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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11-20 订阅

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Novel Real-Time EEG signal Decoding Algorithm based on Deep Learning 3

Novel Real-Time EEG Signal Decoding Algorithm based on Deep ...

引用

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

作者： Jin, Hulin Jin, Zhiran Kim, Yong-Guk Li, Kun Anhui University School of Computer Science and Technology Hefei230031 China Foothill Preparatory School Temple CityCA91780 United States Sejong University Department of Computer Engineering Seoul05006 Korea Republic of Anhui University School of Electronic and Information Engineering Hefei230031 China

ISBN: (纸本)9798331509675

This study proposes the novel real-time electroencephalogram (EEG) signal decoding algorithm based on deep learning from the theoretical modeling level. The original intention of the design of this algorithm is to optimize the decoding accuracy and timeliness of EEG signals in the brain-computer interface (BCI) system. Considering the low efficiency of feature extraction and data processing in traditional EEG signal decoding methods, this study adopted a multi-core collaborative signal transmission architecture and deep learning model for overall optimization. First, the study used multi-core parallel computing mode to increase data processing speed. This operation can reduce system latency and achieve efficient decoding of real-time EEG signals. In terms of core architecture, convolutional neural network (CNN) is used to extract and classify EEG signals. This operation can further improve the accuracy of signal decoding. In addition, in order to solve the problem of artifacts in EEG signals, independent component analysis (ICA) is used to pre-process the original signal to remove the interference of noise actions such as eye movement, electromyography and the other noise on data. The experimental results show that the proposed deep learning algorithm can accurately analyze the characteristics of EEG signals in the different frequency bands, thereby significantly improving the decoding accuracy. © 2025 IEEE.

关键词： Convolutional neural networks

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Attack detection on multi-architecture Internet of Vehicles honeypots and response mechanism research 24

Attack detection on multi-architecture Internet of Vehicles ...

引用

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

作者： Ning, Yuqiao Huo, Quanrui Mu, Shiyu Xue, Shihao China Automobile Data of Tianjin Co. Ltd. Tianjin China CATARC Intelligent and Connected Technology Co. Ltd. Tianjin China

ISBN: (纸本)9798400710834

Intelligent connected vehicles (ICVs) refer to a new vehicle type that utilizes advanced information and communication technology to enable information exchange among vehicles, between vehicles and roads, and between vehicles and the internet. Malicious actors may exploit vulnerabilities in ICVs to gain malicious control, steal data, or launch denial-of-service attacks, resulting in significant economic losses and personal harm. Therefore, it is crucial to research attack detection and response mechanisms for ICVs and build effective network defense systems to ensure the safe operation of these vehicles. However, different manufacturers may adopt different architectures, posing significant challenges to network security defense for these vehicles. To address this issue, this paper proposes an attack detection and response mechanism based on Docker container technology for Multi-Architecture Vehicle Honeypots (MAV-honeypots). The aim is to simulate ICVs under various architectures using honeypot technology, attracting and inducing attackers to carry out attacks, thus achieving identification, analysis, and response to attackers. © 2024 Copyright held by the owner/author(s).

关键词： Network security

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FPGA-Based Implementation of Cuffless Blood Pressure Measurement Using Photoplethysmogram signal 3rd

FPGA-Based Implementation of Cuffless Blood Pressure Measur...

引用

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

作者： Yudha Raja Singam, D. Mukhopadhyay, Sumitra Gupta, Rajarshi Institute of Radio Physics and Electronics University of Calcutta Kolkata India Electrical Engineering Applied Physics University of Calcutta Kolkata India

ISBN: (纸本)9789819795772

In recent years, there has been research on non-invasive blood pressure (NIBP) measurement using physiological signals. However, the main challenges are the selection of physiological signals, low complexity algorithm development and its hardware realization. This paper focuses on FPGA-based realization of a NIBP measurement model using Photoplethysmogram (PPG) signal. The PPG signal is pre-processed, beats are segmented, autoencoder features are extracted, and among 12 different regression models the most competitive model is selected to predict the systolic (SBP) and diastolic blood pressure (DBP) from PPG beat. The model was evaluated with PhysioNet MIMIC-III waveform database and achieved mean absolute error (MAE) and standard deviation (SD) of 3.69 and 5.06 mmHg respectively for SBP, and 1.96 and 3.68 mmHg for DBP respectively. The model has been implemented into Zynq-7000 SoC ZC702 standalone hardware with the lower detection latency (∼6.24 ms for 1 PPG beat) reported so far in the literature and low memory requirement (∼78 KB). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Field Programmable Gate Array (FPGA) NIBP Photoplethysmogram (PPG)

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data security decision under interval-valued intuitionistic fuzzy environment with new score function and combined weight 24

Data security decision under interval-valued intuitionistic ...

