针对人工提取雷达辐射源信号特征耗时长、特征不明显等问题，提出一种基于深度学习卷积神经网络和模糊函数主脊坐标变换的雷达辐射源信号识别方法。该方法通过快速离散分数傅里叶变换提取信号的模糊函数主脊，并将模糊函数主脊极坐标域的二维时频图作为卷积神经网络的输入，实现对不同雷达信号的分选识别。仿真实验结果表明：新方法不仅在信噪比为0 dB以上保持100%的识别率，在-6 dB时识别准确率也稳定在90%以上；相对于传统的雷达信号识别方法和其他深度学习模型识别方法，在识别率和鲁棒性上均有较大提升，具有一定的工程应用价值。

In the military field, airborne multiple input multiple output (MIMO) radars must detect maneuvering targets and prevent them from being intercepted by intercepted receivers. Aiming at this problem, an improved multiple signal classification (MUSIC) algorithm for non-uniform array monostatic MIMO radar with low intercept is proposed. By performing dimensionality reduction processing, whitening processing, time-frequency analysis, time-frequency point filtering, orthogonal joint diagonalization and other signal processing on the received signal after MIMO radar matching filtering, the direction angle estimation under low signal-noise ratio (SNR) and low signal duration is realized. The research results show that compared with the time-frequency MUSIC algorithm of MIMO radar in the same environment, the low intercept improved MUSIC algorithm has achieved a higher accuracy of spatial spectrum pointing, which can distinguish adjacent targets with an angle difference of only 1°.The applicable SNR is reduced by 2 dB, which ensures low interception performance.

In this digital world, encryption plays an important role in various domains. Securing the information is the main goal when transfer of information takes place. Image encryption is a very important part of this as it applies to various domains like medical, multimedia, defence etc. A new method for image encryption is proposed here taking into account the “confusion-diffusion” structure. While working with secure data, requirements like fast computation, compression and processing are important issues. Deoxyribonucleic Acid (DNA) encoding has the capability to cope up with this requirement. In this paper a new algorithm is proposed based on composite chaos map for pixel scrambling and hence image encryption. First the algorithm takes the image and XORs with a composite chaotic map which further goes into dynamic DNA encoding followed by pixel scrambling which results in the image being very random thus it becomes less prone to attacks. The method discussed performs efficiently as shown in the experimental results.

Fractal’s spatially nonuniform phenomena and chaotic nature highlight the function utilization in fractal cryptographic applications. This paper proposes a new composite fractal function (CFF) that combines two different Mandelbrot set (MS) functions with one control parameter. The CFF simulation results demonstrate that the given map has high initial value sensitivity, complex structure, wider chaotic region, and more complicated dynamical behavior. By considering the chaotic properties of a fractal, an image encryption algorithm using a fractal-based pixel permutation and substitution is proposed. The process starts by scrambling the plain image pixel positions using the Henon map so that an intruder fails to obtain the original image even after deducing the standard confusion-diffusion process. The permutation phase uses a Z-scanned random fractal matrix to shuffle the scrambled image pixel. Further, two different fractal sequences of complex numbers are generated using the same function i.e. CFF. The complex sequences are thus modified to a double datatype matrix and used to diffuse the scrambled pixels in a row-wise and column-wise manner, separately. Security and performance analysis results confirm the reliability, high-security level, and robustness of the proposed algorithm against various attacks, including brute-force attack, known/chosen-plaintext attack, differential attack, and occlusion attack.

Cycling behavior involving steady-states and periodic solutions is known to be a generic feature of continuous dynamical systems with symmetry. Using Chua's circuit equations and Lorenz equations, Dellnitz et al. [1995] showed that \"cycling chaos\", in which solution trajectories cycle around symmetrically related chaotic sets, can also be found generically in coupled cell systems of differential equations with symmetry. In this work, we use numerical simulations to demonstrate that cycling chaos also occurs in discrete dynamical systems modeled by one-dimensional maps. Using the cubic map f (x, λ) = λx - x3 and the standard logistic map, we show that coupled iterated maps can exhibit cycles connecting fixed points with fixed points and periodic orbits with periodic orbits, where the period can be arbitrarily high. As in the case of coupled cell systems of differential equations, we show that cycling behavior can also be a feature of the global dynamics of coupled iterated maps, which exists independently of the internal dynamics of each map.