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arXiv 2024年

作者： Xie, Songjie He, Hengtao Li, Hongru Song, Shenghui Zhang, Jun Zhang, Ying-Jun Angela Letaief, Khaled B. Dept. of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Dept. of Information Engineering The Chinese University of Hong Kong Hong Kong

Deep learning-based joint source-channel coding (DJSCC) is expected to be a key technique for the next-generation wireless networks. However, the existing DJSCC schemes still face the challenge of channel adaptability as they are typically trained under specific channel conditions. In this paper, we propose a generic framework for channel-adaptive DJSCC by utilizing hypernetworks. To tailor the hypernetwork-based framework for communication systems, we propose a memory-efficient hypernetwork parameterization and then develop a channel-adaptive DJSCC network, named Hyper-AJSCC. Compared with existing adaptive DJSCC based on the attention mechanism, Hyper-AJSCC introduces much fewer parameters and can be seamlessly combined with various existing DJSCC networks without any substantial modifications to their neural network architecture. Extensive experiments demonstrate the better adaptability to channel conditions and higher memory efficiency of Hyper-AJSCC compared with state-of-the-art baselines. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep neural networks

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Analysis of Distributed Algorithms for Big-data

arXiv

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arXiv 2024年

作者： Purohit, Rajendra Chowdhary, K.R. Purohit, S.D. Dept. of Computer Science and Engineering Jodhpur Institute of Engineeering and Technology Jodhpur India Rajasthan Technical University Kota India

The parallel and distributed processing are becoming de facto industry standard, and a large part of the current research is targeted on how to make computing scalable and distributed, dynamically, without allocating the resources on permanent basis. The present article focuses on the study and performance of distributed and parallel algorithms their file systems, to achieve scalability at local level (OpenMP platform), and at global level where computing and file systems are distributed. Various applications, algorithms,file systems have been used to demonstrate the areas, and their performance studies have been presented. The systems and applications chosen here are of open-source nature, due to their wider applicability. © 2024, CC BY.

关键词： MapReduce

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CAs Based Student-Alumni Management System

CAs Based Student-Alumni Management System

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2021 International Conference on Communication, Control and Information Sciences, ICCISc 2021

作者： Nishanth, D.V. Satish Narasimha, Niteesh S. Sandesh, H.J. Bhargavi, K. Siddaganga Institute of Technology Dept. of Computer Science and Engineering TumakuruKarnataka India

ISBN: (纸本)9781665412797

Students and alumni management system of any university is a collaborative framework which integrates the knowledge between the students who are pursuing education and the students who have already successfully established themselves after graduation. There are various profession choices that are available wherein a person could choose from and this proposed framework is a predictive system which tries to predict the career option based on the success rate of the student in terms of his credentials CGPA, number of projects, number of publication, and so on and the credentials of the alumni like number of years of experience, proprietorship, number of recognitions, designation reached, and many more. The proposed system tries to collaborate students with alumni or the ones who have graduated out of the university and together they are provided an option to choose their career based on all the experiences they have had so far and the necessary credentials they own. Cognitive-Agents (CAs) which exhibits rational thinking which is applied in the paper to collaborate the students and alumni where in the beliefs over the students and alumni are formulated using the Behaviour-Observation-Belief (BOB) model. The performance achieved by the proposed CAs based framework towards alumni management in university is found to be good with respect to parameters like employment rate, student alumni association, and so on. © 2021 IEEE.

关键词： Students

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Optimized ECG Signal Compression and Security for Efficient Cardiac Abnormality Detection 4

Optimized ECG Signal Compression and Security for Efficient ...

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4th IEEE International Conference on ICT in Business Industry and Government, ICTBIG 2024

作者： Roy, Vandana Baghel, Abhishek Singh Jain, Sapan Kumar Mohanty, Sribidhya Srinivasarao, G. Bhatt, Anushka Ggits Dept of Elrectronics Communication M.P Jabalpur India Ies Institute of Technology and Management Ies University Department of Computer Science & Engineering M.P Bhopal India Gyan Ganga Institute of Technology & Sciences Dept of Cse MP Jabalpur India Graphic era Deemed to be University India Chalapathi Institute of Technology Dept. of Cse AP Guntur India Ggits MP India

ISBN: (纸本)9798331518981

Protecting biological data during dissemination, this ECG waves, is becoming more important, and this work is relevant to that requirement. It is of the extreme importance to guarantee the security of transferred medical information as the amount of data sent over the web continues to rise. It proposes a watermarking approach for compressing the ECG waves in addition to securing the information. By including subject information in ECG waves in a way that doesn't change their diagnostic usefulness, watermarking makes signal blocking possible. The frequency region and the domain of space are the two primary categories of watermarking methods. Improving signal quality to accept watermarking is also covered in the chapter using the Fourier Decomposition Method (FDM). In a setting with a large amount of ECG information moving around, FDM is used to accomplish realistic data compression as well, which is similar to decoding. This prevents the expansion of storage and bandwidth requirements while yet achieving a viable signal. The proposed technique beats its predecessors in relations of SNR, PRD, and compression coefficient, as shown by simulation results. As a whole, this approach improves the security of ECG signals and data minimisation for hospitals that are handled electronically. © 2024 IEEE.

