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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Yafis, Balqis Huang, Jane-Hwa Yu, Chih-Min Wang, Li-Chun National Yang Ming Chiao Tung University Eecs Int'l Graduate Program Hsinchu Taiwan National Chi Nan University Dept. of Electrical Engineering Nantou County Taiwan Chung Yuan Christian University Department of Information and Computer Engineering Taoyuan City Taiwan

ISBN: (纸本)9798350351255

Reconfigurable Intelligent Surface (RIS) is a transformative technology using the passive beamforming capabilities to create the programmable wireless environments. However, the control overhead is a challenging issue. The system needs to deliver the high-precision reflection coefficients to the RIS controller, for adjusting the phase shifts of the numerous RIS elements. Therefore, we propose a Spirograph-based discrete beamdirection strategy, which selects a set of non-uniformly distributed beam directions based on the generic patterns generated by the Spirograph (SG). We evaluate the achievable data rate and the number of controlling bits of SG-based method. We compare the SG-based method, the ideal continuous phase-shift method, and the SISO method. Simulation results show that with the properly-selected discrete beam directions, the SG-based method can achieve a comparable data rate as the ideal method, while having a lower control overhead. © 2024 IEEE.

关键词： Beamforming

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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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Enhancing Modulation Bandwidth of Parallel Micro-LED Arrays for Visible Light Communication

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第6期46卷 944-947页

作者： Guo, Wen-An Wan, Ming-He Liu, Shi-Biao Liu, Zhong Lu, Ting-Wei Chen, Guo-Long Lu, Yi-Jun Kuo, Hao-Chung Wu, Ting-Zhu Chen, Zhong Xiamen University National Integrated Circuit Industry and Education Integration Innovation Platform Department of Electronic Science Xiamen361005 China National Yang Ming Chiao Tung University College of Electrical and Computer Engineering Department of Photonics Graduate Institute of Electro-Optical Engineering Hsinchu30010 Taiwan Hon Hai Research Institute Semiconductor Research Center Taipei11492 Taiwan

In this letter, parallel micro-LED arrays with numbers of 1×1, 1×2, 2×2 and 2×3 were fabricated and the p-electrode patterns of arrays were optimized. The results show that both the light output power (LOP) and modulation bandwidth increase with the parallel array numbers. Moreover, optimizing the p-electrode pattern can effectively facilitate current spreading, mitigate thermal crowding, and enhance the LOP. Under combined with the LOP and thermal effect, the 2 × 2 ring-electrode parallel array achieved the highest modulation bandwidth of 172 MHz and maximum LOP of 5.70 mW, which were improved by 17.28% and 13.98%, respectively. This work provides a reference to design micro-LED chip for high bandwidth. © 1980-2012 IEEE.

关键词： Bandwidth

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Reachability Analysis of Nonlinear Discrete-Time Systems Using Polyhedral Relaxations and Constrained Zonotopes 63

Reachability Analysis of Nonlinear Discrete-Time Systems Usi...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Rego, Brenner S. Raffo, Guilherme V. Terra, Marco H. Scott, Joseph K. Georgia Institute of Technology Department of Chemical and Biomolecular Engineering 311 Ferst Dr. AtlantaGA30318 United States University of Säo Paulo Department of Electrical and Computer Engineering SP Säo Carlos13566-590 Brazil Federal University of Minas Gerais Department of Electronics Engineering and the Graduate Program in Electrical Engineering MG Belo Horizonte31270-901 Brazil

ISBN: (纸本)9798350316339

This paper presents a novel algorithm for reachability analysis of nonlinear discrete-time systems. The proposed method combines constrained zonotopes (CZs) with polyhedral relaxations of factorable representations of nonlinear functions to propagate CZs through nonlinear functions, which is normally done using conservative linearization techniques. The new propagation method provides better approximations than those resulting from linearization procedures, leading to significant improvements in the computation of reachable sets in comparison to other CZ methods from the literature. Numerical examples highlight the advantages of the proposed algorithm. © 2024 IEEE.

关键词： Numerical methods

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Triple-path transformer network for multi-channel speech enhancement 24

Triple-path transformer network for multi-channel speech enh...

