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International Conference on Solid State Sensors and Actuators (TRANSDUCERS)

作者： Joshua A. Levy Michael A. Straker Luke A. Beardslee Reza Ghodssi Department of Materials Science and Engineering University of Maryland College Park Maryland USA Institute for Systems Research University of Maryland College Park Maryland USA Robert E. Fischell Institute for Biomedical Devices University of Maryland College Park Maryland USA Fischell Department of Bioengineering University of Maryland College Park Maryland USA Department of Electrical and Computer Engineering University of Maryland College Park Maryland USA

This manuscript demonstrates the first cantilever actuator for highly targeted and directionally specific delivery of drug-eluting microneedles to the gastrointestinal (GI) tract from ingestible devices. The actuator achieves the following advantages over current technology: on-command deployment in under 500 ms, low power consumption, spatial conservation, and prolonged deployment shelf life. These advancements enable integration of multiple actuators per device for multidirectional deployment. These attributes are achieved by the optimization of heater design and use of low-profile and highly relaxation resistant PEEK polymers. In all, these features enable higher delivery precision in both longitudinal and polar directions, potentiating site targeting in the GI tract.

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Compact and Field Portable Biophotonic Sensors for Automated Cell Identification (Plenary Address)

Compact and Field Portable Biophotonic Sensors for Automated...

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International Conference on Optics and Electro-optics, ICOL 2019

作者： Javidi, Bahram O’Connor, Timothy Anand, Arun Moon, Inkyu Stern, Adrian Martinez-Corral, Manuel Department of Electrical and Computer Engineering University of Connecticut Storrs CT06269 United States Department of Biomedical Engineering University of Connecticut Storrs CT06269 United States Department of Applied Physics M.S. University of Baroda Vadodara390001 India Department of Robotics Engineering DGIST 333 Techno Jungang-daero Daegu42988 Korea Republic of Electro-Optics and Photonics Engineering Ben Gurion University of Negav Beer-Sheva84105 Israel Department of Optics University of Valencia Burjassot46100 Spain

ISBN: (纸本)9789811592584

In this Plenary address paper, we overview recently published work for automated cell identification using 3D optical imaging in compact and field portable biophotonic sensors. Digital holographic microscopy systems and lensless pseudorandom phase encoding systems capture 3D information of biological cells and make highly accurate automated cell identification possible. Overviewed systems include sickle cell disease diagnosis based on spatio-temporal cell dynamics in a field-portable 3D-printed shearing digital holography as well as lensless cell identification of both single and multicell samples using pseudorandom phase encoding. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cells

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Additive networks of chen-fliess series: Local convergence and relative degree

arXiv

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arXiv 2021年

作者： Steven Gray, W. Duffaut Espinosa, Luis A. Ebrahimi-Fard, Kurusch Department of Electrical and Computer Engineering Old Dominion University NorfolkVA23529 United States Department of Electrical and Biomedical Engineering University of Vermont BurlingtonVT05405 United States Trondheim7491 Norway

— Given an additive network of input-output systems where each node of the network is modeled by a locally convergent Chen-Fliess series, two basic properties of the network are established. First, it is shown that every input-output map between a given pair of nodes has a locally convergent Chen-Fliess series representation. Second, sufficient conditions are given under which the input-output map between a pair of nodes has a well defined relative degree as defined by its generating series. This analysis leads to the conclusion that this relative degree property is generic in a certain *** Codes 93C10 Copyright © 2021, The Authors. All rights reserved.

关键词： Additives

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Highly sensitive vibrational imaging by fluorescence detection of mid-infrared photothermal effect

Highly sensitive vibrational imaging by fluorescence detecti...

