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IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Ahmed Hadded Mossaad Ben Ayed Shaya A. Alshaya National Engineering School Of Sfax Sfax University Computer and Embedded System Laboratory Sfax Tunisia Electronic Industrial ENISo Sousse University Tunsia Computer and Embedded System Laboratory ENIS Sfax University Tunisia Sfax Tunisia Department of Computer Science Faculty of Sciences and Humanities Sciences Majmaah University Dean of Digital Transformation and e-Transactions Prince Sultan University Majmaah Saudi Arabia

In 2020, the world suffered from the COVID-19 pandemic. This situation highlighted the considerable need for systems to detect viruses quickly. Providing an accurate e-health system that detects, monitors, and controls virus spread became necessary. Countries attempt to reduce the virus's spread using basic processes such as quarantine and lockdown. Early virus detection is requested to reduce the threat and decrease the propagation of the pandemic. This paper proposes an attempt to avoid speed-up spreading based on airborne virus detection. The suggested detection based on the SiNW-FET biosensor is not approved as a standardized diagnostic test. To support the airborne detection methodology, a metasurface technology based on Reflector cylindrical antenna with a sensing unit is proposed to detect viruses. The proposal considered the propagation delays and concentration of a virus. Conducted results prove that the receiver's volume is considered a primary key to an accurate detection rather than the virus's concentration.

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Deep Learning for Sleep Stages Classification: Modified Rectified Linear Unit Activation Function and Modified Orthogonal Weight Initialisation

arXiv

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arXiv 2022年

作者： Bhusal, Akriti Alsadoon, Abeer Prasad, P.W.C. Alsalami, Nada Rashid, Tarik A. School of Computing and Mathematics Charles Sturt University Sydney Australia Information System Department Sydney Australia Computer Science Department Worcester State University MA United States Computer Science and Engineering University of Kurdistan Hewler KRG Erbil Iraq

Background and Aim Each stage of sleep can affect human health, and not getting enough sleep at any stage may lead to sleep disorder like parasomnia, apnea, insomnia, etc. Sleeprelated diseases could be diagnosed using Convolutional Neural Network Classifier. However, this classifier has not been successfully implemented into sleep stage classification systems due to high complexity and low accuracy of classification. The aim of this research is to increase the accuracy and reduce the learning time of Convolutional Neural Network Classifier. Methodology The proposed system used a modified Orthogonal Convolutional Neural Network and a modified Adam optimisation technique to improve the sleep stage classification accuracy and reduce the gradient saturation problem that occurs due to sigmoid activation function. The proposed system uses Leaky Rectified Linear Unit (ReLU) instead of sigmoid activation function as an activation function. Results The proposed system called Enhanced Sleep Stage Classification system (ESSC) used six different databases for training and testing the proposed model on the different sleep stages. These databases are University College Dublin database (UCD), Beth Israel Deaconess Medical Center MIT database (MIT-BIH), Sleep European Data Format (EDF), Sleep EDF Extended, Montreal Archive of Sleep Studies (MASS), and Sleep Heart Health Study (SHHS). Our results show that the gradient saturation problem does not exist anymore. The modified Adam optimiser helps to reduce the noise which in turn result in faster convergence time. Conclusion The convergence speed of ESSC is increased along with better classification accuracy compared to the state of art solution. © 2022, CC BY.

关键词： Convolution

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Spectral Efficiency Analysis for Cell-Free Massive MIMO systems With Low-Resolution ADCs Under Imperfect CSI

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IEEE Internet of Things Journal 2025年

作者： Ni, Weiyi He, Yiling Xiao, Hailin Chronopoulos, Anthony Theodore Ioannou, Petros A. Hubei University Key Laboratory of Intelligent Sensing System and Security Ministry of Education Wuhan 430062 China University of Manchester School of Electrical and Electronic Engineering Manchester M13 9PL United Kingdom Hubei University School of Computer Science Wuhan 430062 China University of Texas Department of Computer Science San Antonio 78249 TX United States University of Patras Department of Computer Engineering and Informatics Rio 26500 Greece University of Southern California Department of Electrical Engineering Los Angeles 90007 CA United States

