scholarly journals Efficient and Unique learning of the Complex Receiver Structure of Galileo E5 AltBOC using an Educational Software in Matlab

2021 ◽  
Author(s):  
Subhan Khan ◽  
Aisha Batool
Keyword(s):  
Fourier Transform ◽  
Educational Software ◽  
Signal Acquisition ◽  
Binary Offset Carrier ◽  
Signal Tracking ◽  
Software Application ◽  
Navigation Data ◽  
Power Spectral ◽  
Unique Method ◽  
Galileo E5

This paper presents an efficient and unique method to learn the complex receiver structure of Galileo E5 AltBOC (Alternative Binary Offset Carrier). The software application is designed in Matlab to present each step involved in the design of software receiver of Galileo E5 signal. This software application contains the fundamental concepts of the Galileo E5 signal in the form of signal acquisition, signal tracking, extraction of the navigation data, power spectral density (PSD) of the AltBOC (15, 10), Fast Fourier Transform (FFT) of the E5a and E5b signal and implementation of the subcarrier used for the AltBOC (15,10). This paper also presents the extraction of navigation data by a novel based approach using the prompt channel of carrier tracking from the code loop discriminator.

scholarly journals Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Irena Orović ◽  
Vladan Papić ◽  
Cornel Ioana ◽  
Xiumei Li ◽  
Srdjan Stanković
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Signal Acquisition ◽  
Transform Domain ◽  
Hermite Transform ◽  
Time Frequency ◽  
Reconstruction Methods ◽  
Fourier Transform Domain

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

scholarly journals Multimodal Affective Computing to Enhance the User Experience of Educational Software Applications

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jose Maria Garcia-Garcia ◽  
Víctor M. R. Penichet ◽  
María Dolores Lozano ◽  
Juan Enrique Garrido ◽  
Effie Lai-Chong Law
Keyword(s):  
User Experience ◽  
Affective Computing ◽  
Educational Software ◽  
Control Group ◽  
Emotional States ◽  
Emotion Detection ◽  
Software Application ◽  
Software Applications ◽  
Learning Progress ◽  
The One

Affective computing is becoming more and more important as it enables to extend the possibilities of computing technologies by incorporating emotions. In fact, the detection of users’ emotions has become one of the most important aspects regarding Affective Computing. In this paper, we present an educational software application that incorporates affective computing by detecting the users’ emotional states to adapt its behaviour to the emotions sensed. This way, we aim at increasing users’ engagement to keep them motivated for longer periods of time, thus improving their learning progress. To prove this, the application has been assessed with real users. The performance of a set of users using the proposed system has been compared with a control group that used the same system without implementing emotion detection. The outcomes of this evaluation have shown that our proposed system, incorporating affective computing, produced better results than the one used by the control group.

scholarly journals Differential Entropy Feature Signal Extraction Based on Activation Mode and Its Recognition in Convolutional Gated Recurrent Unit Network

2021 ◽  
Vol 8 ◽  
Author(s):  
Yongsheng Zhu ◽  
Qinghua Zhong
Keyword(s):  
Feature Extraction ◽  
Time Scales ◽  
Signal Acquisition ◽  
Differential Entropy ◽  
Signal Sequences ◽  
Power Spectral ◽  
Similar Accuracy ◽  
Gated Recurrent Unit ◽  
Unit Network ◽  
Different Time Scales

In brain-computer-interface (BCI) devices, signal acquisition via reducing the electrode channels can reduce the computational complexity of models and filter out the irrelevant noise. Differential entropy (DE) plays an important role in emotional components of signals, which can reflect the area activity differences. Therefore, to extract distinctive feature signals and improve the recognition accuracy based on feature signals, a method of DE feature signal recognition based on a Convolutional Gated Recurrent Unit network was proposed in this paper. Firstly, the DE and power spectral density (PSD) of each original signal were mapped to two topographic maps, and the activated channels could be selected in activation modes. Secondly, according to the position of original electrodes, 1D feature signal sequences with four bands were reconstructed into a 3D feature signal matrix, and a radial basis function interpolation was used to fill in zero values. Then, the 3D feature signal matrices were fed into a 2D Convolutional Neural Network (2DCNN) for spatial feature extraction, and the 1D feature signal sequences were fed into a bidirectional Gated Recurrent Unit (BiGRU) network for temporal feature extraction. Finally, the spatial-temporal features were fused by a fully connected layer, and recognition experiments based on DE feature signals at the different time scales were carried out on a DEAP dataset. The experimental results showed that there were different activation modes at different time scales, and the reduction of the electrode channel could achieve a similar accuracy with all channels. The proposed method achieved 87.89% on arousal and 88.69% on valence.

