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Brain Fingerprinting-Based Community Structure 22

Brain Fingerprinting-Based Community Structure

引用

22nd IEEE International Symposium on biomedical Imaging, ISBI 2025

作者： Athamnah, Sema Aviyente, Selin Michigan State University Department of Biomedical Engineering Department of Electrical and Computer Engineering East LansingMI United States

ISBN: (纸本)9798331520526

The ability to uniquely characterize individual subjects based on their functional connectome (FC), i.e., fingerprinting, is key for progress toward precision medicine. Over the past few years, a variety of methods have been proposed for fingerprinting. While early approaches focused on using either the whole brain connec-tivity or pre-defined subnetwork connectivity to discriminate between subjects, more recently different feature extraction methods such as principal component analysis (PCA) have been used to ex-tract discriminative features. However, these methods do not iden-tify functionally interpretable network components, i.e., commu-nities, that contribute most to discrimination across subjects. In this paper, we introduce a fingerprinting approach based on community structure. In particular, we introduce a signed multilayer modularity optimization approach to identify the community structure across a group of subjects from resting state fMRI FCs. The optimal community structure for identifying subjects and discrimi-nating between different tasks is learned through a community in-dicator vector which quantifies the relevance of each community for a subject. The proposed framework is tested on the Midnight Scan Club (MSC) data across different tasks. High identification rates between resting state and different tasks indicate that an indi-vidual's community indicator vector is intrinsic to that individual independent of the task. The proposed framework also provides interpretability to fingerprinting by determining the most discrimi-native brain regions between task pairs. © 2025 IEEE.

关键词： community detection fingerprinting Functional connectivity multilayer graphs signed graph

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Accuracy Testing of PQ Analyzers Using the OMICRON CMC 356 23

Accuracy Testing of PQ Analyzers Using the OMICRON CMC 356

引用

23rd International Scientific Conference on Electric Power engineering, EPE 2023

作者： Hlavaty, Lukas Bilik, Petr VSB-Technical University of Ostrava Faculty of Electrical Engineering and Computer Science Department of Cybernetics and Biomedical Engineering Ostrava Czech Republic

ISBN: (纸本)9798350335934

This article deals with the accuracy check of the PQ analyzers using the OMICRON CMC 356 calibrator. This article describes the principle and concept of three applications used to test the accuracy of various quantities important in PQ measurements. Furthermore, the GUI of each application, its principle, and description of functionality, software architecture, and implementation in the LabVIEW graphical programming environment are described. The purpose of the applications is the automated generation of a three-phase voltage at the output of the OMICRON CMC 356 calibrator with parameters defined by the operator to test the accuracy of the PQ analyzers. The first application is used to generate the harmonic components according to EN 50160 with definable deviations from the standard and duration intervals. The second application is to generate defined sequence of events (overvoltages, undervoltages, faults) with a definable duration. The third application is used to simulate different types of voltage fluctuations called flicker at the output of the calibrator. © 2023 IEEE.

关键词： Power quality

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A Graph Learning Based Multi-Modal Video Action Recognition

A Graph Learning Based Multi-Modal Video Action Recognition

引用

2023 IEEE International Conference on Image Processing Challenges and Workshops, ICIPCW 2023

作者： Gao, Lei Liu, Kai Guan, Ling Toronto Metropolitan University Electrical Computer and Biomedical Engineering 350 Victoria St TorontoON Canada

ISBN: (纸本)9798350302585

Due to the robustness to variations of viewpoints and environment, 3D skeleton-based action recognition has drawn considerable interests in academic and industrial sectors. Recently, deep neural networks (DNNs)-based algorithms have been applied to skeleton-based action recognition extensively. Amongst many contemporary approaches, graph learning plays a significant role in 3D skeleton-based action recognition. In addition, with the advancement of multi-sensory technology, multi-modal action recognition has grown at an extremely rapid pace. In this work, a graph learning based multi-modal framework is proposed with application to action recognition. Specifically, a two-stream heterogeneous network is designed to extract the complementary features from 3D skeleton and RGB modalities jointly. Then, a discriminative adaptation model (DAM) is presented and then applied to the designed heterogeneous network for multi-modal action recognition. To validate the effectiveness of the proposed model, experiments are conducted on two multi-modal action recognition database with different scales: NTU RGB+D 120, and SYSU. Experimental results show the power of the generated features and the DAM model on multi-modal action recognition. © 2023 IEEE.

关键词： Heterogeneous networks

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Learning from Future: Prediction-based Data Augmentation to Enhance Power Grids Fault Detection 11

Learning from Future: Prediction-based Data Augmentation to ...

