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IEEE Instrumentation and Measurement Technology Conference

作者： Nova Eka Budiyanta Eko Mulyanto Yuniarno Tsuyoshi Usagawa Mauridhi Hery Purnomo Dept. of Electrical Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember Surabaya Indonesia Department of Computer Science and Electrical Engineering Graduate School of Science and Technology Kumamoto University Kumamoto Japan Dept. of Electrical Engineering Dept. of Computer Engineering Faculty of Intelligent Electrical and Informatics Technology (F-Electics) Institut Teknologi Sepuluh Nopember University Center of Excellence on Artificial Intelligence for Healthcare and Society (UCE AIHeS) Surabaya Indonesia

The use of 3-Dimensional Light Detection and Ranging (3D LiDAR) point cloud as the alternative data to reduce privacy exposure in monitoring systems has been carried out in several studies. Unfortunately, various challenges in using point clouds intersect with the amount of data and computational costs. Several studies attempted to optimize the point cloud processing approach by segmenting the ground plane to get the object clusters separated. However, many unnecessary points can still burden the computation process. Since the ground plane mainly represents the horizontal planar plane on the x, y axis, this study tried to reduce the points on the vertical planar plane on the x, z and y, z axes with the x, y horizontal planar plane as well based on the surface normal vector direction of each point. The proposed approach has successfully reduced the raw point cloud by 79.29% removing the point cloud that indicates the planar surface of the three axes while maintaining the essential object of the monitoring system on the KITTI raw dataset. Therefore, the object cluster can be minimized, supporting the computational costs for further research in human activity monitoring systems.

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Atherosclerotic Carotid Plaque AM-FM Texture Feature Variability in Ultrasound Video

Atherosclerotic Carotid Plaque AM-FM Texture Feature Variabi...

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IEEE International Symposium on biomedical Imaging

作者： Kyriacos P. Constantinou Ioannis P. Constantinou Christos P. Loizou Efthyvoulos Kyriacou Andreas Panayides Andrew N. Nicolaides Constantinos S. Pattichis Marios S. Pattichis Department of Computer Science University of Cyprus Nicosia Cyprus Istognosis Ltd Nicosia Cyprus Dept. of Electrical Computer Engineering and Informatics Cyprus University of Technology Cyprus CYENS Centre of Excellence Nicosia Cyprus Cyprus Cardiovascular Disease Educational Research Trust Nicosia Cyprus Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

The objective of this work was to investigate the Amplitude Modulation - Frequency Modulation (AM-FM) texture feature variability in carotid ultrasound video during the cardiac cycle at systole and diastole. The goal here was to identify AM-FM features that are associated with increased risk of stroke. We computed the instantaneous amplitude, instantaneous phase and the magnitude of instantaneous frequency to extract plaque histogram features. A small dataset of 5 asymptomatic and 3 symptomatic videos were analyzed. Selected AM-FM plaque histogram texture features extracted during the cardiac cycle at the systolic and diastolic states were statistically significantly different between asymptomatic and symptomatic videos. However, further evaluation with more subjects needs to be carried out to exploit the usefulness of the proposed analysis in the clinical context.

关键词： Histograms Ultrasonic imaging Frequency modulation Stability criteria Atherosclerosis Feature extraction Object recognition

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Design a compact wireless IoT gait monitor wearable sensory system

Design a compact wireless IoT gait monitor wearable sensory ...

