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

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang-Ming Chiao Tung University Parallel and Scientific Computing Laboratory Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Electrical Engineering and Computer Science International Graduate Program Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Institute of Communications Engineering Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Department of Electrical Engineering and Computer Engineering Hsinchu300 Taiwan National Yang-Ming Chiao Tung University Institute of Biomedical Engineering Hsinchu300 Taiwan Center for mmWave Smart Radar System and Technologies National Yang-Ming Chiao Tung University 1001 Ta-Hsueh Rd. Hsinchu300 Taiwan

ISBN: (纸本)9781665409230

In this paper, for the first time, deep learning (DL) based artificial neural network (ANN) is applied to model the effects of various random variations: work function fluctuation, random dopant fluctuation, and interface trap fluctuation, on gate-all-around silicon nanosheet MOSFETs. The number of fluctuations for each source variation is used as input features and their effects on devices of interest are studied qualitatively and quantitatively. The key figures of merit (FoM) are also extracted accurately from the transfer characteristics, which shows the competency of the ANN model in the domain of device modeling. © 2022 IEEE.

关键词： Nanosheets

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Design and Optimization of Single-Shunt Diode RF Rectifier Using Electromagnetic Co-Simulation

Design and Optimization of Single-Shunt Diode RF Rectifier U...

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international electrical engineering Congress (iEECON)

作者： Kirote Arpanutud Tiwat Pongthavornkamol Prayoot Akkaraekthalin Suramate Chalermwisutkul Faculty of Engineering at Sriracha Kasetsart University Sriracha Campus Chonburi Thailand National Electronics and Computer Technology Center The National Science and Technology Development Agency Pathumthani Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand The Sirindhorn International Thai-German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

In this paper, an efficient design process for a single-shunt diode RF rectifier is proposed. The technique focuses on tuning microstrip line sections in electromagnetic and circuit co-simulation. The design study can achieve an RF-to-DC conversion efficiency of 51.27% at the center frequency of 950 MHz with -10 dBm input power. Good agreement between full wave electromagnetic simulation and electromagnetic/ circuit co-simulation is demonstrated, providing substantial support for the proposed RF rectifier design technique.

关键词： Radio frequency Power transmission lines Simulation Rectifiers Transmission line measurements Mobile handsets Electromagnetics

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Deep Joint Semantic Coding and Beamforming for Near-Space Airship-Borne Massive MIMO Network

arXiv

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arXiv 2024年

作者： Wu, Minghui Gao, Zhen Wang, Zhaocheng Niyato, Dusit Karagiannidis, George K. Chen, Sheng School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China Tsinghua Shenzhen International Graduate School Shenzhen518055 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece Lebanon The School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

Near-space airship-borne communication network is recognized to be an indispensable component of the future integrated ground-air-space network thanks to airships’ advantage of long-term residency at stratospheric altitudes, but it urgently needs reliable and efficient Airship-to-X link. To improve the transmission efficiency and capacity, this paper proposes to integrate semantic communication with massive multiple-input multiple-output (MIMO) technology. Specifically, we propose a deep joint semantic coding and beamforming (JSCBF) scheme for airship-based massive MIMO image transmission network in space, in which semantics from both source and channel are fused to jointly design the semantic coding and physical layer beamforming. First, we design two semantic extraction networks to extract semantics from image source and channel state information, respectively. Then, we propose a semantic fusion network that can fuse these semantics into complex-valued semantic features for subsequent physical-layer transmission. To efficiently transmit the fused semantic features at the physical layer, we then propose the hybrid data and model-driven semantic-aware beamforming networks. At the receiver, a semantic decoding network is designed to reconstruct the transmitted images. Finally, we perform end-to-end deep learning to jointly train all the modules, using the image reconstruction quality at the receivers as a metric. The proposed deep JSCBF scheme fully combines the efficient source compressibility and robust error correction capability of semantic communication with the high spectral efficiency of massive MIMO, achieving a significant performance improvement over existing approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： Base stations

