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2020 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2020

作者： Thoti, Narasimhulu Li, Yiming Kola, Sekhar Reddy Parallel and Scientific Computing Laboratory Eecs International Graduate Program Institute of Communications Engineering Department of Electrical and Computer Engineering Center for MmWave Smart Radar System and Technologies National Chiao Tung University 1001 Ta-Hsueh Rd Hsinchu300 Taiwan

ISBN: (纸本)9781728142326

The scope of the work is to investigate limitations in device scaling by identifying various parameters of short channel effects (SCEs) in current challenging geometries of the line tunnel field effect transistors (TFETs). Key factors of the considered device, such as the doping and the thickness (tn) of n-epitaxial region, and source-to-drain length (LSDeff) scaling cannot be tuned anymore to boost the device characteristics for the sub-8-nm technology node. The main results of this study indicates that the engineering acceptable performance are achieved at a low doping of 5 1018 cm-3, an optimal tn as low (about 0.5 nm), and a LSDeff greater than 12.5 nm. Hence, the line TFETs below sub-8-nm faces serious bottleneck of scaling and cannot be further scaled with the conventional scaling rule at all. © 2020 IEEE.

关键词： Tunnel field effect transistors

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A Genetic Algorithm to Optimize Cargo Stability in Container Transportation

A Genetic Algorithm to Optimize Cargo Stability in Container...

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IEEE Global Conference on Consumer Electronics (GCCE)

作者： Chi-Jung Hsieh Kuang-Sung Wu Tin-Yu Wu Fu Jie Tey Yi-Kai Chen Jye Yi Ho Department of Computer Science and Information Engineering National Ilan University Ilan County Taiwan Master Program of E-Learning for Multimedia and Network Communications National Ilan University Yilan Taiwan Department of Management Information Systems National Pingtung University of Science and Technology Pingtung County Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan

ISBN: (纸本)9781665492331

In the era of the global village, frequent cross-border trade in goods has made container transportation a significant part in delivery of cargo. However, rollover accidents of container trucks often occur because of tall truck height, improper cargo stowage or crooked roads. Therefore, how to find the optimal center of gravity (CG) of the packed cargo remains an issue that the transportation industry should not neglect. To optimize cargo stability and reduce the incidence of rollovers, this paper proposes a genetic algorithm that scores the cargo stacking process to find the best CG of the cargo based on the CG of the vehicle. According to the deviation angle and the point closest to the best CG, we can Figure out the optimal cargo stacking method.

关键词： Rollover Transportation industry Roads Stacking Transportation Containers Safety

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An Enhanced System for Wrong-Way Driving Vehicle Detection with Road Boundary Detection Algorithm

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Procedia computer Science 2022年 204卷 164-171页

作者： Pintusorn Suttiponpisarn Chalermpol Charnsripinyo Sasiporn Usanavasin Hiro Nakahara TAIST Tokyo Tech ICTES Program Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathum Thani Thailand School of ICT Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand Dept. of Information and Communications Engineering Tokyo Institute of Technology Tokyo Japan

Driving in the wrong direction is one of the main reasons that cause road accidents in Thailand. To efficiently detect wrong direction driving vehicles, we proposed a system that can track those moving vehicles from CCTVs using deep learning and image processing techniques. Our proposed system is composed of two main algorithms: Road Lane Boundary detection from CCTV algorithm (RLB-CCTV) and Majority-Based Correct Direction Detection algorithm (MBCDD). In this paper, we introduced the RLB-CCTV algorithm and the enhanced version of MBCDD algorithms for detecting road boundaries and wrong-direction driving vehicles. Based on our experiments, our system can identify the road lane boundary and track vehicles driving in the wrong direction on each lane with an accuracy of 96.61%.

