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6th International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2024

作者： Perdana, Ridho Hendra Yoga Nguyen, Toan-Van Pramitarini, Yushintia An, Beongku Hongik University Dept. of Software and Communications Engineering in Graduate School Korea Republic of San Diego State University Department of Electrical and Computer Engineering San DiegoCA United States Hongik University Dept. of Software and Communications Engineering Korea Republic of

ISBN: (纸本)9798350344349

In this paper, we study a deep learning (DL)-based energy efficiency maximization (EEM) problem in massive multiple-input multiple-output (MIMO)-non-orthogonal multiple access (NOMA) networks with multiple reconfigurable intelligent surfaces (RISs). These RISs are deployed randomly at the edge of the near radius to assist the base station (BS) in communicating with near and far users. We formulate the problem of jointly optimizing the precoding matrix and phase shift of the RISs to maximize the overall energy efficiency subject to the quality-of-service of each user, phase shift of RISs, and power budget of the BS constraints. To address this challenging non-convex problem with mixed-integer constraints, the original problem is decoupled into phase shift and beamforming sub-problems, then addressed them separately. We introduce the bisection search algorithm to address the challenge of the phase shifts optimization problem. For the beamforming optimization, it is transformed into an equivalent non-convex problem but with a more tractable form. Then, we propose an iterative algorithm based on the inner approximation method for its solution. To support real-Time optimization, we design a deep learning framework to predict optimal solutions of phase shifts at RISs and precoding matrix under different parameter settings. Simulation results show that the proposed DL-based approach can predict the optimal solution with high accuracy in a short time compared to the conventional approach. Additionally, the effect of the maximum power budget at the base station, the number of RISs, and BS's antennas are evaluated thoroughly. © 2024 IEEE.

关键词： Energy efficiency

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Secured intelligent transportation with privacy retention through blockchain framework

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10507-10521页

作者： Kaur, Gaganpreet Shobana, M. Kavin, F. Sellakumar, S. Meenakshi, D. Bharathiraja, N. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology KTR Chengalput Tamil Nadu Chennai India Department of Electronics and Instrumentation M.S. Ramaiah Institute of Technology Karnataka India Department of Mechanical Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Tagore Engineering College Tamil Nadu India

The Secured Independent Intelligent Transport System (SIITS) is poised to revolutionize traditional transport management systems, leveraging autonomous vehicles (AVs) connected through an open-channel Internet to link Traffic Command Centers (TCCs), Road Side Units (RSUs), and AVs within the SIITS framework. However, this reliance on the Internet exposes users to various security risks, safety vulnerabilities, and other challenges that impede the progress of SIITS applications. In this method, ensuring robust security management and trustworthiness is paramount for the long-term adoption of this innovative trend. While previous efforts have focused on integrating security methods from multiple disciplines into a unified reference design, this article presents a reference architecture primarily centered around ITS safety. Additionally, the article introduces a proposed framework for enhancing ITS safety, addressing the confidence issues. To further address these challenges, the article offers categorization of goods, Big Data methods and services, and validates the utility of ITS business analytics for corporate applications through a groundbreaking multi-tier ITS security architecture. © 2024 - IOS Press. All rights reserved.

关键词： Big data

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Image Denoising Framework Employing Auto Encoders for Image Reconstruction 1

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3rd International Conference on Emergent Converging Technologies and Biomedical Systems, ETBS 2023

作者： Jain, Shruti Bharti, Monika Jindal, Himanshu Department of Electronics and Communications Engineering Jaypee University of Information Technology Himachal Pradesh Waknaghat Solan173234 India Department of Computer Science Engineering ASET Amity University Punjab Punjab Mohali140603 India

