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11th EAI International Conference on Broadband Communications, Networks, and Systems, BROADNETS 2020

作者： Liang, Guangjun Xin, Jianfang Wang, Qun Xia, Lingling Li, Meng Department of Computer Information and Network Security Jiangsu Police Institute Nanjing China Jiangsu Provincial Public Security Department Key Laboratory of Digital Forensics Nanjing China Electrical Engineering School Anhui Polytechnic University Wuhu China

ISBN: (纸本)9783030687366

This paper conducts research on the RF energy harvesting relay network, and proposes an improved energy harvesting relay protocol, which allows the energy harvesting source node to retransmit data to improve the system diversity gain, and constructs energy harvesting slot allocation, subcarrier pairing, and power Optimized model of distributed system energy efficiency. A resource allocation algorithm based on optimal energy efficiency is further proposed. The Dinkelbach method is used to transform the nonlinear programming problem into a linear programming problem. Then, the Hungarian algorithm and the sub-gradient method are used to obtain the iterative algorithm based on energy efficiency optimization. Simulation shows that the algorithm reduces the complexity of the algorithm and has good global convergence. © 2021, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Energy harvesting

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An end-to-end food image analysis system

An end-to-end food image analysis system

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2021 Imaging and Multimedia Analytics in a Web and Mobile World Conference, IMAWM 2021

作者： He, Jiangpeng Mao, Runyu Shao, Zeman Wright, Janine L. Kerr, Deborah A. Boushey, Carol J. Zhu, Fengqing School of Electrical and Computer Engineering Purdue University West LafayetteIN United States School of Public Health Curtin University PerthWA Australia Cancer Epidemiology Program University of Hawaii Cancer Center HonoluluHI United States

Modern deep learning techniques have enabled advances in image-based dietary assessment such as food recognition and food portion size estimation. Valuable information on the types of foods and the amount consumed are crucial for prevention of many chronic diseases. However, existing methods for automated image-based food analysis are neither end-to-end nor are capable of processing multiple tasks (e.g., recognition and portion estimation) together, making it difficult to apply to real life applications. In this paper, we propose an image-based food analysis framework that integrates food localization, classification and portion size estimation. Our proposed framework is end-to-end, i.e., the input can be an arbitrary food image containing multiple food items and our system can localize each single food item with its corresponding predicted food type and portion size. We also improve the single food portion estimation by consolidating localization results with a food energy distribution map obtained by conditional GAN to generate a four-channel RGB-Distribution image. Our end-to-end framework is evaluated on a real life food image dataset collected from a nutrition feeding study. © 2021, Society for Imaging Science and Technology

关键词： Deep learning

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Assessing gender fairness in EEG-based machine learning detection of Parkinson's disease: A multi-center study 31

Assessing gender fairness in EEG-based machine learning dete...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Kurbatskaya, Anna Jaramillo-Jimenez, Alberto Ochoa-Gomez, John Fredy Brønnick, Kolbjørn Fernandez-Quilez, Alvaro Department of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway Stavanger University Hospital Stavanger Norway Department of Public Health Faculty of Health Sciences University of Stavanger Stavanger Norway Grupo de Neurociencias de Antioquia Universidad de Antioquia School of Medicine Medellín Colombia Grupo Neuropsicología y Conducta Universidad de Antioquia School of Medicine Medellín Colombia Department of Radiology Stavanger University Hospital Stavanger Norway

ISBN: (纸本)9789464593600

As the number of automatic tools based on machine learning (ML) and resting-state electroencephalography (rs-EEG) for Parkinson's disease (PD) detection keeps growing, the assessment of possible exacerbation of health disparities by means of fairness and bias analysis becomes more relevant. Protected attributes, such as gender, play an important role in PD diagnosis development. However, analysis of sub-group populations stemming from different genders is seldom taken into consideration in ML models' development or the performance assessment for PD detection. In this work, we perform a systematic analysis of the detection ability for gender sub-groups in a multi-center setting of a previously developed ML algorithm based on power spectral density (PSD) features of rs-EEG. We find significant differences in the PD detection ability for males and females at testing time (80.5% vs. 63.7% accuracy) and significantly higher activity for a set of parietal and frontal EEG channels and frequency sub-bands for PD and non-PD males that might explain the differences in the PD detection ability for the gender subgroups. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Electroencephalography

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Gene Name Recognition in Gene Pathway Figures Using Siamese Networks

Gene Name Recognition in Gene Pathway Figures Using Siamese ...

