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7th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2022

作者： Liu, Zhexu Zhao, Zhen College of Electronic Information and Automation Civil Aviation University of China Tianjin China

ISBN: (纸本)9781665487115

Data distribution service (DDS) has been widely used for the data communication of distributed computing system. But the existing DDS is based on the Simple Discovery Protocol (SDP). Its high network data transmission cannot meet the real-time requirement of the large-scale distributed computing. For this problem, this paper proposes a lightweight automatic discovery algorithm for DDS. In this algorithm, a new Bloom Filter with symbol is presented to compress the information transmitted between the distributed computing nodes, which is called Symbol Bloom Filter (SBF). Then it is combined with the SDP to reduce the consumption of the automatic discovery process in DDS. Experiments show that the proposed SDP_SBF can present a lower fault positive ratio while reducing data transmission, and therefore improves the real-time performance of DDS in the large-scale distributed computing. © 2022 IEEE.

关键词： Cloud computing Protocols Symbols Distributed databases filtering algorithms Information filters Real-time systems

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Research on Escaping the Big-Data Traps in O2O Service Recommendation Strategy

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IEEE TRANSACTIONS ON BIG DATA 2021年第1期7卷 199-213页

作者： Xiao Xue Shuai Huangfu Zhang, Lejun Wang, Shufang Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Henan Polytech Univ Sch Comp Sci & Technol Jiaozuo 454010 Henan Peoples R China Henan Polytech Univ Jiaozuo 454010 Henan Peoples R China Yangzhou Univ Sch Comp Sci & Technol Yangzhou Jiangsu Peoples R China Tianjin Normal Univ Tianjin Peoples R China

Internet business can be divided into two categories: pure online business and Online to Offline (O2O) business. Currently, the recommendation technology for online business is maturing, such as news, movies, products, and so forth. However, traditional recommendation technology can easily cause the overcrowding at some O2O services because of the big data traps. In the end, the users' experience with the O2O service recommendation is useless or very poor because they have to wait for a long time and can't enjoy the service immediately. Hence, how to improve the performance of O2O service recommendation has become a vital problem. To solve the problem, this paper proposes a research framework based on the continuous feedback learning mechanism between cyber layer and social layer. Then, the continuous feedback ideas are implemented in the design of the O2O service recommendation strategy step by step. Furthermore, the computational experiment system is constructed to perform performance analysis of these service strategies. The results show that our research framework is conductive to help O2O service recommendation to escape the big-data traps and to improve user experience.

关键词： Big Data Business Prediction algorithms Clustering algorithms Learning systems Collaboration filtering algorithms Online to offline continuous feedback learning service recommendation strategy computational experiment

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Accelerated 4PCS Algorithm for Point Cloud Registration 27

Accelerated 4PCS Algorithm for Point Cloud Registration

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27th International Computer Conference, Computer Society of Iran, CSICC 2022

作者： Pazoki, Fatemeh Mousavinia, Amir Abdoni, Fatemeh K. N. Toosi University Of Technology Electrical Engineering Department Tehran Iran

ISBN: (纸本)9781665480277

Point cloud registration is one of the most vital steps in 3D machine vision and photogrammetry. Most registration methods have low accuracy and speed, and 4PCS (Four Points Congruent Sets), one of the primary methods, is no exception. That is due to the vastness of the search space for the corresponding points, which usually yields a large number of candidate points. By down-sampling the input point cloud and then adding a new feature in the search step, this article presents a modified 4PCS algorithm that is much faster without reducing the accuracy. Simulation results show at least a 60-percentage increase in speed. © 2022 IEEE.

关键词： Point cloud compression Three-dimensional displays Simulation Object segmentation filtering algorithms Labeling

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Area-Delay-Power Efficient VLSI Architecture of FIR Filter for Processing Seismic Signal

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2021年第11期68卷 3451-3455页

作者： Bose, Sudipta De, Arijit Chakrabarti, Indrajit Indian Inst Technol Kharagpur Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

Low-complexity, high-speed and re-configurability are the primary requirements of the finite impulse response (FIR) filters employed for the processing of acquired seismic signal in real-time seismic-alert-system. The common sub-expression elimination (CSE) technique is employed widely to reduce the hardware complexity by minimizing the logic operators (LOs) and logic depths (LDs) in digital FIR filter. In this brief, a novel matrix grouped CSE (MCSE) algorithm has been proposed which outperforms the existing CSE algorithms in terms of LOs and LDs minimization. Moreover, a new half-unit biased (HUB) based rounding technique is incorporated in the proposed design to reduce the truncation error while maintaining low-complexity and the cut-set retiming technique is employed to reduce the critical-path-delay (CPD). Two hardware efficient FIR filter architectures (I and II) involving the proposed canonical signed digit (CSD) based MCSE algorithm, HUB rounding and cut-set retiming approach have been presented. Further, the architecture II represents a hardware efficient realization of a reconfigurable FIR filter. The hardware implementation of the architectures is performed on both FPGA and ASIC platforms. The hardware implementation of the proposed architecture I yields more than 25%, 49%, 38%, 36% and, 31% reduction in LOs, CPD, effective latency, area-delay-product (ADP) and, power-delay-product (PDP), respectively, over the state-of-the-art CSE based architectures. Whereas the reconfigurable architecture II exhibits nearly 48%, 12% and, 13% reduction in CPD, ADP and, PDP over the counterpart.

