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Explicitly Solvable Continuous-Time Inference for Partially Observed Markov Processes

IEEE TRANSACTIONS ON signal processing 2022年 70卷 6232-6242页

作者： Chen, Daniel Strang, Alexander G. Eckford, Andrew W. Thomas, Peter J. Case Western Reserve Univ Dept Math Appl Math & Stat Cleveland OH 44106 USA Case Western Reserve Univ Dept Elect Control & Syst Engn Cleveland OH 44106 USA Case Western Reserve Univ Dept Comp & Data Sci Cleveland OH 44106 USA Case Western Reserve Univ Dept Biol Cleveland OH 44106 USA Univ Chicago Dept Stat Chicago IL 60637 USA York Univ Dept Elect Engn & Comp Sci Toronto ON M3J 1P3 Canada

Many natural and engineered systems can be modeled as discrete state Markov processes. Often, only a subset of states are directly observable. Inferring the conditional probability that a system occupies a particular hidden state, given the partial observation, is a problem with broad application. In this paper, we introduce a continuous-time formulation of the sum-product algorithm, which is a well-known discrete-time method for finding the hidden states' conditional probabilities, given a set of finite, discrete-time observations. From our new formulation, we can explicitly solve for the conditional probability of occupying any state, given the transition rates and observations within a finite time window. We apply our algorithm to a realistic model of the cystic fibrosis transmembrane conductance regulator (CFTR) protein for exact inference of the conditional occupancy probability, given a finite time series of partial observations.

关键词： Markov processes signal processing algorithms Sum product algorithm Inference algorithms Hidden Markov models Biological system modeling Message passing Biomedical signal processing differential equations hidden markov models inference algorithms

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Sequential Change-Point Detection for High-Dimensional and Non-Euclidean Data

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 4498-4511页

作者： Chu, Lynna Chen, Hao Iowa State Univ Dept Stat Ames IA 50011 USA Univ Calif Davis Dept Stat Davis CA 95616 USA

In many applications, it is often of practical and scientific interest to detect anomaly events in a streaming sequence of high-dimensional or non-Euclidean observations. We study a non-parametric framework that utilizes nearest neighbor information among the observations to detect changes in an online setting. It can be applied to data in arbitrary dimension and non-Euclidean data as long as a similarity measure on the sample space can be defined. We consider new test statistics under this framework that can detect anomaly events more effectively than the existing test while keeping the false discovery rate controlled at a fixed level. Analytic formulas approximating the average run lengths of the new approaches are derived to make them fast applicable to modern datasets. Simulation studies are provided to support theoretical results. The proposed approach is illustrated with an analysis of the NYC taxi dataset.

关键词： Delays Indexes Streaming media signal processing algorithms Image edge detection Wearable sensors Surveillance Anomaly detection online change-point streaming data graph-based tests non-parametric

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Effective Tensor Completion via Element-Wise Weighted Low-Rank Tensor Train With Overlapping Ket Augmentation

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第11期32卷 7286-7300页

作者： Zhang, Yang Wang, Yao Han, Zhi Chen, Xi'ai Tang, Yandong Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Chinese Acad Sci Shenyang Inst Automat Inst Robot & Intelligent Mfg Shenyang 110016 Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China Xi An Jiao Tong Univ Ctr Intelligent Decis Making & Machine Learning Sch Mangement Xian 710049 Peoples R China

Tensor completion methods based on the tensor train (TT) have the issues of inaccurate weight assignment and ineffective tensor augmentation pre-processing. In this work, we propose a novel tensor completion approach via the element-wise weighted technique. Accordingly, a novel formulation for tensor completion and an effective optimization algorithm, called tensor completion by parallel weighted matrix factorization via tensor train (TWMac-TT), is proposed. In addition, we specifically consider the recovery quality of edge elements from adjacent blocks. Different from traditional reshaping and ket augmentation, we utilize a new tensor augmentation technique called overlapping ket augmentation, which can further avoid blocking artifacts. We then conduct extensive performance evaluations on synthetic data and several real image data sets. Our experimental results demonstrate that the proposed algorithm TWMac-TT outperforms several other competing tensor completion methods. The code is available at https://***/yzcv/ TWMac-TT-OKA

