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Accelerated Structure-Aware Reinforcement Learning for Delay-Sensitive Energy Harvesting Wireless Sensors

IEEE TRANSACTIONS ON signal processing 2020年第0期68卷 1409-1424页

作者： Sharma, Nikhilesh Mastronarde, Nicholas Chakareski, Jacob SUNY Buffalo Dept Elect Engn Buffalo NY 14260 USA New Jersey Inst Technol Ying Wu Coll Comp Newark NJ 07103 USA

We consider a time-slotted energy-harvesting wireless sensor transmitting delay-sensitive data over a fading channel. The sensor injects captured data packets into its transmission queue and relies on ambient energy harvested from the environment to transmit them. We aim to find the optimal scheduling policy that decides how many packets to transmit in each time slot to minimize the expected queuing delay. No prior knowledge of the stochastic processes that govern the channel, captured data, and harvested energy dynamics is assumed, thereby necessitating online learning to optimize the scheduling policy. We formulate this problem as a Markov decision process (MDP) with state-space spanning the sensor's buffer, battery, and channel states, and show that its optimal value function is non-decreasing and has increasing differences, in the buffer state, and that it is non-increasing and has increasing differences, in the battery state. We exploit this value function structure knowledge to formulate a novel accelerated reinforcement learning (RL) algorithm based on value function approximation that can solve the scheduling problem online with controlled approximation error, while inducing limited computational and memory complexity. We rigorously capture the trade-off between approximation accuracy and computational/memory complexity savings associated with our approach. Our simulations demonstrate that the proposed algorithm closely approximates the optimal offline solution, which requires complete knowledge of the system state dynamics. Simultaneously, our approach achieves competitive performance relative to a state-of-the-art RL algorithm, at orders of magnitude lower complexity. Moreover, considerable performance gains are demonstrated over the widely popular Q-learning RL technique.

关键词： Sensors Batteries Heuristic algorithms Energy harvesting Complexity theory Approximation algorithms signal processing algorithms Energy harvesting delay-sensitive remote sensing transmission scheduling accelerated reinforcement learning structural properties value function post-decision state learning virtual experience learning

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Hunting for Paper Moldmates Among Rembrandt's Prints Chain-line pattern matching

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IEEE signal processing MAGAZINE 2015年第4期32卷 28-37页

作者： Johnson, C. Richard, Jr. Sethares, William A. Ellis, Margaret Holben Haqqi, Saira Cornell Univ Engn Ithaca NY 14850 USA Rijksmuseum Amsterdam Netherlands Netherlands Inst Art Hist The Hague Netherlands van Gogh Museum Thread Count Automat Project Amsterdam Netherlands Museum Modern Art Hist Photog Paper Classificat Challenge New York NY USA Morgan Lib & Museum Chain Line Pattern Matching Project New York NY 10016 USA NYU Inst Fine Arts New York NY 10003 USA Morgan Lib & Museum Thaw Conservat Ctr New York NY 10016 USA NYU Inst Fine Arts Conservat Ctr New York NY 10003 USA

Early paper manufacturing used sieve-like molds through which paper pulp was drained. Two pieces of paper are called moldmates if they were made using the same mold. When a large body of one artist?s work on paper exists, the identification of moldmates can help in establishing chronology, suggest paper preferences, and indicate periods of intense activity of the artist. Rembrandt is an especially good example. With several thousand prints in existence today, the study of Rembrandt?s prints has occupied scholars for over two centuries, and the study of his printing papers occupies a prominent place within this scholarship [1]. This article examines the feasibility of moldmate identification via chain-line pattern matching and conducts a series of experiments that demonstrate how accurately the measurements can be made, how straight and parallel the lines may be, and provides a rule of thumb for the number of chain lines required for accurate moldmate identification using a simplified model. The problem of identifying moldmates among Rembrandt?s prints is presented as a pair of image/signal processing tasks; our strategy is to provide basic solutions to these tasks and to then reveal the shortcomings of these solutions in the hopes of encouraging future work in the signal processing community. With the support of the Morgan Library & Museum and the Metropolitan Museum of Art, both of which are in New York, we have made high-resolution data available [2] to facilitate this quest.

关键词： Watermarking signal processing algorithms signal processing Paper making Pattern matching Paper pulp Art

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Network Inference From Consensus Dynamics With Unknown Parameters

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IEEE TRANSACTIONS ON signal AND INFORMATION processing OVER NETWORKS 2020年 6卷 300-315页

作者： Zhu, Yu Schaub, Michael T. Jadbabaie, Ali Segarra, Santiago Rice Univ Dept Elect & Comp Engn Houston TX 77005 USA MIT Inst Data Syst & Soc 77 Massachusetts Ave Cambridge MA 02139 USA Univ Oxford Dept Engn Sci Oxford OX1 2JD England

We explore the problem of inferring the graph Laplacian of a weighted, undirected network from snapshots of a single or multiple discrete-time consensus dynamics, subject to parameter uncertainty, taking place on the network. Specifically, we consider three problems in which we assume different levels of knowledge about the diffusion rates, observation times, and the input signal power of the dynamics. To solve these underdetermined problems, we propose a set of algorithms that leverage the spectral properties of the observed data and tools from convex optimization. Furthermore, we provide theoretical performance guarantees associated with these algorithms. We complement our theoretical work with numerical experiments, that demonstrate how our proposed methods outperform current state-of-the-art algorithms and showcase their effectiveness in recovering both synthetic and real-world networks.

