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Convolutional Neural Network-Based Identity Recognition Using ECG atDifferent Water Temperatures During Bathing

computers, Materials & Continua 2022年第4期71卷 1807-1819页

作者： Jianbo Xu Wenxi Chen Biomedical Information Engineering Laboratory Graduate School of Computer Science and EngineeringUniversity of AizuAizu-Wakamatsu965-8580Japan

This study proposes a convolutional neural network(CNN)-based identity recognition scheme using electrocardiogram(ECG)at different water temperatures(WTs)during bathing,aiming to explore the impact of ECG length on the recognition *** data was collected using non-contact electrodes at five different WTs during *** young student subjects(seven men and three women)participated in data *** ECG recordings were collected at each preset bathtub WT for each *** recording is 18 min long,with a sampling rate of 200 *** total,150 ECG recordings and 150 WT recordings were *** R peaks were detected based on the processed ECG(baseline wandering eliminated,50-Hz hum removed,ECG smoothing and ECG normalization)and the QRS complex waves were *** segmented waves were then transformed into binary images,which served as the *** each subject,the training,validation,and test data were taken from the first,second,and third ECG recordings,*** number of training and validation images was 84297 and 83734,*** the test stage,the preliminary classification results were obtained using the trained CNN model,and the finer classification results were determined using the majority vote method based on the preliminary *** validation rate was 98.71%.The recognition rates were 95.00%and 98.00%when the number of test heartbeats was 7 and 17,respectively,for each subject.

关键词： Electrocardiogram QRS recognition rate water temperatures convolutional neural network majority vote

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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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On the stability of operating points of transistor circuits

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IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND computer scienceS 1997年第9期E80A卷 1540-1547页

作者： Nishi, T Ogata, M Department of Computer Science and Communication Graduate School of Information Science Electrical Engineering Kyushu University Fukuoka-shi 812-81 Japan

In this paper we study on the stability of an operating points of a nonlinear resistive circuits including transistors. A set of sufficient conditions for the operating point to be unstable are proposed. These conditi... 详细信息

关键词： dc operating point nonlinear circuit stability

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Plug-and-Play DISep: Separating Dense Instances for Scene-to-Pixel Weakly-Supervised Change Detection in High-Resolution Remote Sensing Images

arXiv

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arXiv 2025年

作者： Zhao, Zhenghui Wu, Chen Ru, Lixiang Wang, Di Chen, Hongruixuan Chen, Cuiqun State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan430072 China Institute of Artificial Intelligence Wuhan University Wuhan430072 China School of Computer Science Wuhan University Wuhan430072 China Ant Group Hangzhou310013 China Graduate School of Frontier Sciences University of Tokyo Chiba277-8561 Japan Institute of Geodesy and Photogrammetry ETH Zürich Zürich999034 Switzerland

Change Detection (CD) focuses on identifying specific pixel-level landscape changes in multi-temporal remote sensing images. The process of obtaining pixel-level annotations for CD is generally both time-consuming and labor-intensive. Faced with this annotation challenge, there has been a growing interest in research on Weakly-Supervised Change Detection (WSCD). WSCD aims to detect pixel-level changes using only scene-level (i.e., image-level) change labels, thereby offering a more cost-effective approach. Despite considerable efforts to precisely locate changed regions, existing WSCD methods often encounter the problem of"instance lumping" under scene-level supervision, particularly in scenarios with a dense distribution of changed instances (i.e., changed objects). In these scenarios, unchanged pixels between changed instances are also mistakenly identified as changed, causing multiple changes to be mistakenly viewed as one. In practical applications, this issue prevents the accurate quantification of the number of changes. To address this issue, we propose a Dense Instance Separation (DISep) method as a plug-and-play solution, refining pixel features from a unified instance perspective under scene-level supervision. Specifically, our DISep comprises a three-step iterative training process: 1) Instance Localization: We locate instance candidate regions for changed pixels using high-pass class activation maps. 2) Instance Retrieval: We identify and group these changed pixels into different instance IDs through connectivity searching. Then, based on the assigned instance IDs, we extract corresponding pixel-level features on a per-instance basis. 3) Instance Separation: We introduce a separation loss to enforce intra-instance pixel consistency in the embedding space, thereby ensuring separable instance feature representations. The proposed DISep adds only minimal training cost and no inference cost. It can be seamlessly integrated to enhance existing WSCD methods. We

关键词： Remote sensing

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Reconfigurable Virtual Accelerator (ReVA) for Large-Scale Acceleration Circuits 30th

Reconfigurable Virtual Accelerator (ReVA) for Large-Scale A...

