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Emerging Research in Computational science (ICERCS), International Conference on

作者： Gunasekaran Thangavel Sheeba Rajan S. Subbulekshmi Arun Anthonisamy Rohini Ganapathi Gowri Velankanni Department of Electronics and Communication Engineering University of Technology and Applied Sciences Muscat Sultanate of Oman School of Computing SRM Institute of Science and Technology Tiruchirapalli India Department of Electronics and Communication Engineering Prathyusha Engineering College Chennai India Department of Computer Science and Business System Panimalar Engineering College Chennai India Department of Electronics and Communication Engineering St. Joseph’s Institute of Technology Chennai India Department of Electronics and Communication Engineering K. Ramakrishnan College of Technology Trichy India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

In B5G networks, accurate symbol timing estimate and estimation of the carrier frequency offset are both absolutely necessary. Particularly in highly mobile and interference-ridden settings, these factors are of paramount importance for sustaining high-throughput, dependable wireless communication. Estimating symbol timing and carrier frequency offset in OTFS can be difficult due to factors including the time-varying nature of Doppler shifts and interference from surrounding cells as well as the non-stationary nature of wireless channels. In such changing settings, traditional algorithms often fail to produce reliable estimations. To account for frequency changes caused by things like Doppler effects and drift, this research uses Machine Learning-Based Carrier Frequency Prediction Analysis (ML-CFPA) to implement a dynamic carrier frequency offset estimation and correction system. To preserve signal coherence and lower interference in B5G settings, carrier frequency offset adjustment is essential. Communication solutions with high performance are required for these applications to ensure they can function normally despite the complexity and changeability of their contexts. The outcomes demonstrate the higher performance of the algorithm in contrast to conventional approaches, highlighting its applicability to B5G networks. Its potential as a crucial enabling technology for the B5G future has been solidified by these simulations, which verify its capacity to maintain reliable communication under a variety of real-world conditions.

关键词： Wireless communication Machine learning algorithms Heuristic algorithms Estimation Symbols Interference Prediction algorithms Frequency estimation Timing Reliability

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Dynamics characterization of the glass formation of twist-bend liquid crystal dimers through dielectric studies

arXiv

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arXiv 2023年

作者： Czarnecka, M. Arakawa, Y. Kocot, A. Merkel, K. Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Agh University of Science and Technology al. Adama Mickiewicza 30 Cracow30-059 Poland Department of Applied Chemistry and Life Science Graduate School of Engineering Toyohashi University of Technology Toyohashi441-8580 Japan Institute of Materials Engineering Faculty of Science and Technology University of Silesia 75 Pulku Piechoty 1a Chorzów41-500 Poland

Broadband dielectric spectroscopy in a frequency range of 10 to 2× 109Hz were used to study the molecular orientational dynamics of the glass-forming, thioether-linked cyanobiphenyl liquid crystal dimers CBS7SCB and CBS7OCB. As was expected theoretically, two different relaxation processes that contributed to the dielectric permittivity of dimers were observed. The low-frequency relaxation mode, m1, was attributed to an "end-over-end rotation" of the dipolar groups parallel to the director. The high-frequency relaxation mode, m2, was associated with the precessional motions of the dipolar groups around the director. The relaxation times for both modes were analyzed over a wide temperature range down to near the glass transition temperature. The different analytic functions that were used to characterize the temperature dependence of the relaxation times of the two modes are discussed. Particularly, the critical-like description via the dynamic scaling model seemed to give not only quite good numerical fittings, but also provided a consistent physical picture of the orientational dynamics on approaching the glass transition. When compared to the IR spectroscopy finding, in the higher temperature region of NTBphase, where the longitudinal correlations of dipoles grew, the m1 mode experienced a sudden increase of enthalpy while m2 changed continuously, which is described by the critical mode coupling behavior. Both types of molecular motion appear to strongly cooperate at a low temperature range of the NTBphase, but changed in a coordinated manner as the temperature of the material approached the glass transition point. As was expected, it was found that both molecular motions determined the glass dynamics at the same glass transition temperature, Tg. © 2023, CC BY.

关键词： Glass transition

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Physical Layer Security for NOMA Systems: Requirements, Issues, and Recommendations

arXiv

引用

arXiv 2023年

作者： Pakravan, Saeid Chouinard, Jean-Yves Li, Xingwang Zeng, Ming Hao, Wanming Pham, Quoc-Viet Dobre, Octavia A. The Department of Electric and Computer Engineering Laval University Quebec CityQCG1V 0A6 Canada The School of Physics and Electronic Information Engineering Henan Polytechnic University Jiaozuo China The School of Information Engineering Zhengzhou University Zhengzhou450001 China The School of Computer Science and Statistics Trinity College Dublin Dublin Ireland The Faculty of Engineering and Applied Science Memorial University St. John’s Canada

Non-orthogonal multiple access (NOMA) has been viewed as a potential candidate for the upcoming generation of wireless communication systems. Comparing to traditional orthogonal multiple access (OMA), multiplexing users in the same time-frequency resource block can increase the number of served users and improve the efficiency of the systems in terms of spectral efficiency. Nevertheless, from a security viewpoint, when multiple users are utilizing the same time-frequency resource, there may be concerns regarding keeping information confidential. In this context, physical layer security (PLS) has been introduced as a supplement of protection to conventional encryption techniques by making use of the random nature of wireless transmission media for ensuring communication secrecy. The recent years have seen significant interests in PLS being applied to NOMA networks. Numerous scenarios have been investigated to assess the security of NOMA systems, including when active and passive eavesdroppers are present, as well as when these systems are combined with relay and reconfigurable intelligent surfaces (RIS). Additionally, the security of the ambient backscatter (AmB)-NOMA systems are other issues that have lately drawn a lot of attention. In this paper, a thorough analysis of the PLS-assisted NOMA systems research state-of-the-art is presented. In this regard, we begin by outlining the foundations of NOMA and PLS, respectively. Following that, we discuss the PLS performances for NOMA systems in four categories depending on the type of the eavesdropper, the existence of relay, RIS, and AmB systems in different conditions. Finally, a thorough explanation of the most recent PLS-assisted NOMA systems is given. © 2023, CC0.

关键词： Efficiency

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Innovative Techniques in the Construction of Very Large Scale Integration Circuits for Low-Power and High-Performance Circuits

Innovative Techniques in the Construction of Very Large Scal...

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Communications and Information Technologies (GCCIT), Global Conference on

作者： V. Saravanan G. Sasikala P. Gomathi D. Kirubakaran A. Rajalingam Jubert R. Pagunuran Department of Nano Electronics Materials and Sensors Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamilnadu India Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Department of ECE V.S.B. Engineering College Department of Computer Science and Engineering R.M.K Engineering College Engineering Department College of Engineering and Technology University of Technology and Applied Sciences Shinas Sultanate of Oman

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Portable electronic gadgets and high-performance computing systems have increased demand for low-power, high-performance integrated circuits (ICs). VLSI technology, which creates integrated circuits with dozens to millions of transistors on a chip, is leading these efforts. This research investigates low-power, high-performance VLSI circuit construction methods. We start with VLSI design fundamentals and power management and performance optimization concerns. The constraints of traditional VLSI design methods in satisfying current electronics' strict requirements are examined. VLSI design methodology development and evaluation are the focus of this research. Advanced power optimization methods include MTCMOS, adaptive body biasing, and power gating. We also study clock tree improvement, signal integrity enhancement, and new materials and device topologies for high performance. In addition, we study how machine learning techniques automate and optimize circuit placement, routing, and timing in VLSI design. AI handles the complexity of current VLSI designs and finds optimal design parameters for minimal power and high performance. Scaling VLSI circuits to newer technology nodes raises process variance, leakage current, and thermal management difficulties, which the research tackles. These issues can be overcome with new ways to keep VLSI circuits scaling while preserving performance and power efficiency. Finally, case studies and experimental findings show that the offered methods work in real-world applications. These findings demonstrate significant power usage and performance benefits over typical VLSI design. In conclusion, our research advances VLSI technology by developing low-power, high-performance integrated circuits. The proposed VLSI design methods and solutions could lead to more efficient and powerful electronic systems.

关键词： Performance evaluation Design methodology Machine learning Very large scale integration Thermal management Routing Topology Timing Leakage currents Signal integrity

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Accelerating projections to kernel-induced spaces by feature approximation

引用

Pattern Recognition Letters 2020年 136卷 31-39页

作者： Adamiak, Krzysztof Kim, Hyongsuk Ślot, Krzysztof Institute of Applied Computer Science Lodz University of Technology Lodz Poland Division of Electronics Engineering Chonbuk National University Jeonju567-54896 Korea Republic of

A method for speeding-up data projections onto kernel-induced feature spaces (derived using e.g. kernel Principal Component Analysis - kPCA) is presented in the paper. The proposed idea is to simplify the derived features, implicitly defined by all training samples and dominant eigenvectors of problem-specific generalized eigenproblems, by appropriate approximations. Instead of employing the whole training set, we propose to use a small pool of its appropriately selected representatives and we formulate a rule for deriving the corresponding weight vectors that replace the considered dominant eigenvectors. The representatives are determined via clustering of training data, whereas weighting coefficients are chosen to minimize original feature approximation errors. The concept has been experimentally verified for kernel-PCA using both artificial and real datasets. It has been shown that the presented approach provides reduction in feature-extraction complexity, which implies a proportional increase in data projection speed, by one-to-two orders of magnitude, without sacrificing data analysis accuracy. Therefore, the proposed approach is well-suited for kernel-based, intelligent data analysis applications that are to be executed on resource-limited systems, such as embedded or IoT devices, or for systems where processing time is critical. © 2020

关键词： Eigenvalues and eigenfunctions

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Stackelberg POMDP: A Reinforcement Learning Approach for Economic Design

arXiv

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arXiv 2022年

作者： Brero, Gianluca Eden, Alon Chakrabarti, Darshan Gerstgrasser, Matthias Greenwald, Amy Li, Vincent Parkes, David C. Data Science Initiative Brown University United States School of Computer Science and Engineering Hebrew University of Jerusalem Department of Industrial Engineering and Operations Research Columbia University United States John A. Paulson School of Engineering and Applied Sciences Harvard University United States Department of Computer Science Brown University United States

We introduce a reinforcement learning framework for economic design where the interaction between the environment designer and the participants is modeled as a Stackelberg game. In this game, the designer (leader) sets up the rules of the economic system, while the participants (followers) respond strategically. We integrate algorithms for determining followers' response strategies into the leader's learning environment, providing a formulation of the leader's learning problem as a POMDP that we call the Stackelberg POMDP. We prove that the optimal leader's strategy in the Stackelberg game is the optimal policy in our Stackelberg POMDP under a limited set of possible policies, establishing a connection between solving POMDPs and Stackelberg games. We solve our POMDP under a limited set of policy options via the centralized training with decentralized execution framework. For the specific case of followers that are modeled as no-regret learners, we solve an array of increasingly complex settings, including problems of indirect mechanism design where there is turn-taking and limited communication by *** demonstrate the effectiveness of our training framework through ablation *** also give convergence results for no-regret learners to a Bayesian version of a coarse-correlated equilibrium, extending known results to correlated types. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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Antithetic Multilevel Methods for Elliptic and Hypo-Elliptic Diffusions with Applications

arXiv

引用

arXiv 2024年

作者： Iguchi, Yuga Jasra, Ajay Maama, Mohamed Beskos, Alexandros Department of Statistical Science University College London LondonWC1E 6BT United Kingdom School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

We present a new antithetic multilevel Monte Carlo (MLMC) method for the estimation of expectations with respect to laws of diffusion processes that can be elliptic or hypo-elliptic. In particular, we consider the case where one has to resort to time discretization of the diffusion and numerical simulation of such schemes. Inspired by recent works, we introduce a new MLMC estimator of expectations, which does not require any Lévy area simulation and has a strong error of order 2 and a weak error of order 2. We then show how this approach can be used in the context of the filtering problem associated to partially observed diffusions with discrete time observations. We illustrate that in numerical simulations our new approaches provide efficiency gains for several problems, particularly when the diffusion process is hypo-elliptic, relative to some existing methods. © 2024, CC BY.

关键词： Differential equations

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Research on Credit Risk Early Warning Model of Commercial Banks Based on Neural Network Algorithm

arXiv

引用

arXiv 2024年

作者： Cheng, Yu Yang, Qin Wang, Liyang Xiang, Ao Zhang, Jingyu Columbia University The Fu Foundation School of Engineering and Applied Science Operations Research New YorkNY United States Microelectronics Science and Engineering Sichuan Chengdu China Washington University in St. Louis Olin Business School Finance St. LouisMO United States University of Electronic Science and Technology of China School of Computer Science & Engineering School of Cybersecurity Digital Media Technology Sichuan Chengdu China The University of Chicago The Division of the Physical Sciences Analytics ChicagoIL United States

In the realm of globalized financial markets, commercial banks are confronted with an escalating magnitude of credit risk, thereby imposing heightened requisites upon the security of bank assets and financial stability. This study harnesses advanced neural network techniques, notably the Backpropagation (BP) neural network, to pioneer a novel model for preempting credit risk in commercial banks. The discourse initially scrutinizes conventional financial risk preemptive models, such as ARMA, ARCH, and Logistic regression models, critically analyzing their real-world applications. Subsequently, the exposition elaborates on the construction process of the BP neural network model, encompassing network architecture design, activation function selection, parameter initialization, and objective function construction. Through comparative analysis, the superiority of neural network models in preempting credit risk in commercial banks is elucidated. The experimental segment selects specific bank data, validating the model's predictive accuracy and practicality. Research findings evince that this model efficaciously enhances the foresight and precision of credit risk management. Copyright © 2024, The Authors. All rights reserved.

关键词： Neural network models

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Complete Neural Networks for Complete Euclidean Graphs

arXiv

引用

arXiv 2023年

作者： Hordan, Snir Amir, Tal Gortler, Steven J. Dym, Nadav Faculty of Mathematics Technion – Israel Institute of Technology Haifa Israel School of Engineering and Applied Sciences Harvard University Cambridge United States Faculty of Computer Science Technion – Israel Institute of Technology Haifa Israel

Neural networks for point clouds, which respect their natural invariance to permutation and rigid motion, have enjoyed recent success in modeling geometric phenomena, from molecular dynamics to recommender systems. Yet, to date, no model with polynomial complexity is known to be complete, that is, able to distinguish between any pair of non-isomorphic point clouds. We fill this theoretical gap by showing that point clouds can be completely determined, up to permutation and rigid motion, by applying the 3-WL graph isomorphism test to the point cloud’s centralized Gram matrix. Moreover, we formulate an Euclidean variant of the 2-WL test and show that it is also sufficient to achieve completeness. We then show how our complete Euclidean WL tests can be simulated by an Euclidean graph neural network of moderate size and demonstrate their separation capability on highly symmetrical point clouds. © 2023, CC BY.

关键词： Molecular dynamics

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A graph-based convolutional neural network stock price prediction with leading indicators

A graph-based convolutional neural network stock price predi...

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作者： Wu, Jimmy Ming-Tai Li, Zhongcui Srivastava, Gautam Tasi, Meng-Hsiun Lin, Jerry Chun-Wei College of Computer Science and Engineering Shandong University of Science and Technology Shandong China Department of Math and Computer Science Brandon University Brandon Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan Department of Management Information Systems National Chung Hsing University Taichung Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

The stock market is a capitalistic haven where the issued shares are transferred, traded, and circulated. It bases stock prices on the issue market, however, the structure and trading activities of the stock market are much more complicated than the issue market itself. Therefore, making an accurate prediction becomes an intricate as well as highly difficult task. On the other hand, because of the potential benefits of stock prediction, it attracts generation after generation of scholars as well as investors to continuously develop various prediction methods from different perspectives, a myriad of theories, a multitude of investment strategies, and different practical experiences. In this article, aiming at the task of time series (financial) feature extraction and prediction of price movements, a new convolutional novel neural network that can be called a framework to improve the prediction accuracy of stock trading is proposed. The method that is proposed is called SSACNN, a short form of stock sequence array convolutional neural network. SSACNN collects data including historical data of prices and its leading indicators (options/futures) for a stock to take an array as the input graph of the convolutional neural network framework. In our experimental results, five Taiwanese and American stocks were used as a benchmark to compare with the previous algorithms and proposed algorithm, the motion prediction performance of SSACNN has been improved significantly and proved that it has the potential to be applied in the real financial market. © 2020 John Wiley & Sons Ltd

关键词： Forecasting

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