ones easy to deal with. The pattern weight and the other parameters (i.e., classifier weight and confusion matrix) are iteratively optimized for obtaining the highest classification accuracy. A cautious decision making strategy is introduced to reduce the errors, and the pattern hard to classify will be cautiously committed to a set of classes, because the partial imprecision of decision is considered better than error in certain case. The effectiveness of the proposed method is demonstrated with various real datasets from UCI repository, and its performances are compared with those of other classical methods.

This paper proposes a novel method based on deformable dictionary learning for detecting the regions of change between multitemporal image pairs. We build on our previous work, which constructed a pair of dictionaries. The main shortcoming of this method was its dependence on a large amount of training data. In practice, there is often a shortage of ground-truthed training images, which limits the expression capability of the resulting dictionaries. This paper overcomes this challenge by incorporating the concept of deformation, wherein each atom of a dictionary is no longer a simple image patch, but instead is a flexible image deformation function. This enables the creation of more expressive dictionaries, capable of generalizing to a far greater variety of image patterns, while using a far smaller amount of ground-truthed images for supervised dictionary training. Deformation similarity is employed for patch matching to find the best set of atoms in the difference image (DI) dictionary for reconstructing image patches for a new input DI. Each such atom can be deformed to achieve a better match, thus extending generality while reducing the number of atoms needed in the dictionary. Multiple deformed atoms are weighted and combined to best reconstruct the input DI patch. Then, the same set of deformations and weights is projected to the corresponding atoms in the CD dictionary to obtain the output change-detection map. Experiments in six realistic synthetic aperture radar data sets demonstrate the robustness and efficiency of the proposed method in comparison with five other state-of-the-art methods from the literature.

The change detection in heterogeneous remote sensing images remains an important and open problem for damage assessment. We propose a new change detection method for heterogeneous images (i.e., SAR and optical images) based on homogeneous pixel transformation (HPT). HPT transfers one image from its original feature space (e.g., gray space) to another space (e.g., spectral space) in pixel-level to make the pre-event and post-event images represented in a common space for the convenience of change detection. HPT consists of two operations, i.e., the forward transformation and the backward transformation. In forward transformation, for each pixel of pre-event image in the first feature space, we will estimate its mapping pixel in the second space corresponding to post-event image based on the known unchanged pixels. A multi-value estimation method with noise tolerance is introduced to determine the mapping pixel using -nearest neighbors technique. Once the mapping pixels of pre-event image are available, the difference values between the mapping image and the post-event image can be directly calculated. After that, we will similarly do the backward transformation to associate the post-event image with the first space, and one more difference value for each pixel will be obtained. Then, the two difference values are combined to improve the robustness of detection with respect to the noise and heterogeneousness (modality difference) of images. Fuzzy-c means clustering algorithm is employed to divide the integrated difference values into two clusters: changed pixels and unchanged pixels. This detection results may contain some noisy regions (i.e., small error detections), and we develop a spatial-neighbor-based noise filter to further reduce the false alarms and missing detections using belief functions theory. The experiments for change detection with real images (e.g., SPOT, ERS, and NDVI) during a flood in U.K. are given to validate the effectiveness of the proposed method.

This paper studies the relative pose problem for autonomous vehicles driving in highly dynamic and possibly cluttered environments. This is a challenging scenario due to the existence of multiple, large, and independently moving objects in the environment, which often leads to an excessive portion of outliers and results in erroneous motion estimation. Existing algorithms cannot cope with such situations well. This paper proposes a new algorithm for relative pose estimation using a multi-camera system with multiple non-overlapping cameras. The method works robustly even when the number of outliers is overwhelming. By exploiting specific prior knowledge of the autonomous driving scene, we have developed an efficient 4-point algorithm for multi-camera relative pose estimation, which admits analytic solutions by solving a polynomial root finding equation, and runs extremely fast (at about 0.5 μs per root). When the solver is used in combination with a new random sample consensus sampling scheme by exploiting the conjugate motion constraint, we are able to quickly prune unpromising hypotheses and significantly improve the chance of finding inliers. Experiments on synthetic data have validated the performance of the proposed algorithm. Tests on real data further confirm the method's practical relevance.