The main challenge for the practical deployment ofcell-free massive multi-input multi-output (CF-mMIMO) systemsunder imperfect channel state information (CSI) is the highhardware cost caused by utilizing high-resolution analog-to-digital converters (ADCs). To tackle this challenge, an effectivesolution is to equip the systems with low-resolution ADCs at allaccess points (APs). In this paper, the uplink pilot contaminationmitigation method is proposed in a CF-mMIMO system withlow-resolution ADCs under imperfect CSI. More specifically, theclosed-form expressions for both uplink and downlink spectralefficiency (SE) are firstly derived from the additive quantizationnoise model (AQNM) and the maximum ratio combining (MRC)receiver filter under imperfect CSI. Then they are utilized toanalyze the effects of the APs number, the user’s transmit power,and ADC resolution on the SE. Also, in order to maximize the SEwhile reducing the hardware cost, an efficient pilot assignmentalgorithm with graph coloring under low-resolution ADCs ispresented that mitigates the pilot contamination. Finally, wepresent numerical results showing that the achievable uplink SEof the 5-bit low-resolution ADCs is close to that of the perfect resolution ADCs, and the low-resolution quantization losses canbe compensated by increasing the antenna number per ***, an optimal balance between the hardware cost and SEcan be achieved by equipping the APs with 5-bit low-resolutionADCs. Furthermore, compared with the existing pilot assignmentalgorithms, the proposed algorithm mitigates better the pilotcontamination and significantly improves the 95%-likely per-userachievable uplink SE. © 2014 IEEE.

关键词： analog-to-digital converters (ADCs) APs selection Cell-free massive MIMO systems pilot assignment

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Experimental Results of Federated Learning in Unmanned Aerial Vehicle Swarm

Experimental Results of Federated Learning in Unmanned Aeria...

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2021 IEEE International Conference on Space-Air-Ground Computing, SAGC 2021

作者： Shen, Yun Dong, Chao Qu, Yuben Ren, Weiqing Zhu, Xiaojun Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China Shanghai Jiao Tong University Department of Computer Science and Engineering China

ISBN: (纸本)9781665400114

Unmanned aerial vehicles (UAVs) need to deploy machine learning (ML) models to execute complex tasks such as target tracking. For traditional centralized ML, UAVs need to transmit the raw data to the ground central server for model training. However, the transmission of raw data not only causes a large delay but also may incur sensitive information leakage. Federated learning (FL) as a distributed machine learning enables multiple UAVs to form a swarm for distributed model training. Nevertheless, most of current researches on FL of UAV swarm are based on theoretical architecture and simulations. In this paper, we focus on verifying the feasibility of deploying FL in a UAV swarm through practical experiments. Moreover, we further explore the impact of the distance change between the training node UAVs and the parameter server UAV on the training accuracy. The experimental results show that the UAV swarm can perform 15 rounds of global training in 5 minutes, with a training accuracy of 84.28%. © 2021 IEEE.

关键词： Antennas

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Language-Queried Target Sound Extraction Without Parallel Training Data

arXiv

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arXiv 2024年

作者： Ma, Hao Peng, Zhiyuan Li, Xu Li, Yukai Shao, Mingjie Kong, Qiuqiang Liu, Ju School of Information Science and Engineering Shandong University Qingdao China Department of Computer Science North Carolina State University NC United States ARC Lab Tencent PCG China Key Laboratory of System and Control AMSS Chinese Academy of Sciences Beijing China The Chinese University of Hong Kong Hong Kong

Language-queried target sound extraction (TSE) aims to extract specific sounds from mixtures based on language queries. Traditional fully-supervised training schemes require extensively annotated parallel audio-text data, which are labor-intensive. We introduce a parallel-data-free training scheme, requiring only unlabelled audio clips for TSE model training by utilizing the contrastive language-audio pre-trained model (CLAP). In a vanilla parallel-data-free training stage, target audio is encoded using the pre-trained CLAP audio encoder to form a condition embedding, while during testing, user language queries are encoded by CLAP text encoder as the condition embedding. This vanilla approach assumes perfect alignment between text and audio embeddings, which is unrealistic. Two major challenges arise from training-testing mismatch: the persistent modality gap between text and audio and the risk of overfitting due to the exposure of rich acoustic details in target audio embedding during training. To address this, we propose a retrieval-augmented strategy. Specifically, we create an embedding cache using audio captions generated by a large language model (LLM). During training, target audio embeddings retrieve text embeddings from this cache to use as condition embeddings, ensuring consistent modalities between training and testing and eliminating information leakage. Extensive experiment results show that our retrieval-augmented approach achieves consistent and notable performance improvements over existing state-of-the-art with better generalizability. © 2024, CC BY.

