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Outperforming Sequential Full-Word Long Addition With Parallelization and Vectorization

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2022年第12期33卷 4974-4985页

作者： Chusov, Andrey Far Eastern Fed Univ Vladivostok 690922 Russia

The article presents algorithms for parallel and vectorized full-word addition of big unsigned integers with carry propagation. Because of the propagation, software parallelization and vectorization of non-polynomial addition of big integers have long been considered impractical due to data dependencies between digits of the operands. The presented algorithms are based upon parallel and vectorized detection of carry origins within elements of vector operands, masking bits which correspond to those elements and subsequent scalar addition of the resulting integers. The acquired bits can consequently be taken into account to adjust the sum using the proposed generalization of the Kogge-Stone method. Essentially, the article formalizes and experimentally verifies parallel and vectorized implementation of carry-lookahead adders applied at arbitrary granularity of data. This approach is noticeably beneficial for manycore, CUDA and vectorized implementation using AVX-512 with masked instructions. Experiments show that the parallel and vectorized implementations of the proposed algorithms can be multiple times faster compared to a sequential ripple-carry adder or adders based on redundant number systems such as one used in the GNU Multiple Precision library.

关键词： Adders Arithmetic signal processing algorithms Additives Complexity theory Energy efficiency Vector processors AVX-512 CUDA full-word addition Long arithmetic parallel arithmetic SIMD SMP vectorization

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An Input Weights Dependent Complex-Valued Learning Algorithm Based on Wirtinger Calculus

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第5期52卷 2920-2932页

作者： Pu, Yi-Fei Xie, Xuetao Cao, Jinde Chen, Hua Zhang, Kai Wang, Jian Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China China Univ Petr East China Coll Sci Qingdao 266580 Peoples R China China Univ Petr East China Coll Petr Engn Qingdao 266580 Peoples R China

Complex-valued neural network is a kind of learning model which can deal with problems in complex domain. Fully complex extreme learning machine (CELM) is a much faster training algorithm than the complex backpropagation (CBP) scheme. However, it is at the cost of using more hidden nodes to obtain the comparable performance. An upper-layer-solution-aware algorithm has been proposed for training single-hidden layer feedforward neural networks, which performs much better than its counterparts, pseudo-inverse learning (PIL)/extreme learning machine and gradient decent-based backpropagation neural networks. Consequently, there exist two challenges that need to be dealt with: 1) How to combine the advantages of CBP and CELM to develop a novel complex learning algorithm? and 2) What is the convergent behavior of the presented algorithm? In this article, an input weights dependent complex-valued (IWDCV) learning algorithm based on Wirtinger calculus has been proposed, which effectively solves the nonanalytic problem of the common activation functions during training neural networks. In addition, the monotonicity of the error function and the deterministic convergence of the proposed model have been strictly proved, which theoretically guarantee the efficiency and effectiveness of the given model, IWDCV. Finally, for real and complex-valued problems, a variety of simulations have been done to demonstrate the comparable performance of the proposed algorithm which support the theoretical observations as well.

关键词： Training Neural networks Calculus Mathematical model signal processing algorithms Backpropagation Petroleum Complex-valued backpropagation (BP) convergence extreme learning machine (ELM) pseudo-inverse learning (PIL) wirtinger calculus

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DOA Estimation for Heterogeneous Wideband Sources Based on Adaptive Space-Frequency Joint processing

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 1657-1672页

作者： Zhang, Jun Bao, Ming Zhang, Xiao-Ping Chen, Zhifei Yang, Jianhua Northwestern Polytech Univ Sch Automat Xian 710129 Peoples R China Chinese Acad Sci Inst Acoust Beijing 100190 Peoples R China Ryerson Univ Dept Elect Comp & Biomed Engn Toronto ON M5B 2K3 Canada

For direction-of-arrival (DOA) estimation of heterogeneous wideband sources, we propose a new adaptive space-frequency joint processing algorithm. The algorithm is implemented in the sparse Bayesian learning (SBL) DOA framework, which is named as ASF-SBL algorithm in this paper. The traditional SBL-based DOA methods suffer from the problem of erroneous DOA estimation due to the structural mismatch between the invariant prior and the sparse coefficients. To solve this problem, the ASF-SBL algorithm employs a new space-frequency correlation prior model that can be adaptively changed to fit heterogeneous DOA scenarios. Specifically, nine alternative space-frequency structural patterns are constructed to represent the joint space-frequency characteristics of spatial sparse signals. By evaluating the space-frequency correlation of the sparse coefficients updated in each iteration under SBL framework, a suitable pattern is selected from the nine choices to determine the adaptive prior of each coefficient. This adaptive method leads to accurate DOA estimation in different wideband sources scenarios. In addition, we introduce a distributed processing method to extend the ASF-SBL algorithm to two-dimensional DOA estimation. This extension is achieved by decoupling the DOA estimation into two one-dimensional estimations. The decoupling avoids the problems of a huge redundant dictionary and excessive computational complexity caused by the combination of azimuth and elevation angles. Numerical simulations show that the ASF-SBL algorithm is superior to existing algorithms in DOA estimation of heterogeneous sources.