This paper considers the parameter estimation problem of linear system by constructing the iterative and stochastic measurement schedule (ISMS) rule for efficiently implementing the maximum likelihood (ML). When the unknown parameter varies or even mutates with the time proceeding, in the existing measurement schedule rule, estimator can not keep both accuracy and speed of parameter estimation due to the fact that the rule is established before communication. That is, the convergency of the parameter estimation is not fast and accurate enough, especially for the mutational parameter. So we propose a novel ISMS rule to solve this problem. Our ISMS rule can smartly choose these important measurements which are close to the current sampling time and correspondingly drop those useless and unimportant measurements far away from the current time. The accuracy of estimator is improved obviously, because these chosen measurements are able to well reflect the parameter variation. Correspondingly, those dropped measurements further contribute to increase the computation complexity and decrease the speed of tracking the mutational parameter. Based on the constructed ISMS rule, we derive the analytical maximum likelihood parameter estimation (MLPE) and prove its unbiasedness. Moreover, a new concept of average windows length (AWL) is defined as the evaluation index of estimator, and its computation expression is derived. Finally, a numerical example is given to demonstrate the superiority of the new ISMS rule and MLPE in quickly and efficiently estimating the constant or time-varying parameter compared with the existing methods.

In the complex pattern classification problem, the reliability of classifier output for the patterns located at different regions of the data set may be different. In order to efficiently improve the classification accuracy, we propose a new method to correct the original classifier output using the local knowledge of the classifier performance in different regions. The training data set can be divided into some small clusters corresponding to different regions. The prior knowledge of the classifier performance on each cluster is characterized by a confusion matrix representing the conditional probability of the pattern belonging to one class but committed to another class by the classifier. The matrix associated with each cluster is learnt by minimizing an error criteria using training data, which is assigned different weights to achieve the highest possible accuracy. If the classification accuracy of the training data in one cluster can be improved according to the corrected classification results, the associated confusion matrix becomes valid. Otherwise, the confusion matrix is invalid and patterns in this cluster cannot be modified any more. For each object, if it lies in the cluster with valid confusion matrix, its classification result will be corrected by the matrix before making the class decision. The above correction process can be regarded as the fusion of local and global information. Several experiments are given to test the performance of the proposed method using real data sets, and it shows that the new method is able to efficiently improve the classification accuracy compared with other related methods.

This paper considers the state estimation of Markovian jump linear systems with random parameters and estimate feedback. The state estimate at the previous epoch is introduced into the dynamical model to depict some phenomena that the system evolvement may depend on the most recent estimate. Then, the linear minimum mean square error estimator is derived for the considered system. A filtering framework for state estimation and data association for multiple maneuvering targets tracking is presented via the considered system, by using the state estimate at previous epoch to model the false echo which is dropped into the (overlapped) validation regions and the random parameters to describe the uncertainty between targets and possible echoes. A simulation about tracking two closely maneuvering targets in clutter shows the effectiveness of the proposed method.

The theory of belief functions is an effective tool to deal with the multiple uncertain information. In recent years, many evidence combination rules have been proposed in this framework, such as the conjunctive rule, the cautious rule, the PCR (Proportional Conflict Redistribution) rules and so on. These rules can be adopted for different types of sources. However, most of these rules are not applicable when the number of sources is large. This is due to either the complexity or the existence of an absorbing element (such as the total conflict mass function for the conjunctive-based rules when applied on unreliable evidence). In this paper, based on the assumption that the majority of sources are reliable, a combination rule for a large number of sources, named LNS (stands for Large Number of Sources), is proposed on the basis of a simple idea: the more common ideas one source shares with others, the more reliable the source is. This rule is adaptable for aggregating a large number of sources among which some are unreliable. It will keep the spirit of the conjunctive rule to reinforce the belief on the focal elements with which the sources are in agreement. The mass on the empty set will be kept as an indicator of the conflict. Moreover, it can be used to elicit the major opinion among the experts. The experimental results on synthetic mass functions verify that the rule can be effectively used to combine a large number of mass functions and to elicit the major opinion.

Vacation rental websites such as Airbnb have become increasingly popular where rentals are typically short-term and travels or vacations related. Reasonable rental prices play a crucial role in improving user experiences and engagements in these websites. However, the unique properties of their rentals challenge traditional house rentals that are often long-term and study or work related. Therefore, in this paper we investigate the novel problem of price recommendation in vacation rental websites. We identify some important factors that affect the rental prices and propose a framework that consists of Multi-Scale Affinity Propagation (MSAP) to cluster houses, Nash Equilibrium filter to remove unreasonable price and Linear Regression model with Normal Noise (LRNN) to predict the reasonable prices. Experimental results demonstrate the effectiveness of the proposed framework. We conduct further experiments to understand the important factors in rental price recommendation.

