Different Values Of Signal Research Articles

Noise Impact on Optimum Parameters for Signal Deconvolution

In many areas, such as speech processing, communication, and measurement systems, where the actual signal is not directly available, Wiener deconvolution is a widely used signal restoration technique. The accuracy of the estimated signal in Wiener deconvolution is determined by the unknown filter parameter. To compute this unknown parameter, complex and time-consuming numerical as well as iterative computation approaches were proposed in the literature, which also often compute an unoptimized unknown filter parameter. To address the computational problem and estimate a more accurate unknown filter parameter, the authors reported an analytical solution in a prior work for a given signal to noise ratio. However, it was revealed that the actual optimum parameter value also varies with noise also. In this paper, the authors propose a new analytical solution for optimum parameter estimation that includes noise as well. The authors compared the modified analytical solution to existing numerical computation methods for different values of Signal to Noise Ratio and demonstrated that the proposed methods’ optimum parameter estimation is always close to the actual optimum value, which resulted in more accurate signal deconvolution. Proposed mathematical solutions are also verified with experimental signals, acquired in a laboratory.

Nakagami-m Fading Channel Identification Using Adaptive Continuous Wavelet Transform and Convolutional Neural Networks

Channel identification is a useful function to support wireless telecommunication operations because the knowledge of the radio frequency propagation channel characteristics can improve communication efficiency and robustness. In recent times, the application of machine learning (ML) algorithms to the problem of channel identification has been proposed in the literature. In particular, Deep Learning (DL) has demonstrated superior performance to ’shallow’ machine learning algorithms for many wireless communication functions. Inspired by the success of DL in literature, the authors in this paper apply Convolutional Neural Networks (CNN) to the problem of channel identification, which is still an emerging research area. CNN is a deep learning algorithm that has demonstrated superior performance to ML algorithms, in particular for image processing tasks. Because the digitized RF signal is a one-dimensional time series, different algorithms are applied to convert the time series to images using various Time Frequency Transform (TFT) including the CWTs, spectrogram, and Wigner Ville distribution. The images are then provided as input to the CNN. The approach is applied to a data set based on weather radar pulse signals generated in the laboratory of the author’s facilities on which different fading models are applied. These models are inspired by the tap-delay-line 3GPP configurations defined in the standards, but they have been customized with Nakagami-m fading distribution (3GPP-like fading models). The results show the superior performance of time–frequency CNN in comparison to 1D CNN for different values of Signal to Noise Ratio (SNR) in dB. In particular, the study shows that the Continuous Wavelet Transform (CWT) has the optimal performance in this data set, but the choice of the mother wavelet remains a problem to be solved (this is a well-known problem in the research literature). Then, this study also proposes an adaptive technique for the choice of the optimal mother wavelet, which is evaluated on the mentioned data set. The results show that the adaptive proposed approach is able to obtain the optimal performance for most of the SNR conditions.

Performance Enhancement of Ytterbium-Doped Fiber Amplifier Employing a Dual-Stage In-Band Asymmetrical Pumping

The performance of doped fiber amplifiers can be enhanced significantly with the help of multi-stage pumping technique provided that various critical parameters of pumps including their optical power and wavelength are optimized. We report the performance enhancement of a ytterbium doped fiber amplifier (YDFA) for a 1.02–1.08 m spectral region with an optimized design based on a novel dual-stage in-band asymmetrical pumping scheme. By accurately adjusting the optical power and wavelength of pumps in both the stages, a record peak gain of around 62.5 dB and output power of 4.5 W are achieved for a signal wavelength of 1.0329 m at an optimized length of Ytterbium-doped silica fiber and optimized doping concentration of Yb. Moreover, a minimum noise figure (NF) of 4 dB is observed for a signal wavelength of 1.0329 m at the optimized parameters. Similarly, the effect of using high and low pump powers at the first and the second stage, respectively, on NF of the amplifier is also investigated at different values of signal powers. It is observed that the value of NF increases significantly by using high pump power at the first stage and low pump power at the second stage.

