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11th International Conference on Intelligent Computing and Applications, ICRTC 2023

作者： Jadhav, Pramod Lanke, Govinda Rajulu Shrivastava, Anurag Patil, Vinod H. Gupta, Saurabh College of Engineering Pune India ResMed Digital Health Technology San DiegoCA United States Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamil Nadu Chennai India College of Engineering Pune India Department of Computer Science and Engineering SRM Institute of Science and Technology Delhi NCR Campus Ghaziabad India

ISBN: (纸本)9789819717231

One approach to software testing is known as model-based testing, where test cases are generated from a model that represents the functional aspects of the system under test. If the system model is explicitly defined and accurately reflects the system’s behaviour, it can serve as a shared and reusable artefact. For example, the model can be utilized to generate a comprehensive test suite for the system under test (SUT), a methodology commonly referred to as model-based testing (MBT). Over time, the efficiency, capability, and maintainability of both computer code design and model-based systems have continually improved. However, there has been relatively little effort made to gather evidence to assess the precise relationship, boundaries, and practical challenges between the two. This review study aims to address the creation and prioritization of test cases, with a specific focus on reducing redundancy in test cases to enhance the system’s efficiency and effectiveness. In addition to addressing redundancy, this study proposes the use of finite state machines (FSM) as a modelling paradigm. Refactoring, which involves making changes to an application’s source code without altering its external functionality, is introduced to enhance code readability, complexity, extensibility, and maintainability. Code smells, which lead to the generation of redundant test cases, are mitigated through these refactoring techniques. Statistical analysis and methods are employed in this study to proactively reduce test cases by identifying instances of "lazy class code." By assessing code cohesion and dependencies and implementing inline class refactoring techniques before test case generation, the study significantly reduces the number of redundant test cases produced. The primary objective of this work is to construct a test model from a UML activity diagram. From this test model, test paths for composite testing are derived. The study also provides various criteria for constructing t

关键词： Maintainability

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A High Resolution Convolutional Neural Network with Squeeze and Excitation Module for Automatic Modulation Classification

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China Communications 2024年第10期21卷 132-147页

作者： Duan Ruifeng Zhao Yuanlin Zhang Haiyan Li Xinze Cheng Peng Li Yonghui School of Information Science and Technology Bejing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing 100083China Department of Computer Science and Information Technology La Trobe UniversityMelbourneVIC 3086Australia School of Electrical and Information Engineering the University of SydneySydneyNSW 2006Australia

Automatic modulation classification(AMC) technology is one of the cutting-edge technologies in cognitive radio communications. AMC based on deep learning has recently attracted much attention due to its superior performances in classification accuracy and robustness. In this paper, we propose a novel, high resolution and multi-scale feature fusion convolutional neural network model with a squeeze-excitation block, referred to as HRSENet,to classify different kinds of modulation *** proposed model establishes a parallel computing mechanism of multi-resolution feature maps through the multi-layer convolution operation, which effectively reduces the information loss caused by downsampling convolution. Moreover, through dense skipconnecting at the same resolution and up-sampling or down-sampling connection at different resolutions, the low resolution representation of the deep feature maps and the high resolution representation of the shallow feature maps are simultaneously extracted and fully integrated, which is benificial to mine signal multilevel features. Finally, the feature squeeze and excitation module embedded in the decoder is used to adjust the response weights between channels, further improving classification accuracy of proposed *** proposed HRSENet significantly outperforms existing methods in terms of classification accuracy on the public dataset “Over the Air” in signal-to-noise(SNR) ranging from-2dB to 20dB. The classification accuracy in the proposed model achieves 85.36% and97.30% at 4dB and 10dB, respectively, with the improvement by 9.71% and 5.82% compared to ***, the model also has a moderate computation complexity compared with several state-of-the-art methods.

关键词： automatic modulation classification deep learning feature squeeze-and-excitation high-resolution multi-scale

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ElectrodeNet - A Deep-Learning-Based Sound Coding Strategy for Cochlear Implants

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IEEE Transactions on Cognitive and Developmental Systems 2024年第1期16卷 346-357页

作者： Huang, Enoch Hsin-Ho Chao, Rong Tsao, Yu Wu, Chao-Min Department of Electrical Engineering National Central University Taoyuan320317 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei115201 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan701401 Taiwan Department of Electrical Engineering Chung Yuan Christian University Taoyuan320314 Taiwan