引用

11th IEEE International Conference on Smart Energy Grid engineering, SEGE 2023

作者： Rogers, Jennifer Danilczyk, William Lin, Hui Sun, Yan Lindsay University of Rhode Island Department of Electrical Computer and Biomedical Engineering KingstonRI United States

ISBN: (纸本)9798350340716

As power grids modernize to include wide area monitoring and advanced metering infrastructures, the applications for data-driven methods based on artificial intelligence (AI) for situational awareness, reliability, and security continue to grow. However, obtaining effective training data that captures normal and anomalous operations in power systems remains a great challenge. This paper proposes a data-augmentation method that can efficiently build effective training data sets, benefiting various data-driven applications used in power grids without increasing the size of training data set. By leveraging load prediction in smart grids, we obtain the knowledge of future operating conditions and potential anomalies, which are integrated with the historical data in the training data set. By utilizing this augmented training data set, we significantly increase the accuracy of data-driven fault detection, e.g., 8.6% on average, compared to the results trained based on historical data only. © 2023 IEEE.

关键词： Fault detection

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Vibrational Nanoscopy of Intracellular Compartments via Visible Fourier Reweighted Stimulated Raman Scattering

Vibrational Nanoscopy of Intracellular Compartments via Visi...

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2023 Computational Optical Sensing and Imaging, COSI 2023 in Optica Imaging Congress - Part of Imaging and Applied Optics Congress 2023

作者： Lin, Haonan Tan, Yuying Cheng, Ji-Xin Department of Biomedical Engineering United States Department of Electrical & Computer Engineering Boston University BostonMA02215 United States

We present visible Fourier-reweighted stimulated Raman scattering (VFR-SRS), a vibrational imaging scheme that achieves sub-100 nm resolution by computationally amplifying high spatial frequency components, enabling l... 详细信息

ISBN: (纸本)9781957171289

关键词： Stimulated Raman scattering

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A Real Time Physics-Based Industrial Control System Honeynet Architecture for the Smart Grid 8

A Real Time Physics-Based Industrial Control System Honeynet...

引用

8th International Conference on Information and Network Technologies, ICINT 2023

作者： De Pace, Gabriel Lin, Hui Sun, Yan Lindsay University of Rhode Island Department of Electrical Computer and Biomedical Engineering KingstonRI United States

ISBN: (纸本)9798350301458

Many details of cyber attacks occurred to Industrial Control Systems (ICS) such as power grids remain as a secret. To reveal those secrets and benefit a broad research community, we propose an original cyber-physical honeypot architecture that can seamlessly couple IT and OT components in an power grid. Specifically, we will provide high interaction with adversaries as physical processes of an ICS move forward, revealing attack strategies according to adversaries' knowledge on physical trajectories of the target system. Our preliminary evaluations demonstrate that the proposed CPS honeypot can present realistic device fingerprints and application-layer payload that piggybacks meaningful measurements conforming to the physical model of a power grid. In addition, by leveraging runtime high-fidelity OPAL-RT simulator, our CPS honeypot causes small latency, at least one order of magnitude smaller compared to the existing work. © 2023 IEEE.

关键词： Smart power grids

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Single-channel, ultraportable, real-time imaging system based on deep learning: a proof-of-concept

Single-channel, ultraportable, real-time imaging system base...

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2023 IEEE International Ultrasonics Symposium, IUS 2023

作者： Meacci, Valentino Bosco, Edoardo Ramalli, Alessandro Boni, Enrico Tortoli, Piero Mazierli, Daniele Spairani, Edoardo Matrone, Giulia University of Florence Department of Information Engineering Florence Italy University of Pavia Department of Electrical Computer and Biomedical Engineering Pavia Italy

ISBN: (纸本)9798350346459

There is a growing demand for user-friendly technologies that may empower individuals to independently monitor their physiological parameters. Ultrasound systems are highly promising for healthcare self-monitoring. To address these challenges, in this work, we present the proof of concept (PoC) of a real-time ultraportable system with a single-channel transmission and reception strategy and a deep learning-based image reconstruction method. The PoC uses the ULA-OP 256 scanner, employing a monostatic synthetic aperture focusing technique, along with a convolutional neural network (CNN) trained to generate B-mode images. Testing was carried out using a CNN running on a mid-range graphics processing unit (GPU), both on phantoms and in vivo scenarios, comparing the image quality achieved by the CNN with that of a delay-and-sum (DAS) beamformer. The results highlight that the CNN outperformed the DAS, showing a better image quality. Furthermore, the CNN achieved a real-time frame rate of 37.0 frames per second, proving that ultraportable ultrasound systems are highly promising for the future of self-monitoring instruments. © 2023 IEEE.

