When observations are autocorrelated, standard formulae for the estimators of variance, s2, and variance of the mean, s2 (x), are no longer adequate. They should be replaced by suitably defined estimators, s2a and s2a (x), which are unbiased given that the autocorrelation function is known. The formula for s2a was given by Bayley and Hammersley in 1946, this work provides its simple derivation. The quantity named effective number of observations neff is thoroughly discussed. It replaces the real number of observations n when describing the relationship between the variance and variance of the mean, and can be used to express s2a and s2a (x) in a simple manner. The dispersion of both estimators depends on another effective number called the effective degrees of freedom Veff. Most of the formulae discussed in this paper are scattered throughout the literature and not very well known, this work aims to promote their more widespread use. The presented algorithms represent a natural extension of the GUM formulation of type-A uncertainty for the case of autocorrelated observations.

This paper deals with the design of an interval state estimator for linear time-varying (LTV) discrete-time systems subject to component faults and uncertainties. These component faults and uncertainties are assumed to be unknown but bounded without giving any other information, whose effect can be approximated using these bounds. In the first part of this work, an interval state estimator for such systems is designed to deal with these component faults and uncertainties. The result is then extended to find an interval state estimator for a non-cooperative LTVdiscrete-time system subject to component faults and uncertainties by similarity transformation of coordinates. The proposed interval state estimator guaranteed bounds on the observed states that are consistent with the system states. The observer convergence is also ensured. The designed method is simple and easy to be implemented. Two numerical examples are given to show the effectiveness of the proposed method.

A non-classical model of interval estimation based on the kernel density estimator is presented in this paper. This model has been compared with interval estimation algorithms of the classical (parametric) statistics assuming that the standard deviation of the population is either known or unknown. The non-classical model does not have to assume belonging of random sample to a normal distribution. A theoretical basis of the proposed model is presented as well as an example of calculation process which makes possible determining confidence intervals of the expected value of long-term noise indicators Aden and LN. The statistical analysis was carried out for 95% interval widths obtained by using each of these models. The inference of their usefulness was performed on the basis of results of non-parametric statistical tests at significance level α = 0.05. The data used to illustrate the proposed solutions and carry out the analysis were results of continuous monitoring of traffic noise recorded in 2004 in one of the main arteries of Krakow in Poland.

This paper presents a novel speed estimator using Reactive Power based Model Reference Neural Learning Adaptive System (RP-MRNLAS) for sensorless indirect vector controlled induction motor drives. The Model Reference Adaptive System (MRAS) based speed estimator using simplified reactive power equations is one of the speed estimation method used for sensor-less indirect vector controlled induction motor drives. The conventional MRAS speed estimator uses PI controller for adaptation mechanism. The nonlinear mapping capability of Neural Network (NN) and the powerful learning algorithms have increased the applications of NN in power electronics and drives. This paper proposes the use of neural learning algorithm for adaptation in a reactive power technique based MRAS for speed estimation. The proposed scheme combines the advantages of simplified reactive power technique and the capability of neural learning algorithm to form a scheme named “Reactive Power based Model Reference Neural Learning Adaptive System” (RP-MRNLAS) for speed estimator in Sensorless Indirect Vector Controlled Induction Motor Drives. The proposed RP-MRNLAS is compared in terms of accuracy, integrator drift problems and stator resistance versions with the commonly used Rotor Flux based MRNLAS (RF-MRNLAS) for the same system and validated through Matlab/Simulink. The superiority of the RP-MRNLAS technique is demonstrated.

The Histogram Test method is a popular technique in analog-to-digital converter (ADC) testing. The presence of additive noise in the test setup or in the ADC itself can potentially affect the accuracy of the test results. In this study, we demonstrate that additive noise causes a bias in the terminal based estimation of the gain but not in the estimation of the offset. The estimation error is determined analytically as a function of the sinusoidal stimulus signal amplitude and the noise standard deviation. We derive an exact but computationally difficult expression as well as a simpler closed form approximation that provides an upper bound of the bias of the terminal based gain. The estimators are validated numerically using a Monte Carlo procedure with simulated and experimental data.

