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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Monir, Md Fahad Hossain, Md Junayed Barkatullah, Mohammad Hoque, Md Mozammal Hassan, Zoheb Ahmed, Tarem Virginia Polytechnic Institute and State University Department of Electrical and Computer Engineering United States Department of Computer Science & Engineering Bangladesh University of Houston Department of Engineering Technology United States Université Laval Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798331517786

Autonomous Vehicle System (AVS) is rapidly advancing and is expected to completely transform the transportation industry, bringing about a new era of mobility. As digital data proliferation strains network resources, the demand for performing resource-intensive edge-assisted Deep Learning tasks in AVS with limited computational resources becomes increasingly challenging. The advent of 5G and future cellular networks (NextG) offers the promise of facilitating the seamless execution of these tasks. This research aims to diminish dataset size, in harmony with the integration capabilities of the Semantic and Flexible Open Radio Access Network (SEMO-RAN) framework, which is anticipated to reduce latency and refine resource allocation in vehicle image processing. The focus of this study is on optimizing image compression without compromising the accuracy of vehicle classification, leveraging esteemed Convolutional Neural Network models like YOLOv5. By employing Generative Adversarial Network compression and Wavelet Image Compression methods, our study achieves an impressive 81.82% reduction in data size while maintaining a 96.97% accuracy in classification tasks. This underscores the potential for significant efficiency gains in AVS through improved data management, supported by Digital Twins (DT), Integrated Sensing and communication (ISAC), and O-RAN. © 2024 IEEE.

关键词： Radio access networks

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Video and image quality improvement using an enhanced optimized dehazing technique

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Multimedia Tools and Applications 2024年 1-19页

作者： Ayoub, Abeer El-Shafai, Walid El-Samie, Fathi E. Abd Hamad, Ehab K. I. EL-Rabaie, El-Sayed M. Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Lab Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

The detrimental effects of atmospheric haze frequently plague outdoor imagery. This phenomenon arises from the scattering of light by minute particles within the ambient environment surrounding the scene to be imaged. Haze engenders an overall whitening of the image, leading to diminished contrast. To address these issues and enhance the quality of hazy images and videos, an enhanced dehazing technique is proposed. The proposed technique includes image enhancement before the optimal dehazing process. The enhancement stage entails the implementation of both homomorphic processing and Contrast Limited Adaptive Histogram Equalization (CLAHE), serving to control the image dynamic range, while concurrently heightening the image contrast. The culminating stage encompasses an optimized dehazing technique, adept at expunging haze-induced artifacts from images. The homomorphic processing and CLAHE, applied in the pre-processing step, establish a foundation for the subsequent dehazing procedure. This proposed methodology is systematically applied to a gamut of visual outputs, including visible videos, Near-Infrared (NIR) frames, and authentic hazy images. Comparative evaluations of the proposed technique, homomorphic-processing-based enhanced dehazing, and standalone dehazing techniques is undertaken on different video types encompassing five frames. It is evident that the proposed technique, synergizing the homomorphic processing, CLAHE, and dehazing, outperforms alternative strategies. Furthermore, the proposed technique is subjected to a comparative study with various existing dehazing techniques over real hazy images. The assessment depends on Peak Signal-to-Noise Ratio (PSNR), correlation, and entropy metrics. The results underscore the efficacy of the proposed technique, particularly in terms of spectral entropy enhancement of dehazed frames. For both visible and NIR frames, the percentages of enhancement by the proposed technique are 17.66% and 118.48%, respectively. ©

关键词： Image enhancement

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Anomaly Detection in Social Media Texts Using Optimal Convolutional Neural Network

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Intelligent Automation & Soft Computing 2023年第4期36卷 1027-1042页

作者： Swarna Sudha Muppudathi Valarmathi Krishnasamy Department of Computer Science and Engineering Ramco Institute of TechnologyRajapalayam626117India Department of Electronics and Communication Engineering P.S.R.Engineering CollegeSivakasi626140India

