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

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2nd International conference on Science, Engineering Management and Information Technology, SEMIT 2023

2nd International Conference on Science, Engineering Managem...

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2nd International conference on Science, Engineering Management and Information Technology, SEMIT 2023

ISBN: (纸本)9783031722868

The proceedings contain 46 papers. The special focus in this conference is on Science, Engineering Management and Information Technology. The topics include: Verification and Validation of Knowledge Engineering Systems: A Life Cycle Framework;The Critical Factors of Success of Gamification in Digital Banking Services: Using Analytic Hierarchy Process (AHP) Approach;e-Shopping Sites Preference Analysis with Multi-criteria Decision-Making Methods;advancing Anemia Diagnosis: Harnessing Machine Learning Methods for Accurate Detection;monkeypox Detection with K-mer Using Machine Learning algorithms;cloud Computing Model for Handling Medical Big Data: A Mobile Hospital Pervasive Healthcare Application;Harnessing advanced AI Techniques: An In-Depth Analysis of Machine Learning Models for Improved Diabetes Prediction;image processing in Toxicology: A Systematic Review;augmented Reality Immersive World with Hologram Special Effect in Early Childhood Education;improving the Visual Ergonomics of Computerised Workplaces Through the Use of Specialised Eye-Rest Software;a Diagnosis Model Based on Federated Learning for Lung Cancer Classification;Arrhythmia Detection from ECG Traces Images Using Transfer Learning Approach;enhancing Traffic Flow Prediction in Urban Areas Through Deep Learning and Probe Information: A Comparative Study;an Approach to Multi-agent Deep Q-Network Optimization of signal Control in Multi-intersection Road Environments to Enhance Urban Traffic Flow;blockchain-Driven Smart Contracts: An Overview of Application Areas and Gap Identification in Construction Management Literature;stochastic Optimization Methodology for Production Planning with Uncertain Demand and Lead Time Based on the Digital Twin;role of Top Management Commitment and Information Technology Investment in Digital Transformation;antecedents of Mobile Banking Apps Adoption Among Consumers in Ghana.

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Epileptic Seizure Prediction through ML And DL Models: A Survey

Epileptic Seizure Prediction through ML And DL Models: A Sur...

引用

International conference of Electronics, Communication and Aerospace Technology (ICECA)

作者： J. Viswanath S. Annamalai S. Ramesh Department of Computer Science and Engineering Jain (Deemed-to-be University) Bangalore Karnataka

ISBN: (数字)9798350367904

ISBN: (纸本)9798350367911

Epileptic seizures are unpredictable and pose significant risks to individuals affected by epilepsy. Electroencephalogram (EEG) signals offer a promising avenue for early seizure prediction, enabling timely intervention and improved quality of life for patients. This survey paper provides a comprehensive review of Deep learning (DL) and Machine learning (ML) learning techniques applied to EEG-based epileptic seizure prediction. It covers traditional ML models, advanced DL architectures, and optimization algorithms, highlighting their effectiveness and limitations. Additionally, the paper discusses feature extraction methods, signal processing techniques, and evaluation metrics used in this domain. Real-world applications, current challenges, and future research directions are explored to offer a holistic view of the field. By examining state-of-the-art methods, this survey aims to provide valuable insights into the development of robust, real-time seizure prediction systems, fostering advancements in clinical applications and patient care.

