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Fast Search and Efficient Placement Algorithm for Reconfigurable Tasks on Modern Heterogeneous FPGAs

IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2022年第4期30卷 474-487页

作者： Yao, Rui Zhao, Yinhua Yu, Yongchuan Zhao, Yihe Zhong, Xueyan Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 211106 Jiangsu Peoples R China Nanjing Inst Railway Technol Nanjing 210031 Jiangsu Peoples R China

To date, only a tiny fraction of reconfigurable task placement algorithms is targeted at modern heterogeneous field-programmable gate array (FPGA) architecture, and they often focus on determining the final placement location and pursuing placement quality. Hence, their real-time performance is poor because feasible location searching and placement speed are rarely taken into consideration. In this article, we propose a fast search strategy based on characteristic target gene sequence (CTGS) and an efficient placement algorithm called prioritization-based minimum cost and marginal compact (P2MC). CTGS ascertains tasks' feasible locations quickly by regarding the relatively few heterogeneous resources on FPGAs as search targets. P2MC first introduces prioritization heuristics based on task characteristics (PHTC) to presort tasks in order to improve the placement success rate and then select the final location according to the principle of minimum cost and marginal compact (2MC) so as to reduce the fragmentation of free space. The proposed algorithms are verified and evaluated on Xilinx's mainstream FPGA families Virtex-5/6/7. Results show that CTGS can accelerate the search speed of tasks' feasible locations by about four to five times, and P2MC can further balance placement speed and success rate. Compared with state-of-the-art heterogeneous task placement algorithms, P2MC can either increase both placement speed and success rate (by about 29% and 4.5%, respectively) or significantly increase the placement speed (by 20 times) at the expense of a bit of placement success rate (by only 5.8%).

关键词： Task analysis Field programmable gate arrays Resource management Costs signal processing algorithms Heuristic algorithms Very large scale integration Field-programmable gate array (FPGA) reconfigurable architecture reconfigurable task task placement algorithm

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A Hybrid Recursive Implementation of Broad Learning With Incremental Features

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第4期33卷 1650-1662页

作者： Liu, Di Baldi, Simone Yu, Wenwu Chen, C. L. Philip Southeast Univ Sch Cyber Sci & Engn Nanjing 210096 Peoples R China Southeast Univ Sch Math Nanjing 210096 Peoples R China Delft Univ Technol Delft Ctr Syst & Control TU Delft NL-2628 CD Delft Netherlands South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China

The broad learning system (BLS) paradigm has recently emerged as a computationally efficient approach to supervised learning. Its efficiency arises from a learning mechanism based on the method of least-squares. However, the need for storing and inverting large matrices can put the efficiency of such mechanism at risk in big-data scenarios. In this work, we propose a new implementation of BLS in which the need for storing and inverting large matrices is avoided. The distinguishing features of the designed learning mechanism are as follows: 1) the training process can balance between efficient usage of memory and required iterations (hybrid recursive learning) and 2) retraining is avoided when the network is expanded (incremental learning). It is shown that, while the proposed framework is equivalent to the standard BLS in terms of trained network weights,much larger networks than the standard BLS can be smoothly trained by the proposed solution, projecting BLS toward the big-data frontier.

关键词： Training Standards Neural networks Supervised learning signal processing algorithms Prediction algorithms Network architecture Big data broad learning system (BLS) recursive learning training time

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An Enhanced Lattice Algorithm for Range Estimation Using Noisy Measurement With Phase Ambiguity

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IEEE TRANSACTIONS ON signal processing 2022年第4期70卷 890-902页

作者： Li, Wenchao Wang, Xuezhi Moran, Bill RMIT Univ Sch Sci Melbourne Vic 3001 Australia Univ Melbourne Dept Elect & Elect Engn Melbourne Vic 3010 Australia

The problem of range estimation based on noisy measurements with phase ambiguity can be solved using signals across multiple frequencies by existing algorithms based on either the Chinese Remainder Theorem or lattice theory. The performances of these algorithms are constrained by the trade-off between SNR of measurements and required estimation accuracy. In this paper, we firstly propose an algorithm to find a set of wavelengths satisfying the conditions to apply the closed-form lattice algorithm from a given interval. Then we propose an enhanced lattice algorithm for range estimation based on existing lattice approaches. In the conventional lattice algorithm, the selection of an integer vector by the estimator often fails to be the ground truth because of noise. As a result, the true value can be a neighbouring lattice point of the estimated point where neighbours are defined in terms of a distance depending on the noise characteristics. In this paper, all neighbouring lattice points of the estimated point obtained by a conventional lattice algorithm are regarded as candidates for the new estimate. Then an efficient estimator is presented to choose one of those candidates to be the new estimate. The performance of the proposed algorithm, i.e. the successful probability of reconstruction, is derived mathematically. Additionally, a computation-reduced algorithm is presented to reduce the overhead of computation. The calculated probability and simulation results demonstrate the efficiency of the proposed algorithm.

