nonlinear detection
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2021 ◽  
Vol 13 (20) ◽  
pp. 4113
Author(s):  
Chandra S. Pappu ◽  
Aubrey N. Beal ◽  
Benjamin C. Flores

In this article, we propose the utilization of chaos-based frequency modulated (CBFM) waveforms for joint monostatic and bistatic radar-communication systems. Short-duration pulses generated via chaotic oscillators are used for wideband radar imaging, while information is embedded in the pulses using chaos shift keying (CSK). A self-synchronization technique for chaotic systems decodes the information at the communication receiver and reconstructs the transmitted waveform at the bistatic radar receiver. Using a nonlinear detection scheme, we show that the CBFM waveforms closely follow the theoretical bit-error rate (BER) associated with bipolar phase-shift keying (BPSK). We utilize the same nonlinear detection scheme to optimize the target detection at the bistatic radar receiver. The ambiguity function for both the monostatic and bistatic cases resembles a thumbtack ambiguity function with a pseudo-random sidelobe distribution. Furthermore, we characterize the high-resolution imaging capability of the CBFM waveforms in the presence of noise and considering a complex target.


2020 ◽  
Author(s):  
Ze Wang ◽  
Mingzhi Chen ◽  
Yu Yang ◽  
Min Lei ◽  
Zhexuan Dong

Abstract Recently steganalysis methods based on convolutional neural networks (CNN) have achieved great improvement. However, detection against adaptive steganographic algorithms with low embedding rates has still been a challenging task. To deal with this problem, we propose a CNN steganalysis model employing the joint domain detection mechanism and nonlinear detection mechanism. For the joint domain detection mechanism, we use not only the high-pass filters from the SRM for spatial residuals, but also the patterns from the DCTR for frequency steganographic impacts. For the nonlinear detection mechanism, we enlarge steganographic effects by nonlinearly transforming the extracted steganographic residual information. In addition, we innovatively put forward a model learning method based on the high learning ability of a model. That is, we use lower embedding rate image datasets to train a model and higher embedding rate image datasets to test the model, which effectively improves sensitivity to steganographic traces. Compared with the existing steganalysis models such as SRM+EC, Ye-Net, Xu-Net, Yedroudj-Net and Zhu-Net, the detection accuracy of our model is about 4%∼6% higher than that of the best Zhu-Net model.


2020 ◽  
Vol 316 ◽  
pp. 01001
Author(s):  
Zaifu Zhan ◽  
Shen Wang ◽  
Songling Huang ◽  
Yang Zheng ◽  
Fuping Wang ◽  
...  

Under harsh environment or during service, the mechanical properties of materials or structure will deteriorate. Most of the simulations exhibit the phenomenon of nonlinearity by introducing the actual small defects, without considering dislocation. In this manuscript, subroutines are written to change the mechanical constitutive behaviour of materials. When the mechanical constitutive behaviour of the material is not linear any more, it is found that the propagation of ultrasonic wave in the material will show more obvious nonlinear phenomenon. Furthermore, the nonlinear detection coefficient is used to characterize the increase of harmonic components. This work provides a new idea for nonlinear ultrasonic testing.


2019 ◽  
Vol 2019 (3) ◽  
Author(s):  
V.S. Vlasov ◽  
◽  
D.A. Pleshev ◽  
V.G. Shavrov ◽  
V.I. Shcheglov ◽  
...  

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Maria Theresia Pöschko ◽  
Victor V. Rodin ◽  
Judith Schlagnitweit ◽  
Norbert Müller ◽  
Hervé Desvaux

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