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Universal distributed Estimation Scheme for a Bandwidth Constrained Ad Hoc Sensor Network

Tsinghua Science and Technology 2009年第1期14卷 121-125页

作者：张莉张贤达高秋彬 Tsinghua National Laboratory for Information Science and Technology (TNList) Department of Automation Tsinghua University

This paper describes a distributed estimation scheme （DES） for a bandwidth constrained ad hoc sensor network. The DES is universal in the sense that operations on all sensors are identical and independent of noise distribution. The scheme requires each sensor to transmit just a 1-bit message per observation. Simulation results show that the scheme achieves much better mean-squares error （MSE） performance than the simplified isotropic universal DES and even outperforms the isotropic universal DES which requires more than twice the bandwidth of this scheme.

关键词： distributed estimation quantization sensor networks distributed signal processing

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distributed Estimation in Sensor Networks with Repetition Coding

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Tsinghua Science and Technology 2011年第3期16卷 295-299页

作者：张莉崔涛张贤达 Science and Technology on Underwater Acoustic Antagonizing Laboratory System Engineering Research Institute Tsinghua National Laboratory for Information Science and Technology Department of AutomationTsinghua University Department of Electrical Engineering California Institute of Technology

This paper considers the distributed estimation of a source parameter using quantized sensor observations in a wireless sensor network with noisy channels. Repetition codes are used to transmit quantization bits of sensor observations and a quasi best linear unbiased estimate is constructed to estimate the source parameter. Simulations show that the estimation scheme achieves a better power and spectral efficiency than the previous scheme.

关键词： distributed estimation quantization distributed signal processing sensor networks

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Formulation and Analysis of LMS Adaptive Networks for distributed Estimation in the Presence of Transmission Errors

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IEEE INTERNET OF THINGS JOURNAL 2016年第2期3卷 146-160页

作者： Ghazanfari-Rad, Saeed Labeau, Fabrice McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 0E9 Canada

Wireless sensor network WSN) technologies and distributed processing are essential to develop ubiquitous sensing in the Internet of Things IoT) paradigm, wherein sensors pervasively collect data and perform information processing and communication tasks to achieve a common objective. This paper presents the formulation and analysis of distributed estimation algorithms based on the diffusion cooperation scheme in the presence of errors due to the unreliable data transfer among nodes. In particular, we highlight the impact of transmission errors on the least-mean squares LMS) adaptive networks. We develop the closed-form expressions for the steady-state mean-square deviation MSD), which is helpful to assess the effects of the imperfect information flow on the behavior of diffusion LMS algorithms in terms of steady-state error. The model is then validated by performing Monte Carlo simulations. It is shown that local and global steady-state MSD values are not necessarily monotonic increasing functions of the error probability. We also assess sufficient conditions that ensure mean and mean-square stability of diffusion LMS strategies in the presence of transmission errors. We examine a practical scenario where errors occur at the medium access control MAC) level. To overcome the problem of unreliable data exchange, we implement a random pairwise strategy that improves the performance of the estimation algorithm in the presence of high transmission error rates. Moreover, issues such as scalability in the sense of network size and regressor size, convergence behavior during the transient phase, spatially correlated observations, as well as the effect of the distribution of the noise variance are studied.

关键词： Adaptive networks diffusion least-mean squares (LMS) algorithm distributed estimation distributed signal processing medium access control (MAC) layer spatially correlated observations spatially correlated regressors

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Characterisation of signal Modality: Exploiting signal Nonlinearity in Machine Learning and signal processing

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JOURNAL OF signal processing SYSTEMS FOR signal IMAGE AND VIDEO TECHNOLOGY 2010年第1期61卷 105-115页

作者： Jelfs, Beth Javidi, Soroush Vayanos, Phebe Mandic, Danilo Univ London Imperial Coll Sci Technol & Med Dept Elect & Elect Engn London England

A novel method for online tracking of the changes in the nonlinearity within both real-domain and complex-valued signals is introduced. This is achieved by a collaborative adaptive signal processing approach based on a hybrid filter. By tracking the dynamics of the adaptive mixing parameter within the employed hybrid filtering architecture, we show that it is possible to quantify the degree of nonlinearity within both real- and complex-valued data. Implementations for tracking nonlinearity in general and then more specifically sparsity are illustrated on both benchmark and real world data. It is also shown that by combining the information obtained from hybrid filters of different natures it is possible to use this method to gain a more complete understanding of the nature of the nonlinearity within a signal. This also paves the way for building multidimensional feature spaces and their application in data/information fusion.

