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检索条件"任意字段=3rd International Conference on Signal and Data Processing, ICSDP 2023"

共 2955 条记录，以下是21-30 订阅

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Research on Network Security Intrusion Detection Based on Devnet 24

Research on Network Security Intrusion Detection Based on De...

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3rd international conference on signal processing, Computer Networks and Communications, SPCNC 2024

作者： Zheng, Hao Sun, Dongxu Han, Xiao Zhang, Xiaohua Zhao, Yan Co. Ltd. Hebei Shijiazhuang China Guodian Hefeng Wind Power Development Co. Ltd. Liaoning Shenyang China School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin China

ISBN: (纸本)9798400710834

With the promotion of industrial 4.0 and intelligent manufacturing, the network security of industrial control system becomes particularly important. In the face of new network attacks, the traditional intrusion detection system (IDS) has been difficult to meet the protection requirements, so it is particularly urgent to research and develop efficient network security detection technology. In this study, an intrusion detection method of industrial control system based on DevNet network is proposed, which uses deep convolution neural network architecture to detect events and locate evidence by analyzing the characteristics of key frames, which significantly improves the performance of multimedia event detection. This method optimizes the anomaly score through end-to-end learning, makes effective use of the limited marked abnormal data, and enhances the accuracy and real-time performance of detection. The research first examines the challenges of network security in industrial control systems, and then describes the DevNet framework and its core algorithms, including exception score learner, normal a priori and Z-Score deviation loss function. Through the experimental evaluation of multiple data sets, DevNet performs well in key evaluation indicators such as AUC-ROC and AUC-PR, especially in dealing with large-scale network traffic and defending against zero-day attacks. The interpretability of DevNet provides strong support for security analysis and enhances the credibility of the model prediction. This research not only enhances the protection of network security, but also promotes technological innovation, and provides new ideas for the field of network security. With the increase of IIoT equipment, DevNet is expected to become the key technology to protect the safety of industrial control system. The results of this research are of great significance in theory, and show broad prospects in practical application, and contribute to the progress of network security. ©

关键词： Network intrusion

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Modified CNN-Bi-LSTM Framework for Forecasting Epileptic Seizures Utilizing Multi-Scale EEG data 3

Modified CNN-Bi-LSTM Framework for Forecasting Epileptic Sei...

引用

3rd international conference on Electronics and Renewable Systems, ICEARS 2025

作者： Nithya, C.K. Kumar, P. Ranjith Vimala, M. Ramasamy, S. Jenitha, R. P.S.R Engineering College Department of Electronics and Communication Engineering Sivakasi626 140 India

ISBN: (纸本)9798331509675

Recurrent convulsions are a defining characteristic of epilepsy, a cognitive disorder that presents significant challenges in term Multi-Scale of management and therapeutic interventions. Predicting seizures accurately is essential to improving patients' quality of life and lowering the risk of injury. A Bidirectional Long Short-Term Memory (Bi-LSTM) network and a modified Convolution Neural Network(CNN) intended to access Multi-Scale(EEG) data are used in this method and is to provide a stand out method for epileptic seizure prediction. Utilizing CNNs' spatial feature extraction capabilities to detect local patterns in EEG signals, the suggested model efficiently describes temporal correlations through forward and backward analysis of the EEG data set. By diminishing noise and boosting important information, wavelet modification pre-processing of Multi-Scale (EEG) dataset that is available to the public, Innovative frameworks further adopt tailored neuro computational methodologies to bolster the accuracy of seizure detection and forecasting, sensitivity, and specificity. Based on the findings, the integration of CNN and Bi-LSTM significantly improves the efficacy of seizure prediction when utilized alongside Multi-Scale signal processing, presenting a potentially valuable approach for the effective management of epilepsy. © 2025 IEEE.

关键词： Convolutional neural networks

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Research On Lane Change Fusion Decision Model For Complex Traffic 24

Research On Lane Change Fusion Decision Model For Complex Tr...

