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2nd IEEE International Conference on Computational Intelligence and Knowledge Economy, ICCIKE 2021

作者： Alon, Helcy D. Ligayo, Michael Angelo D. Melegrito, Mark P. Franco Cunanan, Christopher Uy Ii, Edgar E. Technological Institute of the Philippines Graduate Program Manila Philippines Quezon City University Department of Electronics Engineering Quezon City Philippines Technological University of the Philippines Department of Electronics and Communications Engineering Manila Philippines Polytechnic University of the Philippines Department of Computer Engineering Philippines Bohol Island State University College of Engineering and Architecture Tagbilaran Philippines

ISBN: (纸本)9781665429214

Sign-Language is to help people with hearing or speaking disabilities who are not able to communicate well with other people. Communicating with deaf people is a challenge for some speakers and people who do not know sign language. What the study proposed is to help people with such disabilities using the American Sign-Language with the corresponding hand gesture. Deaf individuals will be able to communicate or interact with other people conveniently. The study proposed hand gesture or hand sign language detection trained by using the YOLOv3 algorithm that aims to detect hand gestures or hand sign language that can recognize its equivalent letter alphabet. The study tools such as LabelImg for annotating the data set, categorizing each image of hand gestures based on their equivalent letter alphabet. In this study, Model 18 with 95.1804% training accuracy, 90.8242% validation accuracy, and mAP of 0.8275 is used for the final testing. As video with different hand gestures is presented, the results of every hand gesture detected range over 90%. © 2021 IEEE.

关键词： Object detection

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Assessment of force control improvement induced by sinusoidal vibrotactile stimulation in dominant and non-dominant hands

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Research on Biomedical engineering 2021年第1期37卷 95-103页

作者： Germer, Carina Marconi Moreira, Luciana Sobral Elias, Leonardo Abdala Department of Electronics and Biomedical Engineering School of Electrical and Computer Engineering University of Campinas CampinasSP Brazil Neural Engineering Research Laboratory Center for Biomedical Engineering University of Campinas CampinasSP Brazil Cellular and Structural Biology Graduate Program Institute of Biology University of Campinas CampinasSP Brazil Faculty of Biological Sciences Center for Life Sciences Pontifical Catholic University of Campinas CampinasSP Brazil

Purpose: Vibrotactile stimulation has been shown to improve sensorimotor integration in the central nervous system, thereby enhancing motor performance in several conditions. Here we aim to address two questions: (1) Would lateral asymmetries yielding handedness influence the force control improvement induced by sinusoidal vibrotactile stimulation? (2) Does the force control improvement depend on finding an optimum stimulus intensity? Methods: Eleven participants performed a visually guided force-matching task, while different intensities of sinusoidal vibrations were applied to the index finger skin. Tasks consisted of abductions of the index fingers of dominant and non-dominant hands at 5% of their maximal voluntary contractions. The force coefficient of variation (CoV) in the condition without vibration was used as the reference (control) for later comparison with five vibration intensities. Also, force CoV in the control condition was compared with those obtained from the optimal vibration selected either from both hands or from a single hand (the one that started the experiment). Results: Despite the known sensory asymmetries in cortical and spinal circuits, handedness did not significantly influence the improvement of force control induced by sinusoidal vibrotactile stimulation during low-intensity contractions. Moreover, there was a significant reduction in force variability irrespective of the criteria used to select the vibration intensity. Conclusion: These findings add to the current knowledge on the effects of sinusoidal vibrotactile stimulation on force control and have potential implications for the development of wearable motor enhancer devices. © 2020, Sociedade Brasileira de Engenharia Biomedica.

关键词： Force control

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Hardware-based parallel firefly algorithm for embedded applications

Hardware-based parallel firefly algorithm for embedded appli...

