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Laser Machined Fiber‑Based Microprobe: Application in Microscale Electroporation

Advanced Fiber Materials 2022年第4期4卷 859-872页

作者： Jongwoon Kim Yajun Zhao Shuo Yang Ziang Feng Anbo Wang Rafael V.Davalos Xiaoting Jia Bradley Department of Electrical and Computer Engineering Virginia TechBlacksburgVA 24060USA Department of Biomedical Engineering and Mechanics Virginia TechBlacksburgVA 24061USA

Microscale electroporation devices are mostly restricted to in vitro experiments(i.e.,microchannel and microcapillary).Novel fiber-based microprobes enable in vivo microscale electroporation and arbitrarily select the cell groups of interest to *** developed a flexible,fiber-based microscale electroporation device through a thermal drawing process and femtosecond laser micromachining *** fiber consists of four copper electrodes(80μm),one microfluidic channel(30μm),and has an overall diameter of 400μ*** dimensions of the exposed electrodes and channel were customizable through a delicate femtosecond laser *** feasibility of the fiber probe was validated through numerical simulations and in vitro *** reversible and irreversible microscale electroporation was observed in a 3D collagen scaffold(seeded with U251 human glioma cells)using fluorescent *** ablation regions were estimated by performing the covariance error ellipse method and compared with the numerical *** computational and experimental results of the working fiber-based microprobe suggest the feasibility of in vivo microscale electroporation in space-sensitive areas,such as the deep brain.

关键词： Fiber probe Polymer Micromachine Irreversible electroporation Reversible electroporation Microscale electroporation

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Enhanced DehazeFormer: an image and video haze removal method based on SwinTransformer for superior quality

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Multimedia Tools and Applications 2025年 1-33页

作者： Ayoub, Abeer El-Shafai, Walid Aouf, Mohamed Abd El-Samie, Fathi E. Hamad, Ehab K. I. El-Rabaie, S. Electrical Engineering Department Faculty of Engineering Aswan University Aswan81542 Egypt Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communication Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Biomedical Engineering Department Higher Technological Institute P.O. Box 228 Tenth of Ramadan City Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh84428 Saudi Arabia

The visual quality of images and videos is significantly degraded by atmospheric particles such as smoke and dust, leading to the haze problem, which is characterized by low contrast and a whitish veil obscuring the content. Dehazing techniques have been developed to mitigate these effects and restore sharp, visually appealing imagery. This paper presents an enhanced dehazing approach, referred to as the Enhanced DehazeFormer technique, which integrates both preprocessing and dehazing stages to produce high-quality dehazed images. The proposed method incorporates a three-step preprocessing phase aimed at reducing noise and enhancing dynamic range, issues commonly introduced by measurement device errors and other external factors. Prior to dehazing, each image or video frame undergoes homomorphic filtering, followed by Contrast Limited Adaptive Histogram Equalization (CLAHE) and a fast dehazing algorithm to further improve visual quality. The dehazing stage utilizes an extended and customized Swin Transformer architecture, known as DehazeFormer, which is tailored specifically for haze removal tasks. The preprocessed frames are input into the modified Swin Transformer to generate dehazed outputs of superior visual quality. The proposed technique is thoroughly evaluated on visible images, Near-Infrared (NIR) frames, and real-world hazy datasets to assess its effectiveness. Evaluation metrics include entropy, Peak Signal-to-Noise Ratio (PSNR), Feature Similarity Index (FSIM), Feature Similarity Index Chromatic (FSIMC), edge intensity, average gradient, and correlation, all of which are used to quantitatively measure dehazing performance. Furthermore, histograms and spectral entropy analyses are employed to compare the proposed method against other dehazing techniques. A comparative analysis is conducted using five frames from each type of visible and NIR videos to assess the performance of the baseline DehazeFormer and the enhanced DehazeFormer technique. Additional eva

关键词： Image enhancement

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Electroencephalography Machine Learning Features and Methods for Early Diagnosis and Classification of Parkinson’s Disease (2013–2023): A Review

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IEEE Sensors Journal 2025年第12期25卷 21017-21032页

作者： Emad-Ud-Din, Muhammad Almuteb, Ibrahim Wang, Ya Hubbard, James E. Texas A&M University Department of Computer Science College StationTX77843 United States Texas A&M University Department of Biomedical Engineering College StationTX77843 United States Department of Electrical and Computer Engineering College Station77843 United States Texas A&M University Department of Mechanical Engineering College StationTX77843 United States

Exploring the Machine Learning (ML) features and methods based on electroencephalography (EEG) and quantitative electroencephalographic (qEEG) holds the potential to early diagnosis and classification of Parkinson’s disease (PD) and can bring convenience to the decision-making process for healthcare professionals. PD is clinically heterogeneous, with high variability between patients in terms of motor and non-motor symptoms. This nature makes it extremely challenging for early diagnosis, and severity classification using traditionally questionnaire-based standards. EEG and qEEG provide objective signals with rich features that can potentially provide a translational alternative. In the past decade, a host of ML-based features and algorithms have been developed to detect and classify PD in patients using a range of EEG and qEEG datasets. Various methodologies, including graph-CNNs, deep RNNs, and hybrid models, are discussed within the context of early diagnosis and classification, each offering unique insights and performance levels. This review highlights the multifaceted nature of PD diagnosis and classification research. It addresses the challenges and limitations encountered in existing techniques, emphasizing the need for further improvements in accuracy, sensitivity, and specificity. This review provides a standardized insight into the evolving landscape of PD diagnosis and classification. It showcases the remarkable progress that has been made in utilizing EEG and qEEG data while emphasizing the ongoing quest for effective and convenient ML based methods for early diagnosis and classification. © 2001-2012 IEEE.

