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8th Workshop on Hyperspectral Image and signal processing - Evolution in Remote Sensing (WHISPERS)

作者： Lahaie, Pierre DRDC Valcartier 2459 De la Bravoure Rd Quebec City PQ Canada

ISBN: (纸本)9781509006083

In applications involving weak light signal like hyperspectral or time distributed signals obtained in applications involving laser induced fluorescence spectral detection, fluorescence lifetime imaging, Raman Spectroscopy or hyperspectral imaging in low light environment, the photons arrive at such a rate that they can be counted or have to be intensified to obtain a usable signal. Detection and classification algorithms need to be designed and evaluated for weak hyperspectral signal processing. A new algorithm, Adaptive Shot Noise (ASN) based on the assumption that a signal respects the Poisson multivariate distribution has been developed using the method of the maximum likelihood. This algorithm demonstrates the capability to be used for detection and classification. Using Monte Carlo simulations its performances are compared with the Adaptive Coherence Estimator (ACE) classification and with an Integrated signal Algorithm (ISA) and ACE for detection. This new algorithm provides a small increase in performance compared to ACE in very weak signal conditions for classification and in some conditions better performance over both ACE and ISA in detection. The algorithm behavior like ACE shows sensitivity to assumption on the spectral characteristics of the source for the detection, which is not the case for ***, classification, algorithm, hyperspectral

关键词： Photonics Classification algorithms Detectors signal processing algorithms Indexes Mathematical model

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The CLEAN Type algorithms for Radar signal processing

The CLEAN Type Algorithms for Radar Signal Processing

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Microwaves, Radar and Remote Sensing Symposium

作者： Kulpa, Krzysztof Warsaw Univ Technol Inst Elect Syst PL-00665 Warsaw Poland

ISBN: (纸本)9781424426898

A new class of signal processing algorithm based on CLEAN methods primary introduced in radio-astronomy is presented in the paper. The classical radar signal processing is based on match filtering concept, which is optimal in mean square sense in case, when single target echo is detected against white or colour Gaussian noise. Such approach was effective when pulse radar have been widely used. The introduction of pulse-compression technique changes significantly the signal model, but still the match filter have been widely. The introduction of continuous wave radars, and especially noise and passive radars changed dramatically the situation. In such radars all echoes are superimposed and interfere with each other and the simple model no longer fits to that case. The straightforward solution - use of inverse problem mathematical solutions such as solving the set of nonlinear equations to find all echoes in received signal - is usually computationally ineffective and often numerically not stable, so suboptimal methods are being developed to improve detections of weak signals in CW radars. One of possible solution is to use concept of CLEAN technique an remove all strong echoes from received signal. When only weak signals and white noise remains in is possible to use matched filter concept without significant loses of radar sensitivity. In the paper several techniques for radar signal processing utilizing CLEAN concept are shown.

关键词： signal processing algorithms Radar signal processing Matched filters Radar detection Passive radar Filtering Colored noise Color Gaussian noise Inverse problems

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Comparison of Statistical signal processing and Machine Learning algorithms for Spectrum Sensing

Comparison of Statistical Signal Processing and Machine Lear...

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

作者： Tiwari, Ayush Chenji, Harsha Devabhaktuni, Vijay Univ Toledo Dept Elect Engn & Comp Sci 2801 W Bancroft St Toledo OH 43606 USA Ohio Univ Sch Elect Engn & Comp Sci Athens OH 45701 USA

ISBN: (纸本)9781538647271

In a cognitive radio network, what is the trade off between statistical signal processing and machine learning algorithms that perform the same task? When should a system use the former and when should it use the latter? In this paper we present an empirical comparison study of different techniques for two tasks: detecting multiple transmitters in the same time-frequency domain, and automatic modulation classification. We develop and improve upon an unsupervised learning technique for the former, based on the log-Rayleigh distribution. Results are based on data generated from GNU Radio Companion, and implementations of these algorithms in software. They show that there is a tradeoff between accuracy and computation/implementation complexity signal processing has a several orders of magnitude advantage over machine learning, but slightly lower accuracy. Thus, there is a need for an overarching framework that can meld machine learning and statistical signal processing.

