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IEEE Open Journal of the Communications Society 2021年 2卷 384-395页

作者： Huang, Nai-Hsuan Chiueh, Tzi-Dar Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan Wireless Communications Technology Mediatek Inc. Hsinchu30078 Taiwan Department of Electrical Engineering National Taiwan University Taipei10617 Taiwan

Massive machine type communications (mMTC) is one of the three major scenarios in 5G wireless networks. mMTC is an uplink-dominant scenario that supports massive connectivity of small-size packets and sporadic traffic. To meet the requirement of mMTC, non-orthogonal multiple access (NOMA) with grant-free transmission is considered a promising technology because of its higher spectrum efficiency and lower signaling overhead. Request-and-grant-based scheduling process is eliminated in grant-free transmission and thus the base stations need to identify active users first before data detection. Previously, several user activity detection (UAD) algorithms were proposed and they can obtain good performance in flat-fading channels. However, the performance of these works degrades significantly in doubly selective fading channels. In this work, we propose an effective solution for uplink grant-free NOMA with a new user sequence scheme and a novel UAD algorithm suitable for practical channel scenarios. Simulation results validated that the proposed sequences can mitigate the inter-user interference at the base stations, and the proposed UAD algorithm can achieve better detection accuracy. Moreover, detailed complexity analysis showed that the proposed UAD algorithm has relatively low complexity and good scalability. © 2020 IEEE.

关键词： Base stations

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Experimental Evaluation of a Lightweight and Backdrivable Powered Knee Prosthesis for Transfemoral Amputees

Experimental Evaluation of a Lightweight and Backdrivable Po...

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Colombian Caribbean Conference (C3), IEEE

作者： Pedro Ulhoa Guilherme Fiorezi Teodiano Filho Rafhael Andrade Department of Electrical Engineering Universidade Federal do Espírito Santo Vitória Brazil Department of Mechanical Engineering Universidade Federal do Espírito Santo Vitória Brazil Graduate Program in Electrical Engineering Universidade Federal do Espírito Santo Vitória Brazil Graduate Program in Mechanical Engineering Universidade Federal do Espírito Santo Vitória Brazil

Rehabilitation of amputees is still mostly limited to passive technology that cannot provide energy to the gait and contribute to a better experience and comfort of walking. To address this shortcoming, active prostheses have been introduced with actuators capable of generating and dissipating energy in a controlled way. However, proposed designs of powered leg prosthesis still present heavy and bulky structure or low backdrivable actuators, which reduce the volitional control and comfort of the user. This work presents the development and control of a lightweight and highly backdrivable powered knee prosthesis. The designed actuator is composed by a coaxial motor-reducer with a 200 W brushless DC motor in series with a harmonic drive with a 51:1 ratio. After assembled the leg prosthesis weights just 3.64 kg. Bench tests were performed to validate the model and tune a position-based controller. Then, a custom force sensor was implemented to measure ground reaction force and identify gait phases so an impedance controller can regulate joint torque. The powered prosthesis was experimented in three able-bodied subjects walking on a treadmill using both the position and impedance controller. The prosthesis presented high backdrivability in bench tests and the treadmill results shown that the proposed impedance controller was more effective to follow the healthy knee trajectory compared to the position controller. In future works we plan to evaluate the prosthesis in amputee subjects.

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Endurance > 1011Cycling of 3D GAA Nanosheet Ferroelectric FET with Stacked HfZrO2to Homogenize Corner Field Toward Mitigate Dead Zone for High-Density eNVM

Endurance > 1011Cycling of 3D GAA Nanosheet Ferroelectric FE...

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2022 IEEE Symposium on VLSI Technology and Circuits, VLSI Technology and Circuits 2022

作者： Liao, C.-Y. Hsiang, K.-Y. Lou, Z. -F. Tseng, H.-C. Lin, C.-Y. Li, Z.-X. Hsieh, F.-C. Wang, C.-C. Chang, F.-S. Ray, W.-C. Tseng, Y.-Y. Chang, S.T. Chen, T.-C. Lee, M.H. National Taiwan Normal University Institute and Undergraduate Program of Electro-Optical Engineering Taipei Taiwan National Yang Ming Chiao Tung University Department of Electronics Engineering and Institute of Electronics Hsinchu Taiwan Synopsys Taiwan Co. Ltd. Tcad Silicon Engineering Group Hsinchu Taiwan National Chung Hsing University Department of Electrical Engineering Taichung Taiwan Taiwan Semiconductor Manufacturing Company Corporate Research Hsinchu Taiwan

