检索结果-内蒙古大学图书馆

2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Meenakshi, B. Gopi, B. Ramalingam, L. Vanathi, A. Sangeetha, S. Murugan, S. Department of Electrical and Electronics Engineering Sri Sairam Engineering College Tamil Nadu Chennai India Tamil Nadu Rasipuram India Tamil Nadu Chennai India Department of Electronics and Communication Engineering Rajalakshmi Institute of Technology Tamil Nadu Chennai India Department of Computer Science and Engineering Karpagam College of Engineering Tamil Nadu Coimbatore India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350305418

Wireless Sensor Networks (WSNs) are essential for IoT-enabled crisis management and executive response. This paper explores WSNs' function in catastrophe situations and proposes new ways to improve emergency response operations. The article begins with a discussion of disaster management and emergency response scenarios and the necessity for real-time data collecting, monitoring, and analysis. WSNs are suitable for such applications due to their dense and dispersed sensing, tolerance to hostile conditions, and low power consumption. The research provides unique WSN deployment, data aggregation, and resource optimization methodologies for disaster management. These solutions will improve network coverage, dependability, and sensor longevity. The research also examines the synchronization of AI and human brainpower calculations with WSNs to enable smart navigation and robotized crisis response. The research study addresses WSN security and privacy in disaster management. It identifies flaws and suggests ways to classify, verify, and share data. A reenacted fiasco is used to evaluate the proposed alternatives. Situational awareness, response coordination, and emergency operations efficiency increase with the provided techniques. This study sheds light on WSNs' application in IoT-enabled emergency response and disaster management. The proposed solutions advance WSN technology and enable more effective and efficient emergency response systems. © 2023 IEEE.

关键词： Disaster Management Internet of Things Machine learning SVM Classifier Wireless Sensor Networks

来源：评论

学校读者我要写书评

暂无评论

Trustworthy actionable perturbations 24

Trustworthy actionable perturbations

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Jesse Friedbaum Sudarshan Adiga Ravi Tandon Program in Applied Mathematics University of Arizona Tucson AZ and Department of Electrical and Computer Engineering University of Arizona Tucson AZ Department of Electrical and Computer Engineering University of Arizona Tucson AZ

Counterfactuals, or modified inputs that lead to a different outcome, are an important tool for understanding the logic used by machine learning classifiers and how to change an undesirable classification. Even if a counterfactual changes a classifier's decision, however, it may not affect the true underlying class probabilities, i.e. the counterfactual may act like an adversarial attack and "fool" the classifier. We propose a new framework for creating modified inputs that change the true underlying probabilities in a beneficial way which we call Trustworthy Actionable Perturbations (TAP). This includes a novel verification procedure to ensure that TAP change the true class probabilities instead of acting adversarially. Our framework also includes new cost, reward, and goal definitions that are better suited to effectuating change in the real world. We present PAC-learnability results for our verification procedure and theoretically analyze our new method for measuring reward. We also develop a methodology for creating TAP and compare our results to those achieved by previous counterfactual methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study of DeepLabCut and Other Open-Source Pupillometry Data Analysis Algorithms – Which to Choose?

引用

Machine Graphics and Vision 2024年第2期33卷 77-90页

作者： Badkul, Amitesh Mishra, Sonakshi Kommajosyula, Srinivasa P. Department of Electrical & Electronics Engineering Birla Institute of Technology and Science Hyderabad Campus Jawahar Nagar Hyderabad India Department of Pharmacy Birla Institute of Technology and Science Hyderabad Campus Jawahar Nagar Hyderabad India Ph.D. Program in Computer Science The Graduate Center The City University of New York New YorkNY United States

