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Anilkumar, A. K. "Application Of Controlled Random Search Optimization Technique In MMLE With Process Noise." Thesis, Indian Institute of Science, 2000. http://hdl.handle.net/2005/232.
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Generally in most of the applications of estimation theory using the Method of Maximum Likelihood Estimation (MMLE) to dynamical systems one deals with a situation where only the measurement noise alone is present. However in many present day applications where modeling errors and random state noise input conditions occur it has become necessary for MMLE to handle measurement noise as well as process noise. The numerical algorithms accounting for both measurement and process noise require significantly an order of magnitude higher computer time and memory. Further more, implementation difficulties and convergence problems are often encountered. Here one has to estimate the quantities namely, the initial state error covariance matrix Po, measurement noise covariance matrix R, the process noise covariance matrix Q and the system parameter 0 and the present work deals with the above.
Since the above problem is fairly involved we need to have a good reference solution. For this purpose we utilize the approach and results of Gemson who considered the above problem via the extended Kalman filter (EKF) route to compare the present results from the MMLE route. The EKF uses the unknown parameters as additional states unlike in MMLE which uses only the system states.
Chapter 1 provides a brief historical perspective followed by parameter identification in the presence of process and measurement noises. The earlier formulations such as natural, innovation, combined, and adaptive approaches are discussed.
Chapter 2 deals with the heuristic adaptive tuning of the Kalman filter parameters for the matrices Q and R by Myers and Tapley originally developed for state estimation problems involving satellite orbit estimation. It turns out that for parameter estimation problems apart from the above matrices even the choice of the initial covariance matrix Po is crucial for obtaining proper parameter estimates with a finite amount of data and for this purpose the inverse of the information matrix for Po is used. This is followed by a description of the original Controlled Random Search (CRS) of Price and its variant as implemented and used in the present work to estimate or tune Q, R, and 0 which is the aim of the present work. The above help the reader to appreciate the setting under which the present study has been carried out.
Chapter 3 presents the results and the analysis of the estimation procedure adopted with respect to a specific case study of the lateral dynamics of an aircraft involving 15 unknown parameters. The reference results for the present work are the ones based on the approach of Gemson and Ananthasayanam (1998). The present work proceeds in two phases. In the first case (i) the EKF estimates for Po, Q, and R are used to obtain 0 and in the second case (ii) the estimate of Po and Q together with a reasonable choice of R are utilized to obtain 0 from the CRS algorithm. Thus one is able to assess the capability of the CRS to estimate only the unknown parameters.
The next Chapter 4 presents the results of utilizing the CRS algorithm with R based on a reasonable choice and for Po from the inverse of the information matrix to estimate both Q and 0. This brings out the efficiency of MMLE with CRS algorithm in the estimation of unknown process noise characteristics and unknown parameters. Thus it demonstratesthofcdifficult Q can be estimated using CRS technique without the attendant difficulties of the earlier MMLE formulations in dealing with process noise.
Chapter 5 discusses the - implementation of CRS to estimate the unknown measurement noise covariance matrix R together with the unknown 0 by utilizing the values of Po and Q obtained through EKF route. The effect of variation of R in the parameter estimation procedure is also highlighted in This Chapter. This Chapter explores the importance of Po in the estimation procedure. It establishes the importance of Po though most of the earlier works do not appear to have recognized such a feature. It turned out that the CRS algorithm does not converge when some arbitrary value of Po is chosen. It has to be necessarily obtained from a scouting pass of the EKF. Some sensitivity studies based on variations of Po shows its importance. Further studies shows the sequence of updates, the random nature of process and measurement noise effects, the deterministic nature of the parameter, play a critical role in the convergence of the algorithm.
The last Chapter 6 presents the conclusions from the present work and suggestions for further work.
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Barajas, Leandro G. "Process Control in High-Noise Environments Using A Limited Number Of Measurements." Diss., Georgia Institute of Technology, 2003. http://hdl.handle.net/1853/7741.