关键词： Object detection Vehicle tracking FastMOT Image processing Hough transform

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Modeling for small cell networks in 5G communication environment

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Telecommunication Systems 2022年第2期80卷 189-214页

作者： Kim, Tae-Yeun Singh, A. K. Ko, Hoon National Program of Excellence in Software Center Chosun University Gwangju Republic of Korea Department of Computer Science and Engineering National Institute of Technology Patna Patna India Research Institute of Computer Information Communications Chungbuk National University E8-7 Chungdae-ro 1 Seowon-ku Chungcheongbuk-do South Korea Instituto Superior de Engenharia do Porto Instituto Politecnico do Porto R. Dr. Antonio Bernardino de Almeida 431 Porto Portugal

The small cell structure in which many cells are arranged per unit area by reducing the size of cells is a candidate technology for an increase in transmission capacity in the 5G environment. However, the decrease in the size of the cell led to additional problems such as increased inter cell interference and frequent cell changes owing to the movement of the terminal. Therefore, the aim of this study was to propose small cell dynamic channel allocation (SDCA) and hybrid and dynamic channel allocation (HDCA) using conventional reuse methods to improve the macro cell performance while efficiently utilizing scarce frequency resources. The proposed method facilitates an improved performance that is lacking for macro-cell users in the center area of the cell boundary for the network where conventional macro cells and small cells are superposed. Furthermore, to improve the performance, it can provide resources that are lacking in the small cells of the center. To evaluate the performance, the proposed method was compared to frequency reuse factor1 (FRF1), frequency reuse factor3 (FRF3), and fractional frequency reuse (FFR) methods in terms of the signal-to-interference/noise-ratio (SINR) of users of each macro cell and small cell, outage, capacity for each user, and total system capacity. As a result of comparing the SINR, it was confirmed that the performance of the macro cell users has improved by an average of 43.88% compared to FRF1, FRF3, and FFR, and the performance of small cell users has improved by an average of 4.31%. Comparison results show that the outage proportions of the macro and small cell users are 61.29% and 70.59% lower on average, respectively. A comparison of results show that the capacities of the macro and small cell users have also improved by 22.5% and 14.5% on average, respectively. As the comparison results of the total system capacity indicate, the proposed method shows an average improvement of 11.67%. In cases in which the added resources o

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Predicting Metabolic Syndrome With Machine Learning Models of Hybrid Analysis

Predicting Metabolic Syndrome With Machine Learning Models o...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Wu, Yi-Syuan Wang, Wei-Yun Wu, Hao-Yi Chen, Chao-Wen Chu, Chi-Ming Wu, Cheng-Wei Division of Trauma and Surgical Critical Care Department of Surgery Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan Master Program of E-Learning for Multimedia and Network Communications National I-Lan University Yilan Taiwan National Defense Medical Center Tri-Service General Hospital Songshan Branch School of Nursing Department of Nursing Taipei Taiwan National Defense Medical Center Department of Nursing Tri-Service General Hospital Taipei Taiwan Kaohsiung Medical University Hospital Kaohsiung Medical University Trauma and Critical Care Service Department of Surgery Kaohsiung Taiwan National Defense Medical Center School of Public Health Taipei Taiwan National I-Lan University Department of Computer Science and Information Engineering Yilan Taiwan

ISBN: (纸本)9798350324174

The study uses machine learning to determine the accuracy of different algorithms for predicting MetS. Early prediction of MetS through routine health screening is an important goal for preventive medicine. The subjects in this study were employees aged 20 to 65 years old who signed a consent form for in-hospital health screening data were collected from 2015 to 2020. Total of 3538 individuals who underwent health screening were included in this study. Baseline characteristics of the 315 participants diagnosed with metabolic syndrome and 3222 participants without metabolic syndrome. This is a machine learning approach to identify metabolic syndrome using biochemical values and lifestyle habits. We found that the F1 score for detection of metabolic syndrome by neural network is the most accurate and higher compared to other models. Our study also identified some new indicators, including heart rhythm, RBC, WBC, Hb, and uric acid, as candidates for better detection of metabolic syndrome. © 2023 IEEE.

