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VLSI Design and Implementation fuels the signal-processing Revolution

IEEE signal processing MAGAZINE 1998年第1期15卷 22-22页

作者： Chen, TH California Univ. Berkeley CA USA

The article provides a comprehensive overview of the history of how signal-processing researchers have been effectively transforming signal-processing algorithms into efficient implementations. Starting from the early days of analog circuits for signal processing, to digital signal processors (DSPs), to application specific DSPs and programmable DSPs, and to the trend of integrating a complete system on a single chip, this article provides a thorough coverage of the past and the present of design and implementation technology for signal processing systems. Moreover, it presents the exciting challenges faced by the study of the design and implementation of current and future signal processing applications. Topics covered include milestones in signal-processing integrated circuits, the past and future of the signal processor, signal processing in consumer applications, and design automation for signal processing.

关键词： Very large scale integration signal design Fuels Digital signal processing chips signal processing signal processing algorithms History Analog circuits Digital signal processors Integrated circuit technology

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An Introduction to Bilevel Optimization: Foundations and applications in signal processing and machine learning

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IEEE signal processing MAGAZINE 2024年第1期41卷 38-59页

作者： Zhang, Yihua Khanduri, Prashant Tsaknakis, Ioannis Yao, Yuguang Hong, Mingyi Liu, Sijia Michigan State Univ Comp Sci & Engn Dept E Lansing MI 48824 USA Wayne State Univ Comp Sci Dept Detroit MI 48202 USA Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA

Recently, bilevel optimization (BLO) has taken center stage in some very exciting developments in the area of signal processing (SP) and machine learning (ML). Roughly speaking, BLO is a classical optimization problem that involves two levels of hierarchy (i.e., upper and lower levels), wherein obtaining the solution to the upper-level problem requires solving the lower-level one. BLO has become popular largely because it is powerful in modeling problems in SP and ML, among others, that involve optimizing nested objective functions. Prominent applications of BLO range from resource allocation for wireless systems to adversarial ML. In this work, we focus on a class of tractable BLO problems that often appear in SP and ML applications. We provide an overview of some basic concepts of this class of BLO problems, such as their optimality conditions, standard algorithms (including their optimization principles and practical implementations) as well as how they can be leveraged to obtain state-of-the-art results for several key SP and ML applications. Further, we discuss some recent advances in BLO theory and its implications for applications, and we point out some limitations of the state of the art that require significant future research efforts. We hope that this article, together with the associated open source BLO toolbox we developed for algorithm benchmarking, can serve to accelerate the adoption of BLO as a generic tool to model, analyze, and innovate on a wide array of emerging SP and ML applications.

关键词： Wireless communication Array signal processing signal processing algorithms Machine learning Benchmark testing Linear programming Optimization methods Resource management Analytical models

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UNITARY EQUIVALENCE - A NEW TWIST ON signal-processing

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IEEE TRANSACTIONS ON signal processing 1995年第10期43卷 2269-2282页

作者： BARANIUK, RG JONES, DL UNIV ILLINOIS COORDINATED SCI LABURBANAIL 61801

Unitary similarity transformations furnish a powerful vehicle for generating infinite generic classes of signal analysis and processing tools based on concepts different from time, frequency, and scale. Implementation of these new tools involves simply preprocessing the signal by a unitary transformation, performing standard processing on the transformed signal, and then (in some cases) transforming the resulting output. The resulting unitarily equivalent systems can focus on the critical signal characteristics in large classes of signals and, hence, prove useful for representing and processing signals that are not well matched by current techniques. As specific examples of this procedure, we generalize linear time-invariant systems, orthonormal basis and frame decompositions, and joint time-frequency and timescale distributions. These applications illustrate the utility of the unitary equivalence concept for uniting seemingly disparate approaches proposed in the literature.

