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40th IEEE International Conference on Acoustics, Speech, and signal processing, ICASSP 2015

作者： Petrut, Teodor Candel, Ion Ioana, Cornel Gipsa-lab Grenoble Institute of Technology 11 rue des Mathématiques BP 46 Saint Martin d'Hères France

ISBN: (纸本)9781467369978

This paper outlines novel approaches to design a new generation of distributed networks of acoustic sensors. The key-concept is marked by the use of time-frequency signal analysis tools directly embedded at the sensor node. The general framework for analysis at the acoustic sensor level is based on spectrogram which is very simple and easy to implement. The choice of the detection threshold in time-frequency domain is always a difficult task, even more complicated in the embedded computing configurations where eventual threshold adaptive selection algorithms might not have the required complexity. That is, the performances in terms of probability of detection classification and localization accuracy are generally strongly depending of the operational conditions. As such, this paper proposes a novel approach of local signal processing based on phase coherence criterion, designed to track the time-frequency multi-components and which is implemented in a distributed network with a minimum number of acoustic sensors. © 2015 IEEE.

关键词： acoustic source localization distributed signal processing time-frequency analysis

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Transient performance analysis of adaptive multitask network based on correntropy criterion

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INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND signal processing 2018年第2期32卷 229-247页

作者： Hajiabadi, Mojtaba Khoshbin, Hossein Hodtani, Ghosheh Abed Ferdowsi Univ Mashhad Dept Elect Engn Mashhad *** Iran

In this paper, we investigate the transient performance of the proposed distributed multitask learning algorithm that is developed based on maximum correntropy criterion. In the first stage, we derive the proposed multitask learning algorithm in which the correntropy-based combination matrix determines which sensors should collaborate together and which sensors should stop the collaboration. In the second stage, according to the variance relation of the error vector, we derive a closed-form relation that shows the transition of mean-square-deviation learning performance. We also find the lower and upper bounds of the step size that ensure the stability of the multitask learning algorithm. The theoretical finding of the transient performance is shown to fit a well match with simulation results.

关键词： adaptive network correntropy criterion distributed signal processing multitask learning transient analysis

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In-Network distributed Least-Mean-Square Identification of Nonlinear Systems Using Volterra-Laguerre Model

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2020年第2期29卷

作者： Gupta, Saurav Kapgate, Sachin N. Sahoo, Ajit Kumar Natl Inst Technol Dept Elect & Commun Engn Rourkela 769008 Odisha India

It is of great importance to model the behavior of nonlinear systems in a distributed fashion using wireless sensor networks (WSNs) because of its computation and energy-efficient data processing. However, least squares methods have been previously employed to estimate the parameters of Volterra model for modeling nonlinear systems. Still, it is more convenient and advantageous to use in-network distributed identification strategy for real-time modeling and control. In this context, a black-box model with generalized structure and remarkable modeling ability called Volterra-Laguerre model is considered in which distributed signal processing is employed to identify the nonlinear systems in a distributed manner. The model cost function is expressed as a separable constrained minimization problem which is decomposed into augmented Lagrangian form to facilitate the distributed optimization. Then, alternating direction method of multipliers is employed to estimate the optimal parameters of the model. Convergence of the algorithm is guaranteed by providing its mean stability analysis. Simulation results for a nonlinear system are obtained under the noisy environment. These results are plotted against the results of noncooperative and centralized methods, demonstrating the effectiveness and superior performance of the proposed algorithm.

关键词： ADMM distributed signal processing least-mean-square nonlinear system identification Volterra model WSNs

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distributed voting in beep model

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signal processing 2020年 177卷

作者： Ghojogh, Benyamin Salehkaleybar, Saber Sharif Univ Technol Dept Elect Engn Tehran Iran

We consider the problem of distributed multi-choice voting in a setting that each node can communicate with its neighbors merely by sending beep signals. Given its simplicity, the beep communication model is of practical importance in different applications such as system biology and wireless sensor networks. Yet, the distributed majority voting has not been resolved in this setting. In this paper, we propose two algorithms, named distributed Voting with Beeps, to resolve this problem. In the first proposed algorithm, the adjacent nodes having the same value form a set called spot. Afterwards, the spots with majority value try to corrode the spots with non-majority values. The second proposed algorithm is based on pairwise interactions between nodes. The proposed algorithms have a termination detection procedure to check whether voting is achieved. We establish theoretical guarantees for the convergence of these algorithms. In particular, we show that the success probability of the first algorithm tends to one as the percentage of the initial votes in majority increases. For the second algorithm, we show that it returns the correct output with high probability. Our experiments show that the algorithms are fairly invariant to the network topology, initial distribution of values, and network size. (C) 2020 Elsevier B.V. All rights reserved.

