Convolutive Blind Source Separation Applied to the Communication Signals

2012 ◽  
Vol 263-266 ◽  
pp. 188-191
Author(s):  
Xiu Ying Sun ◽  
Peng Fei Xu
Keyword(s):  
Independent Component Analysis ◽  
Time Domain ◽  
Wavelet Decomposition ◽  
Threshold Model ◽  
Automatic Method ◽  
Blind Separation ◽  
Communication Signals ◽  
Communication Signal ◽  
Soft Threshold ◽  
Convolutive Blind Source Separation

In this paper, a method for convolutive blind separation for communication sources is introduced. The method works in time-domain, and it is based on the recently very successful algorithm EFICA for Independent Component Analysis, which is an enhanced version of more famous FastICA. In addition, an automatic method of wavelet de-noising processing is proposed, using the 'mini-maxi' soft-threshold model, wavelet decomposition is performed at level 5 for the noisy separated communication signal, it can improve the performance of BSS system, and this is confirmed in the experiment for communication signals with same carrier frequencies and modulation.

Convolutive Blind Source Separation Based on Wavelet De-Noising

2013 ◽  
Vol 756-759 ◽  
pp. 3356-3361 ◽  
Author(s):  
Hong Bin Zhang ◽  
Peng Fei Xu
Keyword(s):  
Blind Source Separation ◽  
Time Domain ◽  
Source Separation ◽  
Noise Pollution ◽  
Processing Method ◽  
Separation System ◽  
Communication Signals ◽  
The Time Domain ◽  
Ica Algorithm ◽  
Convolutive Blind Source Separation

The paper discusses the time-domain blind seperation applied to communication signals, using an ICA algorithm EFICA together with a wavelet de-noising processing method. In the Blind source separation system, regardless of the mixed signals and separated signals, noise pollution occurs frequently, it increases the complexity of BSS and the difficulty of dealing with the aftermath. So an automatic method of and wavelet de-noising processing is proposed finally. It yields good results in the experiment and improves the performance of BSS system.

Blind Separation of Excavator Noise Signals in Frequency Domain

2011 ◽  
Vol 105-107 ◽  
pp. 723-728
Author(s):  
Li Da Liao ◽  
Qing Hua He ◽  
Zhong Lin Hu
Keyword(s):  
Independent Component Analysis ◽  
Modal Analysis ◽  
Frequency Domain ◽  
Time Domain ◽  
Component Analysis ◽  
Independent Component ◽  
Blind Separation ◽  
Noise Sources ◽  
Amplitude Spectra ◽  
Complex Valued

In order to identify noise sources of an excavator in non-library environment, a complex-valued algorithm in frequency domain was applied. Firstly, an acoustic camera was used to acquire excavator’s noise signals, which were convolutive mixtures in time domain interfered by echo. Secondly, signals in time domain transformed into frequency domain by FT, turned to be complex-valued mixtures. Then, independent components of noise signals were obtained through separation of complex-valued mixtures using complex-valued algorithm based on independent component analysis. Finally, according to noise of diesel with muffler was mainly consist of surface noise, the relationship between principal frequencies and structrual parts was founded by comparing frequency-amplitude spectra and modal analysis in Ansys. Research shows that complex-valued algorithm based on fast fixed-point independent component analysis can effectively separate noise signals from an excavator in time domain, and noise sources can be well ascertained by comparing the modal analysis with blind separation components.

Research of Terahertz Spectra of Terbutaline Sulfate Based on Wavelet De-Noising

2011 ◽  
Vol 63-64 ◽  
pp. 327-332
Author(s):  
Xiai Chen ◽  
Ping Jie Huang ◽  
Di Bo Hou ◽  
Xu Sheng Kang ◽  
Guang Xin Zhang ◽  
...  
Keyword(s):  
Time Domain ◽  
High Frequency ◽  
Wavelet Decomposition ◽  
Signal To Noise Ratio ◽  
Noise Signal ◽  
Discrete Wavelet ◽  
Frequency Noise ◽  
Signal To Noise ◽  
Terbutaline Sulfate ◽  
Soft Threshold

Terahertz spectra of terbutaline sulfate in the range of 0.2 to 2.2 THz was obtained by THz time-domain spectroscopy. The discrete wavelet transform was applied to de-noising terahertz waveforms. The signal was decomposed into five layers by wavelet decomposition, and then the high-frequency noise signal was eliminated by wavelet reconstruction. Another try was through calculating the standard deviation of the noise signal by the 1-th level signals which got from wavelet decomposition, and then the soft threshold and hard threshold de-noising method was employed respectively. The robustness of these wavelet de-noising methods was testified in this paper, and the absorption and refraction spectra of terbutaline sulfate were got at last. The result of experiment indicts that wavelet can enhance the signal to noise ratio of system and this paper provides a new way for the detection of terbutaline sulfate.

