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2022 IEEE Creative Communication and Innovative Technology, ICCIT 2022

作者： Pangestu, Gusti Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Bina Nusantara University School of Computer Science Computer Science Department Binus Graduate Program Computer Science Computer Science Department Jakarta Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9781665468763

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computer science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising. © 2022 IEEE.

关键词： Convolutional neural networks

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Requirements specification and evaluation for transient faults in communication protocols based on RT-FRIDA framework

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IFAC-PapersOnLine 2018年第10期51卷 76-81页

作者： Roque, Alexandre S. Nunes, Gerson L. Freitas, Edison P. Pereira, Carlos E. Graduate Program in Electrical Engineering Federal University of Rio Grande do Sul UFRGS Porto Alegre Brazil Computer Science and Engineering Department Regional Integrated University of High Uruguay and Missions URI Santo Angelo Brazil

This paper presents an evaluation of requirements specification for two critical real-time control systems, which depends of industrial communications protocols. It is proposed the use of aspect-oriented modeling (AOM) to map transient fault behaviors in design phases, with RT-FRIDA framework extended for fault modeling capabilities. Requirements specification for an Active suspension control system and for a fault detection control system applied in agricultural implements was made as case study. Faults are specified as non-functional requirements and evaluated applying a quantitative softgoal weight method. Results presented indicates aspect-oriented fault modeling as an important ally to specify faults that have potential to degrade performance of real-time control systems. © 2016

关键词： Suspensions (components) Active suspension systems Agricultural implements Control systems Fault detection Fault tree analysis Interactive computer systems Network protocols Real time control Real time systems Specifications Active suspension control Aspect oriented Aspect Oriented Modeling Industrial communications Non functional requirements Requirements analysis Requirements specifications Transient faults

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Simulation for image traffic sensor in malaysia traffic dilemma control systems

Simulation for image traffic sensor in malaysia traffic dile...

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Asian Simulation Conference 2005, ASC 2005 and the 6th international Conference on System Simulation and Scientific Computing, ICSC 2005

作者： Nadiah, Hanim Mohd, Nazmi Takaba, Sadao Department of Computer Science Graduate School Tokyo University of Technology Japan Department of Electrical and Computer Engineering International Islamic University Malaysia Malaysia School of Computer Science Tokyo University of Technology Japan

This paper focuses on the method used for traffic control system in dilemma-zone detection. Our research systems based on a general purpose of minimizing the probability for accident that resulted from signal phase termination especially at night. This objective is accomplished by using image detectors to monitor vehicle existence in the dilemma zone and then extending the phase until the dilemma zone is cleared or until a maximum green is reached. The systems described have additional objectives that are intended to provide other safety or operational benefits. Arena simulations are used to determine the existence of vehicle in the dilemma zone. Our main goal is to instruct drivers' decision while approaching the intersection by using the traffic signal control.

关键词： Online systems

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Simulation and evaluation for decentralized signal control on four intersections in Malaysia traffic systems

Simulation and evaluation for decentralized signal control o...

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Asian Simulation Conference 2005, ASC 2005 and the 6th international Conference on System Simulation and Scientific Computing, ICSC 2005

作者： Mohd, Nazmi Nadiah, Hanim Takaba, Sadao Department of Electrical and Computer Engineering International Islamic University Malaysia Malaysia Department of Computer Science Graduate School Tokyo University of Technology Japan School of Computer Science Tokyo University of Technology Japan

This research is to propose and develop the new signal control system which is to be integrated into Malaysia Traffic System. The control system obtains information from image detectors which are installed inside the intersection;predict the future traffic flows at various levels and conditions. A dynamic network system could capture the problem of the network traffic congestions, estimates the information on each particular link (neighboring intersection), in terms of vehicles flow and traffic can be calculated. Referring to the network flow control, the information data let the system to set the parameter itself then allow the system to allocate green time for each different demand pattern and each phase. These decisions are determined not at a central system but at the intersection control system. Simulation is done to find any difficulty of the system.

关键词： Control systems

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Configurable clutter models for radar simulation

Configurable clutter models for radar simulation

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international Waveform Diversity and Design Conference, WDD 2012

作者： Jezak, Joseph A. Gadola, Guy Berdanier, Charles Levitan, Steven P. Chiarulli, Donald M. Computer Engineering Graduate Program University of Pittsburgh United States Department of Electrical Engineering Wright State University United States Department of Electrical and Computer Engineering University of Pittsburgh United States Department of Computer Science University of Pittsburgh United States

ISBN: (纸本)9781509005987

In this paper we describe an extension to the MATLAB Phased Array Toolkit that adds a configurable clutter object to model clutter signals returned along a specified signal path. The clutter model is based on the Simkins Unified Clutter Model[1]. The current implementation supports sea clutter in any of five sea states with configurable polarization, grazing angle, and beam width. We describe the implementation and give examples of modeled clutter returns. © 2012 IEEE.

