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Modification of the Prony Method for Pulse Signal modeling

Modification of the Prony Method for Pulse Signal Modeling

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Annual Siberian Russian Workshop on Electron Devices and Materials (EDM)

作者： Alexey B. Gavrilov Arina I. Bakulina Faculty of Automation and Computer Engineering Novosibirsk State Technical University Novosibirsk Russia

ISBN: (数字)9798350389234

ISBN: (纸本)9798350353051

This paper considers a modification of the Prony parametric approximation method for modeling impulse signals. It is assumed that the experimentally measured signal is represented as a time series. The method is based on the construction of a model of the experimental signal under study as a sum of damped sinusoids. The problem, which does not allow approximating impulse signals with the required accuracy, is revealed. On the basis of the analysis of the classical formulation of the method, its modification is proposed, which allows to solve this problem. Mathematical substantiation of the modified method is presented. The possibility of interference filtering on the basis of analyzing the spectral structure of the signal model is substantiated. The possibility of identification of dynamic characteristics of elements of automatic control systems in analytical form using the developed approach is investigated. The results of modeling a test signal in the form of a rectangular pulse are given. The directions of development of the modified method are discussed, as well as possible areas of practical application.

关键词： Analytical models Accuracy Time series analysis Noise Process control Materials reliability Time measurement

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Real-time Arm Motion Tracking and Hand Gesture Recognition Based on a Single Inertial measurement Unit

Real-time Arm Motion Tracking and Hand Gesture Recognition B...

引用

international conference on Internet of Things: systems, Management and Security

作者： Tien-Chiao Chang Yu-Chi Wu Chin-Chuan Han Chao-Shu Chang Dept. of Electrical Engineering National United University Miao-Li Taiwan Dept. of Computer Science and Information Engineering National United University Miao-Li Taiwan Dept. of Information Management National United University Miao-Li Taiwan

ISBN: (数字)9798350366501

ISBN: (纸本)9798350366518

With the development of virtual reality (VR) and augmented reality (AR) devices, handheld controllers and camera-based hand tracking are the most common methods for interacting with the virtual world. This paper proposes arm motion tracking and hand gesture recognition methods based on a single IMU (Inertial measurement Unit) sensor embedded in a wristband. This approach is more convenient than handheld controllers and more energy-efficient than camera-based tracking. We utilize two deep learning models: one for arm motion tracking and the other for hand gesture recognition. When training the arm motion tracking model, we use two IMU sensors-one placed on the wrist to provide forearm pose data and the other placed above the elbow to provide upper arm pose data. We use the elbow’s IMU data as the reference for training the model and calculate the real-time upper arm pose vector based on the wrist’s pose data. We train the hand gesture recognition model using only the wrist-mounted IMU sensor. This model captures different vibrations in the IMU during various gestures (such as grab, release, snap, and tap) for training. We achieve real-time arm motion tracking and hand gesture recognition using only a single IMU sensor. The angle error for arm motion tracking is only 15 degrees, and the hand gesture recognition accuracy reaches 95%.

关键词： Training Wrist Solid modeling measurement units Accuracy Tracking Gesture recognition Inertial navigation Real-time systems Data models

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Dual-Proxy modeling for Masked Face Recognition 7

Dual-Proxy Modeling for Masked Face Recognition

引用

7th international conference on Intelligent, Interactive systems and Applications, IISA2022

作者： Shuhui, Wang Xiaochen, Mao Shanghai Institute of Measurement and Testing Technology 1500 Zhang Heng Road Shanghai201203 China

With the recent worldwide COVID-19 pandemic, almost everyone wears a mask daily, leading to severe degradation in the accuracy of conventional face recognition *** works improve the performance of masked faces by adopting synthetic masked face images for ***, such methods often cause performance degradation on unmasked faces, raising the contradiction between the face recognition system's accuracy on unmasked and masked *** this paper, we propose a dual-proxy face recognition training method to improve masked faces' performance while maintaining unmasked faces' ***, we design two fully-connected layers as the unmasked and masked feature space proxies to alleviate the significant difference between the two data *** cross-space constraints are adopted to ensure the intra-class compactness and inter-class *** experiments on popular unmasked face benchmarks and masked face benchmarks, including real-world mask faces and the generated mask faces, demonstrate our method's superiority over the state-of-the-art methods on masked faces without incurring a notable accuracy degradation on unmasked faces. © 2022 The Authors. Published by Elsevier B.V.