关键词： Tensors Matrix decomposition Task analysis Automation signal processing algorithms Optimization Mathematical models Tensor completion tensor train overlapping element-wise weighted ket augmentation

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The Diffusion Entropy Stochastic Gradient Descent Algorithm With Quasi-Optimal Combiners: Formulation and Analysis

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 3948-3963页

作者： Xia, Wei Xia, Guoqing Chen, Junbin Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Sichuan Peoples R China Univ Leicester Sch Engn Leicester LE1 7RH Leics England TP LINK Int Chengdu Co Ltd Chengdu 610000 Sichuan Peoples R China

We consider herein the diffusion entropy stochastic gradient descent (DE-SGD) algorithm for non-circular complex-valued signals. We theoretically analyze the mean stability and the steady-state network mean-square-deviation (MSD) of the DE-SGD. We develop both the quasi-optimal static and adaptive combination strategies to enhance the DE-SGD algorithm so as to utilize the spatial variations of the noise circularity coefficients, noise variances, the regressor powers and the step sizes across the entire network. We validate the proposed adaptive combiners theoretically and experimentally in small step size scenarios. We also demonstrate the consistency of the convergence and steady-state performance of both the static combiners and the proposed adaptive combiners. Illustrative simulations and real-world data evaluation validate the superior transient and steady-state performance of the DE-SGD algorithm with the proposed quasi-optimal combination strategies.

关键词： signal processing algorithms Entropy Steady-state Adaptive systems Adaptation models Convergence Stochastic processes Non-circular signals adaptive network distributed estimation Gaussian entropy adaptive combiners

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Robust 2-D DOA Estimation in a Polarization Sensitive Single User Environment

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IEEE ACCESS 2022年 10卷 115914-115929页

作者： Hasan, Md Imrul Saquib, Mohammad Univ Texas Dallas Dept Elect Engn Richardson TX 75080 USA

Apart from the conventional parameters (such as signal-to-noise ratio (SNR), array geometry and size, and the number of samples), several other factors (e.g., alignment of the antenna elements, polarization parameters) influence the performance of direction of arrival (DOA) estimation algorithms. When all the antenna elements are aligned in the same direction, the polarization parameters uniformly affect the steering vectors, which is the underlying assumption of almost all conventional DOA algorithms. Unfortunately, in this case, for a given set of DOA angles, there exists a range of polarization parameters resulting in a very low SNR across all the antenna elements in the array and vice versa. To avoid this type of unwanted event, different antenna elements must be aligned differently. However, this fact will make almost all commonly used DOA estimation algorithms inoperable since the steering vectors are contaminated unevenly by the polarization parameters. To the best of our knowledge, no work in the literature handles this issue using simple hardware and signal processing techniques even for a single user environment. In this paper, that line of inquiry is pursued. We consider a circular array with the minimum number (i.e., 4) of short dipole antenna elements and propose an antenna alignment scheme. This ensures that at any given point no more than one element will suffer significantly from low SNR due to the contribution of polarization. A thresholding technique to isolate the antenna element after being seriously contaminated by the polarization parameters is developed and analyzed. Two algorithms that are suited for operating reliably in all possible DOA and polarization environments are addressed. The first algorithm follows the working principle of the popular MUSIC algorithm after cleaning the polarization contributions from the non-signal subspace. The other one, which is found as a byproduct of the process of cleaning the non-signal subspace, can estima

关键词： Direction-of-arrival estimation signal-to-noise ratio Dipole antennas Antenna arrays signal processing algorithms Receiving antennas signal to noise ratio Dipole antennas Multiple signal classification Polarization DOA estimation polarization closed-form estimation MUSIC antenna alignment antenna mapping

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Meta-Material Sensor Based Internet of Things: Design, Optimization, and Implementation

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IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第8期70卷 5645-5662页