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Medical Artificial Intelligence (MedAI), IEEE International Conference on

作者： Micheal Olaolu Arowolo Muhammad Azam Fei He Mihail Popescu Dong Xu Department of Public Health Sciences Health Informatics Program Xavier University of Louisiana New Orleans Louisiana USA Department of Electrical Engineering and Computer Science Bond Life Sciences Center University of Missouri Columbia Missouri USA Department of Health Management and Informatics University of Missouri Columbia Missouri USA

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

The number of scientific papers related to biology is rapidly increasing, and with it, the number of pathway figures containing crucial biological information. However, manually annotating these figures is a time-consuming process, and current optical character recognition (OCR) methods are not effective in identifying gene names within these figures. To improve gene name recognition from pathway figures, we utilized a Siamese network that maps image segments to a library of pictures containing known gene name, like object recognition in many photo applications. We combined the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net) to create a CNN triple loss function. We trained and tested the developed TSPP-Net network using 563 gene images from 45 pathway pictures, 1000 images of alphanumeric characters and gene names from HUGO and KEGG. To benchmark against major available OCR technologies, we used 20 randomly selected curated pathway genes. Additionally, we compared several models, such as VGG19, VGG16, Resnet-50 Resnet-18, Densnet-121, and Xception for the Siamese backbone network model. VGG16 achieved the best performance with an accuracy of 93%, which is much higher than the results obtained using OCR. Our code is publicly available at https://***/MuhammadAzam636/Siamese-Network.

关键词： Image recognition Accuracy Sensitivity Text recognition Biological system modeling Optical character recognition Neural networks Libraries Object recognition Biological information theory

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Multi-Level ACE-based IoT Knowledge Sharing for Personalized Privacy-Preserving Federated Learning

Multi-Level ACE-based IoT Knowledge Sharing for Personalized...

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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Jing Wang Xi Lin Jun Wu Qinghua Mao Bei Pei Jianhua Li Suchang Guo Baitao Zhang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Integrated Administration Technologies for Information Security Shanghai China The Third Research Institute of The Ministry of Public Security China No.30 Institute of CETC Chengdu Sichuan China School of Public Policy and Management Tsinghua University Beijing China

ISBN: (数字)9798350358261

ISBN: (纸本)9798350358278

The emerging federated learning (FL) enables distributed data mining for Internet of Things (IoT) big data while avoiding data outsourcing privacy risks via local data training and knowledge (i.e., model) sharing. However, only simplified local knowledge sharing will also cause user privacy leaks due to advanced attacks (e.g., model inversion or gradient leakage). Further, how to realize fine-grained and personalized privacy protection for IoT users is still a challenge. In this paper, we first propose a hierarchical cloud-edge orchestrated federated learning architecture for IoT, named HCE-FL, which aims to provide intelligent and distributed data analysis for IoT users. To address the FL privacy issues, we then design a multi-level access control encryption-based IoT knowledge sharing approach for HCE-FL. In our approach, IoT users could be classified into different levels according to their individual privacy requirements. In addition, the proposed multi-level access control encryption algorithm could ensure the confidentiality of the IoT knowledge flow, which runs through local clients, edge sanitizers, and cloud servers in HCE-FL. Moreover, security theoretical analysis shows that our HCE-FL could satisfy“no read” and no write” security rules for the mandatory IoT knowledge access control. Finally, we conduct experiments based on classic MNIST and CIFARIO datasets to evaluate our HCE-FL. The experimental results demonstrate that our solution can achieve personalized privacy-preserving FL without losing IoT data availability and users can obtain better model accuracy and convergence rate through secure IoT knowledge access and sharing.

