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International Colloquium on Signal Processing & Its Applications, CSPA

作者： Jing Wei Teoh Wei Lin Teoh Laila El-Ghandour Zhi Lin Chong Sin Yin Teh School of Mathematical and Computer Sciences Heriot-Watt University Malaysia Putrajaya Malaysia School of Mathematical and Computer Sciences Heriot-Watt University Edinburgh United Kingdom Department of Electronic Engineering Faculty of Engineering and Green Technology Universiti Tunku Abdul Rahman Kampar Perak Malaysia School of Management Universiti Sains Malaysia Penang Malaysia

ISBN: (数字)9798350382310

ISBN: (纸本)9798350382327

In most quality control practices, the mean control chart is used in conjunction with the dispersion control chart to maintain the stability of a production process. Existing literature has suggested setting the in-control average run length (ARL 0 ) of the mean chart and the dispersion chart to be equal. While such designs have the advantage of convenience, it is often unrealistic to assume that the mean and dispersion charts will share a common sensitivity, since mean shifts and variance shifts may occur with different probabilities in practice. In this paper, we propose a mismatched in-control performances strategy for designing the joint sequential probability ratio test (SPRT) mean and dispersion chart. We also develop new optimization designs for the joint SPRT scheme. Results show that the mismatched in-control performances strategy effectively boosts the detection sensitivity of the joint SPRT scheme towards mean shifts alone or variance shifts alone. It is also revealed that the mismatched joint SPRT scheme with a larger ARL on the dispersion chart performs better than the traditional matched joint SPRT scheme for a wide range of process shifts.

关键词： Sensitivity Process control Control charts Quality control Production Dispersion Optimization

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Adaptive Differentially Quantized Subspace Perturbation (ADQSP): A Unified Framework for Privacy-Preserving Distributed Average Consensus

arXiv

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arXiv 2023年

作者： Li, Qiongxiu Gundersen, Jaron Skovsted Lopuhaä-Zwakenberg, Milan Heusdens, Richard Tsinghua University China The Department of Electronic Systems Aalborg University Denmark University of Twente Netherlands Netherlands The Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands

Privacy-preserving distributed average consensus has received significant attention recently due to its wide applicability. Based on the achieved performances, existing approaches can be broadly classified into perfect accuracy-prioritized approaches such as secure multiparty computation (SMPC), and worst-case privacy-prioritized approaches such as differential privacy (DP). Methods of the first class achieve perfect output accuracy but reveal some private information, while methods from the second class provide privacy against the strongest adversary at the cost of a loss of accuracy. In this paper, we propose a general approach named adaptive differentially quantized subspace perturbation (ADQSP) which combines quantization schemes with so-called subspace perturbation. Although not relying on cryptographic primitives, the proposed approach enjoys the benefits of both accuracy-prioritized and privacy-prioritized methods and is able to unify them. More specifically, we show that by varying a single quantization parameter the proposed method can vary between SMPC-type performances and DP-type performances. Our results show the potential of exploiting traditional distributed signal processing tools for providing cryptographic guarantees. In addition to a comprehensive theoretical analysis, numerical validations are conducted to substantiate our results. © 2023, CC BY.

关键词： Differential privacy

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Non return to zero line coding with suppressed carrier in FSO transceiver systems under light rain conditions

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Journal of Optical Communications 2024年第0期45卷 s495-s505页

作者： Rashed, Ahmed Nabih Zaki Daher, Malek G. Ahammad, S. K. Hasane Montalbo, Francis Jesmar P. Sorathiya, Vishal Asaduzzaman, Sayed Rehana, Hasin Zuhayer, Asif Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Physics Department Islamic University of Gaza P.O. Box 108 Gaza Palestine School of Physics Universiti Sains Malaysia Penang11800 Malaysia Department of ECE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522302 India College of Informatics and Computing Sciences Batangas State University Batangas4200 Philippines Faculty of Engineering and Technology Parul Institute of Engineering and Technology Parul University Waghodia Road 391760 Gujarat Vadodara India Department of Computer Science and Engineering Rangamati Science and Technology University Rangamati Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Electronics & Telecommunication Engineering Chittagong University of Engineering and Technology Chattogram4349 Bangladesh