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Label-free biomedical Imaging and Sensing, LBIS 2021

作者： Zhang, Yi Zong, Cheng Zong, Haonan Zhang, Meng Cheng, Ji-Xin Dartment of Physics Boston University Boston 590 Commonwealth Avenue # 255 BostonMA02215 United States Department of Electrical and Computer Engineering Boston University Boston 8 St Marys St # 324 BostonMA02215 United States Department of Biomedical Engineering Boston University Boston 44 cummington BosotnMA02215 United States

ISBN: (纸本)9781510641457

Mid-infrared photothermal (MIP) imaging is an emerging technique where a visible beam is deployed to sense the photothermal lensing effect induced by infrared absorption of molecules. This technology provides sub-micron spatial resolution defined by the visible probe beam. Yet, the photothermal lensing effect is a weak effect for most materials. For instance, the diffraction coefficient of poly (methyl methacrylate) changes around 0.1% per Kelvin. Thus, highly sensitive probes are desired to improve the detection sensitivity. Here, we report a fluorescence-enhanced mid-infrared photothermal (FE-MIP) microscope with high sensitivity. MIP microscopy uses a pump-probe strategy in which a mid-infrared light vibrationally excites a sample and a visible light probes the thermal effect. Instead of measuring the scattering modulated by mid-infrared absorption, we deploy thermal sensitive fluorescent dyes as the probe, and measure the modulated fluorescence intensity in both confocal mode and wide field mode. We demonstrate high imaging sensitivity and component specificity through fluorescence labeling. © 2021 SPIE.

关键词： Infrared absorption

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Determination of the Charge Attached to Micro-scale Devices Used in Fluidic Self-Assembly Processes

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MRS Online Proceedings Library 2021年第1期737卷 735-741页

作者： Eric Tkaczyk Vandna Handa Sangwoo Lee Helen McNally Lichuan Gui Steve Wereley Rashid Bashir School of Electrical and Computer Engineering Purdue University W. Lafayette USA School of Mechanical Engineering Purdue University W. Lafayette USA Department of Biomedical Engineering Purdue University W. Lafayette USA

Self-assembly of micron sized and smaller particles has previously been demonstrated using biologically inspired events such as DNA hybridization and interactions of ligands and receptors. In order to implement these techniques to create practical electronic devices, a quantitative measure of the amount of substance attached to the device surface just prior to the final assembly is essential. In the present investigation, this crucial quantity was investigated from the electrophoretic mobility of particles, which was ascertained by examining their motion under applied electric fields ranging from 0 to 1 V/mm. Sequential CCD camera images processed with custom software enabled calculation of particle velocities during their viscous motion in an inexpensive electrophoresis chamber filled with a low-conductivity buffer solution. A linear fit through the velocity vs. electric field data points yielded the electrophoretic mobility, which was utilized in the Stokes equation to calculate the net amount of charge present on each device. For 5.44 micron carboxyl-coated polystyrene beads, this method indicated a charge of 2.69e-15 C per particle. The manufacturer of the beads, Spherotech corporation, quoted 6.37e-11 C as the expected charge. The more than three orders of magnitude discrepancy is at least partially attributable to the electrophoretic retardation and relaxation effects of small electrolyte ions in the buffer solution. The method was also applied to silicon islands in the shape of a cone frustum with similar dimensions to the beads. A mercapto-ethane-sulfonate monolayer, attached via thiol bonds to the gold-coated surface of the islands, provided the charge. The amount of charge on an island was calculated to average 2.48e-15 C, corresponding to a density of 3.82e10 mercapto-ethane-sulfonate groups per square centimeter of Au surface.

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Artificial intelligence on biomedical signals:technologies,applications,and future directions

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Med-X 2024年第1期2卷 416-445页

作者： Yoon Jae Lee Cheoljeong Park Hodam Kim Seong J.Cho Woon-Hong Yeo Wearable Intelligent Systems and Healthcare Center(WISH Center) Institute for Matter and SystemsGeorgia Institute of TechnologyAtlantaGA 30332USA School of Electrical and Computer Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Department of Mechanical Engineering Chungnam National UniversityDaejeon 34134Korea George W.Woodruff School of Mechanical Engineering Georgia Institute of TechnologyAtlantaGA 30332USA Wallace H.Coulter Department of Biomedical Engineering Georgia Tech and Emory University School of MedicineAtlantaGA 30332USA Parker H.Petit Institute for Bioengineering and Biosciences Georgia Institute of TechnologyAtlantaGA 30332USA

Integrating artificial intelligence(AI)into biomedical signal analysis represents a significant breakthrough in enhanced precision and efficiency of disease diagnostics and *** traditional computational models to advanced machine learning algorithms,AI technologies have improved signal processing by efficiently handling complexity and interpreting intricate *** physiological data,which requires highly trained professionals,is now more accessible;in regions with limited access,AI tools expand healthcare accessibility by providing high-level diagnostic insights,ultimately improving health *** review explores various AI methodologies,including supervised,unsupervised,and reinforcement learning,and examines their synergy for biomedical signal analysis and future directions in medical *** capturing a comprehensive overview of the current state and prospects of AI-driven healthcare,this paper highlights the transformative potential of AI in analyzing biomedical signals.

