This article established a novel multi-input multioutput (MIMO) communication network, in the presence of full-duplex (FD) transmitters and receivers with the assistance of dual-side intelligent omni surface (IOS). Compared with the traditional IOS, the dual-side IOS allows signals from both sides to reflect and refract simultaneously, which further exploits the potential of metasurfaces to avoid frequency dependence, and size, weight, and power (SWaP) limitations. By considering both the downlink and uplink transmissions, we aim to maximize the weighted sum rate, subject to the transmit power constraints of the transmitter, the users and the dual-side reflecting and refracting phase shifts constraints. However, the formulated sum rate maximization problem is not convex, hence we exploit the weighted minimum mean square error (WMMSE) approach, and tackle the original problem iteratively by solving two subproblems. For the beamforming matrices optimization of the downlink and uplink, we resort to the Lagrangian dual method combined with a bisection search to obtain the results. Furthermore, we resort to the quadratically constrained quadratic programming (QCQP) method to optimize the reflecting and refracting phase shifts of both sides of the IOS. Simulation results validate the efficacy of the proposed algorithm and demonstrate the superiority of the dual-side IOS.

Distributed reconfigurable intelligent surfaces (RISs) provide rich macro-diversity coverage due to different locations of the RISs, which is beneficial to combat coverage holes. However, the system performance relies on the effective coordination of multiple RISs. In particular, distributed RIS-assisted power allocation and the phase shifts of RISs should be jointly designed under nonlinear scheduling constraints. Thus, the resource allocation scheme for distributed RIS-assisted multiuser system is a crucial challenge. To tackle these issues, joint power allocation, phase shifts and communication scheduling design for distributed RIS-assisted systems is investigated in this paper, where all RISs simultaneously and cooperatively serve multiple users. To overcome the formulated nonconvex optimization problem, the original problem is decoupled into three subproblems and solved in an iterative manner. Specifically, we first consider the subproblem of power allocation, which can be solved via maximizing the ergodic achievable rate. By applying the ergodic rate, an approximate closed-form solution is formed for the power allocation. Subsequently, the phase shifts are optimized using the minimization-maximization optimization methods. Finally, a communication scheduling scheme is presented to address the scheduling variables. Numerical simulations are conducted to demonstrate that the considered solution outperforms the existing benchmark and achieves a near-optimal spectral efficiency.

In the Internet of Everything (IoE) scenarios, the extensive deployment of devices may result in more stringent power and communication needs. Within this context, we utilize the reconfigurable intelligent surface (RIS) to support the simultaneous wireless information and power transfer (SWIPT) system, whereby the stable transmission of energy and information services can be guaranteed. Specifically, we construct the system model through electromagnetics (EMs), which is based on the scattering-parameter (S-parameter) analysis, for revealing the crucial factors of the practical hardware. Relying on the model, the energy-efficient (EE) maximization problem constrained to the Quality of Services (QoS) is proposed for the users with the framework of co-located receiver (Rx). However, the problem is more intractable due to the introduced channel model. To resolve it, we propose an effective optimization scheme. First, the Neuman series approximation method is adopted to deconstruct the EM transfer model. Then the reformed problem, which includes the variables (i.e., the power splitting ratio, the active beamformer, and the reflection-coefficient matrix), can be addressed through the strategy of alternative optimization (AO). Further, the inner convex approximation (INCA) scheme and Dinkelbach’s algorithm are applied to tackle each subproblem. In the numerical simulation, we demonstrate that the array configuration can influence not only the hardware properties of RIS but also the EE performance of the whole system. What is more, the proposed scheme performs better for the tightly coupled RIS owing to the awareness of the mutual-coupling (MC) effect.

The space-air-ground integrated network (SAGIN) has become a crucial research direction in future wireless communications due to its ubiquitous coverage, rapid and flexible deployment, and multi-layer cooperation capabilities. However, integrating hierarchical federated learning (HFL) with edge computing and SAGINs remains a complex open issue to be resolved. This paper proposes a novel framework for applying HFL in SAGINs, utilizing aerial platforms and low Earth orbit (LEO) satellites as edge servers and cloud servers, respectively, to provide multi-layer aggregation capabilities for HFL. The proposed system also considers the presence of inter-satellite links (ISLs), enabling satellites to exchange federated learning models with each other. Furthermore, we consider multiple different computational tasks that need to be completed within a limited satellite service time. To maximize the convergence performance of all tasks while ensuring fairness, we propose the use of the distributional soft-actor-critic (DSAC) algorithm to optimize resource allocation in the SAGIN and aggregation weights in HFL. Moreover, we address the efficiency issue of hybrid action spaces in deep reinforcement learning (DRL) through a decoupling and recoupling approach, and design a new dynamic adjusting reward function to ensure fairness among multiple tasks in federated learning. Simulation results demonstrate the superiority of our proposed algorithm, consistently outperforming baseline approaches and offering a promising solution for addressing highly complex optimization problems in SAGINs.

In recent years, the amalgamation of satellite communications and aerial platforms into space-air-ground integrated network (SAGINs) has emerged as an indispensable area of research for future communications due to the global coverage capacity of low Earth orbit (LEO) satellites and the flexible Deployment of aerial platforms. This paper presents a deep reinforcement learning (DRL)-based approach for the joint optimization of offloading and resource allocation in hybrid cloud and multi-access edge computing (MEC) scenarios within SAGINs. The proposed system considers the presence of multiple satellites, clouds and unmanned aerial vehicles (UAVs). The multiple tasks from ground users are modeled as directed acyclic graphs (DAGs). With the goal of reducing energy consumption and latency in MEC, we propose a novel multi-agent algorithm based on DRL that optimizes both the offloading strategy and the allocation of resources in the MEC infrastructure within SAGIN. A hybrid action algorithm is utilized to address the challenge of hybrid continuous and discrete action space in the proposed problems, and a decision-assisted DRL method is adopted to reduce the impact of unavailable actions in the training process of DRL. Through extensive simulations, the results demonstrate the efficacy of the proposed learning-based scheme, the proposed approach consistently outperforms benchmark schemes, highlighting its superior performance and potential for practical applications.

This article presents a model predictive control (MPC) design based on dynamic event-triggered mechanism (DETM). In the sensor-to-controller channel, the networks are unreliable in the sense that the transmitted signals may suffer from channel fading which is characterized by a stochastic process. In the controller-to-actuator network, the Denial-of-Service (DoS) attacks are taken into consideration whose dynamic behavior is described by a binary Markov process. Moreover, the external disturbance is considered. First, a DETM is employed to schedule the data transmission with the aim of reducing the communication burden. Then, an$H_{\infty}$-type cost function is applied in the MPC design to improve the system’s robustness against disturbance. Different from the conventional MPC, the novelty of developed MPC is that it can improve the communication efficiency and enhance the robustness against these network-induced issues simultaneously. At last, the validity and superiority of the proposed technique are demonstrated by simulation studies.Note to Practitioners—With the development of information technologies, the practical control systems are highly integrated with wireless networks. In such case, the network-induced issues are very important. For example, in the platooning control of automated vehicles, the conventional time-triggered control scheme leads to unnecessary waste of communication resource and thus heavy communication burden. Especially, this issue becomes particularly important when the number of vehicles increases. This article was motivated by this and it suggests a new control design based on DETM. Moreover, in control systems, it is necessary to utilize the state measurement to compute control signals. However, when the measured state information is affected by DoS attacks and channel fading, the system performance and even stability will be seriously damaged. Furthermore, the external disturbances widely exist in practical engineering. Considering the influence of these issues, this paper studies the design of MPC. The obtained results aim to provide a helpful reference for the controller design under various network-induced issues, involving bandwidth constraints, DoS attacks and channel fading. It is expected that the proposed MPC design can be applied to more practical engineering systems.

User-centric (UC) philosophy is a promising network formation method in light emitting diode enabled visible light communication (VLC) systems. Nevertheless, the nonlinear channel impairments restrict the overall system performance and have not been fully considered in the association structure designing. In this paper, an adaptive user association approach within the UC-cells formation of dense VLC networks is investigated under the consideration of practical nonlinear impairments and adjacent interference. It is mathematically formulated to be an achievable data rate maximization problem by coordinately determining the optimal candidates of access point, clipping ratio and information-carrying power. We divide this mixed combinatorial and non-convex optimization problem into two subproblems and delicately transform them to be binary nonlinear programming and constrained linear programming problems, respectively. In addition, we develop an efficient approach to obtain the local optimal solution with low-computational complexity in an alternating iterative way. Simulation results demonstrate that the proposed scheme has relatively fast convergence and shows robustness to the variation of complex interference patterns and nonlinear impairments. Moreover, it can achieve significant throughput gain as compared with the conventional schemes, demonstrating the prospect and validity of this methodology for dense VLC networks with actual nonlinear devices.

Reconfigurable intelligent surfaces (RISs) have attracted extensive attention in millimeter wave (mmWave) systems because of the capability of configuring the wireless propagation environment. However, due to the existence of a RIS between the transmitter and receiver, a large number of channel coefficients need to be estimated, resulting in more pilot overhead. In this paper, we propose a joint sparse and low-rank based two-stage channel estimation scheme for RIS-assisted mmWave systems. Specifically, we first establish a low-rank approximation model against the noisy channel, fitting in with the precondition of the compressed sensing theory for perfect signal recovery. To overcome the difficulty of solving the low-rank problem, we propose a trace operator to replace the traditional nuclear norm operator, which can better approximate the rank of a matrix. Furthermore, by utilizing the sparse characteristics of the mmWave channel, sparse recovery is carried out to estimate the RIS-assisted channel in the second stage. Simulation results show that the proposed scheme achieves significant performance gain in terms of estimation accuracy compared to the benchmark schemes.

This article investigates the finite-time control problem of the switched affine systems via an event-triggered strategy. It is well known that the existence of affine terms brings great difficulties in analysis of the finite-time property of such systems. Furthermore, the design of the globally feasible event-triggered mechanism (ETM) under a finite-time control framework is challenging. Thus, a two-step hybrid control scheme is proposed in this article. The first step focuses on the event-triggered finite-time control for practical stability, while the second step aims to achieve finite-time stabilization. Particularly, in step one, by constructing the intersection between the affine term's threshold and feasible state region of the established ETM, it is verified that the Zeno behavior can be excluded. Thereafter, an affine state-dependent switching law and sufficient conditions are provided for achieving practical stability. Meanwhile, an estimation for the practical settling time to enter the bounded set is provided. In step two, the criteria for finite-time stabilization of the considered systems are further presented, and an overall settling-time upper bound is derived. Finally, a numerical example is illustrated to demonstrate the effectiveness of our proposed method.

The problem of resilient optimal bipartite tracking control for heterogeneous multi-agent systems with multiple targets under denial-of-service (DoS) attacks is investigated in this paper. A bipartite tracking mechanism is devised in which the agents track the targets under bipartite consensus control, which becomes non-autonomous. This is owing to DoS attacks, as the agents cannot obtain real-time information on the tracked targets and neighboring agents. Consequently, A target observer with a storage module has been developed for the efficient estimation of agent states and the storage of observed information as historical data. By recalling the historical data of the observed state, a new type of distributed resilient optimal controller is formulated, which can achieve the control objective in the case of communication blockage, while minimizing the performance index function of the system. Numerical simulations are performed to verify the proposed secure control design.

The simultaneous wireless information and power transfer (SWIPT) technology assisted by the reconfigurable intelligent surface (RIS) can bring flexibility and stability to the end nodes of the internet of things (IoT) during the deployment. In this paper, we propose a RIS-aided SWIPT system based on a hardware transfer model from the electromagnetic perspective. Particularly, an energy efficiency (EE) maximization problem subject to the quality of service (QoS) demands, power resource budget and circuit restrains is introduced. Furthermore, the active beamforming vectors of the BS and the circuit parameters at the RIS are optimized jointly. The problem can be decomposed into two sub-problems and solved iteratively until convergence. In particular, semi-definite relaxation (SDR), successive convex approximation (SCA), Dinkelbach's algorithm are applied to the solutions of the sub-problems. Numerical results reveal the influences of the various QoS requirements on EE performance. Moreover, the actual generated beams of the BS and the RIS are shown to demonstrate the effectiveness of the proposed optimization strategy.

Reconfigurable intelligent surfaces (RISs) have attracted extensive attention in millimeter wave (mmWave) systems because of the capability of configuring the wireless propagation environment. However, due to the existence of a RIS between the transmitter and receiver, a large number of channel coefficients need to be estimated, resulting in more pilot overhead. In this paper, we propose a joint sparse and low-rank based two-stage channel estimation scheme for RIS-assisted mmWave systems. Specifically, we first establish a low-rank approximation model against the noisy channel, fitting in with the precondition of the compressed sensing theory for perfect channel recovery. To overcome the difficulty of solving the low-rank problem, we propose a trace operator to replace the traditional nuclear norm operator, which can better approximate the rank of a matrix. Furthermore, by utilizing the sparse characteristics of the mmWave channel, sparse recovery is carried out to estimate RIS-assisted channels in the second stage. Simulation results show that the proposed scheme achieves significant performance gain in terms of estimation accuracy compared to the benchmark schemes.

The widespread deployment of the Internet of Things (IoT) is promoting interest in simultaneous wireless information and power transfer (SWIPT), the performance of which can be further improved by employing a reconfigurable intelligent surface (RIS). In this article, we propose a novel RIS-enhanced SWIPT system built on an electromagnetic-compliant framework. The mutual-coupling effects in the whole system are presented explicitly. Moreover, the reconfigurability of RIS is no longer expressed by the reflection-coefficient matrix but by the impedances of the tunable circuit. For comparison, both the no-coupling and the coupling-awareness cases are discussed. In particular, the energy efficiency (EE) is maximized by cooperatively optimizing the impedance parameters of the RIS elements as well as the active beamforming vectors at the base station (BS). For the coupling-awareness case, the considered problem is split into several subproblems and solved alternatively due to its nonconvexity. First, it is transformed into a more solvable form by applying the Neuman series approximation, which can be resolved iteratively. Then, an alternative optimization (AO) framework and semidefinite relaxation (SDR), successive convex approximation (SCA), and Dinkelbach’s algorithm are applied to solve each subproblem decomposed from it. Owning to the similarity between the two cases, the no-coupling one can be viewed as a reduced form of the coupling case and, thus, solved through a similar approach. Numerical results reveal the influence of mutual-coupling effects on the EE, especially in the RIS with closely spaced elements. In addition, physical beam designs are presented to demonstrate how the RIS assists SWIPT through various reflecting states in different conditions.

Combining simultaneous wireless information and power transfer (SWIPT) and an intelligent reflecting surface (IRS) is a feasible scheme to enhance energy efficiency (EE) performance. In this paper, we investigate a multiuser IRS-aided multiple-input single-output (MISO) system with SWIPT. For the purpose of maximizing the EE of the system, we jointly optimize the base station (BS) transmit beamforming vectors, the IRS reflective beamforming vector, and the power splitting (PS) ratios, while considering the maximum transmit power budget, the IRS reflection constraints, and the quality of service (QoS) requirements containing the minimum data rate and the minimum harvested energy of each user. The formulated EE maximization problem is non-convex and extremely complex. To tackle it, we develop an efficient alternating optimization (AO) algorithm by decoupling the original nonconvex problem into three subproblems, which are solved iteratively by using the Dinkelbach method. In particular, we apply the successive convex approximation (SCA) as well as the semi-definite relaxation (SDR) techniques to solve the non-convex transmit beamforming and reflective beamforming optimization subproblems. Simulation results verify the effectiveness of the AO algorithm as well as the benefit of deploying IRS for enhancing the EE performance compared with the benchmark schemes.

