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Development and current status of special units of the Fire Rescue Service of the Czech Fire Brigade
Šembera, Ondřej ; Fiala, Miloš (advisor) ; Vilášek, Josef (referee)
Title: Development and current status of special unit of the Fire Rescue Service of the Czech Fire Brigade. Objective: The aim of this work is to get a detailed overview of the development and the current functioning of the Rescue Service of the Czech Fire Brigade. Another aim is to analyze the individual rescue units of the Rescue Service of the Czech Fire Brigade with a focus on their professional training, specialization courses, organizational structure, firefighting machinery, tasks and activities including rescue and liquidation services in the events of major emergencies from 2009 to the present. Methods: The theoretical part will be drawn up on the basis of a study of printed and electronic sources related to the topic. It will be focused on the history and development of the Rescue Service of the Czech Fire Brigade, organizational structure, tasks, focus and actions, education system and firefighting machinery to the extent that will provide support for the practical part of the work. The practical part will deal with the analysis of selected rescue teams and the specialized team of the Rescue Service of the Czech Fire Brigade. The internal and public sources will be the basis for the examination of their professional training. The organizational structure will be verified by local survey...
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Adaptive Blind Separation of Instantaneous Linear Mixtures of Independent Sources
Šembera, Ondřej ; Tichavský, Petr ; Koldovský, Zbyněk
In many applications, there is a need to blindly separate independent sources from their linear instantaneous mixtures while the mixing matrix or source properties are slowly or abruptly changing in time. The easiest way to separate the data is to consider off-line estimation of the model parameters repeatedly in time shifting window. Another popular method is the stochastic natural gradient algorithm, which relies on non-Gaussianity of the separated signals and is adaptive by its nature. In this paper, we propose an adaptive version of two blind source separation algorithms which exploit non-stationarity of the original signals. The results indicate that the proposed algorithms slightly outperform the natural gradient in the trade-off between the algorithm’s ability to quickly adapt to changes in the mixing matrix and the variance of the estimate when the mixing is stationary.
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Special forces Rescue Unit of the Czech Republic Fire Rescue Service
Šembera, Ondřej ; Fiala, Miloš (advisor) ; Vilášek, Josef (referee)
1 Abstract Title: Special forces Rescue Unit of the Czech Republic Fire Rescue Service. Objective: The aim is to analyze single troops of Special forces Rescue Unit of the Czech Republic Fire Rescue Service and comparison of the statistics of the rescue and disposal work from the beginning of the formation of the department to the present day. Methods: For the teoretical part of the work it is an elaboration of the research from the materials available. For the practical part it is the use of the statistics of the General Directorate of the Fire Rescue Service of the Czech Republic. Results: Based on the statistics, this department is beneficial both for the Fire Rescue Service of the Czech Republic with their rescue and disposal work and for the training centers. Keywords: Troops, Firemen, Rescue, Winding-up work.
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Blind Separation of Mixtures of Piecewise AR(1) Processes and Model Mismatch
Tichavský, Petr ; Šembera, Ondřej ; Koldovský, Zbyněk
Modeling real-world acoustic signals and namely speech signals as piecewise stationary random processes is a possible approach to blind separation of linear mixtures of such signals. In this paper, the piecewise AR(1) modeling is studied and is compared to the more common piecewise AR(0) modeling, which is known under the names Block Gaussian SEParation (BGSEP) and Block Gaussian Likelihood (BGL). The separation based on the AR(0) modeling uses an approximate joint diagonalization (AJD) of covariance matrices of the mixture with lag 0, computed at epochs (intervals) of stationarity of the separated signals. The separation based on the AR(1) modeling uses the covariances of lag 0 and covariances of lag 1 jointly. For this model, we derive an approximate Cram´er-Rao lower bound on the separation accuracy for estimation based on the full set of the statistics (covariance matrices of lag 0 and lag 1) and covariance matrices with lag 0 only. The bounds show the condition when AR(1) modeling leads to significantly improved separation accuracy.
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