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A microfluidic biosensor for the diagnosis ofchronic wasting disease

Microsystems & Nanoengineering 2023年第4期9卷 263-279页

作者： Sura A.Muhsin Amjed Abdullah Estela kobashigawa Muthana Al-Amidie Sherri Russell Michael Z.Zhang Shuping Zhang Mahmoud Almasri University of Missouri–Columbia Electrical Engineering and Computer ScienceColumbiaMOUSA University of Missouri–Columbia College of Veterinary MedicineVeterinary Medical Diagnostic LaboratoryColumbiaMOUSA Missouri Department of Conservation ColumbiaMOUSA

Cervids are affected by a neurologic disease that is always fatal to individuals and has population effects. This disease iscalled chronic wasting disease (CWD) and is caused by a misfolded prion protein. The disease is transmitted viacontact with contaminated body fluids and tissue or exposure to the environment, such as drinking water or *** CWD diagnosis depends on ELISA screening of cervid lymph nodes and subsequent immunohistochemistry(IHC) confirmation of ELISA-positive results. The disease has proven to be difficult to control in part because ofsensitivity and specificity issues with the current test regimen. We have investigated an accurate, rapid, and low-costmicrofluidic microelectromechanical system (MEMS) biosensing device for the detection of CWD pathologic prions inretropharyngeal lymph nodes (RLNs), which is the current standard type of CWD diagnostic sample. The deviceconsists of three novel regions for concentrating, trapping, and detecting the prion. The detection region includes anarray of electrodes coated with a monoclonal antibody against pathologic prions. The experimental conditions wereoptimized using an engineered prion control antigen. Testing could be completed in less than 1 hour with highsensitivity and selectivity. The biosensor detected the engineered prion antigen at a 1:24 dilution, while ELISA detectedthe same antigen at a 1:8 dilution. The relative limit of detection (rLOD) of the biosensor was a 1:1000 dilution of aknown strong positive RLN sample, whereas ELISA showed a rLOD of 1:100 dilution. Thus, the biosensor was 10 timesmore sensitive than ELISA, which is the currently approved CWD diagnostic test. The biosensor’s specificity andselectivity were confirmed using known negative RPLN samples, a negative control antibody (monoclonal antibodyagainst bovine coronavirus BCV), and two negative control antigens (bluetongue virus and Epizootic hemorrhagicdisease virus). The biosensor’s ability to detect pathogenic prions was

关键词： fir diagnosis specificity

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Network-Agnostic Voltage-to-Power Sensitivities of a Real-Time Reduced Active Distribution Network

Network-Agnostic Voltage-to-Power Sensitivities of a Real-Ti...

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2023 IEEE PES Innovative Smart Grid Technologies - Asia, ISGT Asia 2023

作者： Prasad, Manorath Rather, Zakir Hussain Razzaghi, Reza Doolla, Suryanarayana Indian Institute of Technology Bombay Department of Energy Science and Engineering Mumbai India Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9798350327748

The advent of distribution-level phasor measurement units (D-PMUs) has enhanced the observability of active distribution networks (ADNs) and improved their energy management and control. Recently, various ADN control strategies that require detailed network parameter information have been proposed based on the voltage-to-power sensitivities of the ADN nodes. This paper proposes a new realistic approach to determining nodal voltage-to-power sensitivities of a synchrophasor measurement-based reduced ADN model. The real-time synchrophasor data dynamically updates the parameters of the reduced equivalent model to follow the time-varying operating conditions of the ADN accurately. The voltage-to-power sensitivities of the retained observable nodes are derived from the power flow Jacobian matrix using reduced equivalent model parameters. Thus, the proposed approach is agnostic about detailed line impedance data of the complex ADN. Further, the computational complexity is significantly decreased with the reduced number of nodes in the equivalent ADN model. The proposed approach has been tested on modified IEEE-33 bus benchmark system with high penetration of photovoltaic inverters using a DIgSILENT PowerFactory MATLAB co-simulation platform. © 2023 IEEE.

关键词： Electric power distribution

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DNN Acceleration: A High-Accuracy Implementation for Base-2 Softmax Layer 3

DNN Acceleration: A High-Accuracy Implementation for Base-2 ...