关键词： distributed voting distributed signal processing Majority voting Consensus Beep model Communication protocols

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Removal of eye blink artifacts in wireless EEG sensor networks using reduced-bandwidth canonical correlation analysis

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JOURNAL OF NEURAL ENGINEERING 2016年第6期13卷 066008页

作者： Somers, Ben Bertrand, Alexander Katholieke Univ Leuven Dept Neurosci Res Grp Expt Otorhinolaryngol Leuven Belgium Katholieke Univ Leuven Dept Elect Engn ESAT Stadius Ctr Dynam Syst Signal Proc & Data Analyt Leuven Belgium

Objective. Chronic, 24/7 EEG monitoring requires the use of highly miniaturized EEG modules, which only measure a few EEG channels over a small area. For improved spatial coverage, a wireless EEG sensor network (WESN) can be deployed, consisting of multiple EEG modules, which interact through short-distance wireless communication. In this paper, we aim to remove eye blink artifacts in each EEG channel of a WESN by optimally exploiting the correlation between EEG signals from different modules, under stringent communication bandwidth constraints. Approach. We apply a distributed canonical correlation analysis (CCA-) based algorithm, in which each module only transmits an optimal linear combination of its local EEG channels to the other modules. The method is validated on both synthetic and real EEG data sets, with emulated wireless transmissions. Main results. While strongly reducing the amount of data that is shared between nodes, we demonstrate that the algorithm achieves the same eye blink artifact removal performance as the equivalent centralized CCA algorithm, which is at least as good as other state-of-the-art multi-channel algorithms that require a transmission of all channels. Significance. Due to their potential for extreme miniaturization, WESNs are viewed as an enabling technology for chronic EEG monitoring. However, multi-channel analysis is hampered in WESNs due to the high energy cost for wireless communication. This paper shows that multi-channel eye blink artifact removal is possible with a significantly reduced wireless communication between EEG modules.

关键词： electroencephalography (EEG) wireless EEG sensor networks artifact removal distributed signal processing canonical correlation analysis blind source separation

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distributed peer-to-peer target tracking in wireless sensor networks

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SENSORS 2007年第6期7卷 1001-1027页

作者： Wang, Xue Wang, Sheng Bi, Dao-Wei Ma, Jun-Jie Tsinghua Univ State Key lab Precis Measurement Technol & Instru Beijing 100084 Peoples R China

Target tracking is usually a challenging application for wireless sensor networks (WSNs) because it is always computation-intensive and requires real-time processing. This paper proposes a practical target tracking system based on the auto regressive moving average (ARMA) model in a distributed peer-to-peer (P2P) signal processing framework. In the proposed framework, wireless sensor nodes act as peers that perform target detection, feature extraction, classification and tracking, whereas target localization requires the collaboration between wireless sensor nodes for improving the accuracy and robustness. For carrying out target tracking under the constraints imposed by the limited capabilities of the wireless sensor nodes, some practically feasible algorithms, such as the ARMA model and the 2-D integer lifting wavelet transform, are adopted in single wireless sensor nodes due to their outstanding performance and light computational burden. Furthermore, a progressive multi-view localization algorithm is proposed in distributed P2P signal processing framework considering the tradeoff between the accuracy and energy consumption. Finally, a real world target tracking experiment is illustrated. Results from experimental implementations have demonstrated that the proposed target tracking system based on a distributed P2P signal processing framework can make efficient use of scarce energy and communication resources and achieve target tracking successfully.

关键词： wireless sensor networks target tracking distributed signal processing peer-to-peer ARMA model

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Adaptive diffusion networks: An overview

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signal processing 2024年 223卷

作者： Tiglea, Daniel Gilio Candido, Renato Silva, Magno T. M. Univ Sao Paulo Escola Politecn Sao Paulo Brazil

This work provides a comprehensive overview of adaptive diffusion networks, from the first papers published on the subject to state-of-the-art solutions and current challenges. These networks consist of a collection of agents that can measure and learn from streaming data locally, and cooperate to improve the overall performance. Since their inception, adaptive diffusion networks have consolidated themselves as interesting tools for distributed estimation and learning, and have spun several types of solutions for these problems. We begin by discussing the technological advances that led to their emergence, and present the many ramifications of the area. We also discuss some of the most critical limitations of these types of networks in practical situations, such as energy consumption, and show techniques that have been proposed to cope with them. Finally, simulations with real -world data are presented in order to illustrate in practice the opportunities and challenges that they pose.

