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4th IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2024

作者： Mistri, Hiranmay Ghosh, Anumoy Sardar, Abdur Rahaman Ramkrishna Mahato Government Engineering College Department of Electronics and Communication Engineering Purulia India National Institute of Technology Mizoram Department of Electronics and Communication Engineering Aizwal India Ramkrishna Mahato Government Engineering College Depatment of Computer Science and Engineering Purulia India

ISBN: (纸本)9798350352931

This article represents a broadband transmission type linear-to-circular polarization converter (LTCPC) realized using a graphene metasurface for the terahertz (THz) frequency regime. The composition of the unit cell includes a lossy-SiO2 substrate with a graphene-based sub-wavelength mono-layer structure on the top and a similar but very thin gold sub-wavelength structure at the back side to cover it partially. This metasurface can be used to transform waves with linear polarization into circularly polarized waves from 9.15 THz to 13.0 THz, i.e., about 3.85 THz bandwidth and about 35 percent of fractional bandwidth (FBW). The chemical potential (CP) and relaxation time (RT) can be varied to tune the graphene metasurface over a vast range of frequencies. All physical measurements of the unit cell geometry are optimized for performance optimization. The proposed lumped parameter circuit for the unit cell is also incorporated. The proposed unit cell can perform invariably up to 40° incident angle. © 2024 IEEE.

关键词： Terahertz waves

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Compete to Win: Enhancing Pseudo Labels for Barely-Supervised Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2023年第11期42卷 3244-3255页

作者： Wu, Huimin Li, Xiaomeng Lin, Yiqun Cheng, Kwang-Ting The Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Hong Kong The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong The Hong Kong University of Science and Technology Shenzhen Research Institute Shenzhen518057 China The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering The Department of Computer Science and Engineering Hong Kong Hong Kong

This study investigates barely-supervised medical image segmentation where only few labeled data, i.e., single-digit cases are available. We observe the key limitation of the existing state-of-the-art semi-supervised solution cross pseudo supervision is the unsatisfactory precision of foreground classes, leading to a degenerated result under barely-supervised learning. In this paper, we propose a novel Compete-to-Win method (ComWin) to enhance the pseudo label quality. In contrast to directly using one model's predictions as pseudo labels, our key idea is that high-quality pseudo labels should be generated by comparing multiple confidence maps produced by different networks to select the most confident one (a compete-to-win strategy). To further refine pseudo labels at near-boundary areas, an enhanced version of ComWin, namely, ComWin + , is proposed by integrating a boundary-aware enhancement module. Experiments show that our method can achieve the best performance on three public medical image datasets for cardiac structure segmentation, pancreas segmentation and colon tumor segmentation, respectively. The source code is now available at https://***/Huiimin5/comwin. © 1982-2012 IEEE.

关键词： Image segmentation

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Differential coding scheme based FSO channel for optical coherent DP-16 QAM transceiver systems

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Journal of Optical communications 2024年第2期45卷 331-345页

作者： Urooj, Shabana Alwadai, Norah Muhammad Sorathiya, Vishal Lavadiya, Sunil Parmar, Juveriya Patel, Shobhit K. Abd El-Aziz, Ismail A. Rashed, Ahmed Nabih Zaki Electronics and Electrical Communications Engineering Department Faculty of Electronic Engineering Menoufia University Menouf 32951 Egypt Department of Electrical Engineering College of Engineering Princess Nourah bint Abdulrahman University Riyadh84428 Saudi Arabia Department of Physics College of Sciences Princess Nourah bint Abdulrahman University Riyadh84428 Saudi Arabia Department of Information and Communication Technology Marwadi University Rajkot India Department of Physics Marwadi University Rajkot360003 India Department of Electronics and Communication Engineering Marwadi University Rajkot360003 India Department of Computer Engineering Marwadi University Rajkot360003 India Benha Faculty of Engineering Benha University Benha Egypt

