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2019 Advances in Science and engineering technology International Conferences, ASET 2019

作者： Pavithran, Deepa Al-Karaki, Jamal N. Thomas, Rajesh Shibu, Charles Gawanmeh, Amjad Abu Dhabi Polytechnic MBZ City Abu Dhabi United Arab Emirates Abu Dhabi Men's College Higher Colleges of Technology Abu Dhabi United Arab Emirates KU Center for Cyber-Physical Systems Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9781538682715

Bitcoin scheme powered by the blockchain technology is a global payment system that posed great challenges and opportunities for economists, entrepreneurs and consumers. As Bitcoin is still considered to be mysterious and not well understood by many stakeholders, it is essential to analyze the capabilities of Bitcoin and its underlying protocol with regard to different financial aspects. Some of these aspects include Bitcoin ownership, Bitcoin saving trends, transaction authenticity, price stability, and energy consumption. Through this paper we present a data driven analysis of price fluctuations, user behavior, and wealth accumulation in Bitcoin transaction. Of particular interest to stakeholders is the understanding of factors that impact Bitcoin price and the volatility over time. We focus on comparison of Bitcoin price trends vs number of users and the number of transactions. We also analyze the richest addresses and the percentage of wealth in these addresses. Important conclusions are finally summarized. © 2019 IEEE.

关键词： Blockchain

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Backscatter-Aided Hybrid Data Offloading for Wireless Powered Edge Sensor Networks

Backscatter-Aided Hybrid Data Offloading for Wireless Powere...

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IEEE Global Communications Conference

作者： Yuze Zou Jing Xu Shimin Gong Yuanxiong Guo Dusit Niyato Wenqing Cheng School of Computer Science and Engineering Nanyang Technological University Singapore School of Electronic Information and Communications Huazhong University of Science and Technology China School of Intelligent Systems Engineering Sun Yat-sen University China Department of Information Systems and Cyber Security University of Texas San Antonio USA

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

In this paper, we consider a backscatter-aided hybrid data offloading scheme for a batteryless wireless sensor network. All sensor devices on the edge are coordinated by a hybrid access point (HAP), while also provides power for them via wireless power transfer. Co-located with the HAP, an edge computing server is set up to provide the computation and caching capabilities for the edge devices with insufficient power and computation resources. Each node is allocated a fixed time-slot for data offloading via either the conventional active communications or the passive backscatter communications. Such a hybrid data offloading scheme can flexibly control the tradeoff between power consumption and data rate in offloading. We aim to minimize the total energy consumption by optimizing the offloading strategy of each edge device and the HAP's wireless power allocation over different edge devices. We show that the energy minimization problem exhibits a convex reformulation. For practical consideration, we devise a distributed algorithm to solve the problem. The numerical results demonstrate that the distributed algorithm can achieve a near-optimal performance. With a fixed transmit power at the HAP, our proposed hybrid offloading scheme provides a higher offloading throughput compared to the state-of-the-art data offloading schemes.

关键词： Backscatter Wireless sensor networks Wireless communication Servers Power demand Silicon Hybrid power systems

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Study of CdZnTeSe Gamma-Ray Detector under Various Bias Voltages

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Materials Sciences and Applications 2020年第8期11卷 553-559页

作者： Mebougna L. Drabo Stephen U. Egarievwe Utpal N. Roy Benicia A. Harrison Carmella A. Goree Emmanuel K. Savage Ralph B. James Department of Mechanical & Civil Engineering and Construction Management Alabama A&M University Huntsville AL USA Nuclear Engineering and Radiological Science Center Alabama A&M University Huntsville AL USA Department of Electrical Engineering and Computer Science Alabama A&M University Huntsville AL USA Department of Nonproliferation and National Security Brookhaven National Laboratory Upton NY USA Science and Technology Directorate Savannah River National Laboratory Aiken SC USA Biological and Environmental Sciences Department Alabama A&M University Huntsville AL USA Department of Physics Chemistry and Mathematics Alabama A&M University Huntsville AL USA

