Blind Source Separation of Mixed Noisy Audio Signals Using an Improved FastICA

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Signal-To-Noise-Ratio of Signal Acquisition In Global Navigation Satellite System Receiver

This paper presents a measurement of signal-to noise ratio (SNR) for some global navigation systems, and making a comparison between such ratios. This ratio is an important measure to the quality of the signal as SNR increases the quality increases and vice versa. A new ratio is developed here, that is (noise to signal ration)NSR, it is found that as the value of NSR increases the quality decreases. The effects of both bit time and bit error rate on both SNR and NSR  is studied.  Both bit time and bit error rate effects on SNR, as such quantities increases SNR decreases. Keywords: signal-to-noise ratio, global navigation system, signal acquisition

Wave File Features Extraction using Reduced LBP

In this work, we present a novel approach for extracting features of a digital wave file. This approach will be presented, implemented and tested. A signature or a key to any wave file will be created.  This signature will be reduced to minimize the efforts of digital signal processing applications. Hence, the features array can be used as key to recover a wave file from a database consisting of several wave files using reduced Local binary patterns (RLBP). Experimental results are presented and show that The proposed RLBP method is at least 3 times faster than CSLBP method, which mean that the proposed method is more efficient

Ensuring telecommunication network security through cryptology: a case of 4G and 5G LTE cellular network providers

This paper aims to present the details regarding telecommunication network security through cryptology protocols. The data was based on scientific data collection and the quantitative method was adopted. The questionnaire was developed and the primary respondents were approached who were working in 4 telecommunication networking companies namely Huawei, Ericsson, SK Telecom and Telefonica. The sample size of the research was 60 participants and the statistical analysis was used to analyze research. The finding shows that cryptology protocol such as SSH, SSL, Kerberos PGP and SET are implemented within the companies in order to secure network

Statistical blind signal processing for single trace and 2D multicomponent seismic wavefield

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Filteration of multicomponent seismic wavefield data using frequency SVD

This paper proposes a new statistical approach based on frequency singular value decomposition (SVD) to enhance the SNR of the noisy multicomponent seismic wavefield. Our filtering algorithm consists of three main steps: Firstly, the frequency transformed multicomponent seismic wavefield data is rearranged into one long vector containing information on all frequencies and all component interactions. Secondly, the reduced dimensional spectral covariance matrix of the long vector data is estimated by means of singular value decomposition. Finally, the separation of the primary seismic waves from the noise is achieved by projecting the dominant eigenvector that has the highest eigenvalue of the reduced dimensional covariance matrix onto the long data vector. The experimental results have shown that the proposed algorithm outperforms the conventional separation technique in terms of accuracy and complexity

Blind seismic wavefield separation using frequency singular value decomposition

This paper presents a new blind statistical approach based on frequency singular value decomposition to enhance the SNR of the full multicomponent seismic wavefield as well as separating the seismic primary waves. A model of wideband polarized seismic wavefield that are received by linear array of three component sensors is used as framework for implementing the proposed algorithm. This algorithm explicitly exploits the Eigen-structure of reduced dimensional spectral covariance matrix. The blind separation of first primary wave is achieved by projecting the first eigenvector that has the highest eigenvalue of this covariance matrix on the long data vector that contains information on all frequencies and all components interactions of the multicomponent seismic wave-field. In addition, the experimental results have shown that the proposed algorithm outperforms the conventional separation technique in terms of accuracy and complexity

Motor fault detection using sound signature and wavelet transform

The use of induction machines has gained fast popularity in many aspects of today’s energy applications and industrial productions. However, just as with any other machine, failure is expected due to a variety of faults in component and system levels. Therefore, it is necessary to improve machine reliability by performing preventive maintenance and exploring faulty indications in advance to avoid future failures. In normal operation, a distinct machine sound signature can be identify. Therefore, at any faulty operation, diagnosis of potential error can be defined based on output signature sound data analysis. Yet, this process of monitoring induction machine sounds and vibration can be hectic and extensive in terms of collecting data and compiling analysis. That is, a huge number of data samples need to be collected and stored in order to define abnormality operation. Therefore, in this work, wavelet-based algorithms were developed as an analysis process to analyze collected data and identify abnormality, with much fewer data samples and compiling process, as special prosperity of wavelet transform. As a result, MATLAB codes were implemented to analyze data based on sound signature technique and wavelet transform algorithms to show a significant improvement in identifying potential error and abnormality conditions