This article has indicated optical coherent differential polarization (DP) 16 quadrature amplitude modulation (QAM) transceiver systems with free-space optical (FSO) channel in the presence of differential coding scheme. The optical coherent DP 16-QAM receiver executes the reverse process conversion of the optical signal into an electrical one that is detected to the users. The proposed optical coherent DP-16 QAM transceiver systems based FSO channel model with differential coding has been presented and compared with the previous model. However, the simulation results have confidence realization about the superiority of the proposed simulation model. Hence the proposed optical coherent DP-16 QAM transceiver systems simulation model with differential coding is verified and validated the enhancement performance based on simulation performance parameters. © 2024 Walter de Gruyter GmbH. All rights reserved.

关键词： Quadrature amplitude modulation

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Fusion of Forward and Backward LSTMs for Effective Occlusion Reconstruction in Gait Sequences

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SN computer Science 2025年第3期6卷 1-20页

作者： Jain, Manav Chattopadhyay, Pratik Paul, Abhishek Jain, Jinesh Department of Computer Science and Engineering Indian Institute of Technology (Banaras Hindu University) Uttar Pradesh Varanasi 221005 India Department of Electronics and Communication Engineering National Institute of Technology West Bengal Durgapur 713209 India

Gait-based person identification in the presence of occlusion is a challenging problem and research work in this domain is still in its infancy. Some methods that have been developed in the past are based on certain assumptions such as gait features over a cycle following a Gaussian, or a complete cycle can be reconstructed from multiple occluded cycles. Recent deep neural network-based approaches to occlusion handling also mostly focus on making feature-level reconstruction instead of frame-level reconstruction, and thus their effectiveness is likely to suffer if several frames in a cycle are corrupted due to occlusion. There exists a single work on LSTM-based occlusion reconstruction which predicts frames based on the previous unoccluded/reconstructed frames only and does not utilize the complete sequence information effectively. In this paper, we improve the existing work on occlusion reconstruction by performing a two-way prediction using LSTMs and finally combining the two predictions. Our reconstruction model is based on (i) a forward LSTM that performs reconstruction in the forward direction by predicting each occluded frame from a few previous unoccluded or already reconstructed frames and (ii) a backward LSTM that carries out reconstruction in the backward direction by predicting each occluded frame from a few succeeding unoccluded or already reconstructed frames. To train the LSTMs, an extensive gallery set is constructed from the CASIA-B and the OU-ISIR LP data, and the mean-square loss between the corresponding generated and target frames is minimized. Next, a fusion network combines the predictions from the two LSTMs to generate the final reconstructed frame corresponding to each occluded frame. Evaluation of our approach has been performed using synthetically occluded sequences generated from the OU-ISIR LP, OU-ISIR MVLP and CASIA-B data and real occluded sequences present in the TUM-IITKGP and GREW data. The effectiveness of the proposed reconstructio

关键词： Gait recognition Long short term memory Occlusion detection and reconstruction Residual block

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Exploring Twitter Sentiments for Predicting Match Outcomes in the Game of Cricket 2

Exploring Twitter Sentiments for Predicting Match Outcomes i...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Sharma, Karan Hota, Lopamudra Tikkiwal, Vinay Anand Kumar, Arun Department of Computer Science and Engineering National Institute of Technology Rourkela769008 India Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida201304 India

In recent years, social media platforms such as Twitter have become popular among fans to discuss and share their opinions about the matches. This research aims to contribute to the growing body of knowledge on utilizing the sentiments expressed through messages on social media platforms to predict outcomes of matches in the sport of cricket. This research work provides insights into the effectiveness of sentiment analysis techniques in forecasting match outcomes, which can be beneficial to cricket fans, and cricket analysts. In this work, sentiment analysis is carried out based on models developed using various machine-learning techniques. The work also explores the impact of different features, such as the number of tweets collected, the timing of tweets, and the sentiment analysis techniques on the accuracy of the prediction. The results of this study have implications for cricket fans, sports analysts, and the broader field of predictive analytics. © 2024 Elsevier B.V.. All rights reserved.

