Image skew correction is an important part of digital image processing system.In this paper, the method of Fourier transforming is popularized to many image processing applications.Firstly,the spectra are extracted after planar FFT transforming,then the log-polar coordinate mapping is obtained.At last,the skew can be calculated by the way of projection.Experiment results show this method can achieve preferable processing results.
In order to overcome the problems of low accuracy and environmental impact of radio frequency fingerprint identification method, this paper proposes a radio frequency fingerprint identification method based on improved resnet50. This method adds random noise to IQ signal to increase network robustness, and then obtains IQ with different sampling rate through hole convolution, and gives different weights to different feature vectors by using attention mechanism Finally, features are extracted and classified by resnet50 backbone. Experimental results show that the improved network achieves 91.70% recognition accuracy in RF fingerprint task.
The common receiver system of software defined radio includes single-channel receiver system based on DDC,channel receiver system based on multi-phase filter.Although these two systems are widely used,there are still some drawbacks in tracking HF signals.Thus the two systems are combined to realize a flexible integrated channelization receiver system,and the simulation has verified this system.
Modulation pattern recognition is an important research direction in communication systems, and plays an important role in electromagnetic spectrum management, communication signal interception and electronic countermeasures. In order to solve the problem of low modulation pattern recognition performance under low signal-to-noise ratio(SNR), a wavelet fusion noise reduction algorithm is proposed. The algorithm uses the Optuna hyperparameter optimization framework to dynamically construct a wavelet basis function and a wavelet threshold search space, and converts the optimal wavelet basis function. and the optimal wavelet threshold range as a parameter, the original IQ signal is denoised and then fused with the IQ signal to form a new data input neural network, which retains more details of the original IQ signal while denoising the signal. The residual neural network(Resnet) obtains a squeeze and excitation transpose one-dimensional ResNet6, and the squeeze and excitation transpose block is added to the one-dimensional residual block to perform secondary noise reduction on the signal, which improves the recognition accuracy of the modulation pattern under low SNR. The effectiveness of the method is verified by extensive experiments under the open source dataset RML2016.10a.
This paper accurately estimates the cover radius of mobile wireless communication based on the measured data by Okumura-Hata and C.Y.LEE model research. This research provides powerful reference for the radio-frequency spectrum management and advanced prediction and analysis of mobile communication network planning.
Deep Joint transmitter-receiver optimized communication system (Deep JTROCS) is a new physical layer communication system. It integrates the functions of various signal processing blocks into deep neural networks in the transmitter and receiver. Therefore, Deep JTROCS can approach the optimal state at the system level by the joint training of these neural networks. However, due to the non-differentiable feature of the channel, the back-propagation of Deep JTROCS training gradients is hindered which hinders the training of the neural networks in the transmitter. Although researchers have proposed methods to train transmitters using auxiliary tools such as channel models or feedback links, these tools are not available in many real-world communication scenarios, limiting the application of Deep JTROCS. In this paper, we propose a new method to use undertrained Deep JTROCS to transmit the training signals and use these signals to reconstruct the training gradient of the neural networks in the transmitter, thus avoiding the use of an additional reliable link. The experimental results show that the proposed method outperforms the additional link-based approach in different tasks and channels. In addition, experiments conducted on real wireless channels validate the practical feasibility of the method.
We have brought in a kind of table look-up way thermistor temperature transmitter-based design.Including design specifications,circuit of electronic bridge,differential that thermistor is composed of enlarge hardware circuit and corresponding software table look-up design principle such as opsonize and AD modulus change,the transmitter has provided a kind of the basis designing the train of thought and the reference to the low cost temperature. This project design has been applied in the field.
This paper proposes a super chaotic image encryption scheme based on deep neural network key generation and dynamic DNA coding, First, we use deep convolutional neural networks to extract features from plaintext images, and use the feature values as the initial values of the chaotic system, and then iterate the super chaotic Lorenz system to obtain the chaotic sequence for encryption, and use the chaotic sequence to dynamically select the DNA coding rules for image pixel matrix and The DNA encoding and operation of the chaotic sequence are used to change the image pixel values, and finally the dislocation and diffusion operations are performed to realize the encryption. The experimental results show that the image encryption scheme proposed in this paper can effectively resist differential attacks and various statistical analyses, and has higher security compared with other algorithms.
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