关键词： Artificial intelligence biomedical devices Signal analysis Healthcare Diagnostics Therapeutics

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Cross-system biological image quality enhancement based on the generative adversarial network as a foundation for establishing a multi-institute microscopy cooperative network

arXiv

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arXiv 2024年

作者： Panek, Dominik Rząca, Carina Szczypior, Maksymilian Sorysz, Joanna Misztal, Krzysztof Baster, Zbigniew Rajfur, Zenon Doctoral School of Exact and Natural Sciences Jagiellonian University ul. Lojasiewicza 11 Kraków30-348 Poland Department of Molecular and Interfacial Biophysics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. Lojasiewicza 11 Kraków30‑348 Poland Undergraduate Program in Biophysics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. Lojasiewicza 11 Kraków30‑348 Poland Undergraduate program in Biomedical Engineering Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Al. A. Mickiewicza 30 Building B-1 Kraków30-059 Poland Division of Computational Mathematics Faculty of Mathematics and Computer Science Jagiellonian University 6 Lojasiewicza St. Kraków Poland

High-quality fluorescence imaging of biological systems is limited by processes like photobleaching and phototoxicity, and also in many cases, by limited access to the latest generations of microscopes. Moreover, low temporal resolution can lead to a motion blur effect in living systems. Our work presents a deep learning (DL) generative-adversarial approach to the problem of obtaining high-quality (HQ) images based on their low-quality (LQ) equivalents. We propose a generative-adversarial network (GAN) for contrast transfer between two different separate microscopy systems: a confocal microscope (producing HQ images) and a wide-field fluorescence microscope (producing LQ images). Our model proves that such transfer is possible, allowing us to receive HQ-generated images characterized by low mean squared error (MSE) values, high structural similarity index (SSIM), and high peak signal-to-noise ratio (PSNR) values. For our best model in the case of comparing HQ-generated images and HQ-ground truth images, the median values of the metrics are 6·10-4, 0.9413, and 31.87, for MSE, SSIM, and PSNR, respectively. In contrast, in the case of comparison between LQ and HQ ground truth median values of the metrics are equal to 0.0071, 0.8304, and 21.48 for MSE, SSIM, and PSNR respectively. Therefore, we observe a significant increase ranging from 14% to 49% for SSIM and PSNR respectively. These results, together with other single-system cross-modality studies, provide proof of concept for further implementation of a cross-system biological image quality enhancement. © 2024, CC BY-SA.

关键词： Fluorescence imaging

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Towards a telehealth infrastructure supported by machine learning on edge/fog for Parkinson's movement screening

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Smart Health 2022年 26卷

作者： Sadhu, Shehjar Solanki, Dhaval Constant, Nicholas Ravichandran, Vignesh Cay, Gozde Saikia, Manob Jyoti Akbar, Umer Mankodiya, Kunal Dept. of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston RI United States Department of Electrical Engineering University of North Florida Jacksonville 32224 FL United States Department of Neurology Brown University Rhode Island Hospital Providence RI United States