关键词： Lattices signal processing algorithms Estimation Wavelength measurement Phase measurement Cathode ray tubes Noise measurement Lattice theory phase ambiguity phase unwrapping

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Calibration and Adjustment Algorithm to Attenuate the Effect of Arm Orientation on an sEMG-Based Muscle Fatigue Indicator

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IEEE SENSORS JOURNAL 2022年第19期22卷 18793-18803页

作者： Boyer, Marianne Bouyer, Laurent J. Roy, Jean-Sebastien Campeau-Lecours, Alexandre Univ Laval Dept Mech Engn Quebec City PQ G1V 0A6 Canada CIUSSS Capitale Natl Ctr Interdisciplinary Res Rehabil & Social Integ Quebec City PQ G1M 2S8 Canada Univ Laval Dept Rehabil Quebec City PQ G1V 0A6 Canada

A limit in the development of preventive interventions for musculoskeletal disorders (MSDs) of the upper limb is the lack of devices that can measure and process surface electromyography (sEMG) signals in order to provide real-time, reliable information on muscular fatigue in relation to the physical demands of the work being carried out. To keep track of the muscle fatigue level, the chosen fatigue indicator (FI) must not be sensitive to arm orientation (i.e., elevation angle and plane). The objective of this article is, therefore, to develop a calibration and adjustment algorithm to provide an FI that is not sensitive to arm orientation. First, the effect of arm orientation on our former FI indicator was verified by comparing initial (beginning of the task) FIs (IFIs) for static contractions in different arm elevation planes and angles. The results confirmed the hypothesis. A personalized calibration and FI adjustment based on current arm orientation were then proposed. The adjustment module's efficiency was first verified for different elevation planes and elevation angles separately. Using paired t-tests, we compared the IFI deviations from the baseline with and without the adjustment module. It was shown that the proposed method significantly reduces the IFI deviations from the baseline for both the elevation plane ( p = 0.005, d = 1.220) and the elevation angle ( p = 0.002, d = 1.455). Finally, a combined adjustment is presented for both the elevation plane and angle at the same time. The IFI deviations from the baseline were statistically significantly lower with the adjustment module than without it ( p < 0.001, d = 1.461).

关键词： Adaptive signal processing biomedical electronics biomedical signal processing electromyography muscle fatigue real-time systems signal processing algorithms

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Feature Selection Method for Nonintrusive Load Monitoring With Balanced Redundancy and Relevancy

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第1期58卷 163-172页

作者： Bao, Sheng Zhang, Li Han, Xueshan Li, Wensheng Sun, Donglei Ren, Yijing Liu, Ningning Yang, Ming Zhang, Boyi Shandong Univ Key Lab Power Syst Intelligent Dispatch & Control Minist Educ Jinan 250100 Peoples R China State Grid Shandong Elect Power Co Econ & Technol Res Inst Jinan 250001 Peoples R China

Nonintrusive load monitoring (NILM) has become a key technology in the power Internet of Things as well as an important information source for load characteristics analysis. Whether the selected features are appropriate determines the effectiveness of the load identification in NILM. In order to reduce the redundancy and improve the relevancy of the selected features, a feature selection method that balances redundancy and relevancy is proposed. Based on the approximate Markov blanket decision, this article puts forward the basis of feature set redundancy elimination. This is used to determine the priority of feature selection by taking the number of features preselection as a measure. The mutual information method is combined with the CRITIC weight to implement redundancy elimination for the initial feature set of the load, and the feature correlation ranking algorithm is established based on the Relief-F method. Based on the idea of balancing redundancy and relevancy, a feature selection strategy is established for the initial feature set, in order to obtain the optimal feature subset with minimum redundancy and maximum relevancy. Finally, the K-nearest neighbor identification algorithm after k-value optimization is used to simulate the proposed method. The results show that compared with the other feature selection methods, the proposed method is promising in terms of robustness and generalization and shows an active effect on improving identification accuracy.

