374 research outputs found
Modelling Aspects of Planar Multi-Mode Antennas for Direction-of-Arrival Estimation
Multi-mode antennas are an alternative to classical antenna arrays, and hence
a promising emerging sensor technology for a vast variety of applications in
the areas of array signal processing and digital communications. An unsolved
problem is to describe the radiation pattern of multi-mode antennas in closed
analytic form based on calibration measurements or on electromagnetic field
(EMF) simulation data. As a solution, we investigate two modeling methods: One
is based on the array interpolation technique (AIT), the other one on wavefield
modeling (WM). Both methods are able to accurately interpolate quantized EMF
data of a given multi-mode antenna, in our case a planar four-port antenna
developed for the 6-8.5 GHz range. Since the modeling methods inherently depend
on parameter sets, we investigate the influence of the parameter choice on the
accuracy of both models. Furthermore, we evaluate the impact of modeling errors
for coherent maximum-likelihood direction-of-arrival (DoA) estimation given
different model parameters. Numerical results are presented for a single
polarization component. Simulations reveal that the estimation bias introduced
by model errors is subject to the chosen model parameters. Finally, we provide
optimized sets of AIT and WM parameters for the multi-mode antenna under
investigation. With these parameter sets, EMF data samples can be reproduced in
interpolated form with high angular resolution
A comparison of multiple techniques for the reconstruction of entry, descent, and landing trajectories and atmospheres
The primary importance of trajectory reconstruction is to assess the accuracy of pre-flight predictions of the entry trajectory. While numerous entry systems have flown, often these systems are not adequately instrumented or the flight team not adequately funded to perform the statistical engineering reconstruction required to quantify performance and feed-forward lessons learned into future missions. As such, entry system performance and reliability levels remain unsubstantiated and improvement in aerothermodynamic and flight dynamics modeling remains data poor. The comparison is done in an effort to quantitatively and qualitatively compare Kalman filtering methods of reconstructing trajectories and atmospheric conditions from entry systems flight data. The first Kalman filter used is the extended Kalman filter. Extended Kalman filtering has been used extensively in trajectory reconstruction both for orbiting spacecraft and for planetary probes. The second Kalman filter is the unscented Kalman filter. Additionally, a technique for using collocation to reconstruct trajectories is formulated, and collocation's usefulness for trajectory simulation is demonstrated for entry, descent, and landing trajectories using a method developed here to deterministically find the state variables of the trajectory without nonlinear programming. Such an approach could allow one to utilize the same collocation trajectory design tools for the subsequent reconstruction.Ph.D.Committee Chair: Braun, Robert; Committee Member: Lisano, Michael; Committee Member: Russell, Ryan; Committee Member: Striepe, Scott; Committee Member: Volovoi, Vital
Data-driven neural mass modelling
The brain is a complex organ whose activity spans multiple scales, both spatial and temporal. The computational unit of the brain is thought to be the neurone. At the microscopic level, neurones communicate via action potentials. These may be observed experimentally by means of precise techniques that work with a small number of these cells and their interactions, and that can be modelled mathematically in a variety of ways. Other techniques consider the averaged activity of large groups of neurones in the mesoscale, or cortical columns; theoretical models of these signals also abound.
The problem of relating the microscopic scale to the mesoscopic is not trivial. Analytical derivations of mesoscopic models are based on assumptions that are not always justified. Also, traditionally there has been a separation between the clinically oriented analysts that process neural signals for medical purposes and the theoretical modelling community.
This Thesis aims to lay bridges both between the microscopic and mesoscopic scales of brain activity, and between the experimental and theoretical angles of its study. This is achieved via the unscented Kalman filter (UKF), which allows us to combine knowledge from different sources (microscopic/mesoscopic and experimental/theoretical). The outcome is a better understanding of the system than each of the sources of information could provide separately.
The Thesis is organised as follows. Chapter 1 is a brief reflection on the current methodology in Science and its underlying motivations. This is followed by chapters 2 to 4, which introduce and contextualise the concepts discussed in the remainder of the work.
Chapter 5 tackles the interrelationship of the microscopic and mesoscopic scales. Although efforts have been made to derive mesoscopic equations from models of microscopic networks, they are based on assumptions that may not always hold. We use the UKF to assimilate the output of microscopic networks into a mesoscopic model and study a variety of dynamical situations. Our results show that using the Kalman filter compensates for the loss of information that is common in analytical derivations.
Chapters 6 and 7 address the combination of experimental data with neural mass models. More specifically, we extend Jansen and Rit's model of a cortical column with a model of the head, which allows us to use electroencephalography (EEG) data. With this, we estimate the state of the system and a relevant parameter of choice.