All existing physiological tremor filtering algorithms, developed for robotic microsurgery, use nonlinear phase prefilters to isolate the tremor signal. Such filters cause phase distortion to the filtered tremor signal and limit the filtering accuracy. We revisited this long-standing problem to enable filtering of the physiological tremor without any phase distortion. We developed a combined estimation–prediction paradigm that offers zero-phase type filtering. The estimation is achieved with the mathematically modified recursive singular spectrum analysis algorithm, and the prediction is delivered with the standard extreme learning machine. In addition, to limit the computational cost, we developed two moving window versions of this structure, which are appropriate for real-time implementation. The proposed paradigm preserved the natural phase of the filtered tremor. It achieved the key performance index of error limitation below$10\mu \text{m}$, yielding the estimation accuracy larger than 70%, at a time delay of 36 ms only. Both moving window versions of the proposed approach restricted the computational cost considerably while offering the same performance. It is the first time that the effective estimation of the physiological tremor is achieved, without any prefiltering and phase distortion. This proposed method is feasible for real-time implantation. Clinical translation of the proposed paradigm can significantly enhance the outcome in hand-held surgical robotics.Note to Practitioners—The imprecision caused by physiological hand tremor in microsurgeries has motivated researchers to innovate an efficient tremor compensating technique that can improve surgical performance. Yet, all the existing tremor filtering algorithms, implemented in hand-held surgical instruments, use nonlinear phase prefilters to separate the tremor signal. The inherent phase distortion caused by such prefilters restricts the filtering performance significantly and renders the existing methods inadequate for hand-held robotic surgery. Motivated by this, we proposed a novel estimator-predictor-based framework, by adopting the modified recursive singular spectrum analysis estimator and the extreme learning machine predictor. The proposed framework filters the tremor signal accurately, without distorting it, but at a small fixed lag. In a set of rigorous testing performed by emulating real-time processing, the proposed algorithm showed higher performance compared with the state-of-the-art algorithms. This validates not only its suitability for real-time implantation but also its potential to improve surgical performance, which has been limited by the distorted filtering. Nonetheless, we have presented a proof-of-principle framework for distortion-free filtering, but its full implementation in a real surgical instrument, such as Micron or ITrem, requires a substantial amount of experimental testing and verification. It can be also applicable in a wide range of areas, including health-care, digital manufacturing, smart automation and control, and various other robotic technologies where efficient filtering of advanced sensor data is highly desirable. In the future, we will develop the multidimensional model of the proposed framework to enable filtering of tremor in thexyz-axes simultaneously.

This paper investigates joint transmit beampattern and phase shifts optimization techniques for a reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) radar in the presence of an eavesdropping target. We propose an optimization technique to maximize the signal-to-interference plus noise ratio (SINR) at the MIMO radar. However, the problem is non-convex due to the non-concavity of the secrecy rate function. To tackle this issue, we apply the block coordinate descent (BCD) algorithm to update the transmit power and the phase shifts of the RIS alternately. Specifically, we utilize the majorization-minimization (MM) algorithm to optimize the phase shifts for a given transmit power and utilize the first-order Taylor expansion to reformulate the problem as a convex problem to optimize the transmit power for a given set of phase shifts. Two transmit beamforming vectors are designed to detect the target and convey information safely to the legitimate receiver. Simulation results show that the RIS-assisted MIMO radar can significantly enhance the SINR compared to an ordinary MIMO radar.

Subspace clustering is a popular method for clustering unlabelled data. However, the computational cost of the subspace clustering algorithm can be unaffordable when dealing with a large data set. Using a set of dimension sketched data instead of the original data set can be helpful for mitigating the computational burden. Thus, finding a way for dimension sketching becomes an important problem. In this paper, a new dimension sketching algorithm is proposed, which aims to select informative dimensions that have significant effects on the clustering results. Experimental results reveal that this method can significantly improve subspace clustering performance on both synthetic and real-world datasets, in comparison with two baseline methods.

Abstract Breast cancer has the highest fatality for women compared with other types of cancer. Generally, early diagnosis of cancer is crucial to increase the chances of successful treatment. Early diagnosis is possible through physical examination, screening, and obtaining a biopsy of the dubious area. In essence, utilizing histopathology slides of biopsies is more efficient than using typical screening methods. Nevertheless, the diagnosing process is still tiresome and is prone to human error during slide preparation, such as when dyeing and imaging. Therefore, a novel method is proposed for diagnosing breast cancer into benign or malignant in a magnification‐specific binary (MSB) classification. Besides, the introduced method classifies each type into four subclasses in a magnification‐specific multi‐category (MSM) fashion. The proposed method involves normalizing the hematoxylin and eosin stains to enhance colour separation and contrast. Then, two types of novel features—deep and shallow features—are extracted using two deep structure networks based on DenseNet and Xception. Finally, a multi‐classifier method based on the maximum value is utilized to achieve the best performance. The proposed method is evaluated using the BreakHis histopathology data set, and the results in terms of diagnostic accuracy are promising, achieving 99% and 92% in terms of MSB and MSM, respectively, compared with recent state‐of‐the‐art methods reported in the survey conducted by Benhammou on the BreakHis data set using deep learning and texture‐based models.

Wireless-powered communication networks (WPCNs) are a promising technology supporting resource-intensive devices in the Internet of Things (IoT). However, their transmission efficiency is very limited over long distances. The newly emerged intelligent reflecting surface (IRS) can effectively mitigate the propagation-induced impairment by controlling the phase shifts of passive reflection elements. In this article, we integrate IRS into WPCNs to assist both the energy and information transmission. We aim to maximize the uplink (UL) sum rate of all IoT devices by jointly optimizing the time allocation variable, energy beam matrix at the power transmitting base station (PTBS), receive beamforming matrix at the information receiving base station, and the phase shifts of the IRS both in the UL and downlink (DL) subject to time allocation constraint, together with transmit power constraint for the PTBS and unit modulus constraints. This problem is very difficult to solve directly due to the highly coupled variables, which results in the optimization problem taking neither linear nor convex form. Hence, we decouple this problem into three subproblems by using the block coordinate descent method. The UL receive beamforing matrix and phase shift are alternatively optimized in the UL optimization subproblem with fixed time allocation and the DL variables. The DL optimization subproblem is solved by the proposed successive convex approximation algorithm. Simulation results demonstrate that the performance of integrating IRS and WPCNs outperforms traditional WPCNs. Besides, the results show that IRS is an effective method to preserve the tradeoff of energy efficiency and transmission efficiency in the IoT.

Beamforming and non-orthogonal multiple access (NOMA) serve as two potential solutions for achieving spectral efficient communication in the fifth generation and beyond wireless networks. In this paper, we jointly apply a hybrid beamforming and NOMA techniques to an unmanned aerial vehicle (UAV)-carried wireless-powered mobile edge computing (MEC) system, within which the UAV is equipped with a wireless power charger and the MEC platform delivers energy and computing services to Internet of Things (IoT) devices. Our aim is to maximize the sum computation rate at all IoT devices whilst satisfying the constraint of energy harvesting and coverage. The resultant optimization problem is non-convex involving joint optimization of the UAV’s 3D placement and hybrid beamforming matrices as well as computation resource allocation in both partial and binary offloading patterns, and thus is quite difficult to tackle directly. By applying the polyhedral annexation method and the deep deterministic policy gradient (DDPG) algorithm, we develop an effective algorithm to derive the closed-form solution for the optimal 3D deployment of the UAV, and find the solution for the hybrid beamformer. Two resource allocation algorithms for partial and binary offloading patterns are thereby proposed. Simulation results verify that our designed algorithms achieve a significant computation performance enhancement as compared to the benchmark schemes.

This paper investigates asynchronous reinforcement learning algorithms for joint buffer-aided relay selection and power allocation in the non-orthogonal-multiple-access (NOMA) relay network. With the hybrid NOMA/OMA transmission, we investigate joint relay selection and power allocation to maximize the throughput with the delay constraint. To solve this complicated high-dimensional optimization problem, we propose two asynchronous reinforcement learning-based schemes: the asynchronous deep Q-Learning network (ADQN)-based scheme and the asynchronous advantage actor-critic (A3C)-based scheme, respectively. The A3C-based scheme achieves better performance and robustness when the action space is large, while the ADQN-based scheme converges faster with a small action space. Moreover, a-prior information is exploited to improve the convergence of the proposed schemes. The simulation results show that the proposed asynchronous learning-based schemes can learn from the environment and achieve good convergence.

As the number of connected devices is exponentially increasing, security in Internet of Things (IoT) networks presents a major challenge. Accordingly, in this work we investigate the secrecy performance of multihop IoT networks assuming that each node is equipped with only two antennas, and can operate in both Half-Duplex (HD) and Full-Duplex (FD) modes. Moreover, we propose an FD Cooperative Jamming (CJ) scheme to provide higher security against randomly located eavesdroppers, where each information symbol is protected with two jamming signals by its two neighbouring nodes, one of which is the FD receiver. We demonstrate that under a total power constraint, the proposed FD-CJ scheme significantly outperforms the conventional FD Single Jamming (FD-SJ) approach, where only the receiving node acts as a jammer, especially when the number of hops is larger than two. Moreover, when the Channel State Information (CSI) is available at the transmitter, and transmit beamforming is applied, our results demonstrate that at low Signal-to-Noise Ratio (SNR), higher secrecy performance is obtained if the receiving node operates in HD and allocates both antennas for data reception, leaving only a single jammer active; while at high SNR, a significant secrecy enhancement can be achieved with FD jamming. Our proposed FD-CJ scheme is found to demonstrate a great resilience over multihop networks, as only a marginal performance loss is experienced as the number of hops increases. For each case, an integral closed-form expression is derived for the secrecy outage probability, and verified by Monte Carlo simulations.

In this paper, a robust Student’s-T distribution aided One-Stage Orientation detector, namely TOSO, is proposed to address orientation target detection in remote sensing images. A one-stage keypoint based network architecture is used to avoid the complicated computation caused by rotation anchor boxes and two main contributions are proposed to enhance the performance. Firstly, a novel geometric transformation method is introduced to provide an orientation bounding box from its surrounding horizontal bounding box, so that the orientation angle is achieved by only regressing the geometric transformation parameters. Secondly, the Student’s-t distribution is used as a joint distribution to associate the classification task with the regression task, which are represented as Gaussian and inverse Gamma distributions, respectively. Experiments on two popular remote sensing public datasets DOTA and HRSC2016 confirm the improvement from our proposed TOSO detector.

In this paper, we derive an analytical expression for the average symbol error rate for the 4/16-QAM hierarchical modulation (HM), of the mapped signal at the relay during the up-link phase, for the HP and LP streams, respectively, in Rician fading environments. Furthermore, by utilizing HM for the proposed HM-PLNC system, we minimize the computational complexity at the relay node by reducing the number of Euclidean distance computations (EDCs) to 32 EDCs in fading channels. Performance evaluations show that the proposed system can significantly enhance E2E throughput for the TWRN systems compared to an equivalent 16-QAM based PLNC system. The results present a closed-form solution for HP and LP streams over slow, flat, Rician fading channels.

Cognitive multihop relaying has been widely considered for device-to-device (D2D) communications for applications in the physical layer of the Internet of Things. In this article, we construct a multihop cellular D2D communications system model with energy harvesting (EH) in underlay cognitive radio networks. The locations of primary user equipments (PUEs) and cellular base stations are considered as a Poisson point process in this model. The transmit power of secondary devices is collected from the power beacon with time-switching EH policy. Two charging policies for different applications are considered in this article. Then, the end-to-end outage probability analysis expressions of these two scenarios for the transmission scheme subject to interferences from PUEs are derived. The optimal harvesting time ratio is obtained to get the maximum capacity for end-to-end D2D communications. The analytical results are validated by performing the Monte Carlo simulation of the end-to-end outage probability, which is based on the half-duplex transmission scheme. The results of this article provide a potential pathway to reduce reliance on grid or battery energy supplies and, hence, further strengthen the benefits for the environment and deployment of future smart devices.

This paper considers secure simultaneous wireless information and power transfer (SWIPT) in cell-free massive multiple-input multiple-output (MIMO) systems. The system consists of a large number of randomly (Poisson-distributed) located access points (APs) serving multiple information users (IUs) and an information-untrusted dual-antenna active energy harvester (EH). The active EH uses one antenna to legitimately harvest energy and the other antenna to eavesdrop information. The APs are networked by a centralized infinite backhaul which allows the APs to synchronize and cooperate via a central processing unit (CPU). Closed-form expressions for the average harvested energy (AHE) and a tight lower bound on the ergodic secrecy rate (ESR) are derived. The obtained lower bound on the ESR takes into account the IUs' knowledge attained by downlink effective precoded-channel training. Since the transmit power constraint is per AP, the ESR is nonlinear in terms of the transmit power elements of the APs and that imposes new challenges in formulating a convex power control problem for the downlink transmission. To deal with these nonlinearities, a new method of balancing the transmit power among the APs via relaxed semidefinite programming (SDP) which is proven to be rank-one globally optimal is derived. A fair comparison between the proposed cell-free and the colocated massive MIMO systems shows that the cell-free MIMO outperforms the colocated MIMO over the interval in which the AHE constraint is low and vice versa. Also, the cell-free MIMO is found to be more immune to the increase in the active eavesdropping power than the colocated MIMO.

Mobile edge caching (MEC) and device-todevice (D2D) communications are two potential technologies to resolve traffic overload problems in the Internet of Things. Previous works usually investigate them separately with MEC for traffic offloading and D2D for information transmission. In this article, a joint framework consisting of MEC and cache-enabled D2D communications is proposed to minimize the energy cost of systematic traffic transmission, where file popularity and user preference are the critical criteria for small base stations (SBSs) and user devices, respectively. Under this framework, we propose a novel caching strategy, where the Markov decision process is applied to model the requesting behaviors. A novel scheme based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users' preference. In particular, a Q-learning algorithm and a deep Q-network algorithm are, respectively, applied to user devices and the SBS due to different complexities of status. To save the energy cost of systematic traffic transmission, users acquire partial traffic through D2D communications based on the cached contents and user distribution. Taking the memory limits, D2D available files, and status changing into consideration, the proposed RL algorithm enables user devices and the SBS to prefetch the optimal files while learning, which can reduce the energy cost significantly. Simulation results demonstrate the superior energy saving performance of the proposed RL-based algorithm over other existing methods under various conditions.

In this paper, a low-complexity cell-edge-aware Antenna Selection (AS) algorithm is proposed for a Multi-User (MU) Massive Multiple-Input Multiple-Output (M-MIMO) downlink system with Matched Filter (MF) precoding. We assume that the users are uniformly distributed in the cell, and therefore, have different Signal-to-Interference plus Noise Ratios (SINRs). At each iteration, the proposed algorithm selects one antenna to reduce the highest interference term between any two users to its minimum value. Furthermore, we utilize a Max-Min Power Allocation (MMPA) scheme to further enhance the performance of cell-edge users and achieve higher fairness. In addition, the complexity of the proposed AS algorithm is evaluated in terms of number of floating-point operations (FLOPs) required for its implementation. Finally, our proposed AS method is compared with other low-complexity AS schemes found in the literature and shown to demonstrate an impressive performance-complexity trade-off.

The selection of step sizes in the progressive Gaussian approximate filter (PGAF) is important, and it is difficult to select optimal values in practical applications. Furthermore, in the PGAF, significant integral approximation errors are generated by the repeated approximate calculations of the Gaussian weighted integrals, which results in an inaccurate measurement noise covariance matrix (MNCM). To solve these problems, in this paper, the step sizes and the MNCM are jointly estimated based on the variational Bayesian (VB) approach. By incorporating the adaptive estimates of step sizes and the MNCM into the PGAF framework, a novel PGAF with variable step size is proposed. Simulation results illustrate that the proposed filter has higher estimation accuracy than existing state-of-the-art nonlinear Gaussian approximate filters.

Labelled data are often required to train an acoustic scene classification system. However, it is time-consuming and expensive to label the data manually. An unsupervised clustering algorithm can be used to facilitate the labelling process by dividing the acoustic data into different categories. Nevertheless, it can be problematic to run a clustering algorithm with growing data volume and dimension due to the sharp increase in the computational and memory costs. We propose a new streaming based subspace clustering algorithm which allows the data to be clustered on the fly, and also resolves data points in the overlapping regions of two subspaces by augmenting the learned low-rank representation with the original data samples. Experimental results show that our method can achieve the clustering objective for overwhelmingly high-volume data in an online fashion, while retaining good accuracy and reducing the memory cost significantly.