In multiple attribute decision analysis (MADA) problems, one often needs to deal with assessment information with uncertainty. The evidential reasoning approach is one of the most effective methods to deal with such MADA problems. As a kernel of the evidential reasoning approach, an original evidential reasoning (ER) algorithm was firstly proposed by Yang et al, and later they modified the ER algorithm in order to satisfy the proposed four synthesis axioms. However, up to the present, the essential difference of the two ER algorithms is still unclear. In this paper, we analyze the ER algorithms in the Dempster-Shafer theory framework and prove that the original ER algorithm follows the reliability discounting and combination scheme, whereas the modified one follows the importance discounting and combination scheme. Based on these new findings, an extended ER (E2R) algorithm is proposed to take into account both the reliability and importance of different attributes, which provides a more general attribute aggregation scheme for MADA with uncertainty. A motorcycle performance assessment problem is examined to illustrate the proposed algorithm.

In classification problem, several different classes may be partially overlapped in their borders. The objects in the border are usually quite difficult to classify. A hybrid classification system (HCS) is proposed to adaptively utilize the proper classification method for each object according to the K-nearest neighbors (K-NNs), which are found in the weighting vector space obtained by self-organizing map (SOM) in each class. If the K-close weighting vectors (nodes) are all from the same class, it indicates that this object can be correctly classified with high confidence, and the simple hard classification will be adopted to directly classify this object into the corresponding class. If the object likely lies in the border of classes, it implies that this object could be difficult to classify, and the credal classification working with belief functions is recommended. The credal classification allows the object to belong to both singleton classes and sets of classes (meta-class) with different masses of belief, and it is able to well capture the potential imprecision of classification thanks to the meta-class and also reduce the errors. Fuzzy classification is selected for the object close to the border and hard to clearly classify, and it associates the object with different classes by different membership (probability) values. HCS generally takes full advantage of the three classification ways and produces good performance. Moreover, it requires quite low computational burden compared with other K-NNs-based methods due to the use of SOM. The effectiveness of HCS is demonstrated by several experiments with synthetic and real datasets.

In complex environments, the presence or absence of multipath signals not only depends on the relative motion between the GNSS receiver and navigation satellites, but also on the environment where the receiver is located. Thus it is difficult to use a specific propagation model to accurately capture the dynamics of multipath signal parameters when the GNSS receiver is moving in urban canyons or other severe obstructions. This paper introduces a statistical model for the line-of-sight and multipath signals received by a GNSS receiver. A multi-correlator based GNSS receiver is also exploited with the advantage to fully characterizing the impact of multipath signals on the correlation function by providing samples of the whole correlation function. Finally, a maximum likelihood-based unscented Kalman filter is investigated to estimate the line-of-sight and multipath signal parameters. Numerical simulations clearly validate the effectiveness of the proposed approach.

Optimal determination of a UAV using a vision-based system to match images against a database is an important problem. It can be reformulated to the problem of using multiregion scene registration to match areas of a noisy and distorted image to a geo-referenced image. Under the assumptions that the mapping between sensed and geo-referenced images preserves gradients of straight lines cross mapping points on images and registration errors are all Gaussian distributed, we derive a two-stage weighted linear least square algorithm which localises the UAV optimally. Performance of the proposed algorithm is demonstrated via Monte Carlo multiple runs along with those available in literature.

We present a drift-free pose estimation scheme for rigid body and its supporting platform by fusing only two gyroscopes and the relative complementary measurements. The fusion design not only provides robust relative attitude estimation between the rigid body and the platform, but also is capable of identifying partial global absolute attitudes without capturing any absolute attitude information. The pose estimation is built on a special design of the coupled kinematic model with the relative measurements between the rigid body and its supporting platform. We compare the fusion design with an alternative kinematic model and the posterior Cramer-Rao bound analyses are presented to show the completely different estimation performances. An extended Kalman filter (EKF) implementation of the fusion design is presented for the bicycle riding application.