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3rd international conference on signal processing, Computer Networks and Communications, SPCNC 2024

作者： Chen, Yuanjing Zhang, Yongjie GuangXi Vocational Normal University Guangxi Nanning China Key Laboratory of Application Technology of Intelligent Connected Vehicle Guangxi Vocational Normal University Education Department of Guangxi Zhuang Autonomous Region Guangxi Nanning China

ISBN: (纸本)9798400710834

Aiming at the problem that driverless cars are unable to accurately detect the behavior of surrounding vehicles when performing lane changing in complex traffic. This paper proposes a lane changing fusion decision model to solve the problem of low effectiveness of driverless cars under complex traffic. The model improves the multi-objective trajectory prediction problem of driverless cars under complex traffic by using multi-source information fusion technology. At the same time, the input data is processed to realise the automatic learning and analysis of a large amount of data in combination with the deep learning algorithm of the artificial neural network. Statistical analysis of the model’s simulation experiment outcomes was conducted, and the fusion outcomes were then contrasted to the single-sensor situation. Under the optimized lane-changing model, the driverless car is improved in both lane-changing prediction and multi-vehicle perception, which solves the problems of inaccurate multi-objective trajectory prediction and inefficient lane-changing decision-making of the driverless car during lane-changing. © 2024 Copyright held by the owner/author(s).

关键词： data fusion

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Classification of Currencies as Authentic or Counterfeit: A data-Driven Approach 3rd

Classification of Currencies as Authentic or Counterfeit: A ...

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3rd international conference on signal and data processing, icsdp 2023

作者： Koushik, K. Venkat Sai, G. Chaurasiya, Rahul K. Department of ECE Maulana Azad National Institute of Technology Bhopal India

ISBN: (纸本)9789819795772

The pervasive issue of counterfeit currency poses a significant threat to global economies, leading to substantial financial losses and eroding trust in financial institutions. While traditional methods of counterfeit detection rely on visual inspections and security feature verification, the advent of sophisticated counterfeiting techniques necessitates more advanced solutions. This study explores the application of machine learning (ML) algorithms to the problem of currency authentication, utilizing a dataset comprising images of banknotes. We employed a convolutional neural network (CNN) architecture, tailored for feature extraction from image data, which has demonstrated exceptional performance in pattern recognition tasks. Our proposed model introduces a novel preprocessing algorithm that enhances the distinctive characteristics of genuine and counterfeit notes, improving CNN's classification efficacy. We benchmarked the performance of our model against traditional ML classifiers, such as Support Vector Machines (SVM) and Random Forests. The analysis reveals the strengths of our model, including its robustness to variations in image quality and its ability to generalize from limited training data. However, limitations were observed in terms of computational intensity and the need for a substantial dataset to optimize model parameters. The findings underscore the potential of automated, ML-driven techniques in revolutionizing the detection of counterfeit currencies, presenting a scalable and effective solution to a complex challenge. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Counterfeit currency notes data-driven approach Machine learning

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Enhanced One-Bit Passive Radar Detection with Dual-Use of Pilot and data signals 24

Enhanced One-Bit Passive Radar Detection with Dual-Use of Pi...

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3rd international conference on signal processing, Computer Networks and Communications, SPCNC 2024

作者： Wu, Peiwen Huang, Lei Xiao, Yuhang Gan, Yuxi Lu, Zhaohua Yan, Qiao Xie, Jian Shenzhen University Guangdong Shenzhen China Intelligent Co. Ltd. Guangdong Shenzhen China ZTE Corporation Guangdong Shenzhen China Shenzhen University Guangdong Shenzhen China Shenzhen Bamboocloud Co. Ltd. Guangdong Shenzhen China

ISBN: (纸本)9798400710834

Unmanned Aerial Vehicles are crucial carriers in the low-altitude economy, and with large-scale applications, ensuring airspace order is becoming increasingly important. Active radar, which occupies significant spectrum resources, is unsuitable for urban deployment, making passive radar the primary solution for low-altitude detection. However, existing radar systems that rely on high-precision quantization face high storage and computational demands in large-scale monitoring. Furthermore, traditional technologies often treat communication signals as completely unknown for target detection, neglecting the potential of prior-known communication information, such as the waveform of pilot signals. To address these issues, this paper proposes a one-bit passive radar target detection method with dual-use of pilot and data signals. The method enhances target detection accuracy by leveraging the information of these signals and reduces system complexity through one-bit quantization. Additionally, we analyze the asymptotic performance of the proposed detector under both null and alternative hypotheses in low SNR conditions. Finally, simulation results validate the accuracy of our theoretical analysis and demonstrate the superiority of the proposed detector. © 2024 Copyright held by the owner/author(s).

