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7th International Conference on computer Science and Computational Intelligence, CSCI 2022

作者： Ramadhan, M. Vickya Muchtar, Kahlil Nurdin, Yudha Oktiana, Maulisa Fitria, Maya Maulina, Novi Elwirehardja, Gregorius Natanael Pardamean, Bens Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh23111 Indonesia Universitas Syiah Kuala Banda Aceh23111 Indonesia Microbiology Department School of Medicine Universitas Syiah Kuala Banda Aceh23111 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

The spread of Corona Virus Disease 19 (COVID-19) in Indonesia is still relatively high and has not shown a significant decrease. One of the main reasons is due to the lack of supervision on the implementation of health protocols such as wearing masks in daily activities. Recently, state-of-The-Art algorithms were introduced to automate face mask detection. To be more specific, the researchers developed various kinds of architectures for the detection of masks based on computer vision methods. This paper aims to evaluate well-known architectures, namely the ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO (You Only Look Once) to recommend the best approach in this specific field. By using the MaskedFace-Net dataset, the experimental results showed that the EfficientNetB4 architecture has better accuracy at 95.77% compared to the YOLOv4 architecture of 93.40%, InceptionV3 of 87.30%, YOLOv3 of 86.35%, ResNet50 of 84.41%, VGG11 of 84.38%, and YOLOv2 of 78.75%, respectively. It should be noted that particularly for YOLO, the model was trained using a collection of MaskedFace-Net images that had been pre-processed and labelled for the task. The model was initially able to train faster with pre-Trained weights from the COCO dataset thanks to transfer learning, resulting in a robust set of features expected for face mask detection and classification. © 2022 Elsevier B.V.. All rights reserved.

关键词： Face recognition

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Data Alignment Design for Robotic program.ing by Demonstration Based on IMU and Optical Tracker

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IEEE Transactions on Instrumentation and Measurement 2023年 72卷

作者： Song, Kai-Tai Li, Bing-Yi Ou, Szu-Chen National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering College of Electrical and Computer Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Graduate Degree Program of Robotics Hsinchu300093 Taiwan

This study proposes a data alignment design for sensor fusion of an optical target (OT) and an inertial measurement unit (IMU) to track the trajectory of a handheld tool for robot program.ing by demonstration (PbD). A novel method is proposed for coordinate and temporal alignment between the OT and the IMU sensor to increase tracking accuracy. The IMU measurements are aligned with the corresponding angular velocities and accelerations derived from the OT to calibrate the IMU-OT relationship. Global and local optimizations are combined to reduce the computation time for the temporal alignment and coordinate calibration. Experimental results show that the proposed method provides improved calibration results with shorter calculation time compared with the two existing methods. The sensor fusion experiments verify the accuracy of trajectory tracking of 1.3 mm using the calibration result, an improvement from 1.4 mm of the data alignment using ruler measurement. Integrated PbD experiments using a 6-DOF manipulator were carried out to evaluate the proposed method. The manipulator imitated the hand motion of a bottle transfer task with a trajectory error of 0.66 mm. Practical robotic experiments demonstrate that the method is suitable for robot program.ing applications. © 1963-2012 IEEE.

关键词： Calibration

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Real-time Vehicle Detection and Tracking on Fisheye Traffic Monitoring Video in Compressed Domain

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APSIPA Transactions on Signal and Information Processing 2023年第4期12卷

