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We proposed a novel approach to self-radiation of Josephson junction arrays. Bicrystal Josephson junction arrays fabricated on a bicrystal substrate were integrated with a half-wavelength dipole array and embedded in a Fabry-Perot resonator. Considered as a dielectric resonator, the substrate could be stimulated to resonate in an appropriate mode at the same frequency as the Fabry-Perot resonator, which realized a coupling between Josephson junctions as well as a coupling between the junctions and microwave structures by means of a resonant interaction between junctions and resonators. Both simulations and experiments were performed. A YBCO bicrystal Josephson junction array with 166 junctions was measured at liquid nitrogen temperature. The maximal self-radiation power of 10 pW was detected at 75.2 GHz, which was in agreement with the simulation results.

In this paper, a new 5-D chaotic system with hidden attractor was presented. The stability and equilibrium points set of the system were analyzed by the traditional dynamical analysis method. Meanwhile, several special phenomena were found in the system, such as chaos degradation, state transition, multiwing chaotic attractors, coexisting-attractors etc. verifying the application of the system in engineering, offset boosting control method is introduced and numerical simulation of the system is implemented. In addition, the complexity of Spectral Entropy (SE) and C 0 are analyzed. Finally, the new system was simulated by the digital signal processing (DSP) technology, and the results agree well with the numerical simulation result. Theoretical analysis and simulation results show that the system has complex dynamical characteristics and can be applied to secure communication and image encryption.

A new four-dimensional chaotic system is designed in the paper. The equilibrium point and stability of the chaotic system are analyzed, and the dynamical behaviors of the system under different parameters are analyzed by using Lyapunov exponents, Bfurcation diagram, SE and C0 complexity algorithms. The special phenomenon of the coexistence of attractors is also found. Finally, the implementation of circuit of the new system is carried out using digital signal processing (DSP) technology, and the results are consistent with the numerical simulation results, which prove the validity of the theoretical analysis. Through analysis and simulation of the system, it can be found that it has relatively rich dynamic characteristics and can be applied in areas such as confidential communication and image encryption.

In view of the current absence of any deep learning algorithm for shellfish identification in real contexts, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multiobject recognition and localization through a second-order detection network and replaces the original feature extraction module with DenseNet, which can fuse multilevel feature information, increase network depth, and avoid the disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects and enhancing the network detection accuracy under multiple objects. By constructing a real contexts shellfish dataset and conducting experimental tests on a vision recognition seafood sorting robot production line, we were able to detect the features of shellfish in different scenarios, and the detection accuracy was improved by nearly 4% compared to the original detection model, achieving a better detection accuracy. This provides favorable technical support for future quality sorting of seafood using the improved Faster R-CNN-based approach.