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2021 International Telecommunications Conference, ITC-Egypt 2021

作者： Abdelhady, Mohamed Elsheikh, Hesham Eldesoky Ahmed, Wael Abouelwafa Cleveland State University Electrical Engineering and Computer Science Dept. ClevelandOH44115 United States Minia University Faculty of Engineering Biomedical Engineering Department Minya Egypt

ISBN: (纸本)9781665445733

The study of human gait analysis is an important endeavor for years, since there are numerous sectors for patients with walking difficulties, such as stroke, sports, and medical rehabilitation. However, such research necessitates the use of equipment that can precisely quantify the movement of body parts. This paper shows a guideline scheme to develop a compact wireless internet of things (IoT) board, starting from embedded system design to kinematic data collection. This work aim is to create an acquisition IoT board that can measure and gather the knee, hip and ankle angles of a person walking normally as well as a patient walking with certain diseases. An enclosure designed to host the board and its associated battery form a wearable unite which it is able to attach to human lower limbs. The electronic board design to establish an inner communication between a high-fidelity inertial measurement unite (IMU) and microcontroller, and an outer communication between microcontroller and radio frequency (RF) module. The board design considers a bidirectional radio communication, though multiple boards are able to share, exchange, and pass IMU data to a personal computer as well. © 2021 IEEE.

关键词： Internet of things

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Three-dimensional quantitative phase imaging evaluating glioblastoma targeting CAR T-cell efficacy 11

Three-dimensional quantitative phase imaging evaluating glio...

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Quantitative Phase Imaging XI 2025

作者： Serafini, Caroline E. Davarzani, Amin Cappabianca, Dan Collazos, Daniela Baez Li, Zhenmin Mamaghani, Deniz Tommasi, Anna Sarko, Lauren La Vonne Denne, Nina Mao, Leidong Saha, Krishanu Karumbaiah, Lohitash Robles, Francisco E. George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA United States Wallace H. Coulter Dept. of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA United States School of Electrical and Computer Engineering College of Engineering University of Georgia AthensGA United States Regenerative Bioscience Center University of Georgia AthensGA United States Department of Biomedical Engineering University of Wisconsin-Madison MadisonWI United States Wisconsin Institute for Discovery University of Wisconsin-Madison MadisonWI United States School of Electrical and Computer Engineering Georgia Institute of Technology 266 Ferst Drive AtlantaGA United States Department of Cellular Biology University of Georgia AthensGA United States Division of Neuroscience Biomedical and Translational Sciences Institute University of Georgia AthensGA United States Edgar L. Rhodes Center for Animal and Dairy Science College of Agriculture and Environmental Science University of Georgia AthensGA United States

ISBN: (纸本)9781510684065

Glioblastoma (GBM) is a highly aggressive brain tumor with limited treatment options and poor patient outcomes. While patient-derived spheroids offer a platform for studying GBM treatment, existing imaging modalities are often destructive or limited to endpoint analyses. Here, we present quantitative Oblique Back- illumination Microscopy (qOBM) as a novel, label-free imaging technique for real-time, non-invasive monitoring of GBM spheroids treated with tumor targeting chimeric antigen receptor T-cells (CAR T-cells). qOBM overcomes existing imaging limitations by providing high-resolution, 3D quantitative phase imaging through epi-illumination, enabling longitudinal visualization of cellular and subcellular processes within thick spheroids. Here, we apply qOBM to monitor the responses of GBM spheroids derived from three unique patient cell lines to targeted CAR T-cell immunotherapy and non-targeted controls. With this, we capture changes in the spheroids over 72 hours of treatment, allowing for visualization of processes such as CAR T-cell infiltration, mitosis, and apoptosis. Further, using images from GBM/CAR T cell cocultures, we develop a machine learning model for predicting spheroid cytotoxicity rates. Our findings establish qOBM as a powerful, non-destructive tool for longitudinal GBM spheroid toxicity monitoring and demonstrate its potential to advance personalized cancer therapy by providing real-time insight into treatment efficacy. © 2025 SPIE.