关键词： MATLAB

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A Neural Computing-Based Access Control Protocol for AI-Driven Intelligent Flying Vehicles in Industry 5.0-Assisted Consumer Electronics

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 3573-3581页

作者： Mahmood, Khalid Tariq, Tayyaba Sangaiah, Arun Kumar Ghaffar, Zahid Saleem, Muhammad Asad Shamshad, Salman National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan National Yunlin University of Science and Technology Department of Computer Science and Information Engineering Douliu64002 Taiwan National Yunlin University of Science and Technology International Graduate School of AI Douliu64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102 2801 Lebanon University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China The University of Lahore Department of Software Engineering Lahore54590 Pakistan

The continuous revolution in Artificial Intelligence (AI) has played a significant role in the development of key consumer applications, including Industry 5.0, autonomous decision-making, fault diagnosis, etc. In practice, autonomous and intelligent robots and flying vehicles (i.e., drones) are also becoming the integral part of Industry 5.0. These vehicles are attaining significant importance nowadays because of their applications in consumer electronics. However, there may be numerous privacy issues in such an infrastructure. Nevertheless, neural computing-driven security solutions have played a significant role in overcoming the security and privacy issues of AI-driven intelligent flying vehicles. Motivated by these facts, this article presents a neural computing-based access control protocol for AI-driven flying vehicles to prevent all possible security threats. A detailed security evaluation of the devised protocol is done formally using the extensively recognized Real-Or-Random (ROR) oracle model. Similarly, the informal security analysis ensures the resistance of our scheme against various potential attacks under the assumptions of CK and DY adversarial models. Moreover, detailed performance scrutiny of the devised protocol with various existing schemes shows the robustness and supremacy of our scheme in terms of computation, communication overhead, and security attributes. © 1975-2011 IEEE.

关键词： Authentication

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A Comprehensive Technique Based on Machine Learning for Device and Circuit Modeling of Gate-All-Around Nanosheet Transistors

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 181-194页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering the Institute of Biomedical Engineering the Department of Electronics and Electrical Engineering the Institute of Pioneer Semiconductor Innovation and the Institute of Artificial Intelligence Innovation Hsinchu300093 Taiwan

Machine learning (ML) is poised to play an important part in advancing the predicting capability in semiconductor device compact modeling domain. One major advantage of ML-based compact modeling is its ability to capture complex relationships and patterns in large datasets. Therefore, in this paper a novel design scheme based on dynamically adaptive neural network (DANN) is proposed to develop fast and accurate compact model (CM). This framework constitutes a powerful yet computationally efficient methodology and exhibits emergent dynamic behaviors. This paper demonstrates that the compact model based on ML can be designed to replicate the performance of conventional compact model for nanodevices. For this work, gate-all-around (GAA) nanosheet (NS) device characteristics are comprehensively analyzed for process variability sources using the proposed model. The device geometry parameters such as channel length, nanosheet width and nanosheet thickness are fed as input features to the DANN model. The adaptive neural network learns dynamically by updating weights of the model in accordance with the input features and achieves accurate neural weight convergence. The proposed model predicted the electrical characteristics of NS devices with less than 1% error rate. The model is also implemented and validated for the simulations of digital circuit designs such as inverter, and logic gates. ©;2023 The Authors.

关键词： Circuit simulation

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Plasmonic bacteria on a nanoporous mirror via hydrodynamic trapping for rapid identification of waterborne pathogens

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Light(science & Applications) 2018年第1期7卷 359-367页

作者： Keumrai Whang Jong-Hwan Lee Yonghee Shin Wooju Lee Young Wan Kim Dongchoul Kim Luke P.Lee Taewook Kang Department of Chemical and Biomolecular Engineering Sogang UniversitySeoul 04107Korea Berkeley Sensor and Actuator Center Departments of BioengineeringElectrical Engineering and Computer ScienceBiophysics Graduate ProgramUniversity of CaliforniaBerkeleyBerkeleyCA 94720USA Department of Mechanical Engineering Sogang UniversitySeoul 04107Korea

A rapid,precise method for identifying waterborne pathogens is critically needed for effective disinfection and better ***,conventional methods,such as culture-based counting,generally suffer from slow detection times and low ***,we developed a rapid detection method for tracing waterborne pathogens by an innovative optofluidic platform,a plasmonic bacteria on a nanoporous mirror,that allows effective hydrodynamic cell trapping,enrichment of pathogens,and optical signal *** designed and simulated the integrated optofluidic platform to maximize the enrichment of the bacteria and to align bacteria on the nanopores and plasmonic mirror via hydrodynamic cell *** nanoparticles are self-assembled to form antenna arrays on the surface of bacteria,such as Escherichia coli and Pseudomonas aeruginosa,by replacing citrate with hydroxylamine hydrochloride in order to amplify the signal of the plasmonic optical *** to the synergistic contributions of focused light via the nanopore geometry,self-assembled nanoplasmonic optical antennas on the surface of bacteria,and plasmonic mirror,we obtain a sensitivity of detecting *** as low as 102 cells/ml via surface-enhanced Raman *** believe that our label-free strategy via an integrated optofluidic platform will pave the way for the rapid,precise identification of various pathogens.

关键词： mirror trapping precise

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Forecasting Bitcoin Prices Using Deep Learning for Consumer-Centric Industrial Applications

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1351-1358页

作者： Roy, Pradeep Kumar Kumar, Abhinav Singh, Ashish Sangaiah, Arun Kumar Indian Institute of Information Technology Surat Department of Computer Science and Engineering Surat394190 India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj211004 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India National Yunlin University of Science and Technology International Graduate Institute of AI Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision. © 1975-2011 IEEE.

关键词： Hidden Markov models

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Author Correction: Dynamical flexible inference of nonlinear latent factors and structures in neural population activity

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Nature biomedical engineering 2025年第2期9卷 283页

作者： Hamidreza Abbaspourazad Eray Erturk Bijan Pesaran Maryam M Shanechi Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los Angeles CA USA. Departments of Neurosurgery Neuroscience and Bioengineering University of Pennsylvania Philadelphia PA USA. Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los Angeles CA USA. shanechi@usc.edu. Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los Angeles CA USA. shanechi@usc.edu.

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