In chapter 6 we use in silico data to test the UKF under a variety of dynamical conditions, comparing simulated intracranial data with simulated EEG. Extracranial estimation is always superior in speed and quality to intracortical estimation, even though intracortical electrodes are closer to the source of activity than extracranial electrodes. We suggest that this is due to the more complete picture of the cortex that is visible with the set of extracranial electrodes.
Chapter 7 feeds experimental EEG data of an epileptic patient into Jansen and Rit's model; the goal is to estimate a parameter that governs the dynamical behaviour of the system, again with the UKF. The estimation of the state closely follows the experimental data, while the parameter shows sensitivity to the changes in brain regimes, especially seizures.
These results show promise for using data assimilation to address some shortcomings of brain modelling techniques. On the one hand, the mutual influence of neural structures at the microscopic and the mesoscopic scales may become better characterised, by means of filtering approaches that bypass analytical limitations. On the other hand, fusing experimental EEG data with mathematical models of the brain may enable us to determine the underlying dynamics of observed physiological signals, and at the same time to improve our models with patient-specific information. The potential of these enhanced algorithms spans a wide range of brain-related applications.El cervell humà és un òrgan de gran complexitat l’activitat del qual es
desenvolupa en mĂşltiples escales, tant espacials com temporals. Es creu
que la unitat computacional del cervell és la neurona, una cèl·lula altament especialitzada que té com a funció rebre, processar i transmetre informació.
A nivell microscòpic, les neurones es comuniquen les unes amb les altres
per potencials d’acció. Aquests es poden observar experimentalment “in vivo” per mitjà de tècniques de gran precisió que només poden tenir en compte un nombre relativament reduït de cèl·lules i interaccions, i que es poden modelar matemà ticament de diverses maneres. Altres tècniques tracten amb grans grups de neurones a escala mesoscòpica, o columnes corticals, i detecten l’activitat mitjana de la població neuronal; en aquest cas també abunden els models teòrics que intenten reproduir aquests senyals.
Malgrat que estĂ ben establert que hi ha una intercomunicaciĂł entre les
escales microscòpica i mesoscòpica, relacionar una escala amb una altra
no Ă©s gens trivial. Les derivacions analĂtiques de models mesoscòpics a
partir de xarxes microscòpiques es basen en suposicions que no sempre
es poden justificar. A part, tradicionalment hi ha hagut una frontera de
separaciĂł entre els analistes clĂnics que processen senyals neuronals amb fins mèdics (i que sovint usen tècniques molt invasives i/o costoses), i la comunitat teòrica que modelitza aquests senyals, per a qui el repte mĂ©s gran Ă©s caracteritzar els parĂ metres que governen els models perquè aquests s’acostin el mĂ©s possible a la realitat.
Aquesta Tesi té com a objectiu, per una banda, fer un pas més a caracteritzar la relació entre les escales microscòpica i mesoscòpica d’activitat cerebral, i, per l’altra, establir ponts entre els punts de vista experimental i teòric del seu estudi. Ho aconseguim amb un algoritme d’assimilació de dades, el filtre de Kalman desodorat (UKF, de les sigles en anglès), que ens permet combinar informació de diverses procedències (microscòpica/mesoscòpica o experimental/teòrica). El resultat és una comprensió més à mplia del sistema estudiat que la que haurien permès les fonts d’informació per separat.
La Tesi estĂ organitzada de la segĂĽent manera. El capĂtol 1 comença amb una breu reflexiĂł sobre la metodologia cientĂfica actual i les seves motivacions subjacents (segons l’autora). El segueixen els capĂtols del 2 al 4, que introdueixen i posen en context els conceptes que s’exposen a la resta del treball.
El capĂtol 5 aborda el problema de la relaciĂł entre l’escala microscòpica
i la mesoscòpica. Tot i que existeixen diverses derivacions d’equacions
mesoscòpiques partint de models de xarxes neuronals, sovint es basen en suposicions frà gils que no es compleixen en situacions més complicades.
Aquà utilitzem l’UKF per assimilar la sortida de xarxes microscòpiques en
un model mesoscòpic simple i estudiar diverses situacions dinà miques.
Els resultats mostren que la manera que el filtre de Kalman gestiona les
incerteses del model compensa les pèrdues d’informació pròpies de les
derivacions analĂtiques de models mesoscòpics.
Els capĂtols 6 i 7 tracten la combinaciĂł de dades experimentals del cervell
amb models de masses neurals que descriuen la dinĂ mica de grups de
neurones. Concretament, estenem el model de Jansen i Rit d’una columna cortical amb un model del cap, el qual ens permet fer servir dades extracranials no invasives. Amb això estimem l’estat del sistema i un parĂ metre d’interès de possible rellevĂ ncia en l’estudi clĂnic d’afeccions com l’epilèpsia.