Preparing for the unpredictable: adaptive feedback enhances the response to unexpected communication signals

2012 ◽  
Vol 107 (4) ◽  
pp. 1241-1246 ◽  
Author(s):  
Gary Marsat ◽  
Leonard Maler
Keyword(s):  
Nervous System ◽  
Sensory Input ◽  
Low Frequency ◽  
Excitatory Input ◽  
Negative Image ◽  
Communication Signals ◽  
First Order ◽  
Communication Signal ◽  
Apical Dendrites ◽  
Spike Bursts

To interact with the environment efficiently, the nervous system must generate expectations about redundant sensory signals and detect unexpected ones. Neural circuits can, for example, compare a prediction of the sensory signal that was generated by the nervous system with the incoming sensory input, to generate a response selective to novel stimuli. In the first-order electrosensory neurons of a gymnotiform electric fish, a negative image of low-frequency redundant communication signals is subtracted from the neural response via feedback, allowing unpredictable signals to be extracted. Here we show that the cancelling feedback not only suppresses the predictable signal but also actively enhances the response to the unpredictable communication signal. A transient mismatch between the predictive feedback and incoming sensory input causes both to be positive: the soma is suddenly depolarized by the unpredictable input, whereas the neuron's apical dendrites remain depolarized by the lagging cancelling feedback. The apical dendrites allow the backpropagation of somatic spikes. We show that backpropagation is enhanced when the dendrites are depolarized, causing the unpredictable excitatory input to evoke spike bursts. As a consequence, the feedback driven by a predictable low-frequency signal not only suppresses the response to a redundant stimulus but also induces a bursting response triggered by unpredictable communication signals.

scholarly journals Numerical modeling of hiding properties of underwater acoUStic communication signals with linear sweep of the carrier

2020 ◽  
Author(s):  
К.Г. Кебкал
Keyword(s):  
Numerical Modeling ◽  
Continuous Spectrum ◽  
Gaussian Noise ◽  
Data Exchange ◽  
Numerical Models ◽  
Underwater Acoustic Communication ◽  
Underwater Acoustic ◽  
Communication Signals ◽  
Communication Signal ◽  
Spectrum Spread

На результатах численного моделирования продемонстрировано, что распределения вероятностей огибающей и фазы смеси гауссова шума и (слабого) гидроакустического сигнала связи, характеризуемого линейной разверткой несущей, могут иметь исчезающе малые отличия от аналогичных распределений, характерных для просто гауссова шума. Использование непрерывного расширения спектра сигнала связи может представлять интерес для задач скрытого обмена данными, в которых обнаружение сеанса связи устройствами перехвата должно быть затруднительным или невозможным. С применением численных моделей проанализированы возможности использования сигналов с непрерывным расширением спектра для скрытой цифровой гидроакустической связи посредством штатных приемоизлучающих гидроакустических систем, находящихся на вооружении действующих кораблей. Based on the results of the numerical modeling, the vanishingly small differences between the probability distributions of the envelope and phase of the sum of Gaussian noise and (weak) underwater acoustic signal with linear carrier sweep, and the same distributions for the Gaussian noise all alone are demonstrated. Utilization of the continuous spectrum spread of the communication signal may be applied to the task of covert data exchange, where detection of the communication session by the intercepting equipment must be complicated or impossible. Using numerical models, we analyzed the capabilities of implementation of the signals with continuous spectrum spread for covert digital underwater acoustic communications through the standard underwater acoustic transducers, which are in service on the operational ready vessels.

Intelligent Fault Diagnosis of Planetary Wheel Bearing Using Constrained Independent Component Analysis and Stacked Sparse Autoencoder

2020 ◽  
Author(s):  
Hongkun Li ◽  
Gangjin Huang ◽  
Jiayu Ou ◽  
Yuanliang Zhang
Keyword(s):  
Independent Component Analysis ◽  
Time Domain ◽  
Component Analysis ◽  
Independent Component ◽  
Mechanical Equipment ◽  
Training Samples ◽  
Sparse Autoencoder ◽  
Stacked Sparse Autoencoder ◽  
Constrained Independent Component Analysis ◽  
Wheel Bearing

Abstract Industrial machinery is developing in the direction of large-scale, automation, and high precision, which brings novel troubles to mechanical equipment management and maintenance. Intelligent diagnosis of mechanical running state based on vibration signals is becoming increasingly important, and it is still a great challenge at pattern recognition. As one of the indispensable components in mechanical equipment, planetary gearboxes are widely used in wind power, aerospace, and heavy industry. However, the problem of automatically maximizing the accuracy of planetary gearbox under different working conditions has not been solved. Therefore, an intelligent diagnosis method for planetary wheel bearing based on constrained independent component analysis (CICA) and stacked sparse autoencoder (SSAE) is presented in this research. Firstly, the fault signal with obvious time-domain characteristics is extracted by constrained independent component analysis (CICA), and the fault signals and noise is separated. Then, calculating the correlation kurtosis value of the time domain signals at different iteration periods as the eigenvalue to obtain the training samples and the test samples. The parameters of the network layer, the number of hidden nodes and learning rate are determined to build the model of SSAE. In the end, the training samples are input into the model for training and the whole network is fine-tuned. The advantages and disadvantages of the model are verified by the test samples. The intelligent classification and diagnosis of the mechanical running state are completed. Experiments analysis with real datasets of planetary wheel bearing show that the proposed method can achieve higher accuracy and robustness for fault classification compared with other data-driven methods. The application of this method in other major machinery industry also has bright prospects.

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