ISBN: (纸本)9781665497725

After 1011 high endurance cycles with memory window (MW) =0.9 V is achieved for the 3D gate-all-around (GAA) nanosheet (NS) ferroelectric field-effect transistor (FeFET) based on double-HZO;the aim is to homogenize the corner field and mitigate dead zones. The interlayer Al2O3 or TiN in the double-HZO exhibits MW enhancement or low access voltage, respectively. The proposed MFMFS GAA-FeFET demonstrates a low VP/E = ±3.5 V (±2.3 MV/cm), large MW = 1.3 V, >1011 robust endurance cycles, and stable storage with data retention of >2×104 s;therefore, physical dimension scaling of the embedded nonvolatile memory (eNVM) is feasible for future generations. © 2022 IEEE.

关键词： Aluminum oxide

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Super-resolution reconstruction using channel splitting residual edge-guided network for wideband magnetic resonance imaging

Super-resolution reconstruction using channel splitting resi...

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International Forum on Medical Imaging in Asia 2021, IFMIA 2021

作者： Hsu, Feng-Yu Chen, Jyh-Horng Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan

ISBN: (数字)9781510644212

ISBN: (纸本)9781510644205

In order to improve the accuracy of surgical analysis and clinical diagnosis, it is very important to obtain brain structure images. And magnetic resonance imaging (MRI) is one of the most commonly used methods. Moreover, High-Resolution (HR) MR image with smaller pixel size provides more important structure and texture details [1] and helps early diagnosis and subsequent analysis. But in fact, it is relatively difficult to obtain high-quality MR images due to many factors, such as hardware equipment, imaging time, required signal to noise ratio (Signal to Noise Ratio, SNR) and motion artifacts, etc. Generally speaking, brain MR images are often obtained with thick slice thickness and lower image quality to reduce the scanning cost and sampling time. However, this is not conducive to further medical analysis. For decades, Super- Resolution (SR) related technologies have been used to improve the quality of MR images to restore important structural information and facilitate clinical diagnosis. Also, as deep learning (DL) develops significantly nowadays, DL-based methods are widely applied to SR issues. Therefore, we propose a convolution neural network (CNN) SR method called Channel Splitting Edge-guided Residual Network (CSERN). Besides, we combine our method with a novel accelerating imaging method called Single-frequency Excitation WideBand (SE-WB) MRI and design a loss function to achieve higher performance on several index such as structural similarity index (SSIM) and Peak signal-to-noise ratio (PSNR). © 2021 SPIE.

关键词： Magnetic resonance imaging

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An Empirical Generation Technique on Background Music Using Gated Recurrent Neural Networks

An Empirical Generation Technique on Background Music Using ...

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IEEE International Conference on Consumer electronics-Taiwan (ICCE-TW)

作者： Varinya Phanichraksaphong Wei-Ho Tsai International Graduate Program of Electrical Engineering and Computer Science (IEECS) National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan

The implementation of Gated Recurrent Neural Networks (GRU) to generate background music (BGM) combines deep learning technology with music that is used for the visual content of a commercial or educational. Indeed, this BGM is necessary to enhance the intended message expressed to the other audience. This work aimed to provide the model network of GRU which is based on RNN to generate multi-label genres of music by using the open source of GTZAN to evaluate the new BGM. Our GRU networks can solve the vanishing gradient problem by utilizing both the reset gate and the update gate on the network. In the results, we achieved a new BGM that synchronized with the human mood which made more variety of sounds.

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Application of 3D Printing and Design in Healthcare: A Focus on Undergraduate Research to Attract Students to Bioengineering and Related Disciplines

Application of 3D Printing and Design in Healthcare: A Focus...