Statistics-based baseline-free approach for rapid inspection of delamination in composite structures using ultrasonic guided waves

Delamination in composite structures is characterized by a resonant cavity wherein a fraction of an ultrasonic guided wave may be trapped. Based on this wave trapping phenomenon, we propose a baseline-free statistical approach for the identification and localization of delamination using sparse sampling and density-based spatial clustering of applications with noise (DBSCAN) technique. The proposed technique can be deployed for rapid inspection with minimal human intervention. The Performance of the proposed technique in terms of its ability to determine the precise location of such defects is quantified through the probability of detection measurements. The robustness of the proposed technique is tested through extensive simulations consisting of different random locations of defects on flat plate structures with different sizes and orientation as well as different values of signal to noise ratio of the simulated data. The simulation results are also validated using experimental data and the results are found to be in good agreement.

Effect of random sampling on spectrum sensing for cognitive radio networks

Cognitive radio is a mechanism allowing dynamic access to spectrum channels. Since its beginnings, researchers have been working on using this inventive technology to control and manage the spectrum resources. Consequently, this research field has been progressing rapidly and important advances have been made. Spectrum sensing is a key function of cognitive radios that helps prevent the harmful interference with licensed users, as well as identifies the available spectrum to improve its utilization. Several spectrum sensing techniques are found in scientific literature. In this paper, we investigate the effect of the random sampling in spectrum sensing. We propose a spectrum sensing approach based on the energy detection and on the maximum eigenvalue detection (MED) combined with random sampling. The performance of the proposed approach is evaluated in terms of the receiver operating characteristics curves and in terms of the detection probability for different values of signal to noise ratio. The obtained results are compared to the uniform sampling case to show the added value of random sampling.

Performance of UWB-WBAN System Over Awgn Channel

AbstractThe major constraints in the design of Wireless Body Area Network(WBAN) can be attributed to the battery autonomy, need for high data rate services and low interference from the devices operating within the industrial, scientific and medical (ISM) bands. To meet the demand for high data rate services and low power spectral density to avoid ISM band interference, an ultra-wide band(UWB) system based technology has been proposed. This paper focuses on the design and demonstration of an UWB modem to be used in the WBAN applications and the evaluation of its performance in a near-real world scenarios affected by additive white Gaussian noise(AWGN) interference. The modem is tested with different values of signal to noise ratio(SNR). Results show that the performance of the modem degrades as the value of SNR decreases.

Enhancement of SNR in fetal ECG signal extraction using combined SWT and WLSR in parallel EKF

A Fetal Electrocardiogram (FECG) signal will contain some potential information which is precise for assisting the clinicians to make appropriate decisions that are timely at the time of pregnancy and labor. Extraction and detection of such FECG signals from their composite maternal signals of the abdomen using very powerful and advanced methodologies is a very critical need in case of fetal monitoring. The mining of pure FECG signals from the abdominal of the prenatal woman is focused in this paper. This FECG signal is very vulnerable to noise and is very difficult to process it accurately with no significant distortion that can imped its use. So, in order to be able to get some proper information on the status of the fetus or its condition it is important to be able to improve the abdominal signal and its SNR. Since the wavelet transform is well in providing information in time and frequency, the combination of Stationary Wavelet Transform (SWT), Weighted Least Square Regression (WLSR) and parallel Extended Kalman Filter (Par-EKF) are used for FECG extraction. The analysis for different values of Signal to Noise Ratio (SNR) between fetal and maternal ECG’s is implemented using MATLAB. As a Result, the highest SNR obtained is 33.5 which showed that the proposed method is efficient in extracting more information about FECG from Maternal ECG (MECG).

Optimal frequency scanning range for parameters extraction from Brillouin scattering spectrum

To improve the accuracy in temperature and strain measurement based on Brillouin scattering, Brillouin scattering spectra with different values of Signal to Noise Ratio (SNR), full width at half maximum (ΔvB) and the number of frequencies scanning are numerically generated. The influence of frequency scanning range on the accuracy in parameters extraction is systematically investigated and the optimal frequency scanning ranges in different cases are obtained. The results reveal that the frequency scanning range has a big influence on the accuracy in parameters extraction of Brillouin scattering spectrum. If the number of frequencies scanning remains unchanged, the error initially decreases with increasing frequency scanning range. Once the minimum error is reached, it increases with increasing frequency scanning range. The SNR, ΔvB and the number of frequencies scanning have little effect on the optimal frequency scanning range. For the cases with SNR ranging from 10 dB to 40 dB and the other parameters falling within the typical range, the optimal frequency scanning ranges are calculated and presented. The systematical investigations reveal that the frequency scanning range can be set to a value ranging from 1.2ΔvB to 1.6ΔvB within which it has little influence on the parameters extraction accuracy. A distributed fiber sensing system based on Brillouin scattering has been set up and the above results are fully validated by the parameters extraction from the measured Brillouin scattering spectra.