Data Acquisition and Vibration Analysis Based on LabVIEW

2012 ◽  
Vol 239-240 ◽  
pp. 468-472
Author(s):  
Li Li Kang ◽  
Ze Zhang ◽  
Jian Ming Yu
Keyword(s):  
Fourier Transform ◽  
Data Acquisition ◽  
Vibration Analysis ◽  
Vibration Signal ◽  
Acquisition System ◽  
Signal Acquisition ◽  
Analysis Methods ◽  
Data Acquisition Card

According to the characteristics and analysis methods of the vibration signal, this paper analyses and processes the vibration signal with the combination of the INV9822A sensor, the PCI-4472 data acquisition card and the signal acquisition system using Fourier Transform. The vibration law is analysed effectively to explain the effectiveness and practicality of the method due to the compressor vibration signal.

Unambiguous sine-phased binary offset carrier modulated signal tracking technique

Optik ◽  
2017 ◽  
Vol 132 ◽  
pp. 284-290 ◽  
Author(s):  
Bo Yin ◽  
Gang Wang ◽  
Yanjie Qi
Keyword(s):  
Binary Offset Carrier ◽  
Signal Tracking ◽  
Tracking Technique

scholarly journals ESTIMASI KETEBALAN SEDIMEN DENGAN ANALISIS POWER SPECTRAL PADA DATA ANOMALI GAYABERAT

GEOMATIKA ◽  
2018 ◽  
Vol 23 (2) ◽  
pp. 65 ◽  
Author(s):  
Mila Apriani ◽  
Admiral Musa Julius ◽  
Mahmud Yusuf ◽  
Damianus Tri Heryanto ◽  
Agus Marsono
Keyword(s):  
Spectral Analysis ◽  
Fourier Transform ◽  
Gravity Anomaly ◽  
Spectral Method ◽  
Gravity Data ◽  
Sediment Thickness ◽  
Transform Method ◽  
Fourier Transform Method ◽  
Power Spectral ◽  
The Fourier Transform

<p align="center"><strong>ABSTRAK</strong></p><p> </p><p>Penelitian dengan analisis <em>power spectral</em> data anomali gayaberat telah banyak dilakukan untuk estimasi ketebalan sedimen. Dalam studi ini penulis melakukan analisis spektral data anomali gayaberat wilayah DKI Jakarta untuk mengetahui kedalaman sumber anomali yang bersesuaian dengan ketebalan sedimen. Data yang digunakan berupa data gayaberat dari BMKG tahun 2014 dengan 197 lokasi titik pengukuran yang tersebar di koordinat 6,08º-6,36º LU dan 106,68º-106,97º BT. Studi ini menggunakan metode <em>power spectral</em>  dengan mentransformasikan data dari domain jarak ke dalam domain bilangan gelombang memanfaatkan transformasi <em>Fourier</em>. Hasil penelitian dengan menggunakan metode transformasi <em>Fourier  </em>menunjukkan bahwa ketebalan sedimen di Jakarta dari arah selatan ke utara semakin besar, di sekitar Babakan ketebalan diperkirakan 92 meter, sekitar Tongkol, Jakarta Utara diperkirakan 331 meter.</p><p><strong> </strong></p><p><strong>Kata kunci</strong>: <em>power spectral</em>, anomali gayaberat, ketebalan sedimen</p><p align="center"><strong><em> </em></strong></p><p align="center"><strong><em>ABSTRACT</em></strong></p><p><em> </em></p><p><em>Studies of spectral analysis of gravity anomaly data have been carried out to estimate the thickness of sediment. In this study the author did spectral analysis of gravity anomaly data of DKI Jakarta area to know the depth of anomaly source which corresponds to the thickness of sediment. The data used in the form of gravity data from BMKG 2014 with 197 locations of measurement points spread in coordinates 6.08º - 6.36º N and 106.68º - 106.97º E. This study used the power spectral method by transforming the data from the distance domain into the wavenumber domain utilizing the Fourier transform. The result of the research using Fourier transform method shows that the thickness of sediment in Jakarta from south to north is getting bigger, in Babakan the thickness of the sediment is around 92 meter, in Tongkol, North Jakarta is around 331 meter.</em></p><p><strong><em> </em></strong></p><p><strong><em>Keywords</em></strong><em>: </em><em>power spectral, gravity anomaly, sediment thickness</em><em></em></p>

