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21st Polish Control Conference, PCC 2023

作者： Pilat, Adam Krzysztof Milanowski, Hubert Bieszczad, Rafal Sikora, Bartiomiej Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering Department of Automatic Control and Robotics AGH-University of Science and Technology Krakow Poland

ISBN: (纸本)9783031351693

This elaboration presents the synthesis of the Takagi-Sugeno type Fuzzy Logic controller realizing the programmable parameters of the state feedback controller together with the steady state current for the active magnetic levitation system. Closed-loop dynamics was calculated precisely with respect to the requested dynamics. Two cases were considered: fixed-and variable-closed-loop dynamics. The Fuzzy Logic Controller was considered under four scenarios with linear, nonlinear, and constant in-range output. The study was supported by simulations and experimental investigations. The position of the levitating sphere stands as an illustration of the system in operation. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Dynamics

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Assessing the stiffness of intact bone marrow with non-contact optical coherence elastography 12

Assessing the stiffness of intact bone marrow with non-conta...

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Optical Elastography and Tissue Biomechanics XII 2025

作者： Singh, Amandeep Singh, Manmohan Aglyamov, Salavat R. Mayerich, David Larin, Kirill V. Department of Biomedical Engineering University of Houston TX77204 United States Department of Mechanical and Aerospace Engineering University of Houston TX77204 United States Department of Electrical and Computer Engineering University of Houston TX77204 United States

ISBN: (纸本)9781510683907

Bone marrow plays an important role in regulating immunity for homeostasis and controlling stromal cell trafficking. Bone marrow cells are sustained by the framework of connective tissues that preserve the mechanical and functional integrity of the tissue. Optical coherence elastography (OCE) is a potential method to measure the biomechanical properties of bone marrow with high spatio-temporal resolution. In this work, we demonstrate the quantification of wave speed at different locations along the diaphysis of mice femur samples using non-contact air-coupled ultrasound OCE (ACUS-OCE). The preliminary results from the current approach show that the ACUS-OCE can characterize the mechanical stiffness of intact bone marrow and can be used for minimally invasive and ex vivo clinical applications. © 2025 SPIE.

关键词： Bone

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Alzheimer Disease Detection Studies: Perspective on Multi-Modal Data

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Yearbook of Medical Informatics 2025年第1期33卷 266-276页

作者： Dehghani, Farzaneh Derafshi, Reihaneh Lin, Joanna Bayat, Sayeh Bento, Mariana Biomedical Engineering Department University of Calgary Canada Computer Science Department University of Calgary Canada Geomatics Engineering Department University of Calgary Canada Hotchkiss Brain Institute Cumming School of Medicine University of Calgary Canada Electrical and Software Engineering Department University of Calgary Canada

OBJECTIVES: Alzheimer's Disease (AD) is one of the most common neurodegenerative diseases, resulting in progressive cognitive decline, and so accurate and timely AD diagnosis is of critical importance. To this end, various medical technologies and computer-aided diagnosis (CAD), ranging from biosensors and raw signals to medical imaging, have been used to provide information about the state of AD. In this survey, we aim to provide a review on CAD systems for automated AD detection, focusing on different data types: namely, signals and sensors, medical imaging, and electronic medical records (EMR). METHODS: We explored the literature on automated AD detection from 2022-2023. Specifically, we focused on various data resources and reviewed several preprocessing and learning methodologies applied to each data type, as well as evaluation metrics for model performance evaluation. Further, we focused on challenges, future perspectives, and recommendations regarding automated AD diagnosis. RESULTS: Compared to other modalities, medical imaging was the most common data type. The prominent modality was Magnetic Resonance Imaging (MRI). In contrast, studies based on EMR data type were marginal because EMR is mostly used for AD prediction rather than detection. Several challenges were identified: data scarcity and bias, imbalanced datasets, missing information, anonymization, lack of standardization, and explainability. CONCLUSION: Despite recent developments in automated AD detection, improving the trustworthiness and performance of these models, and combining different data types will improve usability and reliability of CAD tools for early AD detection in the clinical practice. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://***/licenses/by/4.0/).

关键词： Alzheimer’s Disease computer-aided Diagnosis Electronic Medical Records Medical Imaging Signals and Sensors

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Optimal Management of a Hydro - Wind Energy System with Hydrogen Storage 12

Optimal Management of a Hydro - Wind Energy System with Hydr...

