Underdetermined Blind Mixing Matrix Estimation Using STWP Analysis for Speech Source Signals

Wavelet packets decompose signals in to broader components using linear spectral bisecting. Mixing matrix is the key issue in the Blind Source Separation (BSS) literature especially in under-determined cases. In this paper, we propose a simple and novel method in Short Time Wavelet Packet (STWP) analysis to estimate blindly the mixing matrix of speech signals from noise free linear mixtures in over-complete cases. In this paper, the Laplacian model is considered in short time-wavelet packets and is applied to each histogram of packets. Expectation Maximization (EM) algorithm is used to train the model and calculate the model parameters. In our simulations, comparison with the other recent results will be computed and it is shown that our results are better than others. It is shown that complexity of computation of model is decreased and consequently the speed of convergence is increased.

Image Super Resolution Reconstruction Based MCA and PCA Dimension Reduction

Image super-resolution (SR) reconstruction is to reconstruct a high-resolution (HR) image from one or a series of low-resolution (LR) images in the same scene with a certain amount of prior knowledge. Learning based algorithm is an effective one in image super-resolution reconstruction algorithm. The core idea of the algorithm is to use the training examples of image to increase the high frequency information of the test image to achieve the purpose of image super-resolution reconstruction. This paper presents a novel algorithm for image super resolution based on morphological component analysis (MCA) and dictionary learning. The MCA decomposition based SR algorithm utilizes MCA to decompose an image into the texture part and the structure part and only takes the texture part to train the dictionary. The reconstruction of the texture part is based on sparse representation, while that of the structure part is based on more faster method, the bicubic interpolation. The proposed method improves the robustness of the image, while for different characteristics of textures and structure parts, using a different reconstruction algorithm, better preserves image details, improve the quality of the reconstructed image.

Open-circuit Fault Diagnosis of Traction Inverter Based on Compressed Sensing Theory

This study proposes a new method of fault diagnosis based on the least squares support vector machine with gradient information(G-LS-SVM)to solve the insulated-gate bipolar transistor(IGBT)open-circuit failure problem of the traction inverter in a catenary power supply system.First,a simulation model based on traction inverter topology is built,and various voltage fault signal waveforms are simulated based on the IGBT inverter open-circuit fault classification.Second,compressive sensing theory is used to sparsely represent the voltage fault signal and make it a fault signal.The new method has a high degree of sparseness and builds an overcomplete dictionary model containing the feature vectors of voltage fault signals based on a double sparse dictionary model to match the sparse signal characteristics.Finally,the space vector transform is used to represent the three-phase voltage scalar in the traction inverter as a composite quantity to reduce the redundancy of the fault signals and data-processing capabilities.A G-LS-SVM fault diagnosis model is then built to diagnose and identify the voltage fault signal feature vector in an overcomplete dictionary.The simulation results show that the accuracy of this method for various types of IGBT tube fault diagnosis is over 98.92%.Moreover,the G-LS-SVM model is robust and not affected by Gaussian white noise.