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arXiv 2023年

作者： Liu, Zhilong Zhang, Jiayi Liu, Ziheng Du, Hongyang Wang, Zhe Niyato, Dusit Guizani, Mohsen Ai, Bo The School of Electronics and Information Engineering The Frontiers Science Center for Smart High-Speed Railway System Beijing Jiaotong University China The School of Computer Science and Engineering Nanyang Technological University Singapore Mohamed Bin Zayed University of Artificial Intelligence United Arab Emirates

Cell-free massive multiple-input multiple-output (mMIMO) and extremely large-scale MIMO (XL-MIMO) are regarded as promising innovations for the forthcoming generation of wireless communication systems. Their significant advantages in augmenting the number of degrees of freedom have garnered considerable interest. In this article, we first review the essential opportunities and challenges induced by XL-MIMO systems. We then propose the enhanced paradigm of cell-free XL-MIMO, which incorporates multi-agent reinforcement learning (MARL) to provide a distributed strategy for tackling the problem of high-dimension signal processing and costly energy consumption. Based on the unique near-field characteristics in XL-MIMO systems, we propose two categories of the low-complexity algorithm design, i.e., antenna selection and power control, to adapt to different cell-free XL-MIMO scenarios and meet the increasing data rate requirement. For inspiration, several critical future research directions pertaining to green cell-free XL-MIMO systems are presented. Copyright © 2023, The Authors. All rights reserved.

关键词： MIMO systems

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Design of a 28nm CMOS Self-Biased Ring Oscillator for Intrinsically Robust PVT TRNG

Design of a 28nm CMOS Self-Biased Ring Oscillator for Intrin...

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Ph.D. Research in Microelectronics and Electronics (PRIME)

作者： João Cabacinho João Casaleiro Luís Oliveira Dept. of Electrical and Computer Engineering NOVA School of Science and Technology Caparica Portugal Center for Technologies and System (CTS) - UNINOVA Dept. Electrical Telecommunications and Computers Engineering ISEL Lisboa Portugal

In this paper we present a dynamic entropy generator based on the oscillator jitter, which uses a self-biasing technique to minimize process, voltage, and temperature (PVT) variations. The proposed circuit has the advantage of its intrinsic robustness due to self-biasing design, which eliminates the need of additional calibration steps and simplifies the circuit topology, with a negligible impact in area and power consumption. A frequency deviation below 15 % and a maximum jitter difference of 32 % under PVT variations, is obtained, in a 28 nm CMOS technology circuit prototype. For validation of the proposed circuit technique the IC prototype is compared with a standard ring oscillator and with traditional calibration techniques.

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A Development of Time-Varying Weight Model Predictive Control for Autonomous Vehicles

A Development of Time-Varying Weight Model Predictive Contro...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Qazani, Mohammad Reza Chalak Asadi, Houshyar Shajari, Arian Najdovski, Zoran Lim, Chee Peng Nahavandi, Saeid Murray State University Murray Department of the Computer Science and Information Systems MurrayKY42071 United States Institute for Intelligent System Research and Innovation Deakin University GeelongVIC43216 Australia Swinburne University of Technology HawthornVIC Australia Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA United States

ISBN: (纸本)9798350337020

Autonomous vehicles, commonly known as self-driving cars, are rapidly gaining popularity due to their numerous advantages, such as reducing traffic, pollution, and emissions while increasing safety, convenience, and transportation connectivity. In order to accurately track the motion signal, these vehicles are now utilising advanced control techniques, such as model predictive control (MPC). However, the efficiency of MPCs heavily relies on properly tuning their weights. The primary function of the MPC is to recalculate the optimal values for the vehicle control commands, such as desired speed, steering angle, etc., while considering the dynamic model of the autonomous vehicle. The existing linear MPC models cannot reach higher efficiency because of using fixed weights without considering the error. This paper introduces a novel approach for developing an MPC model with a time-varying weights algorithm for autonomous vehicles. The study aims to minimise motion tracking errors such as lateral position and yaw angle errors. Relevant MPC weights are calculated online using fuzzy logic-based units considering the lateral position and yaw angle errors. The proposed linear time-varying MPC was designed and developed using MATLAB software, resulting in improved motion tracking performance with 31.62% and 20.89% reduction of the root means square error of lateral position and yaw angle. © 2023 IEEE.

