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SENSOR ARRAY signal-processing VIA A PROCRUSTES ROTATIONS BASED EIGENANALYSIS OF THE ESPRIT DATA PENCIL

IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND signal processing 1989年第6期37卷 832-861页

作者： ZOLTOWSKI, MD STAVRINIDES, D School of Electrical Engineering Purdue University West Lafayette IN USA

The authors propose the procrustes-rotation-based ESPRIT algorithm, PRO-ESPRIT, for estimating characteristic parameters associated with a scene of radiating sources, given data derived from two identical sensor arrays, the X and Y arrays, which are displaced relative to one another. PRO-ESPRIT uses the inherent redundancy built into the ESPRIT array system to compensate for deviations from the ideal array model. This redundancy is manifested by two fundamental properties of the noiseless X and Y data matrices comprising the ESPRIT data pencil: (a) they have the same row space, the signal subspace; and (2) they have the same column space, the source subspace. These properties are utilized by invoking two primary tools: (1) the solution to the procrustes problem for optimally approximating an invariant subspace rotation; and (2) the total-least-squares method for perturbing each of two estimates of a common subspace in a minimal fashion until the two perturbed spaces are the same.< >

关键词： Sensor arrays Array signal processing signal processing algorithms Sensor phenomena and characterization Multiple signal classification Least squares methods Parameter estimation Least squares approximation Linear systems Differential equations

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Discrete Sine and Cosine Transforms for signal processing Spectral Overlap Saliencies of Induction Machine

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2018年第1期65卷 189-199页

作者： Metwaly, Mohamed K. Elkalashy, Nagy I. Zaky, Mohamed S. Menoufia Univ Fac Engn Dept Elect Engn Shibin Al Kawm 32511 Egypt

A novel separation procedure applied to overlap condition of saliencies in induction machine is presented. The machine's current reaction to voltage step excitation is used to obtain saliency signals. The proposed procedure is based on a digital signal processing implementation of the discrete sine and cosine transforms for extracting the slotting saliency signal at close and equal frequencies of the slotting and saturation saliencies under heavy load operation. The proposed algorithms are processed based on an online adaptive threshold. Using the proposed separation algorithm guarantees precise estimation of rotor/flux angle and efficient sensorless control of the induction machine. A sensorless induction motor drive is implemented in the laboratory. Extensive experimental results are presented. A performance comparison between the proposed algorithm and previousworks is provided. The robustness and effectiveness of the applied signal processing algorithms for sensorless torque control are verified at low speed and heavy load.

关键词： Discrete cosine transform (DCT) discrete sine transform (DST) induction motors (IMs) machine vector control sensorless control signal processing algorithms spectral analysis torque control

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Features of a combined FFT and Hilbert transform for phase Doppler signal processing

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MEASUREMENT SCIENCE AND TECHNOLOGY 1997年第4期8卷 409-421页

作者： Lehmann, P Schombacher, EH University of Bremen Meß- und Regelungstechnik/Verfahrenstechnik FB4 Postfach 330440 D-28334 Bremen Germany

Phase Doppler anemometry (PDA) is a laser optical method to determine particle diameters and velocities within two-phase or multiphase flows simultaneously and with high spatial and temporal resolution. The measured phase difference between two Doppler bursts is related to the particle diameter. The proposed method for signal processing is based on a combined Hilbert transform and Fast Fourier transform (FFT) phase Doppler burst analysis. The numerical determination of the burst envelopes gives an estimation of the time delay between two related signals or, more generally, of the burst maximum position within the record time. This estimate is completed by the conventional FFT based signal analysis which is used to estimate frequency and phase difference. By this two-step estimation the restriction to the [0, 360 degrees] interval resulting from conventional signal processing can be avoided. The feasibility of the method in terms of an on-line determination of absolute phase differences is investigated by Monte Carlo simulations and demonstrated by means of a standard PDA arrangement with ball lenses rotating as spherical particles through the measurement volume. Phase differences up to 2500 degrees were determined reliably. In addition, algorithms to calculate Doppler frequencies and Doppler intensities, burst lengths and further characteristic parameters taking the signal quality into account are introduced and studied by Monte Carlo simulations. Based on these results validation strategies to reduce the influence of maltriggered bursts, trajectory or Gaussian beam effects on measurement results can be developed. Furthermore, by using the information of burst length and maximum position, optimized signal processing algorithms can be realized in order to achieve maximum accuracy in frequency, phase difference and signal-to-noise ratio (SNR) estimation.

