|
|
Deconvolution of hemodynamic response from fMRI data
Bartoň, Marek ; Kolář, Radim (referee) ; Havlíček, Martin (advisor)
This paper deals with the variability of HRF, which may have crucial impact on outcomes of fMRI neuronal activation detection in some cases. There are three methods described - averaging, regression deconvolution and biconjugate gradient method - which provide HRF shape estimation. In frame of simulations regression method, which uses B-spline curves of 4-th order for window length of 30 s, was chosen as the most robust method. Deconvolution estimates was used as HRF models for classic analyse of fMRI data, concretely visual oddball paradigm, via general linear model. Enlargement of localizated areas was observed and after expert consultation with scientific employees from neurology clinic, outcomes was evaluated as relevant. Furthermore Matlab application, which provides confortable observation of HRF variability among brain areas, was made.
|
|
|
Measuring of Vehicle Instant Velocity
Štencel, Jakub ; Petyovský, Petr (referee) ; Horák, Karel (advisor)
The theoretical part of this paper summarizes the methods for motion analysis in an image. Inspected in detail are the methods used for motion detection in the image sequences. The practical part is focused on finding the significant points in the image (the targeting reticles on the body of the vehicle) which are then used to calculate the speed of the vehicle.
|
|
|
Canny's Operator and Other Useful Edge Detectors
Janda, Miloš ; Juránek, Roman (referee) ; Venera, Jiří (advisor)
This work introduces main approaches for digital image processing and defines fundamental terms for successful understanding. Main aim is description of several suitable methods used in digital image pre-processing, methods for edge detection and consequent post-processing of these. The final goal of this work is effective implementation and complex comparison of methods for edge detection.
|
|
|
TESTING THE METHOD OF MULTIPLE SCALES AND THE AVERAGING PRINCIPLE FOR MODEL PARAMETER ESTIMATION OF QUASIPERIODIC TWO TIME-SCALE MODELS
Papáček, Štěpán ; Matonoha, Ctirad
Some dynamical systems are characterized by more than one timescale, e.g. two well separated time-scales are typical for quasiperiodic systems. The aim of this paper is to show how singular perturbation methods based on the slow-fast decomposition can serve for an enhanced parameter estimation when the slowly changing features are rigorously treated. Although the ultimate goal is to reduce the standard error for the estimated parameters, here we test two methods for numerical approximations of the solution of associated forward problem: (i) the multiple time-scales method, and (ii) the method of averaging. On a case study, being an under-damped harmonic oscillator containing two state variables and two parameters, the method of averaging gives well (theoretically predicted) results, while the use of multiple time-scales method is not suitable for our purposes.
|
|
|
Verification And Adjustment Of Hf-Ecg
Preprocessing In Experimental Cardiology
Novotna, Petra
The aim of this article is to propose an approach to High-Frequency ECG (HF-ECG) preprocessing with an intention to verify the settled methods of signal preprocessing in the perspective of the new requirements and possibilities in the area of signal processing. The method using Butterworth filters is often used. Nevertheless, for the presented type of analysis is not suitable. FIR filtering alongside with clustering and signal averaging were used for preprocessing of data from isolated rabbit hearts. Frequency bands for further analysis were chosen according to the estimated SNR (signal-to-noise ratio).
|
|
|
Canny's Operator and Other Useful Edge Detectors
Janda, Miloš ; Juránek, Roman (referee) ; Venera, Jiří (advisor)
This work introduces main approaches for digital image processing and defines fundamental terms for successful understanding. Main aim is description of several suitable methods used in digital image pre-processing, methods for edge detection and consequent post-processing of these. The final goal of this work is effective implementation and complex comparison of methods for edge detection.
|
|
|
Deconvolution of hemodynamic response from fMRI data
Bartoň, Marek ; Kolář, Radim (referee) ; Havlíček, Martin (advisor)
This paper deals with the variability of HRF, which may have crucial impact on outcomes of fMRI neuronal activation detection in some cases. There are three methods described - averaging, regression deconvolution and biconjugate gradient method - which provide HRF shape estimation. In frame of simulations regression method, which uses B-spline curves of 4-th order for window length of 30 s, was chosen as the most robust method. Deconvolution estimates was used as HRF models for classic analyse of fMRI data, concretely visual oddball paradigm, via general linear model. Enlargement of localizated areas was observed and after expert consultation with scientific employees from neurology clinic, outcomes was evaluated as relevant. Furthermore Matlab application, which provides confortable observation of HRF variability among brain areas, was made.
|
|
|
Measuring of Vehicle Instant Velocity
Štencel, Jakub ; Petyovský, Petr (referee) ; Horák, Karel (advisor)
The theoretical part of this paper summarizes the methods for motion analysis in an image. Inspected in detail are the methods used for motion detection in the image sequences. The practical part is focused on finding the significant points in the image (the targeting reticles on the body of the vehicle) which are then used to calculate the speed of the vehicle.
|
|
|
Numerical comparison of different choices of interface weights in the BDDC method
Čertíková, M. ; Burda, P. ; Šístek, Jakub
Balancing Domain Decomposition by Constraints (BDDC) belongs to the class of primal substructuring Domain Decomposition (DD) methods. DD methods are iterative methods successfully used in engineering to parallelize solution of large linear systems arising from discretization of second order elliptic problems. Substructuring DD methods represent an important class of DD methods. Their main idea is to divide the underlying domain into nonoverlapping subdomains and solvemany relatively small, local problems on subdomains instead of one large problem on the whole domain. In primal methods, it has to be specified how to distribute interface residuals among subdomains and how to obtain global, interface values of solution from local values on adjacent subdomains. Usually a weighted average is used with some simple choice of weights.
|
guest ::
