The primary goal of this work is to arrive at direction tomography (DT) algorithms freed from the severe linearization in the formulation, and as-assumptions on variation of the refractive index distribution (RID), involved in the earlier approaches based on Born and Royton approximations and the Fourier di reaction theorem (FDT). To start with, a direct single-step re-covery of RID from intensity measurements is demonstrated, replacing the common two-step procedure involving, rest the recovery of phase from in-density followed by the inversion of scattered led for the RID. The information loss, unavoidable in a two-step procedure is thus successfully addressed. Secondly, an iterative method which works with a forward model obtained directly from the Helmholtz equation is developed. This forward model, though has simplifying assumptions, is more general and can accommodate larger variations in RID than that allowed in the previous linear models. The iterative procedure has an update step which uses a linearization of the forward model and a re-linearization step at the updated RID. The procedure which directly employs the measured intensities is used as part of a deterministic Gauss-Newton algorithm and a stochastic optimization algorithm which uses the ensemble Kalman lter to arrive at the recursive update. The stochastic method is found to be more noise-tolerant and efficient to take care of process model inaccuracies. The proof is seen in better reconstructions from experimental data for two example objects, namely, a graded-index optical bre and a photonic-crystal bre. It is further ob-served that the reconstructions from photonic crystal bre are blurred, noisy and less accurate. Identifying the inaccurate implementation of the modemed Helmholtz equation for large k values employing the current sampling rate as the shortcoming, a new procedure, which splits the bandwidth into smaller components using short-time Fourier Transform is developed. The set of equations arrived at, each t for a narrow frequency band, is solved and the solutions are reassembled to obtain the scattered led for the original problem. The simulated di rated intensities so obtained are better matched to their measured experimental counterparts. However, the impel-mentation of the mode end procedure is computation-intensive, for which a parallel-processing machine can be a good solution. The recovery of RID with this mode cation is not attempted in this work and is left for future implementation.

The separation of independent sources from mixed observed data is a fundamental problem in many practical situations. A typical example is speech recordings made in an acoustic environment in the presence of background noise or other speakers. Problems of signal separation are explored by a group of methods called Blind Source Separation. Blind Source Separation (BSS) consists on estimating a set of N unknown sources from P observations resulting from the mixture of these sources and unknown background. Some existing solutions for instantaneous mixtures are reviewed and in Matlab implemented , i.e Independent Componnent Analysis (ICA) and Time-Frequency Analysis (TF). The acoustic signals recorded in real environment are not instantaneous, but convolutive mixtures. In this case, an ICA algorithm for separation of convolutive mixtures in frequency domain is introduced and in Matlab implemented. This diploma thesis examines the useability and comparisn of proposed separation algorithms.

This paper discusses the possible use of Hilbert-Huang transform to analyze the data obtained from physical experiments. Specifically for the analysis of acoustic emission in the form of acoustic shock. The introductory section explains the concept of acoustic emission and its detection process. Subsequently are discussed methods for signal analysis in time-frequency domain. Specifically, short-term Fourier transform, Wavelet transform, Hilbert transform and Hilbert-Huang transform. The final part contains the proposed method for measuring the performance and accuracy of different approaches.

Diplomová práce je zaměřena na problematiku analýzy signálů za účelem detekce poruchového oblouku, přičemž analýza signálů je prováděna v časové, frekvenční a smíšené časově-frekvenční oblasti. Práce stručně shrnuje existující normy pro zařízení pro detekci poruchového oblouku. Práce dokumentuje testy a měření, které byly realizovány v souladu s normami IEC 62606:2013 a UL 1699B. Z důvodu nedostatečnosti stávajících norem je v práci popsána nová metoda iniciace poruchového oblouku. naměřená data byla analyzována s využitím rychlé Fourierovy transformace, krátkodobé Fourierovy transformace a vlnkové transformace. Na základě provedeného literárního průzkumu a s využitím výsledků provedených analýz signálu je v práci proveden návrh nové detekční metody pro účely detekce poruchového oblouku v systémech napájených střídavým i stejnosměrným napětím.

Tato práce se zabývá vylepšením algoritmu pro sdružování (clustering) ERP signálů pomocí analýzy časových a prostorových vlastností pseudo-signálů získaných za pomocí metody analýzy nezávislých komponent (Independent Component Analysis). Naším zájmem je nalezení nových vlastností, které by zlepšily stávající výsledky. Tato práce se zabývá použitím Fourierovy transformace (Fourier Transform), FIR filtru a krátkodobé Fourierovy transformace ke zkvalitnění informace pro sdružovací algoritmy. Princip a použitelnost metody jsou popsány a demonstrovány ukázkovým algoritmem. Výsledky ukázaly, že pomocí dané metody je možné získat ze vstupních dat zajímavé informace, které mohou být úspěšně použity ke zlepšení výsledků.