关键词： signal processing algorithms Doppler frequency Doppler signal phase difference time delay signal quality Hilbert BURSTS signal processing Phase Hilbert transformations Doppler effect Monte Carlo technique Fast Fourier transform burst duration

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Area-efficient multipliers for digital signal processing applications

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-ANALOG AND DIGITAL signal processing 1996年第2期43卷 90-95页

作者： Kidambi, SS ElGuibaly, F Antoniou, A UNIV VICTORIA DEPT ELECT & COMP ENGNVICTORIABC V8W 3P6CANADA

An area-efficient parallel sign-magnitude multiplier that receives two N-bit numbers and produces an N-bit product, referred to as a truncated multiplier, is described, The quantization of the product to N bits is achieved by omitting about half the adder cells needed to add the partial products but in order to keep the quantization error to a minimum, probabilistic biases are obtained and are then fed to the inputs of the retained adder cells, The truncated multiplier requires approximately 50% of the area of a standard parallel multiplier. The paper then shows that this design strategy can also be applied for the design of two's-complement multipliers. The paper concludes with the application of the truncated multiplier for the implementation of a digital filter and it is shown that the signal-to-noise ratio of the digital filter using a truncated multiplier is better than that using a standard multiplier.

关键词： Digital signal processing Digital filters Quantization Digital signal processing chips signal processing algorithms Estimation error Senior members Adders signal to noise ratio

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EP-SAM: An Edge-Detection Prompt SAM Based Efficient Framework for Ultra-Low Light Video Segmentation

EP-SAM: An Edge-Detection Prompt SAM Based Efficient Framewo...

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International Conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Zhitao Wang Jiangtao Wen Yuxing Han Tsinghua University China Research Institute of Tsinghua University in Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The Segment Anything Model (SAM) excels at generating high-quality object masks with various prompts but struggles in ultra-low light. We developed EP-SAM (Edge-Detection Prompt SAM) with a Low Light Edge-Detection Network (LLEN), offering strong robustness and lightweight performance in ultra-low light. Using edge information as prompts, EP-SAM achieves high-precision segmentation in low-light *** combining motion estimation with reference frame optimization, the initial frame can predict the next 29 frames, reducing inference time by over 80% and computational complexity by 86%. Tests show LLEN accurately extracts edges even with about 80 photons per pixel, enabling EP-SAM to produce precise masks and significantly outperform SAM and SAM-2. EP-SAM improves mean Intersection over Union (mIoU) by 5.85% over SAM on the CamVid dataset. Video demos: https://***/wzt22thu/EP-SAM/releases/tag/DEMO.

关键词： Visualization Image edge detection Motion segmentation Motion estimation Surveillance signal processing algorithms signal processing Robustness Speech processing Optimization

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Online Extraction of Electrochemical Impedance Spectroscopy Pattern Based on EV Load Profile and Short Time Fourier Transform for Diagnosis of Lithium-ion Battery Safety

Online Extraction of Electrochemical Impedance Spectroscopy ...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Miyoung Lee Dongcheol Lee Youngmin Bae Jongchan An Garam Yang Woonki Na Jonghoon Kim Department of Electrical Engineering Chungnam National University Daejeon Republic of Korea Department of Electrical and Computer Engineering California State University Fresno United States

