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2018 International Conference on computer and Applications, ICCA 2018

作者： Idriss, Hanan Mouallem, Aly El Ahmad, Abdel-Mehsen Bazzal, Zouhair El Muhieddine, Ahmad Lebanese International University Department of Computer and Communications Engineering Bekaa Lebanon

ISBN: (纸本)9781538643716

Vehicular Ad-hoc Networks (VANETs) have made a great evolution in vehicular system, where they tend to decrease the percentage of accidents annually by maintaining the security of drivers in the road. In recent years, much research has been devoted to the study of the importance of VANETs and improving it;nevertheless, these researches still lack of ensuring broadcasting safety messages and sending non-safety data with less response time, high throughput and a good percentage of successfully received safety packets all at the same time. Our focus in this work goes for allowing vehicles to communicate with each other with less response time, high data throughput, and an acceptable percentage of received safety packets. © 2018 IEEE.

关键词： Vehicular ad hoc networks

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Exact learning augmented naive Bayes classifier 9

Exact learning augmented naive Bayes classifier

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9th International Conference on Probabilistic Graphical Models, PGM 2018

作者： Sugahara, Shouta Uto, Masaki Ueno, Maomi Department of Computer and Network Engineering University of Electro-Communications Tokyo Japan

For classification problems, Bayesian networks are often used to infer a class variable when given feature variables. Earlier reports have described that classification accuracies of Bayesian networks achieved by maximizing the marginal likelihood (ML) were lower than those achieved by maximizing the conditional log likelihood (CLL) of a class variable given the feature variables. However, the reports stated no reason why CLL outperformed ML. Differences between the two scores’ performances in those earlier studies might depend on their respective learning algorithms: they were approximate learning algorithms, not exact ones. The present study compared the classification performances of Bayesian networks with exact learning using ML and those with approximate learning using CLL. Results demonstrate that the performance of Bayesian networks achieved by maximizing ML is not necessarily worse than that achieved by maximizing CLL. However, the results also show that classification accuracies with exact learning by ML are much worse than those by other methods when the class variable has numerous parents and few children. To resolve this difficulty, this study proposed exact learning augmented naive Bayes (ANB) using Markov blanket feature selection. Some comparison experiments demonstrated that the proposed method outperforms the other methods. © 2018 Proceedings of Machine Learning Research. All rights reserved.

关键词： Bayesian networks

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An exploration study on developing blockchain systems–the practitioners' perspective

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Information and Software Technology 2025年 186卷

作者： Bakheet Aljedaani Aakash Ahmad Mahdi Fehmideh Arif Ali Khan Jun Shen Computer Science Department Aljumum University College Umm Alqura University Makkah Saudi Arabia School of Computing and Communications Lancaster University Leipzig Germany School of Business at University of Southern Queensland Queensland Australia M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland School of Computing and Information Technology University of Wollongong Wollongong Australia

Context Blockchain-based software (BBS) builds upon the foundational technologies of cryptocurrencies like Bitcoin, utilising decentralised, immutable ledgers, to support the development and operation of security-critical and transaction-intensive systems and services. In recent years, a number of research studies have investigated the strategic benefits and technical limitations of BBS that is central to the operations of a wide variety of systems ranging from cyber security, healthcare, education, and financial technologies. Despite an increasing interest both from academia and industry in BBS, there is a dearth of empirical evidence resulting in a lack of understanding about processes, methods, and techniques to enable a systematic development of this class of software systems. Objectives Existing research lacks a consolidated view, particularly empirically-driven guidelines based on published evidence and development practices. Therefore, our objective is to derive new or leverage existing development processes, patterns, and models to design, implement, and validate BBS systems. Method Tied to this knowledge gap, we conducted a two-phase research that unifies the findings of (i) a systematic literature review and (ii) practitioners’ survey to derive and validate the development process for BBS systems. First, we conducted a systematic literature review of 58 studies to derive a process comprising of 26 activities, to develop BBS systems. We than engaged 102 blockchain practitioners from, 35 countries across 6 continents to validate the BBS system development processes. Results Our results revealed a statistically significant difference ( p -value < .001) in the importance ratings of 24 out of 26 BBS activities by our participants. The only two activities that were not statistically significant were incentive protocol design and granularity design. Our study also presented some of the activities that have been emphasised by our participants within the different

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A Deep learning based wearable healthcare iot device for ai-enabled hearing assistance automation

arXiv

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arXiv 2020年

作者： Young, Fraser Zhang, Li Jiang, Richard Liu, Han Wall, Conor Department of Computer and Information Sciences Faculty of Engineering and Environment Northumbria University NewcastleNE1 8ST United Kingdom School of Computing and Communications Faculty of Science and Technology Lancaster University Lancaster United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom

With the recent booming of artificial intelligence (AI), particularly deep learning techniques, digital healthcare is one of the prevalent areas that could gain benefits from AI-enabled functionality. This research presents a novel AI-enabled Internet of Things (IoT) device operating from the ESP-8266 platform capable of assisting those who suffer from impairment of hearing or deafness to communicate with others in conversations. In the proposed solution, a server application is created that leverages Googles online speech recognition service to convert the received conversations into texts, then deployed to a micro-display attached to the glasses to display the conversation contents to deaf people, to enable and assist conversation as normal with the general population. Furthermore, in order to raise alert of traffic or dangerous scenarios, an urban-emergency classifier is developed using a deep learning model, Inception-v4, with transfer learning to detect/recognize alerting/alarming sounds, such as a horn sound or a fire alarm, with texts generated to alert the prospective user. The training of Inception-v4 was carried out on a consumer desktop PC and then implemented into the AI-based IoT application. The empirical results indicate that the developed prototype system achieves an accuracy rate of 92% for sound recognition and classification with real-Time performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Internet of things

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Noise bias compensation for reconstructed HDR images in two layer coding

IEEJ Transactions on Electronics, Information and Systems

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IEEJ Transactions on Electronics, Information and Systems 2019年第12期139卷 1442-1451页

作者： Minewaki, Sayaka Hamzah, Fairoza Amira Binti Yoshida, Taichi Iwahashi, Masahiro Kiya, Hitoshi Department of Computer Science and Engineering National Institute of Technology Yuge College 1000 Kamijima-cho Ochi-gun Ehime794-2593 Japan Kyoto College of Graduate Studies for Informatics 7 Tanakamonzen-cho Sakyo-ku Kyoto606-8225 Japan Department of Computer and Network Engineering University of Electro-Communications 1-5-1 Chofugaoka Chofu Tokyo182-8585 Japan Department of Electrical Electronics and Information Engineering Nagaoka University of Technology 1603-1 Kamitomioka Nagaoka Niigata940-2188 Japan Department of Information and Communication Systems Faculty of System Design Tokyo Metropolitan University 6-6 Asahigaoka Hino Tokyo191-0065 Japan

We propose a two-layer coding method for HDR images with noise bias compensation (NBC). Although the mean of image noise is assumed to be zero originally, it becomes nonzero (that is, noise bias is present) after tone mapping, because of the nonlinearity of tone mapping. In the conventional two-layer coding, the reconstructed LDR image is decoded using a lossy encoder of a base layer that includes noise;the noise is included in an HDR image that is inverse tone mapped from a reconstructed LDR image with noise bias. We aim to improve the quality of reconstructed HDR images by compensating for noise bias. NBC classifies pixels in a noisy image into several subsets according to the pixel value after tone mapping, and compensates for the pixel value in each subset with a compensation value determined in advance. The compensation values are determined by using all pixel values in the LDR image that is input to the base layer, in addition to those in the HDR image that is inversely tone mapped from the reconstructed LDR image. We perform NBC following inverse tone mapping for the reconstructed LDR image. Experimental results show the effectiveness of the proposed method. © 2019 The Institute of Electrical Engineers of Japan.

关键词： Image denoising

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Preface

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Lecture Notes on Data engineering and communications Technologies 2022年 116卷 ix页

作者： Suma, V. Fernando, Xavier Du, Ke-Lin Wang, Haoxiang Department of Information Science and Engineering Research and Industry Incubation Center Dayananda Sagar College of Engineering Karnataka Bengaluru India Ryerson Communications Lab TorontoON Canada Department of Electrical and Computer Engineering Concordia University MontrealQC Canada Go Perception Laboratory Cornell University IthacaNY United States

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Photonic convolutional accelerator and neural network in the Tera-OPs regime based on soliton crystal Kerr microcombs

Photonic convolutional accelerator and neural network in the...

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Signal Processing in Photonic communications, SPPCom 2021 - Part of OSA Advanced Photonics Congress 2021

作者： Tan, M. Xu, X. Wu, J. Boes, A. Corcoran, B. Nguyen, T. Chu, S.T. Little, B.E. Morandotti, R. Lowery, A.J. Mitchell, A. Hicks, D. Moss, D.J. Optical Sciences Centre Swinburne University of Technology HawthornVIC3122 Australia Dept. of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia School of Engineering Rmit University MelbourneVIC3001 Australia Department of Physics and Material Science City University of Hong Kong Tat Chee Avenue Hong Kong Xi'an Institute of Optics and Precision Mechanics Precision Mechanics of Cas Xi'an China INRS-Énergie Matériaux et Télécommunications 1650 Boulevard Lionel-Boulet VarennesQCJ3X 1S2 Canada Walter and Eliza Hall Institute of Medical Research Bioinformatics Division ParkvilleVIC3052 Australia

ISBN: (纸本)9781557528209

We report a new approach to ONNs based on integrated Kerr micro-combs that is programmable, highly scalable and capable of reaching ultra-high speeds. We demonstrate a single neuron perceptron at 11.9 Giga-OPS at 8 bits per OP, or 95.2 Gbps. We then demonstrate a convolutional accelerator operating beyond 11 TeraOPs/s. We test the perceptron on handwritten-digit recognition and cancer-cell detection - achieving over 90% and 85% accuracy, respectively. © 2021 OSA - The Optical Society. All rights reserved.

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Performance Evaluation of Deep Recurrent Neural Networks Architectures: Application to PV Power Forecasting 2

Performance Evaluation of Deep Recurrent Neural Networks Arc...