关键词： Face recognition

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Heart Disease Prediction Using Machine Learning

Heart Disease Prediction Using Machine Learning

引用

international conference on computer and Communications Technologies (ICCCT)

作者： P. Dhanalakshmi Annapureddy Titoo Reddy Durgam Lokanandam Dasari Saikumar Bhavish Kumar Shah Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College (Autonomous) Tirupati India Department of Computer Science and Engineering Sree Vidyanikethan Engineering College (Autonomous) Tirupati India

ISBN: (数字)9798331537579

ISBN: (纸本)9798331537586

The heart disease remains among the leading causes of death worldwide, which emphasizes the importance of early detection to enhance results and avoid complications like heart attacks. Traditional diagnostic methods are often expensive and time-consuming, creating a demand for faster, data-driven solutions. The prediction capabilities of Machine learning technology for heart disease detection have proven effective yet remain limited due to the problem known as class imbalance. computational complexity, and extensive hyperparameter tuning. Existing models like Gradient Boosting, SVM, and Neural Networks stands as their main achievement while they encounter problems with efficiency alongside limited generalization capabilities. This research proposes a new framework which applies the joint use of Synthetic Minority Oversampling Technique (SMOTE) and Naive Bayes Classifier to resolve these problems, balancing computational simplicity with robust predictive power. The combination of SMOTE and Standard Scaler helps overcome class imbalance by creating new samples for minority classes and normalizes features to maintain model stability. Additionally, A novel feature selection method is presented, and the model accuracy is enhanced through this action which identifies the fundamental features that contribute to heart disease prediction. This innovative approach improves interpretability and performance, offering an efficient, scalable, and accessible solution for early heart disease detection, Healthcare systems benefit through improved patient outcomes and decreased healthcare burdens when nurses participate in the design phase of healthcare systems.

关键词： Heart Accuracy Computational modeling Medical services Predictive models Feature extraction Naive Bayes methods Tuning Standards Diseases

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Local Attention base on Time Masks for Weakly Supervised Video Anomaly Detection

Local Attention base on Time Masks for Weakly Supervised Vid...

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international conference on Information systems Engineering (ICISE)

作者： Xinghai Zhu Qing Sheng Daoao Li Shangze Li School of Computer Science and Technology Anhui University Hefei China Department of Electronic Engineering Nanjing Vocational Institute of Mechatronic Technology Nanjing China

Video anomaly detection (VAD) aims to use computer technology to automatically detect and recognize abnormal patterns in videos. Based on multi-instance learning, a weakly supervised anomaly detection method treats training videos as bags and assigns video labels by selecting key video segments from the bags to achieve fine-grained classification of video frames. Videos contain rich temporal information, and previous works model the global temporal information of videos to detect anomalies. However, monitoring videos usually have long duration and contain a lot of redundant information. Therefore, this paper proposes a local attention module that integrates video segments of different lengths through four different masking mechanisms to guide the network to model short-term temporal relationships in videos, abandoning the global modeling approach. Additionally, a joint distance metric ranking loss is proposed to guide the network to generate more discriminative feature representations for better anomaly detection performance. The proposed method is extensively tested on two publicly available anomaly detection datasets and its effectiveness is validated.

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Predictive Analytics for Learning Performance in First-Year University Programming Course

Predictive Analytics for Learning Performance in First-Year ...

