The development of digital detectors for X-ray imaging in medical diagnostics receives an increasing amount of attention. The detector under development at the Department of Radiation Sciences at Uppsala University is a hybrid pixel detector, which consists of a semiconductor sensor mounted onto a readout chip. The readout chip is capable of performing photon counting and has an externally adjustable threshold. A simulation tool for the detector and a model applying the linear-systems transfer theory to X-ray hybrid pixel detectors have been developed. Also a characterization of the readout chip has been done. In order to estimate the potential of the detector for diagnostic radiology, we investigate the image quality using the spatial frequency dependent detective quantum efficiency (DQE). By means of the detector simulations, the influence of threshold setting, noise sources, level of exposure and charge sharing on the DQE have been studied. By means of the linear-systems theory, a single analytical expression is provided to obtain the DQE of a hybrid pixel detector. The method developed in this thesis will make it possible to optimize a detector design according to a particular medical application. It will also permit modifications and new features to be included without having to construct a full detector system.

This paper proposes a general convex framework for the identification of switched linear systems. The proposed framework uses over-parameterization to avoid solving the otherwise combinatorially forbidding identification problem, and takes the form of a least-squares problem with a sum-of-norms regularization, a generalization of the ℓ1-regularization. The regularization constant regulates the complexity and is used to trade off the fit and the number of submodels. / <p>Funding Agencies|Swedish foundation for strategic research in the center MOVIII||Swedish Research Council in the Linnaeus center CADICS||European Research Council|267381|Sweden-America Foundation||Swedish Science Foundation||</p>

There are occasions when it is preferable that an aircraft flies asclose to the ground as possible. It is difficult for a pilot to predict the topography when he cannot see beyond the next hill, and this makes it hard for him to find the optimal flight trajectory. With the help of a terrain database in the aircraft, the forthcoming topography can be found in advance and a flight trajectory can be calculated in real-time. The main goal is to find an optimal control sequence to be used by the autopilot. The optimization algorithm, which is created for finding the optimal control sequence, has to be run often and therefore, it has to be fast. This thesis presents a terrain following algorithm based on Model Predictive Control which is a promising and robust way of solving the optimization problem. By using trajectory optimization, a trajectory which follows the terrain very good is found for the non-linear model of the aircraft.

We consider a system of two identical Morris-Lecar neurons coupled via electrical coupling. We focus our study on the effects that the coupling strength, γ , and the coupling time delay, τ , cause on the dynamics of the system. For small γ we use the phase model reduction technique to analyze the system behavior. We determine the stable states of the system with respect to γ and τ using the appropriate phase models, and we estimate the regions of validity of the phase models in the γ , τ plane using both analytical and numerical analysis. Next we examine asymptotic of the arbitrary conductance-based neuronal model for γ → +∞ and γ → −∞. The theory of nearly linear systems developed in [30] is extended in the special case of matrices with non-positive eigenvalues. The asymptotic analysis for γ > 0 shows that with appropriate choice of γ the voltages of the neurons can be made arbitrarily close in finite time and will remain that close for all subsequent time, while the asymptotic analysis for γ < 0 suggests the method of estimation of the boundary between “weak” and “strong” coupling.

We consider a system of two identical Morris-Lecar neurons coupled via electrical coupling. We focus our study on the effects that the coupling strength, γ , and the coupling time delay, τ , cause on the dynamics of the system. For small γ we use the phase model reduction technique to analyze the system behavior. We determine the stable states of the system with respect to γ and τ using the appropriate phase models, and we estimate the regions of validity of the phase models in the γ , τ plane using both analytical and numerical analysis. Next we examine asymptotic of the arbitrary conductance-based neuronal model for γ → +∞ and γ → −∞. The theory of nearly linear systems developed in [30] is extended in the special case of matrices with non-positive eigenvalues. The asymptotic analysis for γ > 0 shows that with appropriate choice of γ the voltages of the neurons can be made arbitrarily close in finite time and will remain that close for all subsequent time, while the asymptotic analysis for γ < 0 suggests the method of estimation of the boundary between “weak” and “strong” coupling.

This study used the multiple methods of classroom observation, interview with teachers and students, and paper-and-pencil test to investigate the ability development of seventh-grade students in learning two-dimensional linear systems of equations and the corresponding error analysis. Hopefully, the results of this study can be as a reference for the middle school math teachers to plan the suitable teaching strategies when they teach two-dimensional linear systems of equations to their students. At the beginning, the researcher entered two seventh-grade classrooms of one middle school in Kaohsiung to make the preliminary observations and let students (also the teachers) to get used to the appearance of the researcher in the classroom during the period that one-variable linear equations were taught. Subsequently, the formal observations were carried out for 40 class periods that two-dimensional linear systems of equations were taught. All the observations made about how teachers taught and how students learned were recorded and content analyzed. Two paper-and-pencil tests were administered during the period of preliminary observations. And three paper-and pencil tests were given during the period of the formal observations. All the test results were collected and analyzed in numerous ways. Based on the literature survey and the interviews with six middle school math teachers, all relevant abilities of mastering two-dimensional linear systems of equations were classified into three categories: Character Symbols (10 sub-abilities), Operational Principals (five sub-abilities), and Other Abilities (16 sub-abilities). Based on the results of the content analyses of classroom observations and the error analyses of five paper-and-pencils tests for each sub-abilities of mastering the subject, it was observed that during the period of developing the abilities on solving two-dimensional linear systems of equations, most students showed some signs of obstacles and puzzles. Even by the end of the course on two-dimensional linear systems of equations, most students still did not master the subject well. Based on the results of this study, it is proposed that the length of teaching period needs to be increased and more efficient learning strategies need to be introduced to the students when two-dimensional linear systems of equations are taught.