关键词： Feature extraction Redundancy Transient analysis signal processing algorithms Steady-state Reactive power Mutual information Feature selection mutual information nonintrusive load monitoring (NILM) redundancy relevancy Relief-F

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QCQP With Extra Constant Modulus Constraints: Theory and Application to SINR Constrained Mmwave Hybrid Beamforming

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 5237-5250页

作者： He, Xin Wang, Jiangzhou Shantou Univ Coll Elect & Informat Engn Shantou 515063 Peoples R China Univ Kent Sch Engn Canterbury CT2 7NZ Kent England

The constant modulus constraint is widely used in analog beamforming, hybrid beamforming, intelligent reflecting surface design, and radar waveform design. The quadratically constrained quadratic programming (QCQP) problem is also widely used in signal processing. However, the QCQP with extra constant modulus constraints was not systematically studied in mathematic programming and signal processing. For example, the multiple quality of service (QoS) constrained analog beamforming is rare, while the QoS constrained digital beamforming methods are abundant. We propose to tackle the QCQP with extra constant modulus constraints problem by solving a series of subproblems with linear programming (LP) under extra constant modulus constraints. Under mild condition, the strong duality between the LP with extra constant modulus constraints and its dual problem is established. Then, by using the optimal solutions from the subproblems, the QCQP with extra constant modulus constraints problem is solved with a monotonically converged algorithm, and all converged solutions are Karush-Kuhn-Tucker (KKT) points. As an application of the proposed method, the signal-to-interference-plus-noise-ratio (SINR) constrained hybrid beamforming is solved. Simulation results show that the transmit power of the proposed method is similar to that of the semidefinite relaxation (SDR) method, while the computational time of proposed method is much smaller than that of the SDR method. The proposed method always provides feasible solution, while the SDR method does not always provide feasible solution.

关键词： Array signal processing Quality of service signal to noise ratio Interference Linear programming signal processing algorithms Radar QCQP LP constant modulus unit modulus MU-MIMO MSE SINR hybrid beamforming analog beamforming

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Algorithm-Driven Advances for Scientific CT Instruments: From model-based to deep learning-based approaches

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IEEE signal processing MAGAZINE 2022年第1期39卷 32-43页

作者： Venkatakrishnan, S. V. Mohan, K. Aditya Ziabari, Amir Koushyar Bouman, Charles A. Oak Ridge Natl Lab Multimodal Sensor Analyt Grp Oak Ridge TN 37831 USA Lawrence Livermore Natl Lab Computat Engn Div Livermore CA 94551 USA Purdue Univ Dept ECE W Lafayette IN 47907 USA Purdue Univ Sch Elect & Comp Engn W Lafayette PA 47907 USA

Multiscale 3D characterization is widely used by materials scientists to further their understanding of the relationships between microscopic structure and macroscopic function. Scientific computed tomography (SCT) instruments are one of the most popular choices for 3D nondestructive characterization of materials at length scales ranging from the angstrom scale to the micron scale. These instruments typically have a source of radiation (such as electrons, X-rays, or neutrons) that interacts with the sample to be studied and a detector assembly to capture the result of this interaction (see Figure 1). A collection of such high-resolution measurements is made by reorienting the sample, which is mounted on a specially designed stage/holder after which reconstruction algorithms are used to produce the final 3D volume of interest. The specific choice of which instrument to use depends on the desired resolution and properties of the materials being imaged. The end goal of SCT scans includes determining the morphology, chemical composition, or dynamic behavior of materials when subjected to external stimuli. In summary, SCT instruments are powerful tools that enable 3D characterization across multiple length scales and play a critical role in furthering the understanding of the structure-function relationships of different materials.

关键词： Three-dimensional displays Instruments Computed tomography Volume measurement signal processing algorithms X-rays Reconstruction algorithms

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Memory-Efficient Convex Optimization for Self-Dictionary Separable Nonnegative Matrix Factorization: A Frank-Wolfe Approach

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 3221-3236页

作者： Nguyen, Tri Fu, Xiao Wu, Ruiyuan Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA Chinese Univ Hong Kong Shatin Dept Elect Engn Shatin Hong Kong Peoples R China