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Automatic melanoma detection using discrete cosine transform features and metadata on dermoscopic images

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Journal of King Saud University - computer and Information sciences 2024年第2期36卷

作者： Yousefi, Shamim Najjar-Ghabel, Samad Danehchin, Ramin Band, Shahab S. Hsu, Chung-Chian Mosavi, Amir Department of Computer Engineering University of Mohaghegh Ardabili Ardabil Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan John von Neumann Faculty of Informatics Budapest Hungary Ludovika University of Public Service Budapest Hungary

Machine learning contributes in improving the accuracy of melanoma detection. There are extensive studies in classic and deep learning-based approaches for melanoma detection in the literature. Still, they are not accurate or require high learning data. This paper proposes a hybrid mechanism for automated melanoma detection on dermoscopic images based on Discrete Cosine Transform features and metadata. It is composed of three steps. First, extra information/artifacts are deleted;the remaining pixels are standardized for accurate processing. Second, the reliability of the mechanism is improved by the Radon transform, extra data is removed using the Top-hat filter, and the detection rate is increased by Discrete Wavelet Transform and Discrete Cosine Transform. Then, the number of features is reduced by Locality Sensitive Discriminant Analysis. The third step divides the images into learning and test ones to create image-based models using learning data. Finally, the best model is selected based on test data and improved by a metadata-based model. Simulation results show that the decision tree provides the most practical image-based model by improving accuracy and sensitivity. Besides, the comparison results demonstrate that our model improves the F-Value to detect melanoma superior to other approaches. © 2024 The Author(s)

关键词： Artificial intelligence Big data Data science Dermoscopic image Discrete cosine transform features Machine learning Melanoma detection Metadata

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BDP-UaiFly System: A Platform for the RoboCup Brazil Open Flying Robot Trial League

BDP-UaiFly System: A Platform for the RoboCup Brazil Open Fl...

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international Conference on Unmanned Aircraft Systems (ICUAS)

作者： Leonardo A. Fagundes-Junior Celso O. Barcelos Daniel C. Gandolfo Alexandre S. Brandão Department of Electrical Engineering and Graduate Program on Computer Science Núcleo de Especialização em Robótica Federal University of Viçosa Viçosa MG Brazil Instituto de Automática National University of San Juan Argentine Department of Electrical Engineering Federal University of Viçosa Viçosa MG Brazil

The Flying Robot Trial League (FRTL), from RoboCup Brazil, is a competition that stimulates the development of autonomous and intelligent flying robots for inspection and operation in pipeline lanes and oil installations. In this context, this work presents the system developed by the BDP-UaiFly Team for the 2022 competition, using the off-the-shelf Parrot Bebop 2 to execute the Equipment Transport phase. This paper presents in detail the system platform and the navigation and sensing strategies implemented for autonomous navigation and image processing. In particular, the strategy adopted for cargo transportation based on servo-visual control is presented. Practical experiments validate the proposed solutions for the phases of the challenge.

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Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approach

Vision-based Semantic Communications for Metaverse Services:...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Guangyuan Liu Hongyang Du Dusit Niyato Jiawen Kang Zehui Xiong Boon Hee Soong Energy Research Institute@NTU Interdisciplinary Graduate Program School of Computer Science and Engineering Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore School of Automation Guangdong University of Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore Department of Electrical and Electronic Engineering Nanyang Technological University Singapore

