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30th European Signal Processing Conference, EUSIPCO 2022

作者： Moral, Özge Taylan Kiliç, Volkan Onan, Aytug Wang, Wenwu Electrical and Electronics Engineering Graduate Program Izmir Katip Celebi University Turkey Department of Computer Engineering Izmir Katip Celebi University Turkey University of Surrey United Kingdom

ISBN: (纸本)9789082797091

Describing the semantic content of an image via natural language, known as image captioning, has recently attracted substantial interest in computer vision and language processing communities. Current image captioning approaches are mainly based on an encoder-decoder framework in which visual information is extracted by an image encoder and captions are generated by a text decoder, using convolution neural networks (CNN) and recurrent neural networks (RNN), respectively. Although this framework is promising for image captioning, it has limitations in utilizing the encoded visual information for generating grammatically and semantically correct captions in the RNN decoder. More specifically, the RNN decoder is ineffective in using the contextual information from the encoded data due to its limited ability in capturing long-term complex dependencies. Inspired by the advantage of gated recurrent unit (GRU), in this paper, we propose an extension of conventional RNN by introducing a multi-layer GRU that modulates the most relevant information inside the unit to enhance the semantic coherence of captions. Experimental results on the MSCOCO dataset show the superiority of our proposed approach over the state-of-the-art approaches in several performance metrics. © 2022 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Recurrent neural networks

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Modeling of communications in the safety assessment of nuclear power plants

Modeling of communications in the safety assessment of nucle...

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2008 Winter Meeting and Nuclear Technology Expo: Nuclear Power-Ready, Steady, Go

作者： Mandelli, D. Kirschenbaum, J. Mangan, L.A. Ekici, E. Aldemir, T. Nuclear Engineering Graduate Program Ohio State University Columbus OH 43210 Department of Computer Science and Engineering Ohio State University Columbus OH 43210 Department of Electrical and Computer Engineering Ohio State University Columbus OH 43210

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3D Image Generation from X-ray Projections Using Generative Adversarial Networks 23

3D Image Generation from X-ray Projections Using Generative ...

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23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： AlShurbaji, Mohammad Dhou, Salam Biomedical Engineering Graduate Program American University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Engineering American University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798350393118

The human brain can effortlessly imagine a 3D image from only 2D images with a little expertise and imagination, but for machines, this is not a trivial task. Because of this, reconstructing 3D images from 2D ones is a hot topic and has many applications. In this paper, we propose a Generative Adversarial Network (GAN)-based approach that generates CT-like images using pairs of orthogonal X-ray projections taken from different angles. In this work, a variety of orthogonal pairs from different angles, ranging from 0 &90 to 60 &150, were considered as input to the 3D image generation model. The effectiveness of the proposed method was assessed by measuring the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), which resulted in values of 0.641 and 29.21, respectively. Furthermore, the model's ability to capture the respiratory motion in the input projections and reflect it in the generated images was also assessed. This work demonstrated the feasibility of generating CT-like images from X-ray projections captured from different orthogonal angles taking into consideration the respiratory motion exhibited in these projections. © 2023 IEEE.

关键词： Generative adversarial networks

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An Improved I-V model for Resistive Random Access Memory

An Improved I-V model for Resistive Random Access Memory

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2022 IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2022

作者： Chung, Harry Shin, Hyungsoon Sun, Wookyung Park, Jisun Graduate Program in Smart Factory Ewha Womans University Dept. of Electronic and Electrical Engineering Seoul Korea Republic of Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of Graduate Program in Smart Factory Ewha Womans University Seoul Korea Republic of

ISBN: (数字)9781665470872

ISBN: (纸本)9781665470872

In this paper, we performed a simulation for a physical resistive random-access device (RRAM) to propose an improved model representing its electrical characteristics. Voltage ThrEshold Adaptive Memristor (VTEAM) model that operates based on the voltage threshold of the device adapted for the RRAM simulation. We applied some physical features to the model to adjust the internal state variable to improve the simulation results. The internal state variable used in the model was designed after the physical feature such as the distance between the end of the filament and the electrode of the device. Therefore, applying some of these physical conditions affects the simulation results. Simulation results with the newly applied model showed optimized I-V characteristics that resemble the physical device's behavior more than the existing model. © 2022 IEEE.

