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3rd IEEE International Multidisciplinary Conference on engineering Technology, IMCET 2021

作者： Haidar Ahmad, Joelle El-Asmar, Alain Abi Zeid Daou, Roy Hayek, Ali Boercsoek, Josef Faculty of Public Health Biomedical Technologies Department Jounieh Lebanon MART Learning Education and Research Center Chananir Lebanon University of Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Germany

ISBN: (纸本)9781665405874

Independent mobility is an important feature of an individual's normal life and with the elderly population continually growing, assistive devices such as walkers and canes provide help and guidance to users. Freezing of Gait (FoG) is an episodic phenomenon that is a common symptom of an age-related disease known as Parkinson Disease (PD). This paper is headed toward implementing a detection, diagnosis and correction system for FoG episodes. The proposed cane will generate visual, auditory and tactile cues to help patients overcome this problem, therefore reducing fall risks. In addition, a vibrating calf cuff will be attached to the calf for muscle stimulation. A compatible android phone application is designed to display heart rate measurements of the patient as well as his exact location in case of fall detection. The system is considered to be an electrically safe, portable, comfortable and a low-cost device. The performance of this system is analyzed thoroughly and the accuracy of all methodologies is considered carefully and evaluated. The results have shown no faulty errors and good accuracy and detection percentages. © 2021 IEEE.

关键词： Diagnosis

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Wireless,multimodal sensors for continuous measurement of pressure,temperature,and hydration of patients in wheelchair

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npj Flexible Electronics 2023年第1期7卷 479-493页

作者： Seokjoo Cho Hyeonseok Han Hyunwoo Park Sung-Uk Lee Jae-Hwan Kim Sung Woo Jeon Mengqiu Wang Raudel Avila Zhaoqian Xi Kabseok Ko Minsu Park Jungyup Lee Myungwoo Choi Je-Sang Lee Weon Gi Min Byeong-Ju Lee Soyeong Lee Jungrak Choi Jimin Gu Jaeho Park Min Seong Kim Junseong Ahn Osman Gul Chankyu Han Gihun Lee Seunghwan Kim Kyuyoung Kim Jeonghyun Kim Chang-Mo Kang Jahyun Koo Sung Soo Kwak Sungbong Kim Dong Yun Choi Seokwoo Jeon Hyung Jin Sung Yong Bae Park Minkyu Je Young Tae Cho Yong Suk Oh Inkyu Park Department of Mechanical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea School of Electrical Engineering Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Advanced 3D Printing Technology Development Division Korea Atomic Energy Research InstituteDaejeon 34057Republic of Korea Department of Electrical and Computer Engineering University of Illinois at Urbana-ChampaignUrbanaIL 61801USA State Key Laboratory of Structural Analysis for Industrial Equipment Department of Engineering MechanicsDalian University of TechnologyDalian 116023P.R.China Department of Mechanical Engineering Northwestern UniversityEvanstonIL 60208USA DUT-BSU Joint Institute Dalian University of TechnologyDalian 116023China Ningbo Institute of Dalian University of Technology Ningbo 315016China Department of Electronics Engineering Kangwon National UniversityChuncheon 24341Republic of Korea Querrey-Simpson Institute for Bioelectronics Northwestern UniversityEvanstonIL 60208USA Sibel Health Inc NilesIL 60714USA Department of Materials Science and Engineering KAIST Institute for The Nanocentury(KINC)Korea Advanced Institute of Science and TechnologyDaejeon 34141Republic of Korea Department of Rehabilitation Medicine Gimhae Hansol Rehabilitation&Convalescent HospitalGimhae 50924Republic of Korea Department of planning and development Gimhae Hansol Rehabilitation&Convalescent HospitalGimhae 50924Republic of Korea Department of rehabilitation medicine Pusan national university hospitalBusan 49241Republic of Korea Department of AI Convergence Network Ajou UniversitySuwon 16499Republic of Korea Department of Electronic Convergence Engineering Kwangwoon UniversitySeoul 01897Republic of Korea Korea Photonics Technology Institute Gwangju 61007Republic of Korea School of Biomedical Engineering Korea UniversitySeoul 02841Republic of Korea Interdisciplinary Program in Precision Public Health Korea UniversitySeoul 02841Republic of Korea Center