To obtain the best pooling effect and higher accuracy in image recognition, an improved method based on optimal search theory for the pooling layer of convolutional neural networks (CNNs) is proposed. The purpose is to solve the problems of the traditional pooling method, namely that it is too simplistic and it is difficult to extract effective features. The basic principle and network structure of CNN are introduced in the study. A new optimum‐pooling method is proposed, and the authors study how to obtain the maximum probability to detect the target function under the constrained condition. Comparison experiments of different pooling methods are performed on three widely used datasets: LFW, CIFAR‐10, and ImageNet. The experimental results show that the proposed method has the characteristics of more effective feature extraction and wide adaptability, and leads to higher accuracy and lower error rate in image recognition.

The nonorthogonal multiple access (NOMA) well improves the spectrum efficiency which is particularly essential in the Internet of Things (IoT) system involving massive number of connections. It has been shown that applying buffers at relays can further increase the throughput in the NOMA relay network. This is however valid only when the channel signal-to-noise ratios (SNRs) are large enough to support the NOMA transmission. While it would be straightforward for the cooperative network to switch between the NOMA and the traditional orthogonal multiple access (OMA) transmission modes based on the channel SNR-s, the best potential throughput would not be achieved. In this paper, we propose a novel prioritization-based buffer-aided relay selection scheme which is able to seamlessly combine the NOMA and OMA transmission in the relay network. The analytical expression of average throughput of the proposed scheme is successfully derived. The proposed scheme significantly improves the data throughput at both low and high SNR ranges, making it an attractive scheme for cooperative NOMA in the IoT.

Multihop relaying is a fundamental technology that will enable connectivity in large-scale networks such as those encounted in Internet of Things applications. However, the end-to-end transmission rate decreases dramatically as the number of hops increases when half-duplex (HD) relaying is employed. In this paper, we investigate the outage probability and symbol-error rate for both HD and full-duplex (FD) transmission schemes in multihop networks subject to interference from randomly distributed third-party devices. We model the locations of the interfering devices as a Poisson point process. We derive a closed-form expression for the outage probability and approximations for the symbol-error rate for HD and FD transmissions employing BPSK and QPSK. The symbol-error rate results are obtained by using a Markov chain model for the multihop decode-and-forward links. This model accurately accounts for the nonlinear dynamical nature of the network, whereby erroneous symbol decoding can be “corrected” by a second erroneous decoding operation later in the network. We verify the analytical results through simulations and show the HD and FD schemes can be utilized to reduce the error-rate and outage probability of the system according to different residual self-interference levels and interferer densities. The results provide clear guidelines for implementing HD and FD in multihop networks.

We consider the optimization of the downlink transmission for simultaneous wireless information and power transfer (SWIPT) in multi-cell massive multiple-input multiple-output systems. The system comprises a two-antenna active energy harvester (EH) which is capable of legitimately harvesting energy via one antenna, and illegitimately and actively eavesdropping the signal intended for certain information user(s) (IU(s)) via the other antenna for the purpose of information decoding or energy harvesting. Thereby, the considered problems are: 1) when the EH eavesdrops for the purpose of information decoding, i.e., the EH is information-untrusted by the base station (BS), we propose to maximize the worst-case secrecy rate under a constraint on a worst-case average harvested energy (AHE) and 2) when the EH eavesdrops one or multiple IUs for energy harvesting, i.e., the EH is information-trusted by the BS, we propose to maximize the sum-rate of the IUs under a constraint on a worst-case AHE by the EH. We derive asymptotic expressions for a lower bound on ergodic secrecy rate (ESR) and AHE in large system limit. Subsequently, we use these results to optimize the power allocation for downlink SWIPT transmissions which include information signals, artificial noise (AN) and energy signal towards the IUs, and legitimate and illegitimate antennas of the EH, respectively. Simulation results show the accuracy of our asymptotic analysis. We show that there is a performance trade-off between the worst-case ESR and the worst-case AHE. In addition, the impact of the combined legitimate/illegitimate operation of the EH on the SWIPT performance is evaluated.

NarrowBand Internet of Things (NB-IoT) is an emerging cellular-based technology that offers a range of flexible configurations for massive IoT radio access from groups of devices with heterogeneous requirements. A configuration specifies the amount of radio resource allocated to each group of devices for random access and for data transmission. Assuming no knowledge of the traffic statistics, there exists an important challenge in “how to determine the configuration that maximizes the long-term average number of served IoT devices at each transmission time interval (TTI) in an online fashion.” Given the complexity of searching for optimal configuration, we first develop real-time configuration selection based on the tabular Q-learning (tabular-Q), the linear approximation-based Q-learning (LA-Q), and the deep neural network-based Q-learning (DQN) in the single-parameter single-group scenario. Our results show that the proposed reinforcement learning-based approaches considerably outperform the conventional heuristic approaches based on load estimation (LE-URC) in terms of the number of served IoT devices. This result also indicates that LA-Q and DQN can be good alternatives for tabular-Q to achieve almost the same performance with much less training time. We further advance LA-Q and DQN via actions aggregation (AA-LA-Q and AA-DQN) and via cooperative multi-agent learning (CMA-DQN) for the multi-parameter multi-group scenario, thereby solve the problem that Q-learning agents do not converge in high-dimensional configurations. In this scenario, the superiority of the proposed Q-learning approaches over the conventional LE-URC approach significantly improves with the increase of configuration dimensions, and the CMA-DQN approach outperforms the other approaches in both throughput and training efficiency.

A novel variational Bayesian (VB)-based adaptive Kalman filter (AKF) is proposed to solve the filtering problem of a linear system with unknown probability of measurement loss. The sum of two likelihood functions is transformed into an exponential multiplication form, and the state vector, the Bernoulli random variable and the probability of measurement loss are jointly inferred based on the VB approach. Simulation results demonstrate the superiority of the proposed AKF as compared with the existing filtering algorithms with unknown probability of measurement loss.

Deep neural networks (DNNs) have been used for dereverberation and separation in the monaural source separation problem. However, the performance of current state-of-the-art methods is limited, particularly when applied in highly reverberant room environments. In this paper, we propose a two-stage approach with two DNN-based methods to address this problem. In the first stage, the dereverberation of the speech mixture is achieved with the proposed dereverberation mask (DM). In the second stage, the dereverberant speech mixture is separated with the ideal ratio mask (IRM). To realize this two-stage approach, in the first DNN-based method, the DM is integrated with the IRM to generate the enhanced time-frequency (T-F) mask, namely the ideal enhanced mask (IEM), as the training target for the single DNN. In the second DNN-based method, the DM and the IRM are predicted with two individual DNNs. The IEEE and the TIMIT corpora with real room impulse responses and noise from the NOISEX dataset are used to generate speech mixtures for evaluations. The proposed methods outperform the state-of-the-art specifically in highly reverberant room environments.

In this paper, we propose a multi-level cooperative fusion approach to address the online multiple human tracking problem in a Gaussian mixture probability hypothesis density (GM-PHD) filter framework. The proposed fusion approach consists essentially of three steps. First, we integrate two human detectors with different characteristics (full-body and body-parts), and investigate their complementary benefits for tracking multiple targets. For each detector domain, we then propose a novel discriminative correlation matching model, and fuse it with spatio-temporal information to address ambiguous identity association in the GM-PHD filter. Finally, we develop a robust fusion center with virtual and real zones to make a global decision based on preliminary candidate targets generated by each detector. This center also mitigates the sensitivity of missed detections in the generalized covariance intersection fusion process, thereby improving the fusion performance and tracking consistency. Experiments on the MOTChallenge Benchmark demonstrate that the proposed method achieves improved performance over other state-of-the-art RFS-based tracking methods.

In this paper, a new robust Kalman filtering framework for a linear system with non-Gaussian heavy-tailed and/or skewed state and measurement noises is proposed through modeling one-step prediction and likelihood probability density functions as Gaussian scale mixture (GSM) distributions. The state vector, mixing parameters, scale matrices, and shape parameters are simultaneously inferred utilizing standard variational Bayesian approach. As the implementations of the proposed method, several solutions corresponding to some special GSM distributions are derived. The proposed robust Kalman filters are tested in a manoeuvring target tracking example. Simulation results show that the proposed robust Kalman filters have a better estimation accuracy and smaller biases compared to the existing state-of-the-art Kalman filters.

The use of multiple data sources (measurements) has been recently demonstrated to improve the accuracy and reliability of a tracking system as it is capable of providing redundancy in different aspects, and also eliminating interferences of individual sources. This paper focuses on addressing the multiple human tracking problem from a multi-detector approach. This approach integrates two detectors with different characteristics (full-body and body-parts) to perform robust collaborative fusion based on data-driven Gaussian Mixture Probability Hypothesis Density (GM-PHD) filters. To leverage the maximum strengths from multiple detectors, we propose a robust fusion center at the track level, which manages to perform Generalized Intersection Covariance (GCI) fusions for survival and birth tracks independently, and also eliminates false tracks caused by a cluttered environment. Moreover, an identity reassignment mechanism is also developed to address the identity mismatching problem in the target birth process, so as to enhance the fusion performance and track consistency. Experimental results on two challenging benchmark video sequences confirm the effectiveness of the proposed approach.

In this paper, a novel robust Rauch-Tung-Striebel smoother is proposed based on the Slash and generalized hyperbolic skew Student's t-distributions. A novel hierarchical Gaussian state-space model is constructed by formulating the Slash distribution as a Gaussian scale mixture form and formulating the generalized hyperbolic skew Student's t-distribution as a Gaussian variance-mean mixture form, based on which the state trajectory, mixing parameters and unknown noise parameters are jointly inferred using the variational Bayesian approach. The posterior probability density functions of mixing parameters of the Slash and generalized hyperbolic skew Student's t-distributions are, respectively, approximated as truncated Gamma and generalized inverse Gaussian. Simulation results illustrate that the proposed robust Rauch-Tung-Striebel smoother has better estimation accuracy than existing state-of-the-art smoothers.

Given the high demand for automated systems for human action recognition, great efforts have been undertaken in recent decades to progress the field. In this paper, we present frameworks for single and multi-viewpoints action recognition based on Space-Time Volume (STV) of human silhouettes and 3D-Histogram of Oriented Gradient (3D-HOG) embedding. We exploit fast-computational approaches involving Principal Component Analysis (PCA) over the local feature spaces for compactly describing actions as combinations of local gestures and L2-Regularized Logistic Regression (L2-RLR) for learning the action model from local features. Outperforming results on Weizmann and i3DPost datasets confirm efficacy of the proposed approaches as compared to the baseline method and other works, in terms of accuracy and robustness to appearance changes.

In this paper, we propose deep discriminative correlation matching within the Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter for online multiple human tracking. In this matching scheme, we mainly exploit the Convolutional Neural Network (CNN) based Discriminative Correlation Filter (DCF) as a target-specific classifier to discriminate the desired target from background and remaining targets. DCFs are learned through the extracted features obtained from the outputs of the last convolutional layers which are capable to encode the target appearances with better discriminativity and robustness to appearance changes. Moreover, we present a hybrid likelihood function that fuses the spatio-temporal relation and correlation matching score to collaboratively enhance the PHD association step. Experimental results on the MOT17 Challenge benchmark [1] confirm the improved performance of our proposed method as compared with other state-of-the-art techniques.

The sequential Monte Carlo probability hypothesis density (SMC-PHD) filter has been shown to be promising for audio-visual multi-speaker tracking. Recently, the zero diffusion particle flow (ZPF) has been used to mitigate the weight degeneracy problem in the SMC-PHD filter. However, this leads to a substantial increase in the computational cost due to the migration of particles from prior to posterior distribution with a partial differential equation. This paper proposes an alternative method based on the non-zero diffusion particle flow (NPF) to adjust the particle states by fitting the particle distribution with the posterior probability density using the non-zero diffusion. This property allows efficient computation of the migration of particles. Results from the AV16.3 dataset demonstrate that we can significantly mitigate the weight degeneracy problem with a smaller computational cost as compared with the ZPF based SMC-PHD filter.

For a linear sensor array, using line spectra is a common technique for estimating directions of arrival (DOA) of single-tone sources. Yet, very few papers consider multitone sources. For the first time, we provide the optimal Bayesian inference for multi-line spectra, i.e. a superposition of line spectra, and estimate the DOAs of the multi-tone sources. For tractable computation via fast Fourier transform, we apply a grid-based method, in which source's tones and sensor's array measure are both uncorrelated. Exploiting this method, we interpret the superposition of sensor's data as a complex Gaussian mixture of multi-tone signals. We then estimate DOA via conjugate Von-Mises, also known as circular Gaussian distribution. Our simulation shows that the multitone method is superior to traditional single-tone method for detecting multi-tone source's frequencies, particularly for the sources with overlapping frequencies. The posterior DOA's resolution can be tuned via Von-Mises' parameter a priori, which enhances the sparsity of DOA's estimation.

The performance of deep neural network (DNN) based monaural speech separation methods is limited in reverberant and noisy room environments. In this paper, we propose a new DNN training target which incorporates geometric information describing the target speaker and microphone to improve the performance in reverberant and noisy room environments. The experiments are based on the IEEE corpus and the NOISEX database and real impulse responses (RIRs). The objective evaluations, short-time objective intelligibility (STOI) and perceptual evaluation of speech quality (PESQ) confirm the efficiency of the proposed direct path ratio mask (DRM).

This paper considers a multiuser multiple‐input multiple‐output (MIMO) space‐time block coded system that operates at a high data rate with full diversity. In particular, they propose to use a full rate downlink algebraic transmission scheme combined with a differential space‐time scheme for multiuser MIMO systems. To achieve this, perfect algebraic space‐time codes and Cayley differential transforms are employed. Since channel state information (CSI) is not needed at the differential receiver, differential schemes are ideal for multiuser systems to shift the complexity from the receivers to the transmitter, thus simplifying user equipment. Furthermore, orthogonal spreading matrices are employed at the transmitter to separate the data streams of different users and enable simple single user decoding. In the orthogonal spreading scheme, the transmitter does not require any knowledge of the CSI to separate the data streams of multiple users; this results in a system which does not need CSI at either end. With this system, to limit the number of possible codewords, a sphere decoder is used to decode the signals at the receiving end. The proposed scheme yields low complexity transceivers while providing full rate full diversity with good performance. Simulation results demonstrate the effectiveness of the proposed scheme.

Finger texture (FT) images acquired from different spectral lighting sensors reveal various features. This inspires the idea of establishing a recognition model between FT features collected using two different spectral lighting forms to provide high recognition performance. This can be implemented by establishing an efficient feature extraction and effective classifier, which can be applied to different FT patterns. So, an effective feature extraction method called the surrounded patterns code (SPC) is adopted. This method can collect the surrounded patterns around the main FT features. It is believed that these patterns are robust and valuable. Furthermore, a novel classifier termed the re‐enforced probabilistic neural network (RPNN) is proposed. It enhances the capability of the standard PNN and provides better recognition performance. Two types of FT images from the multi‐spectral Chinese Academy of Sciences Institute of Automation (CASIA) database were employed as two types of spectral sensors were used in the acquiring device: the white (WHT) light and spectral 460 nm of blue (BLU) light. Supporting comparisons were performed, analysed and discussed. The best results were recorded for the SPC by enhancing the equal error rates at 4% for spectral BLU and 2% for spectral WHT. These percentages have been reduced to 0% after utilising the RPNN.

We enhance the efficacy of an existing dictionary pair learning algorithm by adding a dictionary incoherence penalty term. After presenting an alternating minimization solution, we apply the proposed incoherent dictionary pair learning (InDPL) method in classification of a novel open-source database of Chinese numbers. Benchmarking results confirm that the InDPL algorithm offers enhanced classification accuracy, especially when the number of training samples is limited.

Structured sparse representation has been recently found to achieve better efficiency and robustness in exploiting the target appearance model in tracking systems with both holistic and local information. Therefore, to better simultaneously discriminate multi-targets from their background, we propose a novel video-based multi-target tracking system that combines the particle probability hypothesis density (PHD) filter with discriminative group-structured dictionary learning. The discriminative dictionary with group structure learned by the hierarchical K-means clustering algorithm implicitly associates the dictionary atoms with the group labels, simultaneously enforcing the target candidates from the same group (class) to share the same structured sparsity pattern. Furthermore, we propose a new joint likelihood calculation by relating the discriminative sparse codes with the maximum voting technique to enhance the particle PHD updating step. Experimental results on two publicly available benchmark video sequences confirm the improved performance of our proposed method over other state-of-the-art techniques in video-based multi-target tracking.

