Informatica

In this paper the gaze tracking system based on the adaptively changing threshold value of the gray level, which automatically detects the pupil position in two dimensional data is proposed. The system detects closed eye using normalized accumulative luminosity function. The detection of closed eye allows the confirmation of voluntary selected command in a more natural way. The presented technique allows recalibration without intervention or help of other assistive persons. Natural head motion is evaluated, employing signals, generated by orientation sensor. Within two experiments, the proposed system is tested at different behavior modes in respect of speed and accuracy of the system.

In this paper, the main measure, an amount of information, of the information theory is analyzed and corrected. The three conceptions of the theory on the microstate, dissipation pathways, and self‐organization levels with a tight connection to the statistical physics are discussed. The concepts of restricted information were introduced as well as the proof of uniqueness of the entropy function, when the probabilities are rational numbers, is presented.

The development of Lithuanian HMM/ANN speech recognition system, which combines artificial neural networks (ANNs) and hidden Markov models (HMMs), is described in this paper. A hybrid HMM/ANN architecture was applied in the system. In this architecture, a fully connected three‐layer neural network (a multi‐layer perceptron) is trained by conventional stochastic back‐propagation algorithm to estimate the probability of 115 context‐independent phonetic categories and during recognition it is used as a state output probability estimator. The hybrid HMM/ANN speech recognition system based on Mel Frequency Cepstral Coefficients (MFCC) was developed using CSLU Toolkit. The system was tested on the VDU isolated‐word Lithuanian speech corpus and evaluated on a speaker‐independent ∼750 distinct isolated‐word recognition task. The word recognition accuracy obtained was about 86.7%.

In this paper, a new digital watermarking method based on vector quantization (VQ) is proposed. In contrast with conventional VQ‐based watermarking schemes, the mean of sub‐blocks is used to train the VQ codebook. In addition, the Anti‐Gray Coding (AGC) technique is employed to enhance the robustness of the proposed watermarking scheme. In this scheme, the secret keys are used to hide the associated information between the original image and the watermark. Then the set of secret keys will be registered to a trusted third party for future verification. Thus, the original image remains unchanged after the watermark is melted into the set of secret keys. Experimental results show that the watermark can survive various possible attacks. Besides that, the size of the secret keys can be reduced.

The use of vector quantization for speaker identification is investigated. This method differs from the known methods in that the number of centroids is not doubled but increases by 1 at every step. This enables us to obtain identification results at any number of centroids. This method is compared experimentally with the method (Lipeika and Lipeikienė, 1993a, 1993b), where feature vectors of investigative and comparative speakers are compared directly.