ISBN: (数字)9798331516116

ISBN: (纸本)9798331516123

Batteries which convert chemical energy into electrical energy and play an important role in our daily lives and across various industries. The chemical reaction that occurs within a battery during operation of an electric vehicle (EV) depends on environmental conditions such as temperature, load, and humidity, and these variables greatly affect battery performance and life. However, there is challenging to analyze these electrochemical phenomena with only simple electric signals, such as direct current (DC) signal. For compensation, passive electrochemical impedance spectroscopy (PEIS) is derived by applying a signal processing algorithm that converts time series signals into frequency-domain signals in this study. Based on this, we propose to learn a siamese convolutional neural network (SCNN) algorithm on the converted PEIS pattern to the image and to effectively diagnose the state of the battery through similarity evaluation.

关键词： Lithium-ion batteries Heuristic algorithms Frequency-domain analysis Time series analysis signal processing algorithms Feature extraction Vectors Convolutional neural networks Vehicle dynamics Electrochemical impedance spectroscopy

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Efficient Residual Shrinkage CNN Denoiser Design for Intelligent signal processing: Modulation Recognition, Detection, and Decoding

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2022年第1期40卷 97-111页

作者： Zhang, Lin Yang, Xiaoling Liu, Heng Zhang, Haotian Cheng, Julian Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Peoples R China Southern Marine Sci & Engn Guangdong Lab Zhuhai 519000 Peoples R China Univ British Columbia Sch Engn Kelowna BC V1V 1V7 Canada

The noises embedded in signals will degrade the signal processing quality. Traditional denoising algorithms might not work in practical systems since the statistical characteristics of noises might not be learned. To address this issue, we propose an efficient residual shrinkage convolutional neural network (RSCNN) aided denoiser based on the principle of the domain transformation, shrinking and inverse transforming operations conducted by the traditional denoiser. The proposed RSCNN is composed by the batch normalization layer, domain transformation layers, the shrinkage module and inverse transformation layers, wherein transformation layers consist of convolutional layers and the nonlinear activation function. Moreover, we propose a thresholds learning subnetwork to automatically determine the thresholds, so as to enhance noise suppressing performances. Furthermore, we compose the data set by preprocessing the received signals, and design the loss function according to different denoising requirements. To validate the efficiency and universality of the RSCNN aided denoiser, we apply the proposed RSCNN denoiser to three different application scenarios, including the modulation recognition, detection and decoding. After the offline training, at the online deployment stage, we utilize the RSCNN denoiser to reduce the noise power and improve the signal to noise ratios. Simulation results demonstrate that the proposed intelligent denoiser can efficiently improve the signal processing capabilities to achieve higher modulation recognition accuracy, better detection and decoding performances with lower complexity than benchmark schemes.

关键词： Noise reduction Decoding Modulation Feature extraction Convolutional neural networks signal processing algorithms Training Noise suppression residual shrinkage convolutional neural network aided denoiser modulation recognition detection belief propagation decoding

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A Ground-Penetrating Radar Clutter Suppression Algorithm Integrating signal processing and Image Fusion

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷

作者： Tang, Xiao-Song Yang, Feng Qiao, Xu Liu, Jia-Lin Li, Fan-Ruo Wen, Yong-Liang Fan, Zhi-Hai Qi, Zhen-Hong Yang, Zhi-Hua China Univ Min & Technol Beijing Sch Artificial Intelligence Beijing 100083 Peoples R China Guangzhou Railway Grp Corp Guangzhou 510000 Peoples R China Shaanxi Shaanmei Tongchuan Min Co Ltd Tongchuan 727000 Shaanxi Peoples R China Shanxi Huayang Grp New Energy Co Ltd Yangquan 045000 Shanxi Peoples R China

