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NATURE REVIEWS METHODS PRIMERS 2023年第1期3卷 1-19页

作者： [Anonymous] Department of Electrical and Computer Engineering The Ohio State University Columbus OH USA Department of Electrical and Computer Engineering University of Toronto Toronto Ontario Canada Department of Electrical and Computer Engineering University of Utah Salt Lake City UT USA Department of Electrical Engineering Pohang University of Science and Technology Pohang Korea Department of Electrical and Electronic Engineering The University of Manchester Manchester UK Biomedical Engineering Department Northwestern University Evanston IL USA Department of Electrical and Software Engineering University of Calgary Calgary Canada Department of Electrical and Computer Engineering Purdue University West Lafayette IN USA

This PrimeView highlights how algorithm and hardware develops could expand the application range of finite-difference time-domain methods.

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A Long Short-Term Memory-Based Interconnected Architecture for Classification of Grasp Types Using Surface-Electromyography Signals

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 434-445页

作者： Erazo, Andres Ko, Seok-Bum University of Saskatchewan Division of Biomedical Engineering SaskatchewanSKS7N 5A2 Canada University of Saskatchewan Department of Electrical and Computer Engineering Division of Biomedical Engineering SaskatchewanSKS7N 5A2 Canada

Reliable classification of grasp types from human limbs has become an important aspect used by applications with humanoid robotic systems, because of their high-accuracy implementations in human movement replication and detection. biomedical features extracted from muscular signals are commonly used for this purpose, however, their extraction and usage have been targeted independently, with time series features not even considered in the classification stage. Recently, studies show deep neural networks could obtain the signal's features in their internal architecture and use them directly over a classification task, avoiding all preprocessing steps and improving the obtained accuracy. Therefore, the current study proposes a deep architecture based on long short-term memory networks for the classification of six grasp types as an end-to-end deep model approach, working with raw surface electromyography signals. Classification accuracy of 99.12% was obtained and compared with previous studies which use different machine learning techniques over the same dataset. Results obtained showed that our model's architecture improves previous results as well as provides a robust solution avoiding overfitting, with an F1-score higher than 99% for all grasp types. © 2020 IEEE.

关键词： Time series analysis

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State-of-the-Art in Cyber-Physical Security for Dynamic Wireless Charging of Electric Vehicles 50

State-of-the-Art in Cyber-Physical Security for Dynamic Wire...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Yazdanipour, Samaneh Oruganti, V.S.R. Varaprasad Son, Jeonggi Arani, Mohammadreza F.M. Williamson, Sheldon S. Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering Toronto Canada Ontario Tech University Department of Electrical Computer and Software Engineering Oshawa Canada

ISBN: (纸本)9781665464543

Dynamic Wireless Charging systems for electric vehicles offers a revolutionary approach to EV charging, enabling vehicles to charge while in motion and overcoming the limitations of stationary wireless charging. Despite their efficiency and convenience, dynamic wireless charging systems introduce complex security vulnerabilities that must be addressed to ensure system reliability and safety. This paper provides a comprehensive review of the cyber-physical security challenges associated with electric vehicle dynamic wireless charging systems, examining potential threats such as cyber attacks and physical tampering. It identifies specific vulnerabilities in communication protocols, hardware, and software, and discusses various mitigation strategies such as encryption and physical layer security enhancements. Furthermore, the paper highlights recent advancements in security technologies and outlines future research directions to enhance the resilience of dynamic wireless charging systems. By addressing these security challenges, stakeholders can foster a safer environment for the deployment of dynamic wireless charging, thereby promoting the broader adoption of electric vehicles and advancing sustainable transportation solutions. © 2024 IEEE.

