In this paper a novel and fast algorithm for the blindsourceseparation in convolutive media is introduced. This method estimates multiple independent source signals, using only their set of received convolutive mixt...
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ISBN:
(纸本)9781424452262
In this paper a novel and fast algorithm for the blindsourceseparation in convolutive media is introduced. This method estimates multiple independent source signals, using only their set of received convolutive mixtures. The number of sources and the delays in the arrival of their echoes are unknown. The channel is estimated by calculating the channel matrix which is not achieved in some other CBSS methods. In this algorithm the independent component analysis (ICA) is used as the first step to separate the signals, lags and noise components. In the second stage, a purely second-order statistic approach estimates the source signals, which is a novel CBSS algorithm. This unique structure results in an efficient and accurate estimation. Another new feature of our approach is the implementation of a fast estimator. The channel variations are usually slow compared to the sampling rate. Therefore, the fast estimator separates the source signals using only the received signals at the sampling instant, based on the estimated channel. The channel matrix and the separated source signals are updated by repeating CBSS process at regular intervals. The permutation ambiguity, which is a common problem in many separation methods, is resolved in this algorithm. This new approach is simpler, faster, more accurate and needs less memory compared to some methods recently introduced by others.
Based on the analysis of existing speech enhancement based on the drawbacks of the traditional algorithm is adopted in the system is proposed based on FastICA blind source separation algorithm design of speech enhance...
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Based on the analysis of existing speech enhancement based on the drawbacks of the traditional algorithm is adopted in the system is proposed based on FastICA blind source separation algorithm design of speech enhancement algorithm,and the transplanted to embedded speech enhancement *** system real-time speech enhancement,by four element microphone arrays is used to sample the space of sound signal and through the built-in speech enhancement algorithm to the voice source signal and noise source signal separation,capable of suppressing co channel noise,active noise and residual background *** on the test results of the algorithm on PC platform,the system meets the design requirements and the effect is good.
An insulation board is an important insulator used between damping capacitors in a converter valve. The potential creeping discharge phenomenon on the insulation board will affect the insulation between the damping ca...
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An insulation board is an important insulator used between damping capacitors in a converter valve. The potential creeping discharge phenomenon on the insulation board will affect the insulation between the damping capacitors and even the safe operation of the converter valve;therefore, a modified chaos particle swarm optimization multiple signal classification (MCSPO-MUSIC) localization algorithm based on a sparse array was proposed. The performance of the localization algorithm and the sparse array was analyzed by MATLAB simulation, and a test platform was established to detect the insulation board discharge position localization. The simulation results showed that the calculation time of this algorithm is about 1.5 s, which is an order of magnitude less than traditional MUSIC algorithm, and it is found that when the sparsity of the 4 x 4 array is 4 (the sparse array elements are 5, 9, 14 and 15), the localization accuracy remains high. Ten groups of experimental data were put into the MCSPO-MUSIC algorithm;the root mean square errors (RMSE) of the localization errors are 1.91 degrees (non-sparse array) and 3.12 degrees (sparse array), respectively. Finally, the blind source separation algorithm was used to remove the field noise, which verifies the algorithm and sparse concept accurate and economical in practical application.
This work presents the application of blindsourceseparation (BSS) algorithms on Dual-hand IR Spectrogram for breast cancer detection and tracing the effect of long-term chemotherapy for breast-cancer patients. We ta...
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ISBN:
(纸本)9781538668122;9781538668115
This work presents the application of blindsourceseparation (BSS) algorithms on Dual-hand IR Spectrogram for breast cancer detection and tracing the effect of long-term chemotherapy for breast-cancer patients. We take Dual-band IR Spectrogram's RAW Data as an input to the BSS algorithms. Integrated into the back-end processor of a Dual-band IR Sensor and Readout Circuit Platform, our Improved Neighbor-based BSS algorithm operated on Dual-band IR Spectrogram provides a better breast cancer detection. Considering the demarcating degree, our Improved Neighbor-based BSS algorithm is approximately 15% better than other algorithms. As to the correctness rate, our improved algorithm approximately increases 10% compared with other algorithms.
Since the colour in painting cultural relics observed by our naked eyes or hyperspectral cameras is usually a mixture of several kinds of pigments, the mixed pigments analysis will be an important subject in the field...
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Since the colour in painting cultural relics observed by our naked eyes or hyperspectral cameras is usually a mixture of several kinds of pigments, the mixed pigments analysis will be an important subject in the field of ancient painting conservation and restoration. This paper aims to find a more effective method to confirm the types of every pure pigment from mixture on the surface of paintings. Firstly, we adopted two kinds of blind source separation algorithms, which are independent component analysis and non-negative matrix factorization, to extract the pure pigment component from mixed spectrum respectively. Moreover, we matched the separated pure spectrum with the pigments spectra library built by our team to determine the pigment type. Furthermore, three kinds of data including simulation data, mixed pigments spectral data measured in laboratory, and the spectral data of an ancient painting were chosen to evaluate the performance of the different algorithms. And the accuracy was compared between the two algorithms. Finally, the experimental results show that non-negative matrix factorization method is more suitable for endmember extraction in the field of ancient painting conservation and restoration.
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