关键词： Electroencephalography

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Extracting 3D Information from Widefield Birefringence Microscopy Images of Sparse Myelinated Fibers in Human Brain Gray Matter

Extracting 3D Information from Widefield Birefringence Micro...

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Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: biomedical Optics

作者： Novoseltseva, Anna Xie, Ting Bigio, Irving J. Department of Biomedical Engineering Boston University BostonMA02215 United States Department of Electrical & Computer Engineering Boston University BostonMA02215 United States

We report the application of widefield birefringence microscopy for 3D rendering of sparse myelinated fibers in human brain gray matter. This inexpensive and simple method offers the potential to inform studies of the... 详细信息

ISBN: (纸本)9781957171340

关键词： Birefringence

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On the Role of ANN on the Estimation of Payload Inertia in Parallel Kinematic Machine 3

On the Role of ANN on the Estimation of Payload Inertia in P...

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3rd IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural engineering, MetroXRAINE 2024

作者： Fuentes, José Alfonso Farías Giuletti, Nicola Carnevale, Marco Giberti, Hermes Università di Pavia Dept. Electrical Computer and Biomedical Engineering Pavia Italy Università di Pavia Dept. Industrial and Information Engineering Pavia Italy

ISBN: (纸本)9798350378009

In the robotic field, accurately estimating the inertial parameters of a robot payload is essential for improving path-following precision in model-based robot control. Nowadays, there are two main approaches to address this problem. One approach involves using direct measurement of the interaction between the robot End-Effector and the payload in terms of motions and exchanged forces-torques. The other approach involves a standard dynamic robot identification problem, considering the payload as an extension of the last robot link. Several of these techniques have been tested and consolidated for serial robots. However, results obtained for the estimation using Parallel Kinematic Machines have been less accurate than those using serial robots, especially when no direct measurement is considered, due to the complexity of their kinematic chain and the greater impact of uncertainties in their geometrical and dynamic parameters. The purpose of this work is to evaluate the performance of an approach for estimating the inertial properties of the payload using a trained data-driven model, namely an Artificial Neural Network (ANN). This model takes as input the same information available to the machine when direct measurements are made (i.e., positions and forces exchanged between the End-Effector and the payload) and returns as output the inertia tensor and center of mass of the payload. The developed method is validated and compared with standard model-based approaches (e.g., Least Square Method based on the Newton-Euler equations). This work is the first step towards developing a novel approach for estimating the inertial properties of the robot payload using data-driven models with the inputs of the indirect method (i.e., actuator's motion and torque), in order to use as few sensors as possible. © 2024 IEEE.

关键词： Euler equations

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Performance Analysis of Heart Disease Detection Using Different Machine Learning Approaches 6

Performance Analysis of Heart Disease Detection Using Differ...

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6th International Conference on electrical engineering and Information and Communication Technology, ICEEICT 2024

作者： Tousif, Md. Noumil Rabbi Ushan, Md. Nahian Akhter, Rumana Mahmud, Sadia Rashid, Sakera Mohammad, Sakib University of Dhaka Department of Biomedical Physics and Technology Dhaka Bangladesh Ahsanullah University of Science and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh European University of Bangladesh Department of Electrical and Electronic Engineering Dhaka Bangladesh Southern Illinois University Carbondale Department of Electrical Computer and Biomedical Engineering CarbondaleIL United States

ISBN: (纸本)9798350385779

The leading cause of illness and mortality globally, heart disease is a serious global health problem. For successful prevention and intervention, timely and precise cardiac disease prediction is essential. Using the well-known Cleveland dataset from the UCI repository, we underwent an extensive performance examination of several ML approaches in the context of heart disease prediction in this study. The 303 cases and 14 features in this collection include essential clinical factors. The KNN, Random Forest, Naive Bayes, AdaBoost, Decision Tree, and Logistic Regression machine learning algorithms are among those being examined. Each is being assessed for its predictive efficacy. Our work reveals fascinating insights into algorithm performance through substantial data preparation, robust model training, and a thorough assessment utilizing measures like accuracy, sensitivity, specificity, and PPV, NPV. Notably, our results show that the KNN algorithm is the most effective at predicting heart disease by 93%. This finding has implications for improving clinical decision support systems and early heart disease detection, thereby lowering the prevalence of the condition and its negative effects on public health. This research highlights the crucial contribution of ML to the development of cardiovascular medicine and the significance of algorithm choice for predicting accuracy. © 2024 IEEE.