Social Networking Sites(SNSs)are nowadays utilized by the whole world to share ideas,images,and valuable contents by means of a post to reach a group of *** use of SNS often inﬂicts the physical and the mental health of the ***,researchers often focus on identifying the illegal beha-viors in the SNS to reduce its negative inﬂ*** state-of-art Natural Language processing techniques for anomaly detection have utilized a wide anno-tated corpus to identify the anomalies and they are often time-consuming as well as certainly do not guarantee maximum *** overcome these issues,the proposed methodology utilizes a Modiﬁed Convolutional Neural Network(MCNN)using stochastic pooling and a Leaky Rectiﬁed Linear Unit(LReLU).Here,each word in the social media text is analyzed based on its *** stochastic pooling accurately detects the anomalous social media posts and reduces the chance of overﬁ*** LReLU overcomes the high computational cost and gradient vanishing problem associated with other activation *** also doesn’t stop the learning process when the values are *** MCNN computes a speciﬁed score value using a novel integrated anomaly detection *** on the score value,the anomalies are identiﬁed.A Teaching Learn-ing based Optimization(TLBO)algorithm has been used to optimize the feature extraction phase of the modiﬁed CNN and fast convergence is *** this way,the performance of the model is enhanced in terms of classiﬁcation *** efﬁciency of the proposed technique is compared with the state-of-art techni-ques in terms of accuracy,sensitivity,speciﬁcity,recall,and *** proposed MCNN-TLBO technique has provided an overall architecture of 97.85%,95.45%,and 97.55%for the three social media datasets namely Facebook,Twitter,and Reddit respectively.

关键词： Anomaly detection convolutional neural network social networking sites stochastic pooling teacher learner-based optimization

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Dynamic Data Placement Strategy with Network Security Issues in Distributed Cloud Environment for Medical Issues: An Overview

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Recent Advances in computer Science and communications 2024年第6期17卷 25-38页

作者： Pradhan, Devasis Behera, Majusha Gheisari, Mehdi Department of Electronics and Communication Engineering Acharya Institute of Technology Karnataka Bangalore560107 India School of Electronics Engineering Campus 12 KIIT deemed to be University Odisha Bhubaneswar India Department of Cognitive Computing Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India

The rapid integration of distributed cloud systems in the healthcare industry has profoundly impacted the management of valuable medical data. While this advancement has significantly improved data handling, protecting sensitive healthcare information in such a complex environment remains daunting. This comprehensive study explores the crucial intersection between dynamic data placement strategies and network security concerns in distributed cloud environments, particularly healthcare. After establishing the significance and context of this research, the survey delves into the growing need to safeguard medical data within the everevolving landscape of cloud-based healthcare systems. It lays out fundamental concepts, such as dynamic data placement and network security, highlighting their unique implications in the medical domain. Ultimately, this survey sheds light on the most effective approaches for balancing dynamic data placement and network security in the healthcare sector. This research delves into examining many tactics, evaluating their effectiveness in handling delicate medical information, and presenting tangible use cases. A key focus of this investigation is the fusion of data organization and network safety within the healthcare industry. It investigates the adaptability of dynamic data positioning techniques in fortifying network security and safeguarding against potential threats unique to the healthcare sector. Case studies of the successful implementation of these strategies in healthcare establishments are also included. © 2024, Bentham Science Publishers. All rights reserved.

关键词： Electronic health record

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Spatiotemporal sequence data mining: Traffic flow prediction algorithm based on Recurrent Neural Networks

Advances in Transportation Studies

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Advances in Transportation Studies 2024年 64卷 25-40页

作者： Liu, Y.P. Liu, B.N. Department of Computer Engineering Taiyuan Institute of Technology Shanxi Taiyuan030008 China Department of Traffic Equipment and Control Engineering School of Traffic Electrical and Control Engineering North China University of Technology Beijing100043 China

Traffic congestion is one of the most common problems in the transportation system. In urban planning and construction, traffic congestion increases the difficulty of control and scheduling, hindering the pace of urban modernization development. The development of intelligent transportation systems has to some extent solved these problems, among which traffic flow prediction is particularly crucial. A period processing layer containing concatenation, dimension amplification, and diffusion convolution was studied and designed. On the grounds of this, a graph neural network prediction model combining spatiotemporal period characteristics was designed. And it further introduced a traffic flow prediction method on the grounds of dynamic topology maps. This model combined adaptive topology generation and parameter learning mechanisms, synchronously processing the complex correlation of traffic data and the heterogeneity between nodes through gated recurrent neural networks. The outcomes indicated that the research model is more excellent than other graph learning methods in three evaluation indicators: Mean square error, mean absolute percentage error, and root mean square error. Their values were 15.63%, 9.87%, and 25.08%, respectively. The evaluation on all four datasets showed excellent performance of the research model, confirming the effectiveness of its adaptive topology and parameter learning strategy. This study had important practical significance for improving the traffic management system, alleviating traffic pressure, and enhancing the travel experience of urban residents. © 2024, Aracne Editrice. All rights reserved.