关键词： Surveys Computational modeling signal processing algorithms Epilepsy Predictive models Brain modeling Feature extraction Prediction algorithms Electroencephalography Optimization

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An Earthquake Alert system using Internet of Things

An Earthquake Alert system using Internet of Things

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International conference on Communications and signal processing

作者： N. Vishnu Teja S.P. Akshay K.Abu Khaleed C.Santan Kumar G.P. Manoj Kumar R.M. Bhavadharini School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

This project introduces an innovative earthquake detection system leveraging accelerometers for seismic sensing. By incorporating accelerometers from widely available devices like smartphones into a distributed sensor network, the system aims to provide a cost-effective and scalable solution for enhancing seismic monitoring capabilities. Real-time data processing, supported by advanced signal processing and machine learning algorithms, enables the system to differentiate between normal vibrations and seismic activities. Upon detecting anomalies, the system generates alerts to facilitate swift response and early warning mechanisms. Noteworthy features of this system include its affordability, easy deployment, and suitability for implementation in diverse environments, both urban and remote. The project contributes to advancing earthquake detection technologies, thereby bolstering preparedness and resilience against seismic events

关键词： Vibrations Accelerometers Power demand Earthquakes signal processing Real-time systems Sensors

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An AMCMD Approach for Robust EEG signal Classification: Unraveling Neurophysiological Insights and BCI Applications 11

An AMCMD Approach for Robust EEG Signal Classification: Unra...

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2023 IEEE 11th International conference on Information, Communication and Networks, ICICN 2023

作者： Aziz, Muhammad Zulkifal Yu, Xiaojun School of Automation Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9798350314014

Electroencephalogram (EEG) signal processing plays a pivotal role in deciphering profound neurophysiological insights from seemingly inconspicuous neural signals, enabling the realization of practical BCI applications. This paper delves into an innovative and sophisticated EEG signal processing model, which employs three distinct combinations of EEG electrodes, four advanced feature extraction methods, and four cutting-edge classification algorithms, in conjunction with an adaptive multivariate chirp mode decomposition (AMCMD) for the analysis of motor imagery EEG signals. The feasibility of this novel approach is corroborated through its successful application to a sizable GigaDB dataset comprising 52 participants, as well as the challenging BCI competition III datasets IVa and IVb. The results unequivocally demonstrate that the seamless integration of the AMCMD mechanism with an 18-electrode combination, spectral features, and a multilayer neural network classifier engenders remarkably robust and discriminating classification outcomes. The classification accuracy for dataset IVa and IVb subjects attains an impressive pinnacle, reaching 99.70%, 99.88%, 99.88%, 99.87%, 100%, and 94.98%, respectively. Furthermore, when scrutinizing the GigaDB dataset, the average classification accuracy, sensitivity, specificity, and f1score exhibit commendable values of 82.71%, 82.76%, 83.04%, and 82.80%, respectively. These salient findings, juxtaposed against the backdrop of earlier studies, decisively manifest a striking 15.4% enhancement in average classification accuracy, irrefutably affirming the exceptional prowess of the proposed model. Consequently, the compelling evidence culminates in the conclusion that the adaptive AMCMD method stands resolutely robust, adept, and universally adaptive in its ability to proficiently classify EEG signals, deftly circumventing subject-to-subject variability across diverse and extensive datasets. © 2023 IEEE.

关键词： Mode decomposition

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The DME Interference Mitigation Based Wavelet Thresholding Algorithm for L-DACS Receiver

The DME Interference Mitigation Based Wavelet Thresholding A...

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6th International conference on Intelligent Computing and signal processing (ICSP)

作者： Xiangqing Peng Jiani Wang Lei Wang Haitao Liu Tianjin Key Lab for Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

The L-DACS operates as an inlay system between two adjacent channels of the Distance Measuring Equipment (DME) system since the spectrum resources in the L-band are scarce and fragmented. This DME signal severely impacts the performance of L-DACS. This paper proposes an approach for mitigating DME interference by integrating spectrum aggregation technology and a wavelet thresholding algorithm. To enhance spectral efficiency, the method utilizes direct wideband aggregation technology at the transmission end to expand the bandwidth. Then, at the receiving end of Orthogonal Frequency Division Multiplexing (OFDM), the received signal is decomposed layer by layer using the wavelet threshold algorithm. The difference between the spectrum-aggregated signal and the DME signal in the wavelet domain is effectively identified to reconstruct the DME signal, thereby achieving interference elimination. Simulation results demonstrate that this method improves spectrum efficiency and data transmission rate while significantly mitigating DME interference, thereby enhancing the link transmission reliability of the L-DACS system.