Time-frequency (T-F) masking algorithms are focused at separating multiple sound sources from binaural reverberant speech mixtures. The statistical modelling of binaural cues i.e. interaural phase difference (IPD) and interaural level difference (ILD) is a significant aspect of such algorithms. In this paper, a Gaussian-Student's t distribution combined mixture model is exploited for robust binaural speech separation. The weights of the distribution components are calculated adaptively with the energy of the speech mixtures. The expectation maximization (EM) algorithm is applied to calculate the parameters of the distributions. The speech signals from the TIMIT database are convolved with the real binaural room impulse responses (BRIRs) from two datasets for the evaluation of the proposed method. The objective performance measure signal to distortion ratio (SDR) confirms the improvement and robustness of the proposed method.

As the complexity of cyber-attacks keeps increasing, new and more robust detection mechanisms need to be developed. The next generation of Intrusion Detection Systems (IDSs) should be able to adapt their detection characteristics based not only on the measureable network traffic, but also on the available highlevel information related to the protected network to improve their detection results. We make use of the Pattern-of-Life (PoL) of a network as the main source of high-level information, which is correlated with the time of the day and the usage of the network resources. We propose the use of a Fuzzy Cognitive Map (FCM) to incorporate the PoL into the detection process. The main aim of this work is to evidence the improved the detection performance of an IDS using an FCM to leverage on network related contextual information. The results that we present verify that the proposed method improves the effectiveness of our IDS by reducing the total number of false alarms; providing an improvement of 9.68% when all the considered metrics are combined and a peak improvement of up to 35.64%, depending on particular metric combination.

Some smartphone applications (apps) pose a risk to users' personal information. Events of apps leaking information stored in smartphones illustrate the danger that they present. In this paper, we investigate the amount of personal information leaked during the installation and use of apps when accessing the Internet. We have opted for the implementation of a Man-in-the-Middle proxy to intercept the network traffic generated by 20 popular free apps installed on different smartphones of distinctive vendors. This work describes the technical considerations and requirements for the deployment of the monitoring WiFi network employed during the conducted experiments. The presented results show that numerous mobile and personal unique identifiers, along with personal information are leaked by several of the evaluated apps, commonly during the installation process.

In this paper, we derive the bit error rate and pairwise error probability (PEP) for massive multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems for different M-ary modulations based upon the approximate noise distribution after channel equalization. The PEP is used to obtain the upper-bounds for convolutionally coded and turbo coded massive MIMO-OFDM systems for different code generators and receive antennas. In addition, complexity analysis of the log-likelihood ratio (LLR) values is performed using the approximate noise probability density function. The derived LLR computations can be time-consuming when the number of receive antennas is very large in massive MIMO-OFDM systems. Thus, a reduced complexity approximation is introduced using Newton's interpolation with different polynomial orders and the results are compared with the exact simulations. The Neumann large matrix approximation is used to design the receiver for a zero-forcing equalizer by reducing the number of operations required in calculating the channel matrix inverse. Simulations are used to demonstrate that the results obtained using the derived equations match closely the Monte Carlo simulations.

Video-based multiple human tracking often involves several challenges, including target number variation, object occlusions, and noise corruption in sensor measurements. In this paper, we propose a novel method to address these challenges based on probability hypothesis density (PHD) filtering with a Markov chain Monte Carlo (MCMC) implementation. More specifically, a novel social force model (SFM) for describing the interaction between the targets is used to calculate the likelihood within the MCMC resampling step in the prediction step of the PHD filter, and a one class support vector machine (OCSVM) is then used in the update step to mitigate the noise in the measurements, where the SVM is trained with features from both color and oriented gradient histograms. The proposed method is evaluated and compared with state-of-the-art techniques using sequences from the CAVIAR, TUD, and PETS2009 datasets based on the mean Euclidean tracking error on each frame, the optimal subpattern assignment metric, and the multiple object tracking precision metric. The results show improved performance of the proposed method over the baseline algorithms, including the traditional particle PHD filtering method, the traditional SFM-based particle filtering method, multi-Bernoulli filtering, and an online-learning-based tracking method.

Traditional iris segmentation methods give good results when the iris images are taken under ideal imaging conditions. However, the segmentation accuracy of an iris recognition system significantly influences its performance especially in nonideal iris images. This paper proposes a novel segmentation method for nonideal iris images. Two algorithms are proposed for pupil segmentation in iris images which are captured under visible and near infrared light. Then, a fusion of an expanding and a shrinking active contour is developed for iris segmentation by integrating a new pressure force to the active contour model. Thereafter, a noncircular iris normalization scheme is adopted to effectively unwrap the segmented iris. In addition, a novel method for closed eye detection is proposed. The proposed scheme is robust in finding the exact iris boundary and isolating the eyelids of the iris images. Experimental results on CASIA V4.0, MMU2, UBIRIS V1, and UBIRIS V2 iris databases indicate a high level of accuracy using the proposed technique. Moreover, the comparison results with the state-of-the-art iris segmentation algorithms revealed considerable improvement in segmentation accuracy and recognition performance while being computationally more efficient.

Sclera blood veins have been investigated recently as a biometric trait which can be used in a recognition system. The sclera is the white and opaque outer protective part of the eye. This part of the eye has visible blood veins which are randomly distributed. This feature makes these blood veins a promising factor for eye recognition. The sclera has an advantage in that it can be captured using a visible-wavelength camera. Therefore, applications which may involve the sclera are wide ranging. The contribution of this paper is the design of a robust sclera recognition system with high accuracy. The system comprises of new sclera segmentation and occluded eye detection methods. We also propose an efficient method for vessel enhancement, extraction, and binarization. In the feature extraction and matching process stages, we additionally develop an efficient method, that is, orientation, scale, illumination, and deformation invariant. The obtained results using UBIRIS.v1 and UTIRIS databases show an advantage in terms of segmentation accuracy and computational complexity compared with state-of-the-art methods due to Thomas, Oh, Zhou, and Das.

In this paper, we propose a novel robust multiple human tracking approach based upon processing a video signal by utilizing a social force model to enhance the particle probability hypothesis density (PHD) filter. In traditional dynamic models, the states of targets are only predicted by their own history; however, in multiple human tracking, the information from interaction between targets and the intentions of each target can be employed to obtain more robust prediction. Furthermore, such information can mitigate the problems of collision and occlusion. The cardinality of variable number of targets can also be estimated by using the PHD filter, hence improving the overall accuracy of the multiple human tracker. In this work, a background subtraction step has also been employed to identify the new born targets and provide the measurement set for the PHD filter. To evaluate tracking performance, sequences from both the CAVIAR and PETS2009 datasets are employed for evaluation, which shows clear improvement of the proposed method over the conventional particle PHD filter.

The scale matrix and degrees of freedom (dof) parameter of a Student's t distribution are important for nonlinear robust inference, and it is difficult to determine exact values in practical application due to complex environments. To solve this problem, an improved robust Gaussian approximate (GA) filter is derived based on the variational Bayesian approach, where the state together with unknown scale matrix and dof parameter are inferred. The proposed filter is applied to a target tracking problem with measurement outliers, and its performance is compared with an existing robust GA filter with fixed scale matrix and dof parameter. The results show the efficiency and superiority of the proposed filter as compared with the existing filter.

In this paper, we present a two-way relay channel (TWRC) network that utilizes a full-duplex physical layer network coding (FD-PLNC) scheme in conjunction with amplify-and-forward (AF) relaying and orthogonal frequency-division multiplexing (OFDM). In order to cope with the self-interference (SI) induced by the FD mode of operation, a self-interference cancellation (SIC) scheme is utilized at each node in the proposed system. The performance of the proposed system is thoroughly investigated in the presence of residual SI by deriving a closed-form expression for the distribution of the tight upper bound end-to-end (E2E) signal to interference and noise ratio (SINR). Furthermore, an exact closed-form expression for the tight upper bound ergodic capacity is derived and used to evaluate the E2E upper bound ergodic capacity of the proposed AF-FD-PLNC system. The obtained results demonstrate the ergodic capacity gain of the proposed AF-FD-PLNC over the ergodic capacity of traditional AF half-duplex (HD) PLNC systems. In particular, the proposed AF-FD-PLNC can increase the ergodic capacity of AF-HD-PLNC by a factor of 2 when the SNR is higher than 25 dB and the SI to noise ratio is less than 0 dB.

The human visual cortex has evolved to determine efficiently objects from within a scene. Hierarchical MAX (HMAX) is an object recognition model which has been inspired by the visual cortex, and sparse coding, which is a characteristic of neurons in the visual cortex, was previously integrated into the HMAX model for improved performance. In this study, in order to further enhance recognition accuracy, we have developed an elastic net-regularized dictionary learning approach for use in the HMAX model. We term this the En-HMAX model. With the En-HMAX model, we can exploit the sparsity-grouping tradeoff, such that correlated but informative features are preserved for object classification. Results show that the En-MAX model outperforms the original HMAX model in recognizing unseen objects by ~40% as well as the two special cases of the HMAX model, i.e., the least absolute shrinkage and selection operator (LASSO)-HMAX (~19%) and Ridge-HMAX (~9%) models.

The probability hypothesis density (PHD) filter is well known for addressing the problem of multiple human tracking for a variable number of targets, and the sequential Monte Carlo implementation of the PHD filter, known as the particle PHD filter, can give state estimates with nonlinear and non-Gaussian models. Recently, Mahler et al. have introduced a PHD smoother to gain more accurate estimates for both target states and number. However, as highlighted by Psiaki in the context of a backward-smoothing extended Kalman filter, with a nonlinear state evolution model the approximation error in the backward filtering requires careful consideration. Psiaki suggests that to minimize the aggregated least-squares error over a batch of data. We instead use the term retrodiction PHD filter to describe the backward filtering algorithm in recognition of the approximation error proposed in the original PHD smoother, and we propose an adaptive recursion step to improve the approximation accuracy. This step combines forward and backward processing through the measurement set and thereby mitigates the problems with the original PHD smoother when the target number changes significantly and the targets appear and disappear randomly. Simulation results show the improved performance of the proposed algorithm and its capability in handling a variable number of targets.

In this letter, a robust Gaussian approximate (GA) fixed-interval smoother for nonlinear systems with heavy-tailed process and measurement noises is proposed. The process and measurement noises are modeled as stationary Student's t distributions, and the state trajectory and noise parameters are inferred approximately based on the variational Bayesian (VB) approach. Simulation results show the efficiency and superiority of the proposed smoother as compared with existing smoothers.

In this paper, the performance of a multi-hop underlay cognitive radio network (UCRN) is thoroughly assessed. The co-existence of a primary transceiver and co-channel interference (CCI) is considered along with an uplink single-input multiple-output system utilizing selection combining and maximal ratio combining techniques at the receiver nodes. First, the equivalent per-hop signal-to-interference-plus-noise ratio (SINR) for the UCRN is formulated. Second, the exact cumulative distribution function (CDF) and the probability distribution function of the per-hop SINR are derived and discussed. Furthermore, approximate expressions exhibiting reduced complexity for the per hop equivalent CDF are derived to provide more insights. From the resulting CDF, the exact outage performance of the CR network is thoroughly assessed. In addition, mathematical formulas are derived for the average error probability and system ergodic capacity. Finally, the derived analytical expressions are validated by presenting numerical and simulation results for different network parameters. The results show that several factors contribute to the degradation of the system performance, namely, the interference power constraint, the primary transmitter power, and the presence of CCI, especially in the case where the CCI increases linearly with the secondary transmission powers.

A common approach to blind source separation is to use independent component analysis. However when dealing with realistic convolutive audio and speech mixtures, processing in the frequency domain at each frequency bin is required. As a result this introduces the permutation problem, inherent in independent component analysis, across the frequency bins. Independent vector analysis directly addresses this issue by modeling the dependencies between frequency bins, namely making use of a source prior. An alternative source prior for real-time (online) natural gradient independent vector analysis is proposed. A Student's t probability density function is known to be more suited for speech sources, due to its heavier tails, and is incorporated into a real-time version of natural gradient independent vector analysis. In addition, the importance of the degrees of freedom parameter within the Student's t distribution is highlighted. The final algorithm is realized as a real-time embedded application on a floating point Texas Instruments digital signal processor platform, where simulated recordings from a reverberant room are used for testing. Results are shown to be better than with the original (super-Gaussian) source prior.

The independent vector analysis (IVA) algorithm employs a multivariate source prior to retain the dependency between different frequency bins of each source and thereby avoids the permutation problem that is inherent to blind source separation (BSS). In this paper, a multivariate Student's t distribution is adopted as the source prior, which because of its heavy tail nature can better model the large amplitude information in the frequency bins. Therefore it can improve the separation performance and the convergence speed of the IVA and fast version of the IVA (FastIVA) algorithms as compared with the IVA algorithm based on another multivariate super Gaussian source prior. Separation performance with real binaural room impulse responses (BRIRs) is evaluated by detailed simulation studies when using the different source priors, and the experimental results confirm that the IVA and the FastIVA with the proposed multivariate Student's t source prior can consistently achieve improved and faster separation performance.

Estimating the geometric properties of an indoor environment through acoustic room impulse responses (RIRs) is useful in various applications, e.g., source separation, simultaneous localization and mapping, and spatial audio. Previously, we developed an algorithm to estimate the reflector's position by exploiting ellipses as projection of 3D spaces. In this article, we present a model for full 3D reconstruction of environments. More specifically, the three components of the previous method, respectively, MUSIC for direction of arrival (DOA) estimation, numerical search adopted for reflector estimation and the Hough transform to refine the results, are extended for 3D spaces. A variation is also proposed using RANSAC instead of the numerical search and the Hough transform wich significantly reduces the run time. Both methods are tested on simulated and measured RIR data. The proposed methods perform better than the baseline, reducing the estimation error.

The model-based expectation maximization source separation and localization (MESSL) technique is a probabilistic time-frequency masking algorithm that achieves underdetermined blind source separation of speech sources. Using only two-channel recordings, MESSL clusters spectrogram points based on their interaural spatial cues. Gaussian mixture models (GMMs) are assumed for the interaural cues and their corresponding parameters are determined by maximum likelihood estimation (MLE) via the expectation maximization (EM) framework. However, the presence of singularities and over-fitting are major drawbacks of MLE. In this paper, we investigate variational Bayesian (VB) inference for clustering spectrogram points based particularly on their interaural phase difference (IPD) cues. Variational inference overcomes the difficulties associated with the likelihood optimization and improves the separation especially when the sources are in close proximity. Simulation studies based on speech mixtures formed from the TIMIT database confirm the advantage of the proposed approach in terms of signal to distortion ratio (SDR).

We present a novel unsupervised fall detection system that employs the collected acoustic signals (footstep sound signals) from an elderly person׳s normal activities to construct a data description model to distinguish falls from non-falls. The measured acoustic signals are initially processed with a source separation (SS) technique to remove the possible interferences from other background sound sources. Mel-frequency cepstral coefficient (MFCC) features are next extracted from the processed signals and used to construct a data description model based on a one class support vector machine (OCSVM) method, which is finally applied to distinguish fall from non-fall sounds. Experiments on a recorded dataset confirm that our proposed fall detection system can achieve better performance, especially with high level of interference from other sound sources, as compared with existing single microphone based methods.

This paper investigates the secrecy performance of full-duplex relay (FDR) networks. The resulting analysis shows that FDR networks have better secrecy performance than half duplex relay networks, if the self-interference can be well suppressed. We also propose a full duplex jamming relay network, in which the relay node transmits jamming signals while receiving the data from the source. While the full duplex jamming scheme has the same data rate as the half duplex scheme, the secrecy performance can be significantly improved, making it an attractive scheme when the network secrecy is a primary concern. A mathematic model is developed to analyze secrecy outage probabilities for the half duplex, the full duplex and full duplex jamming schemes, and the simulation results are also presented to verify the analysis.