关键词： Forecasting

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Application of Deep Artificial Neural Network to Model Characteristic Fluctuation of Multi-Channel Gate-All-Around Silicon Nanosheet and Nanofin MOSFETs Induced by Random Nanosized Metal Grains

Application of Deep Artificial Neural Network to Model Chara...

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IEEE Electron Devices Technology and Manufacturing Conference (EDTM)

作者： Sagarika Dash Yiming Li Wen-Li Sung Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University Hsinchu Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University Hsinchu Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University Hsinchu Taiwan Center for mm Wave Smart Radar System and Technologies National Yang-Ming Chiao Tung University Hsinchu Taiwan

In this work, we propose a deep artificial neural network (D-ANN) to estimate the work function fluctuation (WKF) on 4-channel stacked gate-all-around (GAA) silicon (Si) nanosheet (NS) and nanofin (NF) MOSFET devices for the first time. The 2-layered simple deep model can well predict the transfer characteristics for both NS/NF FET with a large number of (128) input features, utilizing considerably lesser (1100 samples) data uniformly. The resultant model is evaluated by the $\mathrm{R}^{2}$ score and RMSE to witness its competency and the average error is $< 4\%$ . We do also discuss the circuit simulation possibility by applying the ANN approach.

关键词： Semiconductor device modeling MOSFET Gallium arsenide Artificial neural networks Predictive models Logic gates Silicon

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Blue micro-LED with a red/green blended polymer film for 3.5-Gbps visible light communication employing adaptive SNR-Flattening Algorithm

Next Nanotechnology

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Next Nanotechnology 2024年 5卷

作者： Ho, Pin-Wei Cheng, Chih-Hsien Liao, Yu-Sheng Chi, Yu-Chieh Sadhu, Annada Sankar Matsumoto, Atsushi Akahane, Kouichi Chen, Li-Yin Kuo, Hao-Chung Lin, Gong-Ru Graduate Institute of Photonics and Optoelectronics and Department of Electrical Engineering National Taiwan University Taipei 106319 Taiwan National Institute of Information and Communications Technology Koganei Tokyo 184-8795 Japan SANWAY OPTOELECTRONICS TECH. CORP New Taipei City 235601 Taiwan Department of Photonics Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan International Ph.D. Program in Photonics (UST) College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu 30010 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei 11492 Taiwan NTU-Tektronix Joint Research Center Tektronix Inc. and National Taiwan University Taipei 106319 Taiwan

By the semipolar blue single GaN μ-LED and blended Ir(piq)2(acac) + CC-MP5 polymer thin film color converter, a high-speed white-light μ-LED-based system is built up for the applications of short-distance VLC. The characteristics and properties of both devices are analyzed to understand the requirements for transmission and illumination. By selecting the growing orient, the influence of QCSE is reduced in this GaN μ-LED. Meanwhile, possessing the low reflection characteristic, it is beneficial for signal modulation. For the polymer thin film color converter with a lifetime of 7.8 ns, low surface reflection and high conversion efficiency are thought of good properties. Color-converted cool white light has a CCT of around 7000 K and high color accuracy with a CRI of about 90. The APD-combined frequency responses of the GaN μ-LED and GaN μ-LED + polymer are measured as 750 MHz and 600 MHz, respectively. After the optimization by utilizing the adaptive SNR-flattening pre-emphasis algorithm, the transmission performance of the white-light μ-LED is significantly promoted. For NRZ-OOK encoding, a 1.4 Gbps 0.15-m free-space transmission is achieved with a rising time of 656.33 ps, a falling time of 493.32 ps, and a Q-factor of 4.75. Besides, in more advanced data formats, the performance of this white-light μ-LED can be better highlighted. For the same 0.15-m free-space VLC, a high-speed 3 Gbps broadband 8-QAM-OFDM transmission is fulfilled with an EVM of 23.9%, an average SNR of 12.5, and a BER below 3.8 × 10^-3;while to the best of our knowledge, a record 3.5 Gbps BL-DMT transmission is implemented as well. This white-light μ-LED can also be integrated into large-scale arrays for multi-functional VLC applications. © 2024 The Authors

关键词： Adaptive SNR-Flattening algorithm Blue micro-LED Red/Green blended polymer Visible light communication

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Leveling up by design: Games design + technical/engineering communication = innovative games and high-functioning teams 46

Leveling up by design: Games design + technical/engineering ...