ISBN: (数字)9789819986460

ISBN: (纸本)9789819986453

Auto Encoder (AE) can be used in denoising of images. It is a type of neural network that can reconstruct the input. An auto-encoder is to represent a (sparse) input dataset in a compressed form that retains the most relevant information such that it may be reconstructed at the output with minimal loss from the compressed representation. In this paper, deep AE, denoising AE, and variational AE are used. Any AE where an extra constraint is put on the bottleneck to have a low KL divergence from a Normal Distribution is a Variational AE. There are multiple ways in which Variational AE is used, but the most common one is generative. The decoders on the top of the bottleneck can be used to generate new data points. Maximum accuracy of 88.85% is observed using the Denoising autoencoder while 76.25% and 81.44% are observed for deep and Variational autoencoder, respectively. 8.3% and 14.18% accuracy improvement is observed in Variational and deep AE, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image reconstruction

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Buffer-Based Adaptive Bitrate Algorithm for Enhanced Quality of Experience

Buffer-Based Adaptive Bitrate Algorithm for Enhanced Quality...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Nos, Tarnim Elmeligy, Ahmed O. Hassan, Mohamed S. Landolsi, Taha Ismail, Mahmoud H. American University of Sharjah Department of Computer Science & Engineering Sharjah United Arab Emirates American University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates Cairo University Faculty of Engineering Department of Electronics and Communications Engineering Giza12613 Egypt

ISBN: (纸本)9798350351408

Video streaming has become a popular method for delivering video content over the Internet, and the number of users and video content is increasing rapidly. The user's quality of experience (QoE) is a crucial factor in the success of video streaming services, as it quantifies the user's satisfaction. The QoE is affected by the video quality, starvation instants, and quality switching. To maximize the QoE, adaptive bitrate (ABR) algorithms select the video quality based on the network conditions and the client's buffer capacity. In this paper, a buffer-based ABR algorithm is proposed to maximize the user's QoE by minimizing the duration and frequency of starvation events and the time spent at the lowest quality level while maintaining smooth quality switching. A SimEvents-based scalable video coding SVC dynamic adaptive streaming over HTTP (DASH) system is developed to simulate the proposed algorithm. The performance of the proposed algorithm is compared to that of a baseline algorithm under different network conditions, and it is shown that the proposed algorithm outperforms the baseline algorithm in terms of buffer occupancy, quality switching and video quality. © 2024 IEEE.

关键词： Video streaming

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Hierarchical vision transformer model for polyp segmentation

Hierarchical vision transformer model for polyp segmentation

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2023 International Conference on Advances in Intelligent Computing and Applications, AICAPS 2023

作者： Geetha, S. Gopakumar, C. Vinod, Vishnu Department of Computer Science and Engineering College of Engineering Chengannur India Department of Electronics and Communications Engineering India College of Engineering Karunagappally India Apj Abdul Kalam Technological University Kerala India

ISBN: (纸本)9798350333817

Medical image analysis plays a powerful role in clinical assistance for the diagnosis and treatment of diseases. Image segmentation is an essential part of the medical imaging process as it extracts the region of interest through semi-automated or automated methods. Deep learning approaches have emerged as a fast-growing research field in medical image analysis. Vision transformers (ViT) are deep learning models that came up as a competing substitute for convolutional neural networks. ViT reports breakthroughs in computer vision tasks including object classification, detection, localization, and segmentation. Colon polyp detection and segmentation is a challenging task in the medical diagnosis and prognosis of colorectal cancer. Early detection and segmentation of polyp regions are of the utmost importance in preventing disease in later stages. In this work, we explore a hierarchical vision transformer as the backbone, replacing convolutional neural networks (CNNs) for the segmentation of polyps. The hierarchical vision transformer is composed of several stages, each having a different resolution. Through the use of a convolutional decoder, the patches from various stages are successively combined to produce full pre-dictions. The transformer backbone has a global receptive field at every stage that provide finer-grained and globally relevant predictions. Experimental results indicate that we can fine-tune the architecture to generate promising results on segmentation metrics even on smaller datasets, with mean Dice and mean IoU scores of 74% and 73% on the Kvasir-SEG dataset. © 2023 IEEE.