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3rd International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2023

作者： Thabit, Doha Alabassy, Bassma Watheq El-Kharashi, M. Safar, Mona Dessouky, Mohamed Ain Shams University Department of Electronics Engineering and Electrical Communications Cairo Egypt Ain Shams University Department of Computer and Systems Engineering Cairo Egypt

ISBN: (纸本)9798350306231

This paper introduces a high-accuracy implementation of the softmax layer in Deep Neural Networks (DNN) used in multi-category classification applications. Calculation of the exponentials or logarithms in the traditional base-e softmax is mathematically complex, which makes it hard to have a high-accuracy hardware implementation without being resource-consuming, so instead of using e as the exponential base, this paper presents a hardware implementation of the base-2 softmax function that makes use of 2 as the exponential base. Thus, the complex operations in base-e softmax will be replaced by simple shift and addition operations, with simpler LUTs and higher accuracy. The implemented hardware model relies on single-precision floating-point arithmetic cores, it achieves classification accuracy equal to 100% relative to a reference software model and has an area of 0.0802 mm2with a power consumption of 8.93 mW when synthesized under TSMC 28nm CMOS technology at the frequency of 1 GHz. © 2023 IEEE.

关键词： Deep neural networks

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Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Resource and Cyberattacks Prediction 19

Spatiotemporal Traffic Analysis: Deep Hybrid Approach for Re...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Lee, Hyeonmin Kim, Dongchan Beom, Donggyu Byun, Gyurin Vo, Van-Vi Choo, Hyunseung Department of AI Systems Engineering Sungkyunkwan University Suwon16419 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Convergence Research Institute Sungkyunkwan University Suwon16419 Korea Republic of Department of Computer Science and Engineering Sungkyunkwan University Suwon16419 Korea Republic of

ISBN: (纸本)9798331507817

This study proposes a hybrid deep learning approach to address the complexity and dynamic characteristics of modern network environments. The research integrates Graph Neural Networks (GNNs) and Convolutional Long and Short-Term Memory (ConvLSTM) networks to predict future network traffic and detect cyberattacks. The model was trained and evaluated on the CICIDS 2018 dataset which includes various types of normal and malicious traffic. Grid-based representations were created from the dataset. The attack type of each timestamp were visualized using distinct colors to the grid images. These images are sued as input of ConvLSTM model. Protocol and destination port are modeled as nodes in a graph, with volume of data flow represented as weighted edges. This graph-based representation is designed to capture unique traffic patterns of specific services and enhance the analysis of spatial and temporal features. Experimental results show a 13% increase in macro F1 score and a 4% increase in weighted F1 score, highlighting the effectiveness of the proposed method. This research highlights its potential to enable proactive responses through the prediction of potential cyberattack occurrences. © 2025 IEEE.

关键词： Convolutional Long Short-Term Memory Cyberattack Detection Deep Learning Graph Neural Networks Network Traffic Prediction

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Predictive modeling of MXene-based solar absorbers using a deep neural network

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Journal of the Optical Society of America B: Optical Physics 2025年第4期42卷 763-772页

作者： Waseer, Waleed Iqbal Baqir, Muhammad Abuzar Saqlain, Muhammad Mughal, Muhammad Junaid Khan, Shujaat School of Physics State Key Laboratory of Electronic Thin Films and Integrated Devices University of Electronic Science and Technology of China Chengdu China Department of Electrical Engineering COMSATS University Islamabad Sahiwal57000 Pakistan Department of Electrical and Computer Engineering COMSATS University Islamabad Islamabad45550 Pakistan Department of Computer Engineering College of Computing and Mathematics King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia SDAIA-KFUPM Joint Research Center for Artificial Intelligence King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia

Nanophotonic structures, such as photonic crystals, plasmonic nanostructures, and metamaterials, present transformative potential in advancing optical devices through innovative design capabilities. Among these, metamaterials—specifically metal–insulator–metal (MIM)-based absorbers—stand out for their ability to achieve high electromagnetic wave absorption across designated frequencies, proving valuable in applications ranging from solar cells to sensors. This study focuses on designing a high-efficiency MXene–SiO2–silver MIM metasurface absorber, leveraging deep learning techniques to streamline and enhance the simulation process traditionally reliant on finite-difference time-domain (FDTD) or finite-element methods (FEMs). By utilizing a deformable convolutional neural network (CNN), the proposed model predicts absorption spectra from metasurface geometries with superior accuracy and reduced computational demand while achieving dynamic spatial transformation handling. The model’s accuracy is further elucidated through Gradient-weighted Class Activation Mapping (Grad-CAM), which provides insights into the absorber’s most influential regions, supporting targeted optimization for specific applications. This deep learning approach demonstrates significant advantages over conventional methods in efficiency and adaptability, advancing the development of high-performance nanophotonic devices. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Time domain analysis