关键词： Decision trees

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What is Affective Touch Made Of? A Soft Capacitive Sensor Array Reveals the Interplay between Shear, Normal Stress and Individuality 24

What is Affective Touch Made Of? A Soft Capacitive Sensor Ar...

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37th Annual ACM Symposium on User Interface Software and Technology, UIST 2024

作者： Mclaren, Devyani Gao, Jian Yin, Xiulun Guerra, Rúbia Reis Vyas, Preeti Morton, Chrys Cang, Xi Laura Chen, Yizhong Sun, Yiyuan Li, Ying Madden, John David Wyndham Maclean, Karon E. Computer Science University of British Columbia VancouverBC Canada Electrical and Computer Engineering University of British Columbia VancouverBC Canada Biomedical Engineering University of British Columbia VancouverBC Canada

ISBN: (纸本)9798400706288

Humans physically express emotion by modulating parameters that register on mammalian skin mechanoreceptors, but are unavailable in current touch-sensing technology. Greater sensory richness combined with data on affect-expression composition is a prerequisite to estimating affect from touch, with applications including physical human-robot interaction. To examine shear alongside more easily captured normal stresses, we tailored recent capacitive technology to attain performance suitable for affective touch, creating a flexible, reconfigurable and soft 36-taxel array that detects multitouch normal and 2-dimensional shear at ranges of 1.5kPa-43kPa and ± 0.3-3.8kPa respectively, wirelessly at 43Hz (1548 taxels/s). In a deep-learning classification of 9 gestures (N=16), inclusion of shear data improved accuracy to 88%, compared to 80% with normal stress data alone, confirming shear stress's expressive centrality. Using this rich data, we analyse the interplay of sensed-touch features, gesture attributes and individual differences, propose affective-touch sensing requirements, and share technical considerations for performance and practicality. © 2024 Owner/Author.

关键词： Shear stress

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Mode-Division Multiplexing at Visible Wavelengths

Mode-Division Multiplexing at Visible Wavelengths

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Shah, Priyansh Mellouk, Elias Ben Levine, Jeremy Mohanty, Aseema Department of Electrical and Computer Engineering Tufts University MedfordMA02155 United States Department of Biomedical Engineering Tufts University MedfordMA02155 United States

We demonstrate mode-division multiplexing at visible wavelengths (473 nm) for the first time using adiabatic mode couplers. We measure less than -15 dB and -20 dB crosstalk for TE2 and TE3 higher-order mode couplers, ... 详细信息

ISBN: (纸本)9781957171395

关键词： Laser beams

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Mode-Division Multiplexing at Visible Wavelengths

Mode-Division Multiplexing at Visible Wavelengths

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

关键词： Tellurium compounds

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On-chip hyperspectral detectors for fluorescence lifetime imaging 9

On-chip hyperspectral detectors for fluorescence lifetime im...

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High-Speed biomedical Imaging and Spectroscopy IX 2024

作者： Ahamed, Ahasan Rawat, Amita McPhillips, Lisa N. Marcu, Laura Saif Islam, M. Electrical and Computer Engineering Department University of California DavisCA95616 United States Biomedical Engineering Department University of California DavisCA95616 United States

ISBN: (纸本)9781510669659

Fluorescence Lifetime Imaging (FLIM) is a powerful technique that measures the decay time of fluorophores present in tissue samples alluding to their constituent molecules. FLIM has gained popularity in biomedical imaging for applications such as detecting cancerous tumors, surgical guidance, etc. However, conventional FLIM systems are limited by a reduced number of spectral bands and long acquisition time. Moreover, the large footprint, complexity, and cost of the instrumentation make it difficult for clinical applications. In this paper, we demonstrate a reconstruction-based hyperspectral detector that can resolve decay time and intensities in broad spectral ranges while providing high sensitivity, high gain, and fast response time. The hyperspectral detector is comprised of an array of efficient, ultrafast avalanche photodetectors integrated with nanophotonic structures. We utilize different nanostructures in the detectors to modulate light-matter interactions in spectral channels. This allows us to computationally reconstruct the spectral profile of the incoming fluorescence spectrum without the need for additional filters or dispersive optics. Also, the nanophotonic structures enhance efficiency (by a factor of 2 to 10 over different wavelengths) while providing fast response time. An innovative detector design has been employed to reduce the breakdown of the avalanche photodetectors to-7.8V while maintaining high gain (∼50) across the spectral range. Therefore, enabling low light detection with a high signal-To-noise ratio for FLIM applications. Added spectral channels would provide valuable information about tissue materials, morphology, and disease diagnosis. Such innovative hyperspectral sensors can now be integrated on-chip capable of miniaturizing the FLIM system and making it a commercially viable tool for clinical use. This technology has the potential to revolutionize the current FLIM system with improved detection capabilities opening doors for new ho

关键词： Hyperspectral imaging

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