作者： Ardianto, Sandy Hang, Hsueh-Ming Cheng, Wen-Huang Electrical Engineering and Computer Science International Graduate Program National Yang Ming Chiao Tung University Hsinchu Taiwan Institute of Electronics National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Our goal is to develop real-time vehicle detection and tracking schemes for fisheye traffic monitoring video using the temporal information in the compressed domain without decoding the entire video. Two algorithms are proposed. The first algorithm starts with a conventional single-frame detector, but we introduce a multi-frame information fusion stage to improve the final detection and tracking accuracy, which is implemented using multi-modal bi-directional LSTM (MM bi-LSTM) network. The second algorithm first constructs multi-frame motion trail image, and then a single-image multi-head detector is designed to produce bounding boxes of an individual frame. The first scheme can be viewed as a detect-to-track design, and the second scheme is track-to-detect. We tested our proposals on the ICIP2020 VIP Cup dataset in H.265 video format. The aforementioned algorithms are applied to the motion fields and residual images in the H.265 compressed data set. It turns out that their detection and tracking performances are on par with their pixel-domain counterparts, and they can achieve the state-of-the-art accuracy of conventional video object detectors and trackers. If the decoding process for video compression is not counted, their computational complexities are much lower than the conventional pixel-domain video object detectors and trackers. © 2023 S. Ardianto, H.-M. Hang and W.-H. Cheng.

关键词： Image compression

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Joint Source Decomposition of Concurrent EEG-FMRI Data in Epilepsy and Control Groups 22

Joint Source Decomposition of Concurrent EEG-FMRI Data in Ep...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Harding, J. Mason Pourmotabbed, Haatef Li, Yamin Rogge-Obando, Kimberly Wang, Kate Goodale, Sarah E. Wang, Shiyu Bibro, Camden Allee, Bergen Martin, Caroline Morgan, Victoria L. Englot, Dario J. Chang, Catie Departments of Electrical and Computer Engineering Vanderbilt University United States Departments of Biomedical Engineering Vanderbilt University United States Departments of Computer Science Vanderbilt University United States Neuroscience Graduate Program Vanderbilt University United States Departments of Radiology Vanderbilt University Medical Center United States Departments of Neurological Surgery Vanderbilt University Medical Center United States Vanderbilt Memory and Alzheimer's Center Vanderbilt University Medical Center United States

ISBN: (纸本)9798331520526

EEG and fMRI are complementary, noninvasive technologies for investigating human brain function. These modalities have been used to uncover large-scale functional networks and their disruptions in clinical populations. Given the high temporal resolution of EEG and high spatial resolution of fMRI, integrating these modalities can provide a more holistic understanding of brain activity. This work explores a multimodal source decomposition technique for extracting shared modes of temporal variation between fMRI BOLD signals and EEG spectral power fluctuations in the resting state. The resulting components are then compared between patients with focal epilepsy and controls, revealing multimodal network differences between groups. © 2025 IEEE.

关键词： Brain mapping

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FDAM: Filter-Dedicated Approximate Multiplier Design for Real-Time CNN Acceleration

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Nguyen, Kihwan Kim Xuan Truong Jeon, Dongsuk Lee, Hyuk-Jae Korea Republic of program Department of Electrical Engineering Seoul08826 Korea Republic of Inter-University Semiconductor Research Center Seoul National University Graduate School of Convergence Science and Technology Research Institute for Convergence Science Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Seoul08826 Korea Republic of

Video super-resolution (VSR) is widely used in various high-definition applications, such as HDTVs and smartphones, requiring a dedicated upscaling technique for realtime full-HD generation. To reduce on-chip buffers for largesize output feature maps, a streaming VSR accelerator may employ an output stationary dataflow, leading to a large energy consumption caused by frequent filter switching. To mitigate this, we introduce a new filter-dedicated multiplier design for realtime VSR acceleration. We replace costly multipliers with adders, shifters, and multiplexers (MUXes), referred to as unified multiple constant multiplications (UMCM). The conventional UMCM, however, may incur considerable area/power overhead due to the unified topology constraint among different filter sets. To address this problem, we propose a new approximated MCM (AMCM) problem to relax the constraint and an approximate compatible graph synthesis (A-CGS) framework to efficiently solve AMCM by jointly searching for approximated filters and constructing a unified graph. Additionally, we suggest a lightweight finetuning method by freezing approximated filters and only finetuning biases, which can recover the original model’s accuracy within a few epochs. Experimental results with synthetic data demonstrate that AMCM reduces the area by up to 49.8%, 44.8%, and 40.3% when considering constant sets of 2, 4, and 8, respectively. Our designs with SR application achieve up to a 73.3% reduction in energy consumption. Experiments with the Set5 and Set14 datasets show that our model with bias correction achieves similar restoration performance compared to the eightbit models. © 1982-2012 IEEE.

