In digital control system, interference, which is mixed in the input signal, has a great influence on the performance of the system. Therefore, processing of input signal has to be done to get useful signal. Finite impulse response (FIR) filter plays an important role in the processing of digital signal. Designing the FIR filter by Matlab can simplify the complicated computation in simulation and improve the performance. By using the methods of window function, frequency sampling and convex optimization techniques, the design of FIR filter has been processed by Matlab. In the view of the designed program of Matlab and I can get the amplitude-frequency characterization. By using the FIR digital filters which have been designed to process the input signal based on the Matlab function, the filtering effect of different digital filters is analyzed by comparing the signal’s amplitude-frequency diagrams which have been generated. The experimental results show that the FIR filters designed in this paper are effective.

This paper describes the methods used mainly in the filtration of audio signals -- noise reduction. It realizes segmentation with variable lenght of segment, constant overlap add and segmentation with variable lenght of segment and overlap add. These methods are then compared with comon methods of segmentation in dependance of the lenght of segment and used window function by the thresholding method. For this purpose it uses the database of audio records. In the first part it desrcibes the technique of audio signal processing with comon method of segmentation. According to this method it continues in design of signal processing by variable segmentation method, in which it is needed to modify the shape of window function, which influences and attributes are explained in next chapter. In practical part it describes the implemented methods in MATLAB programming language with each steps of testing. It continues with chart of enclosed files and the evaluation of the results of hearing tests.

The early diagnosis of CP (Cerebral Palsy) in infants is important for developing meaningful interventions. One of the major symptoms of the CP is lack of the coordinated movements of a baby. The bilateral coordinated movement (BCM) is that a baby shows in the early development stage. Each limb movement shows various ranges of speed and angle with fluency in a normal infant. When a baby has CP the movements are cramped and more synchronized. A quantitative method is needed to diagnose the BCM. Data is collected from 3-axis accelerometers, which are connected, to each limb of the baby. Signal processing the collected data using short time Fourier transforms, along with the formation of time-dependent transfer functions and the coherence property is the key to the diagnostic approach. Combinations of each limb's movement and their relationship can represent the correlated movement. Data collected from a normal baby is used to develop the technique for identifying the fidgety movement. Time histories and the resulting diagnostic tool are presented to show the regions of the described movement. The evaluation of the transduction approach and the analysis is discussed in detail. The application of the quantitative tool for the early diagnosis of CP offers clinicians the opportunity to provide interventions that may reduce the debilitating impact this condition has on children. Tools such as this can also be used to assess motor development in infants and lead to the identification and early intervention for other conditions. / Master of Science

This paper describes the simulation of the motion of a sound source in space with several speakers. It deals mainly with the study and subsequent implementation of events necessary for simulating the motion of the virtual sound source. The theoretical part is therefore concerned with finding these phenomena and with their description. Mainly that means description of panning of the sound and of the Doppler effect. In the following practical part of the work, the previously described phenomena are applied into Matlab. In the created interface , it is then possible to select the parameters of the simulation, such as the trajectory of the motion of the virtual sound source, the number of the points of the trajectory, etc. Analyses of the generated signal are then made of the selected referential parameters of the simulation.

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