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

作者： Gao, Pengzhan Xia, Yuanqi Zhao, Rixi Wang, Zhiping School of Maritime Economics and Management Dalian Maritime University Liaoning Dalian China School of Economics and Management Xiamen University Malaysia Selangor Sepang Malaysia School of Science Dalian Maritime University Liaoning Dalian China

ISBN: (纸本)9798400710834

The score function and weight have great influence on the decision result of interval valued intuitionistic fuzzy (IVIF) environment. Historically, decision-making methods have encountered unsatisfactory and even counter-intuitive outcomes when dealing with extreme data. Therefore, in this paper, a new decision-making method based on the interval-valued intuitionistic fuzzy environment is proposed in this paper to address this issue. Firstly, a new score function for interval-valued intuitionistic fuzzy numbers (IVIFNs) is introduced. This function takes into consideration the membership degree, non-membership degree, and data dispersion degree, while also including a pessimism coefficient that can be adjusted by both conservative and radical decision-makers. Secondly, a hybrid method for obtaining objective weights for decision-making criteria is proposed, and the objective weights are integrated with subjective weights. Finally, the effectiveness of the proposed method in dealing with extreme data and providing reasonable sorting of schemes is demonstrated through a case study of data security decision-making. A comparative analysis is also provided. The results demonstrate that the proposed method has both strong theoretical and practical significance. © 2024 Copyright held by the owner/author(s).

关键词： Fuzzy sets

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Large-Scale Flight Delay Propagation Prediction Based on Spatio-Temporal Dynamics Network 24

Large-Scale Flight Delay Propagation Prediction Based on Spa...

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3rd international conference on Artificial Intelligence and Intelligent Information processing, AIIIP 2024

作者： Qu, Jingyi Wang, Junbo Tianjin Key Laboratory of Advanced Signal Processing Civil Aviation University of China Tianjin China

ISBN: (纸本)9798400707308

Capturing the delay propagation mechanism between multiple airports is fundamental to delay prediction, which is crucial for aviation professionals to make correct decisions. The essence of this problem is how to effectively utilize the temporal and spatial characteristics of flight data and other influencing factors. However, previous research has only considered fragmented spatio-temporal features and other influencing factors, or used dynamic models to simulate the delay propagation mechanism. Spatio-Temporal Dynamics Network (STDN) is proposed in this paper, which combines propagation dynamics models with deep learning methods for the prediction of large-scale flight delays. The model adaptively improves the parameter learning method of the Graph Attention Network (GAT), and combines the propagation dynamics method for knowledge representation and reasoning. It predicts the number of flight delays by modeling the flight delay propagation process. Using real historical flight data for prediction, the results show that STDN effectively learns the flight delay propagation pattern. © 2024 Copyright held by the owner/author(s).

关键词： Knowledge representation

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Dynamic Analysis of SCIMR Rumor Propagation Model with Refutation Mechanism 24

Dynamic Analysis of SCIMR Rumor Propagation Model with Refut...

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

作者： Liu, Shichen Lu, Youjun Chen, Congli College of Data Science and Information Engineering Guizhou Minzu University Guizhou Guiyang China

ISBN: (纸本)9798400710834

The phenomenon is that those who know the truth or possess a strong knowledge background in social networks will refute the rumors. The government agencies and official news outlets will likewise dispute the rumors through corresponding reports. A SCIMR rumor propagation model with a dual rumor debunking mechanism is proposed. From the theoretical perspective, the basic reproduction number R0 and the equilibrium of the proposed model are provided. We further analyze the local and global stability of the equilibrium by applying Lyapunov stability theory and LaSalle’s invariance principle. Finally, to substantiate the effectiveness of the proposed model, a series of numerical simulations are presented, which show that increasing the rate of rumor debunking can effectively curb the propagation of rumors. © 2024 Copyright held by the owner/author(s).

关键词： Equilibrium

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Multi-Agent Deep Reinforcement Learning-Based UAV Trajectory Optimization for data Collection 24

Multi-Agent Deep Reinforcement Learning-Based UAV Trajectory...