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12th International Conference on Power Science and engineering, ICPSE 2023

作者： Alberizzi, J.C. Perez Estevez, M.A. Renzi, M. Jin, L. Rossi, M. Alberizzi, A. Free University of Bozen - Bolzano Faculty of Engineering Bozen-Bolzano Italy Italy University of Pavia Electrical Computer and Biomedical Engineering Pavia Italy

ISBN: (纸本)9798350342383

The exploitation of renewable energy sources is nowadays a fundamental factor for the sustainable development and the transition to low a carbon energy production system. However, the fluctuating and unpredictable behavior of energy sources like wind complicates their integration into energy systems and they need to be coupled with storage systems to fully exploit their potential. The main objective of this paper is thus to develop a novel Mixed Integer Linear Programming (MILP) algorithm capable of providing the optimal sizing and management of a hydrogen storage unit within a Hybrid Renewable Energy System (HRES) in which the produced energy is sold to the power grid. The algorithm has been validated with data from a case study located in Italy. The Hybrid Renewable Energy System under investigation is constituted by a hydropower plant, a wind turbine and a hydrogen storage system. The latter is composed of a Polymer Electrolyte Membrane (PEM) electrolyzer, four pressurized hydrogen tanks, and a PEM fuel cell. The optimal management has been obtained by responding to a unit commitment analysis. © 2023 IEEE.

关键词： Hydrogen storage

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Automated lumpy skin grading in bovine images using novel deep convolutional neural networks

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Multimedia Tools and Applications 2024年 1-26页

作者： Muhammad, Abdul Malik ur Rehman, Muhammad Zia Mushtaq, Zohaib Qureshi, Muhammad Farrukh Department of Biomedical Engineering Riphah International University Islamabad44000 Pakistan Department of Electrical Electronics and Computer Systems University of Sargodha Sargodha40100 Pakistan Department of Electrical Engineering Namal University Mianwali Mianwali42250 Pakistan Center of AI and BigData Namal University Mianwali Mianwali42250 Pakistan

The diagnosis and prevention of lumpy skin disease, a viral ailment affecting cattle and buffalo, present significant financial implications for the livestock industry. Traditional methods for identifying lumpy skin disease rely on manual visual inspection by veterinarians, which can be labor-intensive, subjective, and prone to errors. To address these challenges, this study proposes a novel deep convolutional neural network (DCNN) model for the automatic recognition and grading of lumpy skin disease from bovine images. The primary contributions of this research include the development of a DCNN architecture specifically tailored for this task, comprising five convolutional layers, five max pooling layers, two fully connected layers with ReLU activation, and a final fully connected layer with softmax activation. The model’s detection accuracy is further enhanced by applying image cropping and patching techniques, which divide each input image into 12 patches to improve local feature extraction. The proposed model was trained and tested using a publicly available dataset from Kaggle. Comparative analysis was conducted against several state-of-the-art models, including InceptionV3, ResNet50, MobileNetV3, VGG19, and Xception. The DCNN model demonstrated superior performance, achieving the highest validation accuracy of 0.96875, outperforming the compared models in terms of accuracy, precision, recall, and F1 score. Additionally, the study explores the potential of transitioning from binary to multiclass classification, which would allow for the assessment of the severity of lumpy skin disease. This future direction aims to provide more nuanced and actionable information for veterinary diagnostics. The significance of this research lies in its potential to offer an objective, efficient, and scalable solution for early disease detection and prevention in livestock, thereby presenting considerable economic benefits for farmers and the livestock industry as a whole. The me

关键词： Convolutional neural networks

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Spectral Learning of Shared Dynamics Between Generalized-Linear Processes 38

Spectral Learning of Shared Dynamics Between Generalized-Lin...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Oganesian, Lucine L. Sani, Omid G. Shanechi, Maryam M. Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Ming Hsieh Department of Electrical and Computer Engineering Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los AngelesCA United States

Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

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Feature Matching via Topology-Aware Graph Interaction Model

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 113-130页

作者： Yifan Lu Jiayi Ma Xiaoguang Mei Jun Huang Xiao-Ping Zhang the Electronic Information School Wuhan UniversityWuhan 430072China IEEE Tsinghua Berkeley Shenzhen Institute Shenzhen 518071China the Department of Electrical Computer and Biomedical EngineeringToronto Metropolitan UniversityToronto ON M5B 2K3Canada

Feature matching plays a key role in computer vision. However, due to the limitations of the descriptors, the putative matches are inevitably contaminated by massive *** paper attempts to tackle the outlier filtering problem from two aspects. First, a robust and efficient graph interaction model,is proposed, with the assumption that matches are correlated with each other rather than independently distributed. To this end, we construct a graph based on the local relationships of matches and formulate the outlier filtering task as a binary labeling energy minimization problem, where the pairwise term encodes the interaction between matches. We further show that this formulation can be solved globally by graph cut algorithm. Our new formulation always improves the performance of previous localitybased method without noticeable deterioration in processing time,adding a few milliseconds. Second, to construct a better graph structure, a robust and geometrically meaningful topology-aware relationship is developed to capture the topology relationship between matches. The two components in sum lead to topology interaction matching(TIM), an effective and efficient method for outlier filtering. Extensive experiments on several large and diverse datasets for multiple vision tasks including general feature matching, as well as relative pose estimation, homography and fundamental matrix estimation, loop-closure detection, and multi-modal image matching, demonstrate that our TIM is more competitive than current state-of-the-art methods, in terms of generality, efficiency, and effectiveness. The source code is publicly available at http://***/YifanLu2000/TIM.