Cadmium zinc telluride selenide (CdZnTeSe) is a new semiconductor material for gamma-ray detection and spectroscopy applications at room temperature. It has very high crystal quality compared to similar materials such as cadmium telluride and cadmium zinc telluride. The consistency of peak position in radiation detection devices is important to practical applications. In this paper, we have characterized a CdZnTeSe planar detector for bias voltages in the range of -20 V to -200 V and amplifier shaping time of 2, 3 and 6 μs. The peak position of the 59.6-keV gamma line of ²⁴¹Am becomes more stable as the absolute value of the applied voltage increases. The best energy resolution of 8.5% was obtained for the 59.6-keV gamma peak at -160 V bias voltage and 3-μs shaping time. The energy resolution was relatively stable in the -120 V to -200 V range for a 6-μs shaping time. Future work will be focused on the study of the peak position and energy resolution over time.

关键词： CdZnTeSe Energy Resolution Energy-Peak Stability Gamma-Ray Spectroscopy Nuclear Detectors

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Facial expression classification using machine learning approach: A review 3rd

Facial expression classification using machine learning appr...

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3rd International Conference on computer and Communication Technologies, IC3T 2016

作者： Baskar, A. Gireesh Kumar, T. Department of Computer Science and Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Amrita University Coimbatore India TIFAC-CORE in Cyber Security Amrita School of Engineering Amrita Vishwa Vidyapeetham Amrita University Coimbatore India

ISBN: (纸本)9789811032226

Automatic Facial Expression analysis has enthralled increasing attention in the research community in excess of two decades and its expedient in many application like, face animation, customer satisfaction studies, human-computer interaction and video conferencing. The precisely classifying different emotion is an essential problem in facial expression recognition research. There are several machine learning algorithms applied to facial expression recognition expedition. In this paper, we surveyed three different machine learning algorithms such as Bayesian Network, Hidden Markov Model and Support Vector machine and we attempt to answer following questions: How classification algorithm used its characteristics for emotion recognition? How various parameters in learning algorithm is devoted for better classification? What are the robust features used for training? Finally, we examined how advances in machine learning technique used for facial expression recognition?. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Support vector machines

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Corrigendum to “A novel trust-based security and privacy model for Internet of Vehicles using encryption and steganography” CAEE, Volume 102, September 2022, 108205

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computers and Electrical engineering 2022年 103卷

作者： Manjari Singh Rathore M. Poongodi Praneet Saurabh Umesh Kumar Lilhore Sami Bourouis Wajdi Alhakami Jude Osamor Mounir Hamdi Oriental Institute of Technology Bhopal Madhya Pradesh India Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Mody University of Science and Technology Lakshmangarh Rajasthan India KIET Group of Institutions Delhi-NCR Ghaziabad Uttar Pradesh India Department of Information Technology College of Computers and Information Technology Taif University P.O. Box 11099 Taif 21944 Saudi Arabia Department of Cyber Security and Networks Glasgow Caledonian University Cowcaddens Road Glasgow Scotland G4 0BA United Kingdom

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Transportation,germs,culture:a dynamic graph model of COVID-19 outbreak

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Quantitative Biology 2020年第3期8卷 238-244页

作者： Xiaofei Yang Tun Xu Peng Jia Han Xia Li Guo Lei Zhang Kai Ye School of Computer Science and Technology Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'an 710049China MOE Key Lab for Intelligent Networks&Networks Security Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'an 710049China School of Automation Science and Engineering Faculty of Electronic and Information EngineeringXi'an Jiaotong UniversityXi'an 710049China Hugobiotech Co. Ltd.Beijing 102600China The School of Life Science and Technology Xi'an Jiaotong UniversityXi'an 710049China China-Australia Joint Research Center for Infectious Diseases School of Public HealthXi'an Jiaotong University Health Science CenterXi'an 710061China Melbourne Sexual Health Centre Alfred HealthMelbourneVIC 3004Australia Central Clinical School Faculty of MedicineNursing and Health SciencesMonash UniversityMelbourneVIC 3004Australia Department of Epidemiology and Biostatistics College of Public HealthZhengzhou UniversityZhengzhou 450001China Genome Institute the First Affiliated Hospital of Xi'an Jiaotong UniversityXi'an 710061China