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2nd International Conference on Smart Grid and Renewable Energy, SGRE 2019

作者： Massaoudi, Mohamed Chihi, Ines Sidhom, Lilia Trabelsi, Mohamed Refaat, Shady S. Oueslati, Fakhreddine S. Texas A and M University at Qatar Department of Electrical and Computer Engineering Doha Qatar National Engineering School of Tunis Tunisia Kuwait College of Science and Technology Department of Electronic and Communications Engineering Kuwait Unité de Recherche de Physique des Semi-Conducteurs et Capteurs Carthage University Tunisia

ISBN: (纸本)9781728129600

Smart grid systems require an accurate energy prediction from renewable sources to ensure high sustainability and power quality. For PV plants, a precise estimation of the generated PV power is crucial for the reduction of the production/demand unbalance. This essential need comes from the high variability of weather parameters during the PV electricity generation. Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) recurrent neural networks proved their high efficiency in forecasting applications. Thus, this paper proposes a comprehensive evaluation of the LSTM and GRU techniques for PV power estimation in the medium/long horizon. The evaluation is based on a fair assessment of the aforementioned architectures for one week and more than three months (98 days) periods. © 2019 IEEE.

关键词： Long short-term memory

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Joint location, bandwidth and power optimization for THz-enabled UAV communications

arXiv

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arXiv 2020年

作者： Xu, Luyao Chen, Ming Chen, Mingzhe Yang, Zhaohui Chaccour, Christina Saad, Walid Hong, Choong Seon National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Electrical Engineering Department Princeton University NJ08544 United States The Department of Engineering King's College London WC2R 2LS United Kingdom The Wireless@VT Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States The Department of Computer Science and Engineering Kyung Hee University Yongin-si Gyeonggi-do17104 Korea Republic of

In this paper, the problem of unmanned aerial vehicle (UAV) deployment, power allocation, and bandwidth allocation is investigated for a UAV-assisted wireless system operating at terahertz (THz) frequencies. In the studied model, one UAV can service ground users using the THz frequency band. However, the highly uncertain THz channel will introduce new challenges to the UAV location, user power, and bandwidth allocation optimization problems. Therefore, it is necessary to design a novel framework to deploy UAVs in the THz wireless systems. This problem is formally posed as an optimization problem whose goal is to minimize the total delays of the uplink and downlink transmissions between the UAV and the ground users by jointly optimizing the deployment of the UAV, the transmit power and the bandwidth of each user. The communication delay is crucial for emergency communications. To tackle this nonconvex delay minimization problem, an alternating algorithm is proposed while iteratively solving three subproblems: location optimization subproblem, power control subproblem, and bandwidth allocation subproblem. Simulation results show that the proposed algorithm can reduce the transmission delay by up to 59.3%, 49.8% and 75.5% respectively compared to baseline algorithms that optimize only UAV location, bandwidth allocation or transmit power control. © 2020, CC BY-SA.

关键词： Unmanned aerial vehicles (UAV)

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Can terahertz provide high-rate reliable low latency communications for wireless VR?

arXiv

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arXiv 2020年

作者： Chaccour, Christina Soorki, Mehdi Naderi Saad, Walid Bennis, Mehdi Popovski, Petar Wireless VT Bradley Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA United States Faculty of Engineering Shahid Chamran University of Ahvaz Ahvaz Iran Centre for Wireless Communications University of Oulu Oulu Finland Department of Electronic Systems Aalborg University Denmark

Wireless virtual reality (VR) imposes new visual and haptic requirements that are directly linked to the quality-of-experience (QoE) of VR users. These QoE requirements can only be met by wireless connectivity that offers high-rate and high-reliability low latency communications (HRLLC), unlike the low rates usually considered in vanilla ultra-reliable low latency communication scenarios. The high rates for VR over short distances can only be supported by an enormous bandwidth, which is available in terahertz (THz) frequency bands. Guaranteeing HRLLC requires dealing with the uncertainty that is specific to the THz channel. To explore the potential of THz for meeting HRLLC requirements, a quantification of the risk for an unreliable VR performance is conducted through a novel and rigorous characterization of the tail of the end-to-end (E2E) delay. Then, a thorough analysis of the tail-value-atrisk (TVaR) is performed to concretely characterize the behavior of extreme wireless events crucial to the real-time VR experience. System reliability for scenarios with guaranteed line-of-sight (LoS) is then derived as a function of THz network parameters after deriving a novel expression for the probability distribution function of the THz transmission delay. Numerical results show that abundant bandwidth and low molecular absorption are necessary to improve the reliability. However, their effect remains secondary compared to the availability of LoS, which significantly affects the THz HRLLC performance. In particular, for scenarios with guaranteed LoS, a reliability of 99:999% (with an E2E delay threshold of 20 ms) for a bandwidth of 15 GHz along with data rates of 18:3 Gbps can be achieved by the THz network (operating at a frequency of 1 THz), compared to a reliability of 96% for twice the bandwidth, when blockages are considered. Copyright © 2020, The Authors. All rights reserved.

关键词： Bandwidth

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