关键词： Sentiment analysis

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Analysis of Deep Learning Models for Potato Leaf Disease Classification and Prediction 2nd

Analysis of Deep Learning Models for Potato Leaf Disease Cla...

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2nd International Conference on Advances in Data-driven Computing and Intelligent Systems, ADCIS 2023

作者： Mathur, Pramod Kumar, Sunil Yadav, Vaishali Sangwan, Dhiraj Department of Computer and Communication Engineering Manipal University Jaipur Jaipur India Central Electronics Engineering Research Institute Rajasthan Pilani India

ISBN: (纸本)9789819995233

Plant diseases reduce yields, directly affecting domestic and global food production systems. Using image classification and early prediction of plant diseases can help us to manage yield production properly. This study evaluated the deep learning models VGG19 and ResNet50 for potato leaf disease classification and prediction. The performance of deep learning models VGG19 and Resnet50 is recorded based on performance metrics such as confusion matrix, precision, recall, accuracy, f1-score, and ROC/AUC. The models VGG19 and Resnet50 achieved the highest accuracy at 93% and 92.58%, respectively. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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Overview of Versatile Video Coding (H.266/VVC) and Its Coding Performance Analysis

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IEIE Transactions on Smart Processing and Computing 2023年第2期12卷 122-154页

作者： Lee, Minhun Song, HyeonJu Park, Jeeyoon Jeon, Byeungwoo Kang, Jungwon Kim, Jae-Gon Lee, Yung-Lyul Kang, Je-Won Sim, Donggyu Department of Computer Engineering Kwangwoon University Seoul Korea Republic of Department of Computer Engineering Sejong University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Kyunggi-do Korea Republic of Communication and Media Research Laboratory Electronics and Telecommunications Research Institute Deajeon Korea Republic of School of Electronics and Information Engineering Korea Aerospace University Kyunggi-do Korea Republic of Department of Electronic and Electrical Engineering Graduate Program in Smart Factory Ewha Womans University Seoul Korea Republic of

Versatile Video Coding (H.266/VVC) is the newest video coding standard jointly developed by the Joint Video Experts Team (JVET), which is organized by the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG). H.266/VVC provides about 40% bitrate reduction compared with High Efficiency Video Coding (H.265/HEVC) for the same visual quality. This paper introduces in detail the core structure of H.266/VVC by highlighting its features within block partitioning structure, intra/inter prediction, transform, quantization, and in-loop filtering, compared to its predecessor (H.265/HEVC). H.266/VVC yields significantly improved the coding performance, but it increased the computational complexity, particularly for the encoder side, which remains a problem to be tackled for successful commercialization in the future. This paper examined the statistical performance of H.266/VVC coding tools from the bitstreams encoded by the VVC test model (VTM12.0) encoder through rate-distortion optimization under the JVET common test conditions. In addition, the complexity and performance analyses are conducted on the block partitioning structure and group of picture structure. It is expected that an optimized H.266/VVC encoder can be designed and developed by minimizing the coding loss based on the analysis data. © 2023 institute of electronics and Information Engineers. All rights reserved.

关键词： Image compression

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Enhanced Humidity Sensing with Nafion-Based Chipless RFID Resonators 6

Enhanced Humidity Sensing with Nafion-Based Chipless RFID Re...

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6th International Conference on Electrical engineering and Information and communication Technology, ICEEICT 2024

作者： Bashar, Abdullah Ibn Mumu, Nadia Islam Ul Islam, Akm Adeeb Islam, Md Aminul Military Institute of Science and Technology Mirpur Cantonment Department of Electrical Electronic and Communication Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798350385779

Chipless radio frequency identification (RFID) has recently been the focus of extensive research due to its efficiency and low-cost manufacturing process. In this article, a highly sensitive, rotation-independent chipless RFID sensor is presented that can be deployed for humidity sensing. Different RFID resonators are designed and simulated, and the results are compared. The proposed RFID resonator is based on an electric-field coupled (ELC) structure. Another part of the study is humidity sensing which is introduced by utilizing the dielectric change of Nafion 117, a material that is strongly interactive with water molecules. A comparative evaluation is done with the commonly used resonators. The response of the sensor is simulated for a variation in relative humidity (RH) ranging from 0%-90%. The results exhibit significant potential to be used in humidity-dependent applications. © 2024 IEEE.