Distributed simulation involves many complex techniques and technologies. There are very few educational resources to support the study of distributed simulation. The Simulation Exploration Experience (SEE) (exploresimulation.com) is a partnership of government, industry, academia and professional associations that is attempting to support distributed simulation education in an exciting and challenging way. This annual programme of activities brings together teams of undergraduate and postgraduate students from across the world to build collaboratively a distributed simulation of a lunar expedition. This paper describes SEE and its lunar environment and discusses the experiences of Brunel University's 2014 student team who developed a hybrid distributed simulation of a lunar mining operation involving an agent-based simulation of a mine, a real-time simulation of an astronaut and a discrete-event simulation of a factory in SIMUL8.

This paper first demonstrates the ability of a recently developed complex adaptive notch filter (CANF) to track a complex-valued multicomponent chirp signal (CMCS), and provides an analysis of the convergence of the frequency parameter. Next the design is extended, to enable the adaptation of both the frequency and bandwidth parameters; highlighting the need for a steepest ascent approach to adapt the bandwidth parameter. Adapting the notch bandwidth parameter(s) to track a complex sinusoid signal (CSS), or when filters are cascaded for multiple CSSs; allows the design to reduce the noise output from the CANF structure; and this performance advantage is shown in simulations.

In multiple‐input multiple‐output (MIMO) systems, multiple‐antenna selection has been proposed as a practical scheme for improving the signal transmission quality as well as reducing realisation cost because of minimising the number of radio‐frequency chains. In this study, the authors investigate transmit antenna selection for MIMO systems with the Golden Code. Two antenna selection schemes are considered: max‐min and max‐sum approaches. The outage and pairwise error probability performance of the proposed approaches are analysed. Simulations are also given to verify the analysis. The results show the proposed methods provide useful schemes for antenna selection.

In this study, the authors evaluate the outage probability performance of an amplify‐and‐forward cooperative relay network where the relays are equipped with cognitive radios. When the number of available relays is more than four the authors use the channel conditions in order to select the best four cognitive relays from a set of M cognitive relay nodes and then they are used for cooperation between the source and the destination nodes. Expressions for outage probability are determined for a frequency flat Rayleigh‐fading environment from the received signal‐to‐noise ratio with perfect and imperfect spectrum acquisition. In addition, a modified distributed quasi‐orthogonal space–time block coding scheme with increased code gain distance is considered for use within the proposed cognitive relay network. To utilise the available spectrum opportunities with the modified quasi‐orthogonal space–time block code, the code matrix can be adapted to the number of available relays. Simulation results show that the four relay selection improves the system performance. This is confirmed by the outage probability analysis. The simulations also show that the modified code can significantly enhance the performance of the system and improve the reliability of the link as compared with the conventional distributed quasi‐orthogonal space–time block coding.

A new near‐optimum detection scheme for asynchronous wireless relay networks is proposed to cancel the interference component at the destination node caused by timing misalignment from the relay nodes. The detection complexity at the destination node as compared with a previous sub‐optimum detection scheme is reduced. Closed‐loop extended orthogonal space time block coding and outer convolutive coding are utilised to maximise end‐to‐end performance. A relay selection technique is also proposed in this study for two dual‐antenna relay nodes to enhance the system performance by selecting the best links and the smallest time delay error among the relay nodes. Simulation results confirm that the proposed near‐optimum detection scheme with relay selection is very effective at removing intersymbol interference at the destination node and achieving full cooperative diversity with unity data transmission rate between the relay nodes and the destination node.

The article studies the steady‐state performance of a diffusion least‐mean squares (LMS) adaptive network with imperfect communications where the topology is random (links may fail at random times) and the communication in the channels is corrupted by additive noise. Using the established weighted spatial–temporal energy conservation argument, the authors derive a variance relation which contains moments that represent the effects of noisy links and random topology. The authors evaluate these moments and derive closed‐form expressions for the mean‐square deviation, excess mean‐square error and mean‐square error to explain the steady‐state performance at each individual node. The mean stability analysis is also provided. The derived theoretical expressions have good match with simulation results. Nevertheless, the important result is that the noisy links are the main factor in performance degradation of a diffusion LMS algorithm running in a network with imperfect communications.

This paper considers the security of transmission in buffer-aided decode-and-forward cooperative wireless networks. An eavesdropper which can intercept the data transmission from both the source and relay nodes is considered to threaten the security of transmission. Finite size data buffers are assumed to be available at every relay in order to avoid having to select concurrently the best source-to-relay and relay-to-destination links. A new max-ratio relay selection policy is proposed to optimize the secrecy transmission by considering all the possible source-to-relay and relay-to-destination links and selecting the relay having the link which maximizes the signal to eavesdropper channel gain ratio. Two cases are considered in terms of knowledge of the eavesdropper channel strengths: exact and average gains, respectively. Closed-form expressions for the secrecy outage probability for both cases are obtained, which are verified by simulations. The proposed max-ratio relay selection scheme is shown to outperform one based on a max-min-ratio relay scheme.

A clinical decision support system forms a critical capability to link health observations with health knowledge to influence choices by clinicians for improved healthcare. Recent trends toward remote outsourcing can be exploited to provide efficient and accurate clinical decision support in healthcare. In this scenario, clinicians can use the health knowledge located in remote servers via the Internet to diagnose their patients. However, the fact that these servers are third party and therefore potentially not fully trusted raises possible privacy concerns. In this paper, we propose a novel privacy-preserving protocol for a clinical decision support system where the patients' data always remain in an encrypted form during the diagnosis process. Hence, the server involved in the diagnosis process is not able to learn any extra knowledge about the patient's data and results. Our experimental results on popular medical datasets from UCI-database demonstrate that the accuracy of the proposed protocol is up to 97.21% and the privacy of patient data is not compromised.

We investigate the problem of visual tracking of multiple human speakers in an office environment. In particular, we propose novel solutions to the following challenges: (1) robust and computationally efficient modeling and classification of the changing appearance of the speakers in a variety of different lighting conditions and camera resolutions; (2) dealing with full or partial occlusions when multiple speakers cross or come into very close proximity; (3) automatic initialization of the trackers, or re-initialization when the trackers have lost lock caused by e.g. the limited camera views. First, we develop new algorithms for appearance modeling of the moving speakers based on dictionary learning (DL), using an off-line training process. In the tracking phase, the histograms (coding coefficients) of the image patches derived from the learned dictionaries are used to generate the likelihood functions based on Support Vector Machine (SVM) classification. This likelihood function is then used in the measurement step of the classical particle filtering (PF) algorithm. To improve the computational efficiency of generating the histograms, a soft voting technique based on approximate Locality-constrained Soft Assignment (LcSA) is proposed to reduce the number of dictionary atoms (codewords) used for histogram encoding. Second, an adaptive identity model is proposed to track multiple speakers whilst dealing with occlusions. This model is updated online using Maximum a Posteriori (MAP) adaptation, where we control the adaptation rate using the spatial relationship between the subjects. Third, to enable automatic initialization of the visual trackers, we exploit audio information, the Direction of Arrival (DOA) angle, derived from microphone array recordings. Such information provides, a priori, the number of speakers and constrains the search space for the speaker's faces. The proposed system is tested on a number of sequences from three publicly available and challenging data corpora (AV16.3, EPFL pedestrian data set and CLEAR) with up to five moving subjects.

We propose a robust formulation for the noncooperative rate-maximization game in MIMO Gaussian interference channels under bounded channel uncertainty. The proposed robust game needs little additional computation and requires no additional information exchange among users when compared to the nominal game and thus maintains the low-complexity and distributed nature of the MIMO waterfilling algorithm. The robust rate-maximization game is shown to be equivalent to the nominal game with modified direct-channel matrices. The equilibrium solution of the robust rate-maximization game and the required iterative algorithm to obtain the solution are presented. Sufficient conditions for the uniqueness of the equilibrium and the convergence of the algorithm are also presented. Simulation results indicate that the robust solution in the presence of channel uncertainty performs better than the nominal solution with zero uncertainty, due to the users being more conservative in their power allocation when there is channel uncertainty.

In this work we propose a new binaural spectral subtraction method for the suppression of late reverberation. The proposed approach is a cascade of three stages. The first two stages exploit distinct observations to model and suppress the late reverberation by deriving a gain function. The musical noise artifacts generated due to the processing at each stage are compensated by smoothing the spectral magnitudes of the weighting gains. The third stage linearly combines the gains obtained from the first two stages and further enhances the binaural signals. The binaural gains, obtained by independently processing the left and right channel signals are combined using a new method. Experiments on real data are performed in two contexts: dereverberation-only and joint dereverberation and source separation. Objective results verify the suitability of the proposed cascaded approach in both the contexts.

The independent vector analysis (IVA) algorithm can theoretically avoid the permutation problem in frequency domain blind source separation by using a multivariate source prior to retain the dependency between different frequency bins of each source. In this paper, a new multivariate generalized Gaussian distribution is adopted as the source prior which can exploit fourth order inter-frequency correlation, and therefore better preserve the dependency between different frequency bins to achieve an improved separation performance as compared with the original IVA algorithm. Separation performances are compared by simulation studies when using different source priors, and the experimental results confirm that IVA with the new source prior can consistently achieve improved separation performance.

A novel two stage data association technique for multi-target tracking is proposed which assigns multiple measurements to a target to mitigate information loss. At the first stage a variational Bayesian (VB) clustering technique is used which groups the measurements automatically into a determined number of clusters. In the second stage a belief propagation (BP) based cluster to target association method is proposed to assign multiple clusters to a target. This is achieved by exploiting the inter-cluster dependency information. The proposed technique is suitable to accommodate non-rigid targets such as humans. Both location and features of clusters are used to re-identify the targets when they emerge from occlusions. The proposed technique is compared with state of the art method due to Laet et al. and evaluations are presented on a real data set.

The authors evaluate the outage probability of a cognitive amplify‐and‐forward relay network with cooperation between certain secondary users, chosen by single and multi‐relay (two and four) selection, based on the underlay approach, which requires adherence to an interference constraint on the primary user. The relay selection is performed either on the basis of a max‐min strategy or one based on maximising exactly the end‐to‐end signal‐to‐noise ratio. To realise the relay selection schemes within the secondary networks, a predetermined threshold for the power of the received signal in the primary receiver is assumed. To assess the performance advantage of adding additional secondary relays, we obtain analytical expressions for the probability density function and cumulative density function of the received SNR and thereby provide closed form and near closed form expressions for outage probability over Rayleigh frequency flat fading channels. In particular, the authors present lower and upper bound expressions for outage probability and then provide a new exact expression for outage probability. These analytical results are verified by numerical simulation.

The authors perform an outage probability analysis of a cooperative communication system which transmits over multi‐path channels with best single, or best two relay pair selection and amplify‐and‐forward two‐hop relaying. The probability density function of the multi‐path links is modelled in the time domain with an Erlang distribution function. The analytical expressions for the probability density function and cumulative density function of the end‐to‐end signal‐to‐noise ratio are obtained for an arbitrary number of relay nodes and multi‐path channel lengths of 2 and 3 with best single and best two relay pair selection from N available relays; from which outage probabilities are calculated. The spatial and temporal cooperative diversity of the network is then analysed. Finally, the theoretical results are compared with simulations to confirm the validity of the analysis, and the advantage of two relay selection is verified through bit error rate evaluation.

Facial expression recognition forms a critical capability desired by human-interacting systems that aim to be responsive to variations in the human's emotional state. Recent trends toward cloud computing and outsourcing has led to the requirement for facial expression recognition to be performed remotely by potentially untrusted servers. This paper presents a system that addresses the challenge of performing facial expression recognition when the test image is in the encrypted domain. More specifically, to the best of our knowledge, this is the first known result that performs facial expression recognition in the encrypted domain. Such a system solves the problem of needing to trust servers since the test image for facial expression recognition can remain in encrypted form at all times without needing any decryption, even during the expression recognition process. Our experimental results on popular JAFFE and MUG facial expression databases demonstrate that recognition rate of up to 95.24 percent can be achieved even in the encrypted domain.

Source separation algorithms that utilize only audio data can perform poorly if multiple sources or reverberation are present. In this paper we therefore propose a video-aided model-based source separation algorithm for a two-channel reverberant recording in which the sources are assumed static. By exploiting cues from video, we first localize individual speech sources in the enclosure and then estimate their directions. The interaural spatial cues, the interaural phase difference and the interaural level difference, as well as the mixing vectors are probabilistically modeled. The models make use of the source direction information and are evaluated at discrete time-frequency points. The model parameters are refined with the well-known expectation-maximization (EM) algorithm. The algorithm outputs time-frequency masks that are used to reconstruct the individual sources. Simulation results show that by utilizing the visual modality the proposed algorithm can produce better time-frequency masks thereby giving improved source estimates. We provide experimental results to test the proposed algorithm in different scenarios and provide comparisons with both other audio-only and audio-visual algorithms and achieve improved performance both on synthetic and real data. We also include dereverberation based pre-processing in our algorithm in order to suppress the late reverberant components from the observed stereo mixture and further enhance the overall output of the algorithm. This advantage makes our algorithm a suitable candidate for use in under-determined highly reverberant settings where the performance of other audio-only and audio-visual methods is limited.

In this paper, we propose a novel computer vision-based fall detection system for monitoring an elderly person in a home care, assistive living application. Initially, a single camera covering the full view of the room environment is used for the video recording of an elderly person's daily activities for a certain time period. The recorded video is then manually segmented into short video clips containing normal postures, which are used to compose the normal dataset. We use the codebook background subtraction technique to extract the human body silhouettes from the video clips in the normal dataset and information from ellipse fitting and shape description, together with position information, is used to provide features to describe the extracted posture silhouettes. The features are collected and an online one class support vector machine (OCSVM) method is applied to find the region in feature space to distinguish normal daily postures and abnormal postures such as falls. The resultant OCSVM model can also be updated by using the online scheme to adapt to new emerging normal postures and certain rules are added to reduce false alarm rate and thereby improve fall detection performance. From the comprehensive experimental evaluations on datasets for 12 people, we confirm that our proposed person-specific fall detection system can achieve excellent fall detection performance with 100% fall detection rate and only 3% false detection rate with the optimally tuned parameters. This work is a semiunsupervised fall detection system from a system perspective because although an unsupervised-type algorithm (OCSVM) is applied, human intervention is needed for segmenting and selecting of video clips containing normal postures. As such, our research represents a step toward a complete unsupervised fall detection system.

The problem of tracking people using multiple cameras is of much current interest as a means of providing cues for audio-visual blind source separation in dynamic environments. Here we investigate the use of one of the current state-of-the-art techniques in object recognition combined with one of the most popular methods of modelling object motion, particle filters, for tracking people. The dictionary learning or Bag-of-Words approach to object recognition has proved to be very effective in recent years, as shown in a number of large comparisons such as the PASCAL Visual Object recognition Challenge (VOC). In this paper we use this proven object recognition method within the framework of a particle filter. This provides a more accurate and robust tracking of people in a multiple camera environment. We also demonstrate that the dictionary learning approach can provide a principled method for the fusion of multiple features.

Cooperative diversity has been recently proposed as a way to form virtual antenna arrays and thereby mitigate the deleterious effect of fading channels in transmission. In an environment where multiple relays are available, selection of a subset of such relays may be required as, for example, in distributed space-time coding. In this study, the authors therefore use local measurements of the instantaneous channel conditions to select the best relay pair from a set of N available relays, which both come from the same cluster or different clusters, and then use these best relays for cooperation between the source and the destination. The authors also show that the best relay pair selection scheme has robustness against feedback error and outperforms a scheme based on selecting only the best single relay. The authors obtain analytical expressions for the probability density function, cumulative density function and the moment generating function of the received signal-to-noise ratio to derive closed-form expressions for outage probability over Rayleigh frequency flat-fading channels. The analytical results are supported by simulation studies.

