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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS 1991年第4期38卷 453-456页

作者： CARROLL, TL PECORA, LM USN RES LABWASHINGTONDC 20375

Although the motions of independent chaotic systems are uncorrelated with each other, it is possible under some conditions to synchronize a subsystem of one chaotic system with a separate chaotic system by sending a signal from the chaotic system to the subsystem. We describe here the conditions necessary for synchronization and demonstrate synchronization with a chaotic circuit.

关键词： Chaos Circuits Frequency synchronization Sensor arrays Voltage signal processing algorithms Speech processing Speech analysis signal resolution

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ON THE PRIME FACTOR DECOMPOSITION ALGORITHM FOR THE DISCRETE SINE TRANSFORM

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IEEE TRANSACTIONS ON signal processing 1994年第11期42卷 3258-3260页

作者： KAR, DC RAO, VVB Department of Electrical Engineering North Dakota State University Fargo ND USA

This correspondence presents a fast algorithm for computing the discrete sine transform (DST) when the transform size N is decomposable into mutually prime factors. Based on the Lee algorithm for the discrete cosine transform (DCT) and from the relations obtained between DST and DCT, a new set of input and output index mappings for DST is presented.

关键词： Discrete transforms Discrete cosine transforms Filters signal processing signal processing algorithms Equations Computational complexity Linear algebra Probability Circuits

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Application of Blind signal processing in Image Watermarking Systems

Application of Blind Signal Processing in Image Watermarking...

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International SoC Design Conference, ISOCC

作者： Chuen-Yau Chen Cheng-Yuan Lin Yi-Ze Zou Hung-Ming Hsiao Yen-Ting Chen National University of Kaohsiung Kaohsiung Taiwan National Taiwan University of Science & Technology Taipei Taiwan

ISBN: (数字)9781728183312

ISBN: (纸本)9781728183329

In this paper, an efficient blind signal processing algorithm called relative-gradient bound component analysis algorithm that combines the concepts of the relative-gradient approach and the bound-component analysis is applied to the image watermarking systems. Unlike to the other blind-signal-processing algorithms, neither the inverse matrix nor the covariance matrix is necessary to be computed in this one. It can successfully extract the embedded watermark without the whittling process. The time complexity and the space complexity of this algorithm are both lower than those of the original bound component analysis algorithm.

关键词： signal processing algorithms Watermarking signal processing Complexity theory Covariance matrices Time complexity

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IoT Medical Device with Digital signal processing for Photoplethysmography Derived Respiratory Rate

IoT Medical Device with Digital Signal Processing for Photop...

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Electronic Communications, Internet of Things and Big Data (ICEIB), International Conference on

作者： Maria Therese B. Bonifacio Jae Malonie B. De Austria Ramon G. Garcia Electrical Electronics and Computer Engineering Mapua University Manila Philippines School of EECE Mapua University Manila Philippines

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

Self-monitoring respiratory and cardiac health is challenging without proper equipment and medical knowledge. While Masimo offers ideal remote monitoring through a pulse oximeter, device accessibility is still a significant issue. Thus, we designed a novel device designed for remote monitoring of vital signs using the Internet of Things (IoT) technology and sensor technology in healthcare. Leveraging the capabilities of the modest MAX30102 sensor and ESP32 microcontroller, the device integrates seamlessly with IoT networks, enabling real-time transmission of heart and respiratory rate (RR) data with general assessment feedback to a convenient cellphone application. A sophisticated digital signal processing (DSP) algorithm was implemented in the device, which accurately derives RR from photoplethysmography (PPG) heart rate signals. This DSP-based device ensures precision in RR measurement and facilitates self-monitoring. Mean Squared Error (MSE) and Bland-Altman analysis results demonstrated the device's accuracy and reliability in capturing vital sign data. By bridging IoT capabilities with advanced DSP techniques, the device is a promising solution for remote patient monitoring to improve healthcare.

关键词： Pulse oximeter Patient monitoring signal processing algorithms Digital signal processing Medical services Photoplethysmography Real-time systems

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

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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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MAXIMUM-LIKELIHOOD LOCALIZATION OF MULTIPLE SOURCES BY ALTERNATING PROJECTION

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND signal processing 1988年第10期36卷 1553-1560页

作者： ZISKIND, I WAX, M RAFAEL Advanced Defense Systems Limited Haifa Israel

An algorithm, referred to as APM, for computing the maximum-likelihood estimator of the locations of simple sources in passive sensor arrays is presented. The algorithm is equally applicable to the case of coherent signals and to the case of a single snapshot. The algorithm is iterative; the maximum of the likelihood function is computed by successive approximations. The convergence of the algorithm to the global maximum is demonstrated for a variety of scenarios. The key to this global convergence is the initialization scheme.< >

关键词： Sensor arrays Maximum likelihood estimation Sensor phenomena and characterization signal processing algorithms Frequency estimation Narrowband Multiple signal classification Passive radar Sonar

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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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Joint Spectrum Sensing and DOA Estimation Based on a Resource-Efficient Sub-Nyquist Array Receiver

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IEEE TRANSACTIONS ON signal processing 2024年 72卷 5354-5370页

作者： Liu, Liang Zhang, Zhan Zhang, Xinyun Wei, Ping An, Jiancheng Li, Hongbin Univ Elect Sci & Technol China Sch Informat & Commun Engn Natl Key Lab Blind Signal Proc Chengdu 611731 Peoples R China Singapore Univ Technol & Design SUTD Engn & Prod Dev Pillar Singapore 487372 Singapore Stevens Inst Technol Dept Elect & Comp Engn Hoboken NJ 07030 USA

As the demand for wireless communication continues to surge, spectrum congestion becomes more severe. Compressive spectrum sensing with joint frequency and Direction Of Arrival (DOA) estimation is instrumental to enable efficient spectrum utilization in ultra-wideband scenarios. To reduce hardware complexity, this paper proposes a resource-efficient array undersampling structure which is distinctive in that each array element only connects to one branch of the Modulated Wideband Converter (MWC), and the modulated signals in different branches have identical periods but different waveforms. The proposed structure integrates spatial sampling by array elements and temporal undersampling by the MWC. A signal model is developed for the proposed sampling structure, which can deal with more general scenarios, involving multiple subband signals, and cross-band signals. Joint spectrum sensing algorithms are proposed based on compressed sensing and subspace decomposition. Additionally, multi-resolution grid optimization strategy is designed to eliminate grid effect with low computational complexity. We also analyze the impact of structural parameters on algorithm performance, which reveals that the number of array elements determines the maximum number of signals that can be estimated within a subband, while the equivalent total number of channels of the reception system determines the maximum number of signals that the system can estimate. Our analysis shows that the proposed sampling structure offers a greater flexibility in structural parameter selection and system design. Finally, simulations show that under the condition of the same or similar average sampling rate, the proposed structure and corresponding methods can achieve higher spectrum and DOA estimation accuracy.

关键词： Estimation Sensors Receivers Hardware Arrays Direction-of-arrival estimation Costs Channel estimation signal processing algorithms Frequency estimation Spectrum sensing DOA estimation sub-Nyquist sampling

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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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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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