关键词： Access control Training Privacy Cloud computing Data analysis Federated learning Smart cities

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Paracetamol Concentration-Sensing Scheme Based on a Linear Cavity Fiber Laser Configuration

SSRN

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SSRN 2023年

作者： Soares, Liliana Perez-Herrera, Rosa Ana Novais, Susana Ferreira, António Frazão, Orlando Silva, Susana INESC TEC Institute for Systems and Computer Engineering Technology and Science Rua do Campo Alegre 687 Porto4150-179 Portugal Department of Engineering Physics Faculty of Engineering University of Porto R. Dr. Roberto Frias s/n Porto4200-465 Portugal Department of Electrical Electronic and Communication Engineering Public University of Navarra Pamplona31006 Spain Public University of Navarra Pamplona31006 Spain LEPABE—Laboratory for Process Engineering Environment Biotechnology and Energy Department of Chemical Engineering Faculty of Engineering University of Porto Rua Dr. Roberto Frias s/n Porto4200-465 Portugal ALiCE -Associate Laboratory in Chemical Engineering Faculty of Engineering University of Porto Rua Dr. Roberto Frias Porto4200-465 Portugal

A paracetamol concentration-sensing scheme based on a linear cavity fiber laser configuration is demonstrated experimentally. The laser cavity has a fiber sensor at one end, that allows refractive index measurements. The refractometer consists of a cleaved fiber tip combined with an FBG functioning as a reflecting mirror. The combination of a fiber loop mirror at the other end allows to reflect all the light from the FBG and refractometer, forming a linear cavity. By measuring the intensity variation of the Fresnel reflection at the fiber-to-liquid interface, the measured concentration is linear and have a concentration sensitivity of [[EQUATION]] µW/(g/kg), over a range of 52.61 to 219.25 g /kg, and with a resolution of 2.77 g/kg. The results obtained present great stability and prove the potential of the fiber laser system to performed real-time measurements of concentration, in a non-invasive way. © 2023, The Authors. All rights reserved.

关键词： Refractometers

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Diffusion Gaussian Mixture Audio Denoise

arXiv

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arXiv 2024年

作者： Wang, Pu Li, Junhui Li, Jialu Guo, Liangdong Zhang, Youshan Department of Mathematics School of Science University of Science and Technology Liaoning Anshan China School of public policy Cornell University IthacaNY United States School of Electronic and Information Engineering University of Science and Technology Liaoning Anshan China Department of Artificial Intelligence and Computer Science Yeshiva University New YorkNY United States

Recent diffusion models have achieved promising performances in audio-denoising tasks. The unique property of the reverse process could recover clean signals. However, the distribution of real-world noises does not comply with a single Gaussian distribution and is even unknown. The sampling of Gaussian noise conditions limits its application scenarios. To overcome these challenges, we propose a DiffGMM model, a denoising model based on the diffusion and Gaussian mixture models. We employ the reverse process to estimate parameters for the Gaussian mixture model. Given a noisy audio signal, we first apply a 1D-U-Net to extract features and train linear layers to estimate parameters for the Gaussian mixture model, and we approximate the real noise distributions. The noisy signal is continuously subtracted from the estimated noise to output clean audio signals. Extensive experimental results demonstrate that the proposed DiffGMM model achieves state-of-the-art performance. © 2024, CC0.

关键词： Diffusion

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Cascading Failures in Power Grids

arXiv

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arXiv 2022年

作者： Meyur, Rounak Pal, Anamitra Youssef, Mina Barrett, Christopher L. Marathe, Achla Eubank, Stephen Vullikanti, Anil Centeno, Virgilio Levin, Simon Poor, H. Vincent Phadke, Arun Marathe, Madhav V. Biocomplexity Institute University of Virginia Charlottesville United States School of Electrical Computer and Energy Engineering Arizona State University Tempe United States Eaton Power Center Omaha United States Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24060 United States Dept of Ecology and Evolutionary Biology Princeton University Charlottesville United States School of Electrical & Computer Engineering Princeton University Charlottesville United States Dept. of Public Health Sciences University of Virginia Charlottesville United States