<p>There are occasions when it is preferable that an aircraft flies asclose to the ground as possible. It is difficult for a pilot to predict the topography when he cannot see beyond the next hill, and this makes it hard for him to find the optimal flight trajectory. With the help of a terrain database in the aircraft, the forthcoming topography can be found in advance and a flight trajectory can be calculated in real-time. The main goal is to find an optimal control sequence to be used by the autopilot. The optimization algorithm, which is created for finding the optimal control sequence, has to be run often and therefore, it has to be fast. </p><p>This thesis presents a terrain following algorithm based on Model Predictive Control which is a promising and robust way of solving the optimization problem. By using trajectory optimization, a trajectory which follows the terrain very good is found for the non-linear model of the aircraft.</p>

Thesis (Ph.D.)--University of Delaware, 2009. / Principal faculty advisor: Richard J. Braun, Dept. of Mathematical Sciences. Includes bibliographical references.

This thesis covers the study of an actively assisted passive walker with discontinuous and impulsive actuation. The dynamics of the passive and active portions are derived, and a comprehensive mathematical model is proposed. An actuation method is also proposed to study the use of multiple discrete actuation events in a walking gait. Two key cases are considered: actuation at the stance point and at the EA point of a non-kneed walker. An experimental walker was designed that is capable of passive walking and has an experimental implementation of the proposed actuation system. A thorough characterization of the model is then performed, with experimental validation to show that: at high ramp angles, energy injection results in an increase in BOA of ~38% on a stable walking gait at a Ct of 0.086, and at low ramp angles, injection results in a stride length increase of ~29% at a Ct of 0.06.

Στην παρούσα διπλωματική εργασία παρουσιάζεται η μέθοδος της αναγνώρισης του μη γραμμικού δυναμικού συστήματος της καρδιαγγειακής ρύθμισης. Συγκεκριμένα, αυτή η μέθοδος εστιάζει στην αναγνώριση του Heart Rate Baroreflex μηχανισμού (ή αντίδραση πιεσοϋποδοχέα του καρδιακού παλμού) μέσω παρατηρήσεων της καρδιακής πίεσης η οποία αποτελεί την είσοδο και του καρδιακού παλμού ο οποίος αποτελεί την έξοδο του προς αναγνώριση συστήματος. Το μοντέλο αυτό βασίζεται στα έμφυτα χαρακτηριστικά του αυτόνομου νευρικού συστήματος, για την απεικόνιση των οποίων αναπτύσσουμε ένα προσαρμοζόμενο νεύρο-ασαφές σύστημα (ANFIS). Αυτή η μέθοδος μας επιτρέπει να ενσωματώσουμε τις φυσιολογικές λειτουργίες του νευρικού μας συστήματος όπως την συμπαθητική και παρασυμπαθητική, δια μέσου της επιλογής κατάλληλων συναρτήσεων συμμετοχής, οι οποίες αντικατοπτρίζουν επακριβώς, την συμπεριφορά των παραπάνω νεύρων για το προσαρμοζόμενο νεύρο-ασαφές σύστημα. Τα αποτελέσματα συμφωνούν με τα φυσικά χαρακτηριστικά του καρδιαγγειακού μηχανισμού ρύθμισης, όπως καθυστέρηση κατά την λειτουργία των παρασυμπαθητικών νευρών, διάρκεια κατά την λειτουργία των συμπαθητικών και διόρθωση ανάμεσα στο σήμα του καρδιακού παλμού και της καρδιακής ρύθμισης. Η σωστή εκτίμηση του Heart Rate Baroreflex μηχανισμού επιτρέπει στους κλινικούς γιατρούς να έχουν πιο αξιόπιστες πληροφορίες για τους ασθενείς τους. / This thesis presents the method of identification of nonlinear dynamic system of cardiovascular regulation. Specifically , this method focuses on the identification of the Heart Rate Baroreflex (or Baroreceptor Reflex ) mechanism by observation of the arterial blood pressure which is the input and heart rate which is the output of the system. This model is based on the inherent characteristics of the autonomic nervous system, so we develop an adaptive neuro- fuzzy inference system (ANFIS) to represent them. This method allows us to incorporate the physiological functions of our nervous system, such as sympathetic and parasympathetic nerves, through the selection of appropriate membership functions , which reflect accurately the behavior of these nerves for the adaptive neuro- fuzzy system . The results agree with the natural characteristics and physiological understanding of the cardiovascular regulatory system, such as delay in the parasympathetic function, durability in the function of sympathetic nerves and the correlation between the heart rate and the arterial blood pressure signals. Proper assessment of Heart Rate Baroreflex mechanism allows clinicians to have more reliable information for their patients.