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24th International Congress on Acoustics, ICA 2022

作者： Park, Kyeong Wan Lee, Geon Woo Kim, Hong Kook School of Electrical Engineering and Computer Science Gwangju Institute of Science and Technology Korea Republic of AI Graduate School Gwangju Institute of Science and Technology Korea Republic of

This paper proposes a neural network-based end-to-end multi-channel speech enhancement model that operates in time domain. To this end, a triple-path transformer network (TPTN) is proposed to extract clean speech features by replacing the attentive recurrent neural networks (RNNs) with transformers in the triple-path attentive recurrent network (TPARN) framework. The proposed TPTN is composed of an intra-chunk transformer, an inter-chunk transformer, and an inter-channel transformer. Thus, a series of TPTNs are applied to the speech features converted from multi-channel speech signals so that extracted features are represented with local and global temporal dependencies as well as spatial dependencies. The performance of the proposed TPTN-based multi-channel speech enhancement model was evaluated on the CHiME3 dataset by measuring the short-time objective intelligibility (STOI) score and perceptual evaluation of speech quality (PESQ) score. It was shown from the experiment that the proposed TPTN-based model achieved similar STOI score to and higher PESQ score by 0.15 than the TPARN-based model, while the number of parameters of the proposed model was smaller than that of the TPARN-based model. © ICA *** rights reserved

关键词： Deep Learning End-to-end model Multi-channel Speech enhancement Transformer

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Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders 41

Purify Unlearnable Examples via Rate-Constrained Variational...

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41st International Conference on Machine Learning, ICML 2024

作者： Yu, Yi Wang, Yufei Xia, Song Yang, Wenhan Lu, Shijian Tan, Yap-Peng Kot, Alex C. Rapid-Rich Object Search Lab Interdisciplinary Graduate Programme Nanyang Technological University Singapore School of Electrical and Electronic Engineering Nanyang Technological University Singapore PengCheng Laboratory Shenzhen China School of Computer Science and Engineering Nanyang Technological University Singapore

Unlearnable examples (UEs) seek to maximize testing error by making subtle modifications to training examples that are correctly labeled. Defenses against these poisoning attacks can be categorized based on whether specific interventions are adopted during training. The first approach is training-time defense, such as adversarial training, which can mitigate poisoning effects but is computationally intensive. The other approach is pre-training purification, e.g., image short squeezing, which consists of several simple compressions but often encounters challenges in dealing with various UEs. Our work provides a novel disentanglement mechanism to build an efficient pre-training purification method. Firstly, we uncover rate-constrained variational autoencoders (VAEs), demonstrating a clear tendency to suppress the perturbations in UEs. We subsequently conduct a theoretical analysis for this phenomenon. Building upon these insights, we introduce a disentangle variational autoencoder (D-VAE), capable of disentangling the perturbations with learnable class-wise embeddings. Based on this network, a two-stage purification approach is naturally developed. The first stage focuses on roughly eliminating perturbations, while the second stage produces refined, poison-free results, ensuring effectiveness and robustness across various scenarios. Extensive experiments demonstrate the remarkable performance of our method across CIFAR-10, CIFAR-100, and a 100-class ImageNet-subset. Code is available at https://***/yuyi-sd/D-VAE. Copyright 2024 by the author(s)

关键词： Variational techniques

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HALF WAVELET ATTENTION ON M-NET+ FOR LOW-LIGHT IMAGE ENHANCEMENT 29

HALF WAVELET ATTENTION ON M-NET+ FOR LOW-LIGHT IMAGE ENHANCE...