ElectrodeNet, a deep-learning-based sound coding strategy for the cochlear implant (CI), is proposed to emulate the advanced combination encoder (ACE) strategy by replacing the conventional envelope detection using various artificial neural networks. The extended ElectrodeNet-CS strategy further incorporates the channel selection (CS). Network models of deep neural network (DNN), convolutional neural network (CNN), and long short-term memory (LSTM) were trained using the fast Fourier transformed bins and channel envelopes obtained from the processing of clean speech by the ACE strategy. Objective speech understanding using short-time objective intelligibility (STOI) and normalized covariance metric (NCM) was estimated for ElectrodeNet using CI simulations. Sentence recognition tests for vocoded Mandarin speech were conducted with normal-hearing listeners. DNN, CNN, and LSTM-based ElectrodeNets exhibited strong correlations to ACE in objective and subjective scores using mean squared error (MSE), linear correlation coefficient (LCC), and Spearman's rank correlation coefficient (SRCC). The ElectrodeNet-CS strategy was capable of producing N-of-M compatible electrode patterns using a modified DNN network to embed maxima selection, and to perform in similar or even slightly higher average in STOI and sentence recognition compared to ACE. The methods and findings demonstrated the feasibility and potential of using deep learning in the CI coding strategy. © 2023 IEEE.

关键词： Cochlear implants

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Crack Detection for Non-Metallic Material Using Split Ring Resonator-based Chipless RFID Sensor 20

Crack Detection for Non-Metallic Material Using Split Ring R...

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20th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON 2023

作者： Nongpromma, Sirisak Janin, Suwan Kaemarungsi, Kamol Boonpoonga, Akkarat Torrungrueng, Danai Bannawat, Lakkhana King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand Rajamangala University of Technology Lanna Lampang Faculty of Engineering Department of Engineering and Technology Lampang52000 Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Department of Electrical and Computer Engineering Bangkok Thailand Department of Teacher Training in Electrical Engineering Bangkok Thailand Rajamangala University of Technology Rattanakosin Faculty of Engineering Department of Computer Engineering Nakhon Pathom Thailand

ISBN: (纸本)9798350310467

The study presented a novel chipless RFID sensor designed for the detection of cracks in non-metallic materials. The sensor utilized square coplanar split-ring resonators (SRR) and was mounted on the material under test (MUT), including materials such as Teflon (PTFE), Polycarbonate, and Kapton (Polyimide), for detecting cracks. To assess the effectiveness of the sensor in detecting cracks, simulation experiments were conducted using the CST Microwave Studio software, which analyzed shifts in the resonant frequency. The resulting RCS spectra demonstrated a clear correlation between the size of the crack and the resonance frequencies of the chipless RFID sensor, confirming the sensor's ability to detect cracks in nonmetallic materials. © 2023 IEEE.

关键词： Nonmetallic materials

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Efficient Federated Learning Using Dynamic Update and Adaptive Pruning with Momentum on Shared Server Data

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ACM Transactions on Intelligent Systems and technology 2024年第6期15卷 1-28页

作者： Liu, Ji Jia, Juncheng Zhang, Hong Yun, Yuhui Wang, Leye Zhou, Yang Dai, Huaiyu Dou, Dejing Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China School of Computer Science and Technology Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Baidu Research Beijing China Key Lab of High Confidence Software Technologies Ministry of Education and Software Institute Peking University Beijing China Department of Computer Science and Software Engineering Auburn University AuburnAL United States Department of Electrical and Computer Engineering North Carolina State University RaleighNC United States Bedi Cloud and School of Computer Science Fudan University Beijing China

Despite achieving remarkable performance, Federated Learning (FL) encounters two important problems, i.e., low training efficiency and limited computational resources. In this article, we propose a new FL framework, i.e., FedDUMAP, with three original contributions, to leverage the shared insensitive data on the server in addition to the distributed data in edge devices so as to efficiently train a global model. First, we propose a simple dynamic server update algorithm, which takes advantage of the shared insensitive data on the server while dynamically adjusting the update steps on the server in order to speed up the convergence and improve the accuracy. Second, we propose an adaptive optimization method with the dynamic server update algorithm to exploit the global momentum on the server and each local device for superior accuracy. Third, we develop a layer-adaptive model pruning method to carry out specific pruning operations, which is adapted to the diverse features of each layer so as to attain an excellent tradeoff between effectiveness and efficiency. Our proposed FL model, FedDUMAP, combines the three original techniques and has a significantly better performance compared with baseline approaches in terms of efficiency (up to 16.9 times faster), accuracy (up to 20.4% higher), and computational cost (up to 62.6% smaller). © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Federated learning

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Hyperbolic Pattern Detection in Ground Penetrating Radar Images Using Faster R-CNN 20

Hyperbolic Pattern Detection in Ground Penetrating Radar Ima...

