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A Detailed Analysis of.Secrecy and Performance Metrics in Hybrid RF/ FSO Communication Systems: Implications of.Composite Weibull and Log-normal Fading with M Turbulent Channels

A Detailed Analysis of Secrecy and Performance Metrics in Hy...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

作者： Upadhyay, Deepak Sharma, Kunj Bihari Gupta, Mridul Upadhyay, Abhay Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India Graphic Era deemed to be University Dept. of Computer Science and Engineering Dehradun India ABV Indian Institute of Information Technology and Management Data Science and Quantum Computing Gwalior India

ISBN: (纸本)9798350379525

In this paper, we have analyzed the secrecy performance and system operation performance for hybrid RF/FSO systems against composite Weibull-log-normal fading channel with M turbulent channels. The performance is evaluated by carrying out extensive simulations to calculate Signal-to-Noise Ratio (SNR), Bit Error Rate (BER) and outage probability, we also re-evaluate channel capacity and secrecy capacity with respect of.environmental conditions. Compared with RF only system, FSO only standalone scheme and some meaningful orthogonal-modulation multicarrier IM/DD-based schemes proposed by the literatures. Similarly, the utilization of.adaptive modulation schemes improved resistance to fading and roughness in systems. The above results confirm the hybridization of.both RF and FSO a comprehensive solution for dependable communication in different and even hostile surrounding conditions which set this work as reliable foundation point towards forthcoming research activity or application relevance within all range telecommunication sector. © 2024 IEEE.

关键词： Bit error rate

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Transfer Learning Approaches for Channel State Prediction in Heterogeneous Fading Networks

Transfer Learning Approaches for Channel State Prediction in...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies, INSPECT 2024

ISBN: (纸本)9798350379525

In this work, we leverage state-of.the-art machine learning algorithms to predict Channel State information (CSI), for enhancing performance of.wireless communication systems. The simulation and analysis stop with transfer learning to finetune, adaptive domain adaptations, multitasking (MTL) as well a few shot meta-learning models framework under different network packet loss rate scenarios. By generalizing better from one domain to another, transfer learning pushes the performance beyond what traditional methods can obtain. Further, fine-tuning also helps to align the source and target domain distributions in feature space by a partial (or full) reduction of.re-stylization error. This work proposes multi-Task learning as a tool to mitigate loses in light of.predicting across fading scenarios. Considering multi-task learning also provides benefits from shared learnings, so experience is pooled between the tasks which indirectly improve prediction as well and it helps in training both of.these algorithms together. Moreover, the few-shot learning models prove to be highly desirable as they deliver state-of.the-art performance with minimal training data for difficult problems. These results show the potential of.AI-based methods to improve CSI prediction, and have implications for improving reliability and throughput when deploying real wireless communication systems. © 2024 IEEE.

关键词： Contrastive Learning

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Steganalysis on Dual-Layer Security of.Messages Using Steganography and Cryptography 15

Steganalysis on Dual-Layer Security of Messages Using Stegan...

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15th International Conference on information Technology and Electrical engineering, ICITEE 2023

作者： Shamsi, Zeba Saha, Anish Kumar Patgiri, Ripon Singh, Khoirom Motilal Singh, Laiphrakpam Dolendro National Institute of Technology Dept. of Computer Science and Engineering Assam Silchar788010 India Indian Institute of Information Technology Dept. of Computer Science and Engineering Manipur Senapati795002 India

ISBN: (纸本)9798350304466

Recently, Bansal et al. proposed a dual-layer message security technique using steganography and cryptography. An index table maps each text data to an index point and an elliptic curve point. The text data are encrypted using elliptic curve cryptography, and the corresponding index values are embedded in a cover image using the Least Significant Bit Inversion technique. The proposed paper breaks the security of.the key exchange technique in the Bansal et al. method and the secret text is extracted. © 2023 IEEE.

关键词： Steganography

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Continual Cell Instance Segmentation of.Microscopy Images 48

Continual Cell Instance Segmentation of Microscopy Images

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chuang, Tzu-Ting Wei, Ting-Yun Hsieh, Yu-Hsing Chen, Chu-Song Yang, Huei-Fang National Sun Yat-sen University Dept. Information Management Taiwan National Taiwan University Dept. Computer Science and Information Engineering Taiwan

ISBN: (纸本)9781728163277

A continual cell instance segmenter aims to continually learn to segment new objects while preserving the ability to localize and distinguish old objects without access to previous data. Besides catastrophic forgetting, background shift, where the background class could contain objects in the old and unseen future classes, could occur. In addition, as acquiring annotations is label-intensive, cell images can be partially labeled. In this paper, we present iMRCNN, which extends Mask R-CNN with knowledge distillation and pseudo labeling, to address these challenges. To preserve the learned skills, the current student distills knowledge from the former teacher at output and feature levels. Furthermore, we employ a pseudo labeling scheme, where the teacher is utilized to identify objects with no labels provided, to deal with background shift and partially labeled data. Experiments on two microscopy image sets demonstrate the effectiveness of.iMRCNN over other alternatives in various incremental learning scenarios. © 2023 IEEE.

