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2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023

作者： Catak, Ferhat Ozgur Catak, Evren Elezaj, Ogerta University of Stavanger Electrical Engineering and Computer Science Stavanger Norway Independent Researcher Stavanger Norway School of Computing and Digital Technology Birmingham City University Birmingham United Kingdom

ISBN: (纸本)9798350346473

Random number generation (RNG) is a crucial element in security protocols, and its performance and reliability are critical for the safety and integrity of digital systems. This is especially true in 5G networks with many devices with low entropy sources. This paper proposes 5G-SRNG, an end-to-end random number generation solution for devices with low entropy sources in 5G networks. Compared to traditional RNG methods, the 5G-SRNG relies on hardware or software random number generators, using 5G spectral information, such as from spectrum-sensing or a spectrum-aware feedback mechanism, as a source of entropy. The proposed algorithm is experimentally verified, and its performance is analysed by simulating a realistic 5G network environment. Results show that 5G-SRNG outperforms existing RNG in all aspects, including randomness, partial correlation and power, making it suitable for 5G network deployments. © 2023 IEEE.

关键词： Random number generation

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Telehealth Data Security and Privacy Solutions for Sensitive Health Records using Cloud Computing and Isolation Forest Algorithm 2

Telehealth Data Security and Privacy Solutions for Sensitive...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Prathik, A. Banu, S. Parveen Sagar, B.S. Prakash, A. John Felix Thamizhamuthu, R. Velmurugan, S. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha College of Liberal Arts and Sciences Department of Commerce Tamil Nadu Chennai India Reva University Department of Electrical and Electronics Engineering Karnataka Bengaluru India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Campus-Chengalpattu Tamil Nadu Chennai India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350368413

Protecting the confidentiality of patient's personal health information as it is sent and kept in the cloud is of the utmost importance due to the increasing number of telehealth services. This research provides a new healthcare data security approach using cloud computing and the Isolation Forest method. To identify important challenges and provide a novel technique, a thorough literature study on telehealth security, privacy issues, and cloud-based solutions. It uses the Isolation Forest method for anomaly detection in combination with encryption and access restriction systems to identify intrusion attempts. It uses simulated telehealth data in trials to test the system's work. Findings show substantial gains in privacy and data security with computational burden. Healthcare practitioners and legislators will find results useful in their pursuit of solutions to the problem of securing patients' private health information throughout the rise of telemedicine. © 2024 IEEE.

关键词： Electronic health record

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Design, Analysis, and Experimental Validation of the Harmonic Compensation for Three-Phase Stand-Alone Voltage-Source UPS Inverters

Design, Analysis, and Experimental Validation of the Harmoni...

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2022 IEEE Industry Applications Society Annual Meeting, IAS 2022

作者： Nazeri, Ahmad Ali Zacharias, Peter Centre of Competence for Distributed Electric Power Technology Faculty of Electrical Engineering Computer Science University of Kassel Kassel Germany

ISBN: (数字)9781665478151

ISBN: (纸本)9781665478151

This paper presents a step-by-step design procedure, an extensive system stability analysis, and proper selection of the discretization for the multi-loop control of three-phase power converters in the synchronous and stationary reference frames. A proportional-integral multi-resonant controller (PI-MR) in the synchronous reference frame is implemented for the regulation of the output voltage loop. Moreover, the inverter inductor current controller in a stationary frame is proposed to provide active damping, and improve transients, and steady-state performance. The instantaneous reference voltage of the converter is used as a feed-forward signal at the output of the converter to robust the system performance and simplify the controller design. The traditional PI-MR controller is compared for different load conditions with the practical proportional multi-resonant (PMR) controller in parallel with the harmonic compensators of orders 5th, 7th, 11th, and 13th to reduce low-order load current harmonics. A comprehensive design guideline for the multi-loop controllers with the stability analysis of the system is presented. The system is simulated in MATLAB/Simulink environment and experimentally implemented on a 7.5 kW inverter with the proposed control scheme to validate the proposed methodology. © 2022 IEEE.

关键词： Controllers

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Cost Optimization for Serverless Edge Computing with Budget Constraints using Deep Reinforcement Learning

arXiv

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arXiv 2025年

作者： Chen, Chen Guan, Peiyuan Chen, Ziru Taherkordi, Amir Hou, Fen Cai, Lin X. Department of Computer Science and Technology University of Cambridge United Kingdom Department of Informatics University of Oslo Norway Department of Electrical and Computer Engineering Illinois Institute of Technology United States Department of Electrical and Computer Engineering University of Macau China

Serverless computing adopts a pay-as-you-go billing model where applications are executed in stateless and short-lived containers triggered by events, resulting in a reduction of monetary costs and resource utilization. However, existing platforms do not provide an upper bound for the billing model which makes the overall cost unpredictable, precluding many organizations from managing their budgets. Due to the diverse ranges of serverless functions and the heterogeneous capacity of edge devices, it is challenging to receive near-optimal solutions for deployment cost in a polynomial time. In this paper, we investigated the function scheduling problem with a budget constraint for serverless computing in wireless networks. Users and IoT devices are sending requests to edge nodes, improving the latency perceived by users. We propose two online scheduling algorithms based on reinforcement learning, incorporating several important characteristics of serverless functions. Via extensive simulations, we justify the superiority of the proposed algorithm by comparing with an ILP solver (Midaco). Our results indicate that the proposed algorithms efficiently approximate the results of Midaco within a factor of 1.03 while our decision-making time is 5 orders of magnitude less than that of Midaco. © 2025, CC BY.

