Much effort has been devoted to recovering sparse signals from one-bit measurements in recent years. However, it is still quite challenging to recover signals with high fidelity which is desired in practical one-bit compressive sensing (1-bit CS) applications. We introduce the notion of Schur-concavity in this paper and propose to construct signals by taking advantage of Schur-Concave Functions which are capable of enhancing sparsity. Specifically, the Schur-concave functions can be employed to measure the degree of concentration, and the sparse solutions are obtained at the minima. As a representative of the Schur-concave family, the normalized $\ell_1$ Shannon entropy function ( $\ell_1$ -SEF) is exploited. Compared with other penalty functions, the $\ell_1$ -SEF can not only promote sparsity, but also encourage concentration of energy to a few entries. The resulting optimization problem is nonconvex. Hence, we convert it into a series of weighted ${\ell _1}$ -norm subproblems, which are solved iteratively by a generalized fixed-point continuation (FPC) algorithm. Numerical results are provided to illustrate the effectiveness and superiority of the proposed 1-bit CS algorithm.

The fixed window function used in the short‐time Fourier transform (STFT) does not guarantee both time and frequency resolution, exerting a negative impact on the subsequent study of time‐frequency analysis (TFA). To avoid these limitations, a post‐processing method that enhances the time‐frequency resolution using a deep‐learning (DL) framework is proposed. Initially, the deconvolution theoretical formula is derived and a post‐processing operation is performed on the time‐frequency representation (TFR) of the STFT via deconvolution, a theoretical calculation to obtain the ideal time‐frequency representation (ITFR). Then, aiming at the adverse influence of the window function, a novel fully‐convolutional encoder‐decoder network is trained to preserve effective features and acquire the optimal time‐frequency kernel. In essence, the generation of the optimal time‐frequency kernel can be regarded as a deconvolution process. The authors conducted the qualitative and quantitative analyses of numerical simulations, with experimental results demonstrate that the proposed method achieves satisfactory TFR, possesses strong anti‐noise capabilities, and exhibits high steady‐state generalisation capability. Furthermore, results of a comparative experiment with several TFA methods indicate that the proposed method yields significantly improved performance in terms of time‐frequency resolution, energy concentration, and computational load.

The tribo-dynamics performance of the piston–cylinder system is affected by multiple physical fields. The current work presents a novel multiphysics coupling method to model and analyze the lubricated piston skirt–cylinder interface considering the cylinder liner vibration. This method is implemented by coupling multibody dynamics of the crank-connecting rod–piston–cylinder system, the heat transfer of the cylinder and piston, hydrodynamics lubrication on the skirt–cylinder interface, vibration of the cylinder liner, and thermal as well as elastic deformation in the piston–cylinder system together with rheological characteristics of lubricating oil. The proposed method is adopted into a four-stroke gasoline engine to predict its dynamics and tribological characteristics, with the purpose of revealing the influence of cylinder liner vibration on the tribo-dynamics implementation of the piston–cylinder system. The results indicate that increasing the stiffness and damping coefficient of the cylinder is beneficial to suppress the vibration of the system, but it has little effect on the tribological characteristics of the piston skirt–cylinder interface.