En el capĂtol 6 fem servir dades “in silico” per provar l’UKF en diversos escenaris dinĂ mics: conjunts de parĂ metres que causen comportaments diferents en les columnes corticals, diferents nivells de soroll de mesura i dues modalitats de transmissiĂł d’informaciĂł; tot això comparant dades intracranials simulades amb simulacions d’electroencefalogrames (EEG). En totes les situacions estudiades, l’estimaciĂł extracranial Ă©s sempre superior, en velocitat i precisiĂł, a l’estimaciĂł intracortical, encara que els elèctrodes intracorticals sĂłn molt mĂ©s propers a la font de l’activitat que els elèctrodes de la superfĂcie cranial.
Suggerim que això pot ser causat per la visió més completa del còrtex que es pot obtenir amb el conjunt d’elèctrodes extracranials. Aquesta idea ve reforçada pels resultats observats amb elèctrodes extracranials individuals treballant de manera independent, que apunten a la sensibilitat espacial de les mesures.
En el capĂtol 7 alimentem el model de Jansen i Rit amb dades experimentals de l’EEG d’un pacient epilèptic; l’objectiu Ă©s estimar un parĂ metre significatiu que governa l’evoluciĂł dinĂ mica del sistema, de nou amb l’UKF. L’estimaciĂł de l’estat Ă©s precisa i el parĂ metre es veu afectat pels canvis de règim, especialment (però no exclusivament) per les convulsions.
Aquests resultats són prometedors a l’hora d’utilitzar l’assimilació de dades per superar les diverses carències de les tècniques de modelització cerebral.
Per una banda, la influència mĂştua entre estructures a escala microscòpica i a escala mesoscòpica es pot caracteritzar millor, grĂ cies a tècniques de filtrat que permeten esquivar les habituals limitacions analĂtiques. Això dĂłna com a resultat una millor comprensiĂł de l’estructura i funciĂł cerebrals.
Per una altra banda, fusionar dades experimentals d’EEG amb els models matemà tics del cervell existents ens pot permetre determinar les dinà miques subjacents dels senyals fisiològics que tenim disponibles, a la vegada que millorem els nostres models amb informació individual de cada pacient.
Aquests algoritmes augmentats tenen potencial per a un ampli espectre
d’aplicacions en el camp de les neurociències, des d’interfĂcies cervell/ordinador fins a tota mena d’usos en medicina personalitzada com el diagnòstic precoç de malalties neurodegeneratives, la predicciĂł de crisis convulsives o la monitoritzaciĂł de la rehabilitaciĂł postisquèmica o posttraumĂ tica, entre molts altres.Postprint (published version
Quaternionic Attitude Estimation with Inertial Measuring Unit for Robotic and Human Body Motion Tracking using Sequential Monte Carlo Methods with Hyper-Dimensional Spherical Distributions
This dissertation examined the inertial tracking technology for robotics and human tracking applications. This is a multi-discipline research that builds on the embedded system engineering, Bayesian estimation theory, software engineering, directional statistics, and biomedical engineering.
A discussion of the orientation tracking representations and fundamentals of attitude estimation are presented briefly to outline the some of the issues in each approach. In addition, a discussion regarding to inertial tracking sensors gives an insight to the basic science and limitations in each of the sensing components.
An initial experiment was conducted with existing inertial tracker to study the feasibility of using this technology in human motion tracking. Several areas of improvement were made based on the results and analyses from the experiment. As the performance of the system relies on multiple factors from different disciplines, the only viable solution is to optimize the performance in each area. Hence, a top-down approach was used in developing this system.
The implementations of the new generation of hardware system design and firmware structure are presented in this dissertation. The calibration of the system, which is one of the most important factors to minimize the estimation error to the system, is also discussed in details. A practical approach using sequential Monte Carlo method with hyper-dimensional statistical geometry is taken to develop the algorithm for recursive estimation with quaternions.
An analysis conducted from a simulation study provides insights to the capability of the new algorithms. An extensive testing and experiments was conducted with robotic manipulator and free hand human motion to demonstrate the improvements with the new generation of inertial tracker and the accuracy and stability of the algorithm. In addition, the tracking unit is used to demonstrate the potential in multiple biomedical applications including kinematics tracking and diagnosis instrumentation.
The inertial tracking technologies presented in this dissertation is aimed to use specifically for human motion tracking. The goal is to integrate this technology into the next generation of medical diagnostic system
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