The development of resistance measurement techniques is very important for characterization of future nanoelectronics. Pulsed IV measurement techniques are very useful for accurate resistance measurements on nanoscale samples because of the efficient removal of e.g. EMF errors. In the project we have designed a pulsed IV-setup based on a state-of-the art current source (6221) and nanovoltmeter (2182A) from Keithley, and used the setup for resistance measurements on ferromagnetic samples. Two different samples were investigated using the pulsed IV system – ferromagnetic wires with a central nanoconstriction and amorphous microwires. We have tested the pulse delta system with different pulse widths, duty cycles and voltage levels. The results show a successful integration of the setup. From the measurement results we confirm that the pulse delta system provides accurate measurements with a low noise of about 0.02Ω. The resistance of the samples increases approximately quadratically with bias which is interpreted as a heating effect due to the very high current density of about 107A∙cm-2.

This study investigates the signal processing required in order to allow for the evaluation of hearing perception prediction models at low signal-to-noise Ratios (SNR). It focusses on speech enhancement and the estimation of the cues from which speech may be recognized, specifically where these cues are estimated from severely degraded speech (SNR ranging from -10 dB to -3 dB). This research has application in the field of cochlear implants (CI), where a listener would hear degraded speech due to several distortions introduced by the biophysical interface (e.g. frequency and amplitude discretization). These difficulties can also be interpreted as a loss in signal quality due to a specific type of noise. The ability to investigate perception in low SNR conditions may have application in the development of CI signal processing algorithms to counter the effects of noise. In the military domain a speech signal may be degraded intentionally by enemy forces or unintentionally owing to engine noise, for example. The ability to analyse and predict perception can be used for algorithm development to counter the unintentional or intentional interference or to predict perception degradation if low SNR conditions cannot be avoided. A previously documented perception model (Svirsky, 2000) is used to illustrate that the proposed signal processing steps can indeed be used to estimate the various cues used by the perception model at SNRs successfully as low as -10 dB. AFRIKAANS : Hierdie studie ondersoek die seinprosessering wat nodig is om ’n gehoorpersepsievoorspellingmodel te evalueer by lae sein-tot-ruis-verhoudings. Hierdie studie fokus op spraakverbetering en die estimasie van spraakeienskappe wat gebruik kan word tydens spraakherkenning, spesifiek waar hierdie eienskappe beraam word vir ernstig gedegradeerde spraak (sein-tot-ruisverhoudings van -10 dB tot -3 dB). Hierdie navorsing is van toepassing in die veld van kogleêre inplantings, waar die luisteraar degradering van spraak ervaar weens die bio-fisiese koppelvlak (bv. diskrete frekwensie en amplitude). Hierdie degradering kan gesien word as ’n verlies aan seinkwaliteit weens ’n spesifieke tipe ruis. Die vermoë om persepsie te ondersoek by lae sein-tot-ruis kan toegepas word tydens die ontwikkeling van kogleêre inplantingseinprosesseringalgoritmes om die effekte van ruis teen te werk. In die militêre omgewing kan spraak deur vyandige magte gedegradeer word, of degradering van spraak kan plaasvind as gevolg van bv. enjingeraas. Die vermoë om persepsie te ondersoek en te voorspel in die teenwoordigheid van ruis kan gebruik word vir algoritme-ontwikkeling om die ruis teen te werk of om die verlies aan persepsie te voorspel waar lae sein-tot-ruis verhoudings nie vermy kan word nie. ’n Voorheen gedokumenteerde persepsiemodel (Svirsky, 2000) word gebruik om te demonstreer dat die voorgestelde seinprosesseringstappe wel suksesvol gebruik kan word om die spraakeienskappe te beraam wat deur die persepsiemodel benodig word by sein-tot-ruis verhouding so laag as -10 dB. Copyright / Dissertation (MEng)--University of Pretoria, 2009. / Electrical, Electronic and Computer Engineering / unrestricted

