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6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

作者： Yousefi, Mohadeseh Naderahmadian, Yashar Beheshti, Soosan University of Guilan Department of Electrical and Computer Engineering Rasht Iran Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering Toronto Canada

ISBN: (纸本)9798331506520

The Generalized Adaptive Weighted Recursive Least Squares (GAWRLS) dictionary learning method has shown potential for unsupervised dictionary learning. This paper advances GAWRLS by incorporating classification error as an additional cost to enable supervised learning tasks and introduces the Label Consistency for online supervised dictionary learning in classification tasks. The new method is denoted as Label Consistent Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning (LC-GAWRLS). By incorporating both sparse representation error and classification error into the cost function, LC-GAWRLS enables simultaneous learning of the dictionary and classifier parameters. Particularly, to ensure label consistency, the proposed algorithm introduces a correction weight to adaptively regulate the impact of each training data during the model update, enhancing robustness against variations in training data compared to previous dictionary learning methods. Simulation results on real datasets demonstrate that LC-GAWRLS achieves higher classification accuracy compared to existing state-of-the-art supervised dictionary learning methods, particularly in scenarios with limited training samples per class. © 2025 IEEE.

关键词： Self-supervised learning

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Semi-Supervised Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning 6

Semi-Supervised Generalized Adaptive Weighted Recursive Leas...

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6th IEEE International Conference on Image Processing, Applications and Systems, IPAS 2025

ISBN: (纸本)9798331506520

This work presents a novel semi-supervised dictionary learning framework that updates the dictionary by online learning and is efficient in utilizing the training data. The method employs a two-stage process to train the dictionary: initial training with limited labeled data, followed by online refinement using abundant unlabeled data. We introduce an adaptive correction weight to control the influence of new unlabeled data on the dictionary update based on its consistency with the current model estimate. This approach enables efficient use of the training data set. Moreover, results in faster dictionary convergence and improves data representation accuracy, especially in scenarios with limited training data. Experimental results demonstrate significant enhancement in the classification accuracy of the proposed method compared to the state-of-the-art semi-supervised dictionary learning methods, particularly when dealing with a limited number of training samples. © 2025 IEEE.

关键词： Self-supervised learning

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Magnetic nanoparticle detection methods in the context of complex fluids

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International Journal of Coal Science & Technology 2024年第6期11卷 173-184页

作者： Alirza Orujov Jon M.Pikal TeYu Chien Jefferson A.Carter Saman A.Aryana Deptartment of Chemical and Biomedical Engineering University of WyomingLaramieWYUSA Deptartment of Electrical Engineering and Computer Science University of WyomingLaramieWYUSA Deptartment of Physics and Astronomy University of WyomingLaramieWYUSA

Foams improve mobility control in injection operations within geological ***,such as iron-oxide,have been shown to enhance the stability of foams when combined with *** this research,we leverage the magnetic properties of these nanoparticles to detect their presence as a surrogate for monitoring the geologic extent of injected fluids in the *** feasibility of using these nanoparticles for monitoring purposes stems from their detectability at low concentrations in subsurface *** developed two distinct methods to detect the presence of magnetite nanoparticles in complex *** simulate complex subsurface fluids in a laboratory setting,we included various ions and surfactants and investigated their effects on the detection of *** this end,we designed an experimental setup and tested two magnetic detection methods:Induction Heating(IH)and Oscillator Frequency Shift(OFS).The IH method involves applying a high-frequency alternating magnetic field to a solution containing small amounts of magnetic nanoparticles and measuring the temperature *** built an experimental setup to generate this magnetic field for different samples,with temperature changes recorded by an infrared *** results indicate that nanoparticle concentrations linearly affect the solution's temperature ***,the presence of ions and surfactants also influences the temperature *** OFS method measures shifts in the resonance frequency of a circuit caused by changes in magnetic permeability inside a *** coil is part of a transistor oscillator circuit that produces a sinusoidal voltage waveform,with the oscillation frequency depending on the coil’s *** presence of nanoparticles causes a shift in resonance frequency,which were precisely measured for various *** drop in resonance frequency is a linear function of nanoparticle concentration,and both methods detect concentrations as low as 15

