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IEEE Internatonal Conference on Mobile Adhoc and Sensor Systems (MASS)

作者： Biao Han Li Ding Yusheng Ji Xiaoyan Wang Baosheng Wang School of Computer National University of Defense Technology Changsha China Information Systems Architecture Research Division National Institute of Informatics Tokyo Japan Graduate School of Science and Engineering Ibaraki University Ibaraki Japan

ISBN: (数字)9781728198668

ISBN: (纸本)9781728198675

Mobile Ad hoc Network (MANET) is an emerging technology that allows users to transmit data without any physical infrastructure. Among those MANET protocols, Temporally Ordered Routing Algorithm (TORA) is an on-demand MANET routing protocol that attempts to find routes according to the directed acyclic graph (DAG). However, the TORA protocol requires strict synchronization. The routing overhead of TORA will increase linearly with the packet transmission rate. Motivated by Apple Wireless Direct Link (AWDL), we propose an ad hoc link-layer protocol called TORA-based Wireless Protocol (TWP) in this article. TWP can be deployed on embedded devices with Linux-kernel systems. Besides, it has unique frame structures and mechanisms. Also, it can implement synchronization and a TORA-like routing function at the link layer. We analyze the performance of TWP via experiments on Raspberry Pis. The results show that TWP can perform routing and data transmission successfully. It performs well in synchronization and can effectively reduce the routing overhead during the process of network routing.

关键词： Wireless communication Performance evaluation Wireless sensor networks Routing Routing protocols Synchronization Mobile ad hoc networks

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FTT-NAS: Discovering fault-tolerant convolutional neural architecture

arXiv

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arXiv 2020年

作者： Ning, Xuefei Ge, Guangjun Li, Wenshuo Zhu, Zhenhua Zheng, Yin Chen, Xiaoming Gao, Zhen Wang, Yu Yang, Huazhong Department of Electronic Engineering Tsinghua University China Weixin Group Tencent China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China School of Electrical and Information Engineering Tianjin University China

With the fast evolvement of embedded deep-learning computing systems, applications powered by deep learning are moving from the cloud to the edge. When deploying neural networks (NNs) onto the devices under complex environments, there are various types of possible faults: soft errors caused by cosmic radiation and radioactive impurities, voltage instability, aging, temperature variations, malicious attackers, etc. Thus the safety risk of deploying NNs is now drawing much attention. In this paper, after the analysis of the possible faults in various types of NN accelerators, we formalize and implement various fault models from the algorithmic perspective. We propose Fault-Tolerant Neural architecture Search (FT-NAS) to automatically discover convolutional neural network (CNN) architectures that are reliable to various faults in nowadays devices. Then we incorporate fault-tolerant training (FTT) in the search process to achieve better results, which is referred to as FTT-NAS. Experiments on CIFAR-10 show that the discovered architectures outperform other manually designed baseline architectures significantly, with comparable or fewer floating-point operations (FLOPs) and parameters. Specifically, with the same fault settings, F-FTT-Net discovered under the feature fault model achieves an accuracy of 86.2% (VS. 68.1% achieved by MobileNet-V2), and W-FTT-Net discovered under the weight fault model achieves an accuracy of 69.6% (VS. 60.8% achieved by ResNet-18). By inspecting the discovered architectures, we find that the operation primitives, the weight quantization range, the capacity of the model, and the connection pattern have influences on the fault resilience capability of NN models. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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The Spatiotemporal Traffic Accident Risk Analysis in Urban Traffic Network 9th

The Spatiotemporal Traffic Accident Risk Analysis in Urban T...

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9th International Conference on Mobile Wireless Middleware, Operating Systems and Applications, MOBILWARE 2020

作者： Zhang, Chijun jin, Jing Huang, Qiuyang Du, Zhanwei Yuan, Zhilu Tang, Shengjun Liu, Yang School of Management Science and Information Engineering Jilin University of Finance and Economics Changchun130117 China Jilin Province Key Laboratory of Fintech Jilin University of Finance and Economics Changchun130117 China College of Computer Science and Technology Jilin University Changchun130012 China Research Institute for Smart Cities and Shenzhen Key Laboratory of Spatial Information Smart Sensing and Services School of Architecture and Urban Planning Shenzhen University Shenzhen China Jilin Big Data Research Center for Business Jilin University of Finance and Economics Changchun130117 China

ISBN: (纸本)9783030622046

Traffic accidents seriously threaten people’s lives and property all over the world. Therefore, it is of great significance to human society to have a long-term traffic accidents data with detail temporal and geographic information in a specific space, which can be used for traffic accident hotspots identification to reduce the incidence of traffic accidents. Here, we obtain a one-year dataset of traffic accidents of the city center in Changchun, Northeast China, in 2017. In this paper, we analyze the risk of traffic accident in urban area, and then discover the characteristics of traffic accidents at the temporal and spatial aspect. We construct a traffic network, which takes crossings as nodes and road sections as edges and weighted by the total number of traffic accidents. In addition, we integrate road structure data and meteorological data to explore the characteristics of the traffic network. © 2020, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Risk analysis

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Lower limb Movements' Classifications using Hemodynamic Response:fNIRS Study

Lower limb Movements' Classifications using Hemodynamic Resp...

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IEEE EMBS Conference on Biomedical engineering and Sciences (IECBES)

作者： Maged S. AL-Quraishi Irraivan Elamvazuthi Tong Boon Tang Muhammed AL-Qurishi S. Parasuraman Alberto Borboni Smart Assistive and Rehabilitative Technology (SMART) Research Group Universiti Teknologi PETRONAS Perak Malaysia Centre for Intelligent Signal and Imaging Research (CISIR) Universiti Teknologi PETRONAS Perak Malaysia Faculty of Information and Computer Science Thamar Univerisity Dhamar Yemen School of Engineering Monash University (of Affiliation) Bandar Sunway Malaysia Universita Degli Studi di Brescia Italy

ISBN: (数字)9781728142456

ISBN: (纸本)9781728142463

Functional near-infrared spectroscopy (fNIRS) has become a viable approach for brain function investigation and is an interesting modality for brain-machine interfaces (BMIs) due to its portability and resistance to electromagnetic noise. In this work, a hemodynamic response based on fNIRS signals was utilized to classify the right and left ankle joint movements. To achieve this objective, 32 optodes (emitters and detectors) were used to measure the hemodynamic responses in the motor cortex area during the motor execution task of the ankle joint movements. Two-channel sets were formed one including the channels directly related to the movement task, and another including all of the proposed channels. The results of this study reveal that the scheme based only on the selected channels outperformed the scheme that uses all channels. The classification accuracies were 91.38 % and 89.86 % respectively. These results demonstrated that fNIRS signal classification can be enhanced by eliminating the redundant channels.

关键词： Support vector machines Resistance Spectroscopy Pattern classification Feature extraction Hemodynamics Task analysis

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Quantum machine learning for corrosion resistance in stainless steel

Materials Today Quantum

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Materials Today Quantum 2024年 3卷

作者： Muhamad Akrom Supriadi Rustad Totok Sutojo De Rosal Ignatius Moses Setiadi Hermawan Kresno Dipojono Ryo Maezono Moses Solomon Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang 50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung 40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa 923-1292 Japan Department of Chemical and Environmental Engineering University of Nottingham Ningbo China Ningbo China

This study evaluates the efficacy of quantum machine learning (QML) models in predicting stainless steel corrosion behaviour. Using two datasets, the quantum support vector classifier (QSVC) outperformed classical models, achieving accuracies of 95.46 % and 94.80 % for Dataset A and Dataset B, respectively. The QSVC excelled in identifying complex corrosion classes and demonstrated robust performance across diverse environments. This QML approach accurately predicts corrosion without experimental testing, saving significant time and cost. Future research will aim to include more environmental variables and steel types, broadening the model's applicability.

关键词： Quantum machine learning QSVC Corrosion Stainless steel Classification

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A secure mist-fog-assisted cooperative offloading framework for sustainable smart city development

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Digital Communications and Networks 2024年

作者： Subhranshu Sekhar Tripathy Sujit Bebortta Mazin Abed Mohammed Muhammet Deveci Haydar Abdulameer Marhoon Radek Martinek School of Computer Engineering KIIT Deemed to be University Bhubaneswar 751024 India Department of Computer Science Ravenshaw University Cuttack 753003 India Department of Artificial Intelligence College of Computer Science and Information Technology University of Anbar Anbar 31001 Iraq Department of Cybernetics and Biomedical Engineering VSB-Technical University of Ostrava Ostrava 70800 Czech Republic Department of Telecommunications VSB-Technical University of Ostrava Ostrava 70800 Czech Republic Department of Industrial Engineering Turkish Naval Academy National Defence University 34942 Tuzla Istanbul Turkey Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon The Bartlett School of Sustainable Construction University College London 1-19 Torrington Place London WC1E 7HB UK Information and Communication Technology Research Group Scientific Research Center Al-Ayen University Thi-Qar Iraq Iraq College of Computer Sciences and Information Technology University of Kerbala Karbala Iraq

Practical applications of smart cities and the Internet of Things (IoT) have multiplied, posing many difficulties in network performance, dependability, and security. Concerns of accessibility, reliability, sustainability, and security too have arisen correspondingly because of the decentralized character of the smart city and IoT systems. Fog computing offers a foundation for various applications, including cognitive support, health and social services, intelligent transportation systems, and pervasive computing and communications. Fog computing can help enhance these apps' productivity and lower the end-to-end delay experienced by such time-sensitive applications. In this research, we propose a reliable and secure service delivery strategy at the network edge for smart cities. To improve the availability and dependability, along with the security of smart city applications, the approach employs a combined method uniting distributed fog servers in addition to mist servers with the help of an intrusion detection system. Simulation findings suggest a reduction of 40.3% in the delay incurred by each service request for highly dense areas and 60.6% for moderately dense environments. Furthermore, the system has low false-negative rates and high detection and accuracy rates, decreasing service requests 2%.

关键词： Security Reliability Trustworthy computing Resource provisioning Threat detection Fog computing Internet of things

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architecture information communication in two OSS projects: The why, who, when, and what

arXiv

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arXiv 2021年

作者： Bi, Tingting Ding, Wei Liang, Peng Tang, Antony School of Computer Science Wuhan University Wuhan430072 China Faculty of Information and Technology Monash University MelbourneVIC3166 Australia Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Software and Services Research Group Vrije Universiteit Amsterdam Amsterdam1101 Netherlands Key Laboratory of Earthquake Geodesy Institute of Seismology China Earthquake Administration China

architecture information is vital for Open Source Software (OSS) development, and mailing list is one of the widely used channels for developers to share and communicate architecture information. This work investigates the nature of architecture information communication (i.e., why, who, when, and what) by OSS developers via developer mailing lists. We employed a multiple case study approach to extract and analyze the architecture information communication from the developer mailing lists of two OSS projects, ArgoUML and Hibernate, during their development life-cycle of over 18 years. Our main findings are: (a) architecture negotiation and interpretation are the two main reasons (i.e., why) of architecture communication;(b) the amount of architecture information communicated in developer mailing lists decreases after the first stable release (i.e., when);(c) architecture communications centered around a few core developers (i.e., who);(d) and the most frequently communicated architecture elements (i.e., what) are architecture Rationale and architecture Model. There are a few similarities of architecture communication between the two OSS projects. Such similarities point to how OSS developers naturally gravitate towards the four aspects of architecture communication in OSS development. © 2021, CC BY.

关键词： Open source software

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Waveguide coupler using direct coupling aperture for Ka-band applications

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AIP Conference Proceedings 2023年第1期2795卷

作者： H. O. Hanoosh M. K. A. Rahim N. A. Murad Y. M. Hussein Advance RF and Microwave Research Group (ARFMRG) School of Electrical Engineering Faculty of Engineering Universiti Teknologi Malaysia UTM Johor Bahru Johor 81310 Malaysia Department of computer techniques engineering College of information technology Imam Ja’afar Al-Sadiq University Samawah 66001 Al Muthanna Iraq Information Technology and Communication (ITC) Al-Furat Al-Awsat Technical University Sammawah Iraq

This paper presents a waveguide coupler realized by direct coupling aperture at Ka bands. At high frequency planar coupler structures are expected to exploit a higher propagation loss and components losses. Therefore, a waveguide with direct coupling technology is proposed. A waveguide coupler is designed using direct coupling between two waveguide structures and cutting in narrow walls. This cutting in waveguide narrow walls controls the coupling and the phase differences of the coupler outputs. The simulated reflection and isolation coefficients are observed to be less than −20 dB at 26 GHz, with transmission coefficients ranging between −2.97 dB and −3.04 dB. The phase differences −90° is observed at the outputs. Overall, the waveguide coupler shows a great potential for Ka bands applications.

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Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques

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Neural Computing and Applications 2020年第3期32卷 625-638页

作者： Abd Ghani, Mohd Khanapi Mohammed, Mazin Abed Arunkumar, N. Mostafa, Salama A. Ibrahim, Dheyaa Ahmed Abdullah, Mohamad Khir Jaber, Mustafa Musa Abdulhay, Enas Ramirez-Gonzalez, Gustavo Burhanuddin, M.A. Biomedical Computing and Engineering Technologies (BIOCORE) Applied Research Group Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Durian Tunggal Malaysia Planning and Follow Up Department University Headquarter University of Anbar Anbar Iraq School of EEE Sastra University Thanjavur India Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Johor Malaysia Department of Otorinolaringologi Hospital Pakar Sultanah Fatimah Muar Muar Johor Malaysia Dijlah University College Baghdad Iraq Department of Biomedical Engineering Jordan University of Science and Technology Irbid Jordan Departamento de Telemática Universidad del Cauca Popayán Cauca 190002 Colombia

Making an accurate diagnosis of nasopharyngeal carcinoma (NPC) disease is a challenging task that involves many parties such as radiology specialists often times need to delineate NPC boundaries on various tumor-bearing endoscopic images. It is a tedious and time-consuming operation exceedingly based on doctors and experience of radiologist. NPC has complex and irregular structures which makes it difficult to diagnose even by an expert physician. However, the diagnosis accuracy results of such methods are still insignificant and need improvement in order to manifest robust solution. The study aim is to develop and propose a new automatic classification of NPC tumor using machine learning techniques and feature-based decision-level fusion scheme from endoscopic images. We have implemented the fusion of the three image texture-based schemes (local binary patterns, the first-order statistics histogram properties, and histogram of gray scale) at the decision level and tested the performance of this scheme using the same experimental setup in the previous section for simple score-level fusion, but for comparison, We used the classifiers methods which are support vector machines (SVM), k-nearest neighbors’ algorithm, and artificial neural network (ANN). The results demonstrate that the majority rule for decision-based fusion is outperformed considerably by the single best performing feature scheme (FFGF) for the SVM classifier, but for the ANN and KNN classifier it is significantly outperformed by each of the components features. The classifiers approaches were listed a high accuracy of 94.07%, the sensitivity of 92.05%, and specificity of 93.07%. © 2018, Springer-Verlag London Ltd., part of Springer Nature.

关键词： Neural networks

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RETRACTED: Optimization using Internet of Agent based Stacked Sparse Autoencoder Model for Heart Disease Prediction

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Expert Systems 2023年第12期41卷

作者： Vaishali Baviskar Madhushi Verma Pradeep Chatterjee Gaurav Singal Thippa Reddy Gadekallu GH Raisoni Institute of Engineering and Technology Pune India Bennett University Greater Noida India TML Business Services Ltd. Pune India Netaji Subhas University of Technology Delhi India School of Information Technology and Engineering Vellore Institute of Technology Vellore India Zhongda Group Jiaxing China Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Division of Research and Development Lovely Professional University Phagwara India College of Information Science and Engineering Jiaxing University Jiaxing China

Recently, machine learning methods have been successfully used for the prediction of cardiovascular disease. Early diagnosis and prediction is necessary for giving effective treatment to avoid higher mortality rates. Several classification algorithms have been developed recently which satisfy the need, but show limited accuracy while predicting the heart disease. Hence, the focus of this study is on early prediction of heart disease and to improve the accuracy of prediction using benchmark heart disease datasets such as UCI Cleveland dataset and Heart disease clinical dataset by implementing effective classification and optimization algorithms. Optimization algorithms generally exhibit the benefit of dealing with complex non-linear issues with better adaptability and flexibility. The Emperor penguin optimization algorithm, which can select the best features for classification has been utilized in this study to improve the efficiency, minimize reconstruction errors, and increase the quality of heart disease classification. Further, the newly developed stacked sparse convolutional neural network based auto encoder (SSC-AE) classification algorithm has been employed for significant feature classification with higher robustness and efficacy. Accuracy, Area Under Curve (AUC), and F1 score are some of the measures used to compare the outcomes of several machine learning algorithms to those of the proposed model in this study. The results show that the proposed model, SSC-AE, is superior to other classification models.

关键词： Emperor penguin optimizer heart disease heart disease clinical dataset stacked sparse CNN based auto encoder UCI Cleveland dataset

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