关键词： Adaptive signal processing distributed signal processing Collaborative signal processing Convex optimisation Machine learning Wind modelling EEG modelling Hybrid filtering

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A Technological Perspective on Information Cascades via Social Learning

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IEEE ACCESS 2017年 5卷 22605-22633页

作者： Rosas, Fernando Hsiao, Jen-Hao Chen, Kwang-Cheng Natl Taiwan Univ Grad Inst Commun Engn Taipei 10617 Taiwan Univ S Florida Dept Elect Engn Tampa FL 33620 USA

Collective behavior in human society is attracting a lot of attention, particularly as the result of novel emergent phenomena associated with online social media and networks. In effect, although crowd wisdom and herding behavior have been well studied in social science, the rapid development of Internet computing and e-commerce brings further needs of in-depth comprehension of their consequences and impact from a technological perspective. Based on social learning, an analytical knowledge originated in social science, we re-examine the well-known phenomenon of information cascade, whereby rational agents can ignore personal knowledge to follow a predominant social behavior triggered by earlier decisions made by peers. Moreover, we look into the cascade behavior from a communication theoretic perspective, interpreting social learning as a distributed data processing scheme. This perspective enables the development of a novel framework, which allows a characterization of the conditions that trigger information cascades and trace their impact on the accuracy of the collective inference. Finally, potential applications and examples of information cascade have been presented under various cyber technological scenarios, illustrating the prolific interplay between communication technology and computational social science.

关键词： Data fusion distributed signal processing sensor networks social networks collective behaviour social learning information cascades

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In-Network distributed Identification of Wiener and Volterra-Laguerre Models for Nonlinear Systems

In-Network Distributed Identification of Wiener and Volterra...

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IEEE Global Conference on Communications (GLOBECOM) / Workshop on Wireless Networking and Control for UAV

作者： Gupta, Saurav Sahoo, Ajit Kumar Sahoo, Upendra Kumar Natl Inst Technol Dept Elect & Commun Engn Rourkela India

ISBN: (纸本)9781538647271

distributed estimation over wireless sensor networks (WSNs) has been used to obtain the parameters of interest with reduced resource consumption, hence gained importance in system modeling and control applications. Unlike least-squares and fusion-center based approaches, distributed signal processing is competent in real-time applications. In this article, Volterra-Laguerre model and Wiener model are identified in a distributed manner through WSNs for modeling of nonlinear systems. A block-structured Wiener model has been widely used as it is characterized by a small number of parameters, but can only model specific nonlinearities. A generalized Volterra model over Wiener model can approximate any nonlinear system to a desired precision but has increased parameter complexity. By expanding nonlinear Volterra kernels with orthogonal Laguerre functions, the parameter complexity is reduced significantly. A distributed recursive algorithm for the identification of abovementioned nonlinear models is designed by minimizing the quadratic prediction error. The algorithm reformulates model identification framework into multiple constrained separable subtasks. These subtasks are optimized using a powerful method called alternating direction method of multipliers. Simulation results for an infinite-order and a 2nd-order nonlinear systems are obtained under the influence of process noise and are compared with the results of non-cooperative estimation showing the superiority of the proposed algorithm.

关键词： distributed signal processing Wiener Volterra nonlinear systems ADMM fusion-center

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distributed peer-to-peer multiplexing using ad hoc relay networks

Distributed peer-to-peer multiplexing using ad hoc relay net...

