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Reflection-mode diffraction tomography of multiple-scattering samples on a reflective substrate from intensity images

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Optica 2025年第3期12卷 406-417页

作者： Li, Tongyu Zhu, Jiabei Shen, Yi Tian, Lei Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States

We introduce a reflection-mode diffraction tomography technique that enables the simultaneous recovery of forward and backward-scattering information for high-resolution 3D refractive index reconstruction. Our technique works by imaging a sample on a highly reflective substrate and employing a multiple-scattering model and a reconstruction algorithm. It combines the modified Born series as the forward model, Bloch and perfect electric conductor boundary conditions to handle oblique incidence and substrate reflections, and the adjoint method for efficient gradient computation in solving the inverse-scattering problem. We validate the technique through simulations and experiments, achieving accurate reconstructions in samples with high refractive index contrasts and complex geometries. Forward scattering captures smooth axial features, while backward scattering reveals complementary interfacial details. Experimental results on dual-layer resolution targets, 3D randomly distributed beads, phase structures obscured by highly scattering fibers, fixed breast cancer cells, and fixed C. elegans demonstrate its robustness and versatility. This technique holds promise for applications in semiconductor metrology and biomedical imaging. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Multiple scattering

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Adaptive algorithm for controlling the power management system on offshore jack-up drilling rigs

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Energy Sources, Part A: Recovery, Utilization and Environmental Effects 2024年第1期46卷 16717-16737页

作者： Cemeljic, Hrvoje Havelka, Juraj Jeremic, Aleksandar Kuzle, Igor Department of Energy and Power Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Electrical & Computer Engineering McMaster School of Biomedical Engineering McMaster University HamiltonON Canada Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

The power systems of offshore jack-up drilling rigs consist of diesel generators running in parallel load-sharing mode, controlled by an automatic Power Management System (PMS). In this paper, the operational performance of the diesel generators (DG) and the PMS on a jack-up drilling rig is investigated, focusing on two critical offshore drilling operations: "pipe tripping" and "pulling/pumping out of the hole" (POOH). During these operations, large power swings occur in the system, subjecting the DGs to sudden load surges. These severe imbalances in the system cause the PMS to intermittently start and stop the engines throughout the operation, leading to various problematic conditions in the power plant. The paper provides a novel solution for intermittent starting and stopping of the DGs in the form of development and implementation of an adaptive PMS algorithm, a mathematical model of the system and a machine learning approach to event prediction of PMS operation in offshore drilling. Research demonstrates a noteworthy decrease in intermittent DG starting and stopping when the adaptive algorithm is implemented, with a 96% reduction during pipe tripping and an 87% reduction during POOH. This improvement comes at the expense of an 11% increase in engine running hours and a 1.5% rise in fuel consumption. Furthermore, by configuring the adaptive algorithm to economy mode, savings of up to 1700 liters of fuel per month are achievable. © 2024 Taylor & Francis Group, LLC.

关键词： Adaptive algorithms

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Computational coherent Raman scattering imaging:breaking physical barriers by fusion of advanced instrumentation and data science

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eLight 2023年第1期3卷 211-229页

作者： Haonan Lin Ji-Xin Cheng Department of Biomedical Engineering Department of Electrical and Computer EngineeringPhotonics CenterBoston UniversityBostonMA 02215USA

Coherent Raman scattering(CRS)microscopy is a chemical imaging modality that provides contrast based on intrinsic biomolecular *** date,endeavors on instrumentation have advanced CRS into a powerful analytical tool for studies of cell functions and in situ clinical ***,the small cross-section of Raman scattering sets up a physical boundary for the design space of a CRS system,which trades off speed,signal fidelity and spectral *** synergistic combination of instrumentation and computational approaches offers a way to break the *** this review,we first introduce coherent Raman scattering and recent instrumentation developments,then discuss current computational CRS imaging methods,including compressive micro-spectroscopy,computational volumetric imaging,as well as machine learning algorithms that improve system performance and decipher chemical *** foresee a constant permeation of computational concepts and algorithms to push the capability boundary of CRS microscopy.

关键词： Coherent anti-Stokes Raman scattering Stimulated Raman scattering Computational imaging Hyperspectral imaging Deep learning

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Discrete Current Controller for SPMSMs at Low Sampling-to-Fundamental Frequency Ratios Using Critical Damping Feedback

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IEEE Transactions on Power Electronics 2025年第8期40卷 11787-11798页

作者： Huang, Zhen Zhu, Rui Zhang, Yun Zanchetta, Pericle Nanchang University School of Information Engineering Nanchang330000 China Tianjin University School of Electrical and Information Engineering Tianjin300072 China University of Pavia Electrical Computer and Biomedical Engineering Department Pavia27100 Italy

The discrete current controllers can generally ensure high-performance machine control. However, for high-speed machines associated with low sampling-to-fundamental frequency ratios (SFR), the unavoidable disturbances will seriously affect the control performance. To address this issue, an advanced discrete current controller is proposed in this article. The critical damping feedback is used to achieve the maximum damping ratio of the disturbance loop at any SFR. Compared with the existing discrete controllers, the proposed one can significantly reduce the impact of multiple parameter mismatches on the performance of the controller while improving the current tracking response and enhancing the antidisturbance capability. Finally, the experimental results on a 10 krpm high-speed permanent magnet synchronous machine with the lowest SFR of 8 have proven the feasibility and advantages of the addressed current controller. © 1986-2012 IEEE.