关键词： State of charge

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Fabrication and characterization of magnesium-based nanocomposites reinforced with Baghdadite and carbon nanotubes for orthopaedical applications

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Journal of Magnesium and Alloys 2024年第12期12卷 5144-5163页

作者： Mojtaba Ansari Shiva Mahdavikia Hossein Eslami Mozhdeh Saghalaini Hamid Taghipour Fatemeh Zare Shahin Shirani Mohammad Hossein Alizadeh Roknabadi Department of Biomedical Engineering Meybod UniversityMeybodIran Department of Biomedical Engineering Amirkabir University of TechnologyTehranIran Center for Education and Research on Macromolecules(CERM) CESAM Research UnitDepartment of ChemistryUniversity of LiègeLiègeBelgium Department of Electrical and Computer Engineering Texas A&M UniversityCollege StationTXUSA Department of Aerospace Engineering Amirkabir University of TechnologyTehranIran

This study explores the potential of Mg/Carbon Nanotubes/Baghdadite composites as biomaterials for bone regeneration and repair while addressing the obstacles to their clinical *** powder was synthesized using the sol-gel method to ensure a fine distribution within the Mg/CNTs ***/1.5 wt.%CNT composites were reinforced with BAG at weight fractions of 0.5,1.0,and 1.5 wt.%using spark plasma sintering at 450℃and 50 MPa after homogenization via ball *** cellular bioactivity of these nanocomposites was evaluated using human osteoblast-like cells and adipose-derived mesenchymal stromal *** proliferation and attachment of MG-63cells were assessed and visualized using the methylthiazol tetrazolium(MTT)assay and SEM,while AD-MSC differentiation was measured using alkaline phosphatase activity *** were also generated to visualize the diameter distributions of particles in SEM images using image processing *** Mg/CNTs/0.5 wt.%BAG composite demonstrated optimal mechanical properties,with compressive strength,yield strength,and fracture strain of 259.75 MPa,180.25 MPa,and 31.65%,*** learning models,including CNN,LSTM,and GRU,were employed to predict stress-strain relationships across varying BAG amounts,aiming to accurately model these curves without requiring extensive physical *** shown by contact angle measurements,enhanced hydrophilicity promoted better cell adhesion and ***,corrosion resistance improved with a higher BAG *** study concludes that Mg/CNTs composites reinforced with BAG concentrations below 1.0 wt.%offer promising biodegradable implant materials for orthopedic applications,featuring adequate load-bearing capacity and improved corrosion resistance.

关键词： Biodegradable material Magnesium-based composite Carbon nanotube Baghdadite Spark plasma sintering

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Cardiac Ischemia Detection Using Self-Organizing Map Feature Selection Method 31

Cardiac Ischemia Detection Using Self-Organizing Map Feature...

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31st National and 9th International Iranian Conference on biomedical engineering, ICBME 2024

作者： Karampour, Ramin Aghili Bakhtiar, Amir Arshia Hajizamani, Kiarash Fallahi, Alireza Zadeh, Hamid Soltanian Hamedan University of Technology Biomedical Engineering Department Hamedan Iran School of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331529710

Cardiac ischemia, a prevalent cause of heart failure, remains the leading cause of death in Iran. Early diagnosis of this condition is crucial, and electrocardiogram (ECG) signal processing techniques offer valuable insights. By analyzing ECG signals from ischemic patients and comparing them to those of normal individuals, we can extract relevant features for classification. Our study extracted some features from ECG signals, including statistical, time-based, frequency, and time-frequency features. We employed the self-organizing maps method (SOM) to identify the most informative features. Subsequently, we evaluated the classification accuracy using several machine learning algorithms, including support vector machines (SVM), Naive Bayes, Linear Discriminant Analysis (LDA), and Logistic Regression. The results demonstrated the superiority of the SOM method in selecting the best features compared to PCA, with accuracies of 9 1.2%, 84.3%, 72.5%, and 88.9% for SVM, LDA, NB, and LR methods, respectively. Our findings contribute to the early detection and management of cardiac ischemia, potentially improving patient outcomes. © 2024 IEEE.