关键词： Graphics processing unit

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Image Enhancement Algorithm for Liver CT Scan using Digital Image Processing 6

Image Enhancement Algorithm for Liver CT Scan using Digital ...

引用

6th International Conference on Digital Medicine and Image Processing, DMIP 2023

作者： Linsangan, Noel B. Martin, Melissa B. Cacnio, Jose Lorenzo I. Su, Jenn-Lung School of Electrical Electronics and Computer Engineering Mapua University Philippines Department of Biomedical Engineering Chung Yuan Christian University Taiwan

ISBN: (纸本)9798400709425

Image Enhancement is defined as the process of improving the quality of a digitally-stored image by manipulating it by means of image processing. Image enhancement is often applied in various images such as a Computed Tomography scan, a medical image employing tomography created by computer processing. The image produced in these scans are grayscale in nature and requires a much simpler image processing technique compared to colored images. The techniques that will be used for the enhancement of a Liver CT scan shall follow the standards of "Digital Image Processing"and shall be coded in computer programming software (Borland C++ Builder 6). Digital image processing is the use of computer algorithms to perform image processing on digital images, the word algorithm being a series of steps, reoccurring or not. This research will demonstrate and describe in detail the different algorithms that were applied (e.g. grayscale transformation, histograms, histogram equalization, etc.) and the resulting recommended algorithm that will be used to reduce the noise of a liver CT scan image. © 2023 ACM.

关键词： computerized tomography

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Interpreting Multiclass Lung Cancer from CT Scans using Grad-CAM on Lightweight CNN Layers 6

Interpreting Multiclass Lung Cancer from CT Scans using Grad...

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

作者： Muntasir, Fahim Datta, Ayon Mahmud, Shakib Brac University Department of Computer Science and Engineering Dhaka Bangladesh Ahsanullah University of Science and Technology Department of Electrical and Electronic Engineering Dhaka Bangladesh Bangladesh University of Engineering and Technology Department of Biomedical Engineering Dhaka Bangladesh

ISBN: (纸本)9798350385779

Lung cancer is one of the most prevalent and deadly cancer kinds. CT scan imaging has made it easier to observe in-depth at different lung cancer conditions. Convolutional Neural Networks have already been utilized for detecting objects in images. This research implements a lightweight CNN architecture in classifying two types of cancerous images and distinguishing them from healthy ones. AlexNet, ResNet101 V2, DenseNet201, MobileNet V2, and other sizable deep learning and transfer learning models were tested against this unique architecture with global max pooling. The proposed CNN model overlaps in performance with much bigger models whilst using about eight times fewer parameters, thus being efficient. Apart from making the architecture smaller, Grad-CAM was used to explain the convolutional flow over different convolution layers. This integration of the Grad-CAM technique into the analysis adds a crucial layer of transparency to the deep-learning models. By vi-sualizing the regions within lung cancer images that significantly influence predictions, we aim to enhance the interpretability of our model, fostering trust and better understanding among healthcare professionals. © 2024 IEEE.

关键词： Convolutional neural networks

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Channel selection and wavelet transformation-based data compression preserve motor unit information 46

Channel selection and wavelet transformation-based data comp...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Siyu Quinn, Kiara N. Slepyan, Ariel Qin, Lang Thakor, Nitish V. Johns Hopkins Whiting School of Engineering Department of Biomedical Engineering BaltimoreMD21218 United States Johns Hopkins Whiting School of Engineering Department of Electrical and Computer Engineering BaltimoreMD21218 United States

ISBN: (纸本)9798350371499

Both motor unit-based control algorithms and implantable interfaces are promising approaches to improve current prosthetic limbs by allowing control of more movements. However, combining the two methods poses a challenge due to the current wireless transmission capabilities which limits the amount of data that can be processed. Capturing the motor unit requires a higher sampling rate and higher channel counts. Thus, this paper investigates a data compression strategy comprised of two independent stages: recording data reduction by selecting fewer channels to be 'active' and minimization of the transmitting data through compression with wavelet transformation. We have demonstrated a promising wavelet compression pipeline using db4, which can compress the raw EMG signal 0.63% of its original size while maintaining low WMSE error (0.109) and high heatmap reconstruction correlation coefficient (0.9741±0.972). In addition, placing high-density electrodes along the muscle fibers during channel selection while utilizing wavelet transformation can compress 99.82% of transmitted data while preserving a MCC of 0.706 ± 0.169. © 2024 IEEE.

关键词： Data compression

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