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Research and implementation of Chirp signal detection based on FRFT 3

Research and implementation of Chirp signal detection based ...

引用

3rd international conference on Electronics and Information Technology, EIT 2024

作者： Guo, Ziwei Su, Yanfei Liu, Kaidi Hefei University of Technology School of Microelectronics Hefei China

ISBN: (纸本)9798350369151

Fractional Fourier transform (FRFT), as a generalised form of traditional Fourier transform, can well analyse the local characteristics of signals in the time-frequency plane, and is widely used in various fields such as optics, communications, radar and image processing, etc. Chirp signals, as a kind of typical time-varying signals, are of theoretical significance and practical value in the research of various fields. Therefore, this paper designs and implements a FRFT-based Chirp signal detection system with phase difference and DFRFT algorithms as the core, and optimises the FFT processor, singular point IFFT algorithm and complex matrix multiplication module of the system to give full play to the advantages of the FRFT algorithm for Chirp signal processing. In this paper, the functionality of the Chirp signal detection system is tested at the board level and evaluated in terms of resource consumption and performance. Finally, several sets of experimental data are compared with software simulation to confirm the feasibility and correctness of the scheme. Compared with the traditional two-dimensional search method, the signal detection speed of this design is improved by nearly 10 times, which has some practical value. © 2024 IEEE.

关键词： Chirp modulation

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Dynamic Analysis of SCIMR Rumor Propagation Model with Refutation Mechanism 24

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

引用

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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The Wireless Network signal Detection Using Transfer Learning 3

The Wireless Network Signal Detection Using Transfer Learnin...

引用

3rd international conference on Smart Generation Computing, Communication and Networking, SMART GENCON 2023

作者： Sharma, Sorabh Centre for Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350319125

Over the last decade, deep learning (DL) applications have exploded in the realm of wireless communications, particularly in the realms of channel estimation and signal identification. However, traditional DL methods need training for each new circumstance. As a result, updating to new wireless systems necessitates retraining. The acquisition of sufficient training data to achieve adequate detection efficiency may be time-consuming and expensive. Traditional DL methods sometimes need extensive amounts of time to train, even if substantial amounts of training data are available. Because of this, it's possible that DL approaches' efficacy and robustness may suffer. This letter presents a transfer learning architecture that may use information gained from an original system to enhance the training procedure of a target system that has access to less data for its training. In regards to channel estimation as well as signal identification, our simulation results show that the suggested solution is superior to state-of-the-art deep learning approaches. © 2023 IEEE.

关键词： DL techniques and Transfer learning signal Detection Transfer Learning Wireless Networks

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Soccer Match Prediction based on Big data Mining and Multimodal Features 3

Soccer Match Prediction based on Big Data Mining and Multimo...

引用

3rd international signal processing, Communications and Engineering Management conference, ISPCEM 2023

作者： Zheng, Jiacheng Hangzhou Xuejun High School Hangzhou Zhejiang China

ISBN: (纸本)9798350307023

The sports industry ushered in new development and opportunities in the context of the big data era, especially the big data mining in the sports industry provides technical support for industrial iteration, promotes the innovative development of the sports industry, and accelerates the process of sports industry technology. As a popular sport in the field of soccer, it has received extensive attention from domestic and foreign fans, while data analysis and data mining in the field of soccer are also developing rapidly. Soccer game prediction is due to its many influencing factors, and there are diversity, uncertainty, ambiguity and other problems among the factors. At present, there are various ways to analyze soccer match prediction, and the rating system is not uniform, such as odds judgment, blind guessing based on historical experience, and it is not yet possible to quantify the internal relationship between features according to a set of uniform data mining process. Therefore, this paper proposes a multimodal feature extraction network based on deep learning, which can effectively extract a fusion of low-dimensional feature representations from many different modal features provided in the raw data of soccer matches. Finally, on the basis of solving the unidimensional prediction problem through traditional machine learning models and integration models, an improved Stacking integration model oriented to multidimensional features is innovatively proposed, and ultimately achieves the goal of improving the prediction accuracy and stability of soccer matches. © 2023 IEEE.

关键词： Forecasting

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Deep Reinforcement Learning algorithms for Low Latency Edge Computing Systems 3

Deep Reinforcement Learning algorithms for Low Latency Edge ...

引用

3rd international conference on Artificial Intelligence and signal processing, AISP 2023

作者： Kumaran, K. Sasikala, E. SRM Institute of Science and Technology Chennai India Department of Information Technology Easwari Engineering College Chennai India SRM Institute of Science and Technology Kattankulathur Department of Data Science & Business Systems Chennai India

ISBN: (纸本)9798350320749

Nowadays, due to the increase of technological development in smart devices, more computational capabilities are needed with better performance. The maximization of the cloud computing resources used in mobile networks and Internet of Things makes good results. The data processing along with delay in the communication network will be easily handled by edge computing technology. The edge server brought as much as close to the end devices to avoid the frequent cloud access. In edge servers, the processing of the computational task is time consuming, which causes latency. The inference of deep learning along with reinforcement models makes the better resource allocation and task scheduling for complex systems. In this work, deep reinforcement models like Q learning, Double Q learning, Deep Q network, Double Deep Q learning has been compared. The developed Deep Reinforcement learning model has been deployed in the edge-based system. As a result, it is inferred that Double Deep Q learning is having the ability to make better decisions in resource allocation by maximizing the gains and rewards. The simulation result shows that Double Deep Q learning performs better with decreased latency and increases the performance of the edge systems. © 2023 IEEE.

关键词： Reinforcement learning

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Research on Intelligent data Mining and processing System Based on Computer Genetic Algorithm 3

Research on Intelligent Data Mining and Processing System Ba...