One difficulty faced in knowledge engineering for Bayesian Network (BN) is the quantification step where the Conditional Probability Tables (CPTs) are determined. The number of parameters included in CPTs increases exponentially with the number of parent variables. The most common solution is the application of the so-called canonical gates. The Noisy-OR (NOR) gate, which takes advantage of the independence of causal interactions, provides a logarithmic reduction of the number of parameters required to specify a CPT. In this paper, an extension of NOR model based on the theory of belief functions, named Belief Noisy-OR (BNOR), is proposed. BNOR is capable of dealing with both aleatory and epistemic uncertainty of the network. Compared with NOR, more rich information which is of great value for making decisions can be got when the available knowledge is uncertain. Specially, when there is no epistemic uncertainty, BNOR degrades into NOR. Additionally, different structures of BNOR are presented in this paper in order to meet various needs of engineers. The application of BNOR model on the reliability evaluation problem of networked systems demonstrates its effectiveness.

In this work, a new prototype-based clustering method named Evidential C -Medoids (ECMdd), which belongs to the family of medoid-based clustering for proximity data , is proposed as an extension of Fuzzy C -Medoids (FCMdd) on the theoretical framework of belief functions . In the application of FCMdd and original ECMdd, a single medoid (prototype), which is supposed to belong to the object set, is utilized to represent one class. For the sake of clarity, this kind of ECMdd using a single medoid is denoted by sECMdd. In real clustering applications, using only one pattern to capture or interpret a class may not adequately model different types of group structure and hence limits the clustering performance. In order to address this problem, a variation of ECMdd using multiple weighted medoids, denoted by wECMdd, is presented. Unlike sECMdd, in wECMdd objects in each cluster carry various weights describing their degree of representativeness for that class. This mechanism enables each class to be represented by more than one object. Experimental results in synthetic and real data sets clearly demonstrate the superiority of sECMdd and wECMdd. Moreover, the clustering results by wECMdd can provide richer information for the inner structure of the detected classes with the help of prototype weights.

In some real-world classification applications, such as target recognition, both training data collected by sensors and expert knowledge may be available. These two types of information are usually independent and complementary, and both are useful for classification. In this paper, a hybrid belief rule-based classification system (HBRBCS) is developed to make joint use of these two types of information. The belief rule structure, which is capable of capturing fuzzy, imprecise, and incomplete causal relationships, is used as the common representation model. With the belief rule structure, a data-driven belief rule base (DBRB) and a knowledge-driven belief rule base (KBRB) are learned from uncertain training data and expert knowledge, respectively. A fusion algorithm is proposed to combine the DBRB and KBRB to obtain an optimal hybrid belief rule base (HBRB). A belief reasoning and decision-making module is then developed to classify a query pattern based on the generated HBRB. An airborne target classification problem in the air surveillance system is studied to demonstrate the performance of the proposed HBRBCS for combining both uncertain sensor measurements and expert knowledge to make classification.

The classification of incomplete patterns is a very challenging task because the object (incomplete pattern) with different possible estimations of missing values may yield distinct classification results. The uncertainty (ambiguity) of classification is mainly caused by the lack of information of the missing data. A new prototype-based credal classification (PCC) method is proposed to deal with incomplete patterns thanks to the belief function framework used classically in evidential reasoning approach. The class prototypes obtained by training samples are respectively used to estimate the missing values. Typically, in a c -class problem, one has to deal with c prototypes, which yield c estimations of the missing values. The different edited patterns based on each possible estimation are then classified by a standard classifier and we can get at most c distinct classification results for an incomplete pattern. Because all these distinct classification results are potentially admissible, we propose to combine them all together to obtain the final classification of the incomplete pattern. A new credal combination method is introduced for solving the classification problem, and it is able to characterize the inherent uncertainty due to the possible conflicting results delivered by different estimations of the missing values. The incomplete patterns that are very difficult to classify in a specific class will be reasonably and automatically committed to some proper meta-classes by PCC method in order to reduce errors. The effectiveness of PCC method has been tested through four experiments with artificial and real data sets.

This study presents the state estimation problem of discrete‐time Markovian jump linear systems with randomly delayed measurements. Here, the delay is modelled as the combination of different number of binary stochastic variables according to the different possible delay steps. In the actually delayed measurement equation, multiple adjacent step measurement noises are correlated. Owing to the stochastic property from the measurement delay, the estimation model is rewritten as a discrete‐time system with stochastic parameters and augmented state reconstructed from all modes with their mode uncertainties. For this system, a novel linear minimum‐mean‐square error (LMMSE, renamed as LMRDE) estimator for the augmented state is derived in a recursive structure according to the orthogonality principle under a generalised framework. Since the correlation among multiple adjacent step noises in the measurement equation, the measurement noises and related second moment matrices of corresponding previous instants in each current step are also needed to be estimated or calculated. A numerical example with possibly delayed measurements is simulated to testify the proposed method.