关键词： T-cells

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Non-collocated Vibration Suppression using Delay-Based Controllers with Multiple Fixed Delays*

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IFAC-PapersOnLine 2023年第2期56卷 180-185页

作者： Adrian Saldanha Wim Michiels Tomáš Vyhlídal Department of Computer Science KU Leuven Celestijnenlaan 200A 3001 Leuven Belgium Dept. Instrumentation and Control Engineering Faculty of Mechanical Engineering and Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6 Czechia

The problem of non-collocated vibration absorption is targeted utilizing a multiparameter delay-based controller. The combined objective is to achieve closed-loop stability and vibration suppression at a target location, which is different from the position of the absorber. This problem can be translated into an optimization problem of shaping the poles of a closed-loop system with delays subject to zero-location constraints. The solution approach involves remodelling the closed-loop system as a system of delay-differential algebraic equations (DDAEs) which allows to systematically account for delays in the control law, thereby enabling a straightforward technique for computation of controller parameters. Finally, the proposed design method is validated by simulation on a lumped parameter model of a mass-spring-damper system.

关键词： vibration suppression time-delay systems DDAE optimization

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DIFFUSION-BASED APPROACHES IN MEDICAL IMAGE GENERATION AND ANALYSIS

arXiv

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arXiv 2024年

作者： al Nomaan Nafi, Abdullah Hossain, Md. Alamgir Rifat, Rakib Hossain Uz Zaman, Md Mahabub Ahsan, Md Manjurul Raman, Shivakumar Department of Biomedical Engineering Islamic University Kushtia7003 Bangladesh Dept. of Computer Science Texas Tech University LubbockTX79409 United States Department of Industrial and Systems Engineering University of Oklahoma Norman Oklahoma73071 United States

Data scarcity in medical imaging poses significant challenges due to privacy concerns. Diffusion models, a recent generative modeling technique, offer a potential solution by generating synthetic and realistic data. However, questions remain about the performance of convolutional neural network (CNN) models on original and synthetic datasets. If diffusion-generated samples can help CNN models perform comparably to those trained on original datasets, reliance on patient-specific data for training CNNs might be reduced. In this study, we investigated the effectiveness of diffusion models for generating synthetic medical images to train CNNs in three domains: Brain Tumor MRI, Acute Lymphoblastic Leukemia (ALL), and SARS-CoV-2 CT scans. A diffusion model was trained to generate synthetic datasets for each domain. Pre-trained CNN architectures were then trained on these synthetic datasets and evaluated on unseen real data. All three datasets achieved promising classification performance using CNNs trained on synthetic data. Local Interpretable Model-Agnostic Explanations (LIME) analysis revealed that the models focused on relevant image features for classification. This study demonstrates the potential of diffusion models to generate synthetic medical images for training CNNs in medical image analysis. © 2024, CC BY.

关键词： Convolutional neural networks

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Co-Design of Approximate Multilayer Perceptron for Ultra-Resource Constrained Printed Circuits

arXiv

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arXiv 2023年

作者： Armeniakos, Giorgos Zervakis, Georgios Soudris, Dimitrios Tahoori, Mehdi B. Henkel, Jörg The School of Electrical and Computer Engineering National Technical University of Athens Athens15780 Greece Karlsruhe76131 Germany The Computer Engineering & Informatics Dept. University of Patras Patras26504 Greece Department of Computer Science Kit Karlsruhe76131 Germany

Printed Electronics (PE) exhibits on-demand, extremely lowcost hardware due to its additive manufacturing process, enabling machine learning (ML) applications for domains that feature ultra-low cost, conformity, and non-toxicity requirements that silicon-based systems cannot deliver. Nevertheless, large feature sizes in PE prohibit the realization of complex printed ML circuits. In this work, we present, for the first time, an automated printed-aware software/hardware co-design framework that exploits approximate computing principles to enable ultra-resource constrained printed multilayer perceptrons (MLPs). Our evaluation demonstrates that, compared to the state-of-the-art baseline, our circuits feature on average 6x (5.7x) lower area (power) and less than 1% accuracy loss. © 2023, CC BY-NC-ND.

