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检索条件"任意字段=Conference on Advanced Signal Processing Algorithms"

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Predicting and Analyzing Student Exam Behavior with Distributed Machine Learning

Predicting and Analyzing Student Exam Behavior with Distribu...

引用

IEEE International conference on Intelligent Techniques in Control, Optimization and signal processing (INCOS)

作者： Atul Kumar Jena P. Joshi Akash Agarwal Amol Gawande Asha Kiran Sikhakolli Manjiri Joshi Kiran Singh A. B. Dadas Deepak Gupta Dr. D. Y. Patil B-School Pune Dr. D. Y. Patil IMR Pune REC Kannauj UP NWIMS&R Pune ITM Gwalior

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Examinations are pivotal milestones in educational settings, serving not just as a measure of academic performance but also as a complex behavioral phenomenon. While traditional studies have explored student behaviors during examinations, the intricate, high-dimensional nature of such behaviors often escapes conventional analytical approaches. This paper introduces a novel methodology that employs Distributed Machine Learning techniques to analyze student behavior during exams compre- hensively. We design a system architecture that captures various behavioral metrics, and apply distributed machine learning algorithms to dissect this multi-faceted data. The findings offer actionable insights into optimizing examination processes and provide a pioneering framework for leveraging advanced com- putational techniques in educational psychology. Our empirical results demonstrate a substantial improvement in understanding student behaviors, potentially influencing educational policies and pedagogical strategies.

关键词： Measurement Machine learning algorithms Scalability Systems architecture Psychology Machine learning signal processing

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An Innovative Approach for Long ECG Synthesis with Wasserstein GAN Model 24th

An Innovative Approach for Long ECG Synthesis with Wasserste...

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24th International conference on Computational Science and Its Applications (ICCSA)

作者： Tran, Thi Diem Dang, Thi Thu Khiet Tran, Ngoc Quoc Univ Informat Technol VNU HCM Ho Chi Minh City 70000 Vietnam Univ Int VNU HCM Ho Chi Minh City 70000 Vietnam Univ Sci John Von Neumann Inst VNU HCM Ho Chi Minh City 70000 Vietnam

ISBN: (纸本)9783031646072;9783031646089

Deep neural networks (DNNs) have set new standards in identifying and classifying irregular patterns in ECG (electrocardiogram) signals, surpassing previous methods. Despite the easy access and affordability of ECG sensors, a critical bottleneck remains the limited availability of reliable data for complex heart rhythms like second and third-degree atrioventricular block, ventricular tachycardia, and supraventricular tachycardia. This shortage has been a significant obstacle to improving DNN algorithms. Recent studies have turned to Generative Adversarial Networks (GANs) to create synthetic ECG data, enhancing the diversity of training datasets. However, much of this research has only managed to produce basic ECG components, missing the intricate details found in real patient data that includes multiple heartbeats. Our research has taken a groundbreaking approach by converting ECG signals into a two-dimensional format, allowing us to utilize advanced GAN models originally developed for image processing. This method has enabled us to generate extended, realistic ECG sequences closely mimicking those from actual patients. We have tested and refined our model using two databases, Physionet and Chapman, and have successfully produced 10-second ECG sequences showcasing a variety of heart rhythms previously unachieved in other studies. Our innovative technique not only surpasses existing methods in generating high-quality, realistic ECG data but also sets a new benchmark in ECG synthesis.

关键词： ECG generating Long ECG signal to Image GAN

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Method and Algorithm of Successive Approximation Analog-to-Digital Conversion of Information in Management Systems

Method and Algorithm of Successive Approximation Analog-to-D...

引用

International conference on advanced Computer Information Technologies (ACIT)

作者： Lubomyr Petryshyn Mykhailo Petryshyn dept. of Enterprise Management AGH University of Krakow Krakow Poland dept. of Computer Science and Information Systems Precarpathian National University Ivano-Frankivsk Ukraine

ISBN: (数字)9798350350043

ISBN: (纸本)9798350350050

Modeling methods and algorithms for analog-to-digital conversion of information in control systems are significant components in the development and design of devices for analog signals digitizing. This process involves several steps, including sampling, quantization, and encoding, to convert continuous signals into a digital format that can be easily manipulated and analyzed by digital *** quest to enhance the speed of converters is driven by the ever-increasing demand for rapid and efficient data processing. The research into additive successive approximation methods has yielded a novel approach: bitwise balancing. This innovative method, supported by a robust mathematical model, promises to revolutionize analog-to-digital conversion by significantly reducing the conversion time. The empirical analysis suggests that this reduction could range from 6 to $25 \%$, marking a substantial improvement over existing methods. This breakthrough shows not only the potential for faster data processing but also sets a new direction for the development of technical apparatuses designed for swift information form conversion.

关键词： Additives Quantization (signal) Data processing Approximation algorithms Control systems Mathematical models Encoding Information technology Analog-digital conversion Information systems

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Research on the Analysis and Prediction Model of Health Monitoring Data Based on the Internet of Things

Research on the Analysis and Prediction Model of Health Moni...