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A Novel Random Pulse Width Modulation for PMSM Drive System Based on Unified Voltage Modulation 6

A Novel Random Pulse Width Modulation for PMSM Drive System ...

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6th International Conference on Energy, Power and Grid, ICEPG 2024

作者： Kan, Jinpeng Meng, Yongqing Shi, Yixin Li, Fengjiao Chen, Wenjie Li, Changyi Xi'an Jiaotong University School of Electrical Engineering Xi'an China Avic Xi'an Flight Automatic Control Research Institute Department of Computer and Electronic Systems Engineering Xi'an China

ISBN: (纸本)9798350377798

The electromagnetic interference (EMI) problem caused by power electronic switching devices and pulse width modulation (PWM) affects the normal operation of the motor drive system. In this paper, a random PWM based on unified modulation is proposed for permanent magnet synchronous motor (PMSM) drive system. The theoretical basis of unified modulation and the implementation scheme of random modulation are analyzed. Finally, the effectiveness of the proposed random PWM is verified by simulation. © 2024 IEEE.

关键词： Pulse width modulation

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Evaluating people's perceptions of an agent as a public speaking coach

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Paladyn 2024年第1期15卷

作者： Forghani, Delara Ghafurian, Moojan Rasouli, Samira Nehaniv, Chrystopher L. Dautenhahn, Kerstin Department of Electrical and Computer Engineering University of Waterloo WaterlooON Canada Departments of Systems Design Engineering and of Electrical and Computer Engineering University of Waterloo WaterlooON Canada

The use of interactive tools, such as voice assistants and social robots, holds promise as coaching aids during public speaking rehearsals. To create a coach that is both effective and likable, it is important to understand how people perceive these agents when they observe them during actual presentation sessions. Specifically, it is important to assess people’s perceptions of the agents’ physical embodiment and nonverbal social behaviour, taking into account both listening and feedback periods. To this end, we conducted an online study with 168 participants who watched videos of agents acting as public speaking coaches. The study had three conditions: two with a humanoid social robot in either (1) active listening mode, using nonverbal backchannelling, (2) passive listening mode, and (3) a voice assistant agent. The results showed that the social robot in both conditions was perceived more positively in terms of its human-like attributes, and likability than the voice assistant agent. The active listener robot was perceived as more satisfying, more engaging, more natural, and warmer than the voice assistant agent, but this difference was not seen between the passive listener robot and the voice assistant agent. Additionally, the active listener robot was found to be more natural than the passive listening robot. However, there were no significant differences in perceived intelligence, competence, discomfort, and helpfulness between the three agents. Finally, participants’ gender and personality traits were found to affect their evaluations of the agents. The study offered insights into general attitudes towards using social robots and voice assistants as public speaking coaches, which can guide the future design and use of these agents as coaches. © 2024 the author(s), published by De Gruyter.

关键词： Social robots

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Spatio-Temporal Hybrid Fusion of CAE and SWin Transformers for Lung Cancer Malignancy Prediction 48

Spatio-Temporal Hybrid Fusion of CAE and SWin Transformers f...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Khademi, Sadaf Heidarian, Shahin Afshar, Parnian Naderkhani, Farnoosh Oikonomou, Anastasia Plataniotis, Konstantinos N. Mohammadi, Arash Concordia University Concordia Institute for Information Systems Engineering Montreal Canada Concordia University Department of Electrical and Computer Engineering Montreal Canada Sunnybrook Health Sciences Centre Department of Medical Imaging Toronto Canada University of Toronto Department of Electrical and Computer Engineering Toronto Canada

