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共 498 条记录，以下是11-20 订阅

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Eye Tracking System Using Convolutional Neural Network 5

Eye Tracking System Using Convolutional Neural Network

引用

5th International Conference on computer and Applications, ICCA 2023

作者： Iskandar, Shatha Jaara, Zahraa Ali, Mohamad Abou Sbeity, Fatima Kasem, Abdallah Hamawy, Lara Lebanese International University Department of Biomedical Engineering Lebanon University of the Basque Country Dept.of Computer Science & Artificial Intelligence Spain Lebanese University Dept. of Physics & Electronics Lebanon Notre Dame University Dept. of Electrical & Computer Engineering Lebanon

ISBN: (纸本)9798350303254

Several diseases and disabilities, including cerebral palsy (CP), cause patients to lose their ability to communicate easily with the outside world. In this paper we aim to detect the movement of the iris that can be used to move a wheelchair for CP patients, write sentences by simply looking at an interactive screen, or even by moving an artificial arm. Thus, the two-dimensional CNN model and the coding method, we proposed, tracks the eye movement, specifically the movement of the iris, using VGG image annotator software and a dataset of several people's eyes images A dataset of 500 images from several patients, where only the eyes were cropped and studied, was taken to train the model about eye movement and detection. The proposed model achieved a mean squared error drop of about 49,720 from the original we started with, which was 50,000. © 2023 IEEE.

关键词： Neural network models

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Implementing a Robust and Scalable Detect-and-Avoid Algorithm for UAV Navigation in ROS 15

Implementing a Robust and Scalable Detect-and-Avoid Algorith...

引用

15th International Conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Meimetis, Dimitrios Daramouskas, Ioannis Lappas, Vaios Perikos, Isidoros Kapoulas, Vaggelis Paraskevas, Michael Nkua Department of Aerospace Science & Technology Athens Greece Computer Technology Institute and Press 'Diophantus' Patras Greece UoP Applied Mechanics Lab Patras Greece UoP Computer Engineering and Informatics Department Patras Greece Computer Technology Institute and Press 'Diophantus' University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350368833

Unmanned aerial vehicles (UAVs) have garnered significant attention from the research community during the last decade, due to their diverse capabilities and potential applications. One of the most critical functions that drones must execute efficiently is navigation in real-world environments. This paper presents a decentralized approach for enabling unmanned aerial vehicles (UAVs) to navigate safely in unknown environments and avoid obstacles. Leveraging the Optimal Reciprocal Collision Avoidance (ORCA) algorithm, implemented in the Robot Operating System (ROS), our method facilitates conflict detection and resolution in 2D environments. Through simulations using ROS, Gazebo, and Iris drones, we validate the effectiveness of our approach in scenarios with initial trajectory conflicts. Our work addresses the pressing need for UAVs to autonomously plan and execute safe flights, laying the groundwork for enhanced UAV capabilities in various real-world applications. The simulation results demonstrate the efficiency and robustness of our approach. © 2024 IEEE.

关键词： Drones

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Instance-Level Safety-Aware Fidelity of Synthetic Data and its Calibration 27

Instance-Level Safety-Aware Fidelity of Synthetic Data and i...

引用

27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Cheng, Chih-Hong Stockel, Paul Zhao, Xingyu Chalmers & Univ. of Gothenburg Dept. of Computer Science & Engineering Sweden University of Hildesheim Department of Informatics Germany University of Warwick WMG CV4 7AL United Kingdom

ISBN: (纸本)9798331505929

Modeling and calibrating the fidelity of synthetic data is paramount in shaping the future of safe and reliable self-driving technology by offering a cost-effective and scalable alternative to real-world data collection. We focus on its role in safety-critical applications, introducing four types of instance-level fidelity that go beyond mere visual input characteristics. The aim is to ensure that applying testing on synthetic data can reveal real-world safety issues, and the absence of safety-critical issues when testing under synthetic data can provide a strong safety guarantee in real-world behavior. We suggest an optimization method to refine the synthetic data generator, reducing fidelity gaps identified by deep learning components. Experiments show this tuning enhances the correlation between safety-critical errors in synthetic and real data. © 2024 IEEE.

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

Heart Failure: Machine Learning Prediction Within a 5-Year F...