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

作者： Guo, Hengpeng Zhang, Bo Fu, Yuanzhong School of Cyber Science and Engineering Zhengzhou University Henan Zhengzhou China

ISBN: (纸本)9798400710834

Deploying low-cost sensors to collect large-scale regional data is necessary in numerous applications such as environmental monitoring. However, the manual collection of sensor data is cost-ineffective. With the development of the Internet of Things (IoT) and unmanned aerial vehicle (UAV) technologies, the collection of sensor data by UAVs has emerged as a promising approach. Therefore, it is often crucial to optimally deploy UAVs for efficient data collection to ensure low Age of Information (AoI) and low energy consumption. To describe data freshness and account for volume„ we propose a new data value measurement metric termed eAoI, inspired by AoI. To consider both the data value and energy consumption, we further define a new concept: data value per energy unit. We consider the scenario where distributed sensors generate data continuously, while a set of UAVs are deployed for efficient data collection. We aim to maximize data collection efficiency, namely, the overall eAoI gain per energy unit cost throughout the collection operation lifecycle. We propose K-MADDPG, where all actors share a single Critic, enhanced with Kolmogorov–Arnold networks (KAN). The UAVs in the K-MADDPG framework had distributed decision-making capability, and could dynamically adjust their trajectory strategy according to the environment. Simulation results demonstrate that K-MADDPG exhibits significant improvement in terms of both convergence speed and performance. © 2024 Copyright held by the owner/author(s).

关键词： Deep reinforcement learning

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Shaft Rotation Monitoring Using Radar signal processing and Wavelet Transform 3rd

Shaft Rotation Monitoring Using Radar Signal Processing and ...

引用

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

作者： Valuyskiy, Denis Vityazev, Sergey Vityazev, Vladimir Ryazan State Radio Engineering University Gagarina. 59/1 Ryazan390005 Russia

ISBN: (纸本)9789819743582

This article discusses the problem of shaft rotation control for continuous testing of industrial equipment using a radar sensor. The FMCW radar with a frequency of 77 Hz is used to irradiate a rotating shaft and obtain information about the uniformity of its rotation. A test bench has been developed and radar reflections are recorded. Then the real signals are analyzed and an approach to signal processing and decision-making rules are proposed. The approach is based on wavelet transform and neural classification of rotating shifts with deep learning. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Radar signal processing

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Hybrid EEG signal processing Framework for Driver Drowsiness Detection Using QWT, EMD, and Bayesian Optimized SVM 3

Hybrid EEG Signal Processing Framework for Driver Drowsiness...

引用

3rd international conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Sardesai, Shilpa Gedam, Rahul Pune India Lcit Department of Electronics and Telecommunication Bilaspur India

ISBN: (纸本)9798331508456

Drowsiness among drivers poses an urgent safety problem in road transportation because it creates serious risks to both drivers and their passengers. Carrying out prolonged driving operations creates a mind state between full wakefulness and sleep. The research presents a new supervised learning approach to evaluate sleep stages which helps detect driver sleepiness and trigger prompt alerts. A deep feature extraction system based on QWT with time-frequency scalogram and EMD artifact removal generates features from EEG signals. The system uses evaluation of decomposition levels' energy measurements to build Energy Vectors which lead to the processing of each EEG signal into feature vectors. data classification in this research occurs through implementation of the BO-SVM classifier which features Bayesian optimization. The BO-SVM classifier delivers exceptional accuracy results because it reaches 99.17% maximum accuracy beyond typical SVM classifier performance levels. This paper combines signal processing methods with machine learning techniques which result in advanced EEG data-based sleep stage diagnosis to enhance driver safety. © 2025 IEEE.

关键词： Image reconstruction

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Generating F1-Score to Predict Parkinson Disease with CNN Algorithm 3rd

Generating F1-Score to Predict Parkinson Disease with CNN Al...

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

作者： Krishnamoorthy, Dhivya Bharathi Padhy, Sasmita School of Computing Science and Engineering VIT Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore 466114 India

ISBN: (纸本)9789819795772

This abstract outlines the application of a Convolutional Neural Network (CNN) algorithm for predicting Parkinson’s disease and calculating the F1-score. The process involves multiple stages, starting with data preprocessing, where the dataset is prepared and divided into training and testing subsets. The CNN model is constructed with convolutional layers, pooling layers, and fully connected layers. The model is compiled using appropriate loss functions and optimizers. data augmentation techniques can be applied to enhance model generalization. After training the model using the training dataset, it is evaluated using the testing dataset. Predictions are generated for the testing data, and the F1-score is calculated using the derived predictions and true labels. The F1-score, which balances precision and recall, provides insight into the model’s performance in predicting Parkinson’s disease. A higher F1-score signifies a better trade-off between false positives and false negatives. The outlined process offers a comprehensive approach to employing CNNs in Parkinson’s disease prediction and performance assessment through the F1-score. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Binary classification Convolutional Neural Network (CNN) data augmentation dataset division F1-score Parkinson’s disease

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