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arXiv 2020年

作者： Kouhpanji, Mohammad Reza Zamani Visscher, Pieter B. Stadler, Bethanie J. H. Department of Electrical and Computer Engineering University of Minnesota MinneapolisMN United States Department of Biomedical Engineering University of Minnesota MinneapolisMN United States Department of Chemical Engineering and Materials Science University of Minnesota MinneapolisMN United States Department of Physics and Astronomy University of Alabama United States

Magnetic nanoparticles (MNPs) have been proposed as an ultimate solution for diverse applications including nanomedicine and logic devices over decades. However, none has emerged revolutionary because realizing their magnetization response in an assembly is, surprisingly, still elusive. We employ our fast and universal magnetic characterization method, called the projection method, to underlie the reversible (irreversible) magnetization response of several assemblies. We then illustrate how the reversible (irreversible) magnetization response is correlated to the intrinsic properties (the coercivity and interaction fields) of the MNPs in an assembly. Our experimental observations indicate that the reversible magnetization does not solely depend on the interaction field, but it is indeed a function of the interaction field to coercivity ratio. Furthermore, for large porosities, the interaction field linearly changes with the porosity highlighting the predominant effects of dipole-dipole interactions on the interaction fields. However, at low porosities, the interaction field shows a nonlinear relationship with the porosity indicating the dipole fluctuations effects dominantly determine the interaction fields. Copyright © 2020, The Authors. All rights reserved.

关键词： Porosity

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Cell detection with convolutional spiking neural network for neuromorphic cytometry

APL Machine Learning

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APL Machine Learning 2024年第2期2卷

作者： Ziyao Zhang Haoxiang Yang Jason K. Eshraghian Jiayin Li Ken-Tye Yong Daniele Vigolo Helen M. McGuire Omid Kavehei School of Biomedical Engineering The University of Sydney New South Wales Australia Department of Electrical and Computer Engineering University of California San Diego California 92161 USA Faculty of Science University of Technology Sydney New South Wales Australia The University of Sydney Nano Institute The University of Sydney Sydney New South Wales Australia School of Medical Sciences Faculty of Medicine and Health The University of Sydney New South Wales Australia

Imaging flow cytometry (IFC) is an advanced cell-analytic technology offering rich spatial information and fluorescence intensity for multi-parametric characterization. Manual gating in cytometry data enables the classification of discrete populations from the sample based on extracted features. However, this expert-driven technique can be subjective and laborious, often presenting challenges in reproducibility and being inherently limited to bivariate analysis. Numerous AI-driven cell classifications have recently emerged to automate the process of including multivariate data with enhanced reproducibility and accuracy. Our previous work demonstrated the early development of neuromorphic imaging cytometry, evaluating its feasibility in resolving conventional frame-based imaging systems’ limitations in data redundancy, fluorescence sensitivity, and compromised throughput. Herein, we adopted a convolutional spiking neural network (SNN) combined with the YOLOv3 model (SNN-YOLO) to perform cell classification and detection on label-free samples under neuromorphic vision. Spiking techniques are inherently suitable post-processing techniques for neuromorphic vision sensing. The experiment was conducted with polystyrene-based microparticles, THP-1, and LL/2 cell lines. The network’s performance was compared with that of a traditional YOLOv3 model fed with event-generated frame data to serve as a baseline. In this work, our SNN-YOLO outperformed the YOLOv3 baseline by achieving the highest average class accuracy of 0.974, compared to 0.962 for YOLOv3. Both models reported comparable performances across other key metrics and should be further explored for future auto-gating strategies and cytometry applications.

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A real-time missing data recovery method using recurrent neural network for multiple transmissions 14th

A real-time missing data recovery method using recurrent neu...

