In order to meet higher performance demands on brushless motor controllers, motor feedback signals must meet correspondingly higher standards. Brushless motor controllers require angular feedback for motor commutation, and generally for one or more of the following: torque, velocity or position regulation. These feedback categories impose different requirements on the control system. In many brushless motor applications, sinusoidal encoders have significant advantages over square-wave encoders. Signal processing techniques for sinusoidal encoder feedback signals are considered in the context of a brushless motor positioning system. In particular, a method is presented for correcting gain and offset measurement errors based on ellipse-fitting techniques. / Master of Science

The objective of this study is to investigate the technique of the PWS (Post-Weld-Shift) measurement in cylindrical-type laser diode module packaging processes. Including measure the shift in focus plane and the run-out between the axis of laser diode and optical fiber. The objective of this technique is to quantify the PWS in packaging processes. This technique can improve the efficient of the laser hammering technique. The measurement technique developed in this study employs a laser displacement meter (Keyence LC-2430) to measure the profile and position the upper housing of laser diode module. Using the concepts of ellipse and symmetric figure, the Ellipse Fitting Method (EFM) and the Symmetric Center Method (SCM) are developed. A simulation program is coded for discussing the relationship between the measurement error and the roundness of the optical fiber housing. Finally, these measuring technique are applied on the laser diode module packaging system (Newport-LW4000) to measure the PWS. The experimental measuring data of PWS is 3.5£gm, which is reasonable in compare to the estimate alignment offset (2.9£gm to 5.9£gm) by the coupling efficiency method. Therefore it is believed that the technique developed in this study is feasible and practical.

abstract: There is a growing interest for improved high-accuracy camera calibration methods due to the increasing demand for 3D visual media in commercial markets. Camera calibration is used widely in the fields of computer vision, robotics and 3D reconstruction. Camera calibration is the first step for extracting 3D data from a 2D image. It plays a crucial role in computer vision and 3D reconstruction due to the fact that the accuracy of the reconstruction and 3D coordinate determination relies on the accuracy of the camera calibration to a great extent. This thesis presents a novel camera calibration method using a circular calibration pattern. The disadvantages and issues with existing state-of-the-art methods are discussed and are overcome in this work. The implemented system consists of techniques of local adaptive segmentation, ellipse fitting, projection and optimization. Simulation results are presented to illustrate the performance of the proposed scheme. These results show that the proposed method reduces the error as compared to the state-of-the-art for high-resolution images, and that the proposed scheme is more robust to blur in the imaged calibration pattern. / Dissertation/Thesis / M.S. Electrical Engineering 2012

In this study a fully automatic and scale invariant feature extractor which does not require manual initialization or special equipment is proposed. Face location and size is extracted using skin segmentation and ellipse fitting. Extracted face region is scaled to a predefined size, later upper and lower facial templates are used for feature extraction. Template localization and template parameter calculations are carried out using Principal Component Analysis. Changes in facial feature coordinates between analyzed image and neutral expression image are used for expression classification. Performances of different classifiers are evaluated. Performance of proposed feature extractor is also tested on sample video sequences. Facial features are extracted in the first frame and KLT tracker is used for tracking the extracted features. Lost features are detected using face geometry rules and they are relocated using feature extractor. As an alternative to feature based technique an available holistic method which analyses face without partitioning is implemented. Face images are filtered using Gabor filters tuned to different scales and orientations. Filtered images are combined to form Gabor jets. Dimensionality of Gabor jets is decreased using Principal Component Analysis. Performances of different classifiers on low dimensional Gabor jets are compared. Feature based and holistic classifier performances are compared using JAFFE and AF facial expression databases.