Individuals who are unable to walk independently spend most of the day in a *** population is at high risk for developing pressure injuries caused by ***,early diagnosis and prevention of these injuries still remain ***,we introduce battery-free,wireless,multimodal sensors and a movable system for continuous measurement of pressure,temperature,and hydration at skin *** device design includes a crack-activated pressure sensor with nanoscale encapsulations for enhanced sensitivity,a temperature sensor for measuring skin temperature,and a galvanic skin response sensor for measuring skin hydration *** movable system enables power harvesting,and data communication to multiple wireless devices mounted at skin-cushion interfaces of wheelchair users over full body *** evaluations and numerical simulations of the devices,together with clinical trials for wheelchair patients,demonstrate the feasibility and stability of the sensor system for preventing pressure injuries caused by sitting.

关键词： injuries modal mounted

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Robust Multimodal Cough and Speech Detection using Wearables: A Preliminary Analysis 46

Robust Multimodal Cough and Speech Detection using Wearables...

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

作者： Chen, Yuhan Barahona, Jeffrey Eldho, Iype Yu, Yanbing Muhammad, Riyaz Kutsche, Bill Hernandez, Michelle L. Carpenter, Delesha Bozkurt, Alper Lobaton, Edgar North Carolina State University Department of Electrical and Computer Engineering RaleighNC27695 United States Murata Electronics North America Inc. SmyrnaGA30080-7604 United States University of North Carolina at Chapel Hill School of Medicine Department of Pediatrics Chapel HillNC27559 United States University of North Carolina at Chapel Hill Division of Pharmaceutical Outcomes and Policy Eshelman School of Pharmacy Chapel HillNC27559 United States

ISBN: (纸本)9798350371499

Cough detection is a crucial tool for long-term monitoring of respiratory illnesses. While clinical methods are accurate, they are not available in a home-based setting. In contrast, wearable devices offer a more accessible alternative, but face challenges in ensuring user speech privacy and detecting coughs accurately in real-world settings due to potential poor audio quality and background noise. This study addresses these challenges by developing a small-size multimodal cough and speech detection system, enhanced with an Out-of-Distribution (OOD) detection algorithm. Through our analyses, we demonstrate that combining transfer learning, a multimodal approach, and OOD detection techniques significantly improves system performance. Without OOD inputs, the system shows high accuracies of 92.59% in the in-subject setting and 90.79% in the cross-subject setting. With OOD inputs, it still maintains overall accuracies of 91.97% and 90.31% in these respective settings by incorporating OOD detection, despite the number of OOD inputs being twice that of In-Distribution (ID) inputs. This research are promising towards a more efficient, user-friendly cough and speech detection method suitable for wearable devices. © 2024 IEEE.

关键词： Audio signal processing

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GSLB: The Graph Structure Learning Benchmark 37

GSLB: The Graph Structure Learning Benchmark

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception and Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

ISBN: (纸本)9781713899921

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Graphic methods

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Colorectal Polyp Localization: From Image Restoration to Real-time Detection with Deep Learning

Colorectal Polyp Localization: From Image Restoration to Rea...

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International Symposium on Applied Computational Intelligence and Informatics ( SACI)

作者： Mahsa Dehghan Manshadi Milad Mousavi Arian Golzarian Madjid Soltani Amir Mosavi Department of Mechanical Engineering K.N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering University of Waterloo Ontario Canada John von Neumann Faculty of Informatics Obuda University Budapest Hungary University of Public Service Budapest Hungary

Increasing demands for artificial intelligence tools in the medical sector show the huge interest of physicians. In recent years, one of the most effective assistant systems in this field is the real-time detection of early-stage colorectal polyps during colonoscopy. Since even experienced physicians may miss polyps during colonoscopy, the real-time assistance system is designed to prevent this and hence contributes to diminish the number of missed critical cases. One challenge in this field is the detection of false positives. These systems are prone to mistake artifacts for colorectal polyps. This review provides an overview over current quality assessment and restoration techniques to make a high-quality training dataset for training deep neural network algorithms. Furthermore, four of the latest, fastest, and most accurate methods are introduced and analyzed in the rest of the review. Our main contribution is to provide an analysis of current methods used to detect colorectal polyps. We present a list of available datasets and present a range of challenges colorectal cancer detection systems face.