Backgrounds Various models have been applied to predict the trend of the epidemic since the outbreak of ***:In this study,we designed a dynamic graph model,not for precisely predicting the number of infected cases,but for a glance of the dynamics under a public epidemic emergency situation and of different contributing factors・Results^We demonstrated the impact of asymptomatic transmission in this outbreak and showed the effectiveness of city lockdown to halt virus spread within a *** further illustrated that sudden emergence of a large number of cases could overwhelm the city medical system,and external medical aids are critical to not only containing the further spread of the virus but also reducing *** Our model simulation showed that highly populated modern cities are particularly vulnerable and lessons learned in China could facilitate other countries to plan the proactive and decisive actions・We shall pay close attention to the asymptomatic transmission being suggested by rapidly accumulating evidence as dramatic changes in quarantine protocol are required to contain SARS・CoV・2 from spreading globally.

关键词： dynamic graph model transportation COVID-19 SARS-CoV-2

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SELWasm: A Code Protection Mechanism for WebAssembly

SELWasm: A Code Protection Mechanism for WebAssembly

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Jian Sun DingYuan Cao XiMing Liu ZiYi Zhao WenWen Wang XiaoLi Gong Jin Zhang College of Computer Science Nankai University Tianjin China College of Cyber Science Nankai University Tianjin China Department of Computer Science University of Georgia Athens GA USA Tianjin Key Laboratory of Network and Data Security Technology Tianjin China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

WebAssembly is a novel front-end programming technology that has been wildly supported by most web browsers. The high-level language can be compiled into WebAssembly binary code, and executed by the browser at almost native speed. With the help of WebAssembly, the complex business logic and algorithms can be integrated into the webpage and migrate the computation overhead to the browsers. However, due to the limit of browsers, it is very difficult for developers to ensure that the intellectual property embedded in the front-end code is well protected. Even though the WebAssembly code is a compiler-generated binary file, it is easy to obtain it from the website for reuse or reverse engineering. This paper proposed a new framework, SELWasm, to protect the WebAssembly code. In the framework, three major mechanisms are provided. The Environment Self-checking mechanism can protect the source code from illegally reusing by any unauthorized website. The Encryption&Decryption mechanism can protect the WebAssembly code from being distributed as plaintext. The Lazy-loading mechanism can improve the performance of page loading, mitigate the overhead introduced by the previous mechanisms. Comprehensive experiments are conducted to measure the effectiveness and efficiency of SELWasm. The results show that the behaviors considered from web attackers can be resisted and the overhead involved is negligible.

关键词： Browsers Encryption Servers Runtime Intellectual property Loading

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Flank wear detection with signal variational mode decomposition and support vector machine algorithm

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AIP Conference Proceedings 2024年第1期3069卷

作者： Achmad Zaki Rahman Khairul Jauhari Mahfudz Al Huda Muizuddin Azka Nimas Ayu Untariyati Juliansyah Rusnaldy Achmad Widodo Department of Mechanical Engineering Diponegoro University Tembalang Semarang 50275 Indonesia Laboratory for Machine Tool Production and Automation National Research and Innovation Agency (BRIN) Puspiptek South of Tangerang 15314 Indonesia Research Center for Structural Strength Technology Research Organization for Energy and Manufacture National Research and Innovation Agency (BRIN) Puspiptek South of Tangerang 15314 Indonesia Research Center for Process and Manufacturing Industry Technology Research Organization for Energy and Manufacture National Research and Innovation Agency (BRIN) Puspiptek South of Tangerang 15314 Indonesia Research Center for Artificial Intelligence and Cyber Security Research Organization for Electronics and Informatics National Research and Innovation Agency (BRIN) Puspiptek South of Tangerang 15314 Indonesia

Milling is one type of cutting process that is most often used in the manufacturing industry. Flank wear is a wear phenomenon on the cutter that usually occurs in milling. This wear can affect the accuracy of the product and the quality of the cutting finish surface and can cause damage to the cutting tool. This paper will discuss how to improve the accuracy of cutting tool wear predictions based on vibration signals obtained from the machining process. The signals from the milling process are decomposed by Variational Mode Decomposition (VMD) method before being applied to the machine learning algorithm. As a comparison level of accuracy, the root mean square error (RMSE) parameter is used to determine the error between the actual value and the predicted value of the trained model. The results of the experiments show that using the VMD decomposition feature can improve the performance of the Support Vector Machine (SVM) algorithm in predicting tool wear when compared to those using the time and frequency domain features or those using only the time domain features