关键词： Information leakage

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Channel Treatment for Internet Addiction with Puskesmas Application: Design Approach with Usability Heuristics

Channel Treatment for Internet Addiction with Puskesmas Appl...

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2021 International Conference Advancement in Data science, E-learning and Information systems, ICADEIS 2021

作者： Lubis, Muharman Handayani, Dini Oktarina Adrian, Qadhli Jafar Gustiarani, Anastassya Madjid, Cheta Kusuma Nurhas, Irawan Telkom University Information System Department Bandung Indonesia Tailor University Innovation Technology Department Selangor Malaysia Universitas Teknokrat Indonesia Computer Engineering and Science Department Bandar Lampung Indonesia Institute of Positive Computing Hochschule Ruhr West University of Applied Science Mülheim Germany

ISBN: (纸本)9781665437097

Developed since 1968, Puskesmas is the most important healthcare facility and is at the forefront of providing basic healthcare services at the community level. Basically, its role is very important and should be one of the keys to Indonesia's success in improving the health and nutritional status of the community. Meanwhile, Internet addiction become potential threat to the community that might be more dangerous compare to drug and sex addiction in term of social consequences. This study offer the integrated solution through usability heuristics design for the purpose of redesigning current apps after the identification of business process in several Puskesmas in the Bandung. It also want to increase the quality of service in order to provide proper communication between health workers and patients. In evaluation phase, the 4 criteria was used to generate sketches in relation to contextual settings. Then, the mockups will be assessed with 12 transaction features to ensure the readiness of its financial management before the application launching. © 2021 IEEE.

关键词： Quality of service

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Multi-objective ant lion optimizer for stochastic robotic disassembly line balancing problem subject to resource constraints 2

Multi-objective ant lion optimizer for stochastic robotic di...

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2nd International Conference on computer Vision and Data Mining, ICVDM 2021

作者： Dong, ChengShun Liu, Peisheng Guo, Xi Wang Qi, Liang Qin, Shujin Xu, Gongdan College of Computer and Communication Engineering Liaoning Petrochemical University Fushun113001 China Department of Computer Intelligent Sci. and Tech. Shandong University of Science and Technology Qingdao266590 China College of Economics and Management Shangqiu Normal University Shangqiu476000 China Institute of System Engineering Macao University of Science and Technology 999078 China

With the development of manufacturing and society, a large amount of end-of-life products are generated but with reuse value. Therefore, it is of great significance to dismantle and reuse them. A robotic linear disassembly line plays an important role and promotes the disassembly efficiency in a disassembly process. In this work, we establish a mathematical model of a linear robotic disassembly line balancing problem, which requires the maximization of profit and minimization of energy consumption. Then, an improved ant lion optimizer is proposed to optimize the objectives of the multi-objective robotic disassembly line balancing problem. The experimental results show that the multi-objective ant lion optimizer has good performance, and the diversity and convergence are better than its peers. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Robotics

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Air-Ground Integrated Federated Learning: An Experimental Implementation

Air-Ground Integrated Federated Learning: An Experimental Im...