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The topic of this dissertation is the derivation, development, and evaluation of novel hybrid algorithms for process control that use a limited number of measurements and that are suitable to operate in the presence of large amounts of process noise.
As an initial step, affine and neural network statistical process models are developed in order to simulate the steady-state system behavior. Such models are vitally important in the evaluation, testing, and improvement of all other process controllers referred to in this work. Afterwards, fuzzy logic controller rules are assimilated into a mathematical characterization of a model that includes the modes and mode transition rules that define a hybrid hierarchical process control. The main processing entity in such framework is a closed-loop control algorithm that performs global and then local optimizations in order to asymptotically reach minimum bias error; this is done while requiring a minimum number of iterations in order to promptly reach a desired operational window.
The results of this research are applied to surface mount technology manufacturing-lines yield optimization. This work achieves a practical degree of control over the solder-paste volume deposition in the Stencil Printing Process (SPP). Results show that it is possible to change the operating point of the process by modifying certain machine parameters and even compensate for the difference in height due to change in print direction.
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Constant, Camille. "Modélisation stochastique et analyse statistique de la pulsatilité en neuroendocrinologie." Thesis, Poitiers, 2019. http://www.theses.fr/2019POIT2330.
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L’objectif de cette thèse est de proposer plusieurs modèles probabilistes pour représenter l’activité calcique des neurones et comprendre son implication dans la sécrétion d’hormone GnRH. Ce travail s’appuie sur des expériences réalisées à l’INRA Centre Val-de-Loire. Le Chapitre 1 propose une modélisation continue, où nous étudions un processus markovien de type shot-noise. Le Chapitre 2 étudie un modèle discret de type AR(1) basé sur la discrétisation du modèle du Chapitre 1 et propose une première estimation des paramètres. Le Chapitre 3 propose un autre modèle discret de type AR(1) où les innovations sont la somme d’une variable de Bernouilli et d’une variable gaussienne représentant un bruit, avec prise en compte d’une tendance linéaire. Des estimations des paramètres sont proposées dans le but d’une détection des sauts dans les trajectoires des neurones. Le Chapitre 4 étudie une expérience biologique comportant 33 neurones. Avec la modélisation du Chapitre 3, nous détectons des instants de synchronisation (saut simultané d’une grande proportion des neurones de l’expérience) puis à l’aide de simulations, nous testons la qualité de la méthode utilisée et la comparons à une méthode expérimentale<br>The aim of this thesis is to propose several models representing neuronal calcic activity and unsderstand its applicatition in the secretion of GnRH hormone. This work relies on experience realised in INRA Centre Val de Loire. Chapter 1 proposes a continuous model, in which we examine a Markov process of shot-noise type. Chapter 2 studies a discrete model type AR(1), based on a discretization of the model from Chapter 1 and proposes a first estimation of the parameters. Chapter 3 proposes another dicrete model, type AR(1), in which the innovations are the sum of a Bernouilli variable and a Gaussian variable representing a noise, and taking into account a linear drift . Estimations of the parameters are given in order to detect spikes in neuronal paths. Chapter 4 studies a biological experience involving 33 neurons. With the modelisation of Chapter 3, we detect synchronization instants (simultaneous spkike of a high proportion of neurons of the experience) and then, using simulations, we test the quality of the method that we used and we compare it to an experimental approach
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Sun, Yucheng. "Essays in volatility estimation based on high frequency data." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/402831.