关键词： signal processing signal analysis Time frequency analysis Fourier transforms Wavelet analysis Wavelet transforms signal processing algorithms Biomedical signal processing Wideband Chirp

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ONE-DIMENSIONAL signal-processing TECHNIQUES FOR AIRBORNE LASER BATHYMETRY

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1994年第1期32卷 35-46页

作者： WONG, H ANTONIOU, A Department of Electrical and Computer Engineering University of Victoria Victoria BC Canada

The application of one-dimensional signal processing techniques for the preprocessing of waveforms obtained in airborne laser bathymetry is investigated. Specifically, a special type of smoothing digital filter is used to remove noise in the waveforms while preserving the information content up to a desired degree. An algorithm, which incorporates a lowpass digital differentiator, is then used to detect the bottom reflection. The optimal cutoff frequency of the differentiator is determined on the basis of the spectral content of the bottom reflection. After preprocessing, a waveform-decomposition technique recently described by the authors is used to separate the surface and bottom reflections for sea-depth estimation. In order to evaluate the effectiveness of the techniques developed, sea-depth estimates obtained are compared to corresponding estimates obtained by a local surveying company, and a high degree of agreement is observed between the two sets of results. When the resolution between the surface and bottom returns is low, the proposed techniques together with waveform decomposition offer a significant improvement in the sea-depth estimates.

关键词： signal processing Optical reflection Laser noise signal processing algorithms Acoustic reflection Surface waves Sea surface Laser beam cutting Smoothing methods Digital filters

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TASK ALLOCATION AND SCHEDULING MODELS FOR MULTIPROCESSOR DIGITAL signal-processing

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND signal processing 1990年第12期38卷 2151-2161页

作者： KONSTANTINIDES, K KANESHIRO, RT TANI, JR Hewlett Packard Laboratories Palo Alto CA USA

Task allocation and scheduling models for distributed digital signal processing are presented. The notions of block-type and stream-type tasks in signal processing application are introduced, and models for sequential and parallel I/O are presented. By extending the traditional models, more accurate schedules can be obtained. Those models can be further enhanced by allowing additional restrictions on the number of parallel I/O ports and the amount of parallelism on memory access. The deterministic nature of digital signal processing algorithms allows for more computationally intensive and accurate task allocation techniques to be performed at compile time. By applying a branch and bound algorithm, the task allocation problem can easily be solved for a variety of scheduling models and various system restrictions.< >

关键词： Digital signal processing Processor scheduling Dynamic scheduling Throughput signal processing algorithms Very large scale integration Memory management Costs Ear Computer architecture

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Automatic signal processing of front monitor radar for tunneling machines

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1997年第2期35卷 354-359页

作者： Sato, T Takeda, K Nagamatsu, T Wakayama, T Kimura, I Shinbo, T NIPPON TELEGRAPH & TEL PUBL CORP CHIBA 261 JAPAN MITSUBISHI HEAVY IND CO LTD CHIYODA KU TOKYO 100 JAPAN MITSUBISHI ELECTR CORP KAMAKURA KANAGAWA 247 JAPAN OSAKA INST TECHNOL DEPT INFORMAT SYST HIRAKATA OSAKA 57301 JAPAN KOMATSU CO LTD KANAGAWA 254 JAPAN

It is planned to install a front monitor impulse radar on the surface of the rotating drill of tunneling machines in order to detect obstacles such as casing pipes of vertical borings, The conventional aperture synthesis technique can no more be applied to such cases because the radar image of a pipe does not constitute a hyperbola as is the case for linear scanning radars. We have developed a special purpose signal processing algorithm with the aid of the discrete model fitting method, which can be used for any pattern of scanning, The details of the algorithm are presented together with the results of numerical simulations and test site experiments.

关键词： Synthetic aperture signal processing algorithms linear sweep impulse radar Pipe automatic signal signal processing radar Hyperbola MONITOR Radar images Automatic incident detection

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An Automatic Extrinsic Calibration Method for LiDAR-Camera Fusion via Combining Semantic and Geometric Features

An Automatic Extrinsic Calibration Method for LiDAR-Camera F...

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

作者： Minqian Wang Libo Weng Fei Gao College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Precise extrinsic calibration is one of the key techniques for LiDAR-camera fusion system. In current methods, the extrinsic calibration is usually not automatic. To address this, an automatic calibration method via combining semantic and geometric features is proposed, which is not dependent on any specific calibration object. First, extrinsics are automatically initialized; semantic objects are utilized to formulate the edge constraints and projection boundary constraints. Then, an efficient global optimization algorithm that synergizes the Jacobian matrix and the stochastic strategy of simulated annealing is put forward to calculate precise extrinsics. A feedback mechanism is designed to evaluate the reliability of the proposed method. Experiments on the KITTI dataset show that the proposed method achieves a rotation error of 0.14°and a translation error of 4.5cm, outperforming most current methods. Besides, the experiment on the proposed optimization algorithm is also conducted to verify its effectiveness and efficiency.