关键词： Time-frequency analysis Wavelet domain Simulation OFDM signal processing algorithms Interference Wavelet analysis Reliability L-band Wideband

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Enhancing Induction Motor Reliability Through advanced Feature Selection and Diagnostic Models in Low-Load Conditions

Enhancing Induction Motor Reliability Through Advanced Featu...

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International conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Chibuzo Nwabufo Okwuosa Jang-Wook Hur Department of Mechanical Engineering (Department of Aeronautics Mechanical and Electronic Convergence Engineering) Kumoh National Institute of Technology Gyeonsangbuk-do Republic of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

In induction motors, stator windings are critical for optimal magnetic field distribution, influencing rotor bar induction and motor performance. Motor component failure is undesirable and often necessitates system overhauls. This paper presents a comparative analysis of feature selection techniques filter-based, wrapper-based, and embedded methodsfor diagnosing stator winding faults in squirrel cage induction motors and selecting a reliable cost-efficient feature selection technique with high efficiency. These techniques are applied using varying motor current data signals under low-load conditions. By leveraging advanced signal processing tools, the study demonstrated that the embedded techniques presented a superior computational efficiency and diagnostic model than the other techniques using the XGboost classifier algorithm. Preliminary results highlight the potential of these techniques to improve fault detection while addressing challenges such as noise and signal complexity. Future research will explore the integration of Explainable AI (XAI) to enhance transparency and trust in diagnostic models.

关键词： Induction motors Explainable AI Magnetic separation Noise Stator windings signal processing algorithms Rotors Transforms Feature extraction Information filters

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Quantum Multi-Path Communication Protocol Based on Maximum Flow Theory

Quantum Multi-Path Communication Protocol Based on Maximum F...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jihao Fan Liang Huang Jun Li Long Shi Yuwen Qian School of Cyber Science and Engineering Nanjing University of Science and Technology Jiangyin China Laboratory for Advanced Computing and Intelligence Engineering Wuxi China School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Quantum networks are an actively researched and promising field, aiming to achieve efficient quantum information transmission by interconnecting quantum nodes. In large-scale quantum networks, end-to-end throughput is a critical factor that affects the overall performance of the network. The maximum flow problem, extensively studied in classical network theory, identifies a set of paths between the source and destination nodes that maximizes the total flow. This study extends the maximum flow problem to quantum networks, focusing on coordinating multiple paths for multi-path quantum communication. We propose a Quantum Multi-Path Communication Protocol (QMCP) that employs maximum flow theory to allocate transmission resources across multiple nodes efficiently, thus maximizing the total transmission capacity from the source to the destination. Our evaluation demonstrates that QMCP significantly enhances end-to-end throughput in quantum networks.

关键词： Quantum entanglement Simulation signal processing algorithms Focusing Information processing signal processing Throughput Routing protocols Quantum networks Speech processing

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Non-Stationary Blind Source Separation and advanced signal processing Techniques for Effective Fetal ECG Extraction

Non-Stationary Blind Source Separation and Advanced Signal P...

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International conference on Circuits, Communication, Control and Computing

作者： Shashidhar V Reddy Varun M V V Hemanth Jayashree R Dept. of ECE PES University Bangalore India Dept. of CSE (AIML) PES University Bangalore India