Pupillometry measures pupil size, and several open-source algorithms are available to analyse pupillometry data. However, only a few studies compared these algorithms’ accuracy and computational resources. This study aims to compare the accuracy of computer vision-based algorithms (Swirski, Starburst, PuRe, ElSe, ExCuSe algorithms) and the machine learning algorithm, DeepLabCut, to the double-blinded human examiners (gold-standard). Training of DeepLabCut with different architectures and a variable number of markers (2-9 markers) was done on an open-source dataset. The duration of training was statistically longer for the ResNet152 model compared to the MobileNet model. The pupil diameters in computer vision-based software such as PuRe, Starburst, and Swirski were statistically different from human measurements. MobileNet 2 and 3 marker models were the closest to the human measurements. In conclusion, this work highlights the efficiency of lower marker models based on MobileNet architecture in DeepLabCut, which consumes fewer computational resources and is more accurate. © 2024 Institute of Information Technology, Warsaw University of Life Sciences - SGGW. All rights reserved.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Cloud-Powered Virtual Doctor Robots for Enhancing Elderly Care and Chronic Disease Management with Recurrent Neural Networks 3

Cloud-Powered Virtual Doctor Robots for Enhancing Elderly Ca...

引用

3rd International Conference on electrical, Electronics, Information and Communication Technologies, ICEEICT 2024

作者： Ramesh, S. Mohankumar, N. Kovarasan, Rajesh Kambattan Selvamanikandan, R. Yuvaraj, S. Sujatha, S. Srm Institute of Science and Technology School of Computing College of Engineering and Technology Department of Computing Technologies Chennai Tamil Nadu Kattankulathur India Maharashtra Nagpur India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Care College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Trichy India Faculty of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350369083

This research introduces cloud-powered virtual doctor robots to improve elderly care and chronic illness management. This system uses recurrent neural networks (RNNs) to give personalized medical care to elderly chronic disease patients. These virtual doctor robots may integrate with cloud computing infrastructure to access huge medical databases for real-time patient data analysis and individualized treatment and care suggestions. Healthcare technology has advanced by using RNNs to learn from patient data, adapt to changing health situations, and predict prospective health issues. RNNs can interpret sequential data, thus virtual doctor robots may identify trends in patient health parameters, forecast illness development, and suggest risk mitigation strategies. Cloud-powered virtual doctor robots provide a scalable solution to conventional healthcare systems rising issues. These robots can work effectively in resource-constrained areas by offloading computational chores to distant servers, providing continuous medical expertise and lowering healthcare staff reliance. This technique might improve aged care and reduce load on overworked healthcare systems. Using proactive monitoring, timely intervention, and personalized guidance, virtual doctor robots with RNNs can improve health outcomes, empower patients to manage chronic conditions, and create a more sustainable healthcare model. © 2024 IEEE.

关键词： Medical robotics

来源：评论

学校读者我要写书评

暂无评论

A Cardiovascular Model for the Development of Mathematical Algorithms for Non-invasive Continuous Blood Pressure Determination 10

A Cardiovascular Model for the Development of Mathematical A...

引用

10th E-Health and Bioengineering Conference, EHB 2022

作者： Mahrova, Vendula Prochazka, Michal Penhaker, Marek Department of Cybernetics and Biomedical Engineering Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava Czech Republic Research Centre UNIZA Faculty of Electrical Engineering and Information Technology University of Zilina Zilina Slovakia

ISBN: (纸本)9781665485579

In the human body, a regulated change of hemodynamic parameters and the possibility of repetitive sensing for refinement and finding other algorithms for continuous measurement of blood pressure non-invasively is not possible. To detect relationships, processes, and dependencies in the human body without the need for invasive input, attention should be paid to the creation of an ideal controlled cardiovascular system with the possibility of controlled change of hemodynamic parameters, repetitive sensing of set simulated biological signals with a known resulting reference pressure measured by a sensor. The cardiovascular model opens the door to the possibility of tracking dependencies in the human body and thus finding more robust algorithms to measure continuous blood pressure non-invasively. The cardiovascular model allows hemodynamic parameters that cannot be changed in a controlled manner in the human body to be changed in a controlled manner, thus allowing the determination of blood pressure dependencies on hemodynamic parameters and the time required for the pressure wave to be transmitted between two locations. Attention is paid to the development of a cardiovascular model and subsequent testing of the correct function of the model using an algorithm for measuring continuous blood pressure non-invasively for physiological parameters. The results show that a model of the cardiovascular system has been developed for subsequent testing and search for further algorithms and relationships to measure continuous blood pressure non-invasively more accurately. © 2022 IEEE.