关键词： Acceleration

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Innovative Portable Quantitative EEG Data Acquisition Based Cyton Biosensing Board using PSD Analysis

Innovative Portable Quantitative EEG Data Acquisition Based ...

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International Conference on Smart Computing & Communications (ICSCC)

作者： Melinda Melinda Prima Dewi Purnamasari Fahmi Fahmi Emerson Sinulingga Muliyadi Muliyadi Imam Ashabul Yamin Dept. electrical and computer engineering Universitas syiah kuala Banda Aceh Indonesia Dept. electrical engineering Universitas Indonesia Depok Indonesia Dept. electrical engineering Universitas sumatera utara Medan Indonesia Dept. electrical engineering (of Affiliation) Universitas sumatera utara Medan Indonesia Doctoral program in engineering dept. electrical engineering Universitas syiah kuala Banda Aceh Indonesia

ISBN: (数字)9798350363104

ISBN: (纸本)9798350363111

Electroencephalography (EEG) is a non-invasive brain imaging technique essential for neuroscience research and clinical applications. Conventional EEG devices are often expensive and impractical for use outside clinical or laboratory settings. This study reports the experience of acquiring EEG data using the Cyton board biosensing, an open-source and portable biosensor device. Detailed procedures for subject preparation, electrode placement, device configuration, and EEG data acquisition are provided. A Butterworth filter was used to eliminate noise from the EEG signals, with results visualized using the Fast Fourier Transform (FFT). Quantitative EEG (qEEG) analysis was conducted using the Power Spectral Density (PSD) method. The highest PSD values were 428.04 µV2/Hz at 5.9 Hz on channel C4 for subject 1,329.77 µV2/Hz at 5.9 Hz on channel C4 for subject 2, and 385.65 µ V2/Hz at 5.9 Hz on channel Fp1 for subject 3, all indicating increased brain activity in the theta wave range. This study provides insights for researchers and practitioners using the Cyton board for EEG data acquisition and contributes to developing portable, affordable EEG methods.

关键词： Electrodes Brain Sensitivity Fast Fourier transforms Data acquisition Noise Data visualization Information filters Electroencephalography Biosensors

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Twenty years of e-learning in health science: A bibliometric

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Procedia computer Science 2023年 216卷 604-612页

作者： Fairuz Iqbal Maulana M. Aldiki Febriantono Dwi Risza Budi Raharja Khaeruddin Ridho Herasmara Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia Diploma III Electronic Technology Vocational Study Program University of Muhammadiyah Malang Indonesia Electrical Engineering Faculty of Science and Technology Raden Rahmat Islamic University Malang Indonesia

Education about health sciences has historically been limited in the curriculum of health professionals and largely inaccessible to the public. In practice, most of the health science education is still running conventionally. Supposedly with the advancement of technology and the use of the internet everywhere, learning such as e-learning can be important, especially in the health sector. Until this research was conducted, only 514 academic documents about e-learning in health sciences were found for 20 years from 2001 to 2020, obtained in searching on the Scopus database. This study presents a comprehensive overview of studies related to E-learning in the Health Sciences sector. This study uses bibliometric analysis and indexed digital methods to map scientific publications throughout the world. This research employs the Scopus database to gather information, as well as the Scopus online analysis tool and Vosviewer to show the bibliometric network. The method consists with five stages: determining search keywords, initial search results, refinement of search results, initial compilation, and data analysis. Among the most published and indexed articles by Scopus, papers published by researchers in the United States have the highest number of publications (80), followed by United Kingdom (63) and Australia with 45 academic publications. The processed data shows the pattern and trend of increasing the number of international publications in E-learning in Health Sciences field, which Scopus index.