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Frontiers in Education (FIE) Conference

作者： Cindy M. Zelada-Quiroz Brian L. Reyes-Santiago Eduardo I. Ortiz-Rivera Bio-Engineering Graduate Program University of Puerto Rico at Mayagüez Mayagüez PR Department of Computer Science and Engineering University of Puerto Rico at Mayagüez Mayagüez PR Department of Electrical and Computer Engineering University of Puerto Rico at Mayagüez Mayagüez PR

ISBN: (数字)9798350351507

ISBN: (纸本)9798350363067

This research-to-practice paper describes developing and analyzing state-of-the-art smart boots created by combining CAD technology and advanced 3D printing techniques to attract students in bio-engineering and related fields. The primary objective of this innovative immobilization boot is to expedite fracture recovery phases through an ergonomic design to ensure optimal patient comfort during its use. Technological solutions are crucial in aiding the rehabilitation process for fractures caused by falls, heavy lifting, or rotational trauma. However, cost and comfort-related issues persist, underscoring the need for alternative approaches. This research addresses these challenges and delves into the broader implications of fracture treatment, catalyzing future projects and investigations in bioengineering. Additionally, this study serves as an educational tool that sparks the interest of high school and engineering students, promoting multidisciplinary collaboration in innovation. By involving students in specialized courses covering 3D design, human bone anatomy, biology, and materials science, this initiative empowers them to deepen their knowledge and develop new technologies to address bone injury problems. Material analyses include evaluating the type of material depending on the fracture site, such as PLA for printing and cotton and silicone gel for the midsection between the splint and the body. This research aims to advance our understanding of the type of fracture, the methods associated with their treatment, and tissue repair processes during bone callus formation. To summarize, this multidisciplinary approach drives advancements in bio-engineering and related fields, aiming to enhance patient outcomes and inspire students to pursue further research in bio-engineering and related fields. As part of this endeavor, a list of university-level courses based on the experience of the University of Puerto Rico at Mayaguez (UPRM), such as biology, bio-materials, 3D

关键词： Technological innovation Materials science and technology Three-dimensional displays Tissue engineering Medical services Three-dimensional printing Bones Mechanical factors Manufacturing Sparks

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SURVEY AND EVALUATION OF CONVERGING ARCHITECTURE IN LLMS BASED ON FOOTSTEPS OF OPERATIONS

arXiv

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arXiv 2024年

作者： Kim, Seongho Moon, Jihyun Oh, Juntaek Choi, Insu Yang, Joon-Sung Department of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Department of Systems Semiconductor Engineering Yonsei University Seoul Korea Republic of BK21 Graduate Program in Intelligent Semiconductor Technology Yonsei University Seoul Korea Republic of

Large Language Models (LLMs), emerging from advancements in Natural Language Processing (NLP) tasks, allow chatbots to provide more sophisticated and human-like text generation by leveraging their vast model sizes, often exceeding billions of parameters, to provide deep knowledge across various domains. Hence, they are becoming more integrated into our daily lives, serving as personal assistants or even as experts across various domains. Initial language models relied on rule-based systems and early neural networks like Recurrent Neural Networks (RNNs). However, it remained an issue to handle long-term dependency and to understand context over extended conversation. The advent of the Attention mechanism and Transformer architecture enables contextually natural text generation and compresses the burden of processing entire source information into singular vectors. Based on these two main ideas, model sizes gradually increases to accommodate more precise and comprehensive information, leading to the current state-of-the-art LLMs being very large, with parameters around 70 billion. As the model sizes are growing, the demand for substantial storage and computational capacity increases. This leads to the development of high-bandwidth memory and accelerators, as well as a variety of model architectures designed to meet these requirements. We note that LLM architectures have increasingly converged. This paper analyzes how these converged architectures perform in terms of layer configurations, operational mechanisms, and model sizes, considering various hyperparameter settings. In this paper, we conduct a concise survey of the history of LLMs by tracing the evolution of their operational improvements. Furthermore, we summarize the performance trends of LLMs under various hyperparameter settings using the RTX 6000, which features the state-of-the-art Ada Lovelace architecture. We conclude that even the same model can exhibit different behaviors depending on the hyperparamete

关键词： Recurrent neural networks

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Characterization of Double HfZrO2based FeFET toward Low-Voltage Multi-Level Operation for High Density Nonvolatile Memory

Characterization of Double HfZrO2based FeFET toward Low-Volt...