作者： Hu, Jingzhi Zhang, Hongliang Di, Boya Han, Zhu Poor, H. Vincent Song, Lingyang Peking Univ Sch Elect Beijing 100871 Peoples R China Princeton Univ Dept Elect Engn Princeton NJ 08540 USA Univ Houston Elect & Comp Engn Dept Houston TX 77004 USA Kyung Hee Univ Dept Comp Sci & Engn Seoul 446701 South Korea

For many applications envisioned for the Internet of Things (IoT), it is expected that the sensors will have very low costs and zero power, which can be satisfied by meta-material sensor based IoT, i.e., meta-IoT. As their constituent meta-materials can reflect wireless signals with environment-sensitive reflection coefficients, meta-IoT sensors can achieve simultaneous sensing and transmission without any active modulation. However, to maximize the sensing accuracy, the structures of meta-IoT sensors need to be optimized considering their joint influence on sensing and transmission, which is challenging due to the high computational complexity in evaluating the influence, especially given a large number of sensors. In this paper, we propose a joint sensing and transmission design method for meta-IoT systems with a large number of meta-IoT sensors, which can efficiently optimize the sensing accuracy of the system. Specifically, a computationally efficient received signal model is established to evaluate the joint influence of meta-material structure on sensing and transmission. Then, a sensing algorithm based on deep unsupervised learning is designed to obtain accurate sensing results in a robust manner. Experiments with a prototype verify that the system has a higher sensitivity and a longer transmission range compared to existing designs, and can sense environmental anomalies correctly within 2 meters.

关键词： Sensors Wireless sensor networks Wireless communication signal processing algorithms Computational modeling Antenna arrays 6G mobile communication Meta-materials Internet of Things passive sensors unsupervised learning

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ORTP: A Video SAR Imaging Algorithm Based on Low-Tubal-Rank Tensor Recovery

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2022年 15卷 1293-1308页

作者： Pu, Wei Wu, Junjie Huang, Yulin Yang, Jianyu UCL Dept Elect & Elect Engn London WC1E 6BT England Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

Video synthetic aperture radar (SAR) is attracting more and more attention because of its continuous imaging capability for ground scene of interest under any weather conditions and any time of the day. To reduce the sampling amount of video SAR, image processing can be formulated into a low-tubal-rank tensor recovery problem. In this article, we proposed an orthogonal rank-1 tensor pursuit (ORTP) algorithm to solve the low-tubal-rank tensor recovery problem in video SAR imaging. The proposed ORTP algorithm is an extension of the orthogonal rank-1 matrix pursuit algorithm in the matrix sensing problem from the matrix case to the tensor case under a tubal-rank model. It is capable of reconstructing the target tensor efficiently without requiring any prior information about the prespecified or pre-estimated tensor tubal-rank value. To achieve this, rank-1 basis tensors and weight tensors of the target tensor are estimated iteratively, and the residual error between the observed tensor and the estimated tensor through linear mapping is utilized as the stop condition. We theoretically prove the convergence and correctness of the proposed ORTP method. The methodology was tested on synthetic data, real video data, and video SAR data. These tests show that the proposed approach outperforms other video SAR imaging algorithms and low-rank tensor recovery algorithms.

关键词： Tensors Synthetic aperture radar Radar polarimetry signal processing algorithms Matching pursuit algorithms Streaming media Sparse matrices Low-tubal-rank tensor recovery orthogonal rank-1 tensor pursuit (ORTP) video synthetic aperture radar (SAR) imaging

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SEOFP-NET: Compression and Acceleration of Deep Neural Networks for Speech Enhancement Using Sign-Exponent-Only Floating-Points

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE processing 2022年 30卷 1016-1031页

作者： Lin, Yu-Chen Yu, Cheng Hsu, Yi-Te Fu, Szu-Wei Tsao, Yu Kuo, Tei-Wei Acad Sinica Res Ctr Informat Technol Innovat Taipei 11529 Taiwan Johns Hopkins Univ Dept Comp Sci Baltimore MD 21287 USA Natl Taiwan Univ Dept Comp Sci & Informat Engn Taipei 10617 Taiwan City Univ Hong Kong Coll Engn Kowloon Tong Hong Kong Peoples R China