La micropile à combustible à respiration est développée conjointement à STMicroelectronics Tours et au CEA Liten de Grenoble. De très faible puissance (stack de 1W), elle sera à moyen terme utilisée dans un système de recharge portable pour petites batteries Li-Ion (téléphones portables). Le fonctionnement et la structure de ces micropiles sont tels qu'elles sont très sensibles, entre autres, aux conditions atmosphériques caractérisant leur environnement. Cette sensibilité résulte en un comportement électrique très marqué et complexe. Or, l'aspect nomade de l'application fait que celle-ci devra pouvoir faire face à des atmosphères diverses et variées. Il est donc nécessaire de comprendre les interactions liant le comportement électrique de la micropile et l'environnement. Leur modélisation pourra par la suite apporter des éléments concrets en termes de pilotage d'auxiliaires (micro ventilateurs…) et de design de packaging, visant à contrôler l'environnement immédiat de la micropile de la meilleure façon possible. A cet effet, de nombreuses mesures, réalisées sous atmosphère maîtrisée, et sous plusieurs régimes de fonctionnement électrique, ont été croisées entre elles. Elles nous ont permis de poser les hypothèses d'un modèle quasistatique macroscopique de la micropile, reliant les conditions atmosphériques et opératoires à la réponse électrique de la micropile. Ce modèle a été développé à partir de la théorie de la diffusion en milieu poreux. Ce modèle quasistatique, faisant intervenir une description de la diffusion protonique cathodique, permet de représenter le comportement de la micropile sur une large gamme de conditions atmosphériques, et illustre physiquement autant les situations d'assèchement que de noyage. L’approche a ensuite été élargie au développement d'un modèle petit signal, paramétré grâce à une approche multi spectrale et multi conditions opératoires. Celui-ci permet entre autres de quantifier la dynamique associée au phénomène de diffusion protonique, tout en consolidant sa description quasistatique, ceci faisant intervenir des paramètres cohérents avec ceux du modèle quasistatique. Enfin, à la croisée des approches quasistatique et petit signal, les bases d'un modèle dynamique fort signal sont proposées. Elles font intervenir le modèle fort signal propre au LAPLACE, en y injectant la réponse dynamique à l'environnement et à la sollicitation électrique du bilan hydrique. Ce modèle, paramétré avec les paramètres issus du quasistatique et du petit signal, permet de représenter le comportement non linéaire de la micropile sur une large gamme de fréquences de sollicitations galvanostatiques fort signal. / The micro breathing fuel cell is developed by STMicroelectronics Tours and the CEA Liten of Grenoble. It is very low power (1W stack) and will eventually be used in a portable charging system for small Li-Ion batteries (cell phones). The structure of these micro fuel cells is such that they are very sensitive, among other things, to weather conditions characterizing their environment. This sensitivity results in a very complex electrical behavior. But the portable aspect of the application implies that it will have to cope with various atmospheres. It is therefore necessary to understand the interactions linking the electrical behavior of the micro fuel cell and the atmosphere. A model may then provide some concrete leads in terms of auxiliary control (micro fans ...) and packaging design, to control the immediate environment of the microcell in the best possible way. To this end, a lot of measure were carried out under controlled atmosphere, and in several electrical operating modes, and were crossed with each other. They let us build the assumptions for a macroscopic steady state model of micro fuel cell, linking atmospheric and operating conditions to the electrical response of the micro fuel cell. This model was inspired by the theory of diffusion in porous media. This steady state model, involving a description of a cathodic protonic diffusion, is used to represent the behavior of the micro fuel cell on a wide range of atmospheric conditions, and physically illustrates both drying out situations than drowning. The approach was then extended to develop a small signal model, configured with a multi spectral and multi-operating conditions approach. It allows among other things to quantify the dynamics associated with the phenomenon of proton diffusion, while consolidating its steady state description, this involving parameters consistent with those of the steady state model. Finally, at the intersection of the steady state and small signal approaches, the bases for a large signal dynamic model are proposed. They involve the large signal model which is specific to the LAPLACE, by injecting in it the dynamic response to environmental stress and to water balance. This model, with parameters set from the steady state and small signal models, turns out to be able to represent the nonlinear behavior of the micro fuel cell over a wide range of frequencies of the galvanostatic strong signal solicitation