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Surface tension measurement of normal human blood samples by pendant drop method

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Journal of Medical engineering and Technology 2020年第5期44卷 227-236页

作者： Yadav, Siddharth Singh Sikarwar, Basant Singh Ranjan, Priya Janardhanan, Rajiv Goyal, Ayush Department of Mechanical Engineering Amity University Noida India Department of Electrical and Electronic Engineering Amity University Noida India Amity Institute of Public Health Amity University Noida India Department of Electrical Engineering and Computer Science Texas A&M University KingsvilleTX United States

The surface tension of blood plays an important role not only in the birth and decompression sickness but also in other functionality of the organism. It also provides capillary action during blood flow process. In this article, a simple and low-cost device is designed and fabricated for measuring the surface tension of blood by pendant drop method. In this device, a droplet of blood is formed in a closed chamber on tip of an 18-gauge blunt needle and it is photographed by a camera in very humid conditions (RH = 99%) to minimise the evaporation. A wetted wick is provided at the bottom of the chamber for maintaining constant relative humidity in chamber. Surface tension of blood is inferred using drop shape factor method and image analysis technique at various experimental conditions. This device is validated and calibrated with surface tension measurements of water and silicone oil. Its measurements are in good agreement against data reported in literature. Post-validation, surface tensions of blood samples with and without anticoagulant of healthy persons at various temperatures (range from 20 to 40 °C) was measured. It was found that the surface tension of normal blood samples strongly correlates with blood temperature. The surface tension of female blood was remarkably different from same of male blood. However, the effect of age (21–60 year) on the surface tension was negligible for all practical purposes. Increased percentage of anticoagulant in blood increases its surface tension. This research specifies a baseline for surface tension of normal blood samples at various conditions which in turn provides new insights to pathologists in identifying various disease conditions. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Surface tension

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Tl-Snn: Event-Driven Visual-Tactile Learning with Temporal and Location Spiking Neurons

SSRN

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SSRN 2024年

作者： Jing, Yang Yin, Baofan Li, Shaobo Su, Zhidong Zhang, Zhaohu School of Mechanical Engineering Guizhou University Jiaxiu South Road Guizhou Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Jiaxiu South Road Guizhou Guiyang550025 China School of Electrical and Computer Engineering Oklahoma State University Stillwater OK OklahomaOK74078 United States

With the continuous development of neuromorphic sensors and spiking neural networks, there is increasing attention on event-driven perception learning in both vision and tactile domains. However, due to the limited information representation capability of existing spiking neurons and the high spatio-temporal complexity of event-driven visual and tactile data, we focus on exploring the application potential of visual and tactile perception in event-driven datasets. We propose an innovative spiking neural network method for integrating visual and tactile perception, aiming to significantly enhance the perceptual and information integration capabilities of the fusion network. Our approach enables the extraction of features from both time and position dimensions, thereby more effectively capturing the spatio-temporal dependencies in event data. Additionally, we introduce a weighted spike loss function to optimize model performance and meet specific task requirements. Experimental results demonstrate that the proposed visual and tactile fusion spiking neural network achieves superior performance compared to baseline algorithms on object recognition, container detection, and slip detection datasets. © 2024, The Authors. All rights reserved.

关键词： Neural networks

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Automatic melanoma detection using discrete cosine transform features and metadata on dermoscopic images

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Journal of King Saud University - computer and Information Sciences 2024年第2期36卷

作者： Yousefi, Shamim Najjar-Ghabel, Samad Danehchin, Ramin Band, Shahab S. Hsu, Chung-Chian Mosavi, Amir Department of Computer Engineering University of Mohaghegh Ardabili Ardabil Iran Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran International Graduate School of Artificial Intelligence National Yunlin University of Science and Technology Douliu Taiwan John von Neumann Faculty of Informatics Budapest Hungary Ludovika University of Public Service Budapest Hungary