The Shannon sampling framework is widely used for discrete representation of analog bandlimited signals, starting from samples taken at the Nyquist rate. In many practical applications, signals are not bandlimited. In order to accommodate such signals within the Shannon-Nyquist framework, one typically passes the signal through an anti-aliasing filter, which essentially performs bandlimiting. In applications such as RADAR, SONAR, ultrasound imaging, optical coherence to-mography, multiband signal communication, wideband spectrum sensing, etc., the signals to be sampled have a certain structure, which could manifest in one of the following forms: (i) sparsity or parsimony in a certain bases; (ii) shift-invariant representation; (iii) multi-band spectrum; (iv) finite rate of innovation property, etc.. By using such structure as a prior, one could devise efficient sampling strategies that operate at sub-Nyquist rates. In this Ph.D. thesis, we consider the problem of sampling and reconstruction of finite-rate-of-innovation (FRI) signals, which fall in one of the two classes: (i) Sum-of-weighted and time-shifted (SWTS) pulses; and (ii) Sum-of-weighted exponential (SWE). Finite-rate-of-innovation signals are not necessarily bandlimited, but they are specified by a finite number of free parameters per unit time interval. Hence, the FRI reconstruction problem could be solved by estimating the parameters starting from measurements on the signal. Typically, parameter estimation is done using high-resolution spectral estimation (HRSE) techniques such as the annihilating filter, matrix pencil method, estimation of signal parameter via rotational invariance technique (ESPRIT), etc.. The sampling issues include design of the sampling kernel and choice of the sampling grid structure. Following a frequency-domain reconstruction approach, we propose a novel technique to design compactly supported sampling kernels. The key idea is to cancel aliasing at certain set of uniformly spaced frequencies and make sure that the rest of the frequency response is specified such that the kernel follows the Paley-Wiener criterion for compactly supported functions. To assess the robustness in the presence of noise, we consider a particular class of the proposed kernel whose impulse response has the form of sum of modulated splines (SMS). In the presence of continuous-time and digital noise cases, we show that the reconstruction accuracy is improved by 5 to 25 dB by using the SMS kernel compared with the state-of-the-art compactly supported kernels. Apart from noise robustness, the SMS kernel also has polynomial-exponential reproducing property where the exponents are harmonically related. An interesting feature of the SMS kernel, in contrast with E-splines, is that its support is independent of the number of exponentials. In a typical SWTS signal reconstruction mechanism, first, the SWTS signal is trans formed to a SWE signal followed by uniform sampling, and then discrete-domain annihilation is applied for parameter estimation. In this thesis, we develop a continuous-time annihilation approach using the shift operator for estimating the parameters of SWE signals. Instead of using uniform sampling-based HRSE techniques, operator-based annihilation allows us to estimate parameters from structured non-uniform samples (SNS), and gives more accurate parameters estimates. On the application front, we first consider the problem of curve fitting and curve completion, specifically, ellipse fitting to uniform or non-uniform samples. In general, the ellipse fitting problem is solved by minimizing distance metrics such as the algebraic distance, geometric distance, etc.. It is known that when the samples are measured from an incomplete ellipse, such fitting techniques tend to estimate biased ellipse parameters and the estimated ellipses are relatively smaller than the ground truth. By taking into account the FRI property of an ellipse, we show how accurate ellipse fitting can be performed even to data measured from a partial ellipse. Our fitting technique first estimates the underlying sampling rate using annihilating filter and then carries out least-squares regression to estimate the ellipse parameters. The estimated ellipses have lesser bias compared with the state-of-the-art methods and the mean-squared error is lesser by about 2 to 10 dB. We show applications of ellipse fitting in iris images starting from partial edge contours. We found that the proposed method is able to localize iris/pupil more accurately compared with conventional methods. In a related application, we demonstrate curve completion to partial ellipses drawn on a touch-screen tablet. We also applied the FRI principle to imaging applications such as frequency-domain optical-coherence tomography (FDOCT) and nuclear magnetic resonance (NMR) spectroscopy. In these applications, the resolution is limited by the uncertainty principle, which, in turn, is limited by the number of measurements. By establishing the FRI property of the measurements, we show that one could attain super-resolved tomograms and NMR spectra by using the same or lesser number of samples compared with the classical Fourier-based techniques. In the case of FDOCT, by assuming a piecewise-constant refractive index of the specimen, we show that the measurements have SWE form. We show how super-resolved tomograms could be achieved using SNS-based reconstruction technique. To demonstrate clinical relevance, we consider FDOCT measurements obtained from the retinal pigment epithelium (RPE) and photoreceptor inner/outer segments (IS/OS) of the retina. We show that the proposed method is able to resolve the RPE and IS/OS layers by using only 40% of the available samples. In the context of NMR spectroscopy, the measured signal or free induction decay (FID) can be modelled as a SWE signal. Due to the exponential decay, the FIDs are non-stationary. Hence, one cannot directly apply autocorrelation-based methods such as ESPRIT. We develop DEESPRIT, a counterpart of ESPRIT for decaying exponentials. We consider FID measurements taken from amino acid mixture and show that the proposed method is able to resolve two closely spaced frequencies by using only 40% of the measurements. In summary, this thesis focuses on various aspects of sub-Nyquist sampling and demonstrates concrete applications to super-resolution imaging.