关键词： CO_(2) storage Monitoring Nanoparticle-stabilized foams Sequestration Subsurface Tracer nanoparticles

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Metaheuristic-Driven Two-Stage Ensemble Deep Learning for Lung/Colon Cancer Classification

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computers, Materials & Continua 2024年第9期80卷 3855-3880页

作者： Pouyan Razmjouei Elaheh Moharamkhani Mohamad Hasanvand Maryam Daneshfar Mohammad Shokouhifar Department of Electrical&Computer Engineering Valie-e-Asr University of RafsanjanKerman***Iran International Academic Relations Office(IRO) Computer Science DepartmentUniversity of HalabjaHalabja46018Iraq Department of Computer Engineering University of Mohaghegh ArdabiliArdabil***Iran Department of Biomedical Engineering Islamic Azad University of MashhadMashhad***Iran Department of Electrical&Computer Engineering Shahid Beheshti UniversityTehran***Iran

This study investigates the application of deep learning,ensemble learning,metaheuristic optimization,and image processing techniques for detecting lung and colon cancers,aiming to enhance treatment efficacy and improve survival *** introduce a metaheuristic-driven two-stage ensemble deep learning model for efficient lung/colon cancer *** diagnosis of lung and colon cancers is attempted using several unique indicators by different versions of deep Convolutional Neural Networks(CNNs)in feature extraction and model constructions,and utilizing the power of various Machine Learning(ML)algorithms for final ***,we consider different scenarios consisting of two-class colon cancer,three-class lung cancer,and fiveclass combined lung/colon cancer to conduct feature extraction using four *** extracted features are then integrated to create a comprehensive feature *** the next step,the optimization of the feature selection is conducted using a metaheuristic algorithm based on the Electric Eel Foraging Optimization(EEFO).This optimized feature subset is subsequently employed in various ML algorithms to determine the most effective ones through a rigorous evaluation *** top-performing algorithms are refined using the High-Performance Filter(HPF)and integrated into an ensemble learning framework employing weighted *** findings indicate that the proposed ensemble learning model significantly surpasses existing methods in classification accuracy across all datasets,achieving accuracies of 99.85%for the two-class,98.70%for the three-class,and 98.96%for the five-class datasets.

关键词： Lung cancer colon cancer feature selection electric eel foraging optimization deep learning ensemble learning

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Fabrication of LCE Microactuator Arrays Through Soft Lithography With Surface Alignment

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Journal of Microelectromechanical Systems 2025年第3期34卷 241-243页

作者： Li, Ke Kim, Jae-Jun Fernandes, Jayer Jiang, Hongrui University of Wisconsin–Madison Department of Electrical and Computer Engineering MadisonWI53705 United States University of Wisconsin–Madison Department of Electrical and Computer Engineering Department of Biomedical Engineering Department of Materials Science and Engineering Department of Ophthalmology and Visual Sciences McPherson Eye Research Institute MadisonWI53706 United States

Liquid crystal elastomers (LCEs) offer potentially programmable actuation through precise molecular alignment, making them ideal for microactuators in soft robotics and optical systems. However, achieving precise microscale alignment for LCE actuator arrays through scalable microfabrication approaches has been challenging. This letter introduces a low-cost surface alignment method to fabricate LCE microactuator arrays, reducing the dependency on expensive equipment, improving accessibility and manufactuarability compared to existing studies using field-assisted alignment methods. Thermal actuation tests demonstrated strong thermal responsiveness and stability. Our method offers a superior approach to integrating LCE microactuator arrays with modern microfabrication processes, promising multitudinous applications in MEMS and beyond. © 2025 IEEE. All rights reserved,.

