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Analysis of Brain Excitability After Transcranial Direct Current Stimulation and Brain-computer Interface Based on Motor Imagery on a Post-stroke Patient 9th

Analysis of Brain Excitability After Transcranial Direct Cu...

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9th Latin American Congress on biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on biomedical engineering, CBEB 2022

作者： Silva, Leticia Lima, Jéssica Schreider, Sheila Delisle-Rodriguez, Denis Krishnan, Sridhar Bastos-Filho, Teodiano Postgraduate Program in Electrical Engineering Universidade Federal do Espirito Santo Vitoria Brazil Postgraduate Program in Biotechnology Universidade Federal do Espirito Santo Vitoria Brazil Edmond and Lily Safra International Institute of Neurosciences Instituto Santos Dumont Macaiba Brazil Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada

ISBN: (纸本)9783031494062

Stroke is a neurological syndrome that may cause severe cognitive and motor impairments for survival. Alternative rehabilitation techniques have been developed to recover lower-limb movements and gait of post-stroke patients, such as Motor Imagery (MI)-based Brain-computer Interfaces (BCIs), Virtual Reality (VR), and robotic devices. Transcranial Direct Current Stimulation (tDCS) is a non-invasive technique to stimulate the brain and modulate cortical excitability. This study aims to analyze the short-term effects of tDCS compared to sham stimulation before a lower-limb MI-BCI pedaling training (through a robotic monocycle) task in a patient with subacute stroke. We studied a post-stroke patient with 2 months of ischemic stroke and right hemiparesis who was submitted to the following tasks: active pedaling, sham tDCS and MI-BCI, and tDCS and MI-BCI. We evaluated the relative power around the foot area (Cz electrode), and a significant (p-value © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Brain computer interface

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Digitally-defined ultrathin transparent wireless sensor network for room-scale imperceptible ambient intelligence

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npj Flexible Electronics 2024年第1期8卷 869-878页

作者： Yunxia Jin Mengxia Yu Dat T.Nguyen Xin Yang Zhipeng Li Ze Xiong Chenhui Li Yuxin Liu Yong Lin Kong John S.Ho Institute for Health Innovation and Technology National University of SingaporeSingapore 117599Singapore Department of Biomedical Engineering National University of SingaporeSingapore 119276Singapore Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore Integrative Sciences and Engineering Programme National University of Singapore Graduate SchoolSingapore 119077Singapore The N.1 Institute for Health National University of SingaporeSingapore 117456Singapore Institute of Materials Research and Engineering Agency for Science Technology and ResearchSingapore 138634Singapore Department of Mechanical Engineering University of UtahSalt Lake CityUT 84112USA

Wireless and battery-free radio-frequency(RF)sensors can be used to create physical spaces that ambiently sense and respond to human *** such sensors ultra-flexible and transparent is important to preserve the aesthetics of living environments,accommodate daily activities,and functionally integrate with ***,existing RF sensors are unable to simultaneously achieve high transparency,flexibility,and the electrical conductivity required for remote room-scale ***,we report 4.5μm RF tag sensors achieving transparency exceeding 90%that provide capabilities in room-scale ambient wireless *** develop a laser-assisted water-based adhesion-reversion process to digitally realize computer-aided RF design at *** individually tagging multiple objects and regions of the human body,we demonstrate multiplexed wireless tracking of humanenvironment interactions and physiological signals at a range of up to *** radio-frequency identification sensors open opportunities for non-intrusive wireless sensing of daily living spaces for applications in health monitoring and elderly care.

关键词： network ambient transparent

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AV Navigation in 3D Urban Environments with Curriculum-based Deep Reinforcement Learning

AV Navigation in 3D Urban Environments with Curriculum-based...