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14th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2018

作者： Lin, Bor-Shing Lin, Yu-Syuan Lee, I-Jung Lin, Bor-Shyh Department of Computer Science and Information Engineering National Taipei University New Taipei City23741 Taiwan College of Electrical Engineering and Computer Science National Taipei University New Taipei City23741 Taiwan Institute of Imaging and Biomedical Photonics National Chiao Tung University Tainan71150 Taiwan

ISBN: (纸本)9783030037444

Data loss and recovery is a critical issue in data transmission. Traditional data recovery methods are impractical for use in real-time systems that require multiple transmissions. To solve this problem, this study proposed a recovery method based on a recurrent neural network, which is then used to build a pre-diction model. When a data gap occurs, the missing data can be recovered immediately using the predicted value. This method distributes the calculation and can immediately recover the data gap. Through a series of experiments, this study optimized different parameters in the neural network, thus optimizing the prediction model. © Springer Nature Switzerland AG 2019.

关键词： Recurrent neural networks

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Artificial intelligence applications in histopathology

Nature Reviews Electrical Engineering

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Nature Reviews electrical engineering 2024年第2期1卷 93-108页

作者： Cagla Deniz Bahadir Mohamed Omar Luigi Marchionni Benjamin Liechty David J. Pisapia Jacob Rosenthal Mert R. Sabuncu Biomedical Engineering Cornell University and Cornell Tech New York NY USA Pathology and Laboratory Medicine Weill Cornell Medicine New York NY USA Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program New York NY USA Radiology Weill Cornell Medicine New York NY USA Electrical and Computer Engineering Cornell University and Cornell Tech New York NY USA

Histopathology is a vital diagnostic discipline in medicine, fundamental to our understanding, detection, assessment and treatment of conditions such as cancer, dementia and heart disease. Traditionally, the standard workflow in histopathology has primarily relied on the visual interpretation of tissue samples carried out by human experts under a light microscope. Since the 2000s, thanks to advances in scanning technologies such as whole-slide imaging, histopathology is undergoing a digital transformation. The rapid increase in digital data is fuelling the development and application of artificial intelligence (AI) methods. In this Review, we delve into the latest progress in AI methods for histopathology, which promise to yield accurate, scalable, useful and affordable support tools for clinical decision. We examine the challenges and opportunities in this domain, exploring historically important approaches and problems that have shaped the field, while also highlighting recent technological breakthroughs that are poised to redefine its future. Furthermore, we offer an overview of publicly available datasets that have been instrumental in propelling the development of AI methods in histopathology.

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Single and multi-mode directional lasing from arrays of dielectric nanoresonators

arXiv

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arXiv 2020年

作者： Azzam, Shaimaa I. Chaudhuri, Krishnakali Lagutchev, Alexei Jacob, Zubin Kim, Young L. Shalaev, Vladimir M. Boltasseva, Alexandra Kildishev, Alexander V. School of Electrical & Computer Engineering Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Weldon School of Biomedical Engineering Purdue University West LafayetteIN47907 United States

The strong electric and magnetic resonances in dielectric subwavelength structures have enabled unique opportunities for efficient manipulation of light-matter interactions. Besides, the dramatic enhancement of nonlinear light-matter interactions near so-called bound states in the continuum (BICs) has recently attracted enormous attention due to potential advancements in all-optical and quantum computing. However, the experimental realizations and the applications of high-Q factor resonances in dielectric resonances in the visible regime have thus far been considerably limited. In this work, we explore the interplay of electric and magnetic dipoles in arrays of dielectric nanoresonators to enhance light-matter interaction. We report on the experimental realization of high-Q factor resonances in the visible through the collective diffractive coupling of electric and magnetic dipoles. Providing direct physical insights, we also show that coupling the Rayleigh anomaly of the array with the electric and magnetic dipoles of the individual nanoresonators can result in the formation of different types of BICs. We utilize the resonances in the visible regime to achieve lasing action at room temperature with high spatial directionality and low threshold. Finally, we experimentally demonstrate multi-mode, directional lasing and study the BIC-assisted lasing mode engineering in arrays of dielectric nanoresonators. We believe that our results enable a new range of applications in flat photonics through realizing on-chip controllable single and multi-wavelength micro-lasers. Copyright © 2020, The Authors. All rights reserved.