We present a multidimensional evidential reasoning (MDER) approach to estimate change detection from the fusion of heterogeneous remote sensing images. MDER is based on a multidimensional (M-D) frame of discernment composed by the Cartesian product of the separate frames of discernment used for the classification of each image. Every element of the M-D frame is a basic joint state that allows to describe precisely the possible change occurrences between the heterogeneous images. Two kinds of rules of combination are proposed for working either with the free model, or with a constrained model depending on the integrity constraints one wants to take into account in the scenario under study. We show the potential interest of the MDER approach for detecting changes due to a flood in the Gloucester area in the U.K. from two real ERS and SPOT images.

A credal classification rule (CCR) is proposed to deal with the uncertain data under the belief functions framework. CCR allows the objects to belong to not only the specific classes, but also any set of classes (i.e. meta-class) with different masses of belief. In CCR, each specific class is characterized by a class center. Specific class consists of the objects that are very close to the center of this class. A meta-class is used to capture imprecision of the class of the object that is simultaneously close to several centers of specific classes and hard to be correctly committed to a particular class. The belief assignment of the object to a meta-class depends both on the distances to the centers of the specific class included in the meta-class, and on the distance to the meta-class center. Some objects too far from the others will be considered as outliers (noise). CCR provides the robust classification results since it reduces the risk of misclassification errors by increasing the non-specificity. The effectiveness of CCR is illustrated by several experiments using artificial and real data sets.

In various computer vision applications, often we need to convert an image in one style into another style for better visualization, interpretation and recognition; for examples, up-convert a low resolution image to a high resolution one, and convert a face sketch into a photo for matching, etc. A semi-coupled dictionary learning (SCDL) model is proposed in this paper to solve such cross-style image synthesis problems. Under SCDL, a pair of dictionaries and a mapping function will be simultaneously learned. The dictionary pair can well characterize the structural domains of the two styles of images, while the mapping function can reveal the intrinsic relationship between the two styles' domains. In SCDL, the two dictionaries will not be fully coupled, and hence much flexibility can be given to the mapping function for an accurate conversion across styles. Moreover, clustering and image nonlocal redundancy are introduced to enhance the robustness of SCDL. The proposed SCDL model is applied to image super-resolution and photo-sketch synthesis, and the experimental results validated its generality and effectiveness in cross-style image synthesis.

Theories of evidence have already been applied more or less successfully in the fusion of remote sensing images. These attempts were based on the classical evidential reasoning which works under the condition that all sources of evidence and their fusion results are related to the same invariable (static) frame of discernment. When working with multitemporal remote sensing images, some change occurrences are possible between two images obtained at a different period of time, and these changes need to be detected efficiently in particular applications. The classical evidential reasoning is adapted for working with an invariable frame of discernment over time, but it cannot efficiently detect nor represent the occurrence of change from heterogeneous remote sensing images when the frame is possibly changing over time. To overcome this limitation, dynamic evidential reasoning (DER) is proposed for the sequential fusion of multitemporal images. A new state-transition frame is defined in DER, and the change occurrences can be precisely represented by introducing a statetransition operator. Two kinds of dynamical combination rules working in the free model and in the constrained model are proposed in this new framework for dealing with the different cases. Moreover, the prior probability of state transitions is taken into account, and the link between DER and Dezert–Smarandache theory is presented. The belief functions used in DER are defined similarly to those defined in the Dempster–Shafer theory. As shown in the last part of this paper, DER is able to estimate efficiently the correct change detections as a postprocessing technique. Two applications are given to illustrate the interest of DER: The first example is based on a set of two SPOT images acquired before and after a flood, and the second example uses three QuickBird images acquired during an earthquake event.

Imaging spectropolarimetry is a new sensing method that can acquire the spectral, polarimetric, and spatial information of an interesting scene. They give the incomplete representations of a scene, respectively, and it is expected that combination of them will improve confidence in target identification and quality of the scene description. In this letter, a divide-and-conquer-based unsupervised spectropolarimetric data classification method is proposed to utilize the spatial, spectral, and polarimetric information jointly. First, a spectropolarimetric projection scheme is proposed to divide the whole data set into two parts: spatial-spectral and spatial-polarimetric domains. Then, a nonparametric technique is used to extract the homogeneous regions in these two domains. Each homogeneous region offers a reference spectrum and polarization, based on which a pseudosupervised spectropolarimetric classification scheme is developed by using evidence theory to fuse the information provided by the spectrum and polarization. The experimental results on real spectropolarimetric data demonstrate that the proposed divide-and-conquer-based classification scheme can achieve higher accuracy than the fuzzyc-means clustering method with spatial information constraints, which takes into account the spatial information during spectral and polarimetric clustering. Moreover, the experimental results also show the potential of spectropolarimetric classification.