关键词： Mean square error

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Data-Driven Cloud Based Soil Nutrient Prediction System for Precision Agriculture

Data-Driven Cloud Based Soil Nutrient Prediction System for ...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Mishra, Amit Dass, Jeevesh Saxena, Siddhant Prakash, Ishan Satya Sargun Garg, Arshia Verma, Karun Bhattacharya, Jhilik Thapar Institute of Engineering & Technology Department of Electronics and Communication Engineering Punjab Patiala India Thapar Institute of Engineering & Technology Department of Computer Science Engineering Punjab Patiala India Thapar Institute of Engineering & Technology Department of Electronics and Computer Engineering Punjab Patiala India

ISBN: (纸本)9798331533311

Decreasing arable acreage and a growing world population are pushing for new farming systems. Achieving high crop production requires maintaining a balanced nutrient level in the soil. This study offers a machine learning-based model that will predict the NPK fertilizer to achieve the higher crop yields. Field sensor data along with meteorological data and remote sensing data from various test fields are integrated. A random forest and decision tree machine learning model are applied to the integrated data to develop a software tool for precision agriculture, enabling accurate estimation of nutrient requirements. A mobile application is used to share the nutrient prediction with farmers, enabling them to make well-informed choices regarding resource utilization efficiency and environmental friendliness. This research can transform agricultural practices globally and contribute towards food security on earth. © 2024 IEEE.

关键词： Smart agriculture

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Physical Layer Security for Amplify-and-Forward Relay Aided Cognitive Radio Networks 17

Physical Layer Security for Amplify-and-Forward Relay Aided ...

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17th International Conference on communication Systems and NETworkS, COMSNETS 2025

作者： Goswami, Abhilash Rao, Shilpa Kashyap, Salil Indian Institute of Information Technology Department of Electronics and Communication Engineering Guwahati India Indian Institute of Technology Department of Electronics and Electrical Engineering Guwahati India

ISBN: (纸本)9798331531195

Wireless systems are susceptible to security attacks due to their broadcast nature. Physical layer security (PLS) technique, which exploits channel properties to secure wireless systems, is a promising alternative to traditional shared key based cryptographic methods. We deploy artificial noise (AN)-assisted PLS technique to secure messages from an eavesdropper (ES) in an instantaneous interference-constrained underlay relay-aided cognitive radio network (CRN). An amplify-and-forward (AF) relay, which is intercepted by an ES, forwards the message from the secondary source (SS) to secondary destination (SD). The transmit power of the SS and the relay are restricted by interference constraints of the primary network. We develop a PLS-integrated transmission protocol for the secondary network, wherein SD sends out AN simultaneously when SS transmits the message to the relay, to hinder interception. We determine secondary's performance using the intercept probability (IP), which is the probability that the SS-relay-ES channel capacity exceeds the difference between transmission and secrecy rates, and the outage probability (OP), which is the probability that the SS-SD channel capacity is less than a specific transmission rate. We derive expressions for OP and IP under Rayleigh fading conditions and validate our theoretical results through Monte Carlo simulations. Our results also show IP and OP variations with changes in secondary's transmit powers and primary's interference thresholds. We observe that as the interference threshold reduces and RY-SD channel gain reduces, IP increases. We note that IP is lower when SD transmits AN when compared to the scenario when SD does not transmit AN. © 2025 IEEE.