关键词： Blood pressure

来源：评论

学校读者我要写书评

暂无评论

Autoencoder for 4-Dimensional Fiber Orientation Distributions from Diffusion MRI 22

Autoencoder for 4-Dimensional Fiber Orientation Distribution...

引用

22nd IEEE International Symposium on biomedical Imaging, ISBI 2025

作者： Huang, Shuo Zhong, Lujia Shi, Yonggang Los AngelesCA90033 United States Alfred E. Mann Department of Biomedical Engineering Los AngelesCA90089 United States Ming Hsieh Department of Electrical and Computer Engineering Los AngelesCA90089 United States

ISBN: (纸本)9798331520526

Fiber orientation distributions (FODs) are widely used in connectome analysis based on diffusion MRI. Spherical har-monics (SPHARMs) are often used for the efficient repre-sentation of FODs;however, SPHARMs over the 3-D image volume are in essence four-dimensional. This makes it highly memory-consuming for applying advanced deep learning methods, such as the transformer and diffusion model, to FODs represented by high order SPHARMs. In this work, we present an order-balanced order-level (OBOL) autoencoder to compress the FODs with high accuracy after decoding. Our OBOL method uses separate encoders for FODs in each SPHARM order to balance the feature map size of FODs in different orders. This helps the encoder to better preserve information from the low-order coefficients that have more information but a smaller number of volumes. In our ex-periments, we demonstrated that the decoded FODs of our OBOL autoencoder have better accuracy than the spatial-level or order-level autoencoder without order balance. We also tested the encoded latent space of the OBOL autoencoder in FOD super-resolution. Results show high accuracy with feasible memory usage in commonly available GPUs. © 2025 IEEE.

关键词： Volume rendering

来源：评论

学校读者我要写书评

暂无评论

Gait-Based User Identification for Smart Home: When Machine Learning Meets FBG Accelerometers

引用

IEEE Transactions on Consumer Electronics 2024年第1期71卷 2332-2341页

作者： Alhussein, Ghada Chi, Hao Ran Alberto, Nelia Antunes, Paulo Hadjileontiadis, Leontios Radwan, Ayman Domingues, M. Fatima Instituto de Telecomunicações-University of Aveiro Aveiro Portugal Instituto de Telecomunicações I3N and Dep. of Physics-University of Aveiro Aveiro Portugal Khalifa University of Science and Technology Department of Biomedical Engineering and Biotechnology Abu Dhabi United Arab Emirates Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki Greece Instituto de Telecomunicações Instituto Superior Tècnico -University of Lisbon Lisbon Portugal

Advancements in fiber optic sensor technology and telecommunications have paved the way for innovative health monitoring systems. However, previous works in this area have often been limited by the lack of comprehensive datasets, hindering the development of accurate and robust solutions. This paper addresses this gap by presenting a novel dataset and approach to gait-based user identification using a set of four optical Fiber Bragg Grating (FBG) sensor-based accelerometers, integrated into smart home environments. By computing features such as entropy, mean, standard deviation, kurtosis, and skewness from raw signals, and employing three unsupervised machine learning models-K-means, DB-scan, and Gaussian Mixture Model (GMM)-we achieve high accuracy in distinguishing individuals. Our dataset results for each sensor are as follows: for Sensor 1, K-means with a Silhouette Score of 0.3038 and Davies-Bouldin Index of 1.1238;for Sensor 2, K-means with a Silhouette Score of 0.3543 and Davies-Bouldin Index of 0.9659;for Sensor 3, DB-scan with a Silhouette Score of 0.2650 and Davies-Bouldin Index of 0.6855;and for Sensor 4, K-means with a Silhouette Score of 0.3949 and Davies-Bouldin Index of 1.0438. This approach not only enhances user identification but also facilitates personalized healthcare applications and unobtrusive monitoring. © 1975-2011 IEEE.

