The paper addresses a problem of tracking multiple number of frequencies using Regularized Autoregressive (RAR) approximation. The RAR procedure allows to decrease approximation bias, comparing to other AR-based Frequency detection methods, while still providing competitive variance of sample estimates. We show that the RAR estimates of multiple periodicities are consistent in probability and illustrate dynamics of RAR in respect to sample size and signal-to-noise ration by simulations.

In non-coherent Direction Of Arrival (DOA) Estimation, the goal is to determine DOA based only on the magnitude of the received sensor array signal. The advantage of the non-coherent DOA Estimation is its robustness against phase errors; despite phase errors present in both sensors and phase shifters, direction of arrival can be estimated. In this paper, DOA is estimated using a Frequency Estimation technique which can be simply implemented by discrete Fast Fourier Transform (FFT) methods. In addition, for removing the ambiguity and solving the nonlinear equations, a reference target with high power emission is used. Simulation results, in both linear and plane array cases show the efficiency and robustness of the proposed algorithm against phase errors.

Introduction: Usually osseointegration takes between three to six months after implant placement but patients are interested to have early loading. There are no definitive criteria for measuring bone mineral density (BMD), insertion torque (IT) (final torque force) and resonance Frequency analysis (RFA) (primary implant stability) to determine exact loading time based on the relationship between the above-mentioned parameters. The aim of this study was to determine the relationship between IT, RFA and BMD in screw-type implants.Materials and Methods: This clinical trial was conducted on 18 patients who were candidates for ITI implant placement. Written consent was taken and jaw bone density was determined via a digital radiography technique before surgery. After implant placement, RFA and IT were measured. Fifty-five ITI implants of the total 62 implants placed were evaluated; the implants were 12 mm long with a diameter of 4.1 mm. Data was analyzed with Pearson’s test using SPSS.15 software (a=0.05).Results: There was a significant relationship between IT, RFA and BMD. Pearson’s test showed a correlation coefficient of 0.872 to 0.789 between the three parameters, indicating a strong relationship between them. The mean bone density was 1.468±0.042 g/cm2; the mean RFA was 66.01±2.2 ISQ and the mean IT was 34.62±3.33 N/cm2.Conclusion: Based on the results of the present study there is a significant relationship between, IT, RFA and BMD (p value=0.001).

In this paper optimization of Frequency offset Estimation technique based on channel Estimation, which is desirable for Frequency selective channels and hasfast convergence speed, for using in HF channel is addressed. Because of time variations coherence time in HF channel is short, which leads to a severe degradation in the performance of above technique in this channel. An optimization of said technique is proposed in which after removing coarse Frequency offset Estimation and knowing residual Frequency offset range, incorrect Estimations can be removed and others can be averaged to give final correct estimate. Simulation is conducted underchannel parameters with respect to MIL-STD-188-110B. the results show that proposed optimization result in improved performance of Frequency offset Estimation technique based on channel Estimation.

This paper presents a new technique for measuring power system Frequency, rate of change of Frequency and the voltage amplitude and phase angle using the Simulated Annealing (SA) based optimization algorithm. The algorithm uses the digitized samples of the voltage signal at the relay location and minimizes a cost function of the sum of the absolute error between the actual estimated signal samples. The proposed algorithm does not need any filter or model for the system Frequency before and during the Estimation process. The effects of the number of samples, sampling Frequency and harmonics contamination on the estimated parameters are tested and discussed in the paper.

This paper addresses the problem of direction of arrival (DOA) Estimation for nonstationary signals using time-Frequency distributions (TFDs). The averaging method is used for selecting the time-Frequency points. For this purpose, a strength indicator is used to remove the points with more noise. The method of obtaining the strength indicator is described and numerical simulations are used to show its effect. In addition, the DOA Estimation method using time-Frequency distributions when the number of sources is more than sensors is described and simulated. In addition to the narrowband model, the broadband model is also used for the output signal of the sensor array, which increases the accuracy of the performance evaluation of DOA Estimation methods. The effect of time-Frequency kernels, number of snapshots, number of sensors, signal-to-noise ratio (SNR), bandwidth and signal type are investigated in simulations. The effect of window length and window type in the Wigner-Ville kernel is also examined. The computational complexities of both conventional and time-Frequency DOA Estimation methods are also compared. The results show that as the strength indicator increases, the Estimation error decreases, but with a large increase in the strength indicator, the number of averaging points also decreases, which increases the error. Simulation results show that for low SNRs, the TFD based DOA Estimation methods perform better, while for high SNR values, both conventional and TFD based approaches have similar performances.

In this article, an attempt has been made to estimate the amount of sound transmission loss in a flat oval channel by applying the approach of statistical energy analysis. Correct Estimation of sound transmission loss in an air conditioning channel is of great importance due to the harmful effects of noise pollution in the environment on human health. Simulation with the statistical energy analysis method is a powerful approach to estimate sound and vibration in problems in which we deal with complex and multi-part systems; is considered. In this method, first, a system is divided into several subsystems, and then by writing a matrix equation that includes the energy exchanges between subsystems and energy loss coefficients; It is investigated from the perspective of vibration and sound Estimation.On average, the model presented in this research is able to estimate the sound transmission loss in different dimensions of the air conditioning channels according to the experimental results in the accuracy range of ± 2.5 dB. Considering that it seems that the results obtained from modeling with this method are in good agreement with the experimental data; The results of this research can be used as an efficient approach to estimate noise in oval shaped channels stretched in different lengths.

This study introduce a new Frequency parameter calledsfcwt, which can be used to estimate earthquake magnitude on the basis of the first few seconds of P-waves, using the waveforms of earthquakes occurring in Japan. This new parameter is introduced using continuous wavelet transform as a tool for extracting the Frequency contents carried by the first few seconds of P-wave.The empirical relationship between the logarithm of tfcwt within the initial 4 s of a waveformand magnitude was obtained. To evaluate the precision of tfcwt, we also calculated parameters tpmax and tc. The average absolute values of observed and estimated magnitude differences (½Mest -Mobs½) were 0.43, 0.49, and 0.66 units of magnitude, as determined using tpmax, tc, and tfcwt, respectively. For earthquakes with magnitudes greater than 6, these values were 0.34, 0.56, and 0.44 units of magnitude, as derived using tpmax, tc, and tfcwt, respectively. The tfcwt parameter exhibited more precision in determining the magnitude of moderate- and small-scale earthquakes than did the tc-based approach. For a general range of magnitudes, however, the tpmax -based method showed more acceptable precision than did the other two parameters.

Grid impedance Estimation is used in many power system applications such as grid connected renewable energy systems and power quality analysis of smart grids. The grid impedance Estimation techniques based on signal injection uses Ohm’ s law for the Estimation. In these methods, one or several signal(s) is (are) injected to Point of Common Coupling (PCC). Then the current through and voltage of PCC are measured. Finally, the impedance is assumed as ratio of voltage to current signals in Frequency domain. In a noisy system, energy of the injected signal must be sufficient for an accurate approximation. However, power quality issues and regulations limit the energy and the voltage levels of the injected signal. There are three main issues in impedance Estimation using signal injection: I) Power quality of grid, II) Frequency range of Estimation, and finally III) accuracy of Estimation. In this paper, the stationary wavelet denoising algorithm is employed instead of increasing the energy of injection signal(s). In the paper, a novel method is proposed for impedance Estimation based on selecting several appropriate injection signals and denoising the measured signals. The proposed method is able to impedance Estimation in a wide Frequency range without any effect on power quality. Finally, simulation results have been carried out to validate the proposed method.