引用

2023 IEEE 3rd international conference on data Science and Computer Application, ICDSCA 2023

作者： Chen, Xin Zhenjiang Vocational Technical College Zhenjiang China

ISBN: (纸本)9798350341546

In this paper, a genetic algorithm for DSS modeling is proposed by integrating GA and DSS. Firstly, SOFM algorithm is used to convert the quantized sample information into semantic value. The decision system is transformed into a fuzzy set of semantic values. The original fuzzy interval is reduced by using fuzzy set similarity analysis and fusion method. This enhances the credibility of the model. Then genetic algorithm is used to further optimize the rules obtained. The corresponding data were selected from UCI database as the test object. The correctness of the method applied in quantitative data is verified. © 2023 IEEE.

关键词： Computer data mining processing system genetic algorithm

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A study on acoustic emission signal processing of wood damage and fracture based on VMD and sample entropy 3

A study on acoustic emission signal processing of wood damag...

引用

3rd IEEE international conference on Power, Electronics and Computer Applications, ICPECA 2023

作者： Jiawen, Zheng Junqiu, Li Qinghui, Zhang Xiaoyan, Jia Zengquan, Lan Southwest Forestry University School of Big Data and Intelligent Engineering Kunming China Southwest Forestry University Green Development Institute Kunming China

ISBN: (纸本)9781665472784

The analysis of acoustic emission signals from wood materials can be used to evaluate the damaged state of wood, but the acoustic emission is easily affected by various disturbances. In order to evaluate the damage state of wood more accurately and suppress the interference of acoustic emission signal and reduce the noise content, the following processing methods were applied to the signal in this study. Firstly, Variational Mode Decomposition (VMD) method was used to decompose the acoustic emission signals generated from wood. A particle swarm algorithm is used to optimize the parameters of the method. Then the effective components obtained from the decomposition are selected by calculating the sample entropy values of each component. Finally, the signal phases are characterized by using alignment entropy. The results show that the various stages of wood can be effectively separated by the research method in this paper. © 2023 IEEE.

关键词： Variational mode decomposition

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Simulation Research on Improving Efficiency of Electronic Information Transmission in Big data Environment 3

Simulation Research on Improving Efficiency of Electronic In...

引用

3rd IEEE international conference on Electronic Communications, Internet of Things and Big data, ICEIB 2023

作者： Jia, Junchao Qingdao Huangdao District Public Employment and Talent Service Center Qingdao China

ISBN: (纸本)9798350333862

Optimizing and controlling the efficiency of electronic information transmission in big data processing significantly reduce the execution cost on the Internet. In addition, the node distance function obtains the gap between nodes during the transmission process, monitoring the efficiency. However, traditional methods often suffer from the maximum latency of a data packet chain by providing the maximum bandwidth, while neglecting the gap between nodes, resulting in low monitoring efficiency. By using genetic principles, the differences between nodes are obtained in information transmission, and an efficient management mechanism is established. Then, the routing method for data transmission is optimized. Based on this, efficient management of electronic information is achieved. The experimental results show that the proposed method has satisfactory control accuracy and improves the sensitivity of data transmission, real-time efficiency, and safety and reliability. © 2023 IEEE.

关键词： Big data

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Implementation of data Mining Algorithm Based on Distributed Cloud Platform 3

Implementation of Data Mining Algorithm Based on Distributed...

引用

3rd IEEE international conference on Electrical Engineering, Big data and Algorithms, EEBDA 2024

作者： Ming, Li Shuai, Gong Dongbo, Yu Min, Zhang Bin, Li Hongbo, Lu State Grid Anhui Electric Power Co. Ltd. Information and Communication Branch Hefei China

ISBN: (纸本)9798350380989

MapReduce and Hadoop distributed data processing technologies are used for systematic research. The system is interactive in distributed computing, data mining, service response and cloud environment. At the level of data mining, the normalized method is used to normalize the data, so that it has a standardized dimension. The converted signal is denoised by ICA fast decomposition method. Cloud DM takes full advantage of Hadoop's open-source cloud platform, which is able to efficiently process large amounts of data. This provides a service model for the development and operation of distributed data mining system. This paper introduces the core components of the business-based cloud computing mining engine and its implementation methods. Combined with data mining technology in cloud computing environment, large-scale data storage, processing and data interoperability are realized. Experiments show that this method can efficiently mine the spatial attributes of the city and improve the efficiency of data mining to a great extent. © 2024 IEEE.

关键词： Cloud computing data mining Hadoop service-oriented architecture

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Nonlinear Characteristics Modeling of RF Receiver Components and its Impact 3

Nonlinear Characteristics Modeling of RF Receiver Components...

引用

3rd IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2023

作者： Tang, Anping Chengdu Tengnuo Technology Co. Ltd Chengdu China

ISBN: (纸本)9798350317022

This paper conducts a thorough investigation into the nonlinear characteristics of RF receiver components and their significant impact on modern wireless communication systems. Nonlinearities within these components can introduce signal distortion, harmonic generation, and degrade overall system performance. The research explores diverse nonlinear modeling techniques, encompassing physics-based modeling and mathematical simulation, to comprehensively understand and characterize these nonlinear effects. The study delves into methods for accurate measurement data collection and processing to capture nonlinearities effectively. Additionally, it scrutinizes the implications of nonlinear characteristics on RF receiver performance, particularly focusing on signal distortion, power amplifier nonlinearities, and their consequential effects on system errors. In addressing the challenges posed by nonlinearities, the paper discusses strategies for compensation and optimization. This involves the development of adaptive algorithms and techniques aimed at enhancing the adaptability of RF receiver components in the presence of nonlinear effects. The findings underscore the critical importance of understanding and mitigating nonlinearities for achieving optimal performance in wireless communication systems. © 2023 IEEE.

关键词： modeling Nonlinear characteristics RF receiver components signal distortion

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