The popularity of Metaverse as an entertainment, social, and work platform has led to a great need for seamless avatar integration in the virtual world. In Metaverse, avatars must be updated and rendered to reflect users' behaviour. Achieving real-time synchronization between the virtual bilocation and the user is complex, placing high demands on the Metaverse Service Provider (MSP)'s rendering resource allocation scheme. To tackle this issue, we propose a semantic communication framework that leverages contest theory to model the interactions between users and MSPs and determine optimal resource allocation for each user. To reduce the consumption of network resources in wireless transmission, we use the semantic communication technique to reduce the amount of data to be transmitted. Under our simulation settings, the encoded semantic data only contains 51 bytes of skeleton coordinates instead of the image size of 8.243 megabytes. Moreover, we implement Deep Q-Network to optimize reward settings for maximum performance and efficient resource allocation. With the optimal reward setting, users are incentivized to select their respective suitable uploading frequency, reducing down-sampling loss due to rendering resource constraints by 66.076% compared with the traditional average distribution method. The framework provides a novel solution to resource allocation for avatar association in VR environments, ensuring a smooth and immersive experience for all users.

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Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using a Deep Learning Approach

Enhancing Diagnostic Accuracy for Knee Osteoarthritis Using ...

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international Symposium on Applied science 2024, ISAS 2024

作者： Raza, Aquib Ching, Congo Tak Shing Liu, Peng-Ta Phan, Thien-Luan Tran, Ngoc-Luan Nguyen, Ha Anh T. Rameez, Mohammad Li, Hung-Chung Linh, Dao Nguyen Phuong Van Hieu, Nguyen Tran, Trung Nghia National Taiwan University Taipei City10673 Taiwan Research Center for Information Technology Academia Sinica Taipei City11529 Taiwan Graduate Institute of Biomedical Engineering National Chung Hsing University Taichung402 Taiwan Department of Electrical Engineering National Chi Nan University Nantou County Taiwan Advanced Plant and Food Crop Biotechnology Center National Chung Hsing University Taichung402 Taiwan International Doctoral Program in Agriculture National Chung Hsing University Taichung402 Taiwan Fall Prevention Center Department of Physical Medicine & Rehabilitation Changhua Christian Hospital Changhua500 Taiwan Department of Physics and Electronic Engineering University of Science Vietnam National University - Ho Chi Minh City Ho Chi Minh City70000 Viet Nam Department of Computer Science University of York York HeslingtonYO10 5DD United Kingdom Undergraduate Program of Intellectual Creativity Engineering National Chung Hsing University Taichung402 Taiwan Ho Chi Minh City72409 Viet Nam HCMUT Vietnam National University Ho Chi Minh City Thu Duc Ho Chi Minh City71308 Viet Nam

This research introduces a deep learning framework that combines convolutional neural networks with autoencoders to improve the diagnostic accuracy of knee osteoarthritis. The study utilized a publicly available dataset from the Mendeley Data repository, where knee joint images were preprocessed using Contrast Limited Adaptive Histogram Equalization to enhance contrast and ensure consistent image sizing. Initially, an autoencoder was employed in an unsupervised manner to learn compressed representations of the data. These representations were then used to train a CNN to classify knee images into 'healthy' and 'osteoarthritic' categories. The model was trained using an 80:20 split for training and testing data, incorporating early stopping and data augmentation techniques to prevent overfitting and enhance generalization. Performance metrics such as accuracy, precision, recall, and the area under the Receiver Operating Characteristic curve (ROCAUC) were used to evaluate the model, resulting in a high test accuracy of 98.95%. The high precision and recall rates further confirmed the model's robustness in diagnosing KOA. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to identify critical regions in the images that influenced the model's decisions. Additionally, t-Distributed Stochastic Neighbor Embedding (t-SNE) was used to visualize the high-dimensional encoded features in a two-dimensional space, providing deeper insights into how the model differentiates between classes. The promising results of this deep learning approach suggest its potential for broader applications in medical imaging diagnostics. By providing healthcare professionals with a tool that enhances diagnostic accuracy and operational efficiency, this framework lays the foundation for developing scalable, AI-driven diagnostic systems for various complex medical conditions. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Joints (anatomy)

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Fast Tracking of Maximum Power in a Shaded Photovoltaic System Using Ali Baba and the Forty Thieves (AFT) Algorithm