关键词： Diseases

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Development of an Energy Monitoring System with Forecasting for Off-Grid Microgrids 10

Development of an Energy Monitoring System with Forecasting ...

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10th International Conference on Power and Energy Systems engineering, CPESE 2023

作者： Pena, Robert Alfie S. Castro, Antonio Rafael MacAbebe, Erees Queen B. School of Science and Engineering Ateneo de Manila University Ateneo Energy Research Laboratory Department of Electronics Computer and Communications Engineering Quezon City Philippines

ISBN: (纸本)9798350327625

The need for energy, especially energy from renewable sources, to be distributed and flexible has driven the development of microgrids in recent years. They have been a key technology in achieving the sustainable development goal for energy particularly in rural off-grid areas. However, safe and reliable operation of microgrids remains a challenge given the difficulty in maintaining the physical system due to the lack of technical know-how in the rural localities where they are installed. A monitoring system with power forecasting capability is proposed in this article to aid in the safe and reliable operation of microgrids. The monitoring system features galvanic isolation in both the ac and dc measurement modules protecting users and the monitoring system itself from the high currents of the microgrid power system. The measurements can then be fed into a machine learning algorithm to forecast the power output of the microgrid. Experimental data were presented to validate the measurements and forecasts of the monitoring system. Users can monitor and forecast the power output of a microgrid and conduct maintenance if significant deviations are observed between forecasts and measurements. © 2023 IEEE.

关键词： Forecasting

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High Performance FIR and IIR Filters Based on FPGA for 16 Hz Signal Processing 5

High Performance FIR and IIR Filters Based on FPGA for 16 Hz...

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5th International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2023

作者： Mudhafar, Yousif Samer Abdulnabi, Saif Hasan College of Technical Engineering The Islamic University Computer Techniques Engineering Department Najaf Iraq University of Kufa Faculty of Engineering Department of Electronic and Communications Engineering Kufa Street Najaf54001 Iraq

ISBN: (纸本)9798350337525

The goal of the research to design and implement digital filters (Finite Impulse Response (FIR) and Infinite Impulse Response (IIR)) based on Field Programmable Gate Array (FPGA) by using the copulation between MATLAB/Simulink and Xilinx ISE Design Suite programs. low pass digital filter was implemented with different types of windowing methods that calculate the filter coefficient of FIR filter and different types of IIR filter with three numbers of filter order that are (5th order, 8th order, and 10th order). These different types of digital filters and filter orders are applied with the addition of a sine signal with a frequency of 16 Hz and a random noise signal. The work was done by two approaches: the first by simulation method through coupling between MATLAB/Simulink and Xilinx ISE Design Suite programs. While the second is by the practical method of loading these simulation block diagrams on FPGA. The performance of the work is measured by the difference between the sine signal and filtered signal and by the difference between the simulation results and practical results. Using FPGA with digital filters in this research gives a major advantage which is the simulation results equal to the practical results (Difference equal to zero). © 2023 IEEE.

关键词： Simulink

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The computer network security situation awareness based on decision tree algorithm

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International Journal of Reasoning-based Intelligent Systems 2024年第2期16卷 125-136页

作者： Kong, Dehua Lu, Lin Xiao, Nian School of Computer Science Wuhan Donghu University Wuhan430000 China CICT Mobile Communication Technology Co. Ltd. Wuhan430000 China Department of Information Engineering Wuhan Vocational College of Communications and Publishing Wuhan430223 China

Aiming at the problems of security awareness error and high channel collision rate in computer network security awareness, a computer network security situation awareness method based on decision tree algorithm is proposed. First, the computer network architecture and security requirements are analysed. Then, the security situation index classification model is constructed, the network status data is collected, and the data features are extracted using the artificial fish swarm algorithm. Finally, the maximum information gain attribute is used as a sub node of the decision tree to generate a decision tree. Combined with cosine similarity, computer network security situational awareness is realised. The experimental results show that the correlation of the impact indicators of this method is higher than 0.5, the absolute percentage error of security situation awareness is lower than 0.5%, and the channel collision rate is 0~0.10 × 10-2 Copyright © 2024 Inderscience Enterprises Ltd.

