A Robust Acquisition Technique Using CFAR Adaptive Thresholding in Hybrid Spread Spectrum Systems

This research designs an intelligent parking system including service application layer, perception layer, data analysis layer, and management layer. The network system adopts opm15 system, and the parking space recognition adopts improved convolution neural networks (CNNs) algorithm and image recognition technology. Firstly, the parking space is occupied and located, and the shortest path (Dynamic Programming, DP) is selected. In order to describe the path algorithm, the parking system model is established. Aiming at the problems of DP low power and adjacent path interference in the path detection system, a method of combining interference elimination technology with enhanced detector technology is proposed to effectively eliminate the interference path signal and improve the performance of the intelligent parking system. In order to verify whether the CNNs system designed in this study has advantages, the simulation experiments of CNNs, ZigBee, and manual parking are carried out. The results show that the parking system designed in this study can control the parking error, has smaller parking error than ZigBee, and has more than 25.64% less parking time than ZigBee, and more than 34.83% less time than manual parking. In terms of parking energy consumption, when there are less free parking spaces, CNNs have lower energy consumption.

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[Retracted] Automatic Parking System Based on Improved Neural Network Algorithm and Intelligent Image Analysis

Guo

Shi

2021

Computational Intelligence and Neuroscience

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Spectrum sensing based on adaptive sampling of received signal

Miao

Tan

Zhang

et al. 2021

J Wireless Com Network

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Spectrum sensing (SS) has been heatedly discussed due to its capacity to discover the idle registered spectrum bands, which effectively alleviates the shortage of spectrum by spectrum reuse. Energy detector (ED) is widely accepted for SS as its complexity is very low. In this paper, an adaptive sampling scheme is proposed to improve the sensing performance of ED, where the sampling point of the received signal is adaptively adjusted with the environment signal-to-noise ratio (SNR). When SNR decreases, the sensing performance can be maintained and even improved by the rise of the sampling point. When SNR increases, the improved ED is considered for idle spectrum detection. The SNR is evaluated based on the joint of convolutional neural network (CNN) and long short-term memory (LSTM) network. Both theoretical derivations and simulation experiments validate the effectiveness of the proposed scheme.

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A Robust Acquisition Technique Using CFAR Adaptive Thresholding in Hybrid Spread Spectrum Systems

Cited by 2 publications

References 26 publications

[Retracted] Automatic Parking System Based on Improved Neural Network Algorithm and Intelligent Image Analysis

[Retracted] Automatic Parking System Based on Improved Neural Network Algorithm and Intelligent Image Analysis

Spectrum sensing based on adaptive sampling of received signal

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