This master’s thesis is focused on sensory systems and analysis of such signals. In the frst part, a theoretical analysis of each sensory system is conducted. Followingly, appropriate flters were used to process these signals. The last part of this thesis deals with the analysis of measured data.

This master’s thesis is focused on sensory systems and analysis of such signals. In the frst part, a theoretical analysis of each sensory system is conducted. Followingly, appropriate flters were used to process these signals. The last part of this thesis deals with the analysis of measured data.

This master thesis deals with implementation of the radar sensor for adaptive cruise control system. It discusses used technologies and processes and documents implementation of signal processing serving for the purpose of adaptive cruise control. It also describes the testing on the real data gathered in traffic. Texas Instrument's AWR1843 radar module was used as the sensor. This sensor represents currently very popular milimeter wave technology radars. Result of this master thesis are two implemented systems processing digital signal. One of them is a prototype application of the adaptive cruise control system, which also visualises the data. The other is implemented firmware of radar module doing real-time on-chip signal processing according to adaptive cruise control logic.

This diploma thesis intends to describe some of identification methods applicable to the induction motor resulting in the profit of some of these parameters by means of mathematical analysis. Afterwards these methods are simulated in MATLAB/Simulink and in the final part algorithms of these methods are consequently implemented to the digital signal processor.

The utilisation of personal computers for a conditioning of audio devices has shown a significant increase, since the digital signal processing (DSP) was introduced. The expansion of the DSP has allowed implementing analyses to obtain frequency and linear characteristics, distortion parameters (THD, THD+N, WHD, SINAD), a rate of crosstalk or a signal-to-noise ratio. In this work a software analyser is developed, which is able to obtain qualitative parameters of hardware audio devices that are connected with a sound card. For an efficient communication between the sound card and the personal computer the ASIO driver is used. The application is capable to measure audio effects that are implemented in VST plug-ins. The software is developed in C++ language and the implemented analyses are based on the AES17 recommendation.

In digital image correlation, an optical full-field analysis method that can determine displacements of an object under load, high-resolution images are preferable. One way to improve the resolution is to improve the camera hardware. This can be expensive, hence another way to enhance the image is by various image processing techniques increase the resolution of the image. There are several ways of doing this and these techniques are called super-resolution. In this thesisthe theory behind several different approaches to super-resolution is presented and discussed. The goal of this Thesis has been to investigate if super-resolutionis possible in a scene with moving objects as well as movement of the camera. It became clear early on that image registration, a step in many super-resolution methods that will be explained in this thesis, was of utmost importance, and a major part of the work became comparing image registration methods. Data has been recorded and then two different super-resolution algorithms have been evaluated on a data set showing that super-resolution is possible.

As an extension of previous work done by Luke Newmeyer in his master's thesis \cite{newmeyer2018efficient}, this report presents an improved signal processing chain for efficient, real-time processing of radar data for small-scale UAV traffic control systems. The HDL design described is for a 16-channel, 2-dimensional phased array feed processing chain and includes mean subtraction, windowing, FIR filtering, decimation, spectral estimation via FFT, cross-correlation, and averaging, as well as a significant amount of control and configuration logic. The design runs near the the max allowable memory bus frequency at 300MHz, and using AXI DMA engines can achieve throughput of 38.3 Gb/s (~0.25% below theoretical 38.4 Gb/s), transferring 2MB of correlation data in about 440us. This allows for a pulse repetition frequency of nearly 2kHz, in contrast to 454Hz from the previous design. The design targets the Avnet UltraZed-EV MPSoC board, which boots custom PetaLinux images. API code and post-processing algorithms run in this environment to interface with the FPGA control registers and further process frames of data. Primary configuration options include variable sample rate, window coefficients, FIR filter coefficients, chirp length, pulse repetition interval, decimation factor, number of averaged frames, error monitoring, three DMA sampling points, and DMA ring buffer transfers. The result is a dynamic, high-speed, small-scale design which can process 16 parallel channels of data in real time for 3-dimensional detection of local UAV traffic at a range of 1000m.

Symbolic dynamics is a current interest in the area of anomaly detection, especially in mechanical systems.  Symbolic dynamics reduces the overall dimensionality of system responses while maintaining a high level of robustness to noise.  Rolling element bearings are particularly common mechanical components where anomaly detection is of high importance.  Harsh operating conditions and manufacturing imperfections increase vibration innately reducing component life and increasing downtime and costly repairs.  This thesis presents a novel way to detect bearing vibrational anomalies through Symbolic Aggregate Approximation (SAX) in the two-dimensional time-frequency domain.  SAX reduces computational requirements by partitioning high-dimensional sensor data into discrete states.  This analysis specifically suits bearing vibration data in the time-frequency domain, as the distribution of data does not greatly change between normal and faulty conditions. Under ground truth synthetically-generated experiments, two-dimensional SAX in conjunction with Markov model feature extraction is successful in detecting anomalies (> 99%) using short time spans (< 0.1 seconds) of data in the time-frequency domain with low false alarms (< 8%).  Analysis of real-world datasets validates the performance over the commonly used one-dimensional symbolic analysis by detecting 100% of experimental anomalous vibration with 0 false alarms in all fault types using less than 1 second of data for the basis of 'normality'. Two-dimensional SAX also demonstrates the ability to detect anomalies in predicative monitoring environments earlier than previous methods, even in low Signal-to-Noise ratios. / Master of Science

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