引用

signal processing, Communications and Engineering Management conference (ISPCEM), International

作者： Liling Zhao Zhaomiao Gong School of Nursing Chongqing Medical and Pharmaceutical College China Personnel Division Chongqing Medical and Pharmaceutical College China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

This research centers on the development of an advanced model for analyzing and forecasting elderly health monitoring data, leveraging the Internet of Things (IoT) technology. A comprehensive health monitoring system specifically designed for the elderly is established, enabling the real-time acquisition and transmission of daily health metrics through the integration of IoT technologies. During the data analysis phase, an association rule mining algorithm is utilized to identify hidden patterns and correlations within the elderly health data, thereby facilitating the prediction of health outcomes. A subsequent health prediction model is formulated, incorporating data mining and machine learning methodologies, with its effectiveness and reliability rigorously assessed through simulation experiments. The outcomes of these simulations demonstrate a significant enhancement in both the precision and efficiency of the prediction model. This progress provides a solid scientific basis for the health management of the elderly population. Further model simulations confirmed the accuracy and practicality of the developed prediction model, underscoring its extensive potential for application in elderly health management.

关键词： Analytical models Data analysis signal processing algorithms Predictive models Prediction algorithms Data models Real-time systems Association rule learning Internet of Things Older adults

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FPGA Implementation Of a High Throughput Low Power advanced Encryption Standard (AES-128) Cipher

FPGA Implementation Of a High Throughput Low Power Advanced ...

引用

International conference on signal processing and Integrated Networks (SPIN)

作者： Jaideep Kala Jeebananda Panda Lavi Tanwar Department of Electronics and Communication Delhi Technological University New Delhi India

Ensuring secure transmission and storage of digital information is critical for any organization to function properly. To address this issue, encryption algorithms are commonly used. advanced Encryption Standard (AES) has been globally adopted as the mainstay cipher algorithm for securing transmission networks and storage devices due to its easy implementation and compatibility with both hardware and software applications. Another reason for its widespread popularity is its uncompromisable nature against existing brute force attacks, making it practically unbreakable on existing computing power. AES implementation for battery operated devices requires an algorithm with low power consumption and high-speed encryption/decryption of digital data. This paper proposes an FPGA implementation of a high throughput parallel pipelined 128-bit AES algorithm with a low power key expansion mechanism for iterative stages. A 128-bit symmetric key has been used for undertaking 10 rounds of transformations. All the encryption and decryption transformations are simulated using iterative design methodology in order to minimize hardware consumption. Xilinx Artix-7 FPGA device is used for hardware evaluation and Verilog HDL for programming. Simulation and synthesis task has been performed on Xilinx Vivado v2021.1 IDE. The results exhibit high-rate encryption of 68 Gb/s and low energy consumption of 7 pJ/bit.

关键词： Ciphers Energy consumption Power demand Software algorithms signal processing algorithms Throughput Hardware

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Continuous Time Digital signal processing and signal Reconstruction

Continuous Time Digital Signal Processing and Signal Reconst...

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IEEE Annual Computing and Communication Workshop and conference (CCWC)

作者： Patrick Jungwirth W. Michael Crowe Advanced Computing Branch DEVCOM Army Research Lab Aberdeen MD Embedded Systems DEVCOM Aviation and Missile Center Redstone Arsenal AL

Continuous Time (CT) Systems offer several advantages over traditional digital signal processing (DSP); however, CT is not linear time invariant like DSP. Since discrete time and DSP are linear time invariant, signal reconstruction is applied after all signal processing steps have been completed. A DSP approach cannot be applied to continuous time systems. For CT systems, level crossing sampling signals are nonlinear. For linearity to hold in CT systems, signal reconstruction must be applied before any signal processing steps. In 2003, Tsividis showed an asynchronous zero order hold is the simplest signal reconstruction algorithm for CT. In 2008, Schell and Tsividis demonstrated an offline exact reconstruction algorithm with greater than 100 dB signal-to-noise and distortion ratio (SINAD). This paper focuses on CT level crossing signal reconstruction. For practical CT signal reconstruction, a low complexity approximation to offline signal reconstruction is preferred with higher signal-to-noise ratio than simple asynchronous zero order hold reconstruction. A Gaussian-sinc( ) reconstruction kernel is investigated as an approximation to offline reconstruction.

关键词： Nonlinear distortion signal processing algorithms Linearity Digital signal processing Reconstruction algorithms signal reconstruction Complexity theory

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Quantitative Analysis of EEG Neurofeedback using optimized 1-DPSO

Quantitative Analysis of EEG Neurofeedback using optimized 1...