关键词： Laplace equations Biological system modeling Heuristic algorithms signal processing algorithms Information processing Network topology Computational modeling Network topology inference sparse graph learning graph Laplacian estimation consensus dynamics graph signal processing

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Factoring very-high-degree polynomials

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IEEE signal processing MAGAZINE 2003年第6期20卷 27-42页

作者： Sitton, GA Burrus, CS Fox, JW Treitel, S Rice Univ Sch Engn Houston TX 77251 USA

In this article, we discuss the current status of polynomial factoring (root finding) algorithms with some historical and mathematical background including size limits, convergence, accuracy and speed. The methods of root approximation versus root refinement are also examined. We then focus on two improved general purpose computational techniques, and in particular the factorization algorithm by Lindsey-Fox (L-F), which makes use of the fast Fourier transform to factor polynomials with random coefficients of degrees as high as 1 million. Computer simulations give insight that result in significant improvements in traditional approaches to an ancient problem.

关键词： Polynomials signal processing algorithms Digital signal processing Algebra Fast Fourier transforms Mathematics Convergence Stability Calculus Arithmetic

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LATTICE algorithms FOR RECURSIVE LEAST-SQUARES ADAPTIVE 2ND-ORDER VOLTERRA FILTERING

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-ANALOG AND DIGITAL signal processing 1994年第3期41卷 202-214页

作者： SYED, MA MATHEWS, VJ UNIV UTAH DEPT ELECT ENGNSALT LAKE CITYUT 84112

This paper presents two computationally efficient recursive least-squares (RLS) lattice algorithms for adaptive nonlinear filtering based on a truncated second-order Volterra system model. The lattice formulation transforms the nonlinear filtering problem into an equivalent multichannel, linear filtering problem and then generalizes the lattice solution to the nonlinear filtering problem. One of the algorithms is a direct extension of the conventional RLS lattice adaptive linear filtering algorithm to the nonlinear case. The other algorithm is based on the QR decomposition of the prediction error covariance matrices using orthogonal transformations. Several experiments demonstrating and comparing the properties of the two algorithms in finite and ''infinite'' precision environments are included in the paper. The results indicate that both the algorithms retain the fast convergence behavior of the RLS Volterra filters and are numerically stable.

关键词： Lattices Filtering algorithms Least squares methods Adaptive filters Nonlinear filters signal processing algorithms Resonance light scattering Transversal filters Maximum likelihood detection Covariance matrix

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Active-LATHE: An Active Learning Algorithm for Boosting the Error Exponent for Learning Homogeneous Ising Trees

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IEEE TRANSACTIONS ON INFORMATION THEORY 2023年第4期69卷 2537-2555页

作者： Zhang, Fengzhuo Tandon, Anshoo Tan, Vincent Y. F. Natl Univ Singapore Dept Elect & Comp Engn Singapore 119077 Singapore Indian Inst Sci IUDX Program Unit SID Bengaluru 560012 India Natl Univ Singapore Dept Math Singapore 119077 Singapore

The Chow-Liu algorithm (IEEE Trans. Inform. Theory, 1968) has been a mainstay for the learning of tree structured graphical models from i.i.d. sampled data vectors. Its theoretical properties have been well-studied and are well understood. In this paper, we focus on the class of trees that are arguably even more fundamental, namely homogeneous trees in which each pair of nodes that forms an edge has the same correlation ?. We ask whether we are able to further reduce the error probability of learning the structure of the homogeneous tree model when active learning is allowed. Our figure of merit is the error exponent, which quantifies the exponential rate of decay of the error probability with an increasing number of data samples. We design and analyze an algorithm ActiveLearning Algorithm for Trees with Homogeneous Edges (ACTIVE-LATHE), which surprisingly boosts the error exponent by at least 40% when ? is at least 0.8. For all other values of ?, we also observe commensurate, but more modest, improvements in the error exponent. Our analysis hinges on judiciously exploiting the minute but detectable statistical variation of the samples to allocate more data to parts of the graph in which we are less confident of being correct.

关键词： Graphical models Vegetation signal processing algorithms Correlation Error probability Random variables Task analysis Active learning graphical models ising models error exponents

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Efficient Multisensor Localization for the Internet of Things Exploring a new class of scalable localization algorithms

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IEEE signal processing MAGAZINE 2018年第5期35卷 153-167页

作者： Win, Moe Z. Meyer, Florian Liu, Zhenyu Dai, Wenhan Bartoletti, Stefania Conti, Andrea MIT 77 Massachusetts Ave Cambridge MA 02139 USA Wireless Informat & Network Sci Lab Cambridge MA USA AT&T Res Labs Florham Pk NJ 07748 USA NASA Jet Prop Lab Washington DC 20546 USA MIT Wireless Informat & Network Sci Lab 77 Massachusetts Ave Cambridge MA 02139 USA NATO Ctr Maritime Res & Experimentat La Spezia Italy Wireless Informat & Network Sci Lab Aeronaut & Astronaut La Spezia Italy Univ Ferrara Ferrara Italy

