Robust Audio Blind Watermarking Algorithm Based on Haar Transform

To solve the problem of low capacity of audio watermarking information and poor robustness of impact resistance,a digital audio watermark algorithm based on wavelet transform is presented in this paper. In this algorithm,the fine and approximate components are obtained when the Haar wavelet base is used to convert each frame of the audio signal to its discrete transform. Then the HAS algorithm is applied to the fine component embedded with watermark to realize encryption process. The original audio carrier signal is not needed in extraction,as long as the signal is embedded after the frame is divided. The first two sections of each frame are implemented in Haar transform respectively to get the fine and approximate component. The watermark images are obtained from the former two fine components of the energy watermark sequence after calculating,comparing and extracting. The simulation results show that there is a certain transparency for the algorithm,a good robustness against the resampling and low pass filtering attack.

A HOS-based Blind Signal Extraction Method for Chaotic MIMO Systems

A novel method to extract multiple input and multiple output (MIMO) chaotic signals was proposed using the blind neural algorithm after transmitting in nonideal channel. The MIMO scheme with different chaotic signal generators was presented. In order to separate the chaotic source signals only by using the sensor signals at receivers, a blind neural extraction algorithm based on higher-order statistic (HOS) technique was used to recover the primary chaotic signals. Simulation results show that the proposed approach has good performance in separating the primary chaotic signals even under nonideal channel.

PARTIALLY BLIND EXTRACTION OF CONTINUOUS CHAOTIC SIGNALS FROM A LINEAR MIXTURE

In this paper, we address the problem of blind extraction and separation of a continuous chaotic signal from a linear mixture consisting of some chaotic signal and/or random signals. The problem of blind extraction is firstly formulated as a problem of the synchronization-based parameter estimation. Then an efficient least square based parameter estimation method is introduced to determine the desired extracting vector. The proposed blind signal extraction scheme is applicable to blind separation of chaotic signals by formulating the separation problem as the extraction of each chaotic source. Numerical simulation shows that the proposed approach can blindly extract and separate the desired chaotic signals and it is also robust to measurement noise.

Time-shared channel identification for adaptive noise cancellation in breath sound extraction

Noise artifacts are one of the key obstacles in applying continuous monitoring and computer-assisted analysis of lung sounds. Traditional adaptive noise cancellation (ANC) methodologies work reasonably well when signal and noise are stationary and independent. Clinical lung sound auscultation encounters an acoustic environment in which breath sounds are not stationary and often correlate with noise. Consequendy, capability of ANC becomes significantly compromised. This paper introduces a new methodology for extracting authentic lung sounds from noise-corrupted measurements. Unlike traditional noise cancellation methods that rely on either frequency band separation or signal/noise independence to achieve noise reduction, this methodology combines the traditional noise canceling methods with the unique feature of time-split stages in breathing sounds. By employing a multi-sensor system, the method first employs a high-pass filter to eliminate the off-band noise, and then performs time-shared blind identification and noise cancellation with recursion from breathing cycle to cycle. Since no frequency separation or signal/noise independence is required, this method potentially has a robust and reliable capability of noise reduction, complementing the traditional methods.

Embedding Color Watermark Image into Color Host Image Based on Contourlet-Domain

A novel image blind watermarking scheme based on Contourlet transform was proposed to embed color watermark image into color host image, which was different from some existing works using the binary or gray-level image as watermark. In the process of embedding watermark, each color component of the color host image was decomposed by Contourlet transform. Then, the low-pass sub-band coeffidents were divided to 4 x 4 non-overlapping blocks, and the eoeffidents in the selected block were quantified for embedding the watermark information. In the process of extraction watermark, the watermark could be extracted from the watermarked image without the help of the original host image or the original watermark image. Experimental results show that the proposed color image scheme has better invisibility and stronger robustness against most attacks such as image compression, filtering, adding noise, cropping, rotation, and scaling.

An efficient semi-blind source extraction algorithm and its applications to biomedical signal extraction

In many applications, such as biomedical engineering, it is often required to extract a desired signal instead of all source signals. This can be achieved by blind source extraction （BSE） or semi-blind source extraction, which is a powerful technique emerging from the neural network field. In this paper, we propose an efficient semi-blind source extraction algorithm to extract a desired source signal as its first output signal by using a priori information about its kurtosis range. The algorithm is robust to outliers and spiky noise because of adopting a classical robust contrast function. And it is also robust to the estimation errors of the kurtosis range of the desired signal providing the estimation errors are not large. The algorithm has good extraction performance, even in some poor situations when the kurtosis values of some source signals are very close to each other. Its convergence stability and robustness are theoretically analyzed. Simulations and experiments on artificial generated data and real-world data have confirmed these results.

Atrial fibrillatory signal estimation using blind source extraction algorithm based on high-order statistics

The analysis and the characterization of atrial fibrillation （AF） requires, in a previous key step, the extraction of the atrial activity （AA） free from 12-lead electrocardiogram （ECG）. This contribution proposes a novel non-invasive approach for the AA estimation in AF episodes. The method is based on blind source extraction （BSE） using high order statistics （HOS）. The validity and performance of this algorithm are confirmed by extensive computer simulations and experiments on realworld data. In contrast to blind source separation （BSS） methods, BSE only extract one desired signal, and it is easy for the machine to judge whether the extracted signal is AA source by calculating its spectrum concentration, while it is hard for the machine using BSS method to judge which one of the separated twelve signals is AA source. Therefore, the proposed method is expected to have great potential in clinical monitoring.

A robust extraction algorithm for biomedical signals from noisy mixtures

Blind source extraction （BSE） is widely used to solve signal mixture problems where there are only a few desired signals. To improve signal extraction performance and expand its application, we develop an adaptive BSE algorithm with an additive noise model. We first present an improved normalized kurtosis as an objective function, which caters for the effect of noise. By combining the objective function and Lagrange multiplier method, we further propose a robust algorithm that can extract the desired signal as the first output signal. Simulations on both synthetic and real biomedical signals demonstrate that such combination improves the extrac- tion performance and has better robustness to the estimation error of normalized kurtosis value in the presence of noise.