Protein-RNA interactions are vitally important to a number of fundamental cellular processes, including regulation of gene expression such as RNA splicing, transport and translation, protein synthesis and assembly of ribosome. More detailed information on the Protein-RNA interaction is helpful for comprehending the function notation and molecular regulatory mechanism, meanwhile, knowing the knowledge of Protein-RNA recognition can also help the biological scientist and researcher understand the site-directed mutagenesis and drug design. In the present work, we proposed a computational approach, based on SVM-KNN algorithm, with evolutionary information of spatial neighbour residues for prediction of protein-RNA interaction sites. The overall success rate obtained by 5-fold cross-validation is 78.00%, which is comparable or better than other existing methods, indicating our method is very promising for identifying and predicting protein-RNA interaction sites.

In this work, an energy based acoustic source localization task in a wireless sensor network (WSN) is considered. Based on data gathered from field experiments, it is revealed that the acoustic energy gathered at sensor nodes exhibits a heavy-tail, non-Gaussian characteristic and should be fitted into a contaminated Gaussian model. This property renders conventional least square and maximum likelihood based location estimation methods ineffective. Leveraging the distributed, in-network processing nature of a WSN, a novel de-centralized robust acoustic source localization (DRASL) algorithm is proposed. With the DRASL, local sensor nodes receive sensor readings broadcast from neighboring sensors and independently compute local location estimates using a light-weight Iterative Nonlinear Reweighted Least Square (INRLS) algorithm. The local location estimate then will be relayed to a fusion center where the final location estimate is obtained as a weighted average of the local estimates. The potential advantage of this algorithm is validated using extensive simulation in a real-world operation scenario. It is show that its performance is superior than existing methods while promising to be more energy efficient.

BACKGROUND: Extracting and visualizing of protein-protein interaction (PPI) from text literatures are a meaningful topic in protein science. It assists the identification of interactions among proteins. There is a lack of tools to extract PPI, visualize and classify the results. RESULTS: We developed a PPI search system, termed PPLook, which automatically extracts and visualizes protein-protein interaction (PPI) from text. Given a query protein name, PPLook can search a dataset for other proteins interacting with it by using a keywords dictionary pattern-matching algorithm, and display the topological parameters, such as the number of nodes, edges, and connected components. The visualization component of PPLook enables us to view the interaction relationship among the proteins in a three-dimensional space based on the OpenGL graphics interface technology. PPLook can also provide the functions of selecting protein semantic class, counting the number of semantic class proteins which interact with query protein, counting the literature number of articles appearing the interaction relationship about the query protein. Moreover, PPLook provides heterogeneous search and a user-friendly graphical interface. CONCLUSIONS: PPLook is an effective tool for biologists and biosystem developers who need to access PPI information from the literature. PPLook is freely available for non-commercial users at http://meta.usc.edu/softs/PPLook.

This correspondence paper studies face recognition by using hyperspectral imagery in the visible light bands. The spectral measurements over the visible spectrum have different discriminatory information for the task of face identification, and it is found that the absorption bands related to hemoglobin are more discriminative than the other bands. Therefore, feature band selection based on the physical absorption characteristics of face skin is performed, and two feature band subsets are selected. Then, three methods are proposed for hyperspectral face recognition, including whole band (2D)2PCA, single band (2D)2PCA with decision level fusion, and band subset fusion-based (2D)2PCA. A simple yet efficient decision level fusion strategy is also proposed for the latter two methods. To testify the proposed techniques, a hyperspectral face database was established which contains 25 subjects and has 33 bands over the visible light spectrum (0.4-0.72 μm). The experimental results demonstrated that hyperspectral face recognition with the selected feature bands outperforms that by using a single band, using the whole bands, or, interestingly, using the conventional RGB color bands.

