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A feature fusion approach for detection and counting of freezing-of-gait episodes in Parkinson’s patients using shank acceleration sensor

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Multimedia Tools and Applications 2025年 1-27页

作者： Khera, Preeti Kumar, Neelesh Khosla, Ashima Maithani, Garima Academy of Scientific and Innovative Research Ghaziabad201002 India CSIR-Central Scientific Instruments Organisation Chandigarh160030 India Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India

Neurodegenerative disorders particularly Freezing-of-Gait (FoG) in Parkinson’s disease (PD) accounts for movement inabilities. Disease assessment is based on subjective criterion leading to unreliability and bias. Therefore, an accurate detection system can aid in strengthening clinical decision-making. This research work aims to develop a machine-learning based feature fusion methodology for FoG detection. Two feature extraction schemes (statistical and spectral) have been adopted to derive signal features from three sensor locations (shank, trunk, and upper leg). In the initial phase, publicly-available DAPHNET dataset of 10PD patients has been used for developing our proposed methodology. Sensor optimization has been accomplished by performing detailed set of experiments, thus contributing to use of single shank acceleration sensor. Top-ranked statistical and spectral features selected by Mutual Information technique have been fused together to form hybrid feature-set of 19 features. Proposed Random Forest model is able to achieve an accuracy, precision, recall, and F1-score of 93 ± 1.4%, 94 ± 1.2%, 93 ± 1.09%, and 93 ± 1.1% respectively using tenfold cross-validation. Using smaller window segments, latency-time is reduced to ≈0.5secs, thus allowing patients to plan postural adjustments. Additionally, design for FoG event count and their duration for clinical needs is implemented. Further validation of proposed methodology on another publicly available dataset of 12PD patients with diverse nature achieved an accuracy of ~ 72–79% for different walking conditions. However, combination of data from two databases resulted in average FoG detection accuracy of 81.66 ± 0.01%. Thus, this factor of heterogeneity contributes to generalizability of the suggested strategy and its use as automated diagnostic tool to screen gait abnormalities. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurodegenerative diseases

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Retrospection on the Performance Analysis Tools for Large-Scale HPC Programs 31

Retrospection on the Performance Analysis Tools for Large-Sc...

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31st Annual IEEE International Conference on High Performance Computing, Data, and Analytics, HiPC 2024

作者： Xuan, Zhibo You, Xin Yang, Hailong Li, Mingzhen Luan, Zhongzhi Liu, Yi Qian, Depei School of Computer Science and Engineering Beihang University China State Key Lab of Processors Institute of Computing Technology Chinese Academy China

ISBN: (纸本)9798331509095

As the performance gap between hardware and software widens, performance analysis tools are essential for understanding the behavior of large-scale High-Performance Computing (HPC) programs. These tools provide insights into the performance bottlenecks and help in optimizing the performance of the programs. In this paper, we present a comprehensive study of performance analysis tools for large-scale HPC systems including both sampling-based and instrumentation-based tools that are commonly adopted in the HPC community. We investigate the abundance and overheads of data collection as well as the analysis capabilities of HPCToolkit, TAU, and Scalasca with representative programs at scale. Our study shows that different performance analysis tools have distinct strengths and weaknesses, and the choice of a performance analysis tool depends on the specific requirements of the user. We also discuss the challenges and future directions in the field of performance analysis tools for large-scale HPC systems. © 2024 IEEE.

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Fusion of Sparse Direct Method and Feature-based Approach in Stereo Point-Line SLAM Algorithm

Fusion of Sparse Direct Method and Feature-based Approach in...

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2024 International Conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Wang, Hao Xu, Zhen Leng, Kuang China Nanhu Academy of Electronics and Information Technology Zhejiang Jiaxing China School of Electronic Engineering and Computer Science University of Science and Technology of China Anhui Hefei China School of Management University of Science and Technology of China Anhui Hefei China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

The development of autonomous driving and augmented reality has led to increased demands for accuracy and speed in visual SLAM. Traditional feature-based visual SLAM performs real-time image analysis with high complexity and computation requirements, necessitating devices with exceptionally high arithmetic power. This poses challenges for deployment on low-power devices. Additionally, relying solely on photometric errors makes the system too sensitive to the environment. To tackle these issues, our algorithm integrates the feature-based method and the sparse direct method. We employ the sparse joint method for pose estimation in the tracking thread, considering both accuracy and speed. In case of tracking failure, we extract feature point lines for tracking. Furthermore,to enhance system robustness in low-texture environments and fuse point-line features, we employ adaptive weighted extraction of point-line features, considering computational resources. Our experimental results, based on public datasets, demonstrate that this algorithm achieves faster operation speeds in simple environments and higher accuracy compared to other SLAM algorithms in complex environments and fast camera movement scenes. © 2025 SPIE.