关键词： Direction-of-arrival estimation Estimation Wideband signal processing algorithms Sparse matrices Correlation Clustering algorithms Direction-of-arrival sparse Bayesian learning space-frequency joint processing adaptive structural pattern prior ASF-SBL algorithm

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Characteristics of Peak and Cliff in Branch Length Similarity Entropy Profiles for Binary Time-Series and Their Application

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IEEE ACCESS 2022年 10卷 107259-107268页

作者： Lee, Sang-Hee Park, Cheol-Min Natl Inst Math Sci Div Ind Math Daejeon 34047 South Korea

A binary time series can be transformed into a Branch Length Similarity (BLS) entropy profile by being mapped to a circumference called a time-circle. In this study, we explored how peaks and cliffs are formed and how they relate to time series. Peaks and cliffs are defined as spike shapes in their entropy profile and are called peaks (or cliffs) when their shape is symmetric (or asymmetric). We found that when signal bands with different signal densities are in the same time series, peaks or cliffs are formed on the side of the band with lower signal density. In addition, we found that when the signal density is moderately high, the distribution of peaks and cliffs appears as a global increase-decrease tendency of the entropy profile. The tendency appeared as a barrier in the entropy profile of the image. As an application of our findings, we successfully detected specific patterns in binary images using peaks, cliffs and the barriers.

关键词： Entropy Time series analysis signal analysis Time measurement Behavioral sciences signal processing algorithms Heuristic algorithms Solid modeling Discrete transforms Classification data structure discrete transforms entropy shape detection signal analysis signal processing algorithms time-series analysis

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Multi-Dimensional Feature Combination Method for Continuous Blood Pressure Measurement Based on Wrist PPG Sensor

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第8期26卷 3708-3719页

作者： Yao, Pan Xue, Ning Yin, Siyuan You, Changhua Guo, Yusen Shi, Yi Liu, Tiezhu Yao, Lei Zhou, Jun Sun, Jianhai Dong, Cheng Liu, Chunxiu Zhao, Ming Chinese Acad Sci Aerosp Informat Res Inst AIR State Key Lab Transducer Technol Beijing 100094 Peoples R China Univ Chinese Acad Sci UCAS Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Univ Sci & Technol China USTC Sch Microelect Hefei 230026 Peoples R China Mtrix Technol Shanghai 201800 Peoples R China Shanghai Univ Sch Microelect Shanghai 200444 Peoples R China Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Ctr 7 Beijing 100700 Peoples R China Chinese Peoples Liberat Army Gen Hosp Med Ctr 1 Dept Neurosurg Beijing 100853 Peoples R China

The cuff-less blood pressure (BP) monitoring method based on photoplethysmo- gram (PPG) makes it possible for long-term BP monitoring to prevent and treat cardiovascular and cerebrovascular events. In this paper, a portable BP prediction system based on feature combination and artificial neural network (ANN) is implemented. The robustness of the model is improved from three aspects. Firstly, an adaptive peak extraction algorithm was used to improve the accuracy of peaks and troughs detection. Secondly, multi-dimensional features were extracted and fused, including three groups of PPG-based features and one group of demographics-based features. Finally, a two-layer feedforward artificial neural networks algorithm was used for regression. Thirty-three subjects distributed in the three BP groups were recruited. The proposed method passed the European Society of Hypertension International Protocol revision 2010 (ESP-IP2). Experimental results show that the proposed method exhibits good accuracy for a diverse population with an estimation error of -0.07 +/- 4.47 mmHg for SBP and 0.00 +/- 3.61 mmHg for DBP. Moreover, the model tracked the BP of two subjects for half a month, laying the foundation work for daily BP monitoring. This work will contribute to the long-term wellness management and rehabilitation process, enabling timely detection and improvement of the user's physical health.

