Detection of Structural Features in Biological Signals

Abstract

In this article structures in biological signals are treated. The simpler-directly visible in the signals, which still demand serious methods and algorithms in the feature detection, similarity investigation and classification. The major actions in this domain are of geometric, thus simpler sort, though there are still hard problems related to simple situations. The other large class of less simple signals unsuitable for direct geometric or statistic approach, are signals with interesting frequency components and behavior, those suitable for spectroscopic analysis. Semantics of spectroscopy, spectroscopic structures and research demanded operations and transformations on spectra and time spectra are presented. The both classes of structures and related analysis methods and tools share a large common set of algorithms, all of which aiming to the full automatization. Some of the signal features present in the brain signal patterns are demonstrated, with the contexts relevant in BCI, brain computer interfaces. Mathematical representations, invariants and complete characterization of structures in broad variety of biological signals are in the central focus.

Measures and Metrics of Biological Signals

“…Following with multitude of single chromosome shapes we are forced to operate with this representations collected into large sets which is a serious complication. In this preliminary part of chromosomal analysis we recommended a rather simple controlled normalization procedure, as follows (Jovanović et al, 2010).…”

“…In particular, with a spectrogram with 50 equidistant spectra we can compensate possibly or certainly erroneous insight and understanding of circumstances induced by analysis of single spectra. Applying some interventions on Fourier spectrograms, e.g., (Jovanović et al, 2010; Spasić et al, 2010), it is possible to achieve very sharp time resolution of the present frequency components and their realistic magnitudes.…”

“…A good time spectrogram can contain features significant for the studied process. Such content often require higher order spectroscopy, with or without the preprocessing involving normalization and application of various measurement, (see Jovanović et al, 2010; Perović et al, 2013).…”

Intelligent real time GIS based classificatory method for maritime surveillance systems

“…This method is commonly used in the processing of bitmap images. In addition to these methods we use similar as shown in references [8], [9], [10], [11], [12], [13], [14]. As required format (patterntemplate) in respect of which the correlation is done we will use the matrix in the form of t (u, v) which roughly resembles a bitmapped point.…”

Нумерички Еталони Кариотипова Erinaceus Europaeus Roumanicus

“…Мјерења хромозома у оквиру анализе кариотипа, односно проучавања величине хромозома, почетно се ослањају на директно мјерење на микроскопским препаратима, или на мјерења на фотографијама, затим се развијају полуатоматско, или аутоматско мјерење (Sybenga, 1959 (Jovanović, et al 2004(Jovanović, et al , 2010) методама (микроскопске) фотометрије и анализе слика уз кориштење различитих софистицираних метода препарирања хромозома. Проучавање величине хромозома, помјерањем од оптичког микроскопског нивоа, на ниво положаја гена и ДНК секвенци, долази се у виртуелни ниво анализе ("digital karyotyping") који се даје у мапама гена хромозома, односно мапама јединица генома (Gilbert, 1966, Wang et al, 1966.…”