关键词： Unmanned aerial vehicles (UAV)

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Aircraft Fuel Flow Modeling and Performance Optimization Using Machine Learning 3rd

Aircraft Fuel Flow Modeling and Performance Optimization Usi...

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3rd international conference on signal and data processing, icsdp 2023

作者： Dharrao, Deepak Misal, Mrinank Rai, Naivedya Joshi, Nirgoon Rajagopalan, Siddharth Pangavhane, Madhuri Bongale, Anupkumar Department of Computer Science and Engineering Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India Department of Information Technology Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India Department of Artificial Intelligence and Machine Learning Symbiosis Institute of Technology Pune Campus Symbiosis International (Deemed University) Pune India

ISBN: (纸本)9789819795772

The modeling of fuel flow in aircraft is a critical element in evaluating their performance. However, many of the existing approaches rely on highly aggregated data, which may not provide the necessary accuracy required by the aviation industry. To address this challenge, a new data-driven approach is proposed in this study. This method leverages machine-learning techniques and uses full-flight data from aircraft sensors to develop fuel flow models. The approach focuses on identifying the features that impact fuel flow rates during different flight phases, using a unique deep-learning approach for modeling. The results demonstrate a significant improvement over traditional practices, providing a more comprehensive and accurate alternative for performance evaluations. As a result, airlines and maintenance providers can optimize their operations and reduce fuel costs while meeting environmental targets. The proposed method achieved a 99.25% adjusted R2 score, highlighting the potential of machine learning tools in developing accurate and detailed fuel flow models based on full-flight data, providing significant benefits for the aviation industry. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Fuel consumption and emissions Fuel flow models Full-flight data Multi-layer perceptron (MLP) Optimization of airline operations

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Radar Characteristics Analysis of the Hailstorm Event in Hohhot on July 27, 2020 24

Radar Characteristics Analysis of the Hailstorm Event in Hoh...

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3rd international conference on signal processing, Computer Networks and Communications, SPCNC 2024

作者： Liu, Tianqi Kong, Wenjia Zhang, Lu Li, Yongli Inner Mongolia Autonomous Region Meteorological Data Center Inner Mongolia Hohhot China Inner Mongolia Autonomous Region Meteorological Observatory Inner Mongolia Hohhot China

ISBN: (纸本)9798400710834

This paper employs mathematical and statistical methodologies to quantitatively analyze the Doppler weather radar data of a hailstorm that occurred in Toketuo County, Hohhot City, on the night of July 27, 2020. T The analysis revealed that convective cells initially underwent rapid vertical development, followed by lateral expansion in area and enlargement in volume. Therefore, in hail forecasting, careful attention should be devoted to monitoring the top height of radar reflectivity during the initial phase. In terms of the volume changes of convective cell, the trends of 20 dBZ and 35 dBZ exhibit consistency, demonstrating rapid development during the initial phase, stable alterations in the mature phase, and gradual diminishment in the dissipation phase. However, notable fluctuations are observed during the mature phase of 45 dBZ;furthermore, strong echo at 55 dBZ exhibits a prolonged incubation period followed by sudden emergence. The initial rapid increase in volume was observed at 20:37, coinciding with the onset of the hailstorm in Toketuo County. At the same time, three-body scatter spike (TBSS) is evident on the intensity map, suggesting an imminent occurrence of hail, which holds significant implications for hail forecasting. A subsequent rapid increase in volume occurred around 21:35, followed by a gradual decline;notably, the echo intensity level corresponding to 55 dBZ exhibited the most rapid decay among large hail echoes, indicating that hail has indeed occurred. © 2024 Copyright held by the owner/author(s).

关键词： Radar reflection

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An FPGA Implementation of the Levinson–Durbin Algorithm for Speech Coding 3rd

An FPGA Implementation of the Levinson–Durbin Algorithm for...