关键词： signal processing algorithms Machine learning algorithms Sensors Mixture models Transmitters Clustering algorithms signal processing

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Synthesis of the algorithms of Adaptive Nonlinear signal processing Using Robust Approach

Synthesis of the Algorithms of Adaptive Nonlinear Signal Pro...

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IEEE East-West Design and Test Symposium (EWDTS)

作者： Artyushenko, V. M. Volovach, V. I. Technol Univ Korolev Russia Volga Reg State Univ Serv Tolyatti Russia

ISBN: (纸本)9781538657102

The synthesis of adaptive nonlinear signal processing algorithms based on the use of a robust approach to determine the optimal parameters of the amplitude transfer characteristics (ATC) is considered. The named processing is carried out at a certain final signal/noise ratio (SNR) at the input of the nonlinear block (NLB). A robust approach to determining the minimum root mean square error (RNISE) and by the minimum criterion of the generalized parameters of the NLB with the finite SNR is considered. The structural diagrams of the NLB, that implement the above mentioned algorithms including the use of multidimensional quadrature generator, is presented. It is shown the difference signal from the useful, the ratios of their parameters and usage in this case, the probabilistic algorithm of Newtonian type.

关键词： signal to noise ratio Generators Finite element analysis signal processing algorithms Probabilistic logic Broadband communication Noise measurement

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From algorithms to Devices: Enabling Machine Learning through Ultra-Low-Power VLSI Mixed-signal Array processing 38

From Algorithms to Devices: Enabling Machine Learning throug...

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38th IEEE Annual Custom Integrated Circuits Conference (CICC)

作者： Joshi, Siddharth Kim, Chul Ha, Sohmyung Cauwenberghs, Gert Univ Calif San Diego La Jolla CA 92093 USA New York Univ Abu Dhabi Abu Dhabi 129188 U Arab Emirates

ISBN: (纸本)9781509051922

Machine learning and related statistical signal processing are expected to endow sensor networks with adaptive machine intelligence and greatly facilitate the Internet of Things. As such, architectures embedding adaptive and learning algorithms on-chip are oft-ignored by the design community and present a new set of design trade-offs. This review focuses on efficient implementation of mixed-signal matrix-vector multiplication as a central computational primitive enabling machine learning and statistical signal processing, with specific examples in spatial filtering for adaptive beamforming We describe adaptive algorithms amenable for efficient implementation with such primitives in the presence of noise and analog variability. We also briefly highlight current trends in high-density integration in emerging memory device technologies and their use in high dimensional adaptive computing.

关键词： Gain Dynamic range signal processing algorithms Parallel processing Switches Digital signal processing

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MCSSA: A Stream-Based Multiconcurrency Systolic Sorting Array Combining Merge Tree

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第4期44卷 1340-1353页

作者： Huang, Lan Gao, Teng Feng, Yang Wang, Kangping Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

The exploration of utilizing reconfigurable circuits with parallel computing capabilities has been conducted to enhance sorting performance and reduce power consumption. However, most sorting algorithms using dedicated processors are based on parallelization designs of serial algorithms without considering the design method of large-scale integrated circuits. This results in various issues, including the overuse of I/O interface resources, on-chip storage resources, and complex layout wiring. In this article, we extend the 2-tuple relation in the uniform recurrence equation (URE) structure used to define the systolic array to n-tuples, and the extended structure is flexible in defining I/O bandwidth and concurrency. Then we define the multiconcurrency systolic sorter array (MCSSA) algorithm based on the extended URE structure, which has a flexible 4N/n time complexity based on the n-tuple relation. Moreover, this systolic array can simultaneously sort two independent sequences, increasing the reuse of resources. Afterwards, we encapsulate each n-tuple into a processing element (PE) cell. The entire MCSSA consists of these interconnected PE cells, each of which can be customized in terms of data bit width and type. Last but not least, we have improved the merge tree structure called MC- merge tree. The concurrency of this algorithm can also be flexibly defined, we use this algorithm combined with MCSSA to cope with large-scale sorting scenarios. In our experiments, we have demonstrated the speed-up ratio of MCSSA relative to other state of the art (SOTA) sorting algorithms. Inheriting the unity and simplicity from the Systolic Array architecture, MCSSA achieves a maximum 73.17x acceleration ratio on the U200. In addition, the MC-merge tree expands the MCSSA sorting scale with a maximum of 450.56 times while maintaining the advantage of the acceleration ratio. The results of our study demonstrate that MCSSA and MC-merge tree have better acceleration, throughput and sc