关键词： Microfabrication

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Fostering Human-AI interaction: development of a Clinical Decision Support System enhanced by eXplainable AI and Natural Language Processing 2

Fostering Human-AI interaction: development of a Clinical De...

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Joint of the 2nd World Conference on eXplainable Artificial Intelligence Late-Breaking Work, Demos and Doctoral Consortium, xAI-2024:LB/D/DC

作者： Bergomi, Laura Department of Electrical Computer and Biomedical Engineering University of Pavia Pavia Italy

Artificial Intelligence (AI) is increasingly integrated into Decision Support Systems (DSS). The explainability of AI-based systems becomes crucial in sensitive and critical domains, such as healthcare, where ethical considerations and reliability are paramount concerns. In the clinical setting, it is important to evaluate how humans and AI can collaborate on cognitive tasks. Collaboration protocols (HAI-CP) allow for the investigation of the usefulness of AI models and their impact on users (both positive and negative). Although research on the application of these methods is blooming, there is little understanding of the impact on clinical decision-making, especially for eXplainable AI (XAI) systems, due to the lack of user studies. Therefore, the goal of this proposal is to develop a clinical DSS enhanced by XAI and Natural Language Processing (NLP): their synergy can add value to the interaction between users and AI, fostering a more linguistically natural, comprehensible, trustworthy, and supporting interfacing, that blends into the existing workflows. This proposal explores potential solutions to tailor natural language explanations and data visualizations to the end-user, improving the comprehensibility of the reasons behind a decision, and increasing the user’s confidence in the decision;investigates and tests possible strategies to "get the patient-in-the-loop";explores uncertainty quantification and counterfactual approaches, and finally assesses the impact on naturalistic (i.e., real-world) decision-making and long-term effects and biases. © 2024 Copyright for this paper by its authors.

关键词： Decision support systems

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Multi-stage Segmentation of Diabetic Foot Ulcers Using Self-supervised Learning 4th

Multi-stage Segmentation of Diabetic Foot Ulcers Using Self...

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4th Diabetic Foot Ulcers Grand Challenge, DFUC 2024 held in Conjunction 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Gupta, Neetika Nangia, Ayush Khan, Naimul Mefraz Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University TorontoON Canada

ISBN: (纸本)9783031808708

Diabetic Foot Ulcers (DFUs) pose a significant healthcare challenge due to their potential to lead to severe complications, including infections and ischemia, often necessitating limb amputation or causing mortality. Accurate diagnosis of ulcer regions, and prompt response can prevent amputations and fatalities through early and reliable DFU detection. However, manual examination of DFUs is labor-intensive and prone to variability. In response to these challenges, we propose DFUC_CAM, a novel method for instance segmentation of DFUs using self-supervised learning (SSL). The proposed approach leverages a multi-stage methodology. Initially, a convolutional neural network (CNN) trained for classification is utilized to generate Class Activation Maps (CAMs), which localize and highlight discriminative regions within DFU images. These CAMs serve as pseudo-labels to train a segmentation network using a SSL framework. For the MICCAI 2024 Diabetic Foot Ulcer Challenge (DFUC 2024), we trained DFUC_CAM using the DFUC 2024 and FUSeg datasets. The FUSeg dataset primarily serves for the quantitative evaluation of our method. During the validation phase of DFUC 2024, our team, ***, achieved a mean Dice score of 0.342, and in the final testing phase, we improved this to 0.385. These results demonstrate the potential of our approach for enhancing DFU detection and segmentation in clinical settings. The code is available at: https://***/RyersonMultimediaLab/DFUC_CAM. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Self-supervised learning

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GraphAush: Combining Adversarial Learning and Graph Embedding for a Novel Shilling Attack Model Towards Robust Recommender Systems 11

GraphAush: Combining Adversarial Learning and Graph Embeddin...