关键词： e-learning health science bibliometric vosviewer education

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An Adaptive TinyML Unsupervised Online Learning Algorithm for Driver Behavior Analysis

An Adaptive TinyML Unsupervised Online Learning Algorithm fo...

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Metrology for Automotive (MetroAutomotive), IEEE International Workshop on

作者： Marianne Silva Thais Medeiros Mariana Azevedo Morsinaldo Medeiros Mikael Themoteo Tatiane Gois Ivanovitch Silva Daniel G. Costa Postgraduate Program in Electrical and Computer Engineering (PPgEEC) Federal University of Rio Grande do Norte Natal Brazil INEGI Faculty of Engineering University of Porto Porto Portugal

The Internet of Things (IoT) has significantly impacted various industries, particularly the automotive sector, due to the growing integration of IoT technologies in vehicles. As a result, the volume of data generated by vehicular sensors has increased, leading to a surge in studies focusing on driver behavior to enhance road safety and optimize transportation networks. However, traditional approaches to analyzing driver behavior have relied on supervised offline learning models, which are unsuitable for handling data streams in online learning environments. This study introduces an unsupervised online k-fix AutoCloud algorithm for detecting driver behavior patterns, leveraging the concepts of typicity and eccentricity while considering the historical-temporal relationships between samples. Furthermore, the algorithm autonomously and adaptively evolves without requiring a supervised training phase, making it compatible with the TinyML concept, encompassing Artificial Intelligence algorithms designed for low-power IoT devices. To validate the proposed method, a real case study was conducted over four days using a vehicle to compare the quantity and quality of clusters generated by the algorithm. The findings demonstrate the potential of the proposed approach for optimizing data processing with minimal computational power.

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A Real-time Affordance-based Object Pose Estimation Approach for Robotic Grasp Pose Estimation

A Real-time Affordance-based Object Pose Estimation Approach...

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International Conference on System Science and engineering (ICSSE)

作者： Shang-Wen Wong Yu-Chen Chiu Chi-Yi Tsai Doctoral Program in Robotics College of Engineering Tamkang University New Taipei City Taiwan Department of Electrical and Computer Engineering Tamkang University New Taipei City Taiwan

This paper proposes a pose estimation system for robot grasping based on a novel Object Affordance Detection and Segmentation (OADS) network. The proposed system consists of four modules: (1) OADS network; (2) point cloud extraction; (3) object pose estimation; (4) grasp pose estimation. Based on the OADS network, the proposed system achieves affordance-based object pose estimation results. The proposed grasp pose estimation system is evaluated on a laboratory-made dual-arm robot. Experimental results show that the proposed system achieves high detection rate and high accuracy in affordance detection and segmentation tasks, leading to a high success rate in object grasping tasks with lab-made dual-arm robot.

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A Preliminary Study on the Collaborative Playing Piano Automatic with Deep Learning

A Preliminary Study on the Collaborative Playing Piano Autom...

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

作者： Varinya Phanichraksaphong Karuna Yampray Department of Electronic Engineering International Graduate Program of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan College of Creative Design and Entertainment Technology Dhurakij Pundit University Bangkok Thailand

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

A great number of deep learning-based models have been recently proposed for automatic piano classification. In this paper, we describe our contribution to the challenge of automatic piano classification when the performer performs at the concert or stage. Among these models in deep learning, we use init-1D-WaveNet and init-2D-MLNet for comparison the accuracy in the piano beginning level of the Christmas song (Jingle bells). Our experimental results show that the assessment using the init-2D-MLNet still achieve high accuracy of 87.5%.

关键词： Deep learning Accuracy Instruments Collaboration Consumer electronics

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