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2022 International Symposium on VLSI Technology, Systems and Applications, VLSI-TSA 2022

作者： Lou, Z.-F. Liao, C.-Y. Hsiang, K.-Y. Lin, C.-Y. Lin, Y.-D. Yeh, P.-C. Wang, C.-Y. Yang, H.-Y. Tzeng, P.-J. Hou, T.-H. Tang, Y.-T. Lee, M.H. National Taiwan Normal University Institute and Undergraduate Program of Electro-Optical Engineering Taipei Taiwan National Yang Ming Chiao Tung University Department of Electronics Engineering Hsinchu Taiwan National Central University Department of Electrical Engineering Taoyuan Taiwan Industrial Technology Research Institute Electronic and Optoelectronic System Research Laboratories Hsinchu Taiwan

ISBN: (纸本)9781665409230

A double HfZrO2 (HZO) ferroelectric field-effect transistor (FeFET) is demonstrated with ultra-low operating voltage as |VP/E| = 3 V to achieve multilevel cell (MLC) nonvolatile memory (NVM). Compared to a single FE-HZO FeFET, the metal/ferroelectric/metal/ferroelectric/Si (MFMFS) structure results not only in a reduction of VP/E, but also provides a feasible memory window (MW) of 1.9 V for MLC operation. In addition, the metal/ferroelectric/insulator/ ferroelectric/Si (MFIFS) FeFET exhibits a MW as high as >2.5 V. The double HZO FeFET has potential to improve power consumption and enhance memory density for MLC-NVM applications. © 2022 IEEE.

关键词： Nonvolatile storage

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Recent Advances in Quantum Computing for Drug Discovery and Development

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IEEE Nanotechnology Magazine 2023年第2期17卷 26-30页

作者： Wang, Pei-Hua Chen, Jen-Hao Yang, Yu-Yuan Lee, Chien Tseng, Yufeng Jane Chung Yuan Christian University Quantum Information Center Taoyuan City320314 Taiwan National Taiwan University College of Electrical Engineering and Computer Science Graduate Institute of Biomedical Electronics and Bioinformatics Taipei10617 Taiwan Chunghwa Telecom Co. Ltd. Taipei Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei10617 Taiwan National Center for Theoretical Sciences Physics Division Taipei10617 Taiwan National Taiwan University Center for Quantum Science and Engineering Taipei10617 Taiwan

Drug discovery and development is a time-consuming and cost-intensive process. Computer-aided drug design can speed up the timeline and reduce costs by decreasing the number of necessary biochemical experiments. The number of studies using quantum computing to solve problems in drug development has been increasing in recent years. In this review, we briefly introduce the main steps in drug discovery and development and how computers help to find potential drug candidates. Recent studies of quantum computing in drug development based on the structure of target proteins are listed chronologically. They include protein structure prediction, molecular docking, quantum simulation, and quantitative structure-activity relationship (QSAR) models. Current quantum devices are still susceptible to noise and error but are well suited for hybrid quantum-classical algorithms. The quantum advantage is demonstrated on hybrid systems and quantum-inspired devices such as quantum annealers. We hope to see more applications of quantum computing in the field of drug discovery and development.

关键词： Proteins

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Low-loss, nonvolatile tuning of Bragg structures with Sb2Se3

Low-loss, nonvolatile tuning of Bragg structures with Sb2Se3

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CLEO: Applications and Technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Nobile, Nicholas A. Lian, Chuanyu Sun, Hongyi Huang, Yi-Siou Mills, Brian Popescu, Cosmin Constantin Callahan, Dennis Hu, Juejun Ríos Ocampo, Carlos A. Youngblood, Nathan Electrical & Computer Engineering Department University of Pittsburgh PittsburghPA15213 United States Department of Materials Science & Engineering University of Maryland College ParkMD20742 United States Institute for Research in Electronics and Applied Physics University of Maryland College ParkMD20742 United States Department of Materials Science & Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Draper Scholar Program The Charles Stark Draper Laboratory Inc. CambridgeMA02139 United States The Charles Stark Draper Laboratory Inc. CambridgeMA02139 United States

Sb2Se3 is used to switch between broadband transparency and enhanced index contrast in two device types leveraging Bragg gratings for tunable stop- and pass-band functionalities. Experimental results highlight fabrication challenges and efficacy of the designs. © Optica Publishing Group 2024, © 2024 The Author(s)

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