关键词： Conformal mapping

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Microstrip Patch Antenna with an Inverted T-Type Notch in the Partial Ground for Breast Cancer Detections

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computer Modeling in engineering & Sciences 2024年第2期138卷 1301-1322页

作者： Nure Alam Chowdhury Lulu Wang Md Shazzadul Islam Linxia Gu Mehmet Kaya Department of Biomedical Engineering and Science Florida Institute of TechnologyMelbourneFL 32901USA Biomedical Device Innovation Center Shenzhen Technology UniversityShenzhen518118China Department of Electrical and Computer Engineering International Islamic University MalaysiaJalan GombakSelangor53100Malaysia

This study designs a microstrip patch antenna with an inverted T-type notch in the partial ground to detect tumorcells inside the human *** size of the current antenna is small enough(18mm×21mm×1.6mm)todistribute around the breast *** operating frequency has been observed from6–14GHzwith a minimumreturn loss of−61.18 dB and themaximumgain of current proposed antenna is 5.8 dBiwhich is flexiblewith respectto the size of *** the distribution of eight antennas around the breast phantom,the return loss curveswere observed in the presence and absence of tumor cells inside the breast phantom,and these observations showa sharp difference between the presence and absence of tumor *** simulated results show that this proposedantenna is suitable for early detection of cancerous cells inside the breast.

关键词： Antenna microwave wideband cancer breast phantom tumor detection

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Semi‑supervised contour‑driven broad learning system for autonomous segmentation of concealed prohibited baggage items

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Visual Computing for Industry,Biomedicine,and Art 2024年第1期7卷 1-18页

作者： Divya Velayudhan Abdelfatah Ahmed Taimur Hassan Muhammad Owais Neha Gour Mohammed Bennamoun Ernesto Damiani Naoufel Werghi Department of Electrical Engineering and Computer Sciences Center for Cyber‑Physical SystemsKhalifa University of Science and TechnologyAbu Dhabi 127788United Arab Emirates Department of Electrical Computer and Biomedical EngineeringAbu Dhabi UniversityAbu Dhabi 59911United Arab Emirates Department of Computer Science and Software Engineering the University of Western AustraliaPerthWA 6009Australia

With the exponential rise in global air traffic,ensuring swift passenger processing while countering potential security threats has become a paramount concern for aviation *** X-ray baggage monitoring is now standard,manual screening has several limitations,including the propensity for errors,and raises concerns about passenger *** address these drawbacks,researchers have leveraged recent advances in deep learning to design threatsegmentation ***,these models require extensive training data and labour-intensive dense pixelwise annotations and are finetuned separately for each dataset to account for inter-dataset ***,this study proposes a semi-supervised contour-driven broad learning system(BLS)for X-ray baggage security threat instance segmentation referred to as *** research methodology involved enhancing representation learning and achieving faster training capability to tackle severe occlusion and class imbalance using a single training routine with limited baggage *** proposed framework was trained with minimal supervision using resource-efficient image-level labels to localize illegal items in multi-vendor baggage *** specifically,the framework generated candidate region segments from the input X-ray scans based on local intensity transition cues,effectively identifying concealed prohibited items without entire baggage *** multi-convolutional BLS exploits the rich complementary features extracted from these region segments to predict object categories,including threat and benign *** contours corresponding to the region segments predicted as threats were then utilized to yield the segmentation *** proposed C-BLX system was thoroughly evaluated on three highly imbalanced public datasets and surpassed other competitive approaches in baggage-threat segmentation,yielding 90.04%,78.92%,and 59.44%in terms of mIoU on GDXray,SIXray,and Compass-XP,***,the lim

关键词： Baggage X-ray imagery Broad learning systems Threat detection Threat segmentation

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DrugEL: Ensemble Learning Model for Identification of Druggable Proteins

DrugEL: Ensemble Learning Model for Identification of Drugga...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ali, Md. Mamun Ahmed, Kawsar Bui, Francis M. Wu, Fang-Xiang University of Saskatchewan Division of Biomedical Engineering Saskatoon Canada University of Saskatchewan Department of Electrical and Computer Engineering Saskatoon Canada University of Saskatchewan Division of Biomedical Engineering Department of Mechanical Engineering Department of Computer Science Saskatoon Canada