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IEEE international conference on Smart Instrumentation, measurement and Applications (ICSIMA)

作者： Mira Kartiwi Teddy Surya Gunawan Nelidya Md Yusoff Department of Information Systems International Islamic University Malaysia Kuala Lumpur Malaysia Electrical and Computer Engineering Dept. International Islamic University Malaysia Kuala Lumpur Malaysia Faculty of Artificial Intelligence Universiti Teknologi Malaysia Kuala Lumpur Malaysia

ISBN: (数字)9798350365283

ISBN: (纸本)9798350365290

The increasing demand for programming skills has highlighted the need for effective teaching strategies to support student success in programming courses. Despite significant advancements in learning analytics, predictive models explicitly tailored to programming courses remain underexplored. This research aims to develop a machine learning model to predict student performance in programming courses offered within IT programs by analyzing gender, type of activity (readings, coding exercises, assignments), and frequency of access to different activities. Our study utilizes log data of the asynchronous learning activities in the learning management systems of the students enrolled in programming courses. We employ machine learning techniques, decision trees, gradient boosting machines (GBM), and logistic regression to build robust predictive models. In this study, the decision tree model outperformed logistic regression (77.77%) and gradient boosting machine (GBM) (86.57%) by achieving the highest accuracy of 89.09% and excelling in predicting 'Poor' student performance with a recall of 90.67%, establishing it as the most effective model for this predictive analysis. The findings from this research offer actionable insights for educators, enabling early intervention for at-risk students and developing tailored teaching strategies to enhance student performance through strategically provisioning the learning materials in programming courses. This study contributes to the growing knowledge of learning analytics and provides a foundation for future research in predictive modeling for diverse educational contexts.

关键词： Analytical models Logistic regression Accuracy Education Predictive models Boosting Decision trees

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A Comparative Analysis of Different CNN Architectures for Malware Classification

A Comparative Analysis of Different CNN Architectures for Ma...

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Mobile Networks and Wireless Communications (ICMNWC), IEEE international conference on

作者： Anisha Mahato Rana Majumdar Swarup Kr Ghosh Department of Computer Science Engineering Sister Nivedita University Kolkata India Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (数字)9798331528348

ISBN: (纸本)9798331528355

The concept of digital privacy remains a contested and often misunderstood topic in the modern, internet-dominated era. Despite numerous advancements in cybersecurity, the pervasive threats to data security continue to erode trust in digital systems. Malware, a term encompassing a wide range of malicious software designed to disrupt operations, compromise systems, or steal sensitive information, represents a major challenge in this regard. Its widespread presence has led to the perception that digital privacy is increasingly unattainable, casting a shadow over cyberspace and intensifying the urgency for robust countermeasures. This study delves into the critical task of malware classification, leveraging the Malimg dataset, a benchmark resource widely used for malware research. To tackle this problem, four cutting-edge Convolutional Neural Network (CNN) architectures-LeNet-5, AlexNet, VGG-16 and ResNet-are employed to train and test the dataset, with the objective of identifying the architecture best suited for this classification task. The models are rigorously evaluated using four essential performance metrics: Accuracy, Precision, Recall, and F1-score, which collectively provide a comprehensive measure of their effectiveness. Among these architectures, ResNet emerges as the most effective, achieving a stellar accuracy of 99.78% and excelling across all other evaluation parameters. This highlights ResNet's superior capability in accurately identifying and classifying malware, thereby offering a promising solution to mitigate the growing threat posed by malicious software in cyberspace.

关键词： Deep learning measurement Wireless communication Privacy Accuracy Computational modeling Cyberspace computer architecture Malware Convolutional neural networks

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Physical Layer Security Strategy for Full-Duplex Power Line Communication Networks

Physical Layer Security Strategy for Full-Duplex Power Line ...

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IEEE international conference on Communications in China (ICCC)

作者： Xin Wen Hui Li Shaoliang Chen Miaozhuang Cai Liyu Huang Zhengmin Kong Measurement Center of Guangzhou Power Supply Bureau of Guangdong Power Grid Guangzhou China School of Electrical Engineering and Automation Wuhan University Wuhan China

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Due to the explosive growth of computer network data, there are security vulnerabilities in the physical systems of power information. This paper focuses on the physical layer security of multi-hop decode-and-forward (DF) relay assisted power line communication (PLC) system and proposes a joint design scheme of artificial noise (AN) matrix and precoding matrix. Considering the full-duplex DF relay and legitimate receivers with residual self-interference, the security rate of the PLC system is maximized considering the constraints of imperfect eavesdropping channels and power constraints through AN techniques and physical layer precoding technology. Considering the constraints of residual self-interference, power constraints, and imperfect eavesdropping channels, the problem of maximizing the security rate is studied. The non-convex nature of the problem makes it difficult to solve; the S-Procedure method is used to convert it into a convex problem, and it’s decoupled and solved based on the block coordinate descent (BCD) algorithm. Simulation experiments show that the proposed joint design of AN matrix and precoding matrix scheme has higher security and robustness compared to traditional schemes.

