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This paper develops a representation of multi‐model based controllers by using artificial intelligence typical structures. These structures will be neural networks, genetic algorithms and fuzzy logic. The interpretation of multimodel controllers in an artificial intelligence frame will allow the application of each specific technique to the design of improved multimodel based controllers. The obtained artificial intelligence based multimodel controllers are compared with classical single model based ones. It is shown through simulation examples that a transient response improvement can be achieved by using multiestimation based techniques. Furthermore, a method for synthesizing multimodel based neural network controllers from already designed single model based ones is presented. The proposed methodology allows to extend the existing single model based neural controllers to multimodel based ones, extending the applicability of this kind of techniques to a more general type of controllers. Also, some applications of genetic algorithms and fuzzy logic to multimodel controller design are proposed. Thus, the mutation operation from genetic algorithms inspires a robustness test which consists of a random modification of the estimates which is used to select the estimates leading to the better identification performance towards parameterizing online the adaptive controller. Such a test is useful for plants operating in a noisy environment. The proposed robustness test improves the selection of the plant model used to parameterize the adaptive controller in comparison to classical multimodel schemes where the controller parameterization choice is basically taken based on the identification accuracy of each model. Moreover, the fuzzy logic approach suggests new ideas to the design of multiestimation structures which can be applied to a broad variety of adaptive controllers such as robotic manipulator controller design.

In this paper a useful educational tool is presented for minimizing low order Boolean expressions. The algorithm follows the Karnaugh map looping approach and provides optimal results. For the implementation, C++ was used on the CodeWarrior for Palm Operating System environment. In order to make the overall implementation efficient, the object oriented approach was used. Two step‐by‐step examples are presented to illustrate the efficiency of the proposed algorithm. The proposed application can be used by students and professors in the fields of electrical and computer engineering and computer science.

The problem of post‐processing of a classified image is addressed from the point of view of the Dempster–Shafer theory of evidence. Each neighbour of a pixel being analyzed is considered as an item of evidence supporting particular hypotheses regarding the class label of that pixel. The strength of support is defined as a function of the degree of uncertainty in class label of the neighbour, and the distance between the neighbour and the pixel being considered. A post‐processing window defines the neighbours. Basic belief masses are obtained for each of the neighbours and aggregated according to the rule of orthogonal sum. The final label of the pixel is chosen according to the maximum of the belief function.

The article presents a limited‐vocabulary speaker independent continuous Estonian speech recognition system based on hidden Markov models. The system is trained using an annotated Estonian speech database of 60 speakers, approximately 4 hours in duration. Words are modelled using clustered triphones with multiple Gaussian mixture components. The system is evaluated using a number recognition task and a simple medium‐vocabulary recognition task. The system performance is explored by employing acoustic models of increasing complexity. The number recognizer achieves an accuracy of 97%. The medium‐vocabulary system recognizes 82.9% words correctly if operating in real time. The correctness increases to 90.6% if real‐time requirement is discarded.

This paper presents a new in‐place pseudo linear radix sorting algorithm. The proposed algorithm, called MSL (Map Shuffle Loop) is an improvement over ARL (Maus, 2002). The ARL algorithm uses an in‐place permutation loop of linear complexity in terms of input size. MSL uses a faster permutation loop searching for the next element to permute group by group, instead of element by element. The algorithm and its runtime behavior are discussed in detail. The performance of MSL is compared with quicksort and the fastest variant of radix sorting algorithms, which is the Least Significant Digit (LSD) radix sorting algorithm (Sedgewick, 2003).

The paper describes a new method to segment ischemic stroke region on computed tomography (CT) images by utilizing joint features from mean, standard deviation, histogram, and gray level co‐occurrence matrix methods. Presented unsupervised segmentation technique shows ability to segment ischemic stroke region.

We consider a problem of nonlinear stochastic optimization with linear constraints. The method of ɛ‐feasible solution by series of Monte‐Carlo estimators has been developed for solving this problem avoiding “jamming” or “zigzagging”. Our approach is distinguished by two peculiarities: the optimality of solution is tested in a statistical manner and the Monte‐Carlo sample size is adjusted so as to decrease the total amount of Monte‐Carlo trials and, at the same time, to guarantee the estimation of the objective function with an admissible accuracy. Under some general conditions we prove by the martingale approach that the proposed method converges a.s. to the stationary point of the problem solved. As a counterexample the maximization of the probability of portfolio desired return is given, too.

A new digital signature scheme in non‐commutative Gaussian monoid is presented. Two algebraic structures are employed: Gaussian monoid and a certain module being compatible with a monoid. For both monoid and module, presentation and action level attributes are defined. Monoid action level is defined as monoid element (word) action on module element as an operator. A module is a set of functions (elements) with special properties and could be treated as some generalization of vector space.

In this article we propose a novel Wavelet Packet Decomposition (WPD)‐based modification of the classical Principal Component Analysis (PCA)‐based face recognition method. The proposed modification allows to use PCA‐based face recognition with a large number of training images and perform training much faster than using the traditional PCA‐based method. The proposed method was tested with a database containing photographies of 423 persons and achieved 82–89% first one recognition rate. These results are close to that achieved by the classical PCA‐based method (83–90%).

This paper describes a preliminary experiment in designing a Hidden Markov Model (HMM)‐based part‐of‐speech tagger for the Lithuanian language. Part‐of‐speech tagging is the problem of assigning to each word of a text the proper tag in its context of appearance. It is accomplished in two basic steps: morphological analysis and disambiguation. In this paper, we focus on the problem of disambiguation, i.e., on the problem of choosing the correct tag for each word in the context of a set of possible tags. We constructed a stochastic disambiguation algorithm, based on supervised learning techniques, to learn hidden Markov model's parameters from hand‐annotated corpora. The Viterbi algorithm is used to assign the most probable tag to each word in the text.