scholarly journals Time-series Imputation Algorithm

2021 ◽  
Author(s):  
David Howe
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Time Series Data ◽  
Series Data ◽  
Allan Deviation ◽  
Original Time Series ◽  
Power Spectral ◽  
Power Spectral Densities ◽  
The Fourier Transform ◽  
Original Time

Statistical imputation is a field of study that attempts to fill missing data. It is commonly applied to population statistics whose data have no correlation with running time. For a time series, data is typically analyzed using the autocorrelation function (ACF), the Fourier transform to estimate power spectral densities (PSD), the Allan deviation (ADEV), trend extensions, and basically any analysis that depends on uniform time indexes. We explain the rationale for an imputation algorithm that fills gaps in a time series by applying a backward, inverted replica of adjacent live data. To illustrate, four intentional massive gaps that exceed 100% of the original time series are recovered. The L(f) PSD with imputation applied to the gaps is nearly indistinguishable from the original. Also, the confidence of ADEV with imputation falls within 90% of the original ADEV with mixtures of power-law noises. The algorithm in Python is included for those wishing to try it.

scholarly journals Band-Pass Sampling in High-Order BOC Signal Acquisition

2018 ◽  
Vol 8 (11) ◽  
pp. 2226 ◽  
Author(s):  
Zhijun Liu ◽  
Baiyu Li ◽  
Xiangwei Zhu ◽  
Lixun Li ◽  
Guangfu Sun
Keyword(s):  
Computational Complexity ◽  
Sampling Technique ◽  
Global Navigation Satellite Systems ◽  
Signal Acquisition ◽  
Binary Offset Carrier ◽  
Satellite Systems ◽  
Sampled Signal ◽  
Band Pass ◽  
Global Navigation Satellite ◽  
High Computational Complexity

The binary offset carrier (BOC) modulation, which has been adopted in modern global navigation satellite systems (GNSS), provides a higher spectral compatibility with BPSK signals, and better tracking performance. However, the autocorrelation function (ACF) of BOC signals has multiple peaks. This feature complicates the acquisition process, since a smaller time searching step is required, which results in longer searching time or greater amounts of hardware resources. Another problem is the high Nyquist frequency, which leads to high computational complexity and power consumption. In this paper, to overcome these drawbacks, the band-pass sampling technique for multiple signals is introduced to BOC signals. The sampling frequency can be reduced significantly. Furthermore, the ACF of the sampled signal has only two secondary peaks, so that the code phase can be searched with a larger searching step. An acquisition structure base on dual-loop is proposed, to completely eliminate the ambiguity and compensate the subcarrier Doppler. The acquisition performance and the computational complexity are also analysed.

Unambiguous Composite BOC Signal Tracking Based on Partitioned Sub-Carriers

2015 ◽  
Vol 764-765 ◽  
pp. 535-539
Author(s):  
Keun Hong Chae ◽  
Hua Ping Liu ◽  
Seok Ho Yoon
Keyword(s):  
Correlation Function ◽  
Standard Deviation ◽  
Tracking Error ◽  
Numerical Results ◽  
Tracking Performance ◽  
Binary Offset Carrier ◽  
Signal Tracking ◽  
Partial Correlations ◽  
Error Standard Deviation

In this paper, we propose an unambiguous signal tracking scheme for composite binary offset carrier (CBOC) signal tracking. First, we obtain partitioned sub-carriers and partial correlations. Based on a recombination of the partial correlations, we propose a novel correlation function without side-peaks. From numerical results, the proposed correlation function is found to offer a better tracking performance than that of the conventional correlation function in terms of the tracking error standard deviation.

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