关键词： Interference cancellation Precoding Power line communications Receivers Full-duplex system Spread spectrum communication Physical layer security

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Modelling a movie recommendation system using a mixed classification method

Modelling a movie recommendation system using a mixed classi...

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Advanced Computing and Emerging Technologies (ACET), international conference on

作者： Anand Prakash Srivastava Sanjeev Kumar Sharma Computer Science & Engineering Sanskriti University Mathura India

ISBN: (数字)9798350367720

ISBN: (纸本)9798350367737

The primary objective of this research is to enhance the accuracy and reliability of movie recommendation systems (MRS) by addressing common challenges such as the cold start problem, data sparsity, and malicious attacks. This study introduces a mixed machine learning approach, combining traditional classification techniques with advanced models like autoencoders and Restricted Boltzmann Machines (RBMs). By incorporating trust metrics into the recommendation process, the system aims to provide more personalized and dependable movie suggestions. The methodology involves using a hybrid classifier that integrates Naïve Bayes, Support Vector Machines, autoencoders, and RBMs to improve recommendation precision. The results demonstrate that the inclusion of trust-based algorithms significantly enhances the recommendation accuracy, especially in scenarios with limited data. The proposed model outperformed traditional methods, achieving higher validation accuracy and effectively addressing the cold start problem. The integration of trust metrics with RBM further improved the system's performance, achieving an accuracy of 93.4%. This study concludes that hybrid machine learning approaches, combined with trust-aware mechanisms, offer a robust solution to the challenges faced by conventional MRS, providing a foundation for future research in developing more efficient and reliable recommendation systems.

关键词： measurement Support vector machines Accuracy System performance Computational modeling Machine learning Motion pictures Classification algorithms Reliability Recommender systems

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MVMSFN: A Multi-View and Multi-Scale Fusion Network for Online Detection of Heterogeneous Gestures

MVMSFN: A Multi-View and Multi-Scale Fusion Network for Onli...

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international joint conference on Neural Networks (IJCNN)

作者： Hao Long Song Wang Hao Hu Yanru Wang Hao Xu Hesong Wang School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Gesture is considered as an important approach to implement natural human-machine interaction, but the existing online gesture detection methods still suffer from the problem of unsatisfactory detection rate and false trigger rate that may lead to failed interactions. In this paper, we propose an improved Multi-View and Multi-Task(MVMT) model called Multi-View and Multi-Scale Fusion Network(MVMSFN) for the problem of inaccurate gesture segmentation from the background data. Firstly, for the adaptive feature fusion, we construct Multi-View Fusion Model(MVFM), which introduces spatial attention weight matrix to learn to enhance information interaction among multi-view branches, and Multi-Scale Fusion Model(MSFM), which extracts correlations among features from different layers with different scales to mitigate the effect of inconsistent gesture lengths. Then we design Decoupled Gesture Progress Module (DGPM), which sets up auxiliary tasks that enable the network to predict the execution progression of static and dynamic gestures respectively, to address the network performance degradation problem caused by applying uniform gesture modeling in the heterogeneous gesture environment(including dynamic and static gestures) where there is great difference in representation between the two main gesture categories. The experimental results on the latest benchmark show that MVMSFN achieves the best performance in metrics, in which the false positive score outperforms the state-of-the-art approach(OO-dMVMT). In summary, MVMSFN provides higher accuracy of gesture segmentation and lower false trigger rate while maintaining relatively low computational time for real-time applications. The code is available at https://***/miixcc/MVMSFN-Gesture.

关键词： measurement Human computer interaction Degradation Adaptation models Neural networks Benchmark testing Predictive models

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