关键词： Memristors

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Transfer learning approach to modeling multichannel gate-all-around nanosheet field-effect transistors under work function fluctuations

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engineering Applications of Artificial Intelligence 2025年 148卷

作者： Dash, Sagarika Li, Yiming Parallel and Scientific Computing Laboratory National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Electrical Engineering and Computer Science International Graduate Program National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Communications Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Biomedical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Institute of Artificial Intelligence Innovation National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Electronics and Electrical Engineering National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan Department of Microelectronics National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300093 Taiwan

Deep learning (DL) has significantly advanced various industries, including semiconductors, by providing sophisticated methods for analyzing emerging device data. Transfer learning (TL), a prominent DL topology, leverages knowledge from a pre-trained model to improve the performance of a specific target task. This study aims to apply TL to predict the impact of work function fluctuation (WKF) on the variability of threshold voltage (σVTH) in gate-all-around silicon nanosheet field-effect transistors (GAA Si NS FETs). We used TL to transfer knowledge from models trained on 1-channel (1ch) GAA Si NS FETs to more complex 4-channel (4ch) counterparts. The prediction accuracy is evaluated by using the mean square error(MSE), root mean square error (RMSE), and the mean absolute error(MAE) metrics. Our TL approach achieved an error rate of less than 1%, demonstrating high accuracy in predicting WKF effects on σ VTH of 4ch GAA Si NS FETs using only 500 samples. This study presents a pioneering application of TL using a one-dimensional (1D) convolutional neural network (CNN) long short-term memory (LSTM) topology in semiconductor device technology. The results effectively address the challenge of predicting σ VTH variations in advanced GAA Si NS FETs because of WKF, which eliminates the need for extensive semiconductor device simulations. © 2025 Elsevier Ltd

关键词： Convolutional neural networks

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DCMSim: Didactic cache memory simulator 33

DCMSim: Didactic cache memory simulator

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33rd Annual Frontiers in Education, FIE 2003

作者： Cordeiro, Eduardo Santos Abdanur Stefani, Italo Giovani Do Amaral Pales Soares, Tays Cristina Da Silva Martins, Carlos Augusto Paiva Pontifical Catholic University of Minas Gerais Graduate Program in Computer Science 500 Av. Dom Cabral Coração Eucaristico Belo HarizonteMinas Gerais Brazil Pontifical Catholic University of Minas Gerais Graduate Program in Computer Science Graduate Program in Electrical Engineering 500 Av. Dom Cabral Coração Eucaristico Belo HarizonteMinas Gerais Brazil

ISBN: (纸本)0780379616

We present a functional and structural didactic simulator of Cache Memory Systems developed at the Pontifical Catholic University of Minas Gerais, Brazil. The development occurred during the undergraduate computer Architecture discipline, in the computer Science course. Its implementation is one part of a new didactic method, in which developers (students of the computer Architecture discipline) must learn the concepts and theory of the discipline topics to correctly apply them in the simulator. In our simulator, DCMSim, there are features to allow students to construct and verify knowledge, testing and comparing several different configurations and memory access traces. © 2003 IEEE.

关键词： Cache memory

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Using immersive Virtual environments for realistic life-Size fire safety training for children

Using immersive Virtual environments for realistic life-Size...

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3rd IASTED International Conference on Human-computer Interaction, HCI 2008

作者： Ericson, Emily Smith, Shana Human-Computer Interaction Graduate Program Iowa State University Taipei United States Department of Mechanical Engineering National Taiwan University Taipei Taiwan

ISBN: (纸本)9780889867253

Injuries from residential fires are often overlooked as a dangerous safety concern for children. While lectures and classroom learning techniques can be effective in mitigating this risk, studies have shown that kids retain information better if they are allowed to practice escape techniques. Virtual Reality has been used to simulate situations that are too dangerous to practice in real life. This paper presents a fire safety program that combines classroom activities and small group interaction with fire professionals with a virtual simulation of an at-risk house. Students help firefighters to identify home fire hazards and then practice escaping from a simulated fire in a virtual environment. A CAVE is used to immerse participants in the fire scene.