引用

24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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Auto-Adaptive Stator Ground Fault Protection for Synchronous Generators in Diesel-Electric Locomotives 14

Auto-Adaptive Stator Ground Fault Protection for Synchronous...

引用

14th IEEE International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2023

作者： Mahtani, Kumar Guerrero, Jose M. Beites, Luis F. Platero, Carlos A. Universidad Politécnica de Madrid Automation Electrical and Electronic Engineering and Industrial Informatics Dept. Madrid Spain Mondragon Unibertsitatea Electronics and Computer Science Dept. Arrasate-Mondragon Spain Universidad Politécnica de Madrid Automation Electrical and Electronic Engineering and Industrial Informatics Department Madrid Spain

ISBN: (纸本)9798350320770

Diesel-electric locomotives are equipped with one or more synchronous generators driven by diesel engines, that provide the necessary electrical energy for their operation. These generators are usually provided with some protection relays, depending on their rated power and their type of service. Standard stator-ground fault protection has a setting for rated conditions. In case of variable speed and voltage operation, the protection does not cover all the operation range of the machine. In addition, unwanted tripping may occur due to incorrect filtering of the third harmonic. In this paper, an auto-adaptative stator ground protection function is presented, for the operation of synchronous machines at variable voltage and frequency. A specific case study is tackled, focused on the main generators of a diesel-electric locomotive. Numerous simulations are carried out to verify the proposed protection method. The obtained results are promising, and they can be used in other synchronous machines operating at variable speed. © 2023 IEEE.

关键词： Stators

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Exhaled Breath Analysis Based Diabetes Detection with k-Nearest Neighbors Classifier 16

Exhaled Breath Analysis Based Diabetes Detection with k-Near...

引用

16th International Symposium on Computational Intelligence and Design, ISCID 2023

作者： Smieja, Piotr J. Lu, Jiang Sun, Qingquan Fu, Xingang Zhang, Ting Dept. of Electrical and Biomedical Engineering University of Nevada Reno Reno United States Department of Computer Engineering University of Houston Clear Lake Houston United States School of Computer Science and Engineering California State University San Bernardino San Bernardino United States Department of Computer Science University of Houston Downtown Houston United States

ISBN: (纸本)9798350317459

The diagnosis of early stages of diseases such as lung cancer or diabetes is challenging as those diseases don’t have many noticeable symptoms. The human breath, however, contains many VOCs (volatile organic compounds) that could be used as a clue for conducting the proper test. In this study, a custom breathalyzer device is developed for noninvasive detection and diagnosis of diabetes with the use of a human breath print. An array of 6 MOS sensors is used in the prototype to collect 8 VOCs of the breath print of the subjects. The k-nearest Neighbor algorithm is implemented to determine the likelihood of a disease. In the results, an accuracy of 93.6% was achieved. ©2023 IEEE.

关键词： Volatile organic compounds

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DCGAN-based Medical Image Augmentation to Improve ELM Classification Performance 11

DCGAN-based Medical Image Augmentation to Improve ELM Classi...

引用

11th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2022

作者： Rando Setiawan, Noor Akhmad Permanasari, Adhistya Erna Rulaningtyas, Riries Suksmono, Andriyan Bayu Sitanggang, Imas Sukaesih Universitas Gadjah Mada Dept. of Electrical and Information Engineering Yogyakarta Indonesia Biomedical Engineering Program Dept. of Physics Universitas Airlangga Surabaya Indonesia Institut Teknologi Bandung School of Electrical Engineering and Informatics Bandung Indonesia Ipb University Department of Computer Science Bogor Indonesia

ISBN: (纸本)9781665460309

Cervical cancer is one of the deadliest diseases in women. One of the cervical cancer screening methods is pap smear method. However, using a pap smear method to detect cervical cancer takes a long time for a pathologist to diagnose. Hence, a rapid development of medical computerization for early detection to get the results quickly is needed. This paper proposes synthetic data augmentation by using Deep Convolutional Generative Adversarial Network (DCGAN) to increase number of pap smear samples in dataset. Gray Level Co-occurrence Matrix (GLCM) is employed to extract features from dataset. Classification of 3 classes which are Adenocarcinoma, High-Grade Squamous Intraepithelial Lesion (HSIL), and Squamous Cell Carcinoma (SCC) is conducted using Extreme Learning Machine (ELM). The result shows that the addition of synthetic data improves the performance of ELM with the accuracy of 90%. This accuracy is better than the accuracy of ELM using only the original dataset which is 85%. © 2022 IEEE.