关键词： Damping

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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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A Novel Linguistic Relational Fuzzy C-Means

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IEEE Access 2025年 13卷 89332-89342页

作者： Phaknonkul, Peerawich Auephanwiriyakul, Sansanee Theera-Umpon, Nipon Chiang Mai University Faculty of Engineering Department of Computer Engineering Chiang Mai50200 Thailand Chiang Mai University Biomedical Engineering and Innovation Research Center Biomedical Engineering Institute Chiang Mai50200 Thailand Chiang Mai University Faculty of Engineering Department of Electrical Engineering Chiang Mai50200 Thailand

In real-world applications, sometimes there are uncertainties in the data sets whether from the collection process or from the natural languages. Moreover, the data may come in the form of fuzzy relation. To analyze data, one might use one of popular tools, i.e., a clustering method. There are several types of clustering including clustering on information from objects themselves or clustering on a relational data. Currently, there are few researches on a fuzzy data clustering and a fuzzy relational data clustering. However, those algorithms are either defuzzifying the data or the relation of the data before implementing the clustering Therefore, in this paper, we develop a linguistic relational fuzzy C-means (LRFCM) that works with fuzzy relation of vectors of fuzzy numbers directly. The algorithm is an extension of the regular relational fuzzy C-means (RFCM). The proposed algorithm does not defuzzify fuzzy attributes beforehand. We found that the results from the LRFCM are similar to that from the RFCM. However, the results from the LRFCM can provide information that contains all the uncertainties from the inputs while the RFCM cannot. We also compare the results with the fuzzy C-means clustering. These comparisons show that the LRFCM behaves the same way as the RFCM and the FCM in partitioning the input space into clusters. © 2013 IEEE.

关键词： Fuzzy clustering

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Label Consistent Generalized Adaptive Weighted Recursive Least Squares Dictionary Learning 6

Label Consistent Generalized Adaptive Weighted Recursive Lea...

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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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A Biasing Scheme for the Gain Compression Point Optimization of HBT Cascode D-Band LNAs

A Biasing Scheme for the Gain Compression Point Optimization...

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2024 IEEE BiCMOS and Compound Semiconductor Integrated Circuits and Technology Symposium, BCICTS 2024

作者： Serra, Lorenzo De Filippi, Guglielmo Piotto, Lorenzo Mazzanti, Andrea University of Pavia Department of Electrical Computer and Biomedical Engineering Italy

ISBN: (纸本)9798331541248

Cascode amplifiers with a matching network between the common-emitter (CE) and the common-base (CB) HBTs are popular in the sub-THz band for their enhanced gain. The stage is analyzed in this paper with focus on the large-signal behavior. It is first observed that the CE-CB interstage network can push the CB out of the linear class-A regime, limiting the input power at 1 dB gain compression. It is then shown that the issue can be avoided with a suitable biasing scheme, leading to enhanced dynamic range with superior power efficiency. The concept is applied to the design of a D-band LNA in BiCMOS technology. With a quiescent power consumption of 20 mW from 2 V supply, the LNA shows 17.5 dB gain and 5.5 dB NF at 170 GHz. The output-referred 1dB compression point is 4.2dBm with 9% power efficiency at 175 GHz. Compared to previously reported LNAs in the same band, measured results demonstrate a remarkable dynamic range improvement with the the highest power efficiency. © 2024 IEEE.

关键词： BiCMOS technology

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Early Diagnosis of Osteoporosis: An Artificial Intelligence-Based Framework 7

Early Diagnosis of Osteoporosis: An Artificial Intelligence-...

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7th International Conference on Advances in biomedical engineering, ICABME 2023

作者： Zaylaa, Amira J. Daher, Alaa Ayache, Mohammad Omeirat, Niveen Mahmoud, Razane Hawili, Bader Abou Beirut Arab University Biomedical Engineering Laboratory Biomedical Engineering Program Electrical and Computer Engineering Faculty of Engineering Lebanon

ISBN: (纸本)9798350325850

Osteoporosis is a common disease characterized by low bone density and structural deterioration of bone tissue. For a successful course of treatment and fracture avoidance, early diagnosis of this disease is essential. The aim was to provide a novel method for osteoporosis prediction using Artificial Intelligence (AI)-based framework. The purpose was to predict the likelihood of osteoporosis based on the Bone Mineral Density (BMD), along with other characteristics such as age, weight, height, gender, and Body Mass Index (BMI) extracted from medical reports and images collected from a comprehensive medical center in Lebanon. Three machine-learning algorithms were implemented and tested, Logistic Regression (LR), Support Vector Machines (SVM), and Decision Trees (DT). Variety of quantitative statistical metrics were used to evaluate the performance of our framework, upon training and testing our algorithms. The metrics that were employed to evaluate our results included accuracy, precision, sensitivity, and F-score, in addition to the Receiver Operating Characteristic (ROC) Curve and the Area Under the Curve (AUC). Experimental Results demonstrated that both the SVM and LR algorithms achieved the highest accuracy of detection of osteoporosis as compared to existing algorithms applied in this field, with an accuracy of 89%. The sensitivity of diagnosis obtained was 98% by LR and 97% by SVM and surpassed the sensitivity obtained by DT. As such LR showed the best performance. The output of the algorithms could help medical doctors assess patients automatically. These findings demonstrated the potential of AI in osteoporosis prediction and thus prevention, highlighting the significance of early diagnosis. Thereby as a future prospect, choosing carefully the framework is crucial and additional algorithms have to be considered and tested. © 2023 IEEE.

关键词： Decision trees

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