The PhD thesis deals with modern methods of image processing, especially image segmentation, classification and evaluation of parameters. It is focused primarily on processing medical images of soft tissues obtained by magnetic resonance tomography (MR) and microscopic images of tissues. It is easy to describe edges of the sought objects using of segmented images. The edges found can be useful for further processing of monitored object such as calculating the perimeter, surface and volume evaluation or even three-dimensional shape reconstruction. The proposed solutions can be used for the classification of healthy/unhealthy tissues in MR or other imaging. Application examples of the proposed segmentation methods are shown in this thesis. Research in the area of image segmentation is focused on methods based on solving partial differential equations. This is a modern method for image processing, often called the active contour method. It is of great advantage in the segmentation of real images degraded by noise with fuzzy edges and transitions between objects. The results of the thesis are methods proposed for automatic image segmentation and classification.

Le suivi de la déformation de la croûte terrestre durant la phase intersismique pour la recherche des transitoires nécessite des instruments précis capables d'opérer pour de très longues durées. Le projet ANR-LINES a visé le développement de trois nouveaux instruments : un sismomètre mono-axial, un inclinomètre hydrostatique à longue base et un inclinomètre de forage pendulaire. Ces trois instruments profitent d'un capteur interférométrique de déplacement à longues fibres optiques du type Fabry-Pérot Extrinsèque (EFFPI). Leurs architectures mécaniques et l'utilisation de longues fibres permettent à ces instruments géophysiques nouvellement fabriqués d'atteindre les objectifs fixés.Le premier objectif de cette étude est de proposer des méthodes adaptées à l'estimation de la phase du chemin optique dans les cavités Fabry-Pérot. Une modulation du courant de la diode laser, suivie par une démodulation homodyne du signal d'interférence et un filtre de Kalman permettent de déterminer la phase en temps réel. Les résultats sont convaincants pour des mesures de courtes durées mais exigent des solutions complémentaires pour se prémunir des effets de la variation des phénomènes environnementaux.Le capteur EFFPI intégré dans l'inclinomètre de forage LINES lui offre l'opportunité d'établir une mesure différentielle de l'oscillation de la masselotte pendulée grâce à trois cavités Fabry-Pérot. Le sismomètre LINES utilise lui aussi le capteur de déplacement EFFPI pour la mesure du déplacement de sa bobine. Une description de l'architecture mécanique de ces instruments et une analyse des phénomènes détectés (mouvements lents, marées, séismes, microséismes . . . ) font partie de cette thèse. / Monitoring crustal deformation during the interseismic phase when searching for earth transients requires precise instruments able to operate for very long periods. The ANR-LINES project aimed to develop three new instruments: a single-axis seismometer, a hydrostatic long base tiltmeter and a borehole pendulum tiltmeter. These three instruments benefit of an extrinsic Fabry-Pérot interferometer (EFFPI) with long optic fibers for displacement detections. Their mechanical architectures and their disposal of long fibers help these newly manufactured geophysical instruments complete their goals.The first objective of this study is to propose appropriate methods for estimating the phase of the optical path in the Fabry-Pérot cavities. A modulation of the laser diode current, followed by a homodyne demodulation of the interference signal and a Kalman filter, allow determining the phase in real time. The results are convincing while taking short periods measurements but require additional solutions for protection against environmental phenomena variations. The EFFPI sensor integrated in the LINES borehole tiltmeter gives it the opportunity to establish a differential measurement of the bob's oscillation thanks to three Fabry-Perot cavities. The LINES seismometer also uses the EFFPI displacement sensor to measure its coil's displacement. A description of the two instruments' mechanical structures and an analysis of the detected phenomena (slow movements, tides, earthquakes, microseisms . . . ) are part of this thesis.Keywords: Laser interferometry, wavelength modulation, synchronous homodyne demodulation, ellipse fitting, Kalman filter, temperature compensation, borehole tiltmeter, simple pendulum, differential measurements, slow drift, seismicobservations, seismometer.

This work presents a new extension for Uniform Marker Field, which is able to detect UMF on the cylinder. First part of the text deals with Augmented reality and focuses on systems using markers. It discusses the actual state-of-the-art systems and its possibilities. After that it focuses more deeply on the marker system Uniform marker field and its grayscale variants. Next part of the work describes properties of the cylinder projected in real space. Important properties for detecting are discussed in detail. Then the proposal and description of detection algorithm is presented. Implementation of algorithm is tested and evaluated on the very end of this thesis.