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2023 International Conference on Unmanned Aircraft Systems, ICUAS 2023

作者： Braathen De Carvalho, Kevin De Oliveira, Iure Rosa L. Brandao, Alexandre S. Universidade Federal de Viçosa Nucleus of Specialization in Robotics Department of Electrical Engineering Graduate Program in Computer Science MG Viçosa36570-900 Brazil Federal University of Viçosa Department of Electrical Engineering MG Viçosa Brazil

ISBN: (纸本)9798350310375

Unmanned Aerial Vehicles (UAVs) are widely used in various applications, from inspection and surveillance to transportation and delivery. Navigating UAVs in complex 3D environments is a challenging task that requires robust and efficient decision-making algorithms. This paper presents a novel approach to UAV navigation in 3D environments using a Curriculum-based Deep Reinforcement Learning (DRL) approach. The proposed method utilizes a deep neural network to model the UAV's decision-making process and to learn a mapping from the state space to the action space. The learning process is guided by a reinforcement signal that reflects the performance of the UAV in terms of reaching its target while avoiding obstacles and with energy efficiency. Simulation results show that the proposed method has a positive trade off when compared to the baseline algorithm. The proposed method was able to perform well in environments with a state space size of 22 millions, allowing the usage in big environments or in maps with high resolution. The results demonstrate the potential of DRL for enabling UAVs to operate effectively in complex environments. © 2023 IEEE.

关键词： Reinforcement learning

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Exploring Shifted Domain Problem within MNIST Dataset

Exploring Shifted Domain Problem within MNIST Dataset

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2023 International Conference on computer Science, Information Technology and engineering, ICCoSITE 2023

作者： Hidayaturrahman Heryadi, Yaya Lukas Suparta, Wayan Arifin, Yulyani Bina Nusatara University BINUS Graduate Program - Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350320954

Domain data can be shifted in any direction so it will be shared in different distributions to its original domain. This could be a problem since the model was trained with different distributions. It is found that adversarial domain adaptation using domain adversarial neural networks (DANN) can help to solve this problem on some scale. DANN can minimize the discrepancy between source and target data so the model can work well in both domains. The experiment is done by utilizing MNIST dataset that shifted into some conditions. In a condition when the shifting of distribution is too far, DANN is struggling to maintain the knowledge extracted from source data which leads to underperformance in the source and target domain. In contrast, when the shifting is closer, DANN can easily fit the model so it can perform well in both domains. It proves DANN is one of the good approaches to performing domain adaptation in small discrepancies. © 2023 IEEE.

关键词： Adversarial Domain Adaptation Domain Shifting Problem MNIST Transfer Learning

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Investigating the Impact of Data Augmentation for Fine-Grained Grocery Product Classification Based on Vision Transformer

Investigating the Impact of Data Augmentation for Fine-Grain...

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2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing, AIRoSIP 2023

作者： Rahmania, Rissa Heryadi, Yaya Lukas, Lukas Suparta, Wayan Sonata, Ilvico Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia Faculty of Engineering Jakarta12930 Indonesia Institut Teknologi Nasional Yogyakarta Faculty of Industrial Technology Department of Electrical Engineering Yogyakarta55281 Indonesia

ISBN: (纸本)9798350327472

The existence of fine-grained image classification supporting smart retail provides effectiveness in recognizing products with high similarity. However, the generic classification method performs poorly in identifying products from a subordinate category. This paper aims to identify augmentation techniques to leverage the Vision Transformer (ViT) model to classify the fine-grained grocery product, which involves embedding patches and transformer encoders to extract the main features. First, we develop a fine-grained image dataset with ColorJitter, CutOut, and combining both augmentations. Secondly, we perform experiments and analysis of ViT size, embedded patch size and image size in the patch embedding process. Lastly, the ViT model are evaluated according to the image sizes 224, 384, and 512 in accuracy, loss, and confusion matrix. The highest accuracy was obtained at 0.9922. The ColorJitter and CutOut improved the confusion matrix in ViT-B/16 and ViT-L/16 with an image size of 384 and 512. The results show that both augmentations in the ViT model are able to distinguish fine-grained grocery products. © 2023 IEEE.