关键词： Distillation

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Implementing Newly Proposed Dynamic Tree Structure: Development, Integration, and Optimization

Implementing Newly Proposed Dynamic Tree Structure: Developm...

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2024 IEEE International Conference on Signal Processing and Advance Research in Computing, SPARC 2024

作者： Banerjee, Pallab Kumari, Sweety Kumar, Biresh Thakur, Kanika Gaurav, Kumar Kumar, Purushottam Amity University Jharkhand Dept. of Computer Science Engineering Jharkhand Ranchi India Galgotia University Dept. of Computer Science Engineering Greater Noida campus India Amity University Jharkhand Amity School of Information Technology Jharkhand Ranchi India

ISBN: (纸本)9798350385199

An innovative data structure called a 'dynamic tree' has important uses in algorithmic time complexity optimization. It creates well-organized trees by modifying the properties of.several single-node trees, therefore preserving the forest structure. This structure is essential to improving the effectiveness of.many algorithms. In this work, I particularly address the 'Maximum Flow method,' a crucial subtopic in the larger field of.dynamic trees. One basic method for solving network flow issues is the Maximum Flow Algorithm, but it frequently runs into issues with time complexity. My research attempts to overcome these obstacles by using dynamic trees to enhance the efficiency of.the algorithm. This work reduces the time complexity of.network flow problems, leading to more effective solutions. The findings have potential implications in a variety of.computing and real-world scenarios. © 2024 IEEE.

关键词： Ancestor Cut Evert Find Key Link Root Top tree

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HOG and Cloud Computing based Face Recognition for Attendance Monitoring 3

HOG and Cloud Computing based Face Recognition for Attendanc...

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3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Sudha, V. Kalaiselvi, R. Jithenthiriya, C.K. Government Polytechnic College Dept. of Computer Engineering Coimbatore India Kumaraguru College of Technology Information Science and Engineering Coimbatore India Kumaraguru College of Technology Dept. Computer Science and Engineering Coimbatore India

ISBN: (纸本)9781665462167

Participation plays a key role in the classroom culture. When calling out the students' names at the beginning and end of.a lecture, lecturers may miss a student, or someone may respond several times to substitute their peers. Face identification can be difficult when using low-definition video and other information systems with the assistance of.facial recognition technologies. This necessitates the need to develop a novel device to quickly and accurately identify human faces from images and videos. Many algorithms and techniques have been recently developed to improve the facial recognition accuracy. Deep learning has been recently implemented in various real-time applications to identify and perceive multiple faces at the same time. When it comes to computers, however, it is unrealistic to attempt any of.the human identity. The identification of.the human identity is a critical aspect of.biometrics. The biometric results are consistent with human characteristics. Remote and applied facial expressions can be used to enhance the results. computers that identify and recognize faces can be used for a variety of.purposes, including criminal recognition, network surveillance, biometric authentication, and so on. In this paper, an automated system for attendance recognition is proposed. © 2023 IEEE.

关键词： Face recognition

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Addressing Weight Competition in Long Short-Term Memory (LSTM) Networks for Improving Temperature Prediction 12

Addressing Weight Competition in Long Short-Term Memory (LST...

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12th International Conference on Awareness science and Technology, iCAST 2023

作者： Lin, Kun-Wei Li, Yu-Fan Lin, Che-Yu Jiang, Zheng-Han Chen, Wei-Jian dept. Computer Science and Information Engineering Chaoyang University of Technology Taichung Taiwan dept. Information Management Chaoyang University of Technology Taichung Taiwan

ISBN: (纸本)9798350324693

This conference paper presents a study on the application of.Long Short-Term Memory (LSTM) networks to predict temperature changes in the upcoming week. During our research, we encountered the issue of.weight competition within the LSTM architecture, which can lead to suboptimal predictions. To overcome this challenge, three novel methods including Independent Time Series (ITS) Model, Arithmetic Segmented Time Series (ASTS) Model and Arithmetic Segmented Rolling Time Series (ASRTS) Model, respectively, were used to address weight competition and enhance the accuracy of.temperature change forecasts. The effectiveness of.these methods were evaluated through extensive experiments and compared against standard LSTM models. The results demonstrate the significance of.mitigating weight competition to achieve more reliable and accurate temperature predictions. © 2023 IEEE.

