发布时间: 2021-06-21 16:56:25 浏览量:
时间：2021.6.24 下午 2.30
标题：《A Multipulse Radar Signal Recognition Approach via HRF-Net Deep Learning Models》
In the field of electronic countermeasure, the recognition of radar signals is extremely important. )is paper uses GNU Radio and Universal Software Radio Peripherals to generate 10 classes of close-to-real multipulse radar signals, namely, Barker, Chaotic, EQFM, Frank, FSK, LFM, LOFM, OFDM, P1, and P2. In order to obtain the time-frequency image (TFI) of the multipulse radar signal, the signal is Choi–Williams distribution (CWD) transformed. Aiming at the features of the multipulse radar signal TFI, we designed a distinguishing feature fusion extraction module (DFFE) and proposed a new HRF-Net deep learning model based on this module. )e model has relatively few parameters and calculations. )e experiments were carried out at the signal-to-noise ratio (SNR) of −14 ∼ 4 dB. In the case of −6 dB, the recognition result of HRF-Net reached 99.583% and the recognition result of the network still reached 97.500% under −14 dB. Compared with other methods, HRF-Nets have relatively better generalization and robustness.
录取期刊：Computational Intelligence and Neuroscience
标题：《Design and FPGA Implementation of a Pseudo-random Number Generator Based on a Hopfield Neural Network Under Electromagnetic Radiation》
When implementing a pseudo-random number generator (PRNG) for neural network chaosbased systems on FPGAs, chaotic degradation caused by numerical accuracy constraints can have a dramatic impact on the performance of the PRNG. To suppress this degradation, a PRNG with a feedback controller based on a Hopfield neural network chaotic oscillator is proposed, in which a neuron is exposed to electromagnetic radiation. We choose the magnetic flux across the cell membrane of the neuron as a feedback condition of the feedback controller to disturb other neurons, thus avoiding periodicity. The proposed PRNG is modeled and simulated on Vivado 2018.3 software and implemented and synthesized by the FPGA device ZYNQ-XC7Z020 on Xilinx using Verilog HDL code. As the basic entropy source, the Hopfield neural network with one neuron exposed to electromagnetic radiation has been implemented on the FPGA using the high precision 32-bit Runge Kutta fourth-order method (RK4) algorithm from the IEEE 754-1985 floating point standard. The postprocessing module consists of 32 registers and 15 XOR comparators. The binary data generated by the scheme was tested and analyzed using the NIST 800.22 statistical test suite. The results show that it has high security and randomness. Finally, an image encryption and decryption system based on PRNG is designed and implemented on FPGA. The feasibility of the system is proved by simulation and security analysis.
录取期刊：Frontiers in Physics
标题：《Fast and robust interactive image segmentation in bilateral space with reliable color modeling and higher order potential》
We propose an optimization framework for interactive image segmentation (IIS) that operates in bilateral space to achieve robust object extraction and instant visual feedback. More specifically, we first resample an input image using a regular bilateral grid with a resolution that is typically coarser than the input image to reduce the complexity of subsequent IIS tasks. We then design a Markov random field energy on the vertices of the bilateral grid that can be solved efficiently using a standard graph cut label assignment. To achieve this, we introduce reliable color models to distinguish the foreground and background despite the presence of extremely difficult cases and a higher-order potential to encourage spatial consistency in segmentation. We conduct comprehensive experiments on three standard interactive segmentation datasets, MSRA 10K, IIS, and PASCAL VOC 2012 segmentation validation set. The results show that the proposed method achieves competitive performance compared with state-of-the-art methods while making the current system efficient in terms of speed.
录取期刊：Journal of Electronic Imaging