关键词： Generative adversarial networks

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Comfortable long term ECG monitoring system by the filtering electrodes

Comfortable long term ECG monitoring system by the filtering...

引用

2024 IEEE International Conference on Consumer Electronics, ICCE 2024

作者： Yang, Chang-Ming Wu, Shu-Cing Huang, Ji-Jer Chen, Chi-Chun Ming Young Biomedical Corp. Miaoli Taiwan National Chin-Yi University of Technology Prospective Technology of Electrical Engineering and Computer Science Taichung Taiwan Southern Taiwan University of Science and Technology Department of Electrical Engineering Tainan Taiwan National Chin-Yi University of Technology Dept. of Electronic Engineering Taichung Taiwan

ISBN: (纸本)9798350324136

This paper introduces a novel standard 3lead ECG monitoring system, designed for enhanced comfort in daily wear. The system utilizes innovative dry electrodes, seamlessly integrated into both underwear and shirts. These electrodes, made of non-filtering silver material, are specifically engineered to maximize wearer comfort while reliably capturing standard 3-lead ECG signals. This design allows for accurate ECG signal acquisition in both stationary and ambulatory states. The system uniquely records life behavior patterns by analyzing the noise components within the 3-lead ECG signals. For physical activities, the system switches to a filtering silver electrode, ensuring the capture of high-fidelity ECG signals without compromising comfort. The primary goal of this research is to develop a versatile standard 3-lead ECG monitoring system suitable for various daily activities, including exercise, Holter monitoring, and sleep tracking. © 2024 IEEE.

关键词： exercise and sleep ECG Holter monitoring technology Innovative dry electrode non-filtering and filtering silver electrodes versatile standard 3-lead ECG monitoring

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The Correlation Between Architecture Smells, Package Stability and Reusability: A Case Study 4

The Correlation Between Architecture Smells, Package Stabili...

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4th IEEE International Maghreb Meeting of the Conference on sciences and Techniques of Automatic Control and computer engineering, MI-STA 2024

作者： Benidris, Mrwan Elfallah, Ehab A. Omar Cheema, Marwa Aslam Alsma, Mohammed Salah Addin Kadiki, Tareq Ziad Elbarsha, Abdelfattah University of Benghazi Faculty of Information Technology Dep. of Computer Science Libya Libyan International Medical University Faculty of Information Technology Dept. of Software Engineering Libya University of Benghazi Faculty of Public Health Health Informatics Department Libya

ISBN: (纸本)9798350372632

the design decisions made in the architecture of a software system are essential to its maintainability, and thus its quality is of great importance. Architecture smells (ASs) can be used to identify any quality issues within the system. To further explore the relationships between ASs, stability, and reusability, Spearman correlation coefficients will be calculated. This paper seeks to examine the connection between architecture smells, software stability, and reusability. To do this, we utilized a dataset created by other researchers and conducted an empirical study of the associations between five types of architecture smells and software stability and reusability. This research is necessary as there is a lack of in-dept. research on architecture smells and their relationship to software stability and reusability. We found a relationship between the presence of Feature Concentration in package and the stability and reusability of the package. Also, package's stability is affected by both Cyclic Dependency and Unstable Dependency. A slight positive correlation has been found between God Class and stability, as well as a very weak negative correlation between Unstable Dependency and reusability. Finally, we found a slight negative correlation between package stability and reusability. © 2024 IEEE.

关键词： Reusability

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Probabilistic Decomposed Linear Dynamical Systems for Robust Discovery of Latent Neural Dynamics 38

Probabilistic Decomposed Linear Dynamical Systems for Robust...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Yenho Mudrik, Noga Johnsen, Kyle A. Alagapan, Sankaraleengam Charles, Adam S. Rozell, Christopher J. Machine Learning Center Georgia Institute of Technology United States School of Electrical and Computer Engineering Georgia Institute of Technology United States Coulter Dept. of Biomedical Engineering Emory University Georgia Institute of Technology United States Department of Biomedical Engineering Mathematical Institute for Data Science Center for Imaging Science Kavli Neuroscience Discovery Institute Johns Hopkins University United States

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model's ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot. © 2024 Neural information processing systems foundation. All rights reserved.

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