This paper studies the consequences of a human-initiated targeted attack on the national electric power system. We consider two kinds of attacks: (i) an attack by an adversary that uses a tactical weapon and destroys a large part of the grid, by physically targeting a large geographic region;(ii) a strategic attack by an adversary that targets selected critical components in the network simultaneously. Our analysis uses (i) a realistic representation of the underlying power grid, including the topology, the control and protection components, (ii) a realistic representation of the targeted attack scenario, and (iii) a dynamic stability analysis, that goes beyond traditional work comprising structural and linear flow analysis. Such realistic analysis is expensive, but critical since it is able to capture cascading failures that result from transient instabilities introduced due to the attack. Our model acknowledges the presence of hidden failures in the protection systems resulting in relay misoperations. We analyze the extent of cascading outages for different levels of hidden failures. Our results show that: (i) not surprisingly the power grid is vulnerable to both these attacks, (ii) the tactical attack has significant social, economic and health damage but need not result in a regional cascade;on the other hand the strategically targeted attack can cause significant cascade and lead to power outage over a large region;(iii) the extent of damage caused by the later type of attack is minimal and hence is easier to recover from. Our work shows that it is essential to harden the power grid not just to cyber-attacks but also to physical attacks. Furthermore, we show that realistic representations and analysis can lead to fundamentally new insights that simplified models are unlikely to capture. The methods and results help us identify critical elements in the grid;the system can then be hardened in a more precise manner to reduce the vulnerabilities. © 2022, CC BY.

关键词： Outages

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Real Time Adjustment to Mitigate the SPV Forecasting Errors with BESS and EV - An Utility Case Study

Real Time Adjustment to Mitigate the SPV Forecasting Errors ...

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IEEE Power India Conference

作者： Piyush A. Sharma Abheejeet Mohapatra Ankush Sharma Sanjeev Pannala Noel Schulz Anurag Srivastava Scott Gibson John Hieb Department of Electrical Engineering Indian Institute of Technology Kanpur India School of EECS Washington State University Pullman WA USA Lane Department of Computer Science and Electrical Engineering West Virginia University Morgantown WV USA Snohomish County Public Utility District Everett WA USA

ISBN: (纸本)9781665459310

Adherence to schedules prepared by operators has become more challenging with an increase in the penetration level of non-dispatchable resources at different scales. The inherent variability and uncertainty associated with such resources including Solar Photo-Voltaic (SPV) based generation poses real-time scheduling challenges. Irrespective of the forecasting technique adopted, errors will always persist. Hence, the power flow must be optimized in real-time. The capability of dispatchable resources including Battery Energy Storage Systems (BESSs) and Electric Vehicles (EVs) can be leveraged to absorb the forecast errors in real-time so that the schedules pre-specified by operators can be effectively followed. A problem formulated as an Optimal Power Flow (OPF) is presented in this paper to mitigate SPV forecasting errors for the Snohomish County public Utility District (SnoPUD)’s Arlington Microgrid in the USA. Real world datasets have been used for testing and validation of the developed approach for the Arlington Microgrid with two test cases 1) grid connected mode and 2) islanded mode of operation. Detailed results are also presented in the article, taking two different seasonal (Spring and Summer) variations into account.

关键词： Schedules Low voltage Uncertainty Microgrids Optimal scheduling Real-time systems Forecasting

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A Framework for Describing and Measuring Bike Riders’ Stress and Subjective Experiences While Riding

SSRN

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SSRN 2022年

作者： Lim, Tommy Beck, Ben Thompson, Jason Tian, Leimin School of Public Health and Preventive Medicine Monash University Australia Transport Health and Urban Design Research Hub Australia Department of Electrical and Computer System Engineering Monash University Australia

Beyond an efficient means of transport, bike riding has numerous health and environmental benefits. Despite these benefits, rates of cycling remain low in many parts of the world. A key reason why people do not bike ride is because they have negative bicycling experiences arising from comfort- and safety-related concerns. However, these bicycling experiences have been poorly defined in bicycling research. The field currently lacks a framework for describing peoples’ bicycling experiences, how they are generated, and how they can be measured. This paper presents a framework that defines bike riders’ subjective experiences as encompassing cognitions (thought and cognitive processes), emotions, and physiological sensations (e.g., physical discomfort from exertion or exposure to weather). The paper draws upon elements of the transactional model of stress and coping to illustrate how cognitive processes mediate the relationship between a stimulus (e.g., a close-call incident) and a subjective riding experience (e.g., subsequent fear). The paper draws upon the circumplex model of affect to demonstrate how subjective experiences, particularly emotions, can be measured on dimensions of valence (how pleasant or unpleasant an emotion is) and arousal (the perceived physiological activation of an emotion). We conclude with future research directions. © 2022, The Authors. All rights reserved.

关键词： Behavioral research

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