关键词： Stereo image processing

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Multilevel Product Classification Using Machine Learning Algorithms Sowjanya 3

Multilevel Product Classification Using Machine Learning Alg...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Sowjanya Arjunan, R. Vijaya Babu, D. Vijendra Manipal Institute of Technology Manipal Academy of Higher Education Computer Science and Engineering Karnataka Manipal India Vellore Institute of Technology School of Electronics Engineering Tamil Nadu Vellore India

ISBN: (纸本)9798350372120

Platforms for e-commerce commonly employ a preestablished, organized structure of product classifications. Traditional product categorization methods have primarily relied on text-based data, which can occasionally fall short of providing a comprehensive understanding of a product's characteristics. However, employing image data in classification models has several advantages. The pictures offer a wealth of visual knowledge by capturing details such as color, shape, texture, and size that text explanations may miss. By incorporating text and image data, the classifier gains greater knowledge of each product, resulting in higher categorization reliability. Furthermore, the inclusion of image data enables the machine learning model to discover aesthetic patterns and features that are tricky to convey through text alone, expanding its ability to precisely assign items to their corresponding categories. The current study validates a new classifier for predicting categories using both textual and image data. This suggested approach is being attempted to enhance the accuracy and fullness of product classification. © 2024 IEEE.

关键词： Image classification

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Lightweight Cloud-Based Solution for Digital Education and Assessment

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science and technology Libraries 2024年第3期43卷 274-286页

作者： Geetha, J. Jayalakshmi, D.S. Naresh, E. Sreenivasa, N. Department of Computer Science & Engineering M. S. Ramaiah Institute of Technology Affiliated to VTU Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of CSE Nitte Meenakshi Institute of Technology Bangalore India

With a shift from the regular in-class education to e-learning-based education, there is a need to have the right platform to conduct assessments and courses in a more effective and user-friendly manner which includes robust monitoring, capability of doubt clearing, easy sharing of reference materials and more. There is a need for a highly customizable lab which supports all languages and is very lightweight. Our solution focuses on the creation of a robust, lightweight lab that provides a plug-and-play model for on-demand compilation capabilities. This virtual lab is hosted on the in-house blade servers and virtualized using metal-as-a-service (maas) along with juju controller. Kubernetes is used for on-demand compilation support as well as serves as the load balancer for the servers. The lab is specific to computer science-related courses and aims to impact the students present in the low bandwidth and rural areas. Moreover, the proposed methodology enhances the capabilities of the institutions to utilize their resources to the fullest with the help of virtualization through maas, juju and open-stack and orchestration through Kubernetes. © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC.

关键词： Laboratories

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Smart Home Automation with IoT Sensor Systems for Energy Conservation using Zigbee and Wifi 5

Smart Home Automation with IoT Sensor Systems for Energy Con...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Jagannathan, Srinivasan Deepa, V. Sakthi Karthi Durai, B. Rajan, M. Sundar Celin, J. Jeya A. Dhamodaran, Sasikala Madanapalle Institute of Technology & Science Department of Computer Applications Madanapalle India Tiruppur Kumaran College for Women Pg and Research Department of Computer Science Tamil Nadu Tiruppur India Kalasalingam Academy of Research and Education Computer Science and Engineering India Arbaminch Institute of Technology Arbaminch University Faculty of Electrical and Computer Engineering Ethiopia Kalasalingam Academy of Research and Education Department of Information Technology Viruthunagar Krishnankoil626126 India Rajasthan Jaipur India

ISBN: (纸本)9798350379945

This study presents a comprehensive study on smart home automation systems utilizing Internet of Things (IoT) sensor technology to achieve efficient energy conservation. The proposed system integrates ZigBee and WiFi communication protocols for effective and reliable connectivity across various smart devices within a home. Through the deployment of interconnected sensors and intelligent controllers, the system allows real-time monitoring and management of household energy usage, optimizing energy consumption and reducing waste. The combination of ZigBee for low-power, long-range sensor networks and WiFi for high-speed data transmission enables seamless interaction between smart appliances, lighting, heating, and cooling systems. Proposesd approach offers a scalable, cost-effective solution for enhancing home automation with an emphasis on energy conservation and sustainability. Also discussed the implementation, challenges, and future potential of IoT-based smart home systems for improved energy efficiency. © 2024 IEEE.