关键词： Budget control

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Bio-inspired wrapper-based feature selection: does the choice of metric matter? 5

Bio-inspired wrapper-based feature selection: does the choic...

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5th IEEE International Conference on Smart Systems and Technologies, SST 2022

作者： Bajer, Dražen Dudjak, Mario Zorić, Bruno Faculty of Electrical Engineering Computer Science and Information Technology Osijek Josip Juraj Strossmayer University of Osijek Osijek Croatia

ISBN: (数字)9781665482158

ISBN: (纸本)9781665482158

Applications of bio-inspired algorithms to feature selection offering promising performance can be frequently encountered in the literature. Serving as wrappers, their application essentially boils down to the selection of a classifier and an appropriate metric for evaluating model performance. Although the choice of classifier was investigated to a viable extent, this is, however, not the case for the choice of metric and its impact on performance. Nevertheless, the utilisation of a wide variety of metrics is clearly apparent, albeit to different proportions. This raises the question of whether some metrics might be a better choice than others for bio-inspired wrapper-based methods. This paper sheds some light on this matter by comparing different metrics from different perspectives, like correlation, general performance and subset sizes. Unexpected results were obtained, to say the least, apart from some minor exceptions. Generally, no single metric appears to have an edge over the others. Yet, further investigation is certainly warranted. © 2022 IEEE.

关键词： Feature Selection

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Red VCSEL Array for Optical Parallel-Interconnected Links with Ultra-Low Energy Consumption

Red VCSEL Array for Optical Parallel-Interconnected Links wi...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Nawal Almaymoni Omar Alkhazragi Fabian Finkbeiner Tien Khee Ng Boon S. Ooi Photonics Laboratory Electrical and Computer Engineering Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia Materials Science and Engineering Division of Physical Science and Engineering King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

We demonstrated a high data rate of 4.7 Gb/s based on a single 650-nm vertical-cavity surface-emitting laser with 2-pJ/bit energy consumption, potentially enabling Tb/s parallel interconnects based on a $14\times 16$... 详细信息

ISBN: (纸本)9798350369311

关键词： Optical fibers Energy consumption Optical interconnections Surface waves Electro-optic effects Light emitting diodes Arrays Vertical cavity surface emitting lasers Optical arrays Optical surface waves

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Enhancement in Skin Cancer Detection using Image Super Resolution and Convolutional Neural Network

Enhancement in Skin Cancer Detection using Image Super Resol...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Lembhe, Ashutosh Motarwar, Pranav Patil, Rudra Elias, Susan School of Electrical and Electronics Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Electronics and Computer Engineering Vellore Institute of Technology Chennai India Centre for Advanced Data Science Vellore Institute of Technology Chennai India

Skin cancer has been one of the major worldwide public health issue with more than 1 million cases every year. Skin cancer is classified into three categories: Basal Cell Carcinoma, Melanoma and Squamous Cell Carcinoma [1]. Melanoma is the most critical category of skin cancer with very thin chances of recovery and survival of the patient. Early diagnosis of skin cancer can drastically improve survival rate to as high as 95 percent. This served as a motivation to contribute to this noble cause using technology based solutions. In the process of diagnosis for the disease, the process is divided into four basic components: image processing including hair removal, noise removal, sharpening, and increasing the resolution of the image given to the skin dimension. Recent developments in identification of skin cancer technology uses machine learning and in-depth based reading segmentation algorithms. Most used algorithms are: InceptionV3, ResNet, VGGNet. This paper suggests an artificial skin cancer screening process using techniques like image processing and machine learning. Image super-resolution (ISR) techniques recreate an image with high resolution or sequence from visual LR images. An approach using deep learning on the Image super resolution was used to boost the accuracy of the convolutional neural network model. This model was developed using the Keras backend and tested the model by modifying the layers of neural network which are used for training. The model is built on publicly sourced dataset from the ISIC data archives. © 2023 The Authors. Published by Elsevier B.V.