关键词： Rayleigh fading

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Feature-enhanced deep learning technique with soft attention for MRI-based brain tumor classification

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International Journal of Information Technology (Singapore) 2024年第3期16卷 1617-1626页

作者： Mohanty, Bipin Ch. Subudhi, P.K. Dash, Ratnakar Mohanty, Bidyadhar Department of Electronics and Communication Engineering BPUT Odisha Rourkela 769015 India Department of Electronics and Communication Engineering Nalanda Institute of Technology Odisha Bhubaneshwar 754005 India Department of Computer Science and Engineering National Institute of Technology Odisha Rourkela 769008 India

Brain tumor classification using Magnetic Resonance Imaging (MRI) is a pivotal area in medical diagnostics, with the potential to influence early detection and subsequent treatment strategies. Over the years, various machine learning and deep learning models have been proposed to enhance the accuracy of MRI-based tumor detection. In this evolving landscape, this paper introduces a distinctive deep-learning model that harnesses the power of a soft attention mechanism. We employ a meticulously designed Convolutional Neural Network (CNN) comprising four convolution layers. One of the significant innovations in our approach is the method of feature extraction. Instead of extracting features solely from the final layer, as is common in many models, our approach aggregates and combines features from all layers. This ensures that the vital characteristics intrinsic to each layer are not lost but rather amalgamated into a robust and comprehensive feature vector. The incorporation of a soft attention mechanism at the terminal stages ensures that the most salient and clinically relevant features are emphasized, enhancing classification accuracy. To validate the efficacy of our proposed model, we employed standard datasets for training and testing. A comparative analysis with existing state-of-the-art models affirms the superiority and potential of our approach in the realm of MRI-based brain tumor classification. © 2024, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： Brain tumor CNN Deep learning MRI Soft-attention

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Hybrid renewable energy storage system using modified power management strategies in standalone microgrid

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International Journal of Power and Energy Conversion 2025年第2期16卷 205-220页

作者： Shankar, Gowri Varatharaju, V.M. Subramanian, Sendil Kumar Sathyabhama, B. Siddharth Institute of Engineering and Technology Andhra Pradesh Puttur517583 India Oman Engineering Department University of Technology and Applied Sciences Ibra Oman Department of Electrical and Electronics Engineering S.A. Engineering College Chennai India Department of Electronics and Communication Engineering Panimalar Engineering College Bangalore Trunk Road Varadharajapuram Poonamallee Tamil Nadu Chennai600123 India

The utilisation of renewable power sources has been more common as a dependable and clean energy source. Two possible alternative sources of energy include renewable energy sources, such as solar energy and wind energy. The audit para monitoring system (APMS) and the versatile energy spectral algorithm (VESA) control based hybrid system that has been discussed have been built with the support of the MATLAB simulation 2017b software. These characteristics include switching losses, electromagnetic interference, efficiency, power factor, and total harmonic distortion. In the proposed system, the average voltage may be generated with a total harmonic distortion (THD) of 4.73% and a switching loss of 3.8%. The proposed VESA was able to produce outcomes that were better. A total harmonic distortion (THD) of 4.09% and a switching loss of around 3.5% are both characteristics of the voltage that it generates. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Hybrid power

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Impact of Process-Induced Inclined Side-Walls on Gate Leakage Current of Nanowire GAA MOSFETs

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IEEE Transactions on Electron Devices 2024年第3期71卷 2196-2202页

作者： Maniyar, Ashraf Raj, Pushp Srinivas, P.S.T.N. Kumar, Arun Chang-Liao, Kuei-Shu Tiwari, Pramod Kumar Indian Institute of Technology Patna Department of Electrical Engineering Bihar Patna801106 India Vellore Institute of Technology School of Electronics Engineering Vellore801106 India National Institute of Technology Silchar Department of Electronics and Communication Engineering Silchar788010 India National Tsing Hua University Department of Engineering and System Science Hsinchu30013 Taiwan

In this work, the influence of process-induced sidewall inclination on direct tunneling gate leakage current (IG) in the trapezoidal (Tz) channel NW GAA MOSFETs has been comprehensively investigated using experimental data and the TCAD simulations results. Variability in device parameters as channel length, height, and width have been taken into account when we examined gate leakage current. The side-wall inclination angle has a significant influence on the gate leakage current, where a rise in IG of up to two times is observed when the inclination angle is increased from 0 to 20. Moreover, it has also been observed that the direct tunneling gate leakage current is significantly influenced by the gate overlap and underlap areas. © 1963-2012 IEEE.

关键词： III-V semiconductors

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