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3rd international conference on Computational Electronics for Wireless Communications, ICCWC 2023

作者： Singh, Dilip Chandel, Rajeevan Department of Electronics and Communication Engineering National Institute of Technology Hamirpur Himachal Pradesh Hamirpur177005 India

ISBN: (纸本)9789819719426

Speech coding is a widely used technique used for digital telephony and secure communication. The estimation of linear prediction coefficients (LPCs) for the development of synthetic speech is crucial and involves the use of the Levinson–Durbin (LD) algorithm. The computational complexity introduced by this algorithm affects the performance of speech codecs utilizing LPCs. In this paper, an FPGA implementation of the Levinson–Durbin algorithm is proposed for efficient autoregressive model parameter estimation. By harnessing FPGA’s parallel processing capabilities, the algorithm’s performance is accelerated, allowing real-time processing of signals. The work focuses on translating the algorithmic steps into hardware modules, optimizing memory access, and evaluating resource utilization, and power efficiency of the implemented hardware. This research contributes to the field of hardware-accelerated algorithms, showcasing the potential of FPGA platforms in enhancing signal processing tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Audio signal processing

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Investigation of the Impact of EEG signal processing Techniques on Classification Performance 3rd

Investigation of the Impact of EEG Signal Processing Techniq...

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3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Babu, Shyam Wadhwani, Arun Kumar Department of Electrical Engineering Madhav Institute of Technology and Science Gwalior India

ISBN: (纸本)9789819743582

This research paper is aimed at a practical analysis of the impact of the electroencephalogram (EEG) signal processing techniques on novel classification algorithms. The study examines how newly developed classification algorithms may perform significantly depending on the methods used to interpret EEG signals (brain activity data). In this context, first, we have conducted a review on recently contributed research articles based on EEG signal classification. Using this review, we are trying to recover the essential datasets, features, feature selection techniques, classification techniques, and applications. Next, the work is extended to study the nature of EEG data and its influence on data scaling. Further, we have also investigated the performance influence of feature selection technique on scaled data. Additionally, the convolutional neural network has been trained to learn from scaled and non-scaled data. Using four different experimental scenarios we have evaluated the performance of implemented data scaling and feature selection techniques over the EEG signal classification. Based on experimental analysis we have found that standardization is much beneficial than normalization. Additionally, it also helps to improve training performance of a classification model. However, with the merging of principal component analysis and both the scaling technique, we found the PCA and standardization are providing better training yield, but at the time of validation the standardized data is superior then other implemented techniques. Finally, conclusion and future extension plan have been discussed. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

3rd International Conference on Machine Vision and Augmented...

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3rd international conference on Machine Vision and Augmented Intelligence, MAI 2023

ISBN: (纸本)9789819743582

The proceedings contain 76 papers. The special focus in this conference is on Machine Vision and Augmented Intelligence. The topics include: Survey on Robustness of Deep Learning Techniques on Adversarial Attacks in WBAN;synergizing Collaborative and Content-Based Filtering for Enhanced Movie Recommendations;exploring Transformer-Based Approaches for Hyperspectral Image Classification: A Comparative Analysis;deep Learning for Cognitive Task and Seizure Classification with Hilbert–Huang Transform and Variational Mode Decomposition;tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features;Tumor Detection and Analysis from Brain MRI Images Using Deep Learning;software Maintenance Prediction Using Stack Ensemble Deep Learning Algorithms;resource Allocation in 6G Network for High-Speed Train Using D2D Outband Communication;controlling the Band-to-Band Tunneling Effect in Charge Plasma Based Dopingless Transistor;Comparison of Different CIC Filter Architectures on the Basis of a Novel Parameter Called Noise Factor for Sigma-Delta Based ADCs;the Scientific Analysis on Effective Yoga Posture Recognition Techniques;impact of Gamma Rays on Emerging Devices for Photonic Applications;shaft Rotation Monitoring Using Radar signal processing and Wavelet Transform;gysel Power Divider Miniaturization Using an Inter-Digital Capacitor-Based Slow-Wave Structure;noise Estimation and Removal in Fundus Images Using Pyramid Real Image Denoising Network;evaluation of Hybrid Encryption Method to Secure Healthcare data;multimodal Face Recognition System Using Hybrid Deep Learning Feature;Classification of Copy and Move Image by Using HELM-FSK Method: An Efficient Approach;analysis of Energy Efficient Smart Home Based on IoT System;role of Explainable Artificial Intelligence Approaches in Cybersecurity.

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