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...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.展开更多
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 g...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.展开更多
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 algori...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.展开更多
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 signa...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.展开更多
基金Supported by the National Natural Science Foundation of China (No.60472059)the Aeronautical Science Foundation of China (2008ZC 52026)
文摘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.
基金The Science and Technology Committee of Shanghai Municipality (No. 05DZ15004, 06DZ15013)The Project-sponsored by SRF for ROCS, SEM
文摘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.
基金Sponsored by the Education Department of Heilongjiang Province (Grant No. 12531113)
文摘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.
基金Hong Wang's research was supported in part by the Anesthesiology Department at Wayne State University and in part by Wayne State University Research Enhancement ProgramLeyi Wang" s research was supported in part by the National Science Foundation ( No.
文摘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.