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Analysis of Financial Risk Behavior Prediction Using Deep Learning and Big Data Algorithms

arXiv

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arXiv 2024年

作者： Yang, Haowei Cheng, Zhan Zhang, Zhaoyang Luo, Yuanshuai Huang, Shuaishuai Xiang, Ao University of Houston Cullen College of Engineering Industrial Engineering Houston United States University of California Irvine Donald Bren School of Information & Computer Science Master of Computer Science Irvine United States University of California San Diego Computational Science San Diego United States Southwest Jiaotong University School of Computing and Artificial Intelligence Computer Science and Technology Chengdu China University of Science and Technology of China Department of Software Software system design Hefei China Northern Arizona University Information Security and Assurance AZ United States

As the complexity and dynamism of financial markets continue to grow, traditional financial risk prediction methods increasingly struggle to handle large datasets and intricate behavior patterns. This paper explores the feasibility and effectiveness of using deep learning and big data algorithms for financial risk behavior prediction. First, the application and advantages of deep learning and big data algorithms in the financial field are analyzed. Then, a deep learning-based big data risk prediction framework is designed and experimentally validated on actual financial datasets. The experimental results show that this method significantly improves the accuracy of financial risk behavior prediction and provides valuable support for risk management in financial institutions. Challenges in the application of deep learning are also discussed, along with potential directions for future research. © 2024, CC BY.

关键词： Data accuracy

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SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro Processors

SWPFOPLD: A Profiling and Optimizing Loader for SW26010Pro P...

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International Conference on computers, Communications, and systems (ICCCS)

作者： Hong Qian Wei Wu Qi Zhu Fei Wang Jinwei Zhao Yan Zheng School of Computer Science and Technology University of Science and Technology of China Hefei China System Software Research Department Jiangnan Institute of Computing Technology Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs.

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TRACKING OBJECTS AND ACTIVITIES WITH ATTENTION FOR TEMPORAL SENTENCE GROUNDING

arXiv

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arXiv 2023年

作者： Xiong, Zeyu Liu, Daizong Zhou, Pan Zhu, Jiahao Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China Wangxuan Institute of Computer Technology Peking University China

Temporal sentence grounding (TSG) aims to localize the temporal segment which is semantically aligned with a natural language query in an untrimmed video. Most existing methods extract frame-grained features or object-grained features by 3D ConvNet or detection network under a conventional TSG framework, failing to capture the subtle differences between frames or to model the spatio-temporal behavior of core persons/objects. In this paper, we introduce a new perspective to address the TSG task by tracking pivotal objects and activities to learn more fine-grained spatio-temporal behaviors. Specifically, we propose a novel Temporal Sentence Tracking Network (TSTNet), which contains (A) a Cross-modal Targets Generator to generate multi-modal templates and search space, filtering objects and activities, and (B) a Temporal Sentence Tracker to track multi-modal targets for modeling the targets' behavior and to predict query-related segment. Extensive experiments and comparisons with state-of-the-arts are conducted on challenging benchmarks: Charades-STA and TACoS. And our TSTNet achieves the leading performance with a considerable real-time speed. Copyright © 2023, The Authors. All rights reserved.

关键词： Arts computing

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Learner Action Patterns in the Problem-Solving Process Related to Program Code Composition Based on Tracking system Activities 7

Learner Action Patterns in the Problem-Solving Process Relat...

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7th International Conference on Informatics and Computing, ICIC 2022

作者： Syahidi, Aulia Akhrian Supianto, Ahmad Afif Hirashima, Tsukasa Watanobe, Yutaka Politeknik Negeri Banjarmasin Smart City Information System Applied Undergraduate Program Banjarmasin City Indonesia Research Center for Data and Information Sciences Bandung City Indonesia Graduate School of Advanced Science and Engineering Hiroshima University Kagamiyama Learning Engineering Laboratory Higashihiroshima Japan University of Aizu Division of Information Systems Department of Computer Science and Engineering Fukushima AizuWakamatsu Japan

ISBN: (纸本)9798350345711

Learning activities are an indicator of the learner's desire to learn during the learning process. The pattern of learner action is related to learning activities. In this case, in extracting the learning process, it is necessary to collect a lot of data through analysis of the learning process. The purpose of this study is to recommend and report the performance of an activity tracking system equipped with visual artifacts as an educational data mining approach to analyze action patterns when learners complete the arrangement of program code lines in each programming problem. In this study, data were taken from activity recordings when learners used interactive learning media for a basic programming subject called TOLSYASUPI-EduMed which had problem-solving learning models embedded in their interactions. Learning Analytics is used as a method of this research and data relationships from learner actions during the learning process using the 'if-then' rule. Log data is used for recording and detecting activities carried out by learners. We confirm that the results of the study are based on the findings of the data relationship that if the lines of program code are too long (more than five lines) then almost all learners experience a bottleneck condition. The completion time of each question and the number of iterations in completing the program code lines for each question have also been recorded and have a pattern of connection between the data. The information that has been obtained is then forwarded to teachers or stakeholders as key information to make appropriate feedback to learners based on the results as a means of analyzing and evaluating the programming learning process. © 2022 IEEE.