This paper presents an artificial ant colony approach to distributed sensor wakeup control (SWC) in wireless sensor networks (WSN) to accomplish the joint task of surveillance and target tracking. Each sensor node is modeled as an ant, and the problem of target detection is modeled as the food locating by ants. Once the food is found, the ant will release pheromone. The communication, invalidation, and fusion of target information are modeled as the processes of pheromone diffusion, loss, and accumulation. Since the accumulated pheromone can measure the existence of a target, it is used to determine the probability of ant-searching activity in the next round. To the best of our knowledge, this is the first biologically inspired SWC method in the WSN. Such a biologically inspired method has multiple desirable advantages. First, it is distributive and does not require a centralized control or cluster leaders. Therefore, it is free of the problems caused by leader failures and can save the communication cost for leader selection. Second, it is robust to false alarms because the pheromone is accumulated temporally and spatially and thus is more reliable for wakeup control. Third, the proposed method does not need the knowledge of node position. Two theorems are presented to analytically determine the key parameters in the method: the minimum and maximum pheromone. Simulations are carried out to evaluate the performance of the proposed method in comparison with representative methods.

Sensor measurements in a wireless sensor network (WSN) may significantly deviate from a commonly used Gaussian noise model due to harsh operating conditions, unreliable wireless communication links, or sensor failures. In this work, a mixed Gaussian and impulse noise model is proposed to more accurately model these types of non-Gaussian noise. However, existing maximum likelihood (ML) acoustic energy based source localization algorithms are very sensitive to non-Gaussian noise perturbations. To mitigate this shortcoming, a novel M-estimate based robust estimation formulation is derived. Extensive simulation results demonstrated superior and consistent performance advantage of this robust estimation approach compared to conventional ML estimates over a wide range of practical scenarios.

This letter presents a new class of discrete-time linear stochastic systems with the statistically-constrained disturbance input, which can represent an arbitrary linear combination of dynamic, random, and deterministic disturbance inputs to generalize the complicated modeling error encountered in actual applications. An adaptive filtering scheme is proposed for such systems by recursively constructing and adaptively minimizing the upper-bounds of covariance matrices of the state predictions, innovations, and estimates. The minimum-upper-bound filter is then obtained via online scalar convex optimization. The experiment on maneuvering target tracking shows that the proposed filter can significantly reduce the peak estimation errors due to maneuvers, compared with the well-known IMM method.

In the past few years, imaging spectroscopy has been widely used. However, it only acquires intensity information in a narrow electromagnetic band, ignoring the polarimetric information of the electromagnetic wave and resulting in inaccurate object detection. According to electromagnetic theory, the reflected spectral signature depends on the elemental composition of objects residing within the scene, and the radiation's polarization characteristic is sensitive to surface features, such as relative smoothness and conductance. Independently, spectral and polarimetric features give incomplete representations of an object of interest. These representations are complementary, and it is expected that the combination of complementary information will reduce false alarms, improve confidence in target identification, and improve the quality of the scene description. Imaging spectropolarimetric technology as a new sensing method can acquire polarimetric information at narrow electromagnetic bands, but there are a few results showing how to combine these complementary features to detect objects in clutter. In this paper, a spectropolarimetric projection scheme is proposed to divide the spectropolarimetric data set into two parts: (1) a polarimetric spectrum data set and (2) a polarimetric data cube. Then, the polarimetric spectrum anomaly feature extraction method is used to deal with the polarimetric spectrum data set, and the adaptive polarimetric information fusion method is proposed to extract the feature from the polarimetric data cube. Finally, these features are combined by the Choquet integral to achieve better detection performance. The algorithm is applied to one complex scene, and detailed detection performance is evaluated.

The estimation of Markov jump systems (MJS) is widely used in target tracking, fault detection, signal processing and digital communications. However, the above researches all assume that state measurement and additional mode observation are synchronous which means both state measurement and mode observation at each sampling time arrive at the fusion centre at the same time. The problem of estimation of MJS that mode observation is one-step lagged to its corresponding state measurement is considered. Along state-augmentation approach and the derivation of image-enhanced interacting multiple model (IE-IMM), a new generic estimation algorithm is proposed. It is shown by simulation result that the proposed algorithm is effective.

This paper is a new attempt to introduce multiple cues to the kernel tracking by adaptive manner to improve the reliability and robustness of target tracking in the time-variant scenario. Based on Fisher rule, we construct the measure of discriminability to represent the ability of each cue in distinguishing the target from the background. According to the discriminability the weight of each cue is adjusted in time to accommodate the scene change, and then the cues are adaptively fused with kernel tracking method by these weights. In addition, we present a selective submodel update strategy via the discriminability for alleviating the model drift. In experiments, our scheme based on color cue and LBP texture cue is shown better effectiveness, compared with the well known mean shift tracker.