In this article is revealed the systems of a good delivery witch implement unmanned aerial vehicles during providing the service. the one channel systems of a goods delivery are a goal of this research work. the close analysing of their functional features, the classification, the types and parameters of different systems from this band are presented. in addition, the modelling of the different types of the one channel systems of goods delivery are has done.

The paper presents the results of biomass estimates of commercial fishes in the South Georgia region made by "swept area" method on the basis of catch statistics of a B-22 Polish trawler in the 1980/1981 season. Total biomass was estimated on about 11 x 104 t.

The paper presents the analysis of different fault states in drive systems with multiphase induction motors. The mathematical models of a five-phase and six-phase induction motor and the MRASCC estimator have been presented and the description of the Space Vector Modulation has been shown. The Direct Field-Oriented Control (DFOC) system is analyzed. Results of the simulation and experimental studies of the Direct Field-Oriented Control systems in the fault conditions are presented. The author’s original contribution includes analysis and studies of the DFOC control method of a five-phase induction motor resistant to the motor speed sensor fault with the use of the MRASCC estimator.

Multiple Input Multiple Output (MIMO (techniques use multiple antennas at both transmitter and receiver for increasing the channel reliability and enhancing the spectral efficiency of wireless communication system.MIMO Spatial Multiplexing (SM) is a technology that can increase the channel capacity without additional spectral resources. The implementation of MIMO detection techniques become a difficult mission as the computational complexity increases with the number of transmitting antenna and constellation size. So designing detection techniques that can recover transmitted signals from Spatial Multiplexing (SM) MIMO with reduced complexity and high performance is challenging. In this survey, the general model of MIMO communication system is presented in addition to multiple MIMO Spatial Multiplexing (SM) detection techniques. These detection techniques are divided into different categories, such as linear detection, Non-linear detection and tree-search detection. Detailed discussions on the advantages and disadvantages of each detection algorithm are introduced. Hardware implementation of Sphere Decoder (SD) algorithm using VHDL/FPGA is also presented.

Rachunek fraktalny jest jedną z szybko rozwijających się dziedzin matematyki i znajduje zastosowanie między innymi w opisie struktur porowych. Stanowi nowe spojrzenie na ich nieregularność i chaotyczność. Aby mógł być stosowanym poprawnie, powinien być wspomagany analizą błędu. W artykule przedstawiono i zweryfikowano niedoskonałości związane z analizą obrazu oraz możliwe sposoby ich korekcji. Jednym z kluczowych aspektów podczas takich badań jest miejsce oraz ilość wykonanych zdjęć. Sfotografowano powiększony obraz gruboziarnistego piaskowca w płytce cienkiej, uzyskany przy użyciu lupy binokularnej. Następnie wykonane zdjęcia zostały połączone w jedno. Otrzymane rozkłady parametrów fraktalnych pokazują ich zmienność oraz potwierdzają, że poprawnie wykonana seria zdjęć struktury porowej powinna zawierać zarówno obszary bardziej, jak i mniej porowate, a ich ilość należy dostosować do próbki. Zbadano wpływ rozdzielczości zdjęcia na wartości wymiaru fraktalnego oraz lakunarności. Wykorzystano zdjęcia wapienia wykonane w SEM z użyciem elektronów wstecznie rozproszonych w zakresie powiększeń 120–2000×. Dodatkowo badaniu poddany został pojedynczy por. Otrzymane wyniki wskazują, że dla dużego zakresu powiększeń wartości wymiaru fraktalnego są zbliżone, natomiast lakunarność każdorazowo się zmienia. Jest to związane ze zmieniającą się jednorodnością zdjęcia. Analizie poddana została również metodyka wyznaczania rozkładu przestrzennego parametrów fraktalnych w oparciu o binaryzację. Stosowane metody zakładają, że binaryzacja następuje przed lub po podziale zdjęcia na mniejsze prostokąty, z których wyznaczane są wartości wymiaru fraktalnego oraz lakunarności. Indywidualna binaryzacja, pomimo czasochłonności, zapewnia lepsze wyniki, które są bardziej zbliżone do rzeczywistości. Nie jest możliwe zdefiniowanie jednej, słusznej metodyki do eliminowania błędów. Przedstawiono zbiór wskazówek, które mogą posłużyć do udoskonalenia wyników w przyszłej analizie obrazu struktur porowych.