关键词： Magnetic resonance

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Optimal Synthesis of Wearable Antenna Array for Interference Reduction in 700MHz Band 19

Optimal Synthesis of Wearable Antenna Array for Interference...

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19th International Symposium on Electromagnetic Fields in Mechatronics, electrical and Electronic engineering, ISEF 2019

作者： Januszkiewicz, Lukasz Di Barba, Paolo Hausman, Slawomir Institute of Electronics Lodz University of Technology Wolczanska 211/215 Street Lodz Poland University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia27100 Italy

ISBN: (纸本)9781728115603

In this paper an optimization procedure for a wearable antenna array is presented. The array has switchable radiation pattern which is controlled by signal phase shift in each of four array elements. Radiation of the antenna can be controlled in such a way that the maximum gain can be directed towards a signal source while its minimum towards an interference. This is achieved by proper placement of array elements in the design stage and the selection of phase shift in elementary antenna feeding circuit during antenna operation. For this sake, an evolutionary-strategy based algorithm was applied together with a significantly simplified and cost-efficient model of the wearable antenna array. The results obtained with the proposed method show a performance improvement with respect to the initial heuristic design. © 2019 IEEE.

关键词： Optimization

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EEG-Based Preference Classification for Neuromarketing Application

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Computational Intelligence and Neuroscience 2023年第1期2023卷

作者： Injamamul Haque Sourov Faiyaz Alvi Ahmed Md. Tawhid Islam Opu Aunnoy K. Mutasim M. Raihanul Bashar Rayhan Sardar Tipu Md. Ashraful Amin Md. Kafiul Islam Biomedical Instrumentation and Signal Processing Lab Department of Electrical and Electronic Engineering Independent University Bangladesh Plot 16 Block B Bashundhara R/A Dhaka 1229 Bangladeshiub.edu.bd Agency Lab Department of Computer Science and Engineering Independent University Bangladesh Plot 16 Block B Bashundhara R/A Dhaka 1229 Bangladeshiub.edu.bd

Neuromarketing is a modern marketing research technique whereby consumers’ behavior is analyzed using neuroscientific approaches. In this work, an EEG database of consumers’ responses to image advertisements was created, processed, and studied with the goal of building predictive models that can classify the consumers’ preference based on their EEG data. Several types of analysis were performed using three classifier algorithms, namely, SVM, KNN, and NN pattern recognition. The maximum accuracy and sensitivity values are reported to be 75.7% and 95.8%, respectively, for the female subjects and the KNN classifier. In addition, the frontal region electrodes yielded the best selective channel performance. Finally, conforming to the obtained results, the KNN classifier is deemed best for preference classification problems. The newly created dataset and the results derived from it will help research communities conduct further studies in neuromarketing.

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Solving Energy-Independent Density for CT Metal Artifact Reduction via Neural Representation

arXiv

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arXiv 2024年

作者： Wu, Qing Guo, Xu Chen, Lixuan Liu, Yanyan He, Dongming Wang, Xudong Ren, Wuwei Chen, Xueli Zhang, Yifeng Zhou, S. Kevin Yu, Jingyi Zhang, Yuyao School of Information Science and Technology ShanghaiTech University Shanghai201210 China Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai201210 China University of Chinese Academy of Sciences Beijing101408 China School of Life Science and Technology ShanghaiTech University Shanghai201210 China Electrical and Computer Engineering University of Michigan MI48105 United States Shanghai United Imaging Intelligence Co. Ltd. Shanghai China Shanghai Ninth People’s Hospital Shanghai Jiao Tong China University School of Medicine Shanghai China Center for Biomedical-photonics and Molecular Imaging Advanced Diagnostic-Therapy Technology and Equipment Key Laboratory of Higher Education Institutions in Shaanxi Province School of Life Science and Technology Xidian University Shaanxi Xi’an710126 China University of Science and Technology of China Suzhou China School of Information Science and Technology Shanghai Engineering Research Center of Intelligent Vision and Imaging ShanghaiTech University Shanghai201210 China