Statistical moments of Gaussian kernel correlation sum and weighted least square estimator of correlation dimension and noise level

In this paper, using non-central chi-squared distribution and polynomial chaos decomposition of a log-normal random variable, we derive the exact expressions for the covariance and variance of the Gaussian kernel correlation sum (Gkcs). The obtained results are combined with U-statistics theory and non-linear models theory to construct the exact confidence intervals for the correlation dimension and for the noise level in the case where deterministic time series are corrupted by additive Gaussian noise. The theoretical results are tested on two continuous chaotic dynamics corrupted by Gaussian noise for different values of signal to noise ratio (SNR). An application to a real data time series has been also conducted.

DSP-based Synchronization Algorithm Implementation for OFDM

Synchronization has been one of the most important research topics in Orthogonal Frequency Division Multiplexing (OFDM) system because of its great sensitivity towards timing and frequency offset errors. This paper describes a real-time implementation of a timing and frequency synchronization for OFDM system using MATLAB software Simulink, DSP processor TMS320C6713 and Code Composer Studio (CCS). The hardware interface converts the baseband signal from the PC to the DSP Processor. Basic transmission and reception performances are evaluated in real time using Real Time data Transfer (RTDX). The practical results and performance evaluation of the synchronization algorithms in OFDM system is presented and discussed. The results are plotted for different offsets of CFO and STO with different values of signal to noise ratio. Also the bit error rate (BER) and the mean square error (MSE) of the system is analyzed.

Averaged multiple L-spectrogram for analysis of noisy nonstationary signals

A time–frequency approach for improved instantaneous frequency estimation of noisy signals has been proposed. This approach is based on the averaged multiple time–frequency distribution obtained by using L-spectrograms with different values of the scaling parameter L. The L- spectrogram with low value of L is characterized by large bias and low variance, while higher L decreases the bias and increases the variance of the instantaneous frequency estimator. The averaged L-spectrogram is considered to provide a good bias-variance compromise, leading to low MSE of the instantaneous frequency estimation in the presence of noise. The proposed approach has been tested in the experiments with different values of signal to noise ratio, showing its superior performance when compared to individual L-spectrograms.

Performance analysis of WLAN physical layers using Markov channel model

Orthogonal Frequency Division Multiplexing (OFDM) and multiple antennas are being used in various standards of Wireless Local Area Network (WLAN) to enhance data rates and to achieve higher reliability in fading channel conditions. Channel modeling plays a crucial role, especially in wireless environment, in evaluating the performance of physical layer in fading environment. The paper presents a Finite State Markov (FSM) channel model, for computation of fading channel performance measures. Frequency and duration analysis is proposed to evaluate the performance of WLAN physical layers for OFDM, OFDM with Selection Combining (SC) and OFDM with Maximum Ratio Combining (MRC) diversity techniques. Level Crossing Rate (LCR) and Average Fade Duration (AFD) are computed with the analysis of FSM model. Bit Error Rate (BER) is then calculated with the help of LCR and AFD. LCR, AFD and BER performances are compared for three physical layers with varying fade depths and for different values of Signal to Noise Ratio (SNR). Results show that OFDM with MRC gives minimum value of BER for fade depths more than 0dB, while BER is minimum for OFDM with SC for fade depths below 0dB. OFDM with diversity always outperforms than OFDM without diversity for all values of fade depths.

Noise-subspace-based technique-resolution improvement for acoustical imaging of concealed rods

High-resolution methods are of major interest for estimating discrete spectra, since they overcome the resolution limit of the Fourier transform and provide very accurate estimates of the signal parameters. The problem of spatial detection and imaging of closely separated objects is investigated. This paper presents a method based on noise subspace of the correlation matrix of the noise-corrupted signal for ultrasonic holographic imaging problem. The in-line holography is employed, where the amplitude and phase of a coherent wave is recorded. The performance of the method is compared with the traditional method, Fourier transform method. The technique is investigated for different values of Signal to Noise Ratio (SNR) and synthetic aperture length.