This study proposes a new technique for communicating over multiple-input multiple-output (MIMO) frequency selective channels. This approach operates by calculating the QR decomposition of the polynomial channel matrix at the receiver on the basis of channel state information, which in this work is assumed to be perfectly known. This then enables the frequency selective MIMO system to be transformed into a set of frequency selective single-input single-output systems without altering the statistical properties of the receiver noise, which can then be individually equalised. A like-for-like comparison with the orthogonal frequency division multiplexing scheme, which is typically used to communicate over channels of this form, is provided. The polynomial matrix system is shown to achieve improved performance in terms of average bit error rate results, as a consequence of time-domain symbol decoding.

A novel multimodal source separation approach is proposed for physically moving and stationary sources which exploits a circular microphone array, multiple video cameras, robust spatial beamforming and time-frequency masking. The challenge of separating moving sources, including higher reverberation time (RT) even for physically stationary sources, is that the mixing filters are time varying; as such the unmixing filters should also be time varying but these are difficult to determine from only audio measurements. Therefore in the proposed approach, visual modality is used to facilitate the separation for both stationary and moving sources. The movement of the sources is detected by a three-dimensional tracker based on a Markov Chain Monte Carlo particle filter. The audio separation is performed by a robust least squares frequency invariant data-independent beamformer. The uncertainties in source localisation and direction of arrival information obtained from the 3D video-based tracker are controlled by using a convex optimisation approach in the beamformer design. In the final stage, the separated audio sources are further enhanced by applying a binary time-frequency masking technique in the cepstral domain. Experimental results show that using the visual modality, the proposed algorithm cannot only achieve performance better than conventional frequency-domain source separations algorithms, but also provide acceptable separation performance for moving sources.

We propose a novel computer vision based fall detection system for monitoring an elderly person in a home care application. Background subtraction is applied to extract the foreground human body and the result is improved by using certain post-processing. Information from ellipse fitting and a projection histogram along the axes of the ellipse are used as the features for distinguishing different postures of the human. These features are then fed into a directed acyclic graph support vector machine (DAGSVM) for posture classification, the result of which is then combined with derived floor information to detect a fall. From a dataset of 15 people, we show that our fall detection system can achieve a high fall detection rate (97.08%) and a very low false detection rate (0.8%) in a simulated home environment.

It is the purpose of the note to point out that the Cumulative Distribution Function (CDF) (Eq. (23)) in Appendix A in the paper "Relay Selection for Secure Cooperative Networks with Jamming" by Krikidis et al. (IEEE Trans. Wireless Commun., vol. 8, no. 10, pp. 5003-5011, Oct. 2009) is not the exact expression but an approximation. We provide the exact solution of the CDF in two forms: one using Beta and hypergeometric functions and the second exploiting a recurrence relationship.

In this paper, a novel closed-loop distributed extended orthogonal space time block code (D-EO-STBC) with one-bit feedback based on selection of phase rotation is proposed for two relay nodes each relay equipped with two antennas. In this scheme, only one-bit of feedback is used to determine the transmission phase terms applied to the symbols from the antennas of each relay nodes. This is considerably lower feedback overhead than previous feedback schemes. This feedback information is based upon channel state information (CSI) available at the destination node. In addition, the transmission rate over each hop in the network is unity and full cooperative diversity is obtained by the approach. Furthermore, this proposed scheme is applied to asynchronous relay networks using orthogonal frequency division multiplexing (OFDM) with cyclic prefix (CP) at the source node to combat the timing error at relay nodes, which operate in a simple distributed STBC mode. End-to-end bit error rate (BER) simulation results show that the proposed scheme can enhance the performance of the system with feedback limited to only one-bit and outperform previous feedback methods.

An improvement in the multimodal approach to the problem of blind source separation (BSS) of moving sources is proposed. The challenge of BSS for moving sources is that the mixing filters are time varying. Thus the unmixing filters should also be time varying, which are difficult to calculate from only statistical information avail able from limited number of audio samples. Therefore, in the pro posed approach a robust least square frequency invariant data independent (RLSFIDI) beamformer is implemented to perform real time speech enhancement and provide separation of the moving sources. Direction of arrival information of the sources is obtained from the visual 3-D tracker based on a Markov Chain Monte Carlo particle filter (MCMC-PF). The uncertainties in source localization and direction of arrival information are controlled by using convex optimization approach in the beamformer design. This provides robust ness with a wider main lobe for source of interest (SOI) and wider attenuation pattern to block the interference. In addition, white noise gain (WNG) constraint is used to control the beamformer sensitivity. Experimental results show that by utilizing RLSFIDI beamformer a significant improvement in BSS performance for moving sources is achieved in a low reverberant environment.

For this year's 36th edition of ICASSP, we received 2946 submissions, which is probably an all-time high: it represents an increase of 5% over last year and 12% over two years ago. The overall acceptance rate was 49%. Distributed over the various technical areas, as covered by the Signal Processing Society Technical Committees (TCs), the submission statistics are as follows:

In this paper, we propose an efficient and robust fall detection system by using a fuzzy one class support vector machine based on video in formation. Two cameras are used to capture the video frames from which the features are extracted. A fuzzy one class support vector machine (FOCSVM) is used to distinguish falling from other activities, such as walking, sitting, standing, bending or lying. Compared with the traditional one class support vector machine, the FOCSVM can obtain a more accurate and tight decision boundary under a training dataset with outliers. From real video sequences, the success of the method is confirmed with less non-fall samples being misclassified as falls by the classifier under an imperfect training dataset.

Peak-to-average power ratio (PAPR) reduction in a distributed extended orthogonal space frequency block coding transmission scheme with two-bit group feedback for broadband cooperative four relays system is addressed. The PAPR reduction technique is based on amplitude clipping at the source, and the relays apply extended orthogonal space frequency block coding (EO-SFBC). The decoding method is based on the iterative amplitude reconstruction (IAR) technique. The simulations of the proposed system verify that the four relay cooperative transmitter diversity scheme with two-bit group feedback and IAR decoding achieves almost the same improvement in bit error rate (BER) as the two-bit pertone feedback relay cooperative transmitter with the same decoding technique, whilst limiting the source PAPR, and reduction the feedback overhead by 93.75%.

In this paper we consider a modified quasi-orthogonal space time type block code with optimum distance property for two-way (TW) wireless relay networks. This modified code is designed by using appropriate signal rotations and set partitioning of two quasi-orthogonal codebooks. The codebooks are formed from the union of two modified quasi-orthogonal codes, then the union is pruned down by one-half. A genetic algorithm (GA) is used to search for the optimum rotation matrix to maximize the distance property between the codes. In simulation studies the new code is shown to outperform previous open and closed loop distributed quasi-orthogonal space-time block coding (D-QO-STBC) schemes in terms of end-to-end codeword error rate, and thereby has potential application in wireless relays networks.

In this paper distributed space time block codes (D-STBCs) are applied within an asynchronous two-way cooperative wireless relay network using two relay nodes. A parallel interference cancelation (PIC) detection scheme with low structural and computational complexity is applied at the terminal nodes in order to overcome the effect of imperfect synchronization among the cooperative relay nodes. Simulation results based on end-to-end bit error rate (BER) illustrate that the PIC detection algorithm can mitigate the inter symbol interference (ISI) introduced by the asynchronism, and that only a small number of iterations is necessary within the PIC detection to improve the system performance.

In this study, a new sub-optimum detection scheme is proposed which employs closed-loop extended orthogonal space–time block coding and outer convolutive coding within an asynchronous cooperative wireless relay network to achieve full data rate and full diversity gain between the relays and destination node together with coding gain. Simulation results confirm that the proposed scheme can effectively suppress intersymbol interference induced by asynchronism between the relay nodes at the destination node.

In this paper we present a novel extended orthogonal space-time block coding (EO-STBC) scheme for three and four relay nodes to use in asynchronous cooperative relay networks. This approach attains full-rate and full-diversity in that each hop attains unity rate and all four uncorrelated paths are utilized. Robustness against the effects of random delays at the relay nodes is enhanced through the use of a low-rate feedback channel. A new low complexity phase feedback scheme has been proposed which can retain the advantage of the perfect feedback scheme with substantial reduction in the feedback overhead. Orthogonal frequency division multiplexing (OFDM) with cyclic prefix (CP) is used at the source node to combat the timing errors at the relay nodes, which operate in a simple amplify-and-forward (AF) mode. Simulations show that our new scheme outperforms the previous schemes and uses a very simple symbol-wise maximum-likelihood (ML) decoder.

The problem of decentralized power allocation for competitive rate maximization in a frequency-selective Gaussian interference channel is considered. In the absence of perfect knowledge of channel state information (CSI), a distribution-free robust game is formulated. A robust-optimization equilibrium (RE) is proposed where each player formulates a best response to the worst-case interference. The conditions for existence, uniqueness and convergence of the RE are derived. It is shown that the convergence reduces as the uncertainty increases. Simulations show an interesting phenomenon where the proposed RE moves closer to a Pareto-optimal solution as the CSI uncertainty bound increases, when compared to the classical Nash equilibrium under perfect CSI. Thus, the robust-optimization equilibrium successfully counters bounded channel uncertainty and increases system sum-rate due to users being more conservative about causing interference to other users.

An algorithm has been recently proposed by the authors for calculating a polynomial matrix singular value decomposition (SVD) based upon polynomial matrix QR decomposition. In this work we examine how this method compares to a previously proposed method of formulating this decomposition. In particular, the performance of the two methods is examined when each is used as part of a broadband multiple-input multiple-output (MIMO) communication system by means of average bit error rate simulations. These results confirm a clear advantage of using the new polynomial matrix SVD method over the existing technique. This paper also discusses the possible errors that are encountered when formulating the SVD of a polynomial matrix and investigates how these errors affect the error rate performance of both SVD methods within the proposed application.

In this paper, we propose a novel and robust fall detection system by using a density method for modeling a fall event as a function of certain video feature.3-D head velocity and human shape information are extracted as feature and three types of density model, single Gaussian, mixture of Gaussians and Parzen window method, are constructed for modeling the density of fall with respect to the extracted video feature. Falls are then detected according to the corresponding obtained density model and the success of the method is confirmed on real video sequences.

A novel algorithm for calculating the QR decomposition (QRD) of polynomial matrix is proposed. The algorithm operates by applying a series of polynomial Givens rotations to transform a polynomial matrix into an upper-triangular polynomial matrix and, therefore, amounts to a generalisation of the conventional Givens method for formulating the QRD of a scalar matrix. A simple example is given to demonstrate the algorithm, but also illustrates two clear advantages of this algorithm when compared to an existing method for formulating the decomposition. Firstly, it does not demonstrate the same unstable behaviour that is sometimes observed with the existing algorithm and secondly, it typically requires less iterations to converge. The potential application of the decomposition is highlighted in terms of broadband multi-input multi-output (MIMO) channel equalisation.

A novel multimodal approach is proposed to solve the problem of blind source separation (BSS) of moving sources. The challenge of BSS for moving sources is that the mixing filters are time varying, thus the unmixing filters should also be time varying, which are difficult to track in real time. In the proposed approach, the visual modality is utilized to facilitate the separation for both stationary and moving sources. The movement of the sources is detected by a 3-D tracker based on particle filtering. The full BSS solution is formed by integrating a frequency domain blind source separation algorithm and beamforming: if the sources are identified as stationary, a frequency domain BSS algorithm is implemented with an initialization derived from the visual information. Once the sources are moving, a beamforming algorithm is used to perform real time speech enhancement and provide separation of the sources. Experimental results show that by utilizing the visual modality, the proposed algorithm can not only improve the performance of the BSS algorithm and mitigate the permutation problem for stationary sources, but also provide a good BSS performance for moving sources in a low reverberant environment.

In the case of a frequency flat multiple-input multiple-output (MIMO) system, QR decomposition can be applied to reduce the MIMO channel equalization problem to a set of decision feedback based single channel equalization problems. Using a novel technique for polynomial matrix QR decomposition (PMQRD) based on Givens rotations, we extend this work to frequency selective MIMO systems. A transmitter design based on Diagonal Bell Laboratories Layered Space Time (D-BLAST) encoding has been implemented. Turbo equalization is utilized at the receiver to overcome the multipath delay spread and to facilitate multi-stream data feedback. The effect of channel estimation error on system performance has also been considered to demonstrate the robustness of the proposed PMQRD scheme. Average bit error rate simulations show a considerable improvement over a benchmark orthogonal frequency division multiplexing (OFDM) technique. The proposed scheme thereby has potential applicability in MIMO communication applications, particularly for TDMA systems with frequency selective channels.

The problem of multiuser multiplexing with a MIMO sub system for each individual user is considered. We demonstrate that the capacity performance of the null space based spatial multiplexing schemes can be improved with iterative power allocation within the iterative design process. We considered water-filling based local and global power allocation and demonstrate that both schemes outperform the existing null space based spatial diversity technique in terms of mean capacity and outage capacity.

For a frequency flat multi-input multi-output (MIMO) system the QR decomposition can be applied to reduce the MIMO channel equalization problem to a set of decision feedback based single channel problems. Using a novel technique for polynomial matrix QR decomposition (PMQRD) based on Givens rotations, we show the PMQRD can do likewise for a frequency selective MIMO system. Two types of transmitter design, based on Horizontal and Vertical Bell Laboratories Layered Space Time (H-BLAST, V-BLAST) encoding have been implemented. Receiver processing utilizes Turbo equalization to exploit multipath delay spread and to facilitate multi-stream data feedback. Average bit error rate simulations show a considerable improvement over a benchmark orthogonal frequency division multiplexing (OFDM) technique. The proposed scheme thereby has potential applicability in MIMO communication applications, particularly for a TDMA system with frequency selective channels.

In this paper, closed-loop quasi-orthogonal space time block coding(QO-STBC) is exploited within a four relay node transmission scheme to achieve full-rate and increase the available diversity gain provided by earlier two relay approaches. The problem of imperfect synchronization between relay nodes is overcome by applying a parallel interference cancellation (PIC) detection scheme at the destination node. Bit error rate simulations confirm the advantages of the proposed methodology for a range of levels of imperfect synchronization and that only a small number of iterations is necessary within the PIC detection.

We address the problem of transmit beamforming under channel uncertainty for a multiuser (MU), single carrier frequency domain equalization multiple input multiple output (SC-FDE-MIMO) system. SC-FDE-MIMO scheme is effective solution with relative low complexity to combat inter -symbol interference (ISI) whilst exploiting multi antennas diversity gain. In our system, the signal to leakage ratio (SLR) criterion is used to design the transmit beamformer and the minimum mean square error (MMSE) criterion is employed for the receiver equalization and combining. Non-robust beamforming techniques require perfect channel state information (CSI) knowledge which is not available in practice. In this paper, we propose a robust transmit beamformer which can successfully tolerate CSI imperfection. Diagonal loading is employed to introduce robustness to channel state information errors. The novelty of this work, however, is the application of the robust beamforming in the context of downlink MU-SC transmission. Average bit error (BER) simulations are presented to verify the efficiency of the proposed method.

A simple extended orthogonal space-frequency coded multiple input single output (MISO) orthogonal frequency division multiplexing (OFDM) transmitter diversity technique for wireless communications over frequency selective fading channels is presented. The proposed technique utilizes OFDM to transform frequency selective fading channels into multiple flat fading sub-channels on which space-frequency coding is applied. A four-branch transmitter diversity system is implemented without bandwidth expansion and with only one receive antenna. The associated simulations verify that the four-branch transmitter diversity scheme achieves a significant improvement in average bit-error rate (BER) performance. The proposed scheme also outperforms the previously reported scheme due to Yu, Keroueden, and Yuan with only single phase feedback, and that improvement is retained with quantized feedback. Since the angle feedback is on a per tone basis, the feedback information would be too large for any practical OFDM system. However, we adopt a method which exploits the correlation among the feedback terms for the subcarriers, i.e. a group based quantization technique to reduce the feedback overhead significantly, rendering this scheme attractive to broadband wireless access systems. The performance improvement of convolutionally concatenated space-frequency block coding (CCSBC) schemes is also investigated.