X-ray CT often suffers from shadowing and streaking artifacts in the presence of metallic materials, which severely degrade imaging quality. Physically, the linear attenuation coefficients (LACs) of metals vary significantly with X-ray energy, causing a nonlinear beam hardening effect (BHE) in CT measurements. Reconstructing CT images from metal-corrupted measurements consequently becomes a challenging nonlinear inverse problem. Existing state-of-the-art (SOTA) metal artifact reduction (MAR) algorithms rely on supervised learning with numerous paired CT samples. While promising, these supervised methods often assume that the unknown LACs are energy-independent, ignoring the energy-induced BHE, which results in limited generalization. Moreover, the requirement for large datasets also limits their applications in real-world scenarios. In this work, we propose Density neural representation (Diner), a novel unsupervised MAR method. Our key innovation lies in formulating MAR as an energy-independent density reconstruction problem that strictly adheres to the photon-tissue absorption physical model. This model is inherently nonlinear and complex, making it a rarely considered approach in inverse imaging problems. By introducing the water-equivalent tissues approximation and a new polychromatic model to characterize the nonlinear CT acquisition process, we directly learn the neural representation of the density map from raw measurements without using external training data. This energy-independent density reconstruction framework fundamentally resolves the nonlinear BHE, enabling superior MAR performance across a wide range of scanning scenarios. Extensive experiments on both simulated and real-world datasets demonstrate the superiority of our unsupervised Diner over popular supervised methods in terms of MAR performance and robustness. To the best of our knowledge, Diner is the first unsupervised MAR method to outperform its supervised counterparts. Copyright © 2024, The

关键词： Tissue

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DETECTION AND MONITORING OF RESPONSE TO TREATMENT OF B CELL LYMPHOMAS BY A SIMPLE EPIGENETIC BLOOD TEST

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Hematological Oncology 2025年第S3期43卷

作者： I. Avivi M. Neaman C. Perry E. Joffe R. Vitkon Y. Michaeli S. Zirkin E. Kastritis J. Deek K. Sablauskas A. Zucenka T. Papajik E. Kriegova P. Gajdos Y. Ebenstein Hematology Tel Aviv Sourasky Medical Center Tel Aviv Israel JaxBio Technologies Ltd. Netanya Israel Department of Clinical Therapeutics National and Kapodistrian University of Athens Athens Greece Department of Chemistry Tel Aviv University Tel Aviv Israel Vilnius University Hospital Santaros Klinikos Hematology Oncology and Transfusion Medicine Center Vilnius Lithuania Department of Hemato-Oncology Palacky University and University Hospital Olomouc Czech Republic Department of Computer Science Faculty of Electrical Engineering and Computer Science Technical University of Ostrava Ostrava Czech Republic Department of Biomedical Engineering Faculty of Engineering Tel Aviv Univesity Tel Aviv Israel

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Incentive Mechanisms for Network Utility Maximization

Incentive Mechanisms for Network Utility Maximization

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2019年上海市“智能计算与智能电网”研究生学术论坛

作者： Jie Gao Lian Zhao Xuemin(Sherman)Sheny Dept.Electrical Biomedical& Computer EngineeringRyerson UniversityTorontoONCanada Dept.Electrical & Computer Engineering University of WaterlooWaterlooONCanada

In this paper,incentive mechanisms for maximizing network utility is *** the considered scenario,the network center intends to implement a solution that maximizes the network utility while the solution may not be optimal for each individual ***,incentive needs to be provided for the users to motivate them in order to implement the solution that maximizes the network *** reporting from users is necessary for the center to determine the incentives and achieve its *** two-user and multiuser cases,we propose two mechanisms to motivate truthful reporting from users while guaranteeing nonnegative utility gains for both the users and the *** proposed mechanisms are proved to be incentive *** are conducted for the D2D application to validate the analytical results and demonstrate the proposed mechanisms.

关键词： incentive mechanism network utility maximization truthful local information reporting

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