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33rd IEEE International Conference on Acoustics, Speech and signal processing

作者： Fazeli-Dehkordy, Siavash Gazor, Saeed Shahbazpanahi, Shahram Queens Univ Dept Elect & Comp Eng Walter Light Hall Kingston ON K7L 3N6 Canada Univ Ontario Inst Technol Fac Engn & Appl Sci London ON LIH 7K4 Canada

ISBN: (纸本)9781424414833

We consider an ad hoc network consisting of d source-destination pairs and R relaying nodes. Each source wishes to transmit its data to its corresponding destination through the relay network. Each relay in the network transmits a properly scaled version of its received signal thereby cooperating with other relays to deliver each source's data to the corresponding destination. Assuming a minimal cooperation among the relaying nodes, we design a distributed beamformer such that the total relay transmit power dissipated by all relays is minimized while, at the same time, the quality of services at all destinations are guaranteed to be above certain pre-defined thresholds. We show that using a semi-definite relaxation approach, the power minimization problem can be turned into a semi-definite programming (SDP) optimization, and therefore, it can be solved efficiently using interior point methods. Our results show that the distributed relay multiplexing is possible and may be beneficial depending on the channel conditions.

关键词： distributed beamforming relay networks semi-definite programming convex feasibility problem distributed signal processing

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Cooperative Learning: Decentralized Data Neural Network

Cooperative Learning: Decentralized Data Neural Network

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International Joint Conference on Neural Networks (IJCNN)

作者： Lewis, Noah Plis, Sergey Calhoun, Vince

ISBN: (纸本)9781509061822

Researchers often wish to study data stored in separate locations, such as when several research entities wish to make inferences from their combined data. The most common solution is to centralize the data in one location. However, certain types of data can be difficult to transfer between entities due to legal or practical reasons. This makes centralizing these types of data problematic. A possible solution is the use of methods that learn from data without moving them to a central location: decentralized algorithms. Only a few algorithms emphasizing that property are known to us, and even fewer are used in the biomedical domain. In this paper, we propose a decentralized neural network that allows data analysis without transferring the data from the sites that host them. Instead, this method only transfers the gradients (or their parts) calculated via back-propagation. Our approach allows us to learn a classifier even when class examples are located at different sites, enabling privacy-aware collaboration across groups with specific research interests. We validate the method in several experiments to test stability, compare performance to a network trained on the centralized data, and investigate the ability to reduce size of data transfer. Our experiments on simulated, benchmark, and neuroimaging addiction data provide strong evidence that the proposed model works as effectively as a pooled centralized model.

关键词： distributed signal processing neural network

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distributed average consensus with increased convergence rate

Distributed average consensus with increased convergence rat...

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33rd IEEE International Conference on Acoustics, Speech and signal processing

作者： Oreshkin, Boris N. Aysal, Tuncer C. Coates, Mark J. McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 2T5 Canada

ISBN: (纸本)9781424414833

The average consensus problem in the distributed signal processing context is addressed by linear iterative algorithms, with asymptotic convergence to the consensus. The convergence of the average consensus for an arbitrary weight matrix satisfying the convergence conditions is unfortunately slow restricting the use of the developed algorithms in applications. In this paper, we propose the use of linear extrapolation methods in order to accelerate distributed linear iterations. We provide analytical and simulation results that demonstrate the validity and effectiveness of the proposed scheme. Finally, we report simulation results showing that the generalized version of our algorithm, when a grid search for the unknown optimum value of mixing parameter is used, significantly outperforms the optimum consensus algorithm based on weight matrix optimization.

关键词： distributed signal processing average consensus linear prediction

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distributed Denoising over Simplicial Complexes using Chebyshev Polynomial Approximation 30

Distributed Denoising over Simplicial Complexes using Chebys...

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30th European signal processing Conference (EUSIPCO)

作者： Kadambari, Sai Kiran Francis, Robin Chepuri, Sundeep Prabhakar Indian Inst Sci Bangalore Karnataka India

ISBN: (纸本)9789082797091

In this work, we focus on denoising smooth signals supported on simplicial complexes in a distributed manner. We assume that the simplicial signals are dominantly smooth on either the lower or upper Laplacian matrices, which are used to compose the so-called Hodge Laplacian matrix. This corresponds to denoising non-harmonic signals on simplicial complexes. We pose the denoising problem as a convex optimization problem, where we assign different weights to the quadratic regularizers related to the upper and lower Hodge Laplacian matrices and express the optimal solution as a sum of simplicial complex operators related to the two Laplacian matrices. We then use the recursive relation of the Chebyshev polynomial to implement these operators in a distributed manner. We demonstrate the efficacy of the developed framework on synthetic and real-world datasets.

关键词： Chebyshev polynomials denoising distributed signal processing simplicial complex

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