Lyubka Doukovska

Adaptive CFAR PI Processor for Radar Target Detection in Pulse Jamming

A new parallel algorithm for signal processing and a parallel systolic architecture of a CFAR processor with adaptive post detection integration (API) are presented in this paper. The processor proposed is used for effective target detection in a single range resolution cell of a radar when echoes from small airborne targets are performed in conditions of pulse jamming. The main property of the algorithm proposed is its ability automatically to determine and censor the unwanted samples corrupted by pulse jamming in both the two-dimensional reference window and the test cell before noise level estimation. In such a way the influence of pulse jamming environment over adaptive thresholding is reduced to minimum. Statistical analysis of the algorithm for target detection shows that the signal-to-noise ratio losses are insignificant even if the power and the frequency of pulse jamming are extremely high. The systolic architecture of the CFAR API is designed. Basic measures of the systolic architecture are the number of processor elements, the computational time and the speed-up needed for real-time implementation.

InterCriteria Decision Making Approach to EU Member States Competitiveness Analysis: Temporal and Threshold Analysis

In this paper, we present some interesting findings from the application of our recently developed InterCriteria Decision Making (ICDM) approach to data extracted from the World Economic Forum’s Global Competitiveness Reports for the years 2008–2009 to 2013–2014 for the current 28 Member States of the European Union. The developed approach which employs the apparatuses of index matrices and intuitionistic fuzzy sets is designed to produce from an existing index matrix with multiobject multicriteria evaluations a new index matrix that contains intuitionistic fuzzy pairs with the correlations revealed to exist in between the set of evaluation criteria, which are not obligatory there ‘by design’ of the WEF’s methodology but exist due to the integral, organic nature of economic data. Here, we analyse the data from the six-year period within a reasonably chosen intervals for the thresholds of the intuitionistic fuzzy functions of membership and non-membership, and make a series of observations about the current trends in the factors of competitiveness of the European Union. The whole research and the conclusions derived are in line with WEF’s address to state policy makers to identify and strengthen the transformative forces that will drive future economic growth.

Performance of Hough Detectors in Presence of Randomly Arriving Impulse Interference

In the following paper are investigated several types of Hough detectors with CFAR processors in order to choose the most efficient one in the presence clatter and randomly arriving impulse interference environment. The Hough detector with fixed threshold is compared to other five types of Hough detectors. These are one and two-dimensional Hough CFAR detectors - a Hough CA CFAR (Cell Average Constant False Alarm Rate) detector, a Hough EXC (Excision) CFAR detector, a Hough CFAR BI (Binary Integration) detector, a Hough EXC CFAR BI (Excision Constant False Alarm Rate with Binary Integration) detector and a Hough API (Adaptive censoring Post detection Integration) CFAR detector. The achieved results reveal that the Hough detector with the API CFAR algorithm is the most effective. The research work is performed in MATLAB computational environment.

InterCriteria Decision Making Approach to EU Member States Competitiveness Analysis: Trend Analysis

In this paper, we continue our investigations of the newly developed InterCriteria Decision Making (ICDM) approach with considerations about the more appropriate choice of the employed intuitionistic fuzzy threshold values. In theoretical aspect, our aim is to identify the relations between the thresholds of inclusion of new elements to the set of strictly correlating criteria and the numbers of correlating pairs of criteria thus formed. We illustrate the findings with data extracted from the World Economic Forum’s Global Competitiveness Reports for the years 2008–2009 to 2013–2014 for the current 28 Member States of the European Union. The study of the findings from the considered six-year period involves trend analysis and computation of two approximating functions: a linear function and a polynomial function of 6th order. The per-year trend analysis of each of the 12 criteria, called ‘pillars of competitiveness’ in the WEF’s GCR methodology, gives an opportunity to prognosticate their values for the forthcoming year 2014–2015.