关键词： Timing circuits

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Konstantina-Helen Tsarapatsani Vasilios D. Tsakanikas Antonis Sakellarios Hans J. Trampisch Efterpi Karapintzou Henrik Rudolf George K. Matsopoulos Dimitrios I. Fotiadis Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Department of Medical Informatics Biometry and Epidemiology Ruhr University Bochum Bochum Germany Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greeece Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina and Biomedical Research Institute-FORTH Ioannina Greece

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction.

关键词： Accuracy Sensitivity Explainable AI Biological system modeling Peptides Receivers Predictive models Indexes Cardiovascular diseases Random forests

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Simulation of Random Deformable Motion in Soft-Tissue Cone-Beam CT with Learned Models 7

Simulation of Random Deformable Motion in Soft-Tissue Cone-B...

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7th International Conference on Image Formation in X-Ray Computed Tomography

作者： Hu, Y. Huang, H. Siewerdsen, J.H. Zbijewski, W. Unberath, M. Weiss, C.R. Sisniega, A. Dept. of Computer Science Johns Hopkins University BaltimoreMD United States Dept of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Russell H. Morgan Department of Radiology Johns Hopkins University BaltimoreMD United States

ISBN: (纸本)9781510656697

Cone-beam CT (CBCT) is widely used for guidance in interventional radiology but it is susceptible to motion artifacts. Motion in interventional CBCT features a complex combination of diverse sources including quasi-periodic, consistent motion patterns such as respiratory motion, and aperiodic, quasi-random, motion such as peristalsis. Recent developments in image-based motion compensation methods include approaches that combine autofocus techniques with deep learning models for extraction of image features pertinent to CBCT motion. Training of such deep autofocus models requires the generation of large amounts of realistic, motion-corrupted CBCT. Previous works on motion simulation were mostly focused on quasi-periodic motion patterns, and reliable simulation of complex combined motion with quasi-random components remains an unaddressed challenge. This work presents a framework aimed at synthesis of realistic motion trajectories for simulation of deformable motion in soft-tissue CBCT. The approach leveraged the capability of conditional generative adversarial network (GAN) models to learn the complex underlying motion present in unlabeled, motion-corrupted, CBCT volumes. The approach is designed for training with unpaired clinical CBCT in an unsupervised fashion. This work presents a first feasibility study, in which the model was trained with simulated data featuring known motion, providing a controlled scenario for validation of the proposed approach prior to extension to clinical data. Our proof-of-concept study illustrated the potential of the model to generate realistic, variable simulation of CBCT deformable motion fields, consistent with three trends underlying the designed training data: i) the synthetic motion induced only diffeomorphic deformations - with Jacobian Determinant larger than zero;ii) the synthetic motion showed median displacement of 0.5 mm in regions predominantly static in the training (e.g., the posterior aspect of the patient laying supine

关键词： Motion compensation

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Exhaled Breath Analysis Based Diabetes Detection with k-Nearest Neighbors Classifier

Exhaled Breath Analysis Based Diabetes Detection with k-Near...

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International Symposium on Computational Intelligence and Design, ISCID

作者： Piotr J Smieja Jiang Lu Qingquan Sun Xingang Fu Ting Zhang Department of Computer Engineering University of Houston - Clear Lake Houston USA School of Computer Science and Engineering California State University San Bernardino San Bernardino USA Dept. of Electrical and Biomedical Engineering University of Nevada Reno Reno USA Department of Computer Science University of Houston - Downtown Houston USA

The diagnosis of early stages of diseases such as lung cancer or diabetes is challenging as those diseases don’t have many noticeable symptoms. The human breath, however, contains many VOCs (volatile organic compounds) that could be used as a clue for conducting the proper test. In this study, a custom breathalyzer device is developed for non-invasive detection and diagnosis of diabetes with the use of a human breath print. An array of 6 MOS sensors is used in the prototype to collect 8 VOCs of the breath print of the subjects. The k-nearest Neighbor algorithm is implemented to determine the likelihood of a disease. In the results, an accuracy of 93.6% was achieved.

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