关键词： Decision trees

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LAORA: Location-Aware Orientation Adjustment for MIMO Magnetic Wireless Charging System 21

LAORA: Location-Aware Orientation Adjustment for MIMO Magnet...

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21st Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2024

作者： Kong, Lingchang Wang, Xinyu Zhou, Hao Zhou, Fengyu Jiang, Shenyao Zhu, Peide Song, Qi Liu, Zhi LINKE Lab School of Computer Science and Technology University of Science and Technology of China China CAS Key Laboratory of Wireless-Optical Communications University of Science and Technology of China China Department of Computer Science City University of Hong Kong Hong Kong School of Mathematics and Statistics Fujian Normal University Fuzhou China Department of Computer and Network Engineering The University of Electro-Communications Tokyo182-8585 Japan

ISBN: (纸本)9798331519186

Wireless power transfer (WPT) systems using magnetic resonant coupling (MRC) have made significant progress recently, leading to various optimization methods in scenarios involving multiple-input multiple-output (MIMO) to improve charging performance. Adjusting the coil orientation of the power transmitter (TX) is a simple but effective method due to the directional nature of magnetic field distribution, but existing approaches often require unnecessary coil rotations. In this study, we introduce a Location-Aware Orientation Adjustment algorithm, known as LAORA, to address these inefficiencies. LAORA focuses on solving the problems of charging devices (RX) localization and location-based optimization. We begin by introducing the concept of Equivalent Impedance Distribution Image (EIDI) and transform the RX localization problem into a combined matching process involving EIDI. In addition, we establish a dynamic simulation framework to predict charging performance using RX-related knowledge, enabling us to obtain optimal TX orientations through reinforcement learning without needing to rotate on mechanical devices physically. We then implement the prototype and conduct extensive experiments. The results show that, compared to other existing orientation adjustment methods, LAORA achieves an average improvement of 186% while reducing the mechanical rotations by 83.3%. © 2024 IEEE.

关键词： Inductive power transmission

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An Enhanced Deep Super-Resolution Generative Adversarial Network Approach for Skin Disease Image Regeneration and Assessment 3

An Enhanced Deep Super-Resolution Generative Adversarial Net...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： El-Fattah, Ibrahim Abd Taha, Taha E. El-Shafai, Walid Menoufia University Faculty of Electronic Engineering Department of Electronics and Electrical Communications Engineering Menouf32952 Egypt Department Electronics and Electrical Communications Engineering Alexandria Egypt Prince Sultan University Security Engineering Lab Computer Science Department Riyadh11586 Saudi Arabia

ISBN: (纸本)9798350323511

Skin cancer is a critical medical concern, posing significant challenges in accurate diagnosis. Algorithmic approaches have seen remarkable advancements across various occupations, including skin disease assessment. This study introduces a distinctive Improved Deep Super-Resolution Generative Adversarial Network (I_DSUR-GAN) methodology for regenerating low-resolution (LOR) skin disease images into super-resolution (SUR) format. Additionally, a modified SUR-dataset inspired by HAM10000 is presented for skin disease applications. The proposed approach incorporates a novel loss function design to provide supplementary information, facilitating the creation of high-quality SUR images. Experimental results demonstrate that our method outperforms existing approaches on the HAM10000 dataset. A comprehensive evaluation and employing sensitive metrics is conducted to assess the effectiveness, training periods, and memory requirements of the proposed framework. The outcomes reveal that the suggested model excels in restoring and identifying hue and texture compared to conventional and earlier models. © 2023 IEEE.

关键词： Generative adversarial networks

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