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Demonstration of motion-resistant three-wavelength spatial frequency domain imaging system with ambient light suppression using an 8-tap CMOS image sensor 15

Demonstration of motion-resistant three-wavelength spatial f...

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Optical Tomography and Spectroscopy of Tissue XV 2023

作者： Feng, Yu Shimada, Yuto Cao, Chen Yasutomi, Keita Kawahito, Shoji Kennedy, Gordon T. Durkin, Anthony J. Kagawa, Keiichiro Graduate School of Integrated Science and Technology Shizuoka University Japan Research Institution of Electronics Shizuoka University Japan Beckman Laser Institute University of California Irvine United States Biomedical Engineering Department University of California Irvine United States

ISBN: (纸本)9781510658578

We demonstrate a motion-resistant, three-wavelength, spatial frequency domain imaging (SFDI) system with ambient light suppression using a new 8-tap CMOS image sensor developed in our laboratory. Compared to the previous sensor (134×150), the new sensor’s readout maximum frame rate has improved to 33fps from 6.28fps, and the new 700×540-pixel sensor allows imaging at a higher spatial resolution over a larger field of view. Furthermore, the number of projected images needed per wavelength is reduced from three to two after applying the Hilbert transform. One image of planar illumination and one image for sinusoidal pattern projection at three wavelengths as well as one image of ambient light are captured by the 8-tap image sensor concurrently. The bias caused by ambient light is removed by subtracting the ambient light image from other images. Suppression of motion artifacts is achieved by reducing the exposure and projection time of each pattern. Sufficient signal level is maintained by repeating the exposure multiple times. In this study, LEDs with wavelengths of 554nm, 660nm, and 730nm were used to estimate oxy-/deoxyhemoglobin and melanin concentrations from in-vivo volar forearm skin. © 2023 SPIE.

关键词： Taps

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Optimal Synergic Deep Learning for COVID-19 Classification Using Chest X-Ray Images

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Computers, Materials & Continua 2023年第6期75卷 5255-5270页

作者： JoséEscorcia-Gutierrez Margarita Gamarra Roosvel Soto-Diaz Safa Alsafari Ayman Yafoz Romany F.Mansour Departament of Computational Science and Electronic Universidad de la CostaCUCBarranquilla080002Colombia Biomedical Engineering Program Universidad Simón BolívarBarranquilla080002Colombia Department of Computer Science and AI Faculty of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

A chest radiology scan can significantly aid the early diagnosis and management of COVID-19 since the virus attacks the *** X-ray(CXR)gained much interest after the COVID-19 outbreak thanks to its rapid imaging time,widespread availability,low cost,and *** radiological investigations,computer-aided diagnostic tools are implemented to reduce intra-and inter-observer *** lately industrialized Artificial Intelligence(AI)algorithms and radiological techniques to diagnose and classify disease is *** current study develops an automatic identification and classification model for CXR pictures using Gaussian Fil-tering based Optimized Synergic Deep Learning using Remora Optimization Algorithm(GF-OSDL-ROA).This method is inclusive of preprocessing and classification based on *** data is preprocessed using Gaussian filtering(GF)to remove any extraneous noise from the image’s ***,the OSDL model is applied to classify the CXRs under different severity levels based on CXR *** learning rate of OSDL is optimized with the help of ROA for COVID-19 diagnosis showing the novelty of the *** model,applied in this study,was validated using the COVID-19 *** experiments were conducted upon the proposed OSDL model,which achieved a classification accuracy of 99.83%,while the current Convolutional Neural Network achieved less classification accuracy,i.e.,98.14%.

关键词： Artificial intelligence chest X-ray COVID-19 optimized synergic deep learning preprocessing public health

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Towards Mathematical Modeling of Tissue-Engineered Skeletal Muscle Considering Contraction-Induced Stiffness Changes*

Towards Mathematical Modeling of Tissue-Engineered Skeletal ...