关键词： Sorting Systolic arrays signal processing algorithms Concurrent computing Hardware Time complexity Integrated circuits Field programmable gate arrays Complexity theory Design automation FPGA parallel sorting systolic array uniform recurrence equations (UREs)

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A Co-Evolutionary Dual Niching Differential Evolution Algorithm for Nonlinear Equation Systems Optimization

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2025年第1期9卷 109-118页

作者： Li, Shuijia Wang, Rui Gong, Wenyin Liao, Zuowen Wang, Ling Natl Univ Def Technol Coll Syst Engn Changsha 410073 Peoples R China Natl Univ Def Technol Xiangjiang Lab Changsha 410205 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Beibu Gulf Univ Beibu Gulf Ocean Dev Res Ctr Qinzhou 535000 Peoples R China Tsinghua Univ Dept Automat BNRIST Beijing 100084 Peoples R China

A nonlinear equation system often has multiple roots, while finding all roots simultaneously in one run remains a challenging work in numerical optimization. Although many methods have been proposed to solve the problem, few have utilised two algorithms with different characteristics to improve the root rate. To locate as many roots as possible of nonlinear equation systems, in this paper, a co-evolutionary dual niching differential evolution with information sharing and migration is developed. To be specific, firstly it utilizes a dual niching algorithm namely neighborhood-based crowding/speciation differential evolution co-evolutionary to search concurrently;secondly, a parameter adaptation strategy is employed to ameliorate the capability of the dual algorithm;finally, the dual niching differential evolution adaptively performs information sharing and migration according to the evolutionary experience, thereby balancing the population diversity and convergence. To investigate the performance of the proposed approach, thirty nonlinear equation systems with diverse characteristics and a more complex test set are used as the test suite. A comprehensive comparison shows that the proposed method performs well in terms of root rate and success rate when compared with other advanced algorithms.

关键词： Optimization Information sharing signal processing algorithms Convergence Genetic algorithms Search problems Nonlinear equations Co-evolutionary differential evolution inform-ation migration niching nonlinear equation system

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An Enhanced Empirical Mode Decomposition Technique for Rotor Fault Detection in Induction Motors

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Arifin, Md. Shamsul Wang, Wilson Uddin, Mohammad Nasir Lakehead Univ Elect & Comp Engn Barrie Campus Barrie ON L4M 3X9 Canada Lakehead Univ Mech & Mechatron Engn Thunder Bay Campus Thunder Bay ON P7B 5E1 Canada

Induction motors or machines (IMs) are the driving force in various industries such as manufacturing, transportation, and wind power generation. Hence it is essential to detect faults in IMs reliably so as to enhance the production quality and avoid operational degradation. However, it is still challenging to detect faults in IMs reliably as fault feature properties could change under variable IM operating conditions. The objective of this article is to propose an enhanced empirical mode decomposition (EEMD) technique to detect the IM broken rotor bar (BRB) fault based on motor current signature analysis (MCSA). In the proposed EEMD technique, first, a phase-insensitive similarity function is suggested to determine the representative intrinsic mode function (IMF). Second, an optimized adaptive multiband filter (OAMF) is proposed to recognize the fault characteristic features from the spectrum. Third, a modified whale optimization (MWO) algorithm is suggested to optimize the parameters in the adaptive multiband filter. A reference function is also proposed to enhance feature properties and IM fault detection. The effectiveness of the proposed EEMD technique is verified experimentally under different IM conditions.