ISBN: (纸本)9781728163277

The paper proposes a novel hybrid discovery Radiomics framework that simultaneously integrates temporal and spatial features extracted from non-thin chest Computed Tomography (CT) slices to predict Lung Adenocarcinoma (LUAC) malignancy with minimum expert involvement. Lung cancer is the leading cause of mortality from cancer worldwide and has various histologic types, among which LUAC has recently been the most prevalent. LUACs are classified as pre-invasive, minimally invasive, and invasive adenocarcinomas. Timely and accurate knowledge of the lung nodules malignancy leads to a proper treatment plan and reduces the risk of unnecessary or late surgeries. Currently, chest CT scan is the primary imaging modality to assess and predict the invasiveness of LUACs. However, the radiologists' analysis based on CT images is subjective and suffers from a low accuracy compared to the ground truth pathological reviews provided after surgical resections. The proposed hybrid framework, referred to as the CAET-SWin, consists of two parallel paths: (i) The Convolutional Auto-Encoder (CAE) Transformer path that extracts and captures informative features related to inter-slice relations via a modified Transformer architecture, and;(ii) The Shifted Window (SWin) Transformer path, which is a hierarchical vision transformer that extracts nodules' related spatial features from a volumetric CT scan. Extracted temporal (from the CAET path) and spatial (from the SWin path) are then fused through a fusion path to classify LUACs. Experimental results on our in-house dataset of 114 pathologically proven SubSolid Nodules (SSNs) demonstrate that the CAET-SWin significantly improves reliability of the invasiveness prediction task while achieving an accuracy of 82.65%, sensitivity of 83.66%, and specificity of 81.66% using 10-fold cross-validation. © 2023 IEEE.

关键词： Lung Adenocarcinoma Lung Nodule Invasiveness Self-Attention SubSolid Nodule Transformer

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Empowering Internet of Underwater Things: An Integrated Simulator for Underwater Magneto-Coupled Wireless Sensor Networks

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IEEE Internet of Things Magazine 2025年第1期8卷 109-114页

作者： Songzuo, Liu Ullah, Irfan Muzzammil, Muhammad Ahmed, Niaz Wahid, Abdul Zhen, Shao Ministry of Industry and Information Technology Key Laboratory of Marine Information China College of Underwater Acoustic Engineering Harbin Engineering University China Sanya Nanhai Innovation and Development Base of Harbin Engineering University China FAST National University of Computer and Emerging Sciences Department of Electrical Engineering Islamabad Campus Pakistan

This research paper presents an integration of finite element method (FEM) based models into MATLAB for developing an end-to-end magneto-coupled (MC) communication system tailored for Internet of Underwater Things (IoUT)-based applications. Magneto-coupled communication has emerged as a promising solution for extreme environments like underwater where conventional wireless acoustic communication poses significant challenges. The article exploits the strengths of two software tools: MATLAB and COMSOL Multiphysics, to seamlessly integrate communication protocols with theoretical insights into magnetic channels. The article also demonstrates the development and evaluation of an end-to-end communication system with both simulation and experimental setup. We believe that this integrated approach is highly effective in achieving reliable communication performance in underwater scenarios, thereby advancing the capabilities of IoUT for various real-world applications. © 2025 IEEE.

关键词： MATLAB

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Adaptive Online Model Update Algorithm for Predictive Control in Networked systems 60

Adaptive Online Model Update Algorithm for Predictive Contro...

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60th Annual Allerton Conference on Communication, Control, and Computing, Allerton 2024

作者： Khatana, Vivek Chang, Chin-Yao Wang, Wenbo University of Minnesota Department of Electrical and Computer Engineering Minneapolis United States National Renewable Energy Laboratory GoldenCO80401 United States

ISBN: (纸本)9798331541033

In this article, we introduce an adaptive on-line model update algorithm designed for predictive control applications in networked systems, particularly focusing on power distribution systems. Unlike traditional methods that depend on historical data for offline model identification, our approach utilizes real-time data for continuous model updates. This method integrates seamlessly with existing online control and optimization algorithms and provides timely updates in response to real-time changes. This methodology offers significant advantages, including a reduction in the communication network bandwidth requirements by minimizing the data exchanged at each iteration and enabling the model to adapt after disturbances. Furthermore, our algorithm is tailored for non-linear convex models, enhancing its applicability to practical scenarios. The efficacy of the proposed method is validated through a numerical study, demonstrating improved control performance using a synthetic IEEE test case. © 2024 IEEE.

关键词： Predictive control systems

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