关键词： Diffusion networks distributed estimation distributed signal processing Multitask learning Kernel adaptive filtering Graph signal processing

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High-Precision Indoor Positioning Method Based on Multifeature Fusion of Inertial Sensor Network

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷 1-1页

作者： Yu, Ning Ma, Xiaofeng Chen, Xuanhe Feng, Renjian Wu, Yinfeng Beihang Univ Sch Instrumentat & Optoelect Engn Beijing 100191 Peoples R China Aalto Univ Dept Elect Engn & Automat Espoo 02150 Finland

Highly accurate positioning information can bring higher enhancement and value to basic applications. In the absence of infrastructure or third-party equipment, relying only on the inertial measurement unit (IMU) itself results in severe positioning drift. Aiming this challenge, we propose a high-precision localization method based on multifeature fusion of body-area inertial sensor networks to achieve reliable and low-drift localization over long periods of time without the need for third-party assistance. The proposed method mainly includes three parts: the information of foot and shank are fused based on a ball hinge model to realize accurate and adaptive step length estimation under variable speed motion;the yaw angle is obtained by weighted fusion of inertial nodes mounted at the waist, shank, and foot;based on global multifeature fusion with landmarks, the removal of stage historical errors of the position and location refinement are realized. The experimental results show that the proposed method can better suppress the dead reckoning (DR) error and effectively improve the positioning accuracy compared with the traditional single-IMU-based DR method. Compared to another multi-IMU fusion approach, our method shows outperformance in the long distance (1270 m, 13 min). The average yaw angle error and average positioning error of the proposed method are 3.5(degrees) and 3.3 m, respectively, and it can better adapt to the variable speed movement.

关键词： Body area networks distributed signal processing fusion multiple inertial measurement units (IMUs) pedestrian indoor positioning

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An Integration of the Multisensory System

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IFAC Proceedings Volumes 1992年第25期25卷 137-142页

作者： Š. Havlík D. Hrubý J. Kalina P. Pokorník Institute of Automation and Communication Slovak Academy of Sciences Severná 5 97400 Banská Bystrica Czechoslovakia

This papers presents a multisensory system for robotic application that uses communication network with specific requirements. The real system uncludes numerous sensors for measuring the mechanical quantities. There are: - multicomponent force/torque, proximity/distance, noncontact position, tactile contact force. The communication system should satisfy some given speed, time response and reliability requirements. Another set of problems is the architecture of the communication network between sensors/actuators and controller. Intel's Bitbus has been chosen. It is a master-slave type field bus, expecially suitable for transmission of short messages. The performance of this multisensory system is described

关键词： sensor network distributed signal processing field bus communication

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A Design Methodology for distributed Adaptive Stream Mining Systems

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Procedia Computer Science 2013年 18卷 2482-2491页

作者： Stephen Won Inkeun Cho Kishan Sudusinghe Jie Xu Yu Zhang Mihaela van der Schaar Shuvra S. Bhattacharyya University of Maryland College Park USA University of California Los Angeles USA

Data-driven, adaptive computations are key to enabling the deployment of accurate and efficient stream mining systems, which invoke suitably configured queries in real-time on streams of input data. Due to the physical separation among data sources and computational resources, it is often necessary to deploy such stream mining systems in a distributed fashion, where local learners have access to disjoint subsets of the data that is to be mined, and forward their intermediate results to an ensemble learner that combines the results from the local learners. In this paper, we develop a design methodology for integrated de- sign, simulation, and implementation of dynamic data-driven adaptive stream mining systems. By systematically integrating considerations associated with local embedded processing, classifier configuration, data-driven adaptation and networked com- munication, our approach allows for effective assessment, prototyping, and implementation of alternative distributed design methods for data-driven, adaptive stream mining systems. We demonstrate our results on a dynamic data-driven application involving patient health care monitoring.

关键词： Adaptive stream mining Dataflow graphs distributed signal processing

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