关键词： Electrocardiography

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Unmasking the Truth: A Deep Learning Approach to Detecting Deepfake Audio Through MFCC Features 11

Unmasking the Truth: A Deep Learning Approach to Detecting D...

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11th International Conference on Information technology, ICIT 2023

作者： Altalahin, Islam Alzu'Bi, Shadi Alqudah, Assal Mughaid, Ala Alzaytoonah University of Jordan Department of Computer Science Amman Jordan The Hashemite University Faculty of Prince Al-Hussien Bin Abdullah for IT Dept. of Information Technology P.O. Box 330127 Zarqa13133 Jordan

ISBN: (纸本)9798350320060

Deepfake content is artificially created or altered using artificial intelligence (AI) methods to appear real. Synthesis can include audio, video, images, and text. Deepfakes may now produce content that looks normal, making it more difficult to identify. Significant progress has been made in identifying video deep fakes in recent years;However, most of the investigations into voice deep fake detection have used the ASVSpoof-2019 dataset and several machine learning and deep learning algorithms. This research uses machine-based and deep-learning approaches to identify fake audio. Melted frequency cepstral coefficients (MFCCs) are used to extract the most useful information from the sound. We choose the 2019 ASVSpoof dataset, which is the latest reference dataset. Experimental results show that Convolutional Neural Networks (CNN): (CNN-LSTM) outperformed other machine learning (ML) models in terms of accuracy, achieving an accuracy of up to 88%. © 2023 IEEE.

关键词： Fake detection

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A Prototype of Proximity Warning System on the Sea, Utilizing Received Signal Strength

A Prototype of Proximity Warning System on the Sea, Utilizin...

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International Symposium on Information Theory and Its Applications, ISITA

作者： Hiroomi Isozaki Yashuhiro Sato Yoshinori Matsuura Masaaki Yamanaka dept. Maritime Safety Technology Japan Coast Guard Academy Kure Japan dept. Telecommunications and Computer Networks Osaka Electro-Communication University Neyagawa Japan

ISBN: (数字)9784885523519

ISBN: (纸本)9798331533335

Our research group aims to realize a low-cost alert system that informs the crews of small-sized ships of approaching floating objects on the sea. We previously proposed a maritime object identification system that uses a Radio Frequency Identification (RFID) tag. However, we concluded that it is difficult to measure the received signal strength, due to the effects of waves and other factors, on the sea, and the difficulty in miniaturizing the transmitter and the receiver devices. In this paper, we discuss the construction of a high-precision positioning system necessary to measure a received signal strength on the sea. We decided to use a ESP32, a low-power-consumption Wi-Fi controller, to elucidate characteristics of the received signal strength of Wi-Fi on the sea, which is necessary for a proximity warning system. Finally We make a prototype of the proximity warning system.

关键词： Accuracy Radio transmitters Sea measurements Prototypes Receivers Alarm systems Position measurement Mathematical models RFID tags Wireless fidelity

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The Dynamic Mode Construction of Mixed English Learning Based on Reinforcement Learning 2

The Dynamic Mode Construction of Mixed English Learning Base...

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2nd IEEE International Conference on Mobile Networks and Wireless Communications, ICMNWC 2022

作者： Liu, Yongfen Tang, Weihua Pareek, Piyush Kumar Qujing Medical College Yunnan655000 China Dept. of Computer Science Engineering AIML & IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India

ISBN: (纸本)9781665491112

With the development of new technologies and theories, the Internet has driven the changes of learning methods. With the support of various online learning platforms and social software, individual learning activities are gradually effectively organized and connected. Learning is developing in the direction of interaction, collectivity and socialization, and individual knowledge converges into group wisdom in an explicit or implicit way. The research on the construction of College English mixed learning dynamic model based on learning analysis is to study the process and results of Constructing College English dynamic model. This study aims to understand its effectiveness in improving students' performance, satisfaction and learning motivation. The main focus of this study is to investigate the effectiveness of using different types of feedback at the same time (such as formative feedback, summative feedback or two forms) to teach and evaluate students' progress. © 2022 IEEE.