Railway signaling is of paramount importance to ensure traffic management andsafety on the rail network. The main lines are divided into sections called ‘blocks’,which are governed by a fixed signal installation. To prevent trains from colliding,each block allows one train at once. In France (and most European countries),train detection is performed by an electrical device called track circuit that consistsof a transmitter and a receiver installed at the track-side, and connected via therails. In the absence of a train, an electrical signal flows from the transmitter tothe receiver through the rails. As a train enters a track circuit, its axles shuntthe rails, provoking a short circuit (also called ‘shunt’): the signal transmitted tothe receiver drops. The detection of that signal drop results in the detection of atrain. This method rarely fails throughout the network, but there can be criticalcases where it may be inefficient. In this Master’s Thesis, new detection criteriaproposed in previous studies have been tested on signals measured in poor shuntingconditions. Three approaches have been tested: one in the time domain and two inthe frequency domain. The time approach compares the short-term and long-termstatistics of the received signals. The observation of a change in the spectra of thereceived signals around the 3rd order harmonic (3OH) has led to the implementationof two frequency criteria: the estimation of the band power around the 3OH andthe detection of the 3OH peaks. The obtained results show that better detection isachieved when the new criteria and the existing one are combined. / Tågsignalsystem är väsentliga för att garantera trafikstyrning och säkerhet i tågnätet.Spåren är indelade i sektioner, s.k. block, som övervakas med fasta signalinstallationer.För att hindra tåg från att krocka, tillåts bara ett tåg i taget per block. IFrankrike (och de flesta andra europeiska länder), detekteras tågen med en elektriskspårkrets som består av en sändare och en mottagare som är installerad bredvidspåret och ansluten till rälsen. När inget tåg finns på spåret, flyter en elektrisk signalfrån sändaren till mottagaren via spåret. När ett tåg anländer, kortsluts kretsenav hjulaxeln och signalen försvinner från mottagaren. Minskningen i signalstyrkaanvänds för att detektera tåget. Denna metod sällan misslyckas i tågnätet, men iovanliga fall kan det uppstå farliga situationer. I detta examensarbete utvärderasnya detektionsmetoder, som har föreslagits i tidigare studier, på signaler som haruppmätts under förhållanden med dålig kontakt mellan hjul och spår. Tre olika metoderhar testats, en i tidsdomänen och två i frekvensdomänen. Tidsdomänsmetodenjämför kortvarig och långvarig statistik för den mottagna signalen. I spektrum förden mottagna signalen, har man observerat en förändring runt den tredje övertonen,samt detektering av frekvenstoppar vid tredje övertonen. De erhållna resultatenvisar på förbättrad detektering när de nya och existerande kriterierna kombineras.

This thesis focuses on the simulation of the gyroscopic effect using the software MSC Adams. A simple shaft-disk system was created and parameter of the sys-tem were changed in order to study the influence of the gyroscopic effect. It was shown that an increasing bearing stiffness reduces the precession motion. Fur-thermore, it was shown that the gyroscopic effect vanishes if the disk of system is placed symmetrically on the shaft, which reduces the system to a Jeffcott-Ro-tor. The second objective of this study was to analyze different defects in a simple fixed axis gear set. In particular, a cracked shaft, a cracked pinion and a chipped pinion as well as a healthy gear system were created and tested in Adams. The contact force between the two gears was monitored and the 2D and 3D frequency spectrum, as well as the Wavelet Transform, were plotted in order to compare the individual defects. It was shown that the Wavelet Transform is a powerful tool, capable of identifying a cracked gear with a non-constant speed. The last part of this study included fault detection with statistical methods as well as with the Sideband Energy Ratio (SER). The time domain signal of the individual faults were used to compare the mean, the standard deviation and the root mean square. Furthermore, the noise profile in the frequency spectrum was tracked with statistical methods using the mean and the standard deviation. It was demonstrated that it is possible to identify a cracked gear, as well as a chipped gear, with statistical methods. However, a cracked shaft could not be identified. The results also show that SER was only capable to identify major defects in a gear system such as a chipped tooth.

Epilepsy affects more than 50 million people and is one of the most prevalent neurological disorders and has a high impact on the quality of life of those suffering from it. However, 70% of epilepsy patients can live seizure free with proper diagnosis and treatment. Patients are evaluated using scalp EEG recordings which is cheap and non-invasive. Diagnostic yield is however low and qualified personnel need to process large amounts of data in order to accurately assess patients. MindReader is an unsupervised classifier which detects spectral anomalies and generates a hypothesis of the underlying patient state over time. The aim is to highlight abnormal, potentially epileptiform states, which could expedite analysis of patients and let qualified personnel attest the results. It was used to evaluate 95 scalp EEG recordings from healthy adults and adult patients with epilepsy. Interictal Epileptiform discharges (IED) occurring in the samples had been retroactively annotated, along with the patient state and maneuvers performed by personnel, to enable characterization of the classifier’s detection performance. The performance was slightly worse than previous benchmarks on pediatric scalp EEG recordings, with a 7% and 33% drop in specificity and sensitivity, respectively. Electrode positioning and partial spatial extent of events saw notable impact on performance. However, no correlation between annotated disturbances and reduction in performance could be found. Additional explorative analysis was performed on serialized intermediate data to evaluate the analysis design. Hyperparameters and electrode montage options were exposed to optimize for the average Mathew’s correlation coefficient (MCC) per electrode per patient, on a subset of the patients with epilepsy. An increased window length and lowered amount of training along with an common average montage proved most successful. The Euclidean distance of cumulative spectra (ECS), a metric suitable for spectral analysis, and homologous L2 and L1 loss function were implemented, of which the ECS further improved the average performance for all samples. Four additional analyses, featuring new time-frequency transforms and multichannel convolutional autoencoders were evaluated and an analysis using the continuous wavelet transform (CWT) and a convolutional autoencoder (CNN) performed the best, with an average MCC score of 0.19 and 56.9% sensitivity with approximately 13.9 false positives per minute.

This thesis describe detection of direction receiving sound with small–size microphone array. Work is based on analyzing methods of time delay estimation, energy decay or phase difference signal. Work focus mainly on finding of angle of arrival in small time difference. Results of measuring, as programming sound, so sound recorded in laboratory conditions and real enviroment, are mentioned in conclusion. All calculations were done by platform Matlab