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IEEE International Conference on Cyborg and Bionic Systems (CBS)

作者： Yoshitake Akiyama Mizuki Nakamura Noriyasu Iwamoto Ken Masuya Biomedical Engineering Department Shinshu University Ueda-shi Nagano JAPAN YKK Corporation Kurobe-shi Toyama JAPAN Mechanical Engineering Program Faculty of Engineering University of Miyazaki Miyazaki-shi Miyazaki JAPAN

ISBN: (数字)9798350388039

ISBN: (纸本)9798350388046

Recently, bio-hybrid devices using muscle tissue or muscle cells as a drive source have been actively studied. Although there have been many reports on the development of bioactuators, most of them are concerned with muscle tissue culture and assembly methods, and very few studies have been conducted from the control engineering perspective of tissue-engineered muscle actuators. It is known that the stiffness of muscle changes between relaxation and excitation, and in particular, empirically, tissue-engineered muscle is immature compared to biological muscle, so the change in stiffness during contraction is relatively very large. Therefore, we will first evaluate the change in stiffness of cultured muscle during relaxation and contraction by a push test and confirm that the stiffness increases significantly with TESM. In addition, it is difficult to represent this large change in stiffness using the conventional Hill’s four-element model. Therefore, this study proposes a model of TESM in which two active elements that are involved only during contraction are added to Hill’s four-element model. The force equations for these isometric tests are derived. As a next step, we plan to experimentally identify the parameters to confirm the validity of this model.

关键词： Actuators Control engineering Biological system modeling Human-machine systems Force Muscles Mathematical models Assembly

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Vision Transformers and CNN-Based Knowledge-Distillation for Histopathological Image Classification 10

Vision Transformers and CNN-Based Knowledge-Distillation for...

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10th International Conference on Fuzzy Systems and Data Mining, FSDM 2024

作者： Boudissa, Seddik Debsarkar, Shyam Sundar Kawanaka, Hiroharu Aronow, Bruce Prasath, V.B. Surya Graduate School of Engineering Mie University 1577 Kurima-machiya Mie Tsu514-8507 Japan Department of Computer Science University of Cincinnati OH45221 United States Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center CincinnatiOH45229 United States Department of Pediatrics University of Cincinnati OH45267 United States Department of Biomedical Informatics College of Medicine University of Cincinnati OH45267 United States

ISBN: (纸本)9781643685694

Histopathological image analysis remains at the forefront of computational pathology presenting numerous challenges and demanding tasks, primarily due to the complex nature of tissue structures and the extensive scale of whole slide images (WSIs). Deep learning models have been widely used in histopathology image analysis, especially convolutional neural network (CNN)-based models for classification. However, CNNs have certain limitations due to their small receptive field. Recent works employed adaptations of the classical transformer architecture to visual data [1] [2]. Models such as Vision Transformer (ViT) and Swin Transformer leverage the powerful multi-head self-attention mechanism and have demonstrated comparable or superior performance to state-of-the-art CNN-based classification models. Despite their successes, these models require huge amounts of training data to effectively learn representations as they lack the inherent inductive biases of CNNs. This work compares Vision Transformers with baseline CNN models using a breast cancer histopathological dataset. Further, we employ a novel knowledge-distillation approach to enhance the learning efficiency of vits, When trained with a limited amount of data, Unlike previous works, we aimed to minimize convolution operations when generating patch embeddings to preserve spatial information before reaching the transformer attention layers, we achieved an accuracy of 87.7% for the ViT-base trained as a student of ResNet50, which represents a 1.2% improvement in accuracy over the standalone ViT-base. [3]. © 2024 The Authors.

关键词： Convolutional neural networks

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A 2-DoF Elbow Exoskeleton with Spherical Scissor Mechanism for ADL Assistance

A 2-DoF Elbow Exoskeleton with Spherical Scissor Mechanism f...

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IEEE International Conference on Robotics and Biomimetics

作者： Thomas M. Kwok Hilary HY Cheng Haoyong Yu NUS Graduate School for Integrative Sciences and Engineering Programme National University of Singapore Singapore Department of Biomedical Engineering National University of Singapore Singapore

This paper presents the design and experimental validation of a 2-DoF elbow exoskeleton with a 2-DoF spherical scissor mechanism (SSME) to assist chronic stroke patients with upper limb impairments in activity-of-daily-living (ADL) tasks. Since forearm pronation/supination was missing in most portable exoskeletons and the existing designs with forearm rotation were too bulky, we propose a 2-DoF elbow exoskeleton with a spherical mechanism for elbow and forearm assistance. Benefiting from the spherical scissor and cable-driven mechanisms, the SSME can assist in elbow flexion/extension and forearm pronation/supination, up to 97.467% ADL workspace, with a mechanical design as light as 0.543 kg, practical for daily use. Besides, the proposed sensing method and kinematic model for the spherical scissor mechanism can measure human joint motions, solving the sensing challenge for forearm motion in most existing upper limb exoskeletons. Furthermore, with inverse kinematics and robot-joint-space impedance control, the SSME can independently control the elbow and forearm angle with minimal influence on another, regardless of its coupled nature. Moreover, our experiments have validated the proposed exoskeleton design in terms of the force control performance of actuators, robot joint impedance control response, and robot assistance in an ADL task.