Approximately 10 million people worldwide live with Parkinson's disease (PD), a progressive and incurable neurological movement disorder. Symptomatic treatment is available for PD but requires patients to make periodic clinic visits (2–3 times per year) for symptom assessment. Advanced telehealth technologies can enhance clinical care for PD but warrant an Internet-of-Things-based (IoT) infrastructure that can enable symptom monitoring in out-of-clinic settings, such as homes. In this paper, we present an edge and Fog device-based IoT framework and a machine learning-based telehealth infrastructure that can detect and classify hand movement tasks based on a clinical test (UPDRS) for remote symptom assessment. We used a pair of smart gloves integrated with finger flex sensors, an inertial measurement unit (IMU), and a wireless embedded system (i.e., an edge device) to record the hand movements. The edge device (ESP32) detected the activity on the edge node and transmitted the data to the Fog node for classification. The Fog node (Raspberry pi) hosted the Machine Learning (ML) based activity classification models to classify UPDRS-based hand movement tasks. In this paper, we present the development of edge-fog-supported ML infrastructure. To develop ML models, we utilized the data of 9 participants (five healthy and four people with PD) who performed hand movements tasks while wearing the smart gloves. We developed and tested different classification models such as K-nearest neighbors (KNN), Support Vector Machine (SVM), and Decision Tree (DT) to identify which one fits best for such an edge-fog infrastructure for remote symptom assessment. Results showed that the SVM model outperformed others by giving 94% training, 94% testing, and 93% validation accuracy with a mean inference time of 560 μs on the Fog node. Our preliminary results are promising to further our research in deploying the edge-fog-driven ML-based telehealth infrastructure in real-world settings. © 2022

关键词： Edge computing Fog computing Internet of things Machine learning Parkinson's disease Telehealth Wearable technology

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Automatic detection of lung nodule in CT scan slices using CNN segmentation schemes: A study

Automatic detection of lung nodule in CT scan slices using C...

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2022 International Conference on Machine Learning and Data engineering, ICMLDE 2022

作者： Kadry, Seifedine Herrera-Viedma, Enrique Crespo, Rubén González Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Research Institute in Data Science and Computational Intelligence University of Granada Andalusian Granada Spain Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja 26006 Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The lung is one of the prime respiratory organs in human physiology, and its abnormality will severely disrupt the respiratory system. Lung Nodule (LN) is one of the abnormalities, and early screening and treatment are necessary to reduce its harshness. The proposed work aims to implement the Convolutional-Neural-Network (CNN) segmentation methodology to extract the LN in various lung CT slices, such as axial, coronal, and sagittal planes. This work consists of the following phases;(i) Image collection and pre-processing, (ii) Ground-truth generation, (iii) CNN-supported segmentation, and (iv) Performance evaluation and validation. In this work, the merit of pre-trained CNN segmentation schemes is verified using (i) One-fold training and (ii) Two-fold training methods. The test images for this study are collected from The Cancer Imaging Archive (TCIA) database. The experimental investigation is executed using Python®, and the outcome of this study confirms that the VGG-SegNet helps to get better values of Jaccard (>88%), Dice (>93%), and Accuracy (>96%) compared to other CNN methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： computerized tomography

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Chaotic American zebra search optimization algorithm for benchmark challenges

Chaotic American zebra search optimization algorithm for ben...

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International Conference on Electronics, computers and Artificial Intelligence (ECAI)

作者： Vikram Kumar Kamboj Ardhala Bala Krishna Chaman Verma Maria Simona Raboaca Constantin Filote Maria Stepanova Schulich School of Engineering University of Calgary Alberta Canada Dept. of Electrcical and Electronics Engineering Malla Reddy Engineering College for Women Hyderabad India Dept. of Media and Edu. Informatics Faculty of Informatics Eötvös Loránd University Budapest Hungary ICSI Energy Department National Research and Development Institute for Cryogenics and Isotopic Technologies Ramnicu Valcea Romania Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Suceava Romania Engineering Business and Management Faculty Bauman Moscow State Technical University Russia

In this research, we presented a modification to the recently developed swarm premised American zebra search optimization algorithm (AZOA) using chaos. The particular Singer mapping was proposed by the so-called chaotic AZOA and was already known to perform better in optimization. Twelve uni, multi modal benchmark functions, three-bar truss and ten bar truss optimal structural designs were tested. The chaotic AZOA with Singer map would perform more effectively, more consistently, and quicker than the classical AZOA and other recent metaheuristics in optimization, according to the results, which also supported the viability of the modifications. It was discussed if the chaotic AZAO could be optimized with the original AZOA, and the chaotic AZOA was suggested for use in applications for actual engineering challenges.

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