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30th International Conference on Parallel and Distributed Processing Techniques, PDPTA 2024, held as part of the World Congress in computer science, computer engineering and Applied Computing, CSCE 2024

作者： Yaguchi, Kazuki Maeda, Eriko Kawai, Shunya Teruya, Daichi Osana, Yasunori Miyoshi, Takefumi Nakajo, Hironori Department of Electrical Engineering and Computer Science Graduate School of Engineering Tokyo University of Agriculture and Technology 2-24-16 Nakacho Tokyo Koganei-shi184-8588 Japan Research and Education Institute for Semiconductors and Informatics Kumamoto University 2-39-1 Kurokami Kumamoto Chuo-ku860-8555 Japan WasaLabo LLC. K-2 Buransyu 6-5-20 Minaminaruse Tokyo Machida-shi194-0045 Japan Division of Advanced Information Technology and Computer Science Institute of Engineering Tokyo University of Agriculture and Technology 2-24-16 Nakacho Tokyo Koganei-shi184-8588 Japan

ISBN: (纸本)9783031856372

In recent years, hardware acceleration in large-scale computing fields such as Artificial Intelligence and High Performance Computing, faces hardware resource shortages. To overcome this problem, we propose Reconfigurable Virtual Accelerator (ReVA), which allows a large-scale acceleration circuit built using multiple FPGAs, processors and memory subsystems, to accelerate application programs. We have designed and implemented a prototype of Virtual Accelerator (VA) Generator for ReVA to investigate its performance. The VA Generator prototype employs an open-source HLS automated split compilation tool, RapidStream, to automatically generate placed-and-routed virtual accelerators that can be implemented on multiple FPGAs based on HLS dataflow designs. The VA Generator, which places VAs on the appropriate regions of FPGAs, allows large circuits to be performed like a single accelerator using several FPGAs. In addition, RapidStream’s parallel compilation technology allows the VA Generator to suppress the increasing compilation time according to the circuit size. Moreover, with our VA Generator prototype we have built and evaluated a VA of a large-scale circuit which cannot be fit in a single FPGA with multiple FPGAs. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Reconfigurable hardware

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A two-stage deep learning-based hybrid model for daily wind speed forecasting

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Heliyon 2025年第1期11卷 e41026页

作者： Band, Shahab S. Ameri, Rasoul Qasem, Sultan Noman Mehdizadeh, Saeid Gupta, Brij B. Pai, Hao-Ting Shahmirzadi, Danyal Salwana, Ely Mosavi, Amir Future Technology Research Center National Yunlin University of Science and Technology Douliu Taiwan Department of Information Management International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan Computer Science Department College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz 6803 Yemen Water Engineering Department Urmia University Urmia Iran Department of Computer Science and Information Engineering Asia University Taichung 413 Taiwan Symbiosis Centre for Information Technology (SCIT) Symbiosis International University Pune India Center for Interdisciplinary Research University of Petroleum and Energy Studies (UPES) Dehradun India Department of Big Data Business Analytics National Pingtung University Pingtung Taiwan of Engineering Science and Technology National Yunlin University of Science and Technology 123 University Road Yunlin Douliou 64002 Taiwan Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Bangi 43600 Malaysia John von Neumann Faculty of Informatics Obuda University Budapest Hungary Ludovika University of Public Service Budapest Hungary

Global adoption of wind energy continues to increase, while improving the efficiency of turbine settings requires reliable wind speed (WS) models. The latest models rely on artificial intelligence (AI) optimizations which constructs tests on a range of novel hybrid models to examine the reliability. Gradient Boosting (GB), Random Forest (RF), and Long Short-Term Memory (LSTM) are used in new combinations for data pre-processing. A Time Varying Filter-based Empirical Mode Decomposition (TVFEMD) model is coupled with the GB and LSTM standalone models, to create TVFEMD-GB and TVFEMD-LSTM hybrids, which are run in competition with each other. Eventually, a preferred hybrid form is established, simultaneous hybridization of TVFEMD with GB and LSTM. This study is the first to hybridize these fundamental systems, and create a TVFEMD-GB-LSTM model that can forecast WS. This study finds that the novel hybrid models exhibit superior performance to standalone GB and LSTM models, opening the pathway to alternative WS prediction techniques. © 2024 The Authors

关键词： Artificial intelligence Big data Data science Forecasting Gradient boosting Long short-term memory Machine learning Wind speed

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An efficient binary salp swarm algorithm for user selection in multiuser MIMO antenna systems

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Scientific Reports 2025年第1期15卷 1-17页

作者： Sasikumar, A. Ravi, Logesh Devarajan, Malathi Almazyad, Abdulaziz S. De, Shuvodeep Xiong, Guojiang Mousavirad, Seyed Jalaleddin Mohamed, Ali Wagdy Department of Data Science and Business Systems Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Centre for Advanced Data Science Tamil Nadu Chennai 600127 India School of Electronics Engineering Vellore Institute of Technology Tamil Nadu Chennai 600127 India School of Computer Science and Engineering Vellore Institute of Technology Chennai 600127 India Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh 11543 Saudi Arabia Virginia Tech Blacksburg United States Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical Engineering Guizhou University Guiyang 550025 China Department of Computer and Electrical Engineering Mid Sweden University Sundsvall Sweden Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza 12613 Egypt Applied Science Research Center Applied Science Private University Amman 11931 Jordan Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India