关键词： Feature extraction Standards Monitoring signal processing algorithms Wrist Biomedical measurement Bioinformatics Artificial neural networks blood pressure feature fusion photoplethysmogram wellness management

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A Review of Millimeter Wave Device-Based Localization and Device-Free Sensing Technologies and Applications

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IEEE COMMUNICATIONS SURVEYS AND TUTORIALS 2022年第3期24卷 1708-1749页

作者： Shastri, Anish Valecha, Neharika Bashirov, Enver Tataria, Harsh Lentmaier, Michael Tufvesson, Fredrik Rossi, Michele Casari, Paolo Univ Trento Dept Informat Engn & Comp Sci I-38123 Povo Italy Lund Univ Dept Elect & Informat Technol S-22100 Lund Sweden Univ Padua Dept Informat Engn I-35131 Padua Italy Ericsson AB Dept Radio Syst Standardizat & Realizat S-22363 Lund Sweden

The commercial availability of low-cost millimeter-wave (mmWave) communication and radar devices is starting to improve the adoption of such technologies in consumer markets, paving the way for large-scale and dense deployments in fifth-generation (5G)-and-beyond as well as 6G networks. At the same time, pervasive mmWave access will enable device localization and device-free sensing with unprecedented accuracy, especially with respect to sub-6 GHz commercial-grade devices. This paper surveys the state of the art in device-based localization and device-free sensing using mmWave communication and radar devices, with a focus on indoor deployments. We overview key concepts about mmWave signal propagation and system design, detailing approaches, algorithms and applications for mmWave localization and sensing. Several dimensions are considered, including the main objectives, techniques, and performance of each work, whether they reached an implementation stage, and which hardware platforms or software tools were used. We analyze theoretical (including signal processing and machine learning), technological, and implementation (hardware and prototyping) aspects, exposing under-performing or missing techniques and items towards enabling a highly effective sensing of human parameters, such as position, movement, activity and vital signs. Among many interesting findings, we observe that device-based localization systems would greatly benefit from commercial-grade hardware that exposes channel state information, as well as from a better integration between standard-compliant mmWave initial access and localization algorithms, especially with multiple access points (APs). Moreover, more advanced algorithms requiring zero-initial knowledge of the environment would greatly help improve the adoption of mmWave simultaneous localization and mapping (SLAM). Machine learning (ML)-based algorithms are gaining momentum, but still require the collection of extensive training datasets, and

关键词： Location awareness Sensors Hardware signal processing algorithms Wireless communication Millimeter wave communication Wireless sensor networks Millimeter waves propagation characteristics channel models communications localization sensing radar practical constraints

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AI-Managed Cognitive Radio Digitizers

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IEEE CIRCUITS AND SYSTEMS MAGAZINE 2022年第1期22卷 10-39页

作者： de la Rosa, Jose M. Univ Seville CSIC IMSE CNM Inst Microelect Seville Parque Tecnol CartujaC Amer Vespuccio 28 Seville 41092 Spain

Embedding Artificial Intelligence (AI) in integrated circuits is one of the technology pillars of the so-called digital transformation. Nowadays, the vast majority of electronic devices benefits from digital signal processing to implement more and more functionalities, which can be further enhanced by the action of AI algorithms and artefacts. Moreover, as the analog/digital interfaces are moving closer and closer to the point where the information is either acquired or transmitted, the so-called AI-managed data converters are becoming key building blocks in an increasingly number of interconnected cyberphysical systems-made up of both software and hardware components. Software Defined Radio (SDR) and Cognitive Radio (CR) systems intended for 5G/6G communications are good examples which can benefit from an early digitization managed by AI engines. In this context, this paper presents an overview of circuits and systems techniques for Al-managed analog/digital interfaces with application in SDR/CR mobile telecom systems. Some design trends and challenges are discussed, going from new communications and computing paradigms for AIoT devices and networks, to digital-based/scaling-friendly analog circuit techniques for an efficient digitization. The state of the art on Analog-to-Digital Converters (ADCs) is surveyed, putting emphasis on highly-programmable Sigma-Delta Modulators (Sigma Delta Ms) as one of the best ADC candidates for SDR/CR transceivers. Some chip examples are shown to illustrate their potential application in Al-enhanced CR end-devices.

关键词： Radio frequency Wireless sensor networks Circuits and systems Market research Transceivers Sensors Embedded systems Artificial intelligence Radio communication Integrated circuits Software radio signal processing algorithms Sigma-delta modulation

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Vector-FRI Recovery of Multi-Sensor Measurements

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 4369-4380页

作者： Guo, Ruiming Li, Yongfei Blu, Thierry Zhao, Hangfang Chinese Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310007 Zhejiang Peoples R China Lab Ocean Observat Imaging Testbed Zhejiang Prov Zhoushan City 316021 Zhejiang Peoples R China Minist Educ Engn Res Ctr Ocean Sensing Technol & Equipment Zhoushan City 316021 Zhejiang Peoples R China Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou 310007 Zhejiang Peoples R China