关键词： Fault detection Feature extraction Rotors Spectral analysis Bars signal processing algorithms Empirical mode decomposition Benchmark testing Adaptive filters Accuracy Adaptive multiband filter broken rotor bars (BRBs) empirical mode decomposition (EMD) fault detection of induction motors/machines (IMs) motor current signature analysis (MCSA)

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A Universal Methodology of Complex Number Computation for Low-Complexity and High-Speed Implementation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年第3期72卷 1308-1320页

作者： Wang, Yu Zhang, Jin Wu, Youlong Lyu, Fei Luo, Yuanyong Nanjing Xiaozhuang Univ Sch Elect Engn Nanjing 211171 Peoples R China Jinling Inst Technol Sch Elect & Informat Engn Nanjing 211169 Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Integrated Circuits Nanjing 211106 Peoples R China Nanjing Univ Sch Phys Nanjing 210093 Peoples R China Huawei Corp Dept Turing Architecture Design Linx Lab HiSilicon Shenzhen 518129 Peoples R China

In complex-valued neural network (CVNN) applications, complex number calculations require high performance rather than high precision. However, most previous studies focused on high-precision approaches, which have low speed and high hardware costs. This paper proposes a universal methodology of complex number computation for low-complexity and high-speed implementation. The proposed methodology is based on the piecewise linear (PWL) method and can be used for different types of complex number computations. Considering that multiplication operations consume considerable resources, multiplication, fused square-add (FSA) and fused multiply-add (FMA) operations are the focus of optimization. The partial products of the square operation are reduced by folding and merging techniques because of their symmetry in the FSA operation. The partial products of the multiplication and FMA operations are reduced via Booth encoding. In addition, the partial products are further reduced by the proposed step-by-step truncation method. The proposed segmenter, which simulates the hardware implementation, automatically divides the nonlinear functions in the complex number computations into the smallest number of segments according to the required precision. The results show that the proposed approach improves performance and reduces hardware costs compared with the state-of-the-art methods for complex number calculations involving square roots, reciprocals and logarithms.

关键词： Hardware Image segmentation Software Neural networks Costs signal processing algorithms Power demand Optimization Merging Encoding Piecewise linear (PWL) approximation method computation for complex numbers step-by-step truncation square root calculation of complex numbers reciprocal calculation of complex numbers logarithm calculation of complex numbers

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Personalized Coupled Tensor Decomposition for Multimodal Data Fusion: Uniqueness and algorithms

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IEEE TRANSACTIONS ON signal processing 2025年 73卷 113-129页

作者： Borsoi, Ricardo A. Usevich, Konstantin Brie, David Adali, Tulay Univ Lorraine French Natl Ctr Sci Res CNRS CRAN Lab F-54000 Vandoeuvre Les Nancy France Univ Maryland Baltimore Cty Dept Comp Sci & Elect Engn Baltimore MD 21250 USA

Coupled tensor decompositions (CTDs) perform data fusion by linking factors from different datasets. Although many CTDs have been already proposed, current works do not address important challenges of data fusion, where: 1) the datasets are often heterogeneous, constituting different "views" of a given phenomena (multimodality);and 2) each dataset can contain personalized or dataset-specific information, constituting distinct factors that are not coupled with other datasets. In this work, we introduce a personalized CTD framework tackling these challenges. A flexible model is proposed where each dataset is represented as the sum of two components, one related to a common tensor through a multilinear measurement model, and another specific to each dataset. Both the common and distinct components are assumed to admit a polyadic decomposition. This generalizes several existing CTD models. We provide conditions for specific and generic uniqueness of the decomposition that are easy to interpret. These conditions employ uni-mode uniqueness of different individual datasets and properties of the measurement model. Two algorithms are proposed to compute the common and distinct components: a semi-algebraic one and a coordinate-descent optimization method. Experimental results illustrate the advantage of the proposed framework compared with the state of the art approaches.

关键词： Tensors Matrix decomposition Functional magnetic resonance imaging Data integration Data models Brain modeling Couplings Vectors signal processing algorithms Image fusion Coupled tensor decomposition personalized learning shared and distinct components uniqueness

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