关键词： Smart homes

来源：评论

学校读者我要写书评

暂无评论

An IC-Enabled Metasurface Producing OAM and Pencil Beams

An IC-Enabled Metasurface Producing OAM and Pencil Beams

引用

2021 IEEE International Symposium on Antennas and Propagation and North American Radio Science Meeting, APS/URSI 2021

作者： Kossifos, Kypros M. Georgiou, Julius Antoniades, Marco A. University of Cyprus Department of Electrical and Computer Engineering Nicosia Cyprus Department of Electrical Computer & Biomedical Engineering Ryerson University Toronto Canada

ISBN: (纸本)9781728146706

The capabilities of a multifunctional metasurface design are explored in this work. The metasurface's meta-atoms are equipped with a programmable integrated circuit. The programable integrated circuit loads the meta-atom at four locations to control the reflection coefficient. With this ability a complex reflection pattern is generated. The generated reflected pattern consists of a pencil beam and a second beam which has an orbital angular momentum. The presented design operates in the vicinity of 4.1 GHz and has the potential to be incorporated in future metasurface-aided wireless telecommunication systems. © 2021 IEEE.

关键词： Timing circuits

来源：评论

学校读者我要写书评

暂无评论

Automatic Intelligent Model for Lung Tumor Detection using Medical Images 3

Automatic Intelligent Model for Lung Tumor Detection using M...

引用

3rd International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2022

作者： Devraj, P V Ashwathy Kumar, S Manoj Sriram, S. Venkatesan, G. Karpagam College of Engineering Department of Computer Science and Engineering Tamil Nadu Coimbatore India KPR Institute of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India PSNA College of Engineering and Technology Department of Biomedical Engineering Tamil Nadu Dindigul India Sri Sai Ram Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai India

ISBN: (纸本)9781665462754

The rising mortality toll from tumor is indicative of a growing global health crisis. To put it simply, a tumor is an unnatural proliferation of abnormal cells that invades healthy cells. Lung tumor (LT) cause cellular imbalances that negatively impact lung function. In this era, LT has been and will continue to be the leading cause of mortality owing to tumor. Early LT prediction is crucial, especially in densely populated and low-income nations. Standard clinical techniques, such as blood tests and other medicines, are used by specialists. Using recent advances in Deep Learning (DL), it is possible to build an environmentally friendly prototype system for the prediction of LT. The goal of this study is to use DL to automate the process of detecting LT. Starting with data collecting and ending with model validation, this study's pipeline depicts the steps taken in the research process. Pre-processing steps like reshaping and resizing the data, building a DL model like a Convolutional Neural Network (CNN) and a Combined Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM), training and validating the model comes in between the data collection and model validation stages. The experimental findings indicates that the CNN-LSTM is effective for LT prediction, with accuracy and loss of 98.12% and 1.87%, respectively. © 2022 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

A new control-oriented METANET model to encompass service stations on highways

A new control-oriented METANET model to encompass service st...

引用

European Control Conference (ECC)

作者： Ayda Kamalifar Carlo Cenedese Michele Cucuzzella Antonella Ferrara Department of Electrical Computer Science and Biomedical Engineering University of Pavia Pavia Italy Department of Information Technology and Electrical Engineering ETH Zürich Zürich Switzerland

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

In this paper, we propose the METANET with service station (METANET-s) model, a second-order macro-scopic traffic model that, compared to the classical METANET, incorporates the dynamics of service stations on highways. Specifically, we employ the (so-called) store-and-forward links to model the stop of vehicles and the possible queue forming in the process of merging back into the highway mainstream. We explore the capability of the METANET-s to capture well both traffic back propagation and capacity drops, which are typically caused by the presence of vehicles joining again the mainstream traffic from the service station. Therefore, capturing these effects is crucial to improving the model's predictive capabilities. Finally, we perform a comparative analysis with the Cell Transmission Model with service station (CTM-s), showcasing that the METANET-s describes the traffic evolution much better than its first-order counterpart.

关键词： Backpropagation Analytical models Merging Europe Predictive models Vehicle dynamics Traffic congestion

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：