The estimation and analysis of signals that have polynomial phase and constant or time-varying amplitudes with the addititve noise is considered in this dissertation.Much work has been undertaken on this problem over the last decade or so, and there are a number of estimation schemes available. The fundamental problem when trying to estimate the parameters of these type of signals is the nonlinear characterstics of the signal, which lead to computationally difficulties when applying standard techniques such as maximum likelihood and least squares. When considering only the phase data, we also encounter the well known problem of the unobservability of the true noise phase curve. The methods that are currently most popular involve differencing in phase followed by regression, or nonlinear transformations. Although these methods perform quite well at high signal to noise ratios, their performance worsens at low signal to noise, and there may be significant bias. One of the biggest problems to efficient estimation of these models is that the majority of methods rely on sequential estimation of the phase coefficients, in that the highest-order parameter is estimated first, its contribution removed via demodulation, and the same procedure applied to estimation of the next parameter and so on. This is clearly an issue in that errors in estimation of high order parameters affect the ability to estimate the lower order parameters correctly. As a result, stastical analysis of the parameters is also difficult. In thie dissertation, we aim to circumvent the issues of bias and sequential estiamtion by considering the issue of full parameter iterative refinement techniques. ie. given a possibly biased initial estimate of the phase coefficients, we aim to create computationally efficient iterative refinement techniques to produce stastically efficient estimators at low signal to noise ratios. Updating will be done in a multivariable manner to remove inaccuracies and biases due to sequential procedures. Stastical analysis and extensive simulations attest to the performance of the schemes that are presented, which include likelihood, least squares and bayesian estimation schemes. Other results of importance to the full estimatin problem, namely when there is error in the time variable, the amplitude is not constant, and when the model order is not known, are also condsidered.

Orientador: Carlos Lenz Cesar / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin / Made available in DSpace on 2018-08-20T14:50:59Z (GMT). No. of bitstreams: 1 Marques_GustavoPires_M.pdf: 1771357 bytes, checksum: 8ee6919633e2615608f25b33bec98e96 (MD5) Previous issue date: 2005 / Resumo: O microscópio de força atômica é uma ferramenta que possibilita a medida de forças precisamente localizadas com resoluções no tempo, espaço e força jamais vistas. No coração deste instrumento está um sensor a base de uma viga (cantilever) que é responsável pelas características fundamentais do AFM. O objetivo desta pesquisa foi usar a deflexão deste cantilever para obter uma calibração rápida e precisa da força da armadilha da pinça óptica, assim como testar e comparar com os método tradicionalmente utilizados para este propósito. Para isso, foi necessário analisar e entender o condicionamento de sinais utilizados no AFM. Foram estudados cantilever tradicionais, cujo sistema de detecção é baseado na deflexão de um feixe laser em conjunto com fotodetectores, bem como cantilevers piezoresistivos. Cantilevers piezoresistivos fornecem uma alternativa simples e conveniente aos cantilevers ópticos. A integração de um elemento sensorial dentro do cantilever elimina a necessidade de um laser externo e de um detector utilizados na maioria dos AFMs. Isto elimina a etapa delicada de alinhamento da laser ao cantilever e fotodetector que normalmente precede uma medida com AFM, uma simplificação que expande o potencial do AFM para o uso em meios adversos, como câmaras de ultra alto vácuo ou, como no caso específico das Pinças Ópticas, onde existem esferas em solução líquida e também restrições de dimensão / Abstract: The atomic force microscope (AFM) is a tool that enables the measurement of precisely localized forces with unprecedented resolution in time, space and force. At the heart of this instrument is a cantilever probe that sets the fundamental features of the AFM. The objective of this research has been using the deflection of this cantilever to get a fast and accurate calibration of optical tweezers trap force, as well as testing and comparing to the traditionally used methods of calibration for this purpose. For that it was necessary to resolve and understand the sensors signals conditioning used in the AFM. Traditional cantilevers, whose