Nonnegative matrix factorization (NMF) often relies on the separability condition for tractable algorithm design. Separability-based NMF is mainly handled by two types of approaches, namely, greedy pursuit and convex programming. A notable convex NMF formulation is the so-called self-dictionary multiple measurement vectors (SD-MMV), which can work without knowing the matrix rank a priori, and is arguably more resilient to error propagation relative to greedy pursuit. However, convex SD-MMV renders a large memory cost that scales quadratically with the problem size. This memory challenge has been around for a decade, and a major obstacle for applying convex SD-MMV to big data analytics. This work proposes a memory-efficient algorithm for convex SD-MMV. Our algorithm capitalizes on the special update rules of a classic algorithm from the 1950s, namely, the Frank-Wolfe (FW) algorithm. It is shown that, under reasonable conditions, the FW algorithm solves the noisy SD-MMV problem with a memory cost that grows linearly with the amount of data. To handle noisier scenarios, a smoothed group sparsity regularizer is proposed to improve robustness while maintaining the low memory footprint with guarantees. The proposed approach presents the first linear memory complexity algorithmic framework for convex SD-MMV based NMF. The method is tested over a couple of unsupervised learning tasks, i.e., text mining and community detection, to showcase its effectiveness and memory efficiency.

关键词： signal processing algorithms Noise measurement Task analysis Costs Convex functions Noise robustness Hyperspectral imaging Unsupervised multimodal analysis sample complexity identifiability

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Accurate Detection of Bearing Faults Using Difference Visibility Graph and Bi-Directional Long Short-Term Memory Network Classifier

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第4期58卷 4542-4551页

作者： Roy, Sayanjit Singha Chatterjee, Soumya Roy, Saptarshi Bamane, Pradip Paramane, Ashish Rao, U. Mohan Nazir, Muhammad Tariq NIT Silchar Elect Engn Dept Silchar 788010 Assam India Birla Inst Technol Elect & Elect Engn Dept Ranchi 835215 Bihar India MirMadan Mohanlal Govt Polytech Elect & Engn Dept Palasi 741156 India Univ Quebec Chicoutimi Dept Appl Sci Chicoutimi PQ G7H 2B1 Canada Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia

This article proposes a novel bearing fault detection framework for the real-time condition monitoring of induction motors based on difference visibility graph (DVG) theory. In this regard, the vibration signals of healthy as well as different rolling bearing defects were acquired from both fan-end and drive-end accelerometers. These data were recorded for three different bearing defects and under four loading conditions. The acquired vibration time series were converted to a topological network using DVG. From the transformed vibration data in the graph domain, degree distribution (DD) was selected as feature to discriminate different fault networks. Using analysis of variance test and false discovery rate correction, most discriminative DD features were selected. These features were subsequently fed as inputs to a deep learning model, i.e., a bidirectional long short-term memory network classifier for fault classification. In this study, 112 classification problems have been addressed, and for all of them, the proposed approach delivered very high fault detection accuracy. Finally, the classification performance of the proposed framework is compared with other well-known deep-learning classifiers all of which delivered satisfactory results.

关键词： Vibrations Time series analysis Rolling bearings Feature extraction signal processing algorithms Convolutional neural networks Induction motors Classification fault diagnosis graph theory induction motors (IMs) long short-term memory (LSTM) neural network

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Multiple Object Trajectory Estimation Using Backward Simulation

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IEEE TRANSACTIONS ON signal processing 2022年 70卷 3249-3263页

作者： Xia, Yuxuan Svensson, Lennart Garcia-Fernandez, Angel F. Williams, Jason L. Svensson, Daniel Granstrom, Karl Chalmers Univ Technol Dept Elect Engn S-41296 Gothenburg Sweden Univ Liverpool Dept Elect Engn & Elect Liverpool Merseyside England Univ Antonio Nebrija ARIES Res Ctr L69 3BX Madrid Spain Commonwealth Sci & Ind Res Org Brisbane Qld 4069 Australia NVIDIA Corp S-41755 Gothenburg Sweden Embark Trucks Inc San Francisco CA 94107 USA

This paper presents a general solution for computing the multi-object posterior for sets of trajectories from a sequence of multi-object (unlabelled) filtering densities and a multi-object dynamic model. Importantly, the proposed solution opens an avenue of trajectory estimation possibilities for multi-object filters that do not explicitly estimate trajectories. In this paper, we first derive a general multi-trajectory backward smoothing equation based on random finite sets of trajectories. Then we show how to sample sets of trajectories using backward simulation for Poisson multi-Bernoulli filtering densities, and develop a tractable implementation based on ranked assignment. The performance of the resulting multi-trajectory particle smoothers is evaluated in a simulation study, and the results demonstrate that they have superior performance in comparison to several state-of-the-art multi-object filters and smoothers.

关键词： Trajectory Mathematical models Smoothing methods signal processing algorithms Probabilistic logic Electrical engineering Density measurement Multi-object tracking random finite sets sets of trajectories forward-backward smoothing backward simulation

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