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11th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2024

作者： Barnett, Clayton Kashef, Rasha Toronto Metropolitan University Electrical Computer and Biomedical Engineering Toronto Canada

ISBN: (纸本)9798350367300

Recommender systems (RS) are integral to modern e-commerce and content platforms. Yet, their reliance on user-item interaction data makes them vulnerable to shilling attacks, where fake data is injected to manipulate recommendations. Traditional shilling attack strategies utilize basic statistical properties of user-item data to create deceptive profiles. Still, recent advancements have shifted towards model-based attacks leveraging machine learning to enhance effectiveness and evade detection. This paper introduces GraphAush, a novel neural shilling attack model that employs Generative Adversarial Networks (GANs) with a novel generator shilling loss tailored to manipulate the user-item interaction graph. By incorporating a novel generator shilling loss function that leverages Node2Vec embeddings, GraphAush optimizes fake profile generation to maximize the effectiveness of attacks while minimizing detectability. This method overcomes the limitations of previous models, which either require intricate knowledge of the target RS or use indirect graph-based approaches. The efficacy of GraphAush was validated through experiments with multiple benchmark datasets, revealing its strong performance against traditional heuristic and other GAN-based attack methods. This innovative approach highlights a significant advancement in adversarial techniques for RS and sets a new benchmark for evaluating shilling attack strategies. © 2024 IEEE.

关键词： Generative adversarial networks

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D-band Stacked Common-Base Power Amplifier with 19.2dBm oP1dBin 55 nm SiGe BiCMOS 50

D-band Stacked Common-Base Power Amplifier with 19.2dBm oP1d...

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50th IEEE European Solid-State Electronics Research Conference, ESSERC 2024

作者： Bilato, Andrea Pirbazari, Mahmoud M. Mazzanti, Andrea University of Pavia Computer and Biomedical Engineering Dept. of Electrical Italy

ISBN: (纸本)9798350388138

This paper presents the design and experimental validation of a stacked common-base power amplifier (PA) tailored for D-band communication systems. Leveraging 55 nm SiGe BiCMOS technology, the proposed PA architecture combines the superior gain compression performance of the common-base topology with the higher voltage swing provided by stacked-transistor power amplifiers. Experimental results demonstrate a peak output power of 19.7 dBm with only a 0.5 dB lower 1-dB compression point (o P1dB) of 19.2 dBm at 135 GHz. The power added efficiency (PAE) reaches 12.1% at saturation and 11.5% at o P1dB. © 2024 IEEE.

关键词： BiCMOS technology

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Intrinsic Entropy: A Novel Adaptive Method for Measuring the Instantaneous Complexity of Time Series

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IEEE Signal Processing Letters 2023年 30卷 160-164页

作者： Huang, Po-Hsun Hsiao, Tzu-Chien Hsinchu300 Taiwan Nycu Department of Computer Science College of Cs and Institute of Biomedical Engineering College of Electrical and Computer Engineering Hsinchu300 Taiwan

The determination of appropriate parameters and an appropriate window size in most entropy-based measurements of time-series complexity is a challenging problem. Inappropriate settings can lead to the loss of intrinsic information within a time series. Therefore, two parameter-free methods, namely the intrinsic entropy (IE) and ensemble IE (eIE) methods, are proposed in this paper. The eIE method requires two parameters, which can be easily determined through an orthogonality test. The proposed approaches can measure instantaneous complexity;thus, they do not require a predetermined window size. White noise and three other varieties of colored noise were used to test the stability of the proposed methods, and five types of synthetic signals and logistic maps were applied for measuring instantaneous complexity and regularity. The results revealed that the IE and eIE methods exhibit satisfactory stability. Both methods provide point-by-point entropy measures for time series. The eIE method is useful for measuring the complexity of frequency and amplitude modulation. Furthermore, the periodicity of time series can be detected using the two proposed methods. © 1994-2012 IEEE.

关键词： Empirical mode decomposition

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