Acoustic vector sensor (AVS) has been recently researched and developed for acoustic wave capturing and signal processing. Conventional array generally employs spatially displayed sensors for signal enhancement, source localisation, target tracking, etc. However, the large size usually limits its implementations on some portable devices. AVS which generally includes one omni-directional sensor and three orthogonally co-located directional sensors has been recently introduced. An AVS is able to provide the four-dimensional information of sound field in space: the acoustic pressure and its three-dimensional particle velocities. A compact assembled AVS could be as small as a match head and the weight can be <;50 g. Benefits from these properties, AVS tends to be more attractive for exploitation and commercialisation than conventional sensor array. To have a well understanding of the research progress on AVS, an overview on its recent developments is first given in this study. Then, discussions of challenges on AVS and extensions on its possible future prospects are presented.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-spr.2016.0111

Acoustic vector sensor: reviews and future perspectives

We detail in this paper an $L_1$ -norm Linearly constrained normalized least-mean-square ( $L_1$ -CNLMS) algorithm and its weighted version ( $L_1$ -WCNLMS) applied to solve problems whose solutions have some degree of sparsity, such as the beamforming problem in uniform linear arrays, standard hexagonal arrays, and (non-standard) hexagonal antenna arrays. In addition to the linear constraints present in the CNLMS algorithm, the $L_1$ -WCNLMS and the $L_1$ -CNLMS algorithms take into account an $L_1$ -norm penalty on the filter coefficients, which results in sparse solutions producing thinned arrays. The effectiveness of both algorithms is demonstrated via computer simulations. When employing these algorithms to antenna array problems, the resulting effect due to the $L_1$ -norm constraint is perceived as a large aperture array with few active elements. Although this work focuses the algorithm on antenna array synthesis, its application is not limited to them, i.e., the $L_1$ -CNLMS is suitable to solve other problems like sparse system identification and signal reconstruction, where the weighted version, the $L_1$ -WCNLMS algorithm, presents superior performance compared to the $L_1$ -CNLMS algorithm.

${{\bf L}_1}$ -Constrained Normalized LMS Algorithms for Adaptive Beamforming

This fascinating, colourful book offers in-depth insights and first-hand working experiences in the production of art works, using simple computational models with rich morphological behaviour, at the edge of mathematics, computer science, physics and biology. It organically combines ground breaking scientific discoveries in the theory of computation and complex systems with artistic representations of the research results. In this appealing book mathematicians, computer scientists, physicists, and engineers brought together marvelous andesoteric patterns generated by cellular automata, which are arrays ofsimple machineswith complex behavior. Configurations produced by cellular automata uncover mechanics of dynamic patterns formation, their propagation and interaction in natural systems: heart pacemaker, bacterial membrane proteins, chemical rectors, water permeation in soil, compressed gas, cell division, population dynamics, reaction-diffusion media and self-organisation. The book inspires artists to take on cellular automata as a tool of creativity and it persuades scientists to converttheir research results into the works of art. The book is lavishly illustrated with visually attractive examples, presented in a lively and easily accessible manner.

Designing Beauty: The Art of Cellular Automata

This paper presents a new interval diagnosis method to detect and isolate actuators faults of an autonomous spacecraft involved in the rendez-vous phase of the Mars Sample Return (MSR) mission. The proposed diagnosis approach is based on the Vertices Principal Component Analysis (VPCA) as an extension of the classical PCA method to interval data. To ensure the feasibility of the proposed Fault Detection and Isolation (FDI) approach, a set of interval data provided by the MSR “high-fidelity” industrial simulator and representing the opening rates of the spacecraft thrusters has been considered. The results have proven the efficiency of the proposed FDI approach in the diagnosing process assuring the detection and the isolation of both single and multiple faults.

Fault Detection and Isolation of spacecraft thrusters using an extended principal component analysis to interval data

Wireless sensor networks with mobile sinks enable a mobile device to move into the sensing area for the purpose of collecting the sensing data. Mobile sinks increase the flexibility and convenience of data gathering in such systems. Taking the energy consumption of the mobile sink into account, the moving distance of the mobile sink must be reduced efficiently. Hence, it is important and necessary to develop an efficient path planning scheme for mobile sinks in large-scale wireless sensor network systems. According to several greedy-based algorithms, we adopt an angle bisector concept to create the moving path for the mobile sink. In this paper, a novel and efficient data collection path planning scheme is proposed to reduce the moving distances and to prolong the lifetimes of mobile sinks in wireless sensor networks. Considering the communication range limitations of sensor nodes and the obstacles within sensing areas, we design an inner center path planning algorithm to reduce the moving distance for the mobile sink. A back-routing avoidance method is included to address the moving path backpropagation problem. We account for the obstacles in sensing area. The reference point of obstacle avoidance is employed to address the obstacle problem. The proposed scheme makes an adaptive decision for creating the moving path of the mobile sink. A suitable moving path planning scheme can be achieved, and the moving distance of the mobile sink can be reduced. The proposed scheme is promising in large-scale wireless sensor networks. When the number of sensor nodes in the sensing area is increased by 50, the proposed scheme yields an average moving distance that is 1.1 km shorter than that of the heuristic tour-planning algorithm, where the sensing area is 5 km × 5 km. Simulation results demonstrate that the proposed data collection path planning scheme outperforms the previously developed greedy-based scheme in terms of the moving paths and moving distances of mobile sinks in wireless sensor networks.