关键词： Finite impulse response filters Computer architecture Hardware Cascading style sheets Signal processing algorithms filtering algorithms Band-pass filters Finite impulse response (FIR) half-unit biased (HUB) canonical signed digit (CSD) common sub-expression elimination (CSE) seismic signal

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A Clothoid-based Local Trajectory Planner with Extended Kalman Filter 20

A Clothoid-based Local Trajectory Planner with Extended Kalm...

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20th IEEE Jubilee World Symposium on Applied Machine Intelligence and Informatics, SAMI 2022

作者： Igneczi, Gergo Horvath, Erno Széchenyi István University Vehicle Industry Research Center Győr Hungary

ISBN: (纸本)9781665497046

The paper introduces a local trajectory planner designed specifically for lateral guidance of autonomous vehicles. The inputs of the planner are the lane edges in the form of corner point coordinates in a two-dimensional plane. The aim of the planner is to provide a series of trajectory points ahead of the vehicle. The trajectory shall be well-conditioned which means no border violation (safety), no high lateral acceleration (comfort) and good tracking properties. The optimal conditions for driving have been found in using clothoid curves. The curvature of the clothoid is a linear function of the distance, which resolves the biggest disadvantage of circle conjunction: the discontinuity of the lateral acceleration. Clothoids have constant lateral jerk profile. In our work an Extended Kalman Filter is used with a clothoid model to consolidate inaccuracies of the lane detection system. The paper is presented as the first part of a research process. The algorithm introduced in this paper is planned to be continued with research on its automatized calibration procedures. © 2022 IEEE.

关键词： Lane detection filtering algorithms Trajectory Safety Calibration Kalman filters Informatics

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A Novel AD-PDA-BACF Algorithm for Real-Time Moving Target Shadow Tracking Using ViSAR Imagery

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Tian, Xiaoqing Liu, Jing Mallick, Mahendra Wang, Jing Ji, Xinyuan Su, Liyu Si, Jingxuan Xi An Jiao Tong Univ Fac Elect & Informat Engn Key Lab Intelligent & Network Secur Minist Educ Xian 710049 Peoples R China

Detection and tracking using video synthetic aperture radar (ViSAR) have attracted a great deal of attention in recent years due to their ability to produce high-resolution videos for regions of interest. In this work, we have chosen the background-aware correlation filter (BACF) as a key algorithm due to its superior performance in real-time tracking scenarios. Dim and small shadows with weak visual features, time-varying shadows, and complex environments pose serious challenges to the target tracking problem using ViSAR videos. These factors lead to a multipeak problem in the BACF and many other correlation filter-based algorithms. As a result, incorrect target detection, template model contamination, and tracker drift or failure during long-term tracking can occur. In this work, we propose a novel appearance-distance information-assisted probabilistic data association (AD-PDA) algorithm to tackle the multipeak problem. Based on the correlation outputs of the BACF, the AD-PDA algorithm selects multiple peak locations as validated measurements. By exploiting the appearance-distance probability distribution functions, the AD-PDA algorithm calculates the mixed appearance-distance association weights and estimates target states accurately. Furthermore, we propose an efficient AD-PDA-BACF algorithm that can track targets accurately by combining the AD-PDA and BACF algorithms. This study conducts experiments using two public ViSAR video datasets released by the Sandia National Laboratories, Albuquerque, NM, USA. Our results demonstrate that the proposed algorithm outperforms several state-of-the-art algorithms in tracking dim and small targets in terms of tracking accuracy, success rate, and tracking speed.

关键词： Target tracking Radar tracking Prediction algorithms Correlation Heuristic algorithms filtering algorithms Object detection Background-aware correlation filter (BACF) probabilistic data association (PDA) real-time target tracking video synthetic aperture radar (ViSAR)

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Convolutional Capsule Network Based in-Loop Filter for HEVC 23

Convolutional Capsule Network Based in-Loop Filter for HEVC

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23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Cao, Mengqing Yu, Yue Chen, Jian Yang, Xiuzhi Chen, Zhifeng Fuzhou University College of Physics and Information Engineering Fuzhou China

ISBN: (纸本)9781665494571

Recently, several convolutional neural network (CNN)-based in-loop filtering algorithms are proposed to improve the high efficiency video coding (HEVC). However, regular CNN-based filters can only apply a single model to the whole image, but a single model usually cannot adapt well to all local features in the image. To solve this problem, we propose an inloop filtering algorithm based on convolutional capsule network (CC-net), and adapt it into the HEVC hybrid video coding framework as a new in-loop filter. This article is the first to apply capsule to the filtering work of video encoding, which is proposed to use localized dynamic routing algorithm to improve the self-adaptability of the filter to different local features in the image, and we integrate the model into HEVC encoding loop. Experimentally, our proposed method brings average 7.9%, 5.4% and 4.1% BD-BR reductions under all intra, random access and low-delay P configurations, respectively, as well as, 0.4dB, 0.2dB and 0.2dB BD-PSNR gains respectively. © 2021 IEEE.