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Processes 2023年第10期11卷

作者： Rehman, Khalil Ur Sajid, Injila Lu, Shiue-Der Ahmad, Shafiq Liu, Hwa-Dong Bakhsh, Farhad Ilahi Tariq, Mohd Sarwar, Adil Lin, Chang-Hua Department of Electrical Engineering ZHCET Aligarh Muslim University Aligarh202002 India Department of Electrical Engineering National Chin-Yi University of Technology Taichung411 Taiwan Industrial Engineering Department College of Engineering King Saud University Riyadh11421 Saudi Arabia Undergraduate Program of Vehicle and Energy Engineering National Taiwan Normal University Taipei106 Taiwan Department of Electrical Engineering National Institute of Technology Srinagar190006 India Department of Electrical and Computer Engineering Florida International University MiamiFL33174 United States Department of Electrical Engineering National Taiwan University of Science and Technology Taipei106 Taiwan

Photovoltaic (PV) generation systems that are partially shaded have a non-linear operating curve that is highly dependent on temperature and irradiance conditions. Shading from surrounding objects like clouds, trees, and buildings creates partial shading conditions (PSC) that can cause hot spot formation on PV panels. To prevent this, bypass diodes are installed in parallel across each panel, resulting in a global maximum power point (GMPP) and multiple local maximum power points (LMPPs) on the power-voltage (P-V) curve. Traditional methods for maximum power point tracking (MPPT), such as perturb and observe (P&O) and incremental conductance (INC), converge for LMPPs on the P-V curve, but metaheuristic algorithms can track the GMPP effectively. This paper proposes a new, efficient, and robust GMPP tracking technique based on a nature-inspired algorithm called Ali Baba and the Forty Thieves (AFT). Utilizing the AFT algorithm for MPPT in PV systems has several novel features and advantages, including its adaptability, exploration-exploitation balance, simplicity, efficiency, and innovative approach. These characteristics make AFT a promising choice for enhancing the efficiency of PV systems under varied circumstances. The performance of the proposed method in tracking the GMPP is evaluated using a simulation model under MATLAB/Simulink environment, the achieved simulation results are compared to particle swarm optimization (PSO). The proposed method is also tested in real-time using the Hardware-in-the-loop (HIL) emulator to validate the achieved simulation results. The findings indicate that the proposed AFT-based GMPP tracking method performs better under complex partial irradiance conditions than PSO. © 2023 by the authors.

关键词： Efficiency

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Towards data security assessments using an IDS security model for cyber-physical smart cities

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Information sciences 2023年第1期648卷

作者： Sangaiah, Arun Kumar Javadpour, Amir Pinto, Pedro International Graduate Institute of AI National Yunlin University of Science and Technology Taiwan ADiT-Lab Electrotechnics and Telecommunications Department Instituto Politécnico de Viana do Castelo Porto4900-347 Portugal Harbin Institute of Technology Shenzhen China Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Technology has enabled many devices to exchange huge amounts of data and communicate with each other as Edge Intelligence in Smart Cities (EISC), as a result of rapid technological advancements. When dealing with personal data, it is paramount to ensure that it is not disclosed and that there is no disclosure of any confidential information. In recent decades, academics and industry have spent considerable time and energy discussing security and privacy. Other systems, known as intrusion detection systems, are required to breach firewalls, antivirus software, and other security equipment to provide complete system security in smart operation systems. There are three aspects to an intrusion detection system: the intrusion detection method, the architecture, and the intrusion response method. In this study, we combined linear correlation feature selection methods and cross-information. The database used in this article is KDD99. This paper examines applying two feature selection methods in predicting attacks in intrusion detection systems based on INTERACT and A multilayer perceptron (MLP). Since the number of records associated with each attack type differs, one of our suggestions is to continue using data balancing techniques. As a result, the number of records associated with each type of network status becomes closer together. The results in the categories can also be improved using information synthesis methods, such as majority voting. © 2023 Elsevier Inc.

关键词： Smart city

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