Recent analytical results due to Walsh, Martin and Johnson showed that optimizing the single lag autocorrelation minimization (SLAM) cost does not guarantee convergence to high signal to interference ratio (SIR), an important metric in channel shortening applications. We submit that we can overcome this potential limitation of the SLAM algorithm and retain its computational complexity advantage by minimizing the square of single autocorrelation value with randomly selected lag. Our proposed lag-hopping adaptive channel shortening algorithm based upon squared autocorrelation minimization (LHSAM) has, therefore, low complexity as in the SLAM algorithm and, more importantly, a low average LHSAM cost can guarantee to give a high SIR as for the SAM algorithm. Simulation studies are included to confirm the performance of the LHSAM algorithm.

In this paper a new adaptive leakage factor variable tap-length learning algorithm is proposed. Through analysis the converged difference between the segmented mean square error (MSE) of a filter formed from a number of the initial coefficients of an adaptive filter, and the MSE of the full adaptive filter, is confirmed as a function of the tap-length of the adaptive filter to be monotonically non-increasing. This analysis also provides a systematic way to select the key parameters in the fractional tap-length (FT) learning algorithm, first proposed by Gong and Cowan, to ensure convergence to permit calculation of the true tap-length of the unknown system and motivates the need for adaptation in the leakage factor during learning. A new strategy for adaptation of the leakage factor is therefore developed to satisfy these requirements with both small and large initial tap-length. Simulation results are presented which confirm the advantages of the proposed scheme over the original FT scheme.

A novel approach for separating heart sound signals (HSSs) from lung sound recordings is presented. The approach is based on blind source extraction (BSE) with second-order statistics (SOS), which exploits the quasi-periodicity of the HSSs. The method is evaluated on both synthetic periodic signals of known period mixed with temporally white Gaussian noise (WGN) as well as on real quasi periodic HSSs mixed with lung sound signals (LSSs). Qualitative evaluation involving comparison of the power spectral densities (PSDs) of the extracted signals, by the proposed method and by the JADE algorithm, and that of the original signal is performed for the case of real data. Separation results confirm the utility of the proposed approach, although departure from strict periodicity may impact performance.

For a narrowband multi-input multi-output (MIMO) system the singular value decomposition has the ability to provide multiple spatial channels for data transmission. We extend this work to obtain spatial diversity techniques for frequency selective MIMO systems using a polynomial matrix decomposition known as the sequential best rotation using second order statistics (SBR2) method. This algorithm diagonalizes a MIMO frequency selective channel yielding various spatial modes for data transmission. We evaluate the diversity performance of the dominant channel provided by the SBR2 based broadband decomposition and compare it with a transmit antenna selection method (TAS) and a MIMO orthogonal frequency-division multiplexing (OFDM) singular value decomposition (SVD) based approach. Simulation results show SBR2 significantly outperforms the average bit error rate (BER) of TAS, making it very suitable for time division multiple access (TDMA) and code division multiple access (CDMA) systems. SBR2 and MIMO-OFDM systems are shown to have identical BER performance, confirming the efficiency of the proposed low delay spatial-temporal scheme.

A full-rate and full-diversity closed-loop quasi-orthogonal space time block coding scheme pioneered by Toker, Lambotharan and Chambers is proposed for application in virtual antenna arrays. The theoretical capacity and throughput gains are evaluated as a function of signal-to-noise ratio. It is shown that the scheme has particular benefits in both ergodic and non-ergodic channel environments, and outperforms virtual antenna arrays based solely upon conventional orthogonal space time block codes.

A full rate and full diversity closed-loop quasi-orthogonal space time block coding scheme due to Toker, Lambotharan and Chambers is proposed for application in virtual antenna arrays. The performance gain is achieved through closed-loop operation involving feedback of phase rotation angle(s) calculated from channel state information (CSI) to the transmitter array. Throughput performance of the proposed scheme, with and without power optimisation, is investigated through Monte Carlo simulation with QPSK constellation signals. The results confirm the improvement in throughput performance over orthogonal space time block codes.

The impact of inaccurate channel state information at the transmitter for a variable rate variable power multilevel quadrature amplitude modulation (VRVP-MQAM) system over a Rayleigh flat-fading channel is investigated. A system model is proposed with rate and power adaptation based on the estimates of instantaneous signal-to-noise ratio (SNR) and bit error rate (BER). A pilot symbol assisted modulation scheme is used for SNR estimation. The BER estimator is derived using a maximum a posteriori approach and a simplified closed-form solution is obtained as a function of only the second order statistical characterization of the channel state imperfection. Based on the proposed system model, rate and power adaptation is derived for the optimization of spectral efficiency subject to an average power constraint and an instantaneous BER requirement. The performance of the VRVP-MQAM system under imperfect channel state information (CSI) is evaluated. We show that the proposed VRVP-MQAM system that employs optimal solutions based on the statistical characterization of CSI imperfection achieves a higher spectral efficiency as compared to an ideal CSI assumption based method.

A novel constrained multimodal approach for convolutive blind source separation is presented which incorporates video information related to geometrical position of both the speakers and the microphones, and the directionality of the speakers into the separation algorithm. The separation is performed in the frequency domain and the constraints are incorporated through a penalty function-based formulation. The separation results show a considerable improvement over traditional frequency domain convolutive BSS systems such as that developed by Parra and Spence. Importantly, the inherent permutation problem in the frequency domain BSS is potentially solved.

A new variable step-size least-mean-square (VSSLMS) algorithm is presented in this paper for applications in which the desired response contains nonstationary noise with high variance. The step size of the proposed VSSLMS algorithm is controlled by the normalized square Euclidean norm of the averaged gradient vector, and is henceforth referred to as the NSVSSLMS algorithm. As shown by the analysis and simulation results, the proposed algorithm has both fast convergence rate and robustness to high-variance noise signals, and performs better than Greenburg's sum method, which is a robust algorithm for applications with nonstationary noise.

Multiple-input, multiple-output (MIMO) communication is an effective scheme to improve wireless communication performance of multiuser applications. However, reliable communication in multiuser systems is affected by the presence of both multi-access interference (MAI) and inter-symbol interference (ISI) in multi-path channels. In this paper, we therefore investigate a transceiver design for a wideband multiuser-MiMO communication system, where the co-channel users are equipped with multiple transmit and multiple receive antennas. In particular, we propose a two-step interference cancellation scheme with an error correction coding technique for the receiver of a multiuser uplink system. The scheme employs orthogonal frequency division multiplexing (OFDM) modulation and space-time block codes (STBC). The receiver performs as a soft output multiuser detector based on minimum mean-squared error (MMSE) interference suppression at the first stage, and then, MAI cancellation is implemented with a bank of single-user channel decoders. The paper also includes computer simulations which help to improve the understanding of specific issues involved in the design of multiuser STBC-OFDM systems, and confirm the utility of the proposed approach.

In this paper we propose a generalization of extended orthogonal space-time block codes (EO-STBCs) for MIMO (multi-input/multi-output) channels using four transmit antennas for quasi-static flat fading channels. Since full rate and complex orthogonal space-time block codes (STBCs) do not exist for more than two transmit antennas, we propose a feedback based STBC scheme. In this scheme, phases of certain symbols are rotated according to the feedback from the receiver which is equivalent to rotating the phases of the corresponding channel coefficients. Simulation results show that this rotation phase feedback method achieves a satisfactory performance and outperforms the previous closed-loop space-time block codes, even when the feedback is quantized.

This letter proposes a new variable tap-length least-mean-square (LMS) algorithm for applications in which the unknown filter impulse response sequence has an exponential decay envelope. The algorithm is designed to minimize the mean-square deviation (MSD) between the optimal and adaptive filter weight vectors at each iteration. Simulation results show the proposed algorithm has a faster convergence rate as compared with the fixed tap-length LMS algorithm and is robust to the initial tap-length choice.

A new method, employing machine learning techniques and a modified low frequency envelope spectrum estimator, for estimating important room acoustic parameters including Reverberation Time (RT) and Early Decay Time (EDT) from received music signals has been developed. It overcomes drawbacks found in applying music signals directly to the envelope spectrum detector developed for the estimation of RT from speech signals. The octave band music signal is first separated into sub bands corresponding to notes on the equal temperament scale and the level of each note normalised before applying an envelope spectrum detector. A typical artificial neural network is then trained to map these envelope spectra onto RT or EDT. Significant improvements in estimation accuracy were found and further investigations confirmed that the non-stationary nature of music envelopes is a major technical challenge hindering accurate parameter extraction from music and the proposed method to some extent circumvents the difficulty.

This paper addresses the underdetermined blind source separation problem, using a filtering approach. We have developed an extension of the FastICA algorithm which exploits the disparity in the kurtoses of the underlying sources to estimate the mixing matrix and thereafter the recovery of the sources is achieved by employing the l1-norm algorithm. Also, we demonstrate how promising FastICA can be to extract the sources, without utilizing the l1-norm algorithm. Furthermore, we illustrate how this scenario is particularly suitable to the separation of the temporomandibular joint (TMJ) sounds, crucial in the diagnosis of temporomandibular disorders (TMDs).

In this paper, we generalize the algorithms of our previously proposed Bayesian estimation based variable rate variable power multilevel quadrature amplitude modulation (VRVP-MQAM) system to incorporate for the first time a maximum a posteriori (MAP) channel predictor and a MQAM scheme adopted with practical constellation sizes. Based on a pilot symbol assisted modulation (PSAM) scheme, we evaluate the performance of our proposed VRVP-MQAM system over a Rayleigh flat-fading channel. We demonstrate in our simulation results that the proposed rate and power algorithms that are derived based on a Bayesian bit error rate (BER) estimation and the second order statistical characterization of the channel state information (CSI) outperforms in terms of spectral efficiency and average BER. This improvement is confirmed by comparison with an alternative rate and power algorithm which exploits an ideal CSI assumption

Orthogonal frequency division multiplexing (OFDM) requires an expensive linear amplifier at the transmitter due to its high peak-to-average power ratio (PAPR). Single carrier with cyclic prefix (SC-CP) is a closely related transmission scheme that possesses most of the benefits of OFDM but does not have the PAPR problem. Although in a multipath environment, SC-CP is very robust to frequency-selective fading, it is sensitive to the time-selective fading characteristics of the wireless channel that disturbs the orthogonality of the channel matrix (CM) and increases the computational complexity of the receiver. In this paper, we propose a time-domain low-complexity iterative algorithm to compensate for the effects of time selectivity of the channel that exploits the sparsity present in the channel convolution matrix. Simulation results show the superior performance of the proposed algorithm over the standard linear minimum mean-square error (L-MMSE) equalizer for SC-CP.

Electroencephalogram (EEG) source localization requires a solution to an ill-posed inverse problem. The additional challenge is to solve this problem in the context of multiple moving sources. An effective and simple technique for both separation and localization of EEG sources is therefore proposed by incorporating an algorithmically coupled blind source separation (BSS) approach. The method relies upon having a priori knowledge of the locations of a subset of the sources. The cost function of the BSS algorithm is constrained by this information, and the unknown sources are iteratively calculated. An important application of this method is to localize abnormal sources, which, for example, cause changes in attention, movement, and behavior. In this application, the Alpha rhythm was considered as the known sources. Simulation studies are presented to support the potential of the approach in terms of source localization.

A convex combination of adaptive filters is utilized to improve the performance of a variable tap-length least-mean-square (LMS) algorithm in a low signal-to-noise environment (SNRles0 dB). As shown by our simulations, the adaptation of the tap-length in the variable tap-length LMS algorithm is highly affected by the parameter choice and the noise level. Combination approaches can improve such adaptation by exploiting advantages of parallel adaptive filters with different parameters. Simulation results support the good properties of the proposed method

This study presents an effective method of blindly classifying large amounts of gene expression data into biologically meaningful groups using a combination of independent component analysis (ICA) and clustering techniques. Specifically, we show that the genes can be classified blindly into several groups based solely on their expression profiles. These groups have a very close correspondence with benchmarks obtained by studies using domain knowledge. These results suggest that ICA can be a very useful pre-processing tool in blind gene classification, rather than using the resulting sources as the final model profiles.

A blind adaptive channel shortening algorithm based on minimizing the sum of the squared auto-correlations (SAM) of the effective channel was recently proposed. We submit that identical channel shortening can be achieved by minimizing the square of only a single auto-correlation. Our proposed single lag auto-correlation minimization (SLAM) algorithm has, therefore, very low complexity and also it does not require, a priori, knowledge of the length of the channel. We also constrain the auto-correlation minimization with a novel stopping criterion so that the shortening signal-to-noise ratio (SSNR) of the effective channel is not minimized by the auto-correlation minimization. Simulations have shown that SLAM achieves higher bit rates than SAM.

The problem of joint transmitter and receiver design for multi-input multi-output (MIMO) channel shortening for frequency-selective fading channel is addressed. A frequency domain approach is followed which is equivalent to infinite length time-domain channel shortening equalizers (TEQ). A practical joint space and frequency waterfilling algorithm is also provided for optimum transmit power loading. It is demonstrated that the finite length TEQ suffers from a flooring effect on the compression ratio performance, whereas the proposed method overcomes this disadvantage. The noise amplification and the compression performance of the proposed joint transceiver method is found to be better than both finite and infinite length receiver-only designs, with a gain of order of 3dB for a 2/spl times/2 MIMO channel.

We investigate the problem of integrating the complementary audio and visual modalities for speech separation. Rather than using independence criteria suggested in most blind source separation (BSS) systems, we use visual features from a video signal as additional information to optimize the unmixing matrix. We achieve this by using a statistical model characterizing the nonlinear coherence between audio and visual features as a separation criterion for both instantaneous and convolutive mixtures. We acquire the model by applying the Bayesian framework to the fused feature observations based on a training corpus. We point out several key existing challenges to the success of the system. Experimental results verify the proposed approach, which outperforms the audio only separation system in a noisy environment, and also provides a solution to the permutation problem.

Artifacts such as eye blinks and heart rhythm (ECG) cause the main interfering signals within electroencephalogram (EEG) measurements. Therefore, we propose a method for artifact removal based on exploitation of certain carefully chosen statistical features of independent components extracted from the EEGs, by fusing support vector machines (SVMs) and blind source separation (BSS). We use the second-order blind identification (SOBI) algorithm to separate the EEG into statistically independent sources and SVMs to identify the artifact components and thereby to remove such signals. The remaining independent components are remixed to reproduce the artifact-free EEGs. Objective and subjective assessment of the simulation results shows that the algorithm is successful in mitigating the interference within EEGs.

A novel variable step-size sign natural gradient algorithm (VS-S-NGA) for online blind separation of independent sources is presented. A sign operator for the adaptation of the separation model is obtained from the derivation of a generalized dynamic separation model. A variable step size is also derived to better match the dynamics of the input signals and unmixing matrix. The proposed sign algorithm is appealing in practice due to its computational simplicity. Experimental results verify the superior convergence performance over conventional NGA in both stationary and nonstationary environments.

The topic of low computational complexity frequency estimation of a single complex sinusoid corrupted by additive white Gaussian noise has received significant attention over the last decades due to the wide applicability of such estimators in a variety of fields. In this letter, we propose a computationally fast and statistically improved hybrid phase-based estimator that outperforms other recently proposed approaches, lowering the signal-to-noise ratio at which the Crame/spl acute/r-Rao lower bound is closely followed.

A novel stereophonic noise reduction method is proposed. This method is based upon a combination of a subspace approach realized in a sliding window operation and two-channel adaptive signal enhancing. The signal obtained from the signal subspace is used as the input signal to the adaptive signal enhancer for each channel, instead of noise, as in the ordinary adaptive noise canceling scheme. Simulation results based upon real stereophonic speech contaminated by noise components show that the proposed method gives improved enhancement quality in terms of both segmental gain and cepstral distance performance indices in comparison with conventional nonlinear spectral subtraction approaches.

We consider estimation of frequency offset (FO) and equalization of a wireless communication channel, within a general framework which allows for different frequency offsets for various multipaths. Such a scenario may arise due to different Doppler shifts associated with various multipaths, or in situations where multiple basestations are used to transmit identical information. For this general framework, we propose an approximative maximum-likelihood estimator exploiting the correlation property of the transmitted pilot signal. We further show that the conventional minimum mean-square error equalizer is computationally cumbersome, as the effective channel-convolution matrix changes deterministically between symbols, due to the multiple FOs. Exploiting the structural property of these variations, we propose a computationally efficient recursive algorithm for the equalizer design. Simulation results show that the proposed estimator is statistically efficient, as the mean-square estimation error attains the Crame/spl acute/r-Rao lower bound. Further, we show via extensive simulations that our proposed scheme significantly outperforms equalizers not employing FO estimation.

