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Scalable Parallel Static Learning

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Scalable Parallel Static Learning

International Test Conference in Asia (ITC-Asia)

作者： Xiaoze Lin Liyang Lai Huawei Li The Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Electrical Engineering Shantou University Shantou Guangdong China Chinese Academy of Sciences Institute of Computing Technology Beijing China

ISBN: (纸本)9781665413350

Static learning is a learning algorithm for finding additional implicit implications between gates in a netlist. In automatic test pattern generation (ATPG) the learned implications help recognize conflicts and redundancies early, and thus greatly improve the performance of ATPG. Though ATPG can further benefit from multiple runs of incremental or dynamic learning, it is only feasible when the learning process is fast enough. In the paper, we study speeding up static learning through parallelization on heterogeneous computing platform, which includes multi-core microprocessors (CPUs), and graphics processing units (GPUs). We discuss the advantages and limitations in each of these architectures. With their specific features in mind, we propose two different parallelization strategies that are tailored to multi-core CPUs and GPUs. Speedup and performance scalability of the two proposed parallel algorithms are analyzed. As far as we know, this is the first time that parallel static learning is studied in the literature.

关键词： Scalability Microprocessors Redundancy Graphics processing units Computer architecture Automatic test pattern generation Heterogeneous networks

On a dualband rectangular dielectric resonator antenna for WLAN applications 5

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On a dualband rectangular dielectric resonator antenna for W...

5th IEEE International Conference on Computational Electromagnetics, ICCEM 2019

作者： Xiaosheng, Fang Shuangming, Chen Lingpeng, Weng Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Shantou University Shantou China

ISBN: (纸本)9781538671115

A slot-fed dualband rectangular dielectric resonator antenna (DRA) for WLAN applications is investigated. ANSYS HFSS was used to study the reflection coefficient, radiation patterns and antenna gain of the dualband DRA. The results show that it can provide two passbands in the ranges of 2.4-2.82 GHz and 5.07-5.54 GHz, which completely cover the 2.4-GHz/5.2-GHz WLAN bands. The proposed dualband DRA can provide a relatively larger high-to-low frequency ratio of ~2.1. © 2019 IEEE.

关键词： Microwave antennas

One to multiple mapping dual learning: Learning multiple sources from one mixed signal

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arXiv 2021年

作者： Liu, Ting Wang, Wenwu Zhang, Xiaofei Yan, Jingwen Guo, Yina The Department of Electronics and Communication Engineering Taiyuan University of Science and Technology Taiyuan030024 China The Centre for Vision Speech and Signal Processing University of Surrey Guildford SurreyGU2 7XH United Kingdom The Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Technology

Single channel blind source separation (SCBSS) refers to separate multiple sources from a mixed signal collected by a single sensor. The existing methods for SCBSS mainly focus on separating two sources and have weak generalization performance. To address these problems, an algorithm is proposed in this paper to separate multiple sources from a mixture by designing a parallel dual generative adversarial Network (PDualGAN) that can build the relationship between a mixture and the corresponding multiple sources to realize one-to-multiple cross-domain mapping. This algorithm can be applied to any mixed model such as linear instantaneous mixed model and convolutional mixed model. Besides, one-to-multiple datasets are created which including the mixtures and corresponding sources for this study. The experiment was carried out on four different datasets and tested with signals mixed in different proportions. Experimental results show that the proposed algorithm can achieve high performance in peak signal-to-noise ratio (PSNR) and correlation, which outperforms state-of-the-art algorithms. © 2021, CC BY.

关键词： Mapping

CrackCLF: Automatic Pavement Crack Detection based on Closed-Loop Feedback

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arXiv 2023年

作者： Li, Chong Fan, Zhun Chen, Ying Lin, Huibiao Moretti, Laura Loprencipe, Giuseppe Sheng, Weihua Wang, Kelvin C.P. The Key Lab of Digital Signal and Image Processing of Guangdong Province College of Engineering Shantou University Shantou515063 China The Department of Civil Construction and Environmental Engineering Sapienza University of Rome Rome00184 Italy The School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States The School of Civil and Environmental Engineering Oklahoma State University StillwaterOK74078 United States

Automatic pavement crack detection is an important task to ensure the functional performances of pavements during their service life. Inspired by deep learning (DL), the encoder-decoder framework is a powerful tool for crack detection. However, these models are usually open-loop (OL) systems that tend to treat thin cracks as the background. Meanwhile, these models can not automatically correct errors in the prediction, nor can it adapt to the changes of the environment to automatically extract and detect thin cracks. To tackle this problem, we embed closed-loop feedback (CLF) into the neural network so that the model could learn to correct errors on its own, based on generative adversarial networks (GAN). The resulting model is called CrackCLF and includes the front and back ends, i.e. segmentation and adversarial network. The front end with U-shape framework is employed to generate crack maps, and the back end with a multi-scale loss function is used to correct higher-order inconsistencies between labels and crack maps (generated by the front end) to address open-loop system issues. Empirical results show that the proposed CrackCLF outperforms others methods on three public datasets. Moreover, the proposed CLF can be defined as a plug and play module, which can be embedded into different neural network models to improve their performances. Copyright © 2023, The Authors. All rights reserved.