Thanks to lowering costs, sensors of all kinds have increasingly been used in a wide variety of disciplines and fields, facilitating the rapid development of new technologies and applications. The information of interest (e.g. source location, refractive index, etc.) gets encoded in the measured sensor data, and the key problem is then to decode this information from the sensor measurements. In many cases, sensor data exhibit sparse features-"innovations"-that typically take the form of a finite sum of sinusoids. In practice, the robust retrieval of such encoded information from multi-sensors data (array or network) is difficult due to the non-uniformity of instrument precision and noise (i.e. different across sensors). This motivates the development of a joint sparse ("vector Finite Rate of Innovation") recovery strategy for multi-sensor data: by fitting the data to a joint parametric model, an accurate sparse recovery can be achieved, even if the noise of the sensors is non-homogenous and correlated. Although developed for one-dimensional sensor data, we show that our method is easily extended to multi-dimensional sensor measurements, e.g. direction-of-arrival data of 2D planar array and interference fringes of underwater acoustics, which provides a generic solution to these applications. A very robust and efficient algorithm is proposed, which we validate in various conditions (simulations, multiple types of real data).

关键词： Sensors Frequency estimation Noise measurement signal processing algorithms Sensor phenomena and characterization Acoustic measurements Sensor systems Vector finite-rate-of-innovation (FRI) multi-sensor measurements data fusion multi-dimensional sparse recovery high-resolution model-fitting

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Joint Activity Detection and Channel Estimation in Cell-Free Massive MIMO Networks With Massive Connectivity

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IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第1期70卷 317-331页

作者： Guo, Mangqing Gursoy, M. Cenk SUNY Syracuse Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

Cell-free massive MIMO is one of the key technologies for future wireless communications, in which users are simultaneously and jointly served by all access points (APs). In this paper, we investigate the minimum mean square error (MMSE) estimation of effective channel coefficients in cell-free massive MIMO systems with massive connectivity. To facilitate the theoretical analysis, only single measurement vector (SMV) based MMSE estimation is considered in this paper, i.e., the MMSE estimation is performed based on the received pilot signals at each AP separately. Inspired by the decoupling principle of replica symmetric postulated MMSE estimation of sparse signal vectors with independent and identically distributed (i.i.d.) non-zero components, we develop the corresponding decoupling principle for the SMV based MMSE estimation of sparse signal vectors with independent and non-identically distributed (i.n.i.d.) non-zero components, which plays a key role in the theoretical analysis of SMV based MMSE estimation of the effective channel coefficients in cell-free massive MIMO systems with massive connectivity. Subsequently, based on the obtained decoupling principle of MMSE estimation, likelihood ratio test and the optimal fusion rule, we perform user activity detection based on the received pilot signals at only one AP, or cooperation among the entire set of APs for centralized or distributed detection. Via theoretical analysis, we show that the error probabilities of both centralized and distributed detection tend to zero when the number of APs tends to infinity while the asymptotic ratio between the number of users and pilots is kept constant. We also investigate the asymptotic behavior of oracle estimation in cell-free massive MIMO systems with massive connectivity via random matrix theory. Moreover, in order to demonstrate the potential performance loss of SMV based MMSE estimation, which does not employ the correlation between the received pilot signals at differe

关键词： Estimation Massive MIMO Approximation algorithms signal processing algorithms Fading channels Mean square error methods Channel estimation MMSE estimation cell-free massive MIMO massive connectivity complex Bayesian approximate message passing activity detection likelihood ratio test

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Quantization for Communication-Efficient Change-Point Detection Over Networks

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 142-157页

作者： Jiao, Yuchen Xu, Xingyu Gu, Yuantao Tsinghua Univ Beijing Natl Res Ctr Informat Sci & Technol Beijing 100084 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Change-Point Detection (CPD) has been researched for a long time in statistical signal processing, and has been widely used in many applications such as monitoring and anomaly detection. When detecting over networks, existing algorithms require sensors to exchange information via transmitting full-precision variables, which requires large bandwidth and consumes a large amount of energy. To address these problems, we explore the idea of quantizing data with low precision without deteriorating the detection performance. Specifically, considering the strong autocorrelation of the transmitted sequence in time domain and the accumulation of quantization error due to the autocorrelation, we design a scheme that combines the differential quantization technique and the error feedback technique. Based on this scheme, we propose a communication-efficient decentralized CPD algorithm named Quantized CUSUM (Q-CUSUM), which only requires small bandwidth and has low energy consumption. We theoretically analyze two commonly-used detection criteria, namely Average Run Length (ARL) and Expected Detection Delay (EDD), and compute their theoretical bounds. It is proved that the detection performance is robust to the quantization error. The proposed quantization scheme can be easily extended to the partial-communication case, where at each time only a subset of sensor pairs are allowed to exchange information. Experiments are implemented on both synthetic and real-world datasets. The results indicate that the proposed quantization scheme can effectively reduce the communication cost without deteriorating the detection performance.

关键词： Quantization (signal) Sensors signal processing algorithms Bandwidth Detection algorithms Delays Protocols Change-point detection decentralized algorithms CUSUM differential quantization error feedback

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