Machine learning contributes in improving the accuracy of melanoma detection. There are extensive studies in classic and deep learning-based approaches for melanoma detection in the literature. Still, they are not accurate or require high learning data. This paper proposes a hybrid mechanism for automated melanoma detection on dermoscopic images based on Discrete Cosine Transform features and metadata. It is composed of three steps. First, extra information/artifacts are deleted;the remaining pixels are standardized for accurate processing. Second, the reliability of the mechanism is improved by the Radon transform, extra data is removed using the Top-hat filter, and the detection rate is increased by Discrete Wavelet Transform and Discrete Cosine Transform. Then, the number of features is reduced by Locality Sensitive Discriminant Analysis. The third step divides the images into learning and test ones to create image-based models using learning data. Finally, the best model is selected based on test data and improved by a metadata-based model. Simulation results show that the decision tree provides the most practical image-based model by improving accuracy and sensitivity. Besides, the comparison results demonstrate that our model improves the F-Value to detect melanoma superior to other approaches. © 2024 The Author(s)

关键词： Artificial intelligence Big data Data science Dermoscopic image Discrete cosine transform features Machine learning Melanoma detection Metadata

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Economic and Reliability Impacts of Combined Solar and Battery Energy Storage as a Non-Wire Alternative

Economic and Reliability Impacts of Combined Solar and Batte...

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Annual North-American Power Symposium

作者： Mary Peterson Olivia O'Reilly Arun-Kaarthick Manoharan Sarangan Rajendran Adithya Melagoda Visvakumar Aravinthan Esther Liu Al Tamimi Charles Yokley Electrical & Computer Engineering Wichita State University Wichita KS USA Wichita East High School Wichita KS USA Transmission Planning & Policy Sunflower Electric Power Corporation Wichita KS USA

The decreasing cost of solar PVs is making them a viable non-wire alternative for rural areas considering infrastructure improvements. However, the intermittency and non-dispatchability of solar generation limits this possibility. Using real data from a small farming town in Kansas, this work analyzes the use of a combined community-scale solar farm and battery energy storage system as a non-wire alternative. In this work, various battery energy storage system operating strategies – such as fixed time discharging, outage mitigation, peak shaving, and market event mitigation – are considered. These operating strategies are evaluated based on financial viability for the utility, impact on battery life, and reliability of power supplied to the community served. The results and analysis provide useful insight regarding economic and reliability impacts with respect to the functionality and complexity of battery energy storage operating strategies.

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Prediction New Cases of COVID-19 in Indonesia Using Vector Autoregression (VAR) and Long-Short Term Memory (LSTM) Methods

Prediction New Cases of COVID-19 in Indonesia Using Vector A...

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2021 International Conference on Artificial Intelligence and Big Data Analytics, ICAIBDA 2021

作者： Danistya, Daffa Rahmansyah Qaulifa, Fahira Ramadani, Yovi Adhi Nurma Yulita, Intan Ardisasmita, Mulya Nurmansyah Agustian, Dwi Research Center for Artificial Intelligence and Big Data Padjadjaran University Bandung Indonesia Department of Electrical Engineering Brawijaya University Malang Indonesia Department of Electrical Engineering Telkom University Bandung Indonesia Department of Informatics Engineering Semarang University Semarang Indonesia Department of Computer Science Padjadjaran University Bandung Indonesia Department of Public Health Padjadjaran University Bandun Indonesia

ISBN: (纸本)9781665408905

The addition of Covid-19 cases is still uncontrolled, especially in Indonesia. Often the addition of Covid-19 cases in Indonesia always experiences a significant upward trend after a slightly loose government policy. This is because the government does not think there will be a spike in cases after cases go down. This is where the importance of predicting new cases of Covid-19 in Indonesia to be a reference for the government in taking policy. With deep learning, the prediction results will be more accurate. The implementation of vector autoregression (VAR) and long-short term memory (LSTM) methods can reach an accretion rate of up to 98%. With this method, the prediction results can be used for the government in anticipating if there is a surge in new cases per day because it has been predicted from the beginning. In fact, this method can predict new cases for up to a year. © 2021 IEEE.

关键词： Long short-term memory

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