关键词： Smart homes

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Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

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IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1105-1121页

作者： Yue Zhou Xin Luo MengChu Zhou the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 the Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714 the Chongqing School University of Chinese Academy of SciencesChongqing 400714China the College of Computer and Information Science Southwest UniversityChongqing 400715China the School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

关键词： Big data analysis cryptocurrency transaction network embedding(CTNE) dynamic network network embedding network representation static network

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Enhancing Human-computer Interaction with Hand Gesture Recognition 2

Enhancing Human-Computer Interaction with Hand Gesture Recog...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Pavithra, N. Sapna, R. Aswitha, A. Preethi Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Computer Science and Engineering Manipal India Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Department of Information Technology Manipal India

ISBN: (纸本)9798350372892

A common means of nonverbal expression is realized through a hand gesture recognition system, offering a contemporary and natural mode of communication. This technology finds wide-ranging applications in sign language interpretation and human-computer interaction, bridging the physical and digital realms. Enhancing user-computer interactions by increasing the computer's receptivity to user demands is the fundamental aim of human-computer interaction. Human-computer interaction has grown in significance due to the widespread use of computers in society. It's commonly accepted that when computer, communication, and display technologies advance even more, the current state of HCI practices could eventually impede the efficient use of the information flow that is already accessible. This work aims to explain a new method of hand gesture identification that relies on the detection of certain shape-based elements. Features are extracted using the Histogram of Gradients feature descriptor, and SVMs trained on HOG features provide 87.4 % accurate results. In order to properly evaluate the efficacy of the recommended method, Metrics such as F-Score, Precision, and Recall provide a more nuanced view of the model's performance than merely accuracy. © 2024 IEEE.

关键词： Gesture recognition

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Fine classification of rice paddy using multitemporal compact polarimetric SAR C band data based on machine learning methods

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Frontiers of Earth science 2024年第1期18卷 30-43页

作者： Xianyu GUO Junjun YIN Kun LI Jian YANG Huimin ZOU Fukun YANG School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China College of Global Change and Earth System Science Beijing Normal UniversityBeijing 100875China

Rice is an important food crop for human *** distinguishing different varieties and sowing methods of rice on a large scale can provide more accurate information for rice growth monitoring,yield estimation,and phenological monitoring,which has significance for the development of modern *** polarimetric(CP)synthetic aperture radar(SAR)provides multichannel information and shows great potential for rice monitoring and ***,the use of machine learning methods to build classification models is a controversial *** this paper,the advantages of CP SAR data,the powerful learning ability of machine learning,and the important factors of the rice growth cycle were taken into account to achieve high-precision and fine classification of rice ***,CP SAR data were simulated by using the seven temporal RADARSAT-2 C-band data ***,20-two CP SAR parameters were extracted from each of the seven temporal CP SAR data *** addition,we fully considered the change degree of CP SAR parameters on a time scale(ΔCP_(DoY)).Six machine learning methods were employed to carry out the fine classification of rice *** results show that the classification methods of machine learning based on multitemporal CP SAR data can obtain better results in the fine classification of rice paddies by considering the parameters ofΔCP_(DoY).The overall accuracy is greater than 95.05%,and the Kappa coefficient is greater than *** them,the random forest(RF)and support vector machine(SVM)achieve the best results,with an overall accuracy reaching 97.32%and 97.37%,respectively,and Kappa coefficient values reaching 0.965 and 0.966,*** the two types of rice paddies,the average accuracy of the transplant hybrid(T-H)rice paddy is greater than 90.64%,and the highest accuracy is 95.95%.The average accuracy of direct-sown japonica(D-J)rice paddy is greater than 92.57%,and the highest accuracy is 96.13%.

关键词： compact polarimetric(CP)SAR rice paddy machine learning fine classification multitemporal

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An Efficient and Secure Vehicular Networks Based on IoT and Cloud Computing

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SN computer science 2022年第3期3卷 1-8页

作者： El Zouka, Hesham A. Computer Engineering Department College of Engineering and Technology Arab Academy for Science Technology and Maritime Transport Alexandria Egypt

Undeniably, the increasing number of vehicles on the roads has automatically led to the unbelievable number of accidents occurring every day and the need for a safe communication system among the vehicles has become quite urgent. Using such systems, vehicles can communicate via wireless communication technologies and protocols like ZigBee, WiMax, Bluetooth, GSM, and RF. This paper introduces a communication system that works on preventing accidents by making all the surrounding vehicles communicate using RF, IoT, and LCD technologies to show all the warnings and the indications sent from other vehicles and a VLC that is connected to every vehicle to detect and send all needed information of occurring accidents on the road to all connected vehicles and to identify the hit or damaged ones. Moreover, the framework proposed in this paper attempts to perform mutual authentication procedures between vehicles and road-side units based on radio frequency identification devices and cloud computing. According to this framework, a low-cost device has been implemented using an LED lamp and an electrical circuit to control the LED luminous flux, and the modulated carrier signal has also been modified to effectively suppress the low-frequency noise. It should be noted that the experiments have shown that vehicular networks must be resilient and self-adapted to failures and potential threats to support coordinated transportation systems. Finally, the security analysis has also shown that the proposed framework is efficient, easily deployed and capable of finding the best routing paths between communicating vehicles. © 2022, The Author(s).

关键词： Anonymity Cloud computing Conditional privacy Identity-based cryptography Internet of things Mutual authentication Vehicular network security

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