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29th IEEE International Conference on Image Processing, ICIP 2022

作者： Fan, Chi-Mao Liu, Tsung-Jung Liu, Kuan-Hsien Department of Electrical Engineering Graduate Institute of Communication Engineering National Chung Hsing University Taiwan Department of Computer Science and Information Engineering National Taichung University of Science and Technology Taiwan

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

Low-Light Image Enhancement is a computer vision task which intensifies the dark images to appropriate brightness. It can also be seen as an ill-posed problem in image restoration domain. With the success of deep neural networks, the convolutional neural networks surpass the traditional algorithm-based methods and become the mainstream in the computer vision area. To advance the performance of enhancement algorithms, we propose an image enhancement network (HWMNet) based on an improved hierarchical model: M-Net+. Specifically, we use a half wavelet attention block on M-Net+ to enrich the features from wavelet domain. Furthermore, our HWMNet has competitive performance results on two image enhancement datasets in terms of quantitative metrics and visual quality. The source code and pretrained model are available at https://***/FanChiMao/HWMNet. © 2022 IEEE.

关键词： Image enhancement

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Enhancing the Sense of Unity with Performers in VR Space by Mutual Presentation of Electroencephalogram Information 11

Enhancing the Sense of Unity with Performers in VR Space by ...

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11th IEEE Global Conference on Consumer Electronics, GCCE 2022

作者： Ide, Kota Horie, Ryota Shibaura Institute of Technology Graduate School of Engineering and Science Department of Electrical Engineering and Computer Science Tokyo Japan Shibaura Institute of Technology Department of Information and Communications Engineering Tokyo Japan

ISBN: (纸本)9781665492324

Performances in VR space, such as VR music concerts, are less likely to convey audience's reactions than in real space, making it difficult to feel the sense of unity. In this paper, we introduce a system that enhances the sense of unity among performers and audience in VR space by generating visual effects based on the beta-alpha ratio signals obtained from a single-channel electroencephalograph and sharing them with the others. We evaluated the significance of the system by conducting juggling-shows in VR space using the system. The results showed the potential to enhance the sense of unity among the performers and the audience. © 2022 IEEE.

关键词： Electroencephalography

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Exponentially Enhanced Scheme for the Heralded Qudit Greenberger-Horne-Zeilinger State in Linear Optics

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Physical Review Letters 2024年第25期133卷 253601-253601页

作者： Seungbeom Chin Junghee Ryu Yong-Su Kim Okinawa Institute of Science and Technology Graduate University Okinawa 904-0495 Japan and Department of Electrical and Computer Engineering Sungkyunkwan University Suwon 16419 Korea

High-dimensional multipartite entanglement plays a crucial role in quantum information science. However, existing schemes for generating such entanglement become complex and costly as the dimension of quantum units increases. In this Letter, we overcome the limitation by proposing a significantly enhanced linear optical heralded scheme that generates the d-level N-partite Greenberger-Horne-Zeilinger (GHZ) state with single-photon sources and linear operations. Our scheme requires dN photons, which is the minimal required photon number, with substantially improved success probability from previous schemes. It employs linear optical logic gates compatible with any qudit encoding system and can generate generalized GHZ states with installments of beam splitters. With efficient generations of high-dimensional resource states, our work opens avenues for further exploration in high-dimensional quantum information processing.

关键词： Photons

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SoC-Oriented Implementation of Machine Learning Based Breast Cancer Classification Algorithm 12

SoC-Oriented Implementation of Machine Learning Based Breast...

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12th Mediterranean Conference on Embedded Computing, MECO 2023

作者： Saeed, Abdelrahman Tawfik, Ayman Mostafa, Hassan Khalil, Ahmed Hussein Cairo University Electronics and Communications Engineering Department Giza12613 Egypt Ajman University Department of Electrical and Computer Engineering United Arab Emirates University of Science and Technology Nanotechnology and Nanoelectronics Program Zewail City of Science and Technology Giza Egypt

ISBN: (纸本)9798350322910

Convolutional Neural Networks (CNN) have drawn the attention of researchers in the medical imaging field. Many researchers have exploited CNN for breast cancer detection. This study provides an Internet of Things (IoT) friendly implementation of CNN for breast cancer detection. To achieve faster time to Market, Deep-learning Processing Unit (DPU) on Field programmable Gate Array (FPGA) is adopted for the CNN hardware implementation. CNN inference on the proposed system achieves a 1.6x speed-up factor and 91.5% reduction in energy consumption compared to the conventional general-purpose multi-core Central Processing Unit (CPU). © 2023 IEEE.

关键词： Convolutional neural networks

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