关键词： Generative adversarial networks

VG-Swarm: A Vision-based Gene Regulation Network for UAVs Swarm Behavior Emergence

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arXiv 2022年

作者： Cai, Yuwei Li, Huanlin Hong, Juncao Xu, Peng Cheng, Hui Zhu, Xiaomin Hu, Bingliang Hao, Zhifeng Fan, Zhun Key Lab of Digital Signal and Image Processing of Guangdong Province College of Engineering University of Shantou Guangdong China School of Computer Science and Engineering University of Sun Yat-Sen Guangdong China College of Systems Engineering National University of Defense Technology Hunan Changsha China Xi’an Institute of Optics and Precision Mechanics Shanxi China

Unmanned Aerial Vehicles (UAVs) dynamic encirclement is an emerging field with great potential. Researchers often get inspiration from biological systems, either from macro-world like fish schools or bird flocks etc, or from micro-world like gene regulatory networks (GRN). However, most swarm control algorithms rely on centralized control, global information acquisition, and communications among neighboring agents. In this work, we propose a distributed swarm control method based purely on vision and GRN without any direct communications, in which swarm agents of e.g. UAVs can generate an entrapping pattern to encircle an escaping target of UAV based purely on their installed omnidirectional vision sensors. A finite-state-machine (FSM) describing the behavioral model of each drone is also designed so that a swarm of drones can accomplish searching and entrapping of the target collectively in an integrated way. We verify the effectiveness and efficiency of the proposed method in various simulation and real-world experiments. Copyright © 2022, The Authors. All rights reserved.

关键词： Drones

Mitigation of Cross-modulation Effects in Radar Receivers with Memory

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Mitigation of Cross-modulation Effects in Radar Receivers wi...

IEEE International Conference on Radar

作者： Euan Ward Bernard Mulgrew Institute for Digital Communications Joint Research Institute in Signal and Image Processing School of Engineering and Electronics The University of Edinburgh Edinburgh UK

ISBN: (数字)9781728168135

ISBN: (纸本)9781728168142

Modern mass produced radar systems offer a wide variety of advantages over their bespoke predecessors however to achieve these benefits the linearity of their RF receivers is often sacrificed. This compromise leaves these modern sensors susceptible to unwanted cross-modulation effects and with the RF spectrum becoming increasingly crowded this problem is only going to become more pertinent. If mitigation techniques are to be developed they must correct for the nonlinear distortion applied to both the amplitude and phase of the returned radar signal. In order to accurately model nonlinear phase effects in the radar receiver we must consider memory. This paper studies how introducing nonlinear memory in the radar receiver affects the cross-modulation distortion generated. It also presents a simple cross-modulation mitigation technique developed in the communications literature and applies it to the radar cross-modulation problem. As a result the simple communications based mitigation technique successfully corrects for cross-modulation distortion generated from a memoryless nonlinear radar receiver. However, it is ineffective when nonlinear memory effects are introduced. This is a significant result as it suggests that if the nonlinearities generating cross-modulation effects in modern radar are found to have memory then sophisticated memory mitigation techniques will have to be developed.

关键词：

Flow Visualization for Complex Fluid Flows via A Structure-enhanced Motion Estimator

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Chen, Jun Wang, He Hao, Zhifeng Cai, Zemin Mei, Ling Foshan University School of Industrial Design and Ceramic Art Foshan528000 China Foshan University school of Mechatronic Engineering and Automation Foshan528231 China Shantou University College of Science Department of Mathematics Guangdong 515063 China Shantou University Department of Electronic Engineering Guangdong Shantou515063 China Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Techniques Guangdong Shantou515063 China Wuhan University of Science and Technology School of Information Science and Engineering 430081 China Huazhong University of Science and Technology School of Artificial Intelligence and Automation 430074 China Western Michigan University Department of Mechanical and Aerospace Engineering KalamazooMI49008 United States