This study presents a clutter suppression algorithm for underground mine ground-penetrating radar (GPR) B-scan profiles, integrating the sparrow search algorithm (SSA), variational mode decomposition (VMD), and image fusion via the improved nonsubsampled shearlet transform (INSST), aimed at addressing clutter under strict explosion-proof conditions. Initially, the raw B-scan data undergoes preprocessing to derive a grayscale profile. Using SSA, an envelope entropy fitness function is introduced to automatically optimize VMD with respect to the average trajectory of the grayscale profile. Finally, enhanced non-subsampled shearlet transform (NSST) processing is applied to the synthesized profile using a scale-direction adaptive threshold with an L2 norm square constraint and a flexible threshold function. This approach reduces Gibbs artifacts and oversmoothing while retaining valid signals within the threshold range. In simulated profiles, the proposed algorithm demonstrates strong robustness across different noise standard deviations, significantly improving image quality metrics such as peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM), while reducing the root-mean-square error (RMSE). Notably, in measured mining profiles, the proposed algorithm outperforms eight other methods by better preserving edge details. Compared to the suboptimal algorithm, the target-to-noise ratio (TNR) improves by 36% in mining fault profile I and by 17% in coal seam floor profile II. Finally, a sensitivity analysis of the proposed algorithm's parameters was conducted.

关键词： Clutter signal processing algorithms Geoscience and remote sensing Radar Noise reduction Wavelet transforms Sensitivity analysis Location awareness Geology Entropy Clutter suppression ground-penetrating radar (GPR) improved nonsubsampled shearlet transform (INSST) sparrow search algorithm (SSA) variational mode decomposition (VMD)

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Networked signal and Information processing: Learning by multiagent systems

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IEEE signal processing MAGAZINE 2023年第5期40卷 92-105页

作者： Vlaski, Stefan Kar, Soummya Sayed, Ali H. Moura, Jose M. F. Imperial Coll London SW7 2AZ England Ecole Polytech Fed Lausanne Adapt Syst Lab Lausanne Switzerland Princeton Univ Elect Engn Dept Princeton NJ USA Ecole Polytech Fed Lausanne CH-1015 Lausanne Switzerland Univ Calif Los Angeles Elect Engn Los Angeles CA USA US Natl Acad Engn Washington DC USA World Acad Sci Trieste Italy Carnegie Mellon Univ Pittsburgh PA 15213 USA Amer Assoc Advancement Sci Washington DC USA Natl Acad Inventors Tampa FL USA Portugal Acad Sci Lisbon Portugal Natl Acad Engn New Delhi India

This article reviews significant advances in networked signal and information processing (SIP), which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments of distributed agents. As these interacting agents cooperate, new collective behaviors emerge from local decisions and actions. Moreover, and significantly, theory and applications show that networked agents, through cooperation and sharing, are able to match the performance of cloud or federated solutions while offering the potential for improved privacy, increased resilience, and conserved resources. A longer version of this manuscript, with examples and illustrative applications, is available at https://***/abs/2210.13767.

关键词： Privacy Protocols Network topology Scalability signal processing algorithms Information processing Robustness

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Channel Capacity Estimation of Communication Systems with Digital Antenna Arrays for Nonlinear Model of Low-Frequency Noises

Channel Capacity Estimation of Communication Systems with Di...

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Digital signal processing and its Applications (DSPA), International Conference on

作者： Alexander Parshin Maxim Grachev LLC “Sphera Lab” Ryazan Russia

ISBN: (数字)9798331518752

ISBN: (纸本)9798331518769

The paper is devoted to modeling algorithms for estimating channel transmission coefficients in a communication system with digital antenna arrays taking into account mutual coupling, the complex of flicker, phase and thermal noises, and integrated signal processing. The primary signal processing in the path of each element of the antenna array is considered and the effect of noise from both individual elements of antenna systems and digital processing noise is evaluated. The influence of a complex of noises on the communication system's channel capacity is revealed by channel capacity estimation for parallel method of signal receiving. Methods for increasing the efficiency of digital signal processing, filtering and noise compensation in the communication system are proposed.

关键词： Phased arrays Adaptation models Channel capacity Communication systems Array signal processing Low frequency noise Noise signal processing algorithms Digital signal processing Antenna arrays

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