关键词： Electric fields

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Securing IoT Systems with AI-Infused Software and Virtual Replica Models 2

Securing IoT Systems with AI-Infused Software and Virtual Re...

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2nd IEEE International Conference on Integrated Intelligence and communication Systems, ICIICS 2024

作者： Supriya, K. Sangeetha Jeno Lovesum, S.P. Arora, Renuka Bhatia, Rishu Yadwad, Sunita Natrayan, L. Department of Computer Science & Engineering Christ University Bengaluru India Department of Computer Science & Engineering Jagannath University NCR Bahadurgarh India Department of Electronics and Communication Engineering Ganga Institute of Technology and Management Jhajjar India Department of Computer Science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Vadodara India Department of Mechanical Engineering Saveetha School of Engineering SIMATS Chennai India

ISBN: (纸本)9798331504960

The proliferation of Internet of Things (IoT) devices has introduced significant security challenges due to the increased attack surface and the inherent vulnerabilities of interconnected systems. This paper proposes a novel security framework that integrates Artificial Intelligence (AI)-infused software and Virtual Replica Models, or Digital Twins, to enhance the robustness of IoT systems. The framework leverages AI-driven anomaly detection and predictive analysis to identify potential threats in real-time, while Digital Twins provide a virtual replica of the IoT ecosystem for continuous monitoring and simulation-based threat assessment. By fusing these technologies, the proposed solution enables proactive security measures, ensuring rapid response to cyberattacks and continuous system optimization. Extensive simulations and experimental results demonstrate the system’s ability to reduce attack detection times by 30%, while improving overall system resilience and minimizing false positive rates. This approach provides a scalable and adaptable solution for securing IoT infrastructures in various industries. © 2024 IEEE.

关键词： Risk assessment

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Intrusion Detection System for Big Data Analytics in IoT Environment

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computer Systems Science & engineering 2022年第10期43卷 381-396页

作者： M.Anuradha G.Mani T.Shanthi N.R.Nagarajan P.Suresh C.Bharatiraja Department of Computer Science and Engineering St.Joseph’s College of EngineeringChennai600119India Department of Computer Science and Engineering University College of Engineering ArniThatchur632326India Department of Electronics&Communication Engineering Kings College of EngineeringPudukkottai613303India Department of Electronics&Communication Engineering K.Ramakrishnan College of EngineeringTiruchirapalli621112India Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electrical and Electronics Engineering SRM Institute of Science and TechnologyChennai603203India

In the digital area,Internet of Things(IoT)and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal *** IoT networks are popular and widely employed in real world applications,security in IoT networks remains a challenging *** intrusion detection systems(IDS)cannot be employed in IoT networks owing to the limitations in resources and ***,this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning(IMFSDL)based classification model,called IMFSDL-IDS for IoT *** proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages:data transformation and data *** manage big data,Hadoop ecosystem is ***,the IMFSDL-IDS model includes a hill climbing with moth flame optimization(HCMFO)for feature subset selection to reduce the complexity and increase the overall detection ***,the beetle antenna search(BAS)with variational autoencoder(VAE),called BAS-VAE technique is applied for the detection of intrusions in the feature reduced *** BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification *** validate the intrusion detection performance of the IMFSDL-IDS system,a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct *** resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25%and 97.39%on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.

关键词： Big data cybersecurity IoT networks intrusion detection deep learning metaheuristics intelligent systems

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