Data association algorithm in multiradar system

In this paper we apply a data association in track-before detect (TBD) with a polar Hough transform (PHT) in a radar network. The proposed algorithm is applied in multiple input multiple output (MIMO) radar system. We study the sensitivity of TBD multi-radar system as a function of the errors of target trajectory parameters measurement. The results are obtained in the presence of Randomly Arriving Impulse Interference (RAII) and the target coordinates (range and azimuth) are measurable with and without errors. The study of the signal processing used in detectors is performed through Monte-Carlo simulations in MATLAB computing environment.

Design and application of Artificial Neural Networks for predicting the values of indexes on the Bulgarian Stock market

The Artificial Neural Networks are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. They are an artificial intelligence method for modeling complex target functions. For certain types of problems, such as learning to interpret complex real-world sensor data, Artificial Neural Networks are among the most effective learning methods currently know. During the last decade they have been widely applied to the domain of financial time series prediction and their importance in this field is growing. In this paper our aim will be to analyze different neural networks for financial time series forecasting. Specifically their ability to predict future values of The Bulgarian Stock exchange - Sofia and the respective representative indexes. In order to yield better results Artificial Neural Networks need to have an optimal architecture and be trained in a suitable way. This will be the main challenge for the authors of this paper. Conclusions made by multiple authors that Artificial Neural Networks do have the capability to forecast the stock markets studied and, if properly trained, can improve the robustness according to the network structure are put to the test in this paper by constructing and applying three different models that will be tested in the environment of the Bulgarian capital market.

Hough detector analysis by means of Monte Carlo simulation approach

In the present paper, an algorithm for target detection that exploits the Hough transform is proposed. The target is detected by the Hough detector. The effectiveness of the algorithm proposed is formulated in terms of a quality parameter - the probability of detection. The quality parameter is estimated using the Monte Carlo simulation approach. We compare the results of detection achieved by both analytical and simulational approaches. The results are obtained for Swerling II target model in conditions of randomly arriving impulse interference.

Implicit GPC Based on Semi Fuzzy Neural Network Model

The model in Model Predictive Control (MPC) takes the central place. Therefore, it is very important to find a predictive model that effectively describes the behavior of the system and can easily be incorporated into MPC algorithm. In this paper it is presented implicit Generalized Predictive Controller (GPC) based on Semi Fuzzy Neural Network (SFNN) model. This kind of model works with reduced number of the fuzzy rules and respectively has low computational burden, which make it suitable for real-time applications like predictive controllers. Firstly, to demonstrate the potentials of the SFNN model test experiments with two benchmark chaotic systems - Mackey-Glass and Rossler chaotic time series are studied. After that, the SFNN model is incorporated in GPC and its efficiency is tested by simulation experiments in MATLAB environment to control a Continuous Stirred Tank Reactor (CSTR).

Working regimes classification for predictive maintenance of mill fan systems

In the present paper, the subject of analysis is a device from Maritsa East 2 thermal power plant - a mill fan. The choice of the given power plant is not occasional. This is the largest thermal power plant on the Balkan Peninsula. Mill fans are main part of the fuel preparation in the coal fired power plants. The possibility to predict eventual damages or wear out without switching off the device is significant for providing faultless and reliable work of the equipment avoiding incidents. Standard statistical and probabilistic (Bayesian) approaches for diagnostics are inapplicable to estimate mill fan vibration state due to non-stationarity, non-ergodicity and the significant noise level of the monitored vibrations. Promising results are obtained only using computational intelligence methods (fuzzy logic, neural and neuro-fuzzy networks). In the present paper, two neuro-fuzzy approaches are applied for classification of a mill fan system working regimes based on analysis of data available from its control system.

Image Processing for Technological Diagnostics of Metallurgical Facilities

Abstract The paper presents an overview of the image-processing techniques. The set of basic theoretical instruments includes methods of mathematical analysis, linear algebra, probability theory and mathematical statistics, theory of digital processing of one-dimensional and multidimensional signals, wavelet-transforms and theory of information. This paper describes a methodology that aims to detect and diagnose faults, using thermographs approaches for the digital image processing technique.