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1
Content available remote O estymacji wartości średniej napięcia sinusoidalnego
PL
W artykule oceniono dokładność wyników estymacji wartości średniej napięcia sinusoidalnego. W tym celu zastosowano estymator wartości średniej obliczany na podstawie próbek napięcia. Wyznaczono obciążenie, wariancję i błąd średniokwadratowy estymatora.
EN
The article evaluates the accuracy of the estimating results of the mean value of a sinusoidal voltage. For this purpose, a mean value estimator calculated from voltage samples has been used. The bias, the variance and the mean squared error of an estimator have been determined.
EN
The article presents new tools for investigating the statistical properties of the harmonic signal autocorrelation function (ACF). These tools enable identification of the ACF estimator errors in measurements in which the triggering of the measurements is non-synchronized. This is important because in many measurement situations the initial phase of the measured signal is random. The developed tools enable testing the ACF estimator of a harmonic signal in the presence of Gaussian noise. These are the formulas on the basis of which the statistical properties of the estimator can be determined, including the bias, the variance and the mean squared error (MSE). For comparison, the article also presents the ACF statistical analysis tools used in the conditions of synchronized measurement triggering, known from the literature. Operation of the new tools is verified by simulation and experimental studies. The conducted research shows that differences between the MSE results obtained with the use of the developed formulas and those attained from simulations and experimental tests are not greater than 1 dB.
EN
This paper presents a new simple and accurate frequency estimator of a sinusoidal signal based on the signal autocorrelation function (ACF). Such an estimator was termed as the reformed covariance for half-length autocorrelation (RC-HLA). The designed estimator was compared with frequency estimators well-known from the literature, such as the modified covariance for half-length autocorrelation (MC-HLA), reformed Pisarenko harmonic decomposition for half-length autocorrelation (RPHD-HLA), modified Pisarenko harmonic decomposition for half-length autocorrelation (MPHD-HLA), zero-crossing (ZC), and iterative interpolated DFT (IpDFT-IR) estimators. We determined the samples of the ACF of a sinusoidal signal disturbed by Gaussian noise (simulations studies) and the samples of the ACF of a sinusoidal voltage (experimental studies), calculated estimators based on the obtained samples, and computed the mean squared error (MSE) to compare the estimators. The errors were juxtaposed with the Cramér-Rao lower bound (CRLB). The research results have shown that the proposed estimator is one of the most accurate, especially for SNR>25dB. Then the RC-HLA estimator errors are comparable to the MPHD-HLA estimator errors. However, the biggest advantage of the developed estimator is the ability to quickly and accurately determine the frequency based on samples collected from no more than five signal periods. In this case, the RC-HLA estimator is the most accurate of the estimators tested.
EN
This paper proposed an enhanced asymmetric cryptosystem scheme for optical image encryption in the fractional Hartley transform domain. Grayscale and binary images have been encrypted separately using double random phase encoding. Phase masks based on optical vortex and random phase masks have been jointly used in spatial as well as in the Fourier planes. The images to be encrypted are first multiplied by optical vortex and random phase mask and then transformed with direct and inverse fractional Hartley transform for obtaining the encrypted images. The images are recovered from their corresponding encrypted images by using the correct parameters of the fractional Hartley transform and optical vortex, whose digital implementation has been performed using MATLAB 7.6.0 (R2008a). The random phase masks, optical vortex and transform orders associated with the fractional Hartley transform are extra keys that cause difficulty to an unauthorized user. Thus, the proposed asymmetric scheme is more secure as compared to conventional techniques. The efficacy of the proposed asymmetric scheme is verified by computing the mean squared error between recovered and the original images. The sensitivity of the asymmetric scheme is also verified with encryption parameters, noise and occlusion attacks. Numerical simulation results demonstrate the effectiveness and security performance of the proposed system.