关键词： Embeddings

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Dual Dynamic Consistency Regularization for Semi-Supervised Domain Adaptation

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IEEE Access 2024年 12卷 36267-36279页

作者： Ngo, Ba Hung Lam, Ba Thinh Nguyen, Thanh Huy Dinh, Quang Vinh Choi, Tae Jong Chonnam National University Graduate School of Data Science Gwangju61186 Korea Republic of University of Science Faculty of Information Technology Ho Chi Minh City700000 Viet Nam National Cheng Kung University Department of Biomedical Engineering Tainan70101 Taiwan Vietnamese-German University School of Electrical Engineering and Computer Science Ho Chi Minh City72000 Viet Nam

The Vision Transformer (ViT) model serves as a powerful model to capture and comprehend global information, particularly when trained on extensive datasets. Conversely, the Convolutional Neural Network (CNN) model is beneficial to training with small datasets for retaining essential local information. Inspired by these properties of ViT and CNN models, we introduce a hybrid framework that smoothly increases the cross-domain generalization in Semi-supervised Domain Adaptation (SSDA). To achieve this goal, we first train the ViT model on abundant labeled source data, while the CNN model is trained on a few labeled target samples. Then, these models dynamically exchange their knowledge for potential generalization to unlabeled target data via the proposed method, named Dual Dynamic Consistency Regularization (D2CR). Specifically, the ViT model provides its pseudo labels to update the global perspective for the CNN model. Similarly, the CNN model offers pseudo labels to complement the local perspective for the ViT model. The previous methods use a fixed threshold algorithm for the pseudo-labeling process. However, we utilize the dynamic threshold strategy to create pseudo labels for the bi-directional consistency regularization learning between the ViT and CNN models. We verify our approach across several SSDA benchmark datasets. The outstanding experimental results provide strong evidence of the effectiveness and superiority of our approach over previous state-of-the-art SSDA methods. © 2013 IEEE.

关键词： Neural network models

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electrical Characteristic and Power Fluctuations of GAA Si NS CFETs by Simultaneously Considering Six Process Variation Factors

IEEE Open Journal of Nanotechnology

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IEEE Open Journal of Nanotechnology 2023年 4卷 229-238页

作者： Kola, Sekhar Reddy Li, Yiming National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Parallel and Scientific Computing Laboratory National Yang Ming Chiao Tung University Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Institute of Biomedical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

Characteristic variability induced by process variation effect (PVE) is one of technological challenges in semiconductor industry. In this work, we computationally study electrical characteristic and power fluctuations induced by six factors of PVE of the gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field-effect-transistors (CFETs) which are formed by vertically stacking n-FET on top of p-FET. Among the six factors, NS thickness (TNS), NS width (WNS), and gate length (LG) are identified as crucial factors contributing to large variations in device characteristics. The p-FET exhibits substantial off-state current fluctuation (about 151%) due to the bottom parasitic channel leakages. Compared with the magnitudes of dynamic and short circuit powers, the static power is marginal, but it possesses the largest fluctuation (up to 148%). If we assume that each factor of PVE has the same probability distribution as the others and all are mutually independent, the statistical sum of their power fluctuations will exhibit more than 50% overestimations, compared with the results when all factors are considered simultaneously. © 2020 IEEE.

关键词： III-V semiconductors

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Parameters Determination for Ill-Defined Edge Detection Using Particle Swarm Optimization

Parameters Determination for Ill-Defined Edge Detection Usin...

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2023 International Technical Conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Panwong, Pannawit Auephanwiriyakul, Sansanee Theera-Umpon, Nipon Chiang Mai University Faculty of Engineering Computer Engineering Department Chiang Mai Thailand Graduate School Chiang Mai University Chiang Mai Thailand Biomedical Engineering Institute Chiang Mai University Chiang Mai Thailand Electrical Engineering Department Faculty of Engineering Chiang Mai University Chiang Mai Thailand

ISBN: (纸本)9798350326413

Segmentation process is one of the preprocess steps in object detection. To achieve segmentation, edge detection is a possible choice. However, if there is noise in image, edge might be ill-defined. Our algorithm for ill-defined edge detection is enhanced in this paper. In particular, we utilized the particle swarm optimization (PSO) in finding optimal parameters in the algorithm, rather than using manual settings as in the original work. There are two data sets, i.e., synthetic and carpal bone data sets, used in the experiment. We found that the intersection over union (IOU) on the blind test data set of the synthetic data set is 0.9200\pm 0.0144. The result on the blind test carpal bone data set is 0.9228\pm 0.0592. For the carpal bone data set, we compare the result with that from the original algorithm. The result shows that the enhanced method performs better than the original one. However, there is still a problem in misleading edge direction because of gradient and edge map generation. © 2023 IEEE.

关键词： Swarm intelligence

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Transferrin-Mediated Nanophotosensitizer to Enhance Phototherapy with Photoacoustic Imaging Under Hypoxia

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CCS Chemistry 2025年第4期7卷 1127-1141页

作者： Mengyao Yang Yunyoung Nah Donghyeon Oh Xingshu Li Heejeong Kim Sinyoung Park Junha Lim Aibing Chen Chulhong Kim Won Jong Kim Juyoung Yoon College of Chemical and Pharmaceutical Engineering Hebei University of Science and TechnologyShijiazhuang 050018 Department of Chemistry and Nanoscience Ewha Womans UniversitySeoul 03760 Department of Chemistry POSTECH-Catholic Biomedical Engineering InstitutePOSTECHPohang 37673 Department of Electrical Engineering Convergence IT EngineeringMechanical Engineeringand Medical Science and EngineeringMedical Device Innovation CenterPOSTECHPohang 37673 Fujian Provincial Key Laboratory of Cancer Metastasis Chemoprevention and Chemotherapy College of ChemistryFuzhou UniversityFuzhou 350108 School of Interdisciplinary Bioscience and Bioengineering POSTECHPohang 37673 Graduate Program in Innovative Biomaterials Convergence Ewha Womans UniversitySeoul 03760

Various obstacles[poor tissue penetration,hypoxic tumor microenvironment,and reactive oxygen species(ROS)production quenching due to aggregation]obstruct imaging-guided high-efficiency phototherapy targeting ***,nanophthalocyanine(ZnPcN4-TF)is elaborately designed based on a synthetic phthalocyanine derivative(ZnPcN4)and nonimmunogenic transferrin(TF)through multiple noncovalent interactions for photoacoustic(PA)imaging-guided *** conjugating electron-rich amino groups effectively suppressed fluorescence and ROS generation due to the strong photoinduced electron transfer effect,ZnPcN4 became an ideal photothermal and PA imaging ***4-TF not only effectively targeted tumor sites and accumulated there,but also,surprisingly,facilitated the enhancement of ROS production via a type I process in an aggregate compared to ZnPcN4 itself,potentially due to accelerated electron ***,ZnPcN4-TF had a substantially more powerful photothermal effect than ZnPcN4 ***,this design effectively overcomes the obstacles to photodynamic therapy(aggregation-related ROS generation quenching in a hypoxic environment).Furthermore,PA imaging solves the tissue penetration challenge in optical *** study provides a broader base for designing novel photosensitizers to improve phototherapy under hypoxia.

关键词： photoacoustic imaging type I reactive oxygen species photothermal effect tumor accumulation self-assembly

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Super-Mossian Dielectrics for Nanophotonics

Super-Mossian Dielectrics for Nanophotonics

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Wang, Xielin Doiron, Chloe F. Li, Zhichao Khurgin, Jacob B. Naik, Gururaj V. Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States

Searching for high-index dielectrics, we identify materials that break the index upper bound set by Moss' rule. We highlight the promise of such super-Mossian materials by demonstrating nanophotonic devices made o... 详细信息

ISBN: (纸本)9781957171258

关键词： Layered semiconductors

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