Flow visualization through motion estimation using time-sequenced images plays a significant role in analyzing and understanding complex flow phenomena, and it is widely used in meteorology, oceanography, medicine, astronomy, experimental fluid mechanics, etc. However, it is difficult for current motion estimators to adapt to illumination changes, remove instable perturbation, and capture diverse motion patterns. In this paper, a novel flow visualization tool is developed to address these issues by employing a structure-enhanced motion estimator composed of a data term and a regularization term. Specifically, a statistical correlation descriptor is designed for the data term to improve the accuracy of motion estimation by enhancing both illumination robustness and matching discrimination. Inspired by the strong distinguishability of a structure-texture distribution in a local window, a structure-enhanced regularizer that considers the physical mechanism of fluid diffusion is introduced to capture different motion patterns, enhance prominent flow structures, and remove unnecessary ripples or textures caused by instable perturbation or noise. The experimental results demonstrate that our approach significantly outperforms current motion estimators in handling illumination changes and predicting complex fluid flows, and it also achieves state-of-the-art evaluation results on the public fluid flow datasets. Furthermore, the designed flow visualization tool successfully captures diverse motion patterns in Jupiter’s White Ovals, which is crucial for understanding the physical mechanisms behind their formation and sustenance. © 1991-2012 IEEE.

关键词： Motion estimation

Speech Stuttering Detection and Removal Using Deep Neural Networks

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Speech Stuttering Detection and Removal Using Deep Neural Ne...

作者： Shaswat Rajput Ruban Nersisson Alex Noel Joseph Raj A.Mary Mekala Olga Frolova Elena Lyakso School of Electrical Engineering Vellore Institute of Technology Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronic Engineering College of Engineering Shantou University School of Information Technology and Engineering Vellore Institute of Technology St.Petersburg State University

There are more than 70 million people worldwide who suffer from stuttering problems. This will affect the confidence of public speaking in people who suffer from this issue. To solve this problem many people take therapy sessions but the therapy sessions are a temporary solution, as soon as they leave therapy sessions this problem might arise again. This work aims to use state of the art machine learning algorithms that have improved over the past few years to solve this problem. We have used the dataset from UCLASS archives which provide the data for stuttered speech *** format with time-aligned transcriptions. We have tried different algorithms and optimized our model by hyper parameter tuning to maximize the model's accuracy. The algorithm is tested on random speech data with low to heavy stuttering from the same dataset, and it is observed that there is significant reduction in the Word Error Rate(WER) for most of the test cases.

关键词： Speech stuttering Classification Deep learning Word Error Rate Filtering

RHA-Net: An Encoder-Decoder Network with Residual Blocks and Hybrid Attention Mechanisms for Pavement Crack Segmentation

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arXiv 2022年

作者： Zhu, Guijie Fan, Zhun Liu, Jiacheng Yuan, Duan Ma, Peili Wang, Meihua Sheng, Weihua Wang, Kelvin C.P. College of Engineering Shantou University China Key Lab of Digital Signal and Image Processing of Guangdong Province Shantou515063 China College of Mathematics and Informatics South China Agricultural University Guangzhou501642 China School of Electrical and Computer Engineering Oklahoma State University StillwaterOK74078 United States School of Civil and Environmental Engineering Oklahoma State University StillwaterOK74078 United States

The acquisition and evaluation of pavement surface data play an essential role in pavement condition evaluation. In this paper, an efficient and effective end-to-end network for automatic pavement crack segmentation, called RHA-Net, is proposed to improve the pavement crack segmentation accuracy. The RHA-Net is built by integrating residual blocks (ResBlocks) and hybrid attention blocks into the encoder-decoder architecture. The ResBlocks are used to improve the ability of RHA-Net to extract high-level abstract features. The hybrid attention blocks are designed to fuse both low-level features and high-level features to help the model focus on correct channels and areas of cracks, thereby improving the feature presentation ability of RHA-Net. An image data set containing 789 pavement crack images collected by a self-designed mobile robot is constructed and used for training and evaluating the proposed model. Compared with other state-of-the-art networks, the proposed model achieves better performance and the functionalities of adding residual blocks and hybrid attention mechanisms are validated in a comprehensive ablation study. Additionally, a light-weighted version of the model generated by introducing depthwise separable convolution achieves better a performance and a much faster processing speed with 1/30 of the number of U-Net parameters. The developed system can segment pavement crack in real-time on an embedded device Jetson TX2 (25 FPS). The video taken in real-time experiments is released at https://***/3XIogk0fiG4. Copyright © 2022, The Authors. All rights reserved.

关键词： Convolution