ISBN: (纸本)9798350386226

Druggable proteins are defined as proteins that can interact with drugs to modulate certain biological activity. The identification of druggable proteins holds significant clinical importance, directly impacting the development of targeted therapies for diseases like cancer and metabolic disorders. Identifying druggable proteins involves various methods, including computational prediction models, mass spectrometry (MS), and biochemical assays, but achieving high accuracy remains a challenge. This study proposes DrugEL, an ensemble learning model that uses Bayesian inference to integrate predictions from multiple algorithms: Random Forest (RF), K-Nearest Neighbor (KNN), LightGBM (LGBM), and Decision Tree (DT) with seven feature extraction methods (LSA, AAC, PAAC, GAAC, NMBroto, AAIndex, and KNN). The results show that DrugEL outperforms existing models in terms of accuracy (0.9758), MCC (0.9515), AUC (0.9758), sensitivity (0.9742) and specificity (0.9774), particularly excelling with the LSA method. © 2024 IEEE.

关键词： Contrastive Learning

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Enhancing Digital Content Accessibility for the Hearing Impaired through AI-Driven Visual Representations 10

Enhancing Digital Content Accessibility for the Hearing Impa...

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10th International Conference on Artificial Intelligence and Robotics, QICAR 2024

作者： Hatami, Mahdieh Chegini, Mehran Faculty of Electrical Computer IT & Biomedical Engineering Islamic Azad Universuty of Qazvin Qazvin Iran

ISBN: (纸本)9798350348873

Ensuring inclusivity for individuals with hearing impairments is paramount in an increasingly digital world. This article explores an innovative solution that leverages artificial intelligence (AI) and machine learning to enhance content comprehension for this demographic. We present a system that utilizes deep learning models and natural language processing algorithms to convert spoken language into visual representations, including emojis and images. Our approach empowers the hearing impaired to access and interpret information more effectively by bridging the gap between audio-based content and visual cues. Extensive experiments and user studies confirm the system's effectiveness, significantly improving content understanding and engagement. This novel technology opens new avenues for independent and comprehensive comprehension of audio-based content by individuals with hearing impairments. ©2024 IEEE.

关键词： Audition

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A Review of Machine Learning Methods for Screening Cardiac Abnormalities using Auscultation 10

A Review of Machine Learning Methods for Screening Cardiac A...

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10th International Conference on engineering and Emerging Technologies, ICEET 2024

作者： Alhasan, Sarah Poorshamsi, Zahra Nasser, Loubna Alzaabi, Alyaziah Ghazal, Mohammed Yousaf, Jawad Hassan, Taimur Abu Dhabi University Department of Electrical Computer and Biomedical Engineering United Arab Emirates

ISBN: (纸本)9798331532895

This paper presents a thorough review of various methodologies employed in heart sound classification, combining both conventional machine learning (ML) algorithms and complex deep learning (DL) approaches. We systematically evaluate different data collection approaches, preprocessing techniques, methodologies, and classification mechanisms. Further-more, the review analyzes several feature extraction methods required for capturing relevant signal characteristics, spanning Mel-frequency cepstral coefficients (MFCC), continuous wavelet transform (CWT), and convolutional neural networks (CNNs). Additionally, we examine a variety of classification methods, from traditional methods like support vector machines (SVMs), Knearest neighbors (KNNs), and Decision Trees (DTs), to more sophisticated parametric approaches such as recurrent neural networks (RNNs), (CNNs), long-short term memory (LSTM), and even ensemble techniques, signifying the potential of combining classification approaches. Key findings reveal a trend toward employing hybrid models that merge the capabilities of multiple techniques to enhance cardiac diagnostics precision. This comprehensive overview aims to explore existing methodologies and move toward bridging the gap in today's technologies, establishing a robust diagnostic tool for medical applications. © 2024 IEEE.

关键词： Long short-term memory

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