关键词： Feature matching graph cut outlier filtering topology preserving

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Date Fruit Classification System using Deep Transfer Learning 10

Date Fruit Classification System using Deep Transfer Learnin...

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10th International Conference on Future Internet of Things and Cloud, FiCloud 2023

作者： Abuowda, Zainab Ramadan, Shorouk Salam, Nour Gad, Abdalla Yousaf, Jawad Hassan, Taimur Ghazal, Mohammed Almajali, Eqab Abu Dhabi University Electrical Computer and Biomedical Engineering Department Abu Dhabi United Arab Emirates University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350316353

Nowadays, developing an accurate computer vision system for classifying the type and maturity stage of date fruit is essential for designing an efficient and smart robotic harvesting solution. In this study, we proposed an effective deep transfer learning-based system for automatically classifying five distinct date types on bunches (Naboot saif, Khalas, Barhi, Meneifi, and Sullaj) with four different maturity stages (Immature, Khalal, Rutab, and Tamar) using transfer learning approach on pre-trained deep convolutions neural networks. Resenet-50 and AlexNet networks were trained on the available dataset for the two classification tasks (date types and maturity stage). The findings of performed various experiments suggested that the proposed Resenet-50 outperforms the AlexNet with maximum achieved accuracies of 95.1% and 94.7% for date type and maturity stage classification, respectively. The study's findings are to be utilized in designing an intelligent robotic harvesting mechanism for efficient monitoring and harvesting of date fruits in a natural environment. © 2023 IEEE.

关键词： Harvesting

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Safer Navigation: The AI-Powered Smart Cane for the Visually Impaired 16

Safer Navigation: The AI-Powered Smart Cane for the Visually...

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16th International Conference on Developments in eSystems engineering, DeSE 2023

作者： Gad, Abdalla Fteiha, Bara' Alatawi, Jawaher Mohammed, Mahra Almansoori, Mahra Ghazal, Mohammed Yousaf, Jawad Almajali, Eqab Hussain, Abir Jafar Abu Dhabi University Electrical Computer and Biomedical Engineering Department Abu Dhabi United Arab Emirates University of Sharjah Department of Electrical Engineering Sharjah United Arab Emirates

ISBN: (纸本)9798350381344

This work proposes a flexible, foldable, lightweight, robust, and smart cane to aid visually impaired individuals and patients in navigating unfamiliar environments safely. The developed smart cane is equipped with an ultrasonic sensor, RFID reader, and an AI machine vision sensor for object detection and identification in the range of 30 cm to 9 m. A mobile application with a text-to-voice feature is developed that connects with the cane using Bluetooth to provide voice warnings to the user based on processed sensor output. The developed cane is also equipped with a vibration sensor activated when an obstacle is closer than 30 cm, serving as a backup of voice warnings. Object detection and classification using multiple sources (AI vision sensor, ultrasonic sensor, and RFID readers) ensures robust and uninterrupted operation in case of failure of one sensor. In emergencies, users can send text messages with their location to a pre-selected caretaker's phone number using the installed SOS button. The rechargeable battery offers more than 24 hours of operation and warns users when the battery is low. The prototype has been successfully executed and tested in various environments and aims to improve the safety and quality of life for visually impaired individuals and hospital patients. © 2023 IEEE.

关键词： Object detection

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Infrastructure-dependent ramp-metering control for METANET-s 63

Infrastructure-dependent ramp-metering control for METANET-s

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Kamalifar, Ayda Cenedese, Carlo Cucuzzella, Michele Ferrara, Antonella University of Pavia Department of Electrical Computer Science and Biomedical Engineering Pavia27100 Italy Eth Zürich Department of Information Technology and Electrical Engineering Zürich Switzerland Nijenborgh 4 Groningen9747 AG Netherlands

ISBN: (纸本)9798350316339

In this paper, we propose a novel infrastructure-dependent ramp-metering control for the recently proposed METANET with service station (METANET-s) model, i.e., a second-order macroscopic traffic model that, compared to the classical METANET, incorporates the dynamics of service stations on highways. We study the effect of a ramp-metering control scheme on a highway stretch with a service station and show that it is capable of actively regulate internal traffic demand attempting to exit the service station via its on-ramp, on top of contributing to decrease the traffic congestion on the mainstream. In fact, the proposed control scheme effectively prevents the backlog of vehicles attempting to merge back onto the mainstream. This dynamic control mechanism is further endowed by a route guidance control strategy increasing the share of vehicles stopping at the service station during main-stream congestion periods, e.g. via incentives. The combined effect of our control schemes allows to take full advantage of the presence of service stations, reducing the overall traffic congestion. Simulation results demonstrate the effectiveness of the proposed control strategies. © 2024 IEEE.

关键词： Traffic congestion

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