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2013 NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2013

作者： Muñoz, Daniel M. Llanos, Carlos H. Dos Santos Coelho, Leandro Ayala-Rincón, Mauricio Electronics Engineering Graduate Program Faculty of Gama University of Brasilia Brasilia DF Brazil Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia DF Brazil Industrial and Systems Engineering Graduate Program LAS/PPGEPS Pontifical Catholic University of Parana Curitiba Parana Brazil Departments of Computer Science and Mathematics University of Brasilia Brasilia DF Brazil

ISBN: (纸本)9781467363839

The firefly algorithm (FA) is a new population-based metaheuristic bioinspired on the behavior of the flashing characteristics of fireflies. As a population-based algorithm, the FA suffers from large execution times specifically for embedded optimization problems with computational limitations. For reducing execution times we propose a hardware parallel architecture of the FA algorithm that facilitates the implementation in Field programmable Gate Arrays (FPGAs). In addition, this work proposes the application of the opposition-based learning (OBL) approach to the FA algorithm. The respective hardware implementation (HPOFA) was mapped into a Virtex5 FPGA device and numerical experiments using four well-known benchmark problems demonstrate that the opposition-based approach allows the FA algorithm to improve its functionality, preserving the swarm diversity and avoiding the premature convergence problem. Synthesis results point out that the HPOFA architecture is effectively mapped in hardware and is suitable for embedded applications. © 2013 IEEE.

关键词： Parallel architectures

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Interval state estimation based on constraint propagation for a lithium-ion cell using an equivalent circuit model ⁎

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IFAC-PapersOnLine 2021年第3期54卷 602-608页

作者： D. Locatelli B.S. Rego G.V. Raffo D.M. Raimondo Dept. of Electrical Computer and Biomedical Engineering University of Pavia Italy Graduate Program in Electrical Engineering Dept. of Electronics Engineering Federal University of Minas Gerais Brazil

Battery management systems (BMSs) are responsible for controlling and monitoring battery operations. In order to be effective, BMSs rely on mathematical models. The parameters of these latter are usually obtained through an identification procedure which provides limited accuracy. In addition, only cell current and voltage are usually measurable. For this reason, the model states need to be estimated. In the following, we propose an interval state estimation of a lithium-ion cell when using an equivalent circuit model, taking into account bounded parametric uncertainty and measurement noise. Simulation results show the suitability of the proposed interval estimator to provide a tight enclosure of the states, which is essential for fault detection and model-based control of a lithium-ion cell.

关键词： Lithium-ion batteries battery management system state of charge estimation interval-based nonlinear state estimation

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Planning Optimization Platform for Cluster Type Micro-grid Installations and Operations

Planning Optimization Platform for Cluster Type Micro-grid I...

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2019 IEEE Sustainable Power and Energy Conference, iSPEC 2019

作者： Iwamura, Kazuaki Nakanishi, Yosuke Lewlomphaisarl, Udom Estoperez, Noel Lomi, Abraham Waseda University Graduate School of Environment and Energy Engineering Tokyo Japan National Electronics and Computer Technology Center Advanced Automation and Electronics Research Unit Bangkok Thailand Mindanao State University Illigan Institute of Technology Iligan City Philippines National Institute of Technology Renewable Energy Research Center and Graduate Program Malang Malang Indonesia

ISBN: (纸本)9781728149301

This paper proposes an architecture for a planning optimization platform called the Grid of Grids Optimal Designer (GGOD) to facilitate the optimization of cluster type micro-grid installation and operation. Micro-grid clusters are groups of micro-grids with renewable energy that operate autonomously without power supply from main grids. The GGOD simulates micro-grid optimizations based on a service-oriented architecture and comprises three layers, i.e., end user, service broker, and service supply layers. The enduser layer corresponds to the client system, and the GGOD corresponds to the service broker and service supply layers. The service broker layer is for service management, and the service supply layer comprises various optimization components. Combinable optimization components are activated based on request text in extensible markup language format from end users. By combining optimization components, different optimizations are executed for a single request, the results of the same type of optimization methods are compared, and sudden operation environment changes are considered. Geospatial integrated resource planning and micro-grid facility planning are demonstrated as examples of GGOD applications. © 2019 IEEE.