Numerous compression and acceleration strategies have achieved outstanding results on classification tasks in various fields. Nevertheless, the same strategies may yield unsatisfactory performance on regression tasks because the nature between regression and classification tasks differs. In this paper, a novel sign-exponent-only floating-point network (SEOFP-NET) technique is proposed to compress the model size and accelerate the inference time for speech enhancement, a regression task of speech signal processing. The proposed method compressed the sizes of deep neural network (DNN)-based speech enhancement models by quantizing the fraction bits of single-precision floating-point parameters during training. Before inference implementation, all parameters in the trained SEOFP-NET model are adjusted to accelerate the inference time by replacing the floating-point multiplier with an integer-adder. The experimental results indicate that the size of SEOFP-NET models can be significantly compressed by up to 81.249% without noticeably downgrading their speech enhancement performance, and the inference time can be accelerated to 1.212x compared with the baseline models. The results also verify that SEOFP-NET can cooperate with other efficiency strategies to achieve a synergy effect for model compression. In addition, results of a just noticeable difference experiment show that the listeners cannot facilely differentiate between the enhanced speech signals processed by the baseline model and SEOFP-NET. To the best of our knowledge, this study is one of the first works that aims to compress the model size and reduce the inference time of speech enhancement while maintaining satisfactory performance. The promising results confirm the potential applicability of SEOFP-NET to lightweight embedded devices.

关键词： Speech enhancement Task analysis Noise reduction Computational modeling Inference algorithms Noise measurement signal processing algorithms Speech enhancement speech dereverberation deep neural network model compression inference acceleration floating-point integer arithmetic circuit

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Statistical-Mechanical Analysis of Adaptive Filter With Clipping Saturation-Type Nonlinearity

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 4867-4882页

作者： Miyoshi, Seiji Kansai Univ Fac Engn Sci Dept Elect Elect & Informat Engn Suita Osaka 5648680 Japan

In most practical adaptive signal processing systems, e.g., active noise control, active vibration control, and acoustic echo cancellation, substantial nonlinearities that cannot be neglected exist. In this paper, we analyze the behaviors of an adaptive system in which the output of the adaptive filter has the clipping saturation-type nonlinearity by a statistical-mechanical method. We discuss the dynamical and steady-state behaviors of the adaptive system by performing asymptotic analysis, steady-state analysis, and numerical calculation. As a result, it is clarified that the saturation value has a critical point at which the system's mean-square stability and instability switch. The obtained theory well explains the strange behaviors around the critical point observed in the computer simulation. Finally, the exact value of the critical point is also derived.

关键词： Adaptive systems Behavioral sciences signal processing algorithms Steady-state Analytical models Linearity Adaptive filters Adaptive filter adaptive signal processing system identification LMS algorithm clipping saturation-type nonlinearity piecewise linearity statistical-mechanical analysis

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Investigation of Seismic signal processing for Detection and Classification of Moving Unarmoured Vehicles

Investigation of Seismic Signal Processing for Detection and...

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Innovations in High Speed Communication and signal processing (IHCSP), International Conference on

作者： Neeta Nathani Udit Narayan Bera Siddharath Bhalerao Department of Electronics and Communication GGITS Jabalpur

The concept of automatically recognising targets has been actively researched for more than three decades at this point. Despite this, research is still being conducted because the technology has so many potential uses, including monitoring of traffic and perimeters, surveillance, and so on. Research work that focuses on the performance of detection and classification algorithms is in high demand. The current body of work in research investigates potential methods for enhancing the efficiency of detection and classification procedures. The frequency of the seismic signal shifts throughout the course of time, which means that the signal is non-stationary. Using this method, the non-stationary signals are evaluated. The time-frequency representation of these signals does, however, contain the cross-terms in question. In light of this, our working hypothesis for this study is that getting rid of these cross terms will result in an improvement in the performance of the detection algorithm. As a result, an improved method has been suggested, which performs better than STFT and thus verifies the hypothesis that was proposed.

关键词： Time-frequency analysis Technological innovation Target recognition Surveillance signal processing algorithms Object detection Traffic control

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