Enhanced probabilistic neural network with local decision circles: A robust classifier

In recent years the Probabilistic Neural Network (PPN) has been used in a large number of applications due to its simplicity and efficiency. PNN assigns the test data to the class with maximum likelihood compared with other classes. Likelihood of the test data to each training data is computed in the pattern layer through a kernel density estimation using a simple Bayesian rule. The kernel is usually a standard probability distribution function such as a Gaussian function. A spread parameter is used as a global parameter which determines the width of the kernel. The Bayesian rule in the pattern layer estimates the conditional probability of each class given an input vector without considering any probable local densities or heterogeneity in the training data. In this paper, an enhanced and generalized PNN (EPNN) is presented using local decision circles (LDCs) to overcome the aforementioned shortcoming and improve its robustness to noise in the data. Local decision circles enable EPNN to incorporate local information and non-homogeneity existing in the training population. The circle has a radius which limits the contribution of the local decision. In the conventional PNN the spread parameter can be optimized for maximum classification accuracy. In the proposed EPNN two parameters, the spread parameter and the radius of local decision circles, are optimized to maximize the performance of the model. Accuracy and robustness of EPNN are compared with PNN using three different benchmark classification problems, iris data, diabetic data, and breast cancer data, and five different ratios of training data to testing data: 90:10, 80:20, 70:30, 60:40, and 50:50. EPNN provided the most accurate results consistently for all ratios. Robustness of PNN and EPNN is investigated using different values of signal to noise ratio (SNR). Accuracy of EPNN is consistently higher than accuracy of PNN at different levels of SNR and for all ratios of training data to testing data.

Back Propagation Neural Network-Modified Covariance Modelling for Improvement of Holographic Imaging

Two approaches are combined to improve the performance of modelling long wavelength holographic imaging (detection) of concealed objects. One approach is to design a multi-layer back propagation neural network that is able to reduce the effect of noise in a captured signal and results in a model as close as possible to the desired one. The other approach is to further process the captured signal by applying a modified covariance spectral estimation method to improve the resolution of the reconstructed image. Different concealing media and different values of signal to noise ratio are used to investigate the performance of such combination. It has been proved that the proposed model can provide an overall improvement in the imaging of concealed objects.

AN ACKNOWLEDGMENT

1<h3>Abstract</h3> The problem of interest here concerns electrophysiological signals from two cortical sites, acquired as invasive intracranial recordings, or from non-invasive estimates of cortical electric neuronal activity computed from EEG or MEG recordings (see e.g. https://doi.org/10.1101/269753). In the absence of other sources, these measured signals consist of an instantaneous linear mixture of the true, actual, unobserved local signals, due to low spatial resolution and volume conduction. A connectivity measure is unreliable as a true indicator of electrophysiological connectivity if it is not invariant to mixing, or if it reports a significant connection for a mixture of independent signals. In (Vinck et al 2011 Neuroimage 55:1548) it was shown that coherence, imaginary coherence, and phase locking value are not invariant to mixing, while the phase lag index (PLI) and the weighted version (wPLI) are invariant to mixing. Here we show that the lagged coherence (LagCoh) measure (2007, https://arxiv.org/abs/0711.1455), not studied in Vinck et al, is invariant to mixing. Additionally, we present here a new mixture-invariant connectivity statistic: the “standardized imaginary covariance” (sImCov). We also include in the comparisons the directed PLI (dPLI) by Stam et al (2012 Neuroimage 62:1415). Fourier coefficients for “N” trials are generated from a linear unidirectional causal time domain model with electrophysiological delay “k” and regression coefficient “b”. 1000 random data sets of “N” trials are simulated, and for each one, and for each connectivity measure, non-parametric randomization tests are performed. The “true positive detection rate” is calculated as the fraction of 1000 cases that have significant connectivity at p&lt;0.05, 0.1, and 0.2. The connectivity methods were compared in terms of detection rates, under non-mixed and mixed conditions, for small and large sample sizes “N”, with and without jitter for “k”, and for different values of signal to noise. Under mixing, the results show that LagCoh outperforms wPLI, PLI, dPLI, and sImCov. Without mixing, LagCoh and sImCov outperform wPLI, PLI, and dPLI. Finally, it is shown that dPLI is an invalid estimator of flow direction, i.e. it reverses and “goes against the flow” by merely changing the sign of one of the time series, a fact that violates the basic definition of Granger causality. For the sake of reproducible research, the supplementary material includes Delphi Pascal source code and all detailed result files in human readable format.