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20th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON 2023

作者： Srimuk, Pachara Boonpoonga, Akkarat Kaemarungsi, Kamol Athikulwongse, Krit Torrungrueng, Danai Chantasen, Nattawat King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand National Science and Technology Development Agency National Electronics and Computer Technology Center Pathumthani Thailand Department of Teacher Training in Electrical Engineering Bangkok Thailand Rajamangala University of Technology Phra Nakhon Faculty of Engineering Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9798350310467

This study presents the application of Faster RCNN, a popular Region Based Convolutional Neural Network, for detecting hyperbolic patterns in Ground Penetrating Radar (GPR) images. GPR is an important tool for subsurface imaging in various fields such as geology, archaeology, and engineering. However, the analysis of GPR images can be challenging due to noise, small objects, and variations in object sizes. To evaluate the performance of the proposed method, 369 simulated GPR B-scan images were generated using GprMax simulation software. These images included single, double, and triple hyperbolic patterns. The results showed that preprocessing improved the detection accuracy and led to higher Intersection over Union (IoU) scores. The experimental results demonstrate that Faster R-CNN is an effective tool for hyperbolic pattern detection in GPR images and provides a promising direction for future research in the field. © 2023 IEEE.

关键词： Ground penetrating radar systems

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A Lightweight Authentication and Privacy-Preserving Aggregation for Blockchain-Enabled Federated Learning in VANETs

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IEEE Transactions on Consumer Electronics 2024年第1期71卷 1274-1287页

作者： Liu, Peng He, Qian Chen, Yiting Jiang, Shan Zhao, Baokang Wang, Xichan State and Local Joint Engineering Research Center for Satellite Navigation and Location Service Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Digital Infrastructure GuangxiZhuang Autonomous Region Information Center Nanning530000 China Department of Computing The Hong Kong Polytechnic University Hong Kong Hong Kong School of Computer National University of Defense Technology Changsha410073 China

Intelligent Transport Systems are designed to revolutionize the performance and efficiency of Vehicular Ad Hoc Networks (VANETs). Consumer electronics encompassing autonomous driving and route planning contribute to a better driving experience. Federated learning enables vehicle collaboration to train global models of intelligent transport without sharing local data for personalized consumer electronics. However, due to the dynamic network topology and unreliable open channels of VANETs, various potential risks undermine the credibility of establishing intermediate model parameters. To address these issues, we propose a lightweight authentication and privacy-preserving aggregation scheme for blockchain-enabled federated learning in VANETs(LPBFL). Specifically, we first construct a distributed secure authentication framework for blockchain federated learning that facilitates seamless authentication of mobile vehicles and security of model transmission. Subsequently, we design a lightweight three-party authentication key agreement based on chaotic map that establishes a session key for secure transmission of the intermediate model. In addition, we propose a continuous authentication adaptive model aggregation algorithms to ensure local model integrity while improving the quality of global models. Finally, security analyses and proofs show that LPBFL enhances the privacy and reliability of intermediate model parameters. Comprehensive experimental evaluations substantiate that the proposed LPBFL facilitates lightweight authentication while maintaining superior model accuracy. © 1975-2011 IEEE.

关键词： Vehicular ad hoc networks

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Solving Fuel-Based Unit Commitment Problem Using Improved Binary Bald Eagle Search

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Journal of Bionic engineering 2024年第6期21卷 3098-3122页

作者： Sharaz Ali Mohammed Azmi Al-Betar Mohamed Nasor Mohammed A.Awadallah Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityP.O.Box 346AjmanUnited Arab Emirates Department of Computer Science Al-Aqsa UniversityGaza4051Palestine Artificial Intelligence Research Center(AIRC) Ajman UniversityP.O.Box 346AjmanUnited Arab Emirates

The Unit Commitment Problem(UCP)corresponds to the planning of power generation *** objective of the fuel-based unit commitment problem is to determine the optimal schedule of power generators needed to meet the power demand,which also minimizes the total operating cost while adhering to different constraints such as power generation limits,unit startup,and shutdown *** this paper,four different binary variants of the Bald Eagle Search(BES)algorithm,were introduced,which used two variants using S-shape,U-shape,and V-shape transfer *** addition,the best-performing variant(using an S-shape transfer function)was selected and improved further by incorporating two binary operators:swap-window and *** variation is labeled Improved Binary Bald Eagle Search(IBBESS2).All five variants of the proposed algorithm were successfully adopted to solve the fuel-based unit commitment problem using seven test cases of 4-,10-,20-,40-,60-,80-,and *** comparative evaluation,34 comparative methods from existing literature were compared,in which IBBESS2 achieved competitive scores against other optimization *** other words,the proposed IBBESS2 performs better than all other competitors by achieving the best average scores in 20-,40-,60-,80-,and 100-unit ***,IBBESS2 demonstrated quicker convergence to an optimal solution than other algorithms,especially in large-scale unit commitment *** Friedman statistical test further validates the results,where the proposed IBBESS2 is ranked the *** conclusion,the proposed IBBESS2 can be considered a powerful method for solving large-scale UCP and other related problems.

关键词： Swarm intelligence Bald eagle search Unit commitment problem Optimization

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Understanding Human Balance Strategies Through Clustering of Motion Analysis Data 10

Understanding Human Balance Strategies Through Clustering of...

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10th International Conference on digital Arts, Media and technology and 8th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2025

作者： Lapawong, Werapat Uttama, Surapong Rueangsirarak, Worasak Temdee, Punnarumol Hu, Shanfeng Aslam, Nauman Mae Fah Luang University Digital Transformation Technology School of Applied Digital Technology Chiang Rai Thailand Mae Fah Luang University Computer and Communication Engineering for Capacity Building Research Center Chiang Rai Thailand Northumbria University Department of Computer and Information Sciences Newcastle upon Tyne United Kingdom

ISBN: (纸本)9798331543273

Balance is the result of complex interactions among individuals, tasks, and the environment. It plays a crucial role in daily activities, relying on sensory systems and joint movement strategies to maintain postural stability. Effective balance not only improves movement and physical activities but also enhances overall quality of life. Balance strategies, such as ankle, hip, knee, and stepping strategies, are essential for maintaining stability in various situations and environments. This study focuses on utilizing public data from the Clinical Test of Sensory Interaction on Balance (CTSIB) to assess balance strategies. Traditional methods rely on subjective observations, limiting the identification of nuanced strategy combinations. To overcome this, motion capture (mocap) data and unsupervised machine learning techniques, clustering and dimensionality. reduction using Agglomerative, Gaussian Mixture, and K-Means and classify balance strategies. The results demonstrate the potential of this approach to identify distinct clusters of balance strategies, offering a more detailed, objective, and comprehensive understanding of postural stability. Among the clustering algorithms applied, K-Means proved to be particularly effective, achieving reliable groupings with consistent results. The Silhouette Scores indicated that k=3, identified as the optimal number of clusters from the elbow test, provided the best balance between cohesion and separation for K-Means. This suggests that K-Means delivers interpretable clusters of balance strategies. The three possible outcomes are good balance, A slight unbalance and a poor balance. This innovative methodology advances the field of balance assessment by providing a clear and systematic approach to classifying balance strategies, allowing for better understanding and targeted interventions. © 2025 IEEE.

关键词： K-means clustering

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Graphene Bridge Heterostructure Devices for Negative Differential Transconductance Circuit Applications

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Nano-Micro Letters 2023年第2期15卷 161-171页

作者： Minjong Lee Tae Wook Kim Chang Yong Park Kimoon Lee Takashi Taniguchi Kenji Watanabe Min‑gu Kim Do Kyung Hwang Young Tack Lee Department of Electrical and Computer Engineering Inha UniversityIncheon 22212Republic of Korea Department of Materials Science and Engineering The University of Texas at DallasRichardsonTX 75080USA Center for Opto‑Electronic Materials and Devices Post‑Silicon Semiconductor InstituteKorea Institute of Science and Technology(KIST)Seoul 02792Republic of Korea Department of Physics Kunsan National UniversityGunsan 54150Republic of Korea Advanced Materials Laboratory National Institute for Materials ScienceTsukuba 305‑0044Japan Department of Information and Communication Engineering Inha UniversityIncheon 22212Republic of Korea Division of Nanoscience and Technology KIST SchoolUniversity of Science and Technology(UST)Seoul 02792Republic of Korea Department of Electronic Engineering Inha UniversityIncheon 22212Republic of Korea

Two-dimensional van der Waals(2D vdW)material-based heterostructure devices have been widely studied for high-end electronic applications owing to their heterojunction *** this study,we demonstrate graphene(Gr)-bridge heterostructure devices consisting of laterally series-connected ambipolar semiconductor/Gr-bridge/n-type molybdenum disulfide as a channel material for field-effect transistors(FET).Unlike conventional FET operation,our Gr-bridge devices exhibit nonclassical transfer characteristics(humped transfer curve),thus possessing a negative differential *** phenomena are interpreted as the operating behavior in two series-connected FETs,and they result from the gate-tunable contact capacity of the Gr-bridge ***-value logic inverters and frequency tripler circuits are successfully demonstrated using ambipolar semiconductors with narrow-and wide-bandgap materials as more advanced circuit applications based on non-classical transfer ***,we believe that our innovative and straightforward device structure engineering will be a promising technique for future multi-functional circuit applications of 2D nanoelectronics.

关键词： Graphene bridge Heterostructure device Non-classical transfer characteristics Multi-value logic inverter Frequency tripler

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