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Erosion-corrosion resistance of Mo-Ti- and Ni-Cr-Mo-alloyed medium-carbon martensitic steels: a critical analysis of synergistic effect of erosion and corrosion

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Journal of Iron and Steel Research International 2022年第8期29卷 1299-1311页

作者： De-fa Li Hang-yu Dong Cheng-yang Hu Kai-ming Wu Serhii Yershov Oleg Isayev School of Mechanical and Vehicle Engineering West Anhui UniversityLuan 237012AnhuiChina The State Key Laboratory of Refractories and Metallurgy Hubei Province Key Laboratory of Systems Science on Metallurgical ProcessingInternational Research Institute for Steel TechnologyWuhan University of Science and TechnologyWuhan 430081HubeiChina Laboratory for Excellence in Advanced Steel Research Materials Science and Engineering ProgramDepartment of MetallurgicalMaterials and Biomedical EngineeringUniversity of Texas at El PasoEl PasoTX 79968USA

Grain refinement renders Mo-Ti-alloyed medium-carbon martensitic steel to exhibit high hardness,high strength,and good toughness,but the erosion-corrosion resistance of steel in a corrosive slurry environment is not ***-Ti-alloyed medium-carbon martensitic steel is compared with Ni-Cr-Mo-alloyed medium-carbon martensitic steel,and the erosion-corrosion resistance of those two steels under impingement by NaCl solution with gravels has been *** components,pure-corrosion rate,pure-erosion rate,and synergistic effect of erosion and corrosion(SEEC)of erosion-corrosion rate,were *** pure-corrosion and pure-erosion rates of Mo-Ti-alloyed steel were higher than those of Ni-Cr-Mo-alloyed one;however,its erosion-corrosion rate was relatively lower because of a weak *** plastic deformation and work hardening due to gravel impingement were the essential reason for SEEC,which could be reduced by grain refinement,and consequently,Mo-Ti-alloyed steel with finer grains had better erosion-corrosion *** refinement could be an effective way to improve the erosion-corrosion resistance of martensitic steels.

关键词： Grain refinement Mo-Ti alloying Ni-Cr-Mo alloying Martensite Erosion-corrosion

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Quantification of limitations in statistical analysis of ultrasound echo envelope amplitudes

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Japanese Journal of Applied Physics 2023年第SJ期62卷 SJ1045-SJ1045页

作者： Mori, Shohei Arakawa, Mototaka Kanai, Hiroshi Hachiya, Hiroyuki Graduate School of Engineering Tohoku University Miyagi Sendai980-8579 Japan Graduate School of Biomedical Engineering Tohoku University Miyagi Sendai980-8579 Japan School of Engineering Tokyo Institute of Technology Meguro Tokyo152-8550 Japan

Ultrasound echo envelope statistics have been widely studied for quantitative tissue characterization. In ultrasound measurements, the size of the region-of-interest (ROI) is limited by several factors, such as the locality of the tissue characteristics and the depth dependence of the acoustic field of the ultrasound beam. In this case, the evaluated echo envelope statistics vary even when the envelope amplitudes follow the same population without any noise. In this study, the statistical variance of the moments caused by this finite number of samples was quantified based on the central limit theorem and the law of error propagation. The proposed principles were validated by random number simulation and used to quantify the statistical variance of Nakagami parameter estimation. Finally, the effective number of independent samples in an ultrasonic measurement was quantified based on the relationship between the ROI size and the ultrasound spatial resolution. © 2023 The Japan Society of Applied Physics.

关键词： Probability density function

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Improving the Cooling Time of Twisted Coiled Actuators in Soft Robotics

Improving the Cooling Time of Twisted Coiled Actuators in So...