关键词： Neural network models

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DPAL-BERT:A Faster and Lighter Question Answering Model

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computer Modeling in engineering & sciences 2024年第10期141卷 771-786页

作者： Lirong Yin Lei Wang Zhuohang Cai Siyu Lu Ruiyang Wang Ahmed AlSanad Salman A.AlQahtani Xiaobing Chen Zhengtong Yin Xiaolu Li Wenfeng Zheng Department of Geography and Anthropology Louisiana State UniversityBaton RougeLA 70803USA School of Automation University of Electronic Science and Technology of ChinaChengdu610054China College of Computer and Information Sciences King Saud UniversityRiyadh11574Saudi Arabia School of Electrical and Computer Engineering Louisiana State UniversityBaton RougeLA 70803USA College of Resources and Environmental Engineering Guizhou UniversityGuiyang550025China School of Geographical Sciences Southwest UniversityChongqing400715China

Recent advancements in natural language processing have given rise to numerous pre-training language models in question-answering ***,with the constant evolution of algorithms,data,and computing power,the increasing size and complexity of these models have led to increased training costs and reduced *** study aims to minimize the inference time of such models while maintaining computational *** also proposes a novel Distillation model for PAL-BERT(DPAL-BERT),specifically,employs knowledge distillation,using the PAL-BERT model as the teacher model to train two student models:DPAL-BERT-Bi and *** research enhances the dataset through techniques such as masking,replacement,and n-gram sampling to optimize knowledge *** experimental results showed that the distilled models greatly outperform models trained from *** addition,although the distilled models exhibit a slight decrease in performance compared to PAL-BERT,they significantly reduce inference time to just 0.25%of the *** demonstrates the effectiveness of the proposed approach in balancing model performance and efficiency.

关键词： DPAL-BERT question answering systems knowledge distillation model compression BERT Bi-directional long short-term memory(BiLSTM) knowledge information transfer PAL-BERT training efficiency natural language processing

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Securing Smart Microgrids: A Cybersecurity Survey

Securing Smart Microgrids: A Cybersecurity Survey

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Nair, Prabha Shreeraj Mandal, Sanjeev Kumar Sharma, Neeraj Noida Institute of Engineering & Technology Department of It and M.Tech Integrated Uttar Pradesh Greater Noida India Computer Science Engineering Department Bangalore India Vivekananda Global University Department of Electrical Engineering Jaipur India

ISBN: (纸本)9798350357769

The cybersecurity concerns of intelligent microgrids are thoroughly investigated in this scholarly work. The intricate link that exists between the cyber grid and smart grid operating processes is examined, exposing the cyber system's susceptibility to different types of attacks. The research highlights the potentially catastrophic consequences, both materially and financially, that might result from hacks that jeopardize the operation of these smart microgrids. Particularly in islanded modes with little inertia, hackers are more likely to affect power electronics-intensive smart small power plants and seriously jeopardize their operational stability. The study starts with a brief synopsis of digital and physical systems in wise micro-grids before delving further into cyberattacks that aim to compromise data availability, confidentiality, and integrity. The research categorizes defensive methods into two groups in order to counter FDI attacks: protection plans, which include safeguarding certain meter readings, and surveillance/mitigation strategies, which comprise perhaps static or dynamic detection techniques. Examples of how to create, detect, and mitigate FDI attacks in smart microgrids are provided. The paper also includes a list of critical cybersecurity guidelines pertaining to smart electricity networks and notable instances of ongoing cybersecurity projects carried out globally. The essay concludes with a discussion of future directions in smart microgrid cybersecurity. © 2023 IEEE.

关键词： AC-DC Microgrids Communication Network Cyber-Physical Systems Cyberattacks Cybersecurity Defense Strategies Detection Strategies Fraudulent Data Injection (FDI) Power Electronics Converters Smart Grid Cybersecurity and Ongoing Cybersecurity Projects Smart Microgrids

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Fully Tensorized Convolutional Long Short-Term Memory for Hyperspectral Image Classification 13

Fully Tensorized Convolutional Long Short-Term Memory for Hy...

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13th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2023

作者： Ma, Tian-Yu Li, Heng-Chao Zheng, Yu-Bang Du, Qian Southwest Jiaotong University School of Information Science and Technology Chengdu610031 China Mississippi State University Department of Electrical and Computer Engineering StarkvilleMS39762 United States

ISBN: (纸本)9798350395570

Convolutional long short-term memory (ConvLSTM) can encode spatial information while modeling long-range dependencies in sequence data for the classification of hyperspectral images (HSIs). However, the design of the special gate structure and the convolution operation result in high model complexity. To solve this problem, an efficient spatial-spectral ConvLSTM 2D neural network model in tensor train (TT) format (namely ETT-SSCL2DNN) is proposed for the feature extraction and classification of HSIs under the premise of low complexity. This approach decomposes the channels of the tensorized convolution kernel and jointly compresses all weight tensors within the recurrent units, significantly reducing the number of parameters and the computation in the model. The experimental results on two HSI data sets demonstrate the superiority of the proposed method in terms of classification performance and model complexity. © 2023 IEEE.

关键词： Convolution

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