关键词： Convolutional codes Adaptation models Technological innovation Image coding Heuristic algorithms filtering algorithms Routing

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Visual Object Tracking: algorithms and Prospectives 3

Visual Object Tracking: Algorithms and Prospectives

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3rd International Conference on Computer Vision, Image and Deep Learning and International Conference on Computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Yuan, Jiehao Virginia Polytechnic Institute and State University College of Engineering Department of Computer Science BlacksburgVA24061 United States

ISBN: (纸本)9781665459112

Visual object tracking is the process of tracking a given target using the initial bounding box as a guide. It has a variety of applications: driverless driving, robotic interface, and so forth. To provide a brief introduction to visual object tracking, this paper gives an in-depth introduction to visual object tracking and summarizes five popular frameworks for this task: correlation filter, Siamese network, multi-domain network, and transformer-based trackers. The author hopes this paper will help beginners recognize visual object tracking and algorithms. Finally, this paper also provides a future perspective on developing the performance of visual trackers. © 2022 IEEE.

关键词： Visualization Target tracking Correlation filtering algorithms Transformers Object tracking Task analysis

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Adaptive Modified Unbiased Minimum-Variance Estimation for Highly Maneuvering Target Tracking With Model Mismatch

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： He, Shan Wu, Panlong Li, Xingxiu Bo, Yuming Yun, Peng Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Nanjing Univ Sci & Technol Sch Math & Stat Nanjing 210094 Peoples R China AVIC Leihua Elect Technol Res Inst Wuxi 214063 Peoples R China

Due to the complexity, time variability, and mutability of the noncooperative maneuvering target, the dynamic tracking model of the target does not match with the actual state, which reduces the tracking accuracy or even completely tracking loss. In this article, an expectation maximization-based adaptive modified unbiased minimum-variance estimation (EM-AMUMVE) algorithm is proposed for highly maneuvering target tracking with model mismatch. First, the virtual maneuvering noise and the first-order Markov process model are integrated to quantitatively describe the maneuvering acceleration according to the boundness of the maneuvering acceleration. Then, the maneuvering acceleration and state estimation are derived based on the unbiased minimum-variance criterion, and the expectation maximization (EM) is introduced in the updating procedure of the quadratic Bayesian filter to adaptively estimate the mean and covariance of virtual maneuvering noise and thus to improve the accuracy of maneuvering acceleration estimation. Finally, the optimality of the adaptive modified maneuvering acceleration estimation is theoretically proved, and the tracking scenarios of time-varying maneuvering target with model mismatch and reentry gliding trajectory of typical hypersonic vehicle hypersonic technology vehicle 2 (HTV-2) are built to demonstrate the validness, convergence, sensitivity, and engineering practicability of the proposed algorithm.

关键词： Target tracking Adaptation models Mathematical models filtering algorithms Aerodynamics Computational modeling State estimation Expectation maximization (EM) quadratic Bayesian filter Index Terms target tracking unbiased minimum variance virtual maneuvering noise

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Microphone Channel Frequency Response Calibration Using Circular Shifting and Spatio-Temporal Prediction

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2023年 72卷 1页

作者： Zhao, Zhao Wang, Ziyi Xu, Zhiyong Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China

Microphone channel frequency response calibration (MCFRC) can compensate for frequency response mismatches among channels and has attracted increasing attention in recent years, which is accompanied by booming applications of microphone arrays. State-of-the-art MCFRC techniques translate the frequency response calibration (FRC) into the optimization of calibration filter coefficients and have shown remarkable performance. However, a compromise between inter-microphone distance and passband bandwidth shrinkage still exists, which limits the wide application of those techniques to a great extent. To address this problem, an MCFRC method using circular shifting and spatio-temporal prediction is proposed in this article. First, all microphones from uncalibrated channels are deployed in a rotating symmetric arrangement, and circular shifting is conducted, making each channel experience all different predetermined positions. Then, for each channel, a spatio-temporal prediction technique is utilized to separately enhance those segments containing received calibration signals at different positions. Next, the summation of all enhanced segments for each channel is calculated, which can almost ensure that differences among microphone channel (MC) outputs are only caused by channel frequency response mismatches. Finally, the Newton algorithm is employed to design the calibration filter coefficients corresponding to each channel. Simulation results reveal that the proposed approach considerably outperforms existing calibration methods across various signal-to-noise ratio (SNR), reverberation time, and inter-microphone distance conditions. Meanwhile, the passband bandwidth shrinkage problem is well handled. Real-world experiments also verify its effectiveness.

关键词： Calibration Frequency response Microphone arrays Passband Bandwidth Wiener filters filtering algorithms Circular shifting frequency response calibration (FRC) microphone channel (MC) spatio-temporal prediction

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