关键词： Dynamic models

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Energy Efficient Coverage Optimization in Mobile Wireless Sensor Network Using Grey Wolf Algorithm

Energy Efficient Coverage Optimization in Mobile Wireless Se...

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International Conference on Computing and Networking technology (ICCNT)

作者： Ankit Gupta Mamatha K. M. Kiran M. Dept. of Information Technology National Institute of Technology Karnataka Surathkal India Dept. of Computer Science and Engineering Viseveshvaraya Technological University Belgaum India

The issue of decreased coverage rate in Mobile Wireless Sensor Networks (MWSNs), caused by mobile sensor nodes being randomly placed inside a monitoring area. Additionally, it becomes extremely important to utilise a sensor node’s energy very effectively due to the finite energy of sensor nodes. Hence, to provide optimised positions for the sensor nodes while using the energy of sensor nodes adept.y authors propose an energy efficient coverage algorithm. Initially, article focus on optimal placement of the sensor nodes within a area to achieve the maximum coverage and later authors have focused on improvising the network lifetime. Article presents a combination of Grey Wolf Optimization and Virtual Force algorithm for optimization of coverage in MWSN. Further, to improve the network lifetime, a GWO-based clustering algorithm is presented using distance and energy as a parameter. The algorithms are implemented and simulated on Matlab. The efficiency of the presented algorithm is observed comparing with other Swarm Intelligence (SI) based optimization algorithms, like GWO, VFA, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant-lion Optimization (ALO) and the results of the GWO-based clustering is compared with the traditional LEACH algorithm and energy-balanced clustering based on PSO. Simulation results demonstrate that the presented algorithms outperform the considered algorithms.

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A Novel Approach Towards Gas Turbine Emission Reduction by Using Neural Networks

A Novel Approach Towards Gas Turbine Emission Reduction by U...

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2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023

作者： Roy, Atanu Pramanik, Sabyasachi Mitra, Kalyan Chakraborty, Manashi Sarisa West Bengal 743368 India Haldia Institute of Technology Dept. of Computer Science and Engineering Hatiberia West Bengal Haldia721657 India Indian Maritime University Dept. of Maritime Studies P - 19 Taratalla Road Kolkata700088 India Sarisa West Bengal 743368 India

ISBN: (纸本)9798350313987

Optimizations of controlling parameters are the key factors to achieve effectual output and emission reduction from machinery running. This study relates prediction technology for gas turbine's (GT's) running optimization and emission reduction. The tool can identify upcoming disputes and alter the engine control setting to achieve highly efficient running operations. This research objects to get a machine learning approach to predict GT performance on emissions. The forecasting capabilities of this system are based on real-time data collected from a GT plant. A neural network is employed to analyze and predict the emissions of exhaust pollutants, primarily carbon monoxide and nitrogen oxides. The model performance is studied by considering the values of MSE, MAE, and residuals. The model's accuracy and precision are revealed by its low MSE (0.0035) and MAE (0.043). This approach is a cost-effective way to gage emissions accurately and can be used to confirm compliance with environmental regulations. It is also a helpful tool for monitoring the health of the turbine and identifying any potential issues with its operation. © 2023 IEEE.

关键词： Convolutional Neural Networks Emission Gas Turbine Predictive Emission Monitoring

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Probabilistic Dynamic Line Rating Forecasting with Line Graph Convolutional LSTM

arXiv

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arXiv 2024年

作者： Kim, Minsoo Dvorkin, Vladimir Kim, Jip Dept. of Energy Engineering Korea Institute of Energy Technology Korea Republic of Dept. of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States

Dynamic line rating (DLR) is a promising solution to increase the utilization of transmission lines by adjusting ratings based on real-time weather conditions. Accurate DLR forecast at the scheduling stage is thus necessary for system operators to proactively optimize power flows, manage congestion, and reduce the cost of grid operations. However, the DLR forecast remains challenging due to weather uncertainty. To reliably predict DLRs, we propose a new probabilistic forecasting model based on line graph convolutional LSTM. Like standard LSTM networks, our model accounts for temporal correlations between DLRs across the planning horizon. The line graph-structured network additionally allows us to leverage the spatial correlations of DLR features across the grid to improve the quality of predictions. Simulation results on the synthetic Texas 123-bus system demonstrate that the proposed model significantly outperforms the baseline probabilistic DLR forecasting models regarding reliability and sharpness while using the fewest parameters. Copyright © 2024, The Authors. All rights reserved.

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