Signal Processing under Active Monitoring

This paper describes a method of signal preprocessing under active monitoring. Suppose we want to solve the inverse problem of getting the response of a medium to one powerful signal, which is equivalent to obtaining the transmission function of the medium, but do not have an opportunity to conduct such an experiment (it might be too expensive or harmful for the environment). Practically the problem can be reduced to obtaining the transmission function of the medium. In this case we can conduct a series of experiments of relatively low power and superpose the response signals. However, this method is conjugated with considerable loss of information (especially in the high frequency domain) due to fluctuations of the phase, the frequency and the starting time of each individual experiment. The preprocessing technique presented in this paper allows us to substantially restore the response of the medium and consequently to find a better estimate for the transmission function. This technique is based on expanding the initial signal into the system of orthogonal functions.

Investigating the Relations of Attacks, Methods and Objects in Regard to Information Security in Network TCP/IP Environment

Possibilities for investigations of 43 varieties of file formats (objects), joined in 10 groups; 89 information attacks, joined in 33 groups and 73 methods of compression, joined in 10 groups are described in the paper. Experimental, expert, possible and real relations between attacks’ groups, method’ groups and objects’ groups are determined by means of matrix transformations and the respective maximum and potential sets are defined. At the end assessments and conclusions for future investigation are proposed.

The Latest Prague Contributions to Written Cultural Heritage Processing

* The following text has been originally published in the Proceedings of the Language Recourses and Evaluation Conference held in Lisbon, Portugal, 2004, under the title of "Towards Intelligent Written Cultural Heritage Processing - Lexical processing". I present here a revised contribution of the aforementioned paper and I add here the latest efforts done in the Center for Computational Linguistic in Prague in the field under discussion.

Bottleneck Problem Solution using Biological Models of Attention in High Resolution Tracking Sensors

Every high resolution imaging system suffers from the bottleneck problem. This problem relates to the huge amount of data transmission from the sensor array to a digital signal processing (DSP) and to bottleneck in performance, caused by the requirement to process a large amount of information in parallel. The same problem exists in biological vision systems, where the information, sensed by many millions of receptors should be transmitted and processed in real time. Models, describing the bottleneck problem solutions in biological systems fall in the field of visual attention. This paper presents the bottleneck problem existing in imagers used for real time salient target tracking and proposes a simple solution by employing models of attention, found in biological systems. The bottleneck problem in imaging systems is presented, the existing models of visual attention are discussed and the architecture of the proposed imager is shown.

The Involvement of Institute for Information Technologies in Text Processing

The activities of the Institute of Information Technologies in the area of automatic text processing are outlined. Major problems related to different steps of processing are pointed out together with the shortcomings of the existing solutions.

Research of the Influence of Methods of Compression on the Co-efficient of Information Security of Objects Under Attacks

In this paper a possibility for quantitative measuring of information security of objects, exposed to information attacks and processed with methods of compression, is represented. A co-efficient of information security, which reflects the influence of the level of compression obtained after applying methods of compression to objects and the time, required by the attack to get access to the corresponding object, is proposed. Methods’ groups with the highest and respectively the lowest values of the co-efficient of information security for all methods’ groups in relation to all attacks’ groups are determined. Assessments and conclusions for future investigations are proposed.

Development of Database for Distributed Information Measurement and Control System

The purpose of this work is the development of database of the distributed information measurement and control system that implements methods of optical spectroscopy for plasma physics research and atomic collisions and provides remote access to information and hardware resources within the Intranet/Internet networks. The database is based on database management system Oracle9i. Client software was realized in Java language. The software was developed using Model View Controller architecture, which separates application data from graphical presentation components and input processing logic. The following graphical presentations were implemented: measurement of radiation spectra of beam and plasma objects, excitation function for non-elastic collisions of heavy particles and analysis of data acquired in preceding experiments. The graphical clients have the following functionality of the interaction with the database: browsing information on experiments of a certain type, searching for data with various criteria, and inserting the information about preceding experiments.