关键词： Jacobian matrices Translation Semantics signal processing algorithms Simulated annealing Reliability engineering Stability analysis Calibration Speech processing Optimization

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Enhancing Time Series Prediction with Evolutionary Algorithm-based Optimization of LSTM

Enhancing Time Series Prediction with Evolutionary Algorithm...

引用

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

作者： Jingyu Sun Hanting Zhang Jianfeng Wang School of Software Taiyuan University of Technology Taiyuan China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The architecture of a neural network serves as the foundation for deep learning models and plays a crucial role in efficient training and application. Designing high-performance neural network architectures typically requires extensive expertise, along with numerous trials and optimizations of hyperparameters and network structure parameters. This process demands significant time and effort. To automate the optimization of LSTM (Long Short-Term Memory), this paper combines evolutionary algorithms for LSTM neural network architecture search and hyperparameter tuning. Additionally, to prevent LSTM from getting stuck in local optima during backpropagation, particle swarm optimization is employed to initialize the weights of LSTM. Experimental results on real-world datasets demonstrate that the proposed algorithm can provide different neural network architectures tailored to different datasets, achieving competitive predictive capabilities.

关键词： Computational modeling Neural networks Time series analysis signal processing algorithms Computer architecture Predictive models Prediction algorithms Particle swarm optimization Long short term memory Optimization

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MIMO Channel as a Neural Function: Implicit Neural Representations for Extreme CSI Compression

MIMO Channel as a Neural Function: Implicit Neural Represent...

引用

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

作者： Haotian Wu Maojun Zhang Yulin Shao Krystian Mikolajczyk Deniz Gündüz Department of Electrical and Electronic Engineering Imperial College London Department of Information Science and Electronic Engineering Zhejiang University Department of Electrical and Computer Engineering University of Macau

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Acquiring and utilizing accurate channel state information (CSI) is crucial for realizing the benefits of massive multiple-input multiple-output (MIMO) technology. Current CSI feedback approaches improve precision by employing advanced deep-learning methods to learn representative CSI features for a subsequent compression process. Diverging from previous works, we treat the CSI compression problem in the context of implicit neural representations. Specifically, each CSI matrix is viewed as a neural function that maps the spatial coordinates (antenna and subchannel) to the corresponding channel gains with physical significance. Rather than transmitting the parameters of the specific neural functions directly, we send low-cost modulations of the CSI matrix, derived through a meta-learning algorithm. These modulations are then applied to a shared base network at the receiver to reconstruct the CSI matrix. Numerical results show that our proposed approach achieves state-of-the-art performance and showcases flexibility in feedback strategies.

关键词： Metalearning Measurement Modulation signal processing algorithms Receivers Antenna feeds Speech processing Channel state information MIMO communication Antennas

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An Intelligent signal processing Method for Motional Vital Signs Detection System Based on Deep Learning

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IEEE ACCESS 2022年 10卷 106463-106481页

作者： Liu, Siyun Qi, Qingjie Cheng, Huifeng Zhang, Jingwen Xian, Wenhao Ma, Tianfang Wang, Yue Liu, Yingjie Li, Dan Chai, Jiamei Chinese Inst Coal Sci CICS Emergency Res Inst Beijing 100013 Peoples R China

Detection of vital signs for motional human targets in complex environment has always been a major challenge in the field of remote detection, remote healthcare and emergency rescue, because polytropic and multimodal interferences make intelligent signal processing more difficult. In this paper, a systematic intelligent signal processing scheme which contains signal preprocessing, vital signs identification, motion trajectory estimation and respiratory signal and heartbeat signal extraction is established. Based on CNN (Convolutional Neural Networks) model, accurate identification of motional vital signs getting rid of the interference of harmonics and distortion can be realized. Then, the misidentified outliers are eliminated with K-means clustering algorithm. Next, the motion trajectory of human targets can be estimated with Kalman filtering algorithm. Finally, the SVD-EEMD algorithm is proposed for respiratory signal and heartbeat signal extraction of dynamic human targets. The introduction of deep learning algorithms makes the proposed method have good performance of high accuracy, good robustness, strong adaptability and high efficiency, which can be observed in actual detection tasks contrast experiments.

关键词： Ultra wideband radar Radar detection Radar signal processing algorithms Radar antennas Convolutional neural networks signal processing Deep learning Kalman filters Ultra wideband radar Intelligent signal processing vital signs detection deep learning convolutional neural networks (CNN) Kalman filtering SVD-EEMD ultra-wideband (UWB) radar

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