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2021 IEEE International Conference on Space-Air-Ground Computing, SAGC 2021

作者： Jing, Yuqian Dong, Chao Qu, Yuben Zhou, Fuhui Ministry of Industry and Information Technology Nanjing University of Aeronautics and Astronautics The Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space China Shanghai Jiao Tong University Department of Computer Science and Engineering China

ISBN: (纸本)9781665400114

With the envision of sixth generation (6G) networks technology, diverse artificial intelligence (AI) services are gradually developed from the network center to the edge, which enables unmanned aerial vehicle (UAV) as a hot spot to provide auxiliary services of machine learning (ML). Due to the sensitive privacy and limited resources, traditional centralized ML may not be used directly in such networks. As a promising distributed collaborative ML, federated learning (FL) is more suitable. However, the current research on air-ground integrated federal learning (AGIFL) mostly focuses on architecture design and network simulation. In this paper, we mainly verify the feasibility of deploying FL between UAV and terrestrial users through practical platform. Extensive experiments demonstrate that the training accuracy is higher when the training nodes is close to the server, and the accuracy is improved from 69.48% to 83.52% when distance is reduced from 20 meters to 5 meters. © 2021 IEEE.

关键词： Antennas

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A review of Smart Contract Blockchain Based on Multi-Criteria Analysis: Challenges and Motivations

arXiv

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arXiv 2023年

作者： Alshahrani, Norah M. Kiah, M.L. Mat Zaidan, B.B. Alamoodi, A.H. Saif, Abdu Department of Computer System & Technology Faculty of Computer Science & IT University of Malaya Lembah Pantai Kuala Lumpur50603 Malaysia Department of Computer Science Applied Collage King Khalid University P.O. Box 62569 Abha Saudi Arabia Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Universiti Pendidikan Sultan Idris Tanjung Malim Malaysia Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia

A smart contract is a digital program of transaction protocol (rules of contract) based on the consensus architecture of blockchain. Smart contracts with Blockchain are modern technologies that have gained enormous attention in scientific and practical applications. A smart contract is the central aspect of a blockchain that facilitates blockchain as a platform outside the cryptocurrency spectrum. The development of blockchain technology, with a focus on smart contracts, has advanced significantly in recent years. However, research on the smart contract idea has weaknesses in the implementation sectors based on a decentralized network that shares an identical state. This paper extensively reviews smart contracts based on multi-criteria analysis, challenges, and motivations. Therefore, implementing blockchain in multi-criteria research is required to increase the efficiency of interaction between users via supporting information exchange with high trust. Implementing blockchain in the multi-criteria analysis is necessary to increase the efficiency of interaction between users via supporting information exchange and with high confidence, detecting malfunctioning, helping users with performance issues, reaching a consensus, deploying distributed solutions and allocating plans, tasks and joint missions. The smart contract with decision-making performance, planning and execution improves the implementation based on efficiency, sustainability and management. Furthermore, the uncertainty and supply chain performance lead to improved users’ confidence in offering new solutions in exchange for problems in smart contacts. Evaluation includes code analysis and performance, while development performance can be under development. © 2023, CC BY.

关键词： Smart contract

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An Effective CU Depth Decision Method for HEVC Using Machine Learning

引用

computer systems science & engineering 2021年第11期39卷 275-286页

作者： Xuan Sun Pengyu Liu Xiaowei Jia Kebin Jia Shanji Chen Yueying Wu Beijing University of Technology Beijing100124China Beijing Laboratory of Advanced Information Networks Beijing100124China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing100124China Department of Computer Science University of PittsburghPittsburgh15260USA School of Physics and Electronic Information Engineering Qinghai Nationalities University810007QinghaiChina

This paper presents an effective machine learning-based depth selection algorithm for CTU(Coding Tree Unit)in HEVC(High Efficiency Video Coding).Existing machine learning methods are limited in their ability in handling the initial depth decision of CU(Coding Unit)and selecting the proper set of input features for the depth selection *** this paper,we first propose a new classification approach for the initial division depth *** particular,we study the correlation of the texture complexity,QPs(quantization parameters)and the depth decision of the CUs to forecast the original partition depth of the current ***,we further aim to determine the input features of the classifier by analysing the correlation between depth decision of the CUs,picture distortion and the *** the found relationships,we also study a decision method for the end partition depth of the current CUs using bit-rate and picture distortion as ***,we formulate the depth division of the CUs as a binary classification problem and use the nearest neighbor classifier to conduct *** proposed method can significantly improve the efficiency of interframe coding by circumventing the traversing cost of the division *** shows that the mentioned method can reduce the time spent by 34.56%compared to HM-16.9 while keeping the partition depth of the CUs correct.

关键词： HEVC inter-frame coding CU depth

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