A Cognitive Science Reasoning in Recognition of Emotions in Audio-Visual Speech

In this report we summarize the state-of-the-art of speech emotion recognition from the signal processing point of view. On the bases of multi-corporal experiments with machine-learning classifiers, the observation is made that existing approaches for supervised machine learning lead to database dependent classifiers which can not be applied for multi-language speech emotion recognition without additional training because they discriminate the emotion classes following the used training language. As there are experimental results showing that Humans can perform language independent categorisation, we made a parallel between machine recognition and the cognitive process and tried to discover the sources of these divergent results. The analysis suggests that the main difference is that the speech perception allows extraction of language independent features although language dependent features are incorporated in all levels of the speech signal and play as a strong discriminative function in human perception. Based on several results in related domains, we have suggested that in addition, the cognitive process of emotion-recognition is based on categorisation, assisted by some hierarchical structure of the emotional categories, existing in the cognitive space of all humans. We propose a strategy for developing language independent machine emotion recognition, related to the identification of language independent speech features and the use of additional information from visual (expression) features.

How to Use a Desktop Version of a DBMS for Client-Server Applications

DBMS (Data base management systems) still have a very high price for small and middle enterprises in Bulgaria. Desktop versions are free but they cannot function in multi-user environment. We will try to make an application server which will make a Desktop version of a DBMS open to many users. Thus, this approach will be appropriate for client-server applications. The author of the article gives a concise observation of the problem and a possible way of solution.

Greedy Approximation with Regard to Bases and General Minimal Systems

*This research was supported by the National Science Foundation Grant DMS 0200187 and by ONR Grant N00014-96-1-1003

Manometry-Based Cough Identification Algorithm

Gastroesophageal reflux disease (GERD) is a common cause of chronic cough. For the diagnosis and treatment of GERD, it is desirable to quantify the temporal correlation between cough and reflux events. Cough episodes can be identified on esophageal manometric recordings as short-duration, rapid pressure rises. The present study aims at facilitating the detection of coughs by proposing an algorithm for the classification of cough events using manometric recordings. The algorithm detects cough episodes based on digital filtering, slope and amplitude analysis, and duration of the event. The algorithm has been tested on in vivo data acquired using a single-channel intra-esophageal manometric probe that comprises a miniature white-light interferometric fiber optic pressure sensor. Experimental results demonstrate the feasibility of using the proposed algorithm for identifying cough episodes based on real-time recordings using a single channel pressure catheter. The presented work can be integrated with commercial reflux pH/impedance probes to facilitate simultaneous 24-hour ambulatory monitoring of cough and reflux events, with the ultimate goal of quantifying the temporal correlation between the two types of events.

Realization of an Optimal Schedule for the Two-machine Flow-Shop with Interval Job Processing Times

Non-preemptive two-machine flow-shop scheduling problem with uncertain processing times of n jobs is studied. In an uncertain version of a scheduling problem, there may not exist a unique schedule that remains optimal for all possible realizations of the job processing times. We find necessary and sufficient conditions (Theorem 1) when there exists a dominant permutation that is optimal for all possible realizations of the job processing times. Our computational studies show the percentage of the problems solvable under these conditions for the cases of randomly generated instances with n ≤ 100 . We also show how to use additional information about the processing times of the completed jobs during optimal realization of a schedule (Theorems 2 – 4). Computational studies for randomly generated instances with n ≤ 50 show the percentage of the two- machine flow-shop scheduling problems solvable under the sufficient conditions given in Theorems 2 – 4.

Neural Control of Chaos and Aplications

Signal processing is an important topic in technological research today. In the areas of nonlinear dynamics search, the endeavor to control or order chaos is an issue that has received increasing attention over the last few years. Increasing interest in neural networks composed of simple processing elements (neurons) has led to widespread use of such networks to control dynamic systems learning. This paper presents backpropagation-based neural network architecture that can be used as a controller to stabilize unsteady periodic orbits. It also presents a neural network-based method for transferring the dynamics among attractors, leading to more efficient system control. The procedure can be applied to every point of the basin, no matter how far away from the attractor they are. Finally, this paper shows how two mixed chaotic signals can be controlled using a backpropagation neural network as a filter to separate and control both signals at the same time. The neural network provides more effective control, overcoming the problems that arise with control feedback methods. Control is more effective because it can be applied to the system at any point, even if it is moving away from the target state, which prevents waiting times. Also control can be applied even if there is little information about the system and remains stable longer even in the presence of random dynamic noise.