detection system is based on the deflection of a laser beam in addition with a photodetector, as well as piezoresistive cantilevers has been studied. Piezoresistive cantilevers provide a simple and convenient alternative to optically detected cantilevers. Integration of a sensing element into the cantilever eliminates the need for the external laser and detector used in most AFMs. This removes the delicate step of aligning the laser to the cantilever and photodetector which usually precedes an AFM measurement, a simplification which expands the potential of the AFM for use in difficult environments such as ultrahigh vacuum chambers or, as in Optical Tweezers specific case, where there are spheres into a liquid solution as well as dimensional constraints / Mestrado / Física / Mestre em Física

Les travaux de cette thèse concernent la conception d’outils algorithmiques permettant l’intégration de connaissances a priori dans la reconstruction de signaux parcimonieux. Le but étant principalement d’améliorer la reconstruction de ces signaux à partir d’un ensemble de mesures largement inférieur à ce que prédit le célèbre théorème de Shannon-Nyquist. Dans une première partie nous proposons, dans le contexte de la nouvelle théorie du « compressed sensing » (CS), l’algorithme NNOMP (Neural Network Orthogonal Matching Pursuit), qui est une version modifiée de l’algorithme OMP dans laquelle nous avons remplacé l'étape de corrélation par un réseau de neurones avec un entraînement adapté. Le but est de mieux reconstruire les signaux parcimonieux possédant des structures supplémentaires, i.e. appartenant à un modèle de signaux parcimonieux particulier. Pour la validation expérimentale de NNOMP, trois modèles simulés de signaux parcimonieux à structures supplémentaires ont été considérés, ainsi qu’une application pratique dans un arrangement similaire au « single pixel imaging ». Dans une deuxième partie, nous proposons une nouvelle méthode de sous-échantillonnage en spectroscopie RMN multidimensionnelle (y compris l’imagerie spectroscopique RMN), lorsque les spectres des acquisitions correspondantes de dimension inférieure, e.g. monodimensionnelle, sont intrinsèquement parcimonieux. Dans cette méthode, on modélise le processus d’acquisition des données et de reconstruction des spectres multidimensionnels, par un système d’équations linéaires. On utilise ensuite des connaissances a priori, sur les emplacements non nuls dans les spectres multidimensionnels, pour enlever la sous-détermination induite par le sous échantillonnage des données. Ces connaissances a priori sont obtenues à partir des spectres des acquisitions de dimension inférieure, e.g. monodimensionnelle. La possibilité de sous-échantillonnage est d’autant plus importante que ces spectres monodimensionnels sont parcimonieux. La méthode proposée est évaluée sur des données synthétiques et expérimentales in vitro et in vivo. / The work of this thesis concerns the proposal of algorithms for the integration of prior knowledge in the reconstruction of sparse signals. The purpose is mainly to improve the reconstruction of these signals from a set of measurements well below what is requested by the famous theorem of Shannon-Nyquist. In the first part we propose, in the context of the new theory of "compressed sensing" (CS), the algorithm NNOMP (Neural Network Orthogonal Matching Pursuit), which is a modified version of the algorithm OMP in which we replaced the correlation step by a properly trained neural network. The goal is to better reconstruct sparse signals with additional structures, i.e. belonging to a particular model of sparse signals. For the experimental validation of NNOMP three simulated models of sparse signals with additional structures were considered and a practical application in an arrangement similar to the “single pixel imaging”. In the second part, we propose a new method for under sampling in multidimensional NMR spectroscopy (including NMR spectroscopic imaging), when the corresponding spectra of lower dimensional acquisitions, e.g. one-dimensional, are intrinsically sparse. In this method, we model the whole process of data acquisition and reconstruction of multidimensional spectra, by a system of linear equations. We then use a priori knowledge about the non-zero locations in multidimensional spectra, to remove the under-determinacy induced by data under sampling. This a priori knowledge is obtained from the lower dimensional acquisition spectra, e.g. one-dimensional. The possibility of under sampling increases proportionally with the sparsity of these one dimensional spectra. The proposed method is evaluated on synthetic, experimental in vitro and in vivo data.