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46th SEFI Annual Conference 2018: Creativity, Innovation and Entrepreneurship for engineering Education Excellence

作者： Nathans-Kelly, T. White, W. Engineering Communications Program College of Engineering Cornell University IthacaNY United States Computer Science College of Engineering Cornell University IthacaNY United States

ISBN: (纸本)9782873520168

Spring semester of 2018 marks the eleventh anniversary of our integration of writing with game development. Agile and continuous development require that we (as instructors) revisit and revise the courses each year. The core feature of our approach is to make sure that the documentation always moves the development process forward, and CATME assists in our continuous assessment efforts. For all documents in the process, we identify for our students exactly why it is important to the agile cycles;we cut documents that don't contribute to the process. By applying these simple rules, we believe that instructors can create highly effective and professional teams that are better prepared to meet demanding early careers in CS or related fields. © Proceedings of the 46th SEFI Annual Conference 2018: Creativity, Innovation and Entrepreneurship for engineering Education Excellence. All rights reserved.

关键词： engineering education

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Noise-robust deep spiking neural networks with temporal information

arXiv

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arXiv 2021年

作者： Park, Seongsik Lee, Dongjin Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of Asri and Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of

Spiking neural networks (SNNs) have emerged as energy-efficient neural networks with temporal information. SNNs have shown a superior efficiency on neuromorphic devices, but the devices are susceptible to noise, which hinders them from being applied in real-world applications. Several studies have increased noise robustness, but most of them considered neither deep SNNs nor temporal information. In this paper, we investigate the effect of noise on deep SNNs with various neural coding methods and present a noise-robust deep SNN with temporal information. With the proposed methods, we have achieved a deep SNN that is efficient and robust to spike deletion and jitter. © 2021, CC BY.

关键词： Deep neural networks

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Training energy-efficient deep spiking neural networks with time-to-first-spike coding

arXiv

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arXiv 2021年

作者： Park, Seongsik Yoon, Sungroh Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Institute of New Media and Communications Seoul National University Seoul08826 Korea Republic of ASRI and Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of

The tremendous energy consumption of deep neural networks (DNNs) has become a serious problem in deep learning. Spiking neural networks (SNNs), which mimic the operations in the human brain, have been studied as prominent energy-efficient neural networks. Due to their event-driven and spatiotemporally sparse operations, SNNs show possibilities for energy-efficient processing. To unlock their potential, deep SNNs have adopted temporal coding such as time-to-first-spike (TTFS) coding, which represents the information between neurons by the first spike time. With TTFS coding, each neuron generates one spike at most, which leads to a significant improvement in energy efficiency. Several studies have successfully introduced TTFS coding in deep SNNs, but they showed restricted efficiency improvement owing to the lack of consideration for efficiency during training. To address the aforementioned issue, this paper presents training methods for energy-efficient deep SNNs with TTFS coding. We introduce a surrogate DNN model to train the deep SNN in a feasible time and analyze the effect of the temporal kernel on training performance and efficiency. Based on the investigation, we propose stochastically relaxed activation and initial value-based regularization for the temporal kernel parameters. In addition, to reduce the number of spikes even further, we present temporal kernel-aware batch normalization. With the proposed methods, we could achieve comparable training results with significantly reduced spikes, which could lead to energy-efficient deep SNNs. © 2021, CC BY.

关键词： Energy efficiency

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