Journal:Informatica
Volume 23, Issue 2 (2012), pp. 283–298
Abstract
The notion of general backlash is introduced where instead of the straight lines determining the upward and downward parts of backlash characteristic general curves are considered. An analytic form of general backlash characteristic description is proposed, which is based on appropriate switching and internal functions. Consequently, this multi-valued mapping is represented by one difference equation. All the parameters in the equation describing this hard nonlinearity are separated; hence the general backlash identification can be solved as a quasi-linear problem using an iterative parameter estimation method with internal variable estimation. Also the identification of cascaded systems consisting of a general input backlash followed by a linear dynamic system is presented. Simulation studies of general backlash identification and that of cascaded systems with general input backlash are included.
Journal:Informatica
Volume 21, Issue 4 (2010), pp. 575–596
Abstract
This paper presents GSM speech coder indirect identification algorithm based on sending novel identification pilot signals through the GSM speech channel. Each GSM subsystem disturbs identification pilot, while speech coder uniquely changes the tempo-spectral characteristics of the proposed pilot signal. Speech coder identification algorithm identifies speech coder with the usage of robust linear frequency cepstral coefficient (LFCC) feature extraction procedure and fast artificial neural networks. First step of speech coder identification algorithm is the exact position detection of the identification pilot signal using normalized cross correlation approach. Next stage is time-domain windowing of the input signal to convolve each frame of the input speech signal and window spectrum. Consecutive step is a short-time Fast Fourier Transformation to produce the magnitude spectrum of each windowed frame. Further, a noise reduction with spectral subtraction based on spectral smoothing is carried out. In last steps we perform the frequency filtering and Discrete Cosine Transformation to receive 24 uncorrelated cepstral coefficients per frame as a result. Speech coder identification is completed with fast artificial neural network classification using the input feature vector of 24 LFCC coefficients, giving a result of identified speech coder. For GSM speech coder indirect identification evaluation, the standardized GSM ETSI bit-exact implementations were used. Furthermore, a set of custom tools was build. These tools were used to simulate and control various conditions in the GSM system. Final results show that proposed algorithm identifies the GSM-EFR speech coder with the accuracy of 98.85%, the GSM-FR speech coder with 98.71%, and the GSM-HR coder with 98.61%. These scores were achieved at various types of surrounding noises and even at very low SNR conditions.
Journal:Informatica
Volume 15, Issue 1 (2004), pp. 45–62
Abstract
Petri net variants are widely used as a real time systems modeling technique. Recently, UML activity diagrams have been used for the same purpose, even though the syntax and semantics of activity diagrams has not been yet fully worked out. Nevertheless, activity diagrams seem very similar to Petri net semantics. UML, being the industry standard as a common object oriented modeling language needs a well‐defined semantic base for its notation. Formalization of the graphical notation enables automated processing and analysis tasks. Petri nets can provide a formal semantic framework for the UML notations plus the behavioral modeling/analysis strength needed to system designers. This paper describes the methodology for creating the model of the RT application that would allow testing the correctness of the algorithm and the fulfillment of the time constraints at the design stage using UML and Petri Nets.
Journal:Informatica
Volume 5, Issues 3-4 (1994), pp. 324–337
Abstract
The problem of the parameters estimation of quasihomogeneous autoregressive random field is considered. An algorithm is proposed for the parameter estimation of certain classes of such fields.
Journal:Informatica
Volume 3, Issue 1 (1992), pp. 98–118
Abstract
We consider a class of identification algorithms for distributed parameter systems. Utilizing stochastic optimization techniques, sequences of estimators are constructed by minimizing appropriate functionals. The main effort is to develop weak and strong invariance principles for the underlying algorithms. By means of weak convergence methods, a functional central limit theorem is established. Using the Skorohod imbedding, a strong invariance principle is obtained. These invariance principles provide very precise rates of convergence results for parameter estimates, yielding important information for experimental design.