EN
The ever-growing need for high data rate, bandwidth efficiency, reliability, less complexity and less power consumption in our communication systems is on the increase. Modern techniques have to be developed and put in place to meet these requirements. Research has shown, that compared to conventional Single Input Single Output (SISO) systems, Multiple-Input Single Output (MISO), and Multiple-Input Multiple-Output (MIMO) can actually increase the data rate of a communication system, without actually requiring more transmit power or bandwidth. This paper aims at the investigation of the existing channel estimation techniques. Based on the pilot arrangement, the block type and comb type are compared, employing the Least Square estimation (L.S) and Minimum Mean Squared Error (MMSE) estimators. Pilots occupy bandwidth, minimizing the number of pilots used to estimate the channel, in order to allow for more bandwidth utilization for data transmission, without compromising the accuracy of the estimates is taken into consideration. Various channel interpolation techniques and pilot-data insertion ratio are investigated, simulated and compared, to determine the best performance technique with less complexity and minimum power consumption. As performance measures, the Mean Squared Error (MSE) and Bit Error Rate (BER) as a function of Signal to Noise power Ratio (SNR) of the different channel estimation techniques are plotted, in order to identify the technique with the most optimal performance. The complexity and energy efficiency of the techniques are also investigated. The system modelling and simulations are carried out using Matlab simulation package. The MIMO gives the optimum performance, followed by the MISO and SISO. This is as a result of the diversity and multiplexing gain experienced in the multiple antenna techniques using the STBC.
6
Content available remote Wybrane zagadnienia aproksymacji charakterystyk statycznych termoanemometrów CTA
PL
W artykule przeanalizowano możliwości aproksymacji odwrotnych charakterystyk termoanemometrów stałotemperaturowych (CTA) za pomocą różnych funkcji. Porównano wyniki aproksymacji z wykorzystaniem funkcji wielomianowych (3-go i 4-go stopnia), wykładniczych (eksponencjalnej, Stirliga oraz Gompertza) a także funkcji potęgowej. Wykorzystując wskaźniki jakości aproksymacji (takie jak współczynnik determinacji czy błąd średniokwadratowy) dokonano ilościowej oceny poszczególnych metod na bazie trzech różnych zestawów danych pochodzących z rzeczywistych wzorcowań termoanemometrów.
EN
Possibilities of approximation of inverse static characteristics of constant temperature hot-wire anemometers, by means of different functions were analyzed in the paper. The results of approximation with use of polynomials (3rd and 4th degree), exponential functions (exponential growth, Stirling and Gompertz function) as well as power function were presented and compared. Applying some coeffi cients describing the quality of approximation (like coefficient of determination or mean squared error), quantitative evaluation of particular methods was done, on the basis of three different data sets, originating from real hot wire calibrations.
7
EN
The representation and processing of uncertainty information is one of the key basic issues of the intelligent information processing in the face of growing vast information, especially in the era of network. There have been many theories, such as probability statistics, evidence theory, fuzzy set, rough set, cloud model, etc., to deal with uncertainty information from different perspectives, and they have been applied into obtaining the rules and knowledge from amount of data, for example, data mining, knowledge discovery, machine learning, expert system, etc. Simply, This is a cognitive transformation process from data to knowledge (FDtoK). However, the cognitive transformation process from knowledge to data (FKtoD) is what often happens in human brain, but it is lack of research. As an effective cognition model, cloud model provides a cognitive transformation way to realize both processes of FDtoK and FKtoD via forward cloud transformation (FCT) and backward cloud transformation (BCT). In this paper, the authors introduce the FCT and BCT firstly, and make a depth analysis for the two existing single-step BCT algorithms. We find that these two BCT algorithms lack stability and sometimes are invalid. For this reason we propose a new multi-step backward cloud transformation algorithm based on sampling with replacement (MBCT-SR) which is more precise than the existing methods. Furthermore, the effectiveness and convergence of new method is analyzed in detail, and how to set the parameters m, r appeared in MBCT-SR is also analyzed. Finally, we have error analysis and comparison to demonstrate the efficiency of the proposed backward cloud transformation algorithm for some simulation experiments.
8
Content available remote Ocena dokładności cyfrowej estymacji podstawowych parametrów sygnałów
PL
Artykuł dotyczy problematyki wyznaczania błędów estymatorów i oceny niepewności estymacji podstawowych parametrów sygnałów otrzymanych na podstawie danych spróbkowanych. Do podstawowych parametrów sygnałów zaliczamy wartość średnią, średniokwadratową skuteczną, międzyszczytową i funkcję gęstości prawdopodobieństwa.
EN
The paper focuses on errors of estimators and the measurement uncertainty of basic signals parameters set with sampled data. As basic signals parameters we regard mean, mean square, root mean square, peak-to-peak amplitude and probability density function.
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