关键词： Long short-term memory

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COVID-19 Improved Diagnoses Based on the Open-morphology Filter and Deep-learning

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Recent Patents on engineering 2022年第3期16卷 122-130页

作者： Younus, Majid D. Zedan, Mohammad J. M. Malallah, Fahad L. Saeed, Mustafa G. Computer and Information Engineering Department College of Electronics Engineering Ninevah University Mosul Iraq Computer Science Dept. College of Science Cihan-Slemani University Slimani Iraq

Background: Coronavirus (COVID-19) has appeared first time in Wuhan, China, as an acute respiratory syndrome and spread rapidly. It has been declared a pandemic by the WHO. Thus, there is an urgent need to develop an accurate computer-aided method to assist clinicians in identifying COVID-19-infected patients by computed tomography CT images. The contribution of.this paper is that it proposes a pre-processing technique that increases the recognition rate compared to the techniques existing in the literature. Methods: The proposed pre-processing technique, which consists of.both contrast enhancement and open-morphology filter, is highly effective in decreasing the diagnosis error rate. After carrying out pre-processing, CT images are fed to a 15-layer convolution neural network (CNN) as deep-learning for the training and testing operations. The dataset used in this research has been publically published, in which CT images were collected from hospitals in Sao Paulo, Brazil. This dataset is composed of.2482 CT scans images, which include 1252 CT scans of.SARS-CoV-2 infected patients and 1230 CT scans of.non-infected SARS-CoV-2 patients. Results: The proposed detection method achieves up to 97.8% accuracy, which outperforms the reported accuracy of.the dataset by 97.3%. Conclusion: The performance in terms of.accuracy has been improved up to 0.5% by the proposed methodology over the published dataset and its method. © 2022 Bentham science Publishers.

关键词： COVID-19

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Human Activity Recognition using ShuffleNetV2 Model

Human Activity Recognition using ShuffleNetV2 Model

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2023 Intelligent Computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Shanufa Nazrin, P.S. Benifa, J.V. Bibal Anu, K.P. Vijayakumar, Aiswarya Dept. of Computer Science Kerala Thrissur India Indian Institute of Information Technology Dept.of Computer Science and Engineering Kerala kottayam India

ISBN: (纸本)9798350394580

HAR is specified as the crucial task within the field of.computer vision and artificial intelligence. Main aim of.HAR is to observing and labelling different human activity images of.distinct data sources by machines such as computer. HAR has applications in various fields. CNN model is an appropriate model for HAR task, due to their ability to automaticaly acquire relevant spatial features, capture hierarchical representations, and handle input of.various sizes and types. So it is utilized for developing HAR model. This model can detect objects and also can classify images. The model contains wide ranging catogories. ShuffleNetV2 is a particular architecture within it, mainly for efficient and lightweight deep learning task. This paper develops an intelligent HAR using ShuffleNetV2 model which acquired an accuracy about 69.52 percentage. © 2023 IEEE.

关键词： HAR Human activity ShuffleNetV2

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Performance Analysis of.a Scalable Federated Learning-Based Agricultural Network 4

Performance Analysis of a Scalable Federated Learning-Based ...

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4th IEEE International Conference on Emerging Techniques in Computational Intelligence, ICETCI 2024

作者： Chakraborty, Shatanik Sarkar, Maharnab Gayen, Avijit Jana, Angshuman Chakraborty, Nilotpal Meghnad Saha Institute of Technology Dept. of Computer Science & Engg. Kolkata India Govt. College Of Engineering & Textile Technology Dept. of Computer Science & Engg. Berhampore India Indian Institute of Information Technology Dept. of Computer Science & Engg. Guwahati India Techno India University Dept. of Computer Science & Engg. West Bengal India

ISBN: (纸本)9798350387063

Although agriculture is a substantial part of.the Indian economy, it encounters several challenges. Precision agri-culture is a farming management strategy that aims to overcome these challenges. It optimizes several components of.the farming process using data and technology, resulting in increased crop yields while reducing waste and raising total resource use effi-ciency. Thus, in its context, we present a study on the development of.a Federated Learning-based Agricultural Network consisting of.a popular federated learning algorithm FedAvg to perform image classification on an open dataset, Plant Village. In this work, we specifically analyze the performance of.the proposed Federated Learning-based Agricultural Network in terms of.its scalable deployment. The proposed network yields an average classification accuracy of.97.52 % on test data (C ≈ 70), and provides a host of.other benefits it derives from its federated nature, such as upholding the privacy of.local client data as only the model updates are shared between the clients and the aggregation server. The prime objective of.our work has been to study and explore the possibility of.applying federated learning in the agriculture sector so that more efficient and privacy- preserving smart farming networks could be developed. © 2024 IEEE.

关键词： Agribusiness

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