In wireless sensor networks (WSN), it is a fundamental issue to balance two conflicted performance indexes: sensing ability and energy cost, via the self-organization (SO). Here each sensor node in the WSN is mapped to an ant in ant colony system and node communication information is modeled by the current pheromone. The SO problem of the WSN is transformed to the swarm intelligence optimization problem of ant colony. If an ant detects an interested target, it will lay pheromone, which can diffuse in its neighbor zone. The accumulated pheromone is calculated to adaptively and distributively determine the waking probability of the ant so that the self organization of the WSN can be implemented automatically. Hence a new swarm intelligence method for the SO of WSN is proposed. The simulations show the effectiveness of our method.

This work presents a real-time system for multiple objects tracking in dynamic scenes. A unique characteristic of the system is its ability to cope with long-duration and complete occlusion without a prior knowledge about the shape or motion of objects. The system produces good segment and tracking results at a frame rate of 15-20 fps for image size of 320 /spl times/ 240, as demonstrated by extensive experiments performed using video sequences under different conditions indoor and outdoor with long-duration and complete occlusions in changing background.

In this paper, the modeling and estimation of a class of dynamic multiscale system, subject to colored state equation noise and measurement equation noise, is proposed. The state equation is whitened firstly and then the measurement equation. The state space projection equation is used to link the scales, then a new system model is built. The new model is in a form suitable for the application of the Kalman filter equations. A Haar-wavelet-based model and estimation algorithm are given. Monte Carlo simulation results demonstrate that the proposed algorithm is effective and powerful in this kind of multiscale estimation problem.

Multiple sensors image fusion is increasingly being employed in video surveillance. This paper presents a color based grayscale-fused image enhancement algorithm. The main advantage of this algorithm is to stand out the meaningful information of a certain sensor in the selected band of the color space, while maintaining the high resolution of the grayscale-fused image. Moreover, a performance evaluation method based on analyzing the average gradients of the color-fused image is presented and experiments are performed using images under complex environment. The results show that the addition of color to grayscale-fused image can significantly increase observer's sensitivity and the accuracy of further classification and recognition.

A fast and efficient multiple layer background maintenance model is built to conserve the original and the current background separately. Fusing the properties of object motion in image pixels and the changes between the input video and the multiple background layers, this method could handle various sources of scene changes, including ghosts, abandon objects and illumination changes. An intelligent video surveillance system is developed to test the performance of the algorithm. Experiments are performed using long video sequences under different conditions indoor and outdoor. The results show that the proposed algorithm is effective and efficient in real-time and accurate background maintenance in complex environment.

MOTIVATION: Since the gap between sharply increasing known sequences and slow accumulation of known structures is becoming large, an automatic classification process based on the primary sequences and known three-dimensional structure becomes indispensable. The classification of protein quaternary structure based on the primary sequences can provide some useful information for the biologists. So a fully automatic and reliable classification system is needed. This work tries to look for the effective methods of extracting attribute and the algorithm for classifying the quaternary structure from the primary sequences. RESULTS: Both of the support vector machine (SVM) and the covariant discriminant algorithms have been first introduced to predict quaternary structure properties from the protein primary sequences. The amino acid composition and the auto-correlation functions based on the amino acid index profile of the primary sequence have been taken into account in the algorithms. We have analyzed 472 amino acid indices and selected the four amino acid indices as the examples, which have the best performance. Thus the five attribute parameter data sets (COMP, FASG, NISK, WOLS and KYTJ) were established from the protein primary sequences. The COMP attribute data set is composed of amino acid composition, and the FASG, NISK, WOLS and KYTJ attribute data sets are composed of the amino acid composition and the auto-correlation functions of the corresponding amino acid residue index. The overall accuracies of SVM are 78.5, 87.5, 83.2, 81.7 and 81.9%, respectively, for COMP, FASG, NISK, WOLS and KYTJ data sets in jackknife test, which are 19.6, 7.8, 15.5, 13.1 and 15.8%, respectively, higher than that of the covariant discriminant algorithm in the same test. The results show that SVM may be applied to discriminate between the primary sequences of homodimers and non-homodimers and the two protein sequence descriptors can reflect the quaternary structure information. Compared with previous Robert Garian's investigation, the performance of SVM is almost equal to that of the Decision tree models, and the methods of extracting feature vector from the primary sequences are superior to Robert's binning function method. AVAILABILITY: Programs are available on request from the authors.