关键词： Simulation platform

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Nano functional neural interfaces

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Nano Research 2018年第10期11卷 5065-5106页

作者： Yongchen Wang Hanlin Zhu Huiran Yang Aaron D. Argall Lan Luan Chong Xie Liang Guo Department of Biomedical Engineering The Ohio State University Columbus 43210 USA Department of Biomedical Engineering The University of Texas at Austin Austin 78712 USA Department of Electrical and Computer Engineering The Ohio State University Columbus 43210 USA Key Laboratory of Flexible Electronics and Institute of Advanced Materials Jiangsu National Synergetic Innovation Center for Advanced Materials Nanjing Tech University Nanjing 211816 China Biomedical Sciences Graduate Program The Ohio State University Columbus 43210 USA Department of Neuroscience The Ohio State University Columbus 43210 USA

Engineered functional neural interfaces （fNIs） serve as essential abiotic-biotic transducers between an engineered system and the nervous system. They convert external physical stimuli to cellular signals in stimulation mode or read out biological processes in recording mode. Information can be exchanged using electricity, light, magnetic fields, mechanical forces, heat, or chemical signals. fNIs have found applications for studying processes in neural circuits from cell cultures to organs to whole organisms, fNI-facilitated signal transduction schemes, coupled with easily manipulable and observable external physical signals, have attracted considerable attention in recent years. This enticing field is rapidly evolving toward miniaturization and biomimicry to achieve long-term interface stability with great signal transduction efficiency. Not only has a new generation of neuroelectrodes been invented, but the use of advanced fNIs that explore other physical modalities of neuromodulation and recording has begun to increase. This review covers these exciting developments and applications of fNIs that rely on nanoelectrodes, nanotransducers, or bionanotransducers to establish an interface with the nervous system. These nano fNIs are promising in offering a high spatial resolution, high target specificity, and high communication bandwidth by allowing for a high density and count of signal channels with minimum material volume and area to dramatically improve the chronic integration of the fNI to the target neural tissue. Such demanding advances in nano fNIs will greatly facilitate new opportunities not only for studying basic neuroscience but also for diagnosing and treating various neurological diseases.

关键词： neural interface neurotechnology nanoelectrode nanomaterial neural recording neural stimulation

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Clustering Analysis of a Spatiotemporal Dataset with a Novel Kernel Density Estimator

Clustering Analysis of a Spatiotemporal Dataset with a Novel...

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2023 International Conference on Machine Learning and Cybernetics, ICMLC 2023

作者： Yu, Jen-Chien Yang, Chun-Chieh Gilbert, John Reuben Liu, Rou-Jun Oyang, Yen-Jen Yang, Meng-Han National Kaohsiung University of Science and Technology Department of Computer Science and Information Engineering Kaohsiung City807618 Taiwan National Taiwan University Master Program in Statistics Taipei106216 Taiwan National Taiwan University Department of Computer Science & Information Engineering Taiwan Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taiwan

ISBN: (纸本)9798350303780

A vast number of spatiotemporal datasets collected from a wide range of sources has motivated scientists to develop effective approaches to identify interesting patterns hidden in these datasets. In this respect, kernel density estimators, which belong to a class of non-parametric estimators in statistics, have been widely exploited in recent years. With this background, we have developed a novel kernel density estimator aiming to provide accurate analysis results. According to the evaluation with a real spatiotemporal dataset, which collected emergency medical service records in a county in the United States, the proposed kernel density estimator can approximate the probability density function significantly more accurately than a conventional kernel density estimator. Furthermore, we have exploited the proposed kernel density estimator to identify interesting patterns hidden in the real spatiotemporal dataset. © 2023 IEEE.

关键词： Probability density function

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Eye-Zheimer: A Deep Transfer Learning Approach of Dementia Detection and Classification from NeuroImaging 7

Eye-Zheimer: A Deep Transfer Learning Approach of Dementia D...

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7th IEEE International Conference on engineering Technologies and Applied Sciences, ICETAS 2020

作者： Alon, Helcy D. Ligayo, Michael Angelo D. Misola, Maribel A. Sandoval, Allan A. Fontanilla, Marites V. Technological Institute of the Philippines Graduate Program Manila Philippines Quezon City University Department of Electronics Engineering Quezon City Philippines FEU Institute of Technology Department of Computer Engineering Manila Philippines St. Paul University Department of Information Technology Quezon City Philippines

ISBN: (纸本)9780738105048

Dementia is a common term for memory loss, speech, problem-solving, and other cognitive skills that are serious enough to interfere with everyday life, and Alzheimer's is the leading cause of dementia. Alzheimer's disease is presumed to develop 20 years or more before symptoms occur, with degenerative changes that are unapparent to the person affected. The deep learning approach for early detection and Alzheimer's disease classification has recently gained significant attention. This study proposed disease detection trained by utilizing the YOLO v3 algorithm that aims to detect Alzheimer's disease based solely on Magnetic Resonance Imaging (MRI). Pascal VOC format and LabelImg tool are used for annotating the datasets, categorizing the image as non-demented and mild-demented. Model 4 was used in the system having 98.617% training accuracy, 98.8207% validation accuracy, and mAP of 96.17%. To test the accuracy of the used model, images of MRI scans are presented and it recorded 80% testing accuracy. © 2020 IEEE.

关键词： Object detection

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Comparison between wire and wireless EEG acquisition systems based on SSVEP in an Independent-BCI

Comparison between wire and wireless EEG acquisition systems...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Richard M. G. Tello Sandra M. T. Müller Teodiano Bastos-Filho Andre Ferreira Post-Graduate Program of Electrical Engineering (PPGEE). UFES Vitoria Brazil Department of Computer and Electronics (CEUNES) Brazil

This paper presents a comparison between two different technologies of acquisition systems (BrainNet36 and Emotiv Epoc) for an Independent-BCI based on Steady-State Visual Evoked Potential (SSVEP). Two stimuli separat... 详细信息

关键词： Visualization Electrodes Indexes Accuracy Brain-computer interfaces Synchronization Feature extraction

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First Demonstration of HZO-LNOI Integrated Ferroelectric Electro-Optic Modulator and Memory to Enable Reconfigurable Photonic Systems

First Demonstration of HZO-LNOI Integrated Ferroelectric Ele...

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2023 International Electron Devices Meeting, IEDM 2023

作者： Xu, Zefeng Chen, Chun-Kuei Lin, Hong-Lin Gao, Yuan Ke, Wei Xu, Baochang Dmitriev, Pavel Arbiz, Carlan Zamburg, Evgeny Touzard, Steven Cai, Xinlun Lee, James Venkatesan, Suresh Danner, Aaron Thean, Aaron Voon-Yew Department of Electrical and Computer Engineering Singapore Nus Graduate School Integrative Sciences and Engineering Program Singapore 117583 Singapore School of Electronics and Information Technology 117583 Singapore Centre for Quantum Technologies Nus Singapore Poet Technologies Canada Sun Yat-Sen University 510275 China

ISBN: (纸本)9798350327670

We have successfully demonstrated, for the first time, an innovative back-end-of-line (BEOL) compatible electro-optic modulator and memory (EOMM) based on Lithium Niobate on Insulator (LNOI) micro-ring resonator (MRR) integrated with Ferroelectric Hafnium Zirconate Hf0.5Zr0.5O (HZO) non-volatile analog memory. High non-volatile memory and modulation performances are both achieved in a single compact device, exhibiting high extinction ratio of 13.3 dB, excellent efficiency of 66pm/V, stable nine-state switching, record-high endurance exceeding 109 cycles. This is accomplished by utilizing Pockels effect in LNOI, induced by electric-field effect from remnant HZO ferroelectric polarization. We studied the system implementation of reconfigurable chiplet-interposer photonic interconnect, enabled by the EOMM and EOMM with hybrid thermal-optical modulation. Our model shows a potential 70% energy efficiency improvement over conventional electrical interposer interconnect. We have also tested the integration of the EOMM with POET technologies' 400G Tx/Rx optical interposer chip and studied a limited scale demonstration of the EOMM device. © 2023 IEEE.

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