ISBN: (数字)9798331528539

ISBN: (纸本)9798331528546

Fetal electrocardiogram (FECG) extraction from maternal electrocardiogram (MECG) signals presents a major obstacle in biomedical signal processing. In this paper Fast Independent Subspace Analysis (FastISA), Fast Independent Component Analysis (FastICA) and Join Approximate Diagonalization of Eigen Matrices(JADE) is used as for Blind Source Separation (BSS) with stern pre-processing steps like band pass filtering, wavelet denoising, orthogonalization and Wiener deconvolution. To evaluate the quality of separation Amari Index has been implemented achieving 0.19 for synthetic data using fastISA and 0.33 for real-world using JADE algorithm providing reliable operation for different situations as FECG extraction is crucial for its insights on fetal hearts electrical activity with non-invasive means to monitor and diagnose early during pregnancy. A predictive model based on Random Forest and a Neural Network along with R Peak Detection based on Pan-Tompkins algorithm has been implemented to predict pregnancy states and physiological metric for analyzing Mothers Health condition. Integration of predictive model and signal processing techniques like FastISA and Pan-Tompkins algorithm provides a wider application of this approach in biomedical engineering for improved healthcare and diagnostics.

关键词： Pregnancy Filtering signal processing algorithms Electrocardiography Predictive models Prediction algorithms Wavelet analysis Blind source separation Data mining Biomedical monitoring

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Research and Application of Multimodal Knowledge Graph in Construction Risk Management of Transmission and Transformation Engineering

Research and Application of Multimodal Knowledge Graph in Co...

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

作者： Zhang You Qian Yi Zhang Yiquan Na Yu Zhao Jundan Yu Ting Hu Yue Lv Peng Engineering Construction Consulting Branch of State Grid Shanghai Electric Power Company Shanghai China Shanghai Power Transmission and Transformation Engineering Co. Ltd. Shanghai China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

This paper proposes a construction risk management method based on multimodal knowledge graph, aiming to improve the risk identification and prediction capabilities in the construction process. The system combines advanced natural language processing (NLP) algorithms, which can automatically extract and integrate construction-related information from multi-source heterogeneous data, and construct a comprehensive knowledge graph covering multimodal data such as text, images, and videos. In terms of algorithm design, an improved NLP model is used to conduct in-depth semantic analysis and association mining of text data, while integrating image recognition and video analysis technologies to achieve all-round data integration and analysis of complex construction scenes. In the system simulation stage, this paper conducts comprehensive verification through a large amount of actual construction data, focusing on evaluating the risk identification and prediction performance of the system in different scenarios. The simulation results show that the accuracy of this method in identifying construction risks has reached more than 95%, and the error rate of predicting construction risks is controlled within 5%, which significantly improves the accuracy and efficiency of construction risk management, fully verifies the practicality and reliability of the system, and provides strong technical support for construction management.

关键词： Accuracy Power transmission signal processing algorithms Knowledge graphs Prediction algorithms Natural language processing Systems simulation Risk management Data mining Videos

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Design and Implementation of Power Data Security and Compliance Management Framework

Design and Implementation of Power Data Security and Complia...

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

作者： Wei Huo Yuanming Ma Yan Ma Xiaoyan Li Lili Xiong Information and Communication Company State Grid Qinghai Electric Power Company China Qinghai Ruifeng Power Technology Co. Ltd Qinghai China State Grid Info-Telecom Great Power Science and Technology Co.Ltd Fuzhou China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

There are numerous data management and security compliance challenges that exist in the power industry such as issues related to data security, compliance and efficient data management. The goal of this research is to design and implement a power data security and compliance management framework. At the beginning of the research, the analysis and research of power data management, access control requirements, data security requirements, and compliance standards were conducted. After the design of the power data security and compliance management framework architecture and the functional modules, it was possible to complete the design. In the final design, four modules were presented: data encryption, access control, identity authentication, and auditing. By further adopting advanced techniques and algorithms such as parallel processing, high-speed network transmission, and data structure optimization, they improved system efficiency and throughput. By reflecting on data management, access control, configuration difficulty, and processing time, it was found that both tagging time and system throughput were shortened, and that there was no competition between data management tasks and concurrent authentication tasks, or between data processing tasks and data structure optimization.

关键词： Access control High-speed networks Data security Authentication signal processing algorithms Parallel processing Throughput Data structures Encryption Optimization

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