Shi Guangming

Adaptive wavelet thresholding for time varying SNR signal denoising

The wavelet transform has become a powerful tool for signal analysis and is widely used in many applications, including signal detection and denoising. This paper deals with signal detection and denoising using wavelet transforms for a class of time varying signal-to-noise-ratio (SNR) signals in B-type ultrasonic echoes. A new threshold estimator is proposed in order to enhance the SNR in time varying signals. With the estimator used on real B-type ultrasonic echo data, the results show that the proposed estimator is superior to the existing threshold estimator in SNR enhancement.

Low complexity DOA estimation approach through multitask Bayesian compressive sensing strategies

Based on the Multitask Bayesian Compressive Sensing (MT-BCS) framework, a novel DOA estimation approach for planar array is proposed in this paper. Different from the traditional CS-based DOA model, where the spatial observation is characterized in one large scale matrix, to reduce the complexity, a separable observation structure is proposed, which separates the joint spatial observation into two individual parts, and thus, the large scale matrix can be split into two small scale matrices. In addition, the Multitask Bayesian Compressive Sensing framework is engaged to build a MT-BCS-based DOA estimation scheme (MT-BCS-DOA). The simulation results show the superior capability of the proposed approach.

Dynamic nonlinear threshold decomposition algorithm for implementing stack filters

Stack filters are a class of nonlinear digital filters possessing threshold decomposition and stacking property. In this paper, new nonlinear threshold decomposition architecture is introduced to reduce the complexity of algorithm. Then by combining with the character of image, a dynamic nonlinear decomposition architecture is proposed to implement stack filters which can shorten the running time in the filtering procedure and improve the quality of the output image simultaneously. Comparisons on the mean absolute error (MAE), mean square error (MSE), power signal-to-noise ratio (PSNR) and on the time of filtering computation between this new algorithm and others are provided to show the validity of proposed algorithm.

Improvement of factorization for two-channel perfect reconstruction FIR filter banks

There is an increasing interest in designing structurally perfect reconstruction (PR) filter banks because the system can be implemented by using sum of powers-of-two (SOPOT) coefficients. The structurally PR filter banks can be designed by factorization based on the lifting scheme. But there exist some problems that are addressed in this paper. Improvement of the factorization to solve the problems is proposed. The procedures of proof for the improvement are given. Finally, the given examples show that the proposed method is effective.

The design of two-channel PR FIR filter bank with linear-phase using evolutionary strategies

A novel method for the design of a two-channel linear-phase perfect reconstruction (PR) FIR filter bank is proposed. In order to ensure the PR property, a lattice architecture has been adopted. Evolutionary strategies (ES) are used to search for an optimal set of coefficients in lattice structure. For increasing the searching speed, an initial set of coefficients is obtained by a constrained optimization method. Some design examples are given to illustrate the proposed method. The results are presented which show that this method can be used to design the filter bank with high stop band attenuation, compared to conventional methods.