/pdf/an-efficient-data-collection-path-planning-scheme-for-32mz12p4rw.pdf

An efficient data collection path planning scheme for wireless sensor networks with mobile sinks

Aiming at high network energy consumption and data delay induced by mobile sink in wireless sensor networks (WSNs), this paper proposes a cluster-based energy optimization algorithm called Cluster-Based Energy Optimization with Mobile Sink (CEOMS). CEOMS algorithm constructs the energy density function of network nodes firstly and then assigns sensor nodes with higher remaining energy as cluster heads according to energy density function. Meanwhile, the directivity motion performance function of mobile sink is constructed to enhance the probability of remote sensor nodes being assigned as cluster heads. Secondly, based on Low Energy Adaptive Clustering Hierarchy Protocol (LEACH) architecture, the energy density function and the motion performance function are introduced into the cluster head selection process to avoid random assignment of cluster head. Finally, an adaptive adjustment function is designed to improve the adaptability of cluster head selection by percentage of network nodes death and the density of all surviving nodes of the entire network. The simulation results show that the proposed CEOMS algorithm improves the cluster head selection self-adaptability, extends the network life, reduces the data delay, and balances the network load.

https://www.mdpi.com/1424-8220/21/7/2523/pdf

A Cluster-Based Energy Optimization Algorithm in Wireless Sensor Networks with Mobile Sink.

In multi-platform surveillance system, a prerequisite for successful fusion is the transformation of data from different platforms to a common coordinate system. However, some stochastic system biases arise during this transformation, and they seriously downgrade the global surveillance performance. Considering that the target state and the system biases are coupled and interactive, the authors present a new recursive joint estimation (RJE) algorithm for registering stochastic system biases and estimating target state. First, the relationship between system biases estimation and target state estimation is derived. Second, the RJE framework is introduced on the basis of the proposed relationship. Representing the different behavioural aspects of the motion of a maneuvering target is difficult to achieve with a single model in a multi-platform target tracking system. By accounting for the non-linear and/or non-Gaussian property of the dynamic system, they modify the interacting multiple model–particle filter framework to estimate parameters. This approach considers not only the influence of the system biases, but also the covariance of state on the basis of multiple-particle statistics. Simulation results reveal the superior performance of the proposed approach with respect to the traditional algorithm under the same conditions.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-spr.2015.0068

Joint estimation of state and system biases in non-linear system

Sensor fault cannot be converted to system equation under the condition of under- measurement system. Aiming to solve this problem, we present a new method which treats sensor fault as state variable to enforce fault diagnosis. Firstly, the system model of sensor fault is constructed by putting sensor fault into the state equation. Then, the residual generator is designed using the space projection operation to solve the relevant parameter matrices. Since the proposed algorithm satisfies one-to-one correspondence of faults and residuals, it can achieve single and multiple sensors FDI. Simulation results show the effectiveness of the proposed approach.

A new method of sensor fault diagnosis for under-measurement system based on space geometry approach

In the Dempster-Shafer evidence theory, how to effectively measure the degree of conflict between two bodies of evidence is still an open question. To solve this problem, we propose a weighted conflict evidence combination method based on Hellinger distance and the belief entropy. This method uses the probability transformation function to deal with the multi-subset focal elements firstly. Next, the Hellinger distance is introduced to measure the degree of conflict among the evidence. Moreover, improved belief entropy is also employed to quantify the uncertainty of the basic belief assignments. Further, Hellinger distance and the improved belief entropy are combined to construct the weight coefficient concerning evidence, and finally, the Dempster combination rule is used for fusion. The final fusion results of proposed method on fault diagnosis experiment and target recognition experiment are 0.9018 and 0.9895 respectively, apparently higher than that of other methods, revealing the advantages of the proposed method.

/pdf/weighted-conflict-evidence-combination-method-based-on-117qt21lq6.pdf

Weighted Conflict Evidence Combination Method Based on Hellinger Distance and the Belief Entropy

Solving the nonconvex and non-differentiable objective function of traditional range-free node localization methods will result in low localization accuracy or high computational complexity. To this end, a novel iterative localization algorithm called CVX-DV-hop is proposed in this letter. It first performs matrix transformation to reformulate the original optimization problem into one with a convex function and nonconvex constraints. And then, the first-order Taylor expansions are employed to tighten the nonconvex constraints into linear inequality constraints. Finally, a successive convex approximation method is designed to iteratively solve the optimization problem. Simulation results show that the proposed algorithm has higher localization accuracy and lower computational burden than those of the particle swarm optimization algorithm.

Yong Jin

Papers

A Cluster-Based Energy Optimization Algorithm in Wireless Sensor Networks with Mobile Sink.

Joint estimation of state and system biases in non-linear system

A new method of sensor fault diagnosis for under-measurement system based on space geometry approach

Weighted Conflict Evidence Combination Method Based on Hellinger Distance and the Belief Entropy

A Novel Range-Free Node Localization Method for Wireless Sensor Networks