关键词： Virtual reality

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Classification of musical styles using liquid state machines

Classification of musical styles using liquid state machines

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

作者： Ju, Han Xu, Jian-Xin Vandongen, Antonius M.J. Program for Neuroscience and Behavioral Disorders Duke-NUS Graduate Medical School Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore

ISBN: (纸本)9781424469178

Music Information Retrieval (MIR) is an interdisciplinary field that facilitates indexing and content-based organization of music databases. Music classification and clustering is one of the major topics in MIR. Music can be defined as 'organized sound'. The highly ordered temporal structure of music suggests it should be amendable to analysis by a novel spiking neural network paradigm: the liquid state machine (LSM). Unlike conventional statistical approaches that require the presence of static input data, the LSM has a unique ability to classify music in real-time, due to its dynamics and fading-memory. This paper investigates the performance of an LSM in classifying musical styles (ragtime vs. classical), as well as its ability to distinguish music from note sequences without temporal structure. The results show that the LSM performs admirably in this task. © 2010 IEEE.

关键词： Music

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Measurement of capacitance and electrical potential of plural points for estimation of permittivity distribution

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IEEJ Transactions on Fundamentals and Materials 2013年第4期133卷 173-179+5页

作者： Okada, Shota Ohchi, Masashi Furukawa, Tatsuya Itoh, Hideaki Fukumoto, Hisao Master's Program in Electrical Graduate School of Engineering Chiba Institute of Technology 2-17-1 Tudanuma Narashino 275-0016 Japan Department of Electrical Electronics and Computer Engineering Faculty of Engineering Chiba Institute of Technology 2-17-1 Tudanuma Narashino 275-0016 Japan Graduate School of Science and Engineering Saga University 1 Honjo-machi Saga 840-8502 Japan

Recently, a method for estimating permittivity distributions was reported. The method combined a numerical technique (Finite Element Method) with Genetic Algorithm (GA). However, methods for estimating the distribution of permittivity have not yet been established. The purpose of this study is to obtain experimental data in order to improve the estimation method. We measured capacitance and electrical potential of plural points when an acrylic rod is placed in various positions within a measurement system. © 2013 The Institute of Electrical Engineers of Japan.

关键词： Capacitance

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Overview of Versatile Video Coding (H.266/VVC) and Its Coding Performance Analysis

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IEIE Transactions on Smart Processing and Computing 2023年第2期12卷 122-154页

作者： Lee, Minhun Song, HyeonJu Park, Jeeyoon Jeon, Byeungwoo Kang, Jungwon Kim, Jae-Gon Lee, Yung-Lyul Kang, Je-Won Sim, Donggyu Department of Computer Engineering Kwangwoon University Seoul Korea Republic of Department of Computer Engineering Sejong University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Kyunggi-do Korea Republic of Communication and Media Research Laboratory Electronics and Telecommunications Research Institute Deajeon Korea Republic of School of Electronics and Information Engineering Korea Aerospace University Kyunggi-do Korea Republic of Department of Electronic and Electrical Engineering Graduate Program in Smart Factory Ewha Womans University Seoul Korea Republic of

Versatile Video Coding (H.266/VVC) is the newest video coding standard jointly developed by the Joint Video Experts Team (JVET), which is organized by the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG). H.266/VVC provides about 40% bitrate reduction compared with High Efficiency Video Coding (H.265/HEVC) for the same visual quality. This paper introduces in detail the core structure of H.266/VVC by highlighting its features within block partitioning structure, intra/inter prediction, transform, quantization, and in-loop filtering, compared to its predecessor (H.265/HEVC). H.266/VVC yields significantly improved the coding performance, but it increased the computational complexity, particularly for the encoder side, which remains a problem to be tackled for successful commercialization in the future. This paper examined the statistical performance of H.266/VVC coding tools from the bitstreams encoded by the VVC test model (VTM12.0) encoder through rate-distortion optimization under the JVET common test conditions. In addition, the complexity and performance analyses are conducted on the block partitioning structure and group of picture structure. It is expected that an optimized H.266/VVC encoder can be designed and developed by minimizing the coding loss based on the analysis data. © 2023 Institute of Electronics and Information Engineers. All rights reserved.

关键词： Image compression

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