关键词： Data mining

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A Novel Multi-layered Minutiae Extractor Based on OCT Fingerprints 16th

A Novel Multi-layered Minutiae Extractor Based on OCT Finge...

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16th Chinese Conference on Biometric Recognition, CCBR 2022

作者： Zeng, Wenfeng Zhang, Wentian Liu, Feng Tan, Xu Li, Qin College of Computer Science and Software Engineering Shenzhen University Shenzhen China SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Senzhen China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Software Engineering Shenzhen Institute of Information Technology Shenzhen China

ISBN: (纸本)9783031202322

Fingerprints of Optical Coherence Tomography (OCT) imaging provide 3D volume data which have the nature property of multi-layered tissue structure. This paper, for the first time, attempts to extract minutiae for OCT-based fingerprint by making full use of the merits multi-layered structure of OCT imaging and powerful convolution al neural network (CNN). In particular, a novel multi-layered feature fusion minutiae extraction network is proposed, involving a multi-layered feature extractor to integrate the rich information of multiple subsurface fingerprints, and a two-stage object detection framework using reweighted concatenation feature to detect minutiae points. Compared with the results achieved by typical traditional and learning-based methods, our method outperforms the best one by about 2% of F1-score in minutiae extraction, and achieves the improvement of 0.09% than the best one in matching performance, which further demonstrate the effectiveness and robustness of our proposed method. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Optical tomography

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Robust Spectral Efficiency Maximization in 5G Networks Using Non-Linear Stochastic Precoding

Robust Spectral Efficiency Maximization in 5G Networks Using...

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Disruptive Technologies (ICDT), International Conference on

作者： Geetha Ponnaian Jayanthi. N R. Sarasu Sajitha. L.P R. Ramalakshmi Manimegalai L Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai India School of Management Studies Vels Institute of Science Technology and Advanced Studies (VISTAS) Pallavaram Chennai Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and business system R. M. K Engineering College Kavaraipettai Tamilnadu India Department of Electronics and Communication Engineering Ramco Institute of Technology Rajapalayam Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Chennai India

This research focuses on enhancing the spectral efficiency of 5G networks through the utilization of nonlinear stochastic precoding techniques. The proposed approach aims to mitigate the detrimental effects of channel impairments and noise by employing advanced precoding schemes that exploit the statistical properties of the wireless channel. By incorporating nonlinear precoding algorithms and stochastic optimization methods, the system can dynamically adapt to varying channel conditions, resulting in improved data transmission rates and overall spectral efficiency. Through extensive simulations and analysis, the effectiveness of the proposed method is demonstrated, showcasing its potential to significantly enhance the performance of 5G networks in real-world scenarios.

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Resilient Train Localization based on GNSS/INS/Trackmap Integration using a MAP-AICKF Method 35

Resilient Train Localization based on GNSS/INS/Trackmap Inte...

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35th International Technical Meeting of the Satellite Division of the Institute of Navigation, ION GNSS+ 2022

作者： Cao, Zhuojian Liu, Jiang Jiang, Wei Cai, Baigen Wang, Jian School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Frontiers Science Center for Smart High-speed Railway System Beijing100044 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of EMC and GNSS Technology for Rail Transportation Beijing100044 China

ISBN: (纸本)9781713871361

GNSS-base train localization is confronted with rigorous safety level requirements, and merged with railway scenario features of polytropic operation environments and constrained trackmap. Integrating GNSS with extra assistant sensors is highly required for the effectiveness of GNSS-enabled train localization under the signal constrained or even the GNSS interference environments. Under an integrated localization architecture, how to address the nonlinearity of the system is a significant issue for precision and performance stability. The Bayesian filter provides a unified recursive framework for the data fusion which approximates the posterior density based on the Linear Minimum Mean Square Error (LMMSE). However, the filter may suffer from a failure under large prior errors. The probability distribution of the measurement noise is required as a priori knowledge to be filtered out which is usually modeled as an additive Gaussian random variable with an invariant variance. Nevertheless, with the potential GNSS signal interference from a deliberate attack, the priori probability cannot describe the likelihood probability correctly. The approximation process of the nonlinearity and the noise distribution of the fusion system is important to guarantee the reliability and resilience of GNSS-based train localization. An Adaptive Iterated Cubature Kalman Filter (AICKF) based on Maximum A Posteriori (MAP) is applied for the integrated train localization to cope with the vulnerability of GNSS under the signal interference scenarios. The solution is capable of realizing autonomous train localization with the enhanced nonlinearity approximation capability. It allows us to deal with specific GNSS interference situations where the probability density of the pseudorange noise for different visible satellites is dynamically adjusted. The proposed solution is tested based on a practical operational scenario on Qinghai-Tibet Railway. GNSS interference injection (e.g., amplitude mod

关键词： Probability distributions

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