We present a method for the estimation of possible cell-cyclic elements in mis-sampled microarray data. Accurate assessment of the frequency content of microarray data gives insight into genes which could be cell-cycle regulated. Cell-cycle regulation is one component of the complex network of genetic regulatory processes and is especially relevant to the study of cancer. As cDNA microarray experiments involve human sampling of cell populations, slight variations in the sampling times invariably occur. We propose estimating the frequency content of microarray data using the recent robust Capon estimator, and formulate a suitable uncertainty region over which to minimize. The estimator is shown to yield robust estimates with real microarray data and to identify cell-cyclic genes that elude both the traditional periodogram and the Capon spectral estimator.

The problem of underdetermined blind source separation is addressed. The sparse assumption which is commonly required in the current underdetermined blind source separation literature is relaxed. By introducing an advanced clustering technique based upon self-splitting competitive learning, the time-frequency plane is partitioned into appropriate blocks where the number of active sources is no more than the number of sensors, resulting in a novel robust block based algorithm. Simulation studies are presented to support the proposed approach for the separation of GMSK sources.

Permutation of the outputs at different frequency bins remains as a major problem in convolutive blind source separation (BSS). A coupled hidden Markov model (CHMM) effectively exploits the psychoacoustic characteristics of signals to mitigate such permutations. A joint diagonalization algorithm has been used for convolutive BSS; it incorporates a non-unitary penalty term within the cross-power spectrum-based cost function in the frequency domain. The proposed CHMM system couples a number of conventional HMMs, equivalent to the number of outputs, by making state transitions in each model dependent, not only on its own previous state, but also on some aspects of the state of the other models. Using this method, the permutation effect is substantially reduced; it is demonstrated using a number of simulation studies.

An adaptive algorithm for the blind separation of periodic sources is proposed in this paper. The method uses only the second order statistics of the data, and exploits the periodic nature of the source signals. Simulation results show that the proposed approach has the ability to restore statistical independence, and its performance is comparable to that of a well-established, higher order, blind source separation method.

Quasi-orthogonal space-time block codes (QO-STBCs) achieve full code rate at the expense of loss in diversity gain. We propose two feedback methods for QO-STBCs to achieve full diversity and full code rate. In the first method, signals radiated from various antennas are rotated by phasors according to feedback from the receiver, whereas the second method is based upon antenna weighting/selection. For high to moderate feedback error rates, it is demonstrated that the proposed methods outperform the quantized transmit beamformer. The performance improvement is also investigated for these closed-loop methods when the transmitted signal is error control coded.

We address the problem of blind recovery of multiple sources from their linear convolutive mixture with the cross-correlation and constant modulus algorithm. The steady state mean-squared error of this algorithm is first derived to justify the proposal of a new cross-correlation and constant modulus type algorithm for PAM-PSK type non-constant modulus signals. Simulation studies are presented to support the improved steady-state performance of the new algorithm.

A normalised natural gradient algorithm (NGA) for the separation of cyclostationary source signals is proposed in this paper. It improves the convergence properties of the cyclostationary natural gradient algorithm (CSNGA) by employing a gradient adaptive learning rate whose value changes in response to some change in the filter parameters. Experimental results demonstrate the improved behaviour of the approach.

The adaptive step-size (AS) code-constrained minimum output energy (CMOE) receiver for nonstationary code-division multiple access (CDMA) channels is proposed. The AS-CMOE algorithm adaptively varies the step-size in order to minimise the CMOE criterion. Admissibility of the proposed method is confirmed via the reformulation of the CMOE criterion as an unconstrained optimisation. The ability of the algorithm to track sudden changes of the channel structure in multipath fading channels is assessed. Sensitivity to the initial values of step-size and the adaptation rate of the algorithm is also investigated.

Orthogonal space-time block coding is a transmit diversity method that has the potential to enhance forward capacity. For a communication system with a complex alphabet, full diversity and full code rate space-time codes are available only for two antennas, and for more than two antennas full diversity is achieved only when the code rate is lower than one. A quasi-orthogonal code could provide full code rate, but at the expense of loss in diversity, which results in degradation of performance. We propose a closed loop feedback scheme for quasi-orthogonal codes which provides full diversity while achieving the full code rate. We investigate, in particular, the performance of this scheme, when the feedback information is quantised and when the fading of the channel is frequency-selective.

To solve the problem of joint blind equalization and source separation, two new quasi-Newton 'adaptive algorithms with rapid convergence property are proposed based upon the cross-correlation and constant modulus (CC-CM) criterion, namely the block-Shanno cross-correlation and constant modulus algorithm (BS-CCCMA) and the fast quasi-Newton cross-correlation and constant modulus algorithm (FQN-CCCMA). Simulations studies are used to show that the convergence properties of these algorithms are much improved upon those of the conventional LMS-CCCMA algorithm.

A combined Kalman filter (KF) and natural gradient algorithm (NGA) approach is proposed to address the problem of blind source separation (BSS) in time-varying environments, in particular for binary distributed signals. In situations where the mixing channel is nonstationary, the performance of the NGA is often poor. Typically, in such cases, an adaptive learning rate is used to help the NGA track the changes in the environment. The Kalman filter, on the other hand, is the optimal, minimum mean square error method for tracking certain non-stationarity. Experimental results are presented, and suggest that the combined approach performs significantly better than NGA in the presence of both continuous and abrupt non-stationarities.

We derive new bounds for the mixing parameter, /spl gamma/, within the cross-correlation constant modulus algorithm (CC-CMA) for blind source separation and equalization in non-ideal multiuser environments. Channel undermodelling and noise are considered when the complex sources are circularly symmetric. These tighter bounds are obtained by surface topography of the error performance surface of the CC-CMA algorithm, and replace earlier work which suggested that /spl gamma/>4/3. The validity of the bounds is confirmed by simulation studies.

In many pattern classification problems, an intelligent neural system is required which can learn the newly encountered but misclassified patterns incrementally, while keeping a good classification performance over the past patterns stored in the network. In the paper, an heuristic pattern correction scheme is proposed using adaptively trained generalized regression neural networks (GRNNs). The scheme is based upon both network growing and dual-stage shrinking mechanisms. In the network growing phase, a subset of the misclassified patterns in each incoming data set is iteratively added into the network until all the patterns in the incoming data set are classified correctly. Then, the redundancy in the growing phase is removed in the dual-stage network shrinking. Both long- and short-term memory models are considered in the network shrinking, which are motivated from biological study of the brain. The learning capability of the proposed scheme is investigated through extensive simulation studies.

A normalized algorithm for on-line adaptation of a recurrent perceptron is derived. The algorithm builds upon the normalized backpropagation (NBP) algorithm for feedforward neural networks, and provides an adaptive learning rate and normalization for a recurrent perceptron learning algorithm. The algorithm is based upon local linearization about the current point in the state-space of the network. Such a learning rate is normalized by the squared norm of the gradient at the neuron, which extends the notion of normalized linear algorithms to the nonlinear case.

Conditions for global asymptotic stability (GAS) of a nonlinear relaxation process realized by a recurrent neural network (RNN) are provided. Existence, convergence, and robustness of such a process are analyzed. This is undertaken based upon the contraction mapping theorem (CMT) and the corresponding fixed point iteration (FPI). Upper bounds for such a process are shown to be the conditions of convergence for a commonly analyzed RNN with a linear state dependence.

The lower bounds for the a posteriori prediction error of a nonlinear predictor realized as a neural network are provided. These are obtained for a priori adaptation and a posteriori error networks with sigmoid nonlinearities trained by gradient-descent learning algorithms. A contractivity condition is imposed on a nonlinear activation function of a neuron so that the a posteriori prediction error is smaller in magnitude than the corresponding a priori one. Furthermore, an upper bound is imposed on the learning rate eta so that the approach is feasible. The analysis is undertaken for both feedforward and recurrent nonlinear predictors realized as neural networks.

We address the choice of the coefficients in the cost function of a modular nested recurrent neural-network (RNN) architecture, known as the pipelined recurrent neural network (PRNN). Such a network can cope with the problem of vanishing gradient, experienced in prediction with RNN's. Constraints on the coefficients of the cost function, in the form of a vector norm, are considered. Unlike the previous cost function for the PRNN, which included a forgetting factor motivated by the recursive least squares (RLS) strategy, the proposed forms of cost function provide "forgetting" of the outputs of adjacent modules based upon the network architecture. Such an approach takes into account the number of modules in the PRNN, through the unit norm constraint on the coefficients of the cost function of the PRNN. This is shown to be particularly suitable, since due to inherent nesting in the PRNN, every module gives its full contribution to the learning process, whereas the unit norm constrained cost function introduces a sense of forgetting in the memory management of the PRNN. The PRNN based upon a modified cost function outperforms existing PRNN schemes in the time series prediction simulations presented.

We propose a new family of mixed constant modulus algorithms for the elimination of local minima associated with the fractionally spaced constant modulus algorithm in the presence of channel noise. A special case of this family is the leaky constant modulus algorithm (L-CMA). We show that L-CMA aims to minimise jointly the intersymbol interference (ISI) and the noise gain introduced by the equalizer. Moreover, we derive a suitable range of leakage factors for which all local minima due to large noise amplification are eliminated.

Conditions for global asymptotic stability (GAS) of a nonlinear relaxation equation realised by a nonlinear autoregressive moving average (NARMA) recurrent perceptron are provided. Convergence is derived through fixed point iteration (FPI) techniques, based upon a contraction mapping feature of a nonlinear activation function of a neuron. Furthermore, nesting is shown to be a spatial interpretation of an FPI, which underpins a pipelined recurrent neural network (PRNN) for nonlinear signal processing.

Beamformers which use only the constant modulus algorithm (CMA) are unable to track properly time-variant signals in fast-fading channels. The Kalman kilter (KF), however, has significant advantage in time-varying channels but needs a training sequence to operate. A combined CMA and KF algorithm is therefore proposed in order to utilise the advantages of both algorithms. The associated stepsize of the combination is also varied in accordance with the magnitude of the output. Simulations are presented to demonstrate the potential of this new approach.

A relationship between the learning rate η in the learning algorithm, and the slope β in the nonlinear activation function, for a class of recurrent neural networks (RNNs) trained by the real-time recurrent learning algorithm is provided. It is shown that an arbitrary RNN can be obtained via the referent RNN, with some deterministic rules imposed on its weights and the learning rate. Such relationships reduce the number of degrees of freedom when solving the nonlinear optimization task of finding the optimal RNN parameters.

We enhance the performance of the nonnegativity and support constraints recursive inverse filtering (NAS-RIF) algorithm for blind image deconvolution. The original cost function is modified to overcome the problem of operation on images with different scales for the representation of pixel intensity levels. Algorithm resetting is used to enhance the convergence of the conjugate gradient algorithm. A simple pixel classification approach is used to automate the selection of the support constraint. The performance of the resulting enhanced NAS-RIF algorithm is demonstrated on various images.

We present an approach for selecting optimal parameters for the pipelined recurrent neural network (PRNN) in the paradigm of nonlinear and nonstationary signal prediction. Although there has recently been progress in terms of algorithms for training the PRNN, no account has been made of some inherent features of the PRNN. We therefore provide a study of the role of nesting, which is inherent to the PRNN architecture. The corresponding number of nested modules needed for a certain prediction task, and their contribution toward the final prediction gain (PG) give a thorough insight into the way the PRNN performs, and offers solutions for optimization of its parameters. In particular, nesting, which is a contractive function by its nature, allows the forgetting factor in the cost function of the PRNN to exceed unity, hence it becomes an emphasis factor. This compensates for the small contribution of the distant modules to the prediction process, due to nesting, and helps to circumvent the problem of vanishing gradient, experienced in RNN's for prediction. The PRNN, with its parameters chosen based upon the established criteria, is shown to outperform the linear least mean square (LMS) and recursive least squares (RLS) predictors, as well as previously proposed PRNN schemes, at no expense of additional computational complexity.

The performance of the constant modulus algorithm can suffer because of the existence of local minima with large mean squared error (MSE). This paper presents a new way of obtaining the optimum MSE over all delays using a second equalizer under a mixed constant modulus and cross correlation algorithm (CM-CCA). Proof of convergence is obtained for the noiseless case. Simulations demonstrate the potential of the method.

Constant modulus algorithms based on a deterministic error criterion are presented. Soft-constraint satisfaction methods yield a general family of blind equalization algorithms employing nonlinear functions of the equalizer output which must satisfy certain conditions. The algorithms are also extended to cover fractionally-spaced blind equalization. A normalization factor which appears as a result of the deterministic formulation of the problem helps the blind equalizer improve its performance. Also, the family supports a wide range of nonlinear functions. Extensive simulations are presented to reveal convergence characteristics which also include signals from the signal processing information base (SPIB).

New constant modulus (CM) algorithms are presented that are based on soft constraint satisfaction. The stationary points of an algorithm in this family are studied for an AR(p) channel and it is shown that Ding-type undesirable local solutions (ULS) do not exist. This is due to the normalisation of the gradient vector and the soft nonlinearity used in these algorithms. Error performance surfaces (EPS) and convergence trajectories from arbitrary initialisations are presented for various channels that support the analytical findings.

We propose a new member of the family of mixed-norm stochastic gradient adaptive filter algorithms for system identification applications based upon a convex function of the error norms that underlie the least mean square (LMS) and least absolute difference (LAD) algorithms. A scalar parameter controls the mixture and relates, approximately, to the probability that the instantaneous desired response of the adaptive filter does not contain significant impulsive noise. The parameter is calculated with the complementary error function and a robust estimate of the standard deviation of the desired response. The performance of the proposed algorithm is demonstrated in a system identification simulation with impulsive and Gaussian measurement noise.

A new normalized constant modulus algorithm is proposed that has a more desirable error performance surface (EPS) than the existing constant modulus blind equalization algorithms. We show that for an autoregressive channel, a well-known class of undesirable local solutions (ULSs) does not exist. The EPSs and convergence of the parameters are shown for a number of channels for which well-known algorithms are known to possess ULSs.

All-pass polyphase networks (APN) are particularly attractive for acoustical echo cancellation (AEC) arranged in sub-bands. They provide lower inter-band aliasing, delay and computational complexity than their FIR counterparts. Moreover, APNs achieve higher echo return loss enhancement (ERLE) performance and faster convergence than full-band processing. In the paper, the finite precision implementation of APNs is addressed. A procedure is presented for re-optimising the all-pass coefficients of the prototype low-pass filter for finite precision operation. Robust finite precision implementation of a prototype low-pass filter is discussed. The results of a set of AEC experiments are reported with full and 16-bit precision implementation.

A novel adaptive line enhancer (ALE) structure is introduced. The adaptive filter has an autoregressive moving average (ARMA) structure which is based on classical Laguerre orthogonal functions. The frequencies and radii of the poles within the orthogonal set admit tuning. Tuning of these terms involves the use of higher-order statistics. Indeed the properties of fourth-order cumulants are exploited to aid harmonic retrieval of the sinusoids embedded in the input signal. Furthermore, adaptation of the feedforward filter parameters is straightforward since they are linearly related to the filter output. Advantages of stability, short filter length, and robustness in the presence of noise are expected. Simulation results are included to show typical performance.>

The authors introduce constrained adaptive notch filters which are synthesized from a numerically robust all-pass filter section. This section is realized as a structurally lossless bounded real function which is canonic in both multipliers and delay elements. The notch filter structures admit orthogonal tuning of their notch frequency and bandwidth. For the two structures, frequency tracking and signal enhancement outputs are derived. The mirror image pair of polynomials present in a real all-pass transfer function is shown to yield significant simplification in the generation of the necessary gradient terms used in parameter adaptation. A cascade of such structures is shown to be suitable for tracking multiple sinusoids. Simulation results verify the utility of these structures for frequency tracking.>