Project proposal


This project focuses on development and implementation of novel measurement techniques and analysis of different types of data mainly originating from real-life sources. It is also the continuation of our scientific research in the field of stochastic signals, time series and data structure analysis.

The project originates from broader area of complex systems’ behavior investigation through their time series or response output. Classical methods for signal processing along with advanced methods for analysis of complex signals and time series such as chaotic and self-affine features detection and extraction methods as well as recent development in digital signal processing will be used.

The research is based on theoretical and experimental investigation, covering all phases of the measurement and analysis processes (measurement hardware development, data acquisition, data processing and storage using dedicated expert programs and algorithms). Scope of investigation includes measurement and data acquisition techniques that will result in gathering of data series appropriate for statistical evaluation (long memory processes statistics), nonlinear features detection (self-affine structures, chaotic behavior etc..) and system functional features interpretation. At the same time development of new and modification of known analytical methods will be considered through testing and verification using acquired time series data.

Project contributes in foundation of knowledge basis for scientific advancement in various progressive areas such as real-system modeling, complex electronic circuits’ behavior research, new communication technologies, bioinformatics and particular areas of medical information systems.

Several areas will be thoroughly investigated, such as timing measurements of synchronization circuits, analysis of single photon detectors, temporal characterization of UWB signals and systems, analysis of bio-systems such as human locomotion and jaw movement through biomechanical data acquisition and interpretation. Investigated data may also comprise biological sequences, natural language text and some other specific types of data such as vector graphics. Characterization of such static alphanumeric databases, with respect to data compression and retrieval using novel approach to indexing will also be considered.