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IEEE/RAS-EMBS International Conference on biomedical Robotics and Biomechatronics (BioRob)

作者： Alex Lizotte Parisa Daemi Brendan DiFabio Ana Luisa Trejos School of Biomedical Engineering (BME) Western University BME Western University Department of Electrical and Computer Engineering (ECE) Department of Mechanical and Materials Engineering Mechatronics Program Western University ECE and BME Western University

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Fabric cooling channels for twisted coiled actuators (TCAs) were recently proposed to achieve the required response times for motion assistance in a manner suitable for soft wearable robotic devices. While previous work demonstrated that the fabric channel reduced the cooling time by 42% in comparison to the same TCA without the cooling channel, the TCAs were still not cooled quickly enough to support human motion. Therefore, in this paper, two variations to the channel are proposed to further reduce the cooling time of the TCAs. The variations include unsealing the inlet and adding vents along the length of the channel to take advantage of air entrainment and natural convection. While both variations reduced the cooling time on their own, when they were employed together there was a 34% reduction in cooling time compared to the original channel design ( $19.1 \pm 2.4\ \mathrm{s}$ vs. $13.5 \pm 0.9\ \mathrm{s}, \mathrm{p} < 0.001$ ). This decrease occurred without any significant differences in the stroke or heating time of the TCA. The modified channel was then compared to the TCA without the cooling apparatus and the cooling time was reduced by 57% ( $25.1 \pm 1.7\ \mathrm{s}$ vs. $14.0 \pm 1.2\ \mathrm{s}, \mathrm{p} < 0.001)$ . This work advances the development of a cooling system for TCAs, making it suitable for soft wearable robotic devices by improving portability, and thereby enabling their use in wearable devices for rehabilitation applications.

关键词： Heating systems Actuators Cooling Vents Soft robotics Fabrics Time factors Wearable devices Convection Testing

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Design and Control of the Portable Upper-limb Elbow-forearm Exoskeleton for ADL Assistance

Design and Control of the Portable Upper-limb Elbow-forearm ...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Hilary HY Cheng Thomas M. Kwok Haoyong Yu Department of Biomedical Engineering National University of Singapore Singapore Integrative Sciences and Engineering Programme NUS Graduate School National University of Singapore Singapore

This paper proposes a portable Unilateral Elbow- Forearm Exoskeleton (UEFE) for assisting chronic stroke patients in the activities of daily living (ADL). UEFE provides users 2DoFs assistance: elbow flexion/extension (eF/E) and forearm pronation/supination (eR). Other than eF/E, forearm rotation is equally crucial for completing ADL tasks; however, limited exoskeletons provide eR assistance for users in ADL. Even though some existing exoskeletons are equipped with eR joints, those devices are not practical to use in ADL due to their heavy weight, insufficient assistance, and obstructing handles. In UEFE, both active joints are actuated by Series Elastic Actuators (SEA) through Bowden cables to ensure safe interactions and a lightweight solution. The design of UEFE is based on the ADL requirements, which include the range of motion (ROM) and torque. The handle-free feature also allows users to pick up objects in ADL. A pill-taking task with impedance control demonstrates the feasibility of using the UEFE in ADL assistance.

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ATP_mCNN: Predicting ATP binding sites through pretrained language models and multi-window neural networks

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Computers in Biology and Medicine 2025年 185卷 109541-109541页

作者： Le, Van-The Malik, Muhammad-Shahid Lin, Yi-Jing Liu, Yu-Chen Chang, Yan-Yun Ou, Yu-Yen Department of Computer Science and Engineering Yuan Ze University Chung-Li32003 Taiwan Graduate Program in Biomedical Informatics Yuan Ze University Chung-Li32003 Taiwan Department of Computer Sciences Karakoram International University Gilgit-Baltistan 15100 Pakistan

Adenosine triphosphate plays a vital role in providing energy and enabling key cellular processes through interactions with binding proteins. The increasing amount of protein sequence data necessitates computational methods for identifying binding sites. However, experimental identification of adenosine triphosphate-binding residues remains challenging. To address the challenge, we developed a multi-window convolutional neural network architecture taking pre-trained protein language model embeddings as input features. In particular, multiple parallel convolutional layers scan for motifs localized to different window sizes. Max pooling extracts salient features concatenated across windows into a final multi-scale representation for residue-level classification. On benchmark datasets, our model achieves an area under the ROC curve of 0.95, significantly improving on prior sequence-based models and outperforming convolutional neural network baselines. This demonstrates the utility of pre-trained language models and multi-window convolutional neural networks for advanced sequence-based prediction of adenosine triphosphate-binding residues. Our approach provides a promising new direction for elucidating binding mechanisms and interactions from primary structure. © 2024 Elsevier Ltd

关键词： Convolutional neural networks

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