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Based on high-frequency price data, this thesis focuses on estimating the realized covariance and the integrated volatility of asset prices, and applying volatility estimation to price jump detection. The first chapter uses the LASSO procedure to regularize some estimators of high dimensional realized covariance matrices. We establish theoretical properties of the regularized estimators that show its estimation precision and the probability that they correctly reveal the network structure of the assets. The second chapter proposes a novel estimator of the integrated volatility which is the quadratic variation of the continuous part in the price process. This estimator is obtained by truncating the two-scales realized variance estimator. We show its consistency in the presence of market microstructure noise and finite or infinite activity jumps in the price process. The third chapter employs this estimator to design a test to explore the existence of price jumps with noisy price data.<br>Basándonos en datos de precios de alta frecuencia, esta tesis se centra en la estimación de la covarianza realizada y la volatilidad integrada de precios de activos, y la aplicación de la estimación de la volatilidad para la detección de saltos en los precios. El primer capítulo utiliza el procedimiento LASSO para regularizar algunos estimadores de matrices de covarianza realizada de alta dimensión. Establecemos propiedades teóricas de los estimadores regularizados que muestran su precisión de estimación y la probabilidad de que revelen correctamente la estructura de red de los activos. En el segundo capítulo se propone un nuevo estimador de la volatilidad integrada que es la variación cuadrática de la parte continua en el proceso de precios. Este estimador se obtiene truncando el estimador de varianza realizado en dos escalas. Demostramos su consistencia en presencia de ruido de microestructura del mercado y saltos de actividad finitos o infinitos en el proceso de precios. El tercer capítulo emplea este estimador para diseñar un test para explorar la existencia de saltos en los precios con ruido.
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Jaoua, Nouha. "Estimation Bayésienne non Paramétrique de Systèmes Dynamiques en Présence de Bruits Alpha-Stables." Phd thesis, Ecole Centrale de Lille, 2013. http://tel.archives-ouvertes.fr/tel-00929691.
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Dans un nombre croissant d'applications, les perturbations rencontrées s'éloignent fortement des modèles classiques qui les modélisent par une gaussienne ou un mélange de gaussiennes. C'est en particulier le cas des bruits impulsifs que nous rencontrons dans plusieurs domaines, notamment celui des télécommunications. Dans ce cas, une modélisation mieux adaptée peut reposer sur les distributions alpha-stables. C'est dans ce cadre que s'inscrit le travail de cette thèse dont l'objectif est de concevoir de nouvelles méthodes robustes pour l'estimation conjointe état-bruit dans des environnements impulsifs. L'inférence est réalisée dans un cadre bayésien en utilisant les méthodes de Monte Carlo séquentielles. Dans un premier temps, cette problématique a été abordée dans le contexte des systèmes de transmission OFDM en supposant que les distorsions du canal sont modélisées par des distributions alpha-stables symétriques. Un algorithme de Monte Carlo séquentiel a été proposé pour l'estimation conjointe des symboles OFDM émis et des paramètres du bruit $\alpha$-stable. Ensuite, cette problématique a été abordée dans un cadre applicatif plus large, celui des systèmes non linéaires. Une approche bayésienne non paramétrique fondée sur la modélisation du bruit alpha-stable par des mélanges de processus de Dirichlet a été proposée. Des filtres particulaires basés sur des densités d'importance efficaces sont développés pour l'estimation conjointe du signal et des densités de probabilité des bruits
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Chen, Hao. "Noise enhanced signal detection and estimation." Related electronic resource:, 2007. http://proquest.umi.com/pqdweb?did=1342743841&sid=2&Fmt=2&clientId=3739&RQT=309&VName=PQD.
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Bareš, Robert. "Environmentally adaptive noise estimation for active sonar." Thesis, Cardiff University, 2012. http://orca.cf.ac.uk/18588/.