In the era of the Internet of Things (IoT), efficient localization is essential for emerging mass-market services and applications. IoT devices are heterogeneous in signaling, sensing, and mobility, and their resources for computation and communication are typically limited. Therefore, to enable location awareness in large-scale IoT networks, there is a need for efficient, scalable, and distributed multisensor fusion algorithms. This article presents a framework for designing network localization and navigation (NLN) for the IoT. Multisensor localization and operation algorithms developed within NLN can exploit spatiotemporal cooperation, are suitable for arbitrary, large-network sizes, and only rely on an information exchange among neighboring devices. The advantages of NLN are evaluated in a large-scale IoT network with 500 agents. In particular, because of multisensor fusion and cooperation, the presented network localization and operation algorithms can provide attractive localization performance and reduce communication overhead and energy consumption.

关键词： Sensors Time measurement signal processing algorithms Navigation Position measurement Current measurement Measurement uncertainty

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PERFORMANCE ANALYSIS OF SPATIAL SMOOTHING WITH INTERPOLATED ARRAYS

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IEEE TRANSACTIONS ON signal processing 1993年第5期41卷 1881-1892页

作者： WEISS, AJ FRIEDLANDER, B UNIV CALIF DAVIS DEPT ELECT & COMP ENGNDAVISCA 95616

The interpolated spatial smoothing algorithm is a computationally efficient method for estimating the directions of arrival (DOA's) of signals, some of which may be perfectly correlated. It extends the spatial smoothing method to arbitrary array geometries. In an earlier paper we derived this algorithm and studied its properties. This paper provides a statistical performance analysis for the algorithm. Closed form expressions for the covariance matrix of the DOA estimation errors are derived using a perturbation analysis. Evaluating these expressions for specific cases and comparing them to the Cramer-Rao lower bound for the DOA estimates, provides insight into the statistical efficiency of this algorithm. The formulas for the error covariance are quite general, and can be specialized to provide results for other DOA estimation algorithms as well.

关键词： Performance analysis Smoothing methods Direction of arrival estimation Interpolation signal processing algorithms Geometry Covariance matrix Estimation error signal resolution Maximum likelihood estimation

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Cluster Analysis for the Separation of Auditory Scenes

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IEEE ACCESS 2021年 9卷 130959-130967页

作者： Daley, Matthew S. Bonacci, Lia M. Gever, David H. Diaz, Krystina Bolkhovsky, Jeffrey B. Naval Submarine Med Res Lab Groton CT 06340 USA

The "cocktail party problem" refers to the ability of human listeners to separate the acoustic signal reaching their ears into its individual components, corresponding to individual sound sources in the environment. Despite this phenomenon appearing trivial for humans, solving the cocktail party problem computationally remains an ambitious challenge. The approach used in this paper takes inspiration from human strategies for separating an acoustic environment into distinct perceptual auditory streams. A series of time-frequency-based features, analogous to those thought to emerge at various stages in the human auditory processing pathway, are derived from biaural auditory inputs. These feature vectors are used as inputs to an unsupervised cluster analysis used to group feature values that are assumed to correspond to the same object. Reconstructed auditory streams are then correlated to the original components used to create the auditory scene. Our model is capable of reconstructing streams that correlate to the original components (r = 0.3-0.7) used to create the complex auditory scene. The success of the reconstructions is largely dependent on the signal-to-noise ratio of the components of the auditory scene.

关键词： Bandwidth Filter banks Feature extraction Time-frequency analysis Kernel Time measurement Spectral shape Biomedical signal processing clustering algorithms machine learning machine learning algorithms pattern clustering signal processing algorithms

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Cooperative Positioning of Wireless Networks in Complex Propagation Environments

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2024年第10期42卷 2877-2889页

作者： Jiang, Peiyue Gu, Xiaobo Zhou, Haibo Guangdong Univ Technol Sch Integrated Circuits Guangzhou 510006 Peoples R China Nanjing Univ Sch Elect Sci & Engn Nanjing 210008 Peoples R China

Cooperative positioning in wireless networks has attracted great attention in recent years, as many applications require the exact location of all member nodes. The pairwise distance between the member nodes is conventionally constructed as an Euclidean Distance Matrix (EDM) for subsequent location estimation. In this paper, we address the problem of cooperative positioning in complex propagation environments, which results in an incomplete EDM. We proposed an improved EDM recovery algorithm based on low tank matrix completion (LRMC), which makes use of the sensor correlation by Laplacian and trace minimization. In addition, we derive a semi-definite relaxation estimator to localize the unknown sensors. Simulations are conducted to evaluate the performance of the proposed algorithm and the results show that the proposed method outperforms existing ones in both matrix completion and positioning accuracy.

关键词： Distance measurement Correlation Wireless sensor networks signal processing algorithms Position measurement Minimization Accuracy Cooperative positioning graph Laplacian low rank matrix completion manifold learning semi-definite programming

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