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International conference on signal processing and Integrated Networks (SPIN)

作者： Ms. Kiran Waghmare Meenakshi Malhotra Reeja Sr Center for Development of Advanced Computing Mumbai India Dayananda Sagar University Bengaluru India School of Computer Science and Engineering AP University India

Brain-Computer Interface (BCI) systems are the leading technology in the world related to Neurosciences. Human intelligence and imagination have no bounds and this has led to vast advancement in BCI systems related to Neurological and allied sciences. This system measures the activity of the Central Nervous System (CNS) using biosignals and output is called Electroencephalography (EEG). The main purpose of BCI is to acquire EEG brain signals, identify patterns, extract features, and produce resultant actions. This process communicates with a modest electronic system designed for movements of physically challenged or paralyzed people. The purpose of this BCI system is to design a model to check the attention level of body movement. The movements are based on the EEG signals captured from the 19-electrode EEG headset. This allows gaining control over optimized real-time feature selection for EEG signals. The dataset of 30 subjects’ sample EEG signals is recorded for classification and analysis purpose. The EEG signals are classified using Logistic Regression, Decision Tree, and Random Forest algorithms. It is shown that Random Forest is the most efficient classifier with the highest accuracy of 99.47%.

关键词： signal processing algorithms Feature extraction Brain modeling Prediction algorithms Electroencephalography Real-time systems Classification algorithms

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Efficient Time-frequency Domain Filter Design Against SAR Interference

Efficient Time-frequency Domain Filter Design Against SAR In...

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signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Xiangyu Zhu Jiadong Wang Yachao Li Endi Zhu Qi Wang Yang Zhang National Key Laboratory of Radar Signal Processing Xidian University Xi’an China Academy of Advanced Interdisciplinary Research Xidian University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Synthetic aperture radar (SAR) could be easily interfered with by a number of active jamming signals from enemy active jammers during its operating cycle, which seriously affects its battlefield information acquisition capability. In order to effectively combat multiple types of interference, this paper proposed an efficient time-frequency domain filtering method, which extracts the jamming-free pulses from received echo, and constructs an adaptive time-frequency domain filter by using its max time-frequency domain energy peaks, which could accurately extract and suppress interference. Simulation results demonstrate the significant suppression effect of the proposed method on the typical types of interference measured.

关键词： Time-frequency analysis Simulation Interference Filtering algorithms Information filters Stability analysis Jamming Time-domain analysis Synthetic aperture radar signal to noise ratio

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Classification algorithms and applications for flight delay prediction based on unbalanced data

Classification algorithms and applications for flight delay ...

引用

2023 International conference on Computer, Artificial Intelligence, and Control Engineering, CAICE 2023

作者： Qu, Jingyi Chen, Bo Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China Tianjin300300 China

ISBN: (纸本)9781510665040

By predicting flight delays, economic losses caused by delay situations can be effectively prevented and reduced. A classification algorithm for flight delay prediction based on unbalanced data is proposed to solve the problem of unbalanced number of delayed and non-delayed samples in the process of flight delay prediction. Firstly, a multiclassification model based on the CycleMLP algorithm is proposed for flight delay prediction. Secondly, the CycleMLP algorithm is improved by using the Focal Loss modified cross-entropy loss function to build the final classification model for flight delay prediction. And the experimental results show that the improved model has better prediction results for delayed flights, with a 5.54% increase in average macro precision and 1.86% increase in accuracy. Finally, the flight delay prediction results are published in the mobile APP application system to provide corresponding advice to passengers and airport departments. © 2023 SPIE.

关键词： Losses

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Applying ML Models for Heart Disease Prediction: A Comparative Analysis

Applying ML Models for Heart Disease Prediction: A Comparati...

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Communication, Computing and signal processing (IICCCS), IEEE International conference on

作者： Ishu Rani Dibyahash Bordoloi Abhishek Tiwari Pradeepta Kumar Sarangi Srikanta Kumar Mohapatra Alok Kumar Agrawal Chitkara University College of Nursing Chitkara University Himachal Pradesh Dept of CSE Graphic Era Hill University Graphic Era Deemed to be University Dehradun India Apex Institute of Technology Chandigarh University Mohali Punjab India Chitkara University School of Engineering and Technology Chitkara University Himachal Pradesh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (数字)9798350390759

ISBN: (纸本)9798350390766

The human body is always susceptible to disease. Heart-related issues are a major concern among them, leading to more annual fatalities than all other diseases combined. It includes various conditions that impact the heart arrhythmias, among others. Alarmingly, heart disease is increasingly prevalent among younger generations, particularly among youth. Research indicates that factors such as chronic stress, smoking, and unhealthy lifestyles contribute to this trend. Given the rising incidence of heart disease, there is a pressing need for advanced algorithms and methodologies for predicting its occurrence. This study aims to determine whether an individual is currently suffering from or is at risk of developing heart disease. Hospitals can leverage automated diagnostic tools that utilize computations for data science and data mining techniques for accurate predictions. This research paper examines the application of supervised ML methods, specifically highlighting the Decision Trees (DT) model, alongside two other models: the Random Forest (RF) model and the LR model. The dataset that was utilized in this investigation came from Kaggle. The Decision Tree and Logistic Regression algorithms demonstrated notable accuracy rates of 88% and 84%, respectively. In contrast, the Random Forest model outperformed the others, achieving an accuracy of 92.74%, almost 93%.

关键词： Heart Accuracy Computational modeling signal processing algorithms Surgery Prediction algorithms Vectors Telecommunication computing Random forests Diseases

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