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Noise is frequently encountered when processing data from the natural environment, and is of particular concern for remote-sensing applications where the accuracy of data gathered is limited by the noise present. Rather than merely accepting that sonar noise results in unavoidable error in active sonar systems, this research explores various methodologies to reduce the detrimental effect of noise. Our approach is to analyse the statistics of sonar noise in trial data, collected by a long-range active sonar system in a shallow water environment, and apply this knowledge to target detection. Our detectors are evaluated against imulated targets in simulated noise, simulated targets embedded in noise-only trial data, and trial data containing real targets. First, we demonstrate that the Weibull and K-distributions offer good models of sonar noise in a cluttered environment, and that the K-distribution achieves the greatest accuracy in the tail of the distribution. We demonstrate the limitations of the Kolmogorov-Smirnov goodness-of-fit test in the context of detection by thresholding, and investigate the upper-tail Anderson-Darling test for goodness-of-fit analysis. The upper-tail Anderson-Darling test is shown to be more suitable in the context of detection by thresholding, as it is sensitive to the far-right tail of the distribution, which is of particular interest for detection at low false alarm rates. We have also produced tables of critical values for K-distributed data evaluated by the upper-tail Anderson-Darling test. Having established suitable models for sonar noise, we develop a number of detection statistics. These are based on the box-car detector, and the generalized likelihood ratio test with a Rician target model. Our performance analysis shows that both types of detector benefit from the use of the noise model provided by the K-distribution. We also demonstrate that for weak signals, our GLRT detectors are able to achieve greater probability of detection than the box-car detectors. The GLRT detectors are also easily extended to use more than one sample in a single test, an approach that we show to increase probability of detection when processing simulated targets. A fundamental difficulty in estimating model parameters is the small sample size. Many of the pings in our trial data overlap, covering the same region of the sea. It is therefore possible to make use of samples from multiple pings of a region, increasing the sample size. For static targets, the GLRT detector is easily extended to multi-ping processing, but this is not as easy for moving targets. We derive a new method of combining noise estimates over multiple pings. This calculation can be applied to either static or moving targets, and is also shown to be useful for generating clutter maps. We then perform a brief performance analysis on trial data containing real targets, where we show that in order to perform well, the GLRT detector requires a more accurate model of the target than the Rician distribution is able to provide. Despite this, we show that both GLRT and box-car detectors, when using the K-distribution as a noise model, can achieve a small improvement in the probability of detection by combining estimates of the noise parameters over multiple pings.
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Lowe, Alexander M. "Estimation of electrochemical noise impedance and corrosion rates from electrochemical noise measurements." Curtin University of Technology, School of Electrical and Computer Engineering, 2002. http://espace.library.curtin.edu.au:80/R/?func=dbin-jump-full&object_id=12723.
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Electrochemical noise refers to the spontaneous fluctuations in potential and current that can be observed on a corroding metal. The use of electrochemical noise for obtaining information on the corrosion process generates much interest in research fields. One important application is the measurement of corrosion rate. This can be achieved using the electrochemical noise of a pair of electrically coupled corroding metals to obtain an estimate of electrochemical impedance - an abstract quantity that reflects various aspects of the corrosion process.There are a number of problems associated with estimation of impedance information from the electrochemical noise data, particularly regarding data pre-treatment, accuracy and precision. In addition, the present methods are incomplete: current literature does not offer information regarding the phase of the impedance; and assumptions regarding symmetry of an electrode pair cannot be tested without additional measurements.The thesis addresses the above mentioned problems. Specifically,analysis of the impedance estimation process is given to determine how precision can be affected by various factors;a novel signal processing technique is described that is shown to yield a local optimum precision;the application of the proposed signal processing to time varying systems is demonstrated by use of a time varying, frequency dependent impedance estimate;a technique for recovering phase information, given certain conditions, is suggested so that Nyquist impedance diagrams can be constructed; anda technique for testing the symmetry of a coupled pair of corroding metals is described.An integral part of electrochemical noise analysis is the software used for numerical computation. The Matlab package from MathWorks inc. provides an extensible platform for electrochemical noise analysis. Matlab code is provided in Appendix A to implement ++<br>much of the theory discussed in the thesis.Impedance analysis and many other electrochemical corrosion monitoring techniques are primarily used for uniform corrosion, where the corrosion patterns occur uniformly over the exposed surface. In order to map localised corrosion, where the corrosion is typically concentrated within a small area, a wire beam electrode can be used. A wire beam electrode is a surface that is divided into a matrix of mini-electrodes so that the corrosion rate at different points can be monitored. However, manual connection of each mini-electrode to the measurement device can prove cumbersome. The final chapter of this thesis describes the design and testing of specialised multiplexing hardware to automate the process.In general, the thesis shows that by careful conditioning of the electrochemical noise prior to analysis, many of the problems with the technique of impedance estimation from the electrochemical noise data can be overcome. It is shown that the electrochemical noise impedance estimation can be extended to encompass a time varying, frequency dependent quantity for studying dynamic systems; that phase information can be recovered from electrochemical noise for the purpose of constructing Nyquist impedance diagrams; and that asymmetric electrodes can be detected without requiring additional measurements.