The past ten years have seen notable research activity and significant advancements in multiuser multiple-input multiple-output (MU-MIMO) antennas. An MU-MIMO antenna system must accommodate many subscribers without additional bandwidth or energy. User scheduling becomes a critical strategy to take advantage of multiuser heterogeneity and acquire maximum gain in systems where the total number of recipients exceeds the number of transmitting antennas. Due to their high computational cost, many user selection methods currently in use, such as greedy algorithms and exhaustive search are unsuitable for MU-MIMO systems. A suitable scheduling mechanism is essential for the various users in an MU-MIMO system to utilise bandwidth and enhance the system’s total rate effectively. In this article, we proposed a user and antenna scheduling with a population-based meta-heuristic approach, namely the binary salp swarm algorithm (binary SSA), to increase the system sum rate with low computing complexity. We specifically used a population-based meta-heuristics optimisation technique to simulate the user scheduling problem in MU-MIMO systems, characterising complicated issues with binary decisions. Additionally, binary SSA significantly outperforms existing population-based models, such as the binary bat algorithm (binary BA), PSO, SSA, FPA and binary flower pollination algorithm (binary FPA), regarding system throughput/sum rate. The proposed binary SSA technique also effectively achieves a system sum rate compared to a random search scheme and other existing suboptimal scheduling methods. Compared to binary BA and binary FPA approaches, the binary SSA has a higher convergence rate and superior searching capabilities. The simulation outcomes show the proposed binary SSA-based scheduling scheme delivers noticeable performance benefits. © The Author(s) 2025.

关键词： Antenna Binary salp swarm algorithm Metaheuristics optimization Multiuser MIMO User scheduling

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Logical entanglement distribution between distant 2D array qubits

arXiv

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arXiv 2025年

作者： Maeda, Yuya Suzuki, Yasunari Kobayashi, Toshiki Yamamoto, Takashi Tokunaga, Yuuki Fujii, Keisuke Graduate School of Engineering Science Osaka University 1-3 Machikaneyama Toyonaka Osaka560-8531 Japan NTT Computer and Data Science Laboratories NTT Corporation Musashino180-8585 Japan JST PRESTO 4-1-8 Honcho Kawaguchi Saitama332-0012 Japan Center for Quantum Information and Quantum Biology Institute for Open and Transdisciplinary Research Initiatives Osaka University Japan Center for Quantum Computing RIKEN Wako Saitama351-0198 Japan

Sharing logical entangled pairs between distant quantum nodes is a key process to achieve fault-tolerant quantum computation and communication. However, there is a gap between current experimental specifications and theoretical requirements for sharing logical entangled states while improving experimental techniques. Here, we propose an efficient logical entanglement distribution protocol based on surface codes for two distant 2D qubit array with nearest-neighbor interaction. A notable feature of our protocol is that it allows post-selection according to error estimations, which provides the tunability between the infidelity of logical entanglements and the success probability of the protocol. With this feature, the fidelity of encoded logical entangled states can be improved by sacrificing success rates. We numerically evaluated the performance of our protocol and the trade-off relationship, and found that our protocol enables us to prepare logical entangled states while improving fidelity in feasible experimental parameters. We also discuss a possible physical implementation using neutral atom arrays to show the feasibility of our protocol. © 2025, CC BY.

关键词： Qubits

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Text-independent and character-dependent online writer verification

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Journal of Convergence information Technology 2012年第16期7卷 161-168页

作者： Chang, Won-Du Yun, Keunsoo Shin, Jungpil Department of Computer and Information Ulsan College Korea Republic of Graduate School of Computer Science and Engineering The University of Aizu Fukushima Japan

This paper proposes a text-independent writer verification scheme by using character level features. Four character features are introduced in the context of handwritten string, which mainly represent a character's relative position in its string. The scheme is implemented by comparing character shapes and relative features of characters in the context of string styles. The system reported 53.11% of successive filtering effect with the features alone and dropped error rates to 1.0% from 1.7% when the features are integrated with character shape comparing function.

关键词： Text-independent Character-dependent Online Writer Verification

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Dementia Detection by Shopping Refugees Support Robot 2

Dementia Detection by Shopping Refugees Support Robot

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2nd IEEE Global Conference on Life sciences and Technologies, LifeTech 2020

作者： Kishimoto, Yuta Arai, Kenichi Imai, Tetsuo Kobayashi, Toru Nagasaki University Computer and Information Science Program Graduate School of Engineering Nagasaki Japan Nagasaki University Division of Electrical Engineering and Computer Science Graduate School of Engineering Nagasaki Japan

ISBN: (纸本)9781728170633

We have developed the Shopping Refugees Support Robot that enables the elderly to order items through Social Networking Service (SNS) by voice conversation without relying on smartphones. This paper proposes a detection method of dementia signs using this Robot. Concretely, deterioration of cognitive function is detected by the evaluation of short-term memory utilizing order history of the Robot. We expect detection of dementia signs on a daily basis without making the elderly person conscious of having a dementia test, by information that only the person knows. We implemented a prototype and confirmed its functional validity. © 2020 IEEE.

关键词： Internet of things

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