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Non-orthogonal multiple access for visible light communications with ambient light and user mobility

arXiv

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arXiv 2019年

作者： Mitra, Rangeet Sofotasios, Paschalis C. Bhatia, Vimal Muhaidat, Sami Indian Institute of Information Technology SriCity517646 India Center for Cyber-Physical Systems Department of Electrical Engineering and Computer Science Khalifa University of Science and Technology PO Box 127788 Abu Dhabi United Arab Emirates Department of Electrical Engineering Tampere University Tampere33101 Finland Department of Electrical Engineering Indian Institute of Technology Indore Indore453441 India Center for Cyber-Physical Systems Department of Electrical Engineering and Computer Science Khalifa University of Science and Technology PO Box 127788 Abu Dhabi United Arab Emirates

The ever-increasing demand for high data-rate applications and the proliferation of connected devices pose several theoretical and technological challenges for the fifth generation (5G) networks and beyond. Among others, this includes the spectrum scarcity and massive connectivity of devices, particularly in the context of the Internet of Things (IoT) ecosystem. In this respect, visible light communication (VLC) has recently emerged as a potential solution for these challenges, particularly in scenarios relating to indoor communications. Additionally, nonorthogonal multiple access (NOMA) for VLC has been envisioned to address some of the key challenges in the next generation wireless networks. However, in realistic environments, it has been shown that VLC systems suffer from additive optical interference due to ambient light, and user-mobility which cause detrimental outages and overall degraded data rates. Motivated by this, in this work, we first derive the statistics of the incurred additive interference, and then analyze the rate of the considered NOMA-VLC channel. An analytical expression is subsequently derived for the rate of NOMA-VLC systems with ambient light and user-mobility, followed by the formulation of a power-allocation technique for the underlying scenario, which has been shown to outperform classical gain-ratio power allocation in terms of achievable rate. The obtained analytical results are corroborated with computer simulations for various realistic VLC scenarios of interest, which lead to useful insights of theoretical and practical interest. For example, it is shown that, in a NOMA-enabled VLC system, the maximum rate at which information can be transmitted over a static VLC communication channel with ambient light asymptotically converges to the Shannon Hartley capacity formula. Copyright © 2019, The Authors. All rights reserved.

关键词： Visible light communication

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A review of uncertainty quantification in deep learning: Techniques, applications and challenges

arXiv

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arXiv 2020年

作者： Abdar, Moloud Pourpanah, Farhad Hussain, Sadiq Rezazadegan, Dana Liu, Li Ghavamzadeh, Mohammad Fieguth, Paul Cao, Xiaochun Khosravi, Abbas Rajendra Acharya, U. Makarenkov, Vladimir Nahavandi, Saeid Deakin University Australia College of Mathematics and Statistics Guangdong Key Lab. of Intelligent Information Processing Shenzhen University Shenzhen518060 China Dibrugarh University Dibrugarh India Department of Computer Science and Software Engineering Swinburne University of Technology Melbourne Australia Center for Machine Vision and Signal Analysis University of Oulu Oulu Finland Google research United States Department of Systems Design Engineering University of Waterloo Waterloo Canada State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing China Department of Electronics and Computer Engineering Ngee Ann Polytechnic Clementi Singapore Department of Computer Science University of Quebec in Montreal MontrealQC Canada

—Uncertainty quantification (UQ) plays a pivotal role in the reduction of uncertainties during both optimization and decision making, applied to solve a variety of real-world applications in science and engineering. Bayesian approximation and ensemble learning techniques are two of the most widely-used UQ methods in the literature. In this regard, researchers have proposed different UQ methods and examined their performance in a variety of applications such as computer vision (e.g., self-driving cars and object detection), image processing (e.g., image restoration), medical image analysis (e.g., medical image classification and segmentation), natural language processing (e.g., text classification, social media texts and recidivism risk-scoring), bioinformatics, etc. This study reviews recent advances in UQ methods used in deep learning, investigates the application of these methods in reinforcement learning, and highlight the fundamental research challenges and directions associated with the UQ field. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

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