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Warner, Carl Michael 1952. "ESTIMATION OF NONSTATIONARY SIGNALS IN NOISE (PROCESSING, ADAPTIVE, WIENER FILTERS, ESTIMATION, DIGITAL)." Thesis, The University of Arizona, 1986. http://hdl.handle.net/10150/291297.
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Dorosh, Anastasiia. "Direction-of-Arrival Estimation in Spherically Isotropic Noise." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-28603.
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Today the multisensor array signal processing of noisy measurements has received much attention. The classical problem in array signal processing is determining the location of an energy-radiating source relative to the location of the array, in other words, direction-of-arrival (DOA) estimation. One is considering the signal estimation problem when together with the signal(s) of interest some noise and interfering signals are present. In this report a direction-of-arrival estimation system is described based on an antenna array for detecting arrival angles in azimuth plane of signals pitched by the antenna array. For this, the Multiple Signal Classication (MUSIC) algorithmis first of all considered. Studies show that in spite of its good reputation and popularity among researches, it has a certain limit of its performance. In this subspace-based method for DOA estimation of signal wavefronts, the term corresponding to additive noise is initially assumed spatially white. In our paper, we address the problem of DOA estimation of multiple target signals in a particular noise situation - in correlated spherically isotropic noise, which, in many practical cases, models a more real context than under the white noise assumption. The purpose of this work is to analyze the behaviour of the MUSIC algorithm and compare its performance with some other algorithms (such as the Capon and the Classical algorithms) and, uppermost, to explore the quality of the detected angles in terms of precision depending on different parameters, e.g. number of samples, noise variance, number of incoming signals. Some modifications of the algorithms are also done is order to increase their performance. Program MATLAB is used to conduct the studies. The simulation results on the considered antenna array system indicate that in complex conditions the algorithms in question (and first of all, the MUSIC algorithm) are unable to automatically detect and localize the DOA signals with high accuracy. Other algorithms andways for simplification the problem (for example, procedure of denoising) exist and may provide more precision but require more computation time.
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Nadadur, Desikachari. "Noise covariance estimation in low-level computer vision /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/5988.
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Niles, Frederick A. "Estimation of noise variances for shuttle proximity operations." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/49609.
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Aparnnaa. "Image Denoising and Noise Estimation by Wavelet Transformation." Kent State University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=kent1555929391906805.
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Granefelt, Håkan. "Applicability of ISO standards & noise estimation tool." Thesis, KTH, MWL Marcus Wallenberg Laboratoriet, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-203817.
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This study has two major purposes: (1) To determine the applicability of ISO standards for calculating sound power levels emitted by acoustic noise sources in-house at Ericsson, Kista. (2) To design a software tool for estimation of emitted sound power levels from a radio installation. To determine the applicability of different ISO standards during the design phase at Ericsson, viable standards were chosen and measurements were taken on the exact same noise source following each chosen standard. The evaluation of these measurements was made with different points in focus. Accuracy Availability of instrumentation and measurement environments Time consumption Complexity of measurement conduction. The ISO standard that showed most promise was the ISO 3744. This standard uses a method with fairly high accuracy and is quite easy to implement compared to other standards tested. All instrumentation required to follow this standard, except for a calibrated microphone calibrator, is available at Ericsson, Kista. The standards ISO 3747 and ISO 9614-2 might also be of interest, during the design phase, if the equipment necessary is acquired. The acoustic noise estimation tