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IEEE International Conference on Micro Electro Mechanical Systems

作者： Anwar Elhadad Yang Gao Seokheun Choi Department of Electrical & Computer Engineering Bioelectronics & Microsystems Laboratory State University of New York at Binghamton New York Binghamton USA Center for Research in Advanced Sensing Technologies & Environmental Sustainability State University of New York at Binghamton New York Binghamton USA

This report presents a ‘Gastrobot’ engineered for extended self-sufficient operation in water bodies. It features microbial fuel cells (MFCs) mimicking a digestive system to convert biochemical into mechanical energy, achieving 135μW/cm power density for autonomous propulsion. The MFCs use hydrophobic/hydrophilic interfaces for nutrient intake and incorporate Bacillus subtilis spores, which germinate in nutrient-rich conditions, fueling the system and enabling motion. A central motor activates for movement, highlighting the robot’s potential as a smart sensor node within an ‘Ocean of Things’ network, advancing global aquatic surveillance and connectivity.

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Paper-Based Moist-Electric Generators for Scalable, Disposable, and Green Power Generation

Paper-Based Moist-Electric Generators for Scalable, Disposab...

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Yang Gao Seokheun Choi Department of Electrical & Computer Engineering Bioelectronics & Microsystems Laboratory State University of New York at Binghamton Binghamton New York USA Center for Research in Advanced Sensing Technologies & Environmental Sustainability State University of New York at Binghamton Binghamton New York USA

Our research presents a novel paper-based moist-electric generator (PMEG) for the Internet of Disposable Things (IoDT) that operates by harnessing ambient moisture. This disposable, eco-friendly power source is crafted from micropatternable hydrophilic paper, whose hygroscopicity is significantly enhanced with a blend of bacterial endospores and polyvinylpyrrolidone (PVP), a superhygroscopic polymer. The PMEG demonstrates remarkable electrical performance, producing a steady voltage of approximately 0.42 V at 90% relative humidity, with power densities reaching 2.3 μW/cm 2 . Remarkably, it sustains a voltage of 0.2 V for over 60 hours even at a lower humidity of 40%. The integration of robust endospores, capable of swelling by up to 12% and withstanding environmental stresses, positions our PMEG as a sustainable and durable solution for powering IoDT devices.

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Microalgae-Enabled Artificial Plants for Indoor Air Quality Improvement and Electricity Generation

Microalgae-Enabled Artificial Plants for Indoor Air Quality ...

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Maryam Rezaie Seokheun Choi Department of Electrical & Computer Engineering Bioelectronics & Microsystems Laboratory State University of New York at Binghamton Binghamton New York USA Center for Research in Advanced Sensing Technologies & Environmental Sustainability State University of New York at Binghamton Binghamton New York USA

This study introduces a bioengineered artificial plant featuring microalgae within artificial leaves connected via microfluidic channels to a plant stem. The microalgae harness solar energy to transform carbon dioxide (CO 2 ) and water into oxygen (O 2 ), thereby enhancing indoor air quality and releasing electrons during photosynthesis. These electrons provide energy for low-power devices through a biosolar cell with a microalgae-included anode, a cathode, and an ion exchange membrane. Encased in a gas-permeable PDMS membrane, the artificial leaves facilitate gas exchange while microporous hydrogels, enriched with Fe 2 O 3 nanoparticles, sustain the microalgae with water and nutrients, optimize CO 2 capture, and facilitate extracellular electron transfer. A single leaf with five biosolar cells notably decreases indoor CO 2 (from 416 ppm to 363 ppm) and increases O 2 (from 13.2 ppm to 19.1 ppm), generating a peak power density of 46 μW/cm 2 . Integrating five leaves magnifies the overall CO 2 reduction, O 2 output, and power generation.

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Integrated Papertronics for a Sustainable Future

Integrated Papertronics for a Sustainable Future

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Zahra Rafiee Anwar Elhadad Seokheun Choi Department of Electrical & Computer Engineering Bioelectronics & Microsystems Laboratory State University of New York at Binghamton Binghamton New York USA Center for Research in Advanced Sensing Technologies & Environmental Sustainability State University of New York at Binghamton Binghamton New York USA

The escalating issue of electronic waste, exacerbated by frequent updates in consumer technology, presents a critical environmental challenge. Conventional electronics, particularly disposable devices, are often not sustainable. Paper-based alternatives, with their favorable mechanical and dielectric properties, offer a sustainable solution. Prior attempts at integrating paper with standard electronic components have fallen short in terms of recyclability and practical deployment. This study introduces an innovative approach for fabricating fundamental electronic components—resistors, capacitors, and transistors— directly onto paper substrates. Employing techniques such as hydrophobic wax patterning, precision ink deposition, and screen-printing, we have successfully created versatile, fully paper-based electronic components. This breakthrough facilitates the development of wholly paper-integrated circuits, representing a leap forward in environmentally sustainable electronics.

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Bacterial Endospore Based Wearable Biosensors for Selective and Sensitive Glucose Monitoring

Bacterial Endospore Based Wearable Biosensors for Selective ...

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Yang Gao Anwar Elhadad Seokheun Choi Department of Electrical & Computer Engineering Bioelectronics & Microsystems Laboratory State University of New York at Binghamton Binghamton New York USA Center for Research in Advanced Sensing Technologies & Environmental Sustainability State University of New York at Binghamton Binghamton New York USA

We present a groundbreaking non-invasive, wearable glucose biosensor offering long-term stability, high selectivity, and biocompatibility at low cost. Utilizing bacterial endospores as novel bioreceptors, our sensor exhibits unprecedented robustness, maintaining viability for extended periods without optimized storage conditions. Upon contact with glucose on human skin, these endospores germinate, initiating a microbial process that converts glucose into electrical signals directly correlating with glucose levels. This biosensor discriminates glucose effectively amidst other skin-emitted substances, ensuring precise monitoring. The electrical signals are converted by an integrated circuit, providing a visual LED alert at specific glucose thresholds.

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Soliton Vortex Comb Generation in an AlGaAs Microresonator for Self-Torque Pulse Synthesis

Soliton Vortex Comb Generation in an AlGaAs Microresonator f...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Liu, Y. Chen, B. Zhou, Y. Ye, C. Cao, Q. Huang, P. Kim, C. Zheng, Y. Oxenløwe, L.K. Yvind, K. Li, J. Li, J. Zhang, Y. Dong, C. Fu, S. Zhan, Q. Wang, X. Liu, J. Pu, M. DTU Electro Department of Electrical and Photonics Engineering Technical University of Denmark Kgs. LyngbyDK-2800 Denmark State Key Laboratory of Optoelectronic Materials and Technologies School of Physics School of Electronics and Information Technology Sun Yat-sen University Guangzhou China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China Zhangjiang Laboratory Shanghai China CAS Key Laboratory of Quantum Information University of Science and Technology of China Hefei China Institute of Advanced Photonics Technology School of Information Engineering Guangdong University of Technology Guangzhou China Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen China

We demonstrate bright soliton generation at room temperature within a grating-dressed AlGaAs-on-insulator microresonator and the simultaneous emission of phase-locked vortices, enabling the synthesis of self-torque pu... 详细信息

关键词： Microresonators

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Soliton Vortex Comb Generation in an AlGaAs Microresonator for Self-Torque Pulse Synthesis

Soliton Vortex Comb Generation in an AlGaAs Microresonator f...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Microcavities

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Evaluation of the Performance of Wearables’ Inertial Sensors for the Diagnosis of Resting Tremor in Parkinson’s Disease

Evaluation of the Performance of Wearables’ Inertial Sensor...

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17th International Joint Conference on Biomedical engineering Systems and technologies, BIOSTEC 2024

作者： Polvorinos-Fernández, Carlos Sigcha, Luis de Pablo, Laura Pereira Borzí, Luigi Cardoso, Paulo Costa, Nelson Costa, Susana López, Juan Manuel de Arcas, Guillermo Pavón, Ignacio Mechanical Engineering Department ETSI Industriales Universidad Politécnica de Madrid Spain Group University of Limerick LimerickV94 T9PX Ireland ALGORITMI Research Center School of Engineering University of Minho Guimarães Portugal PolitoBIOMed Lab–Biomedical Engineering Lab Politecnico di Torino Turin10129 Italy ANTHEA Lab–Data Analytics and Technologies for Health Lab Department of Control and Computer Engineering Politecnico di Torino Turin10129 Italy Physical Electronics Electrical Engineering and Applied Physics Department ETS de Ingeniería y Sistemas de Telecomunicación Universidad Politécnica de Madrid Spain

Currently, objective monitoring of resting tremor in Parkinson’s disease (PD) involves wearable devices and machine learning. Smartwatches may present an affordable method for remote and unintrusive tremor monitoring. However, the development of optimized systems is necessary to perform accurate monitoring in free-living settings. In this study, the potential of inertial sensors to detect resting tremors is evaluated. A smartwatch was placed on the wrist of six subjects with PD during the execution of MDS-UPDRS motor tasks. Data were collected over eight weeks from triaxial accelerometer and gyroscope simultaneously and used to implement machine learning algorithms to detect resting tremor. The best performance (accuracy 97.0% in tremor detection) was achieved using accelerometer data analysed with a Random Forest classifier, while the gyroscope showed lower performance (93.0%). The results indicates that the use of the accelerometer in commercial smartwatches can offer effective results for detecting resting tremors, while reducing computational workload. These results show opportunities for the development of robust free-living tremor monitoring systems using commodity devices and algorithms using a single sensor. © 2024 by SCITEPRESS–Science and Technology Publications, Lda.

关键词： Gyroscopes

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Self-supervised model-informed deep learning for low-SNR SS-OCT domain transformation

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Scientific Reports 2025年第1期15卷 1-20页

作者： Rakhshani, Sajed Arbab, Amirali Habibi, Aref Pourazizi, Mohsen Tajmirriahi, Mahnoosh Sedighin, Farnaz Rabbani, Hossein Medical Image and Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Department of Ophthalmology Isfahan Eye Research Center Isfahan University of Medical Sciences Isfahan Iran

This article introduces a novel deep-learning based framework, Super-resolution/Denoising network (SDNet), for simultaneous denoising and super-resolution of swept-source optical coherence tomography (SS-OCT) images. The novelty of this work lies in the hybrid integration of data-driven deep-learning with a model-informed noise representation, specifically designed to address the very low signal-to-noise ratio (SNR) and low-resolution challenges in SS-OCT imaging. SDNet introduces a two-step training process, leveraging noise-free OCT references to simulate low-SNR conditions. In the first step, the network learns to enhance noisy images by combining denoising and super-resolution within noise-corrupted reference domain. To refine its performance, the second step incorporates Principle Component Analysis (PCA) as self-supervised denoising strategy, eliminating the need for ground-truth noisy image data. This unique approach enhances SDNet’s adaptability and clinical relevance. A key advantage of SDNet is its ability to balance contrast-texture by adjusting the weights of the two training steps, offering clinicians flexibility for specific diagnostic needs. Experimental results across diverse datasets demonstrate that SDNet surpasses traditional model-based and data-driven methods in computational efficiency, noise reduction, and structural fidelity. The framework excels in improving both image quality and diagnostic accuracy. Additionally, SDNet shows promising adaptability for analyzing low-resolution, low-SNR OCT images, such as those from patients with diabetic macular edema (DME). This study establishes SDNet as a robust, efficient, and clinically adaptable solution for OCT image enhancement addressing critical limitations in contemporary imaging workflows. © The Author(s) 2025.

关键词： Denoising Low-SNR OCT Model-aware deep learning Self-supervised learning Super-resolution

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Partially Occluded Face Expression Recognition with CBAM-Based Residual Network for Teaching Scene

Partially Occluded Face Expression Recognition with CBAM-Bas...

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2023 China Automation Congress, CAC 2023

作者： Bai, Yanxing Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey Tokyo Institute of Technology Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

In this paper, a deep residual network based on convolutional block attention module (CBAM) is proposed, which is utilized for feature extraction of partially occluded face expression data. The proposed method overcomes the problem of localized occlusion face feature extraction by focusing on the regions and channels containing important information in the occluded face data through CBAM. Multi-task cascaded convolutional networks (MTCNN) are firstly utilized to localize the key regions of face emotion, and then deep emotion features are extracted by CBAM-ResNet network. The final emotion labels are generated. The effectiveness of this paper's method is verified on the RAF-DB dataset and the occluded CK+ dataset. The experimental accuracy in the RAF-DB dataset is 76.3%, which is 3.74% and 1.64% higher than the accuracy produced by the method of RGBT, and the WLS-RF, respectively. Application experiments are carried out in the real teaching scenario, which verifies the applicability of the algorithm in the real teaching scene. © 2023 IEEE.

关键词： Emotion Recognition

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