This paper aims to simulate performance efficiency of carrier suppressed non return to zero line coding based FSO transceiver systems under light rain conditions with amplification units at 40 Gbps. The max. Q, BER and total optical power are simulated and demonstrated after FSO channel and PIN Photodetector Receiver under light rain weather conditions at maximum reach of 1.2 km at 10 Gbps. As well as the max. Q Factor variations versus max reach variations are clarified after PIN photodetector receiver under light rain weather conditions at 10, 40 Gbps with/without amplification units. Besides the total optical power variations versus max reach variations are assured after FSO channel under light rain weather conditions at 10, 40 Gbps with/without amplification units. © 2022 Walter de Gruyter GmbH, Berlin/Boston.

关键词： Rain

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Algorithm for Resource Allocation and Computing Offloading in 6G Networks: Deep Reinforcement Learning-based

Algorithm for Resource Allocation and Computing Offloading i...

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International Conference on Mechatronics (ICOM)

作者： Mamoon M. Saeed Rashid A. Saeed Elmustafa Sayed Ali Rania A. Mokhtar Othman O. Khalifa Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Yemen Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Electrical & Electronic Engineering Faculty of Engineering Red Sea University PortSudan Sudan Department of Electronics Engineering Faculty of Engineering Sudan University of Science and Technology Khartoum Sudan Dept of Elec and Comp Eng Inter. Islamic Uni. Malaysia KL Malaysia Libyan Center for Engineering Research and Info. Tech. Bani Walid Libya

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

The emergence of the sixth generation (6G) wireless networks brings new challenges and opportunities for efficient computing offloading and resource allocation. This paper proposes a novel Deep Reinforcement Learning-based Computing Offloading and Resource Allocation (DRL-CORA) algorithm for 6G networks. The algorithm leverages the power of deep reinforcement learning to dynamically determine the optimal computing offloading decisions and resource allocation strategies. The Deep Reinforcement Learning-based DCORA algorithm for computation offloading and resource allocation is effective, as demonstrated by our simulations. When compared directly, the suggested DCORA algorithm performs 15% better than other baseline systems.

关键词： 6G mobile communication Energy consumption Mechatronics Heuristic algorithms Wireless networks Quality of service Deep reinforcement learning

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Evaluating RNN Models for Multi-Step Traffic Matrix Prediction

Evaluating RNN Models for Multi-Step Traffic Matrix Predicti...

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Information technology and Data Science (CITDS), IEEE Conference on

作者： Mohammed Nsaif Gergely Kovásznai Hasanein D. Rjeib Ali Malik Ruairí de Fréin Faculty of informatics University of Debrecen Hungary Faculty of Computer Science and Mathematics University of Kufa Iraq Department of Business Management Al-imam University College Balad Iraq Faculty of informatics Eszterházy Károly Catholic University Hungary Faculty of Engineering University of Kufa Iraq Institute of Information Technology University of Miskolc Hungary School of Electrical and Electronic Engineering Technological University Dublin Ireland

ISBN: (数字)9798350387889

ISBN: (纸本)9798350387896

Network traffic matrix prediction is used to estimate the patterns of future network flows before they are initiated. Traffic matrix prediction is needed by a wide range of network management functions such as network monitoring and it relies on historical data. In this paper, we address the task of multi-time step traffic matrix prediction using Recurrent Neural Networks (RNN). Our objective is to conduct a comparative study of different types of RNNs and to evaluate their ability to predict multi-time step Origin-Destination traffic matrices. Experiments show that RNNs are capable of predicting multiple steps of traffic matrices, however, the RMSE of the predictions increases as we move further away from the last true value. Our primary finding is that the RNN-GRU show has the best prediction ability in the very beginning steps with an RMSE of 0.048, while RNN-LSTM demonstrated higher capability with the last steps, having an RMSE value of 0.058.

关键词： Recurrent neural networks Power demand Telecommunication traffic Predictive models Data science Data models computer networks Numerical models Information technology Monitoring

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Beam divergence and operating wavelength bands effects on free space optics communication channels in local access networks

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Journal of Optical Communications 2023年第s1期44卷 S823-S831页

作者： Kumar Paul, Bikash Alatwi, Aadel M. Rashed, Ahmed Nabih Zaki Shahriar Parvez, Ahm Ahmed, Kawsar Department of Software Engineering Daffodil International University Sukrabad Dhanmondi Dhaka1207 Bangladesh Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Tangail Santosh1902 Bangladesh Electrical Engineering Department Faculty of Engineering University of Tabuk Tabuk Saudi Arabia Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32951 Egypt Department of Computer Science and Engineering Bangladesh University 15/1 Iqbal Road Mohammadpur Dhaka1207 Bangladesh Department of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur Bangladesh Group of Bio-photomati Mawlana Bhashani Science and Technology University Tangail Santosh1902 Bangladesh

The paper outlines the divergence of beam and operating wavelength bands effects on free-space optics communication channels in local access networks. The max Q factor and min bit error rate are measured based on the divergence of beam and operating wavelength bands variations. The signal is tested also at the receiver side by using an optical power meter visualizer at various operating signal wavelength bands and under the optimum values of the divergence of the beam. The study has presented the optimum operating conditions for upgrading network operation efficiency. © 2023 Walter de Gruyter GmbH. All rights reserved.

关键词： Bit error rate

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When Zero-Trust Meets Federated Learning

When Zero-Trust Meets Federated Learning

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xinran Zhang Dan Wang Yifei Zhu Weilong Chen Zheng Chang Zhu Han School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computing The Hong Kong Polytechnic University Hong Kong UM-SJTU Joint Institute Shanghai Jiao Tong University China Faculty of Information Technology University of Jyväskylä Jyväskylä Finland Department of Electrical and Computer Engineering University of Houston Houston TX USA Department of Computer Science and Engineering Kyung Hee University Seoul South Korea

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Nowadays, Federated Learning (FL) has emerged as a promising and critical machine learning scheme to protect data privacy and reduce communication overhead. As the scale and connectivity expand in the FL system, enhancing the model’s robustness against security threats from malicious clients grows ever more critical. An effective defensive solution involves selecting benign clients appropriately, thereby mitigating the vulnerability of the FL system to malicious attacks. However, clients exhibit varying behaviors over time, which complicates the task of accurately modeling their future trustworthiness. Moreover, blindly trusting clients with high trust values poses risks, given the potential for severe losses from betrayal. To tackle these problems, we propose a zero-trust policy in FL aimed at establishing continuous trust in each client while maintaining skepticism towards potential betrayal attacks. Specifically, we develop a Dirichlet-based trust evaluation technique to enable a comprehensive selection of trustworthy participants. This technique leverages the posterior distribution to estimate clients’ trust values from their evolving behavior records over time. Then, we anticipate potential betrayal from a selected client and formulate a min-max optimization problem to minimize the worst-case betrayal loss, thereby boosting the system’s betrayalaware robustness. Next, we convert this problem into a convex optimization problem and utilize the interior point method for resolution. We conduct extensive simulations to validate the efficacy of our proposed zero-trust policy in accurately assessing trust and enhancing the model’s robustness to betrayal.

关键词： Data privacy Accuracy Federated learning Boosting Robustness Convex functions Security Global communication Optimization

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Performance Analysis between Feature Extraction and Fusion in Familiar and Unfamiliar Typing Biometric Authentication

Performance Analysis between Feature Extraction and Fusion i...

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2021 International Conference on Biomedical engineering, ICoBE 2021

作者： Amalina, I. Saidatul, A. Fook, C.Y. Navea, R.F. Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Arau Perlis Malaysia Universiti Malaysia Perlis Arau Perlis Malaysia Department of Electronics and Computer Engineering De La Salle University Manila Philippines

The brain signals recorded by EEG devices are largely developed in for biometric authentication purposes. Those signals are very informative and reliable to be classified using signal processing. In this paper, the feature extraction and feature fusion are further studied to observe their performance towards the typing tasks. The signals are pre-processed to eliminate the unwanted noise present in the signals. The feature extraction method such as Welch’s method, Burg’s method and Yule Walk’s method are applied to extract the mean, median, standard deviation and variance in the data. Nonlinear feature such as fuzzy entropy is also been extracted. The extracted features are further classified by using k-Nearest Neighbour (k-NN), Random Forest (RF) and Ensemble Bagged Tree (EBT). The performance of feature extraction and feature fusion through concatenation are recorded and compared. For comparison, the feature fusion shows a better performance accuracy rather than feature extraction. The highest percentage accuracy was produced by Burg’s method for frontal-parietal lobes feature fusion which is 95.94% using Ensemble Bagged Tree (EBT). © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Feature extraction

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Tracking and Calculating Speed of Mixing Vehicles Using YOLOv4 and DeepSORT 9

Tracking and Calculating Speed of Mixing Vehicles Using YOLO...

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9th NAFOSTED Conference on Information and computer Science, NICS 2022

作者： Huu, Phat Nguyen Anh, Bang Nguyen Nam, Vu Tran Ngoc Hoang, Tran Manh Nguyen, Tien Dzung Minh, Quang Tran School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Viet Nam Specialized Math Class HUS High School for Gifted Students Hanoi Viet Nam Ho Chi Minh City University of Technology Faculty of Computer Science and Engineering Department of Information Systems Hochiminh Viet Nam Vietnam National University HoChiMinh City Hochiminh Viet Nam

ISBN: (纸本)9781665454223

Today, strong development of socio-economic has promoted participation in traffic. As a result, traffic management is becoming more and more difficult. To effectively solve the problem, artificial intelligence (AI) applications are applied for the improvement of urban traffic management and administration. Therefore, we propose an intelligent algorithm for monitoring and detecting vehicles. We use data collected from cameras and apply deep learning technology to track objects in the paper. The proposed algorithm uses YOLOv4 combined with DeepSORT for tracking and calculating the speed of detected vehicles. Results show that the proposed algorithm improves accuracy up to 95% which is suitable to apply for real applications. © 2022 IEEE.

关键词： Object detection

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A Concise Review of Deep Learning Deployment in 3D computer Vision Systems 4

A Concise Review of Deep Learning Deployment in 3D Computer ...

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4th International Symposium on Agents, Multi-Agents Systems and Robotics, ISAMSR 2021

作者： Shaqwi, Farooq Sijal Audah, Lukman Hassan, Mustafa Hamid Jubair, Mohammed Ahmed Abd Wahab, Mohd Helmy Mostafa, Salama A. Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Johor Malaysia College of Information Technology Ja'Afar Al-Sadiq University Department of Computer Technical Engineering Al-Muthanna Iraq

ISBN: (纸本)9781728166544

Deep Learning (DL) has advanced the concept of digital image processing, but this does not rule out the relevance of the conventional computer vision techniques as they have continuously been rebranded even before the emergence of DL. DL techniques have over the years been the method of choice in several fields due to their superior performance compared to the state-of-the-art machine learning methods. Among the numerous DL methods, computer vision has to be one of the most prominent methods, and given this, this article overviewed the concept of DL in computer vision, focusing more on the recent advancements in tasks that involve 3D visual data. Some of the DL schemes that are relevant in solving computer vision problems were also briefly reviewed. Furthermore, the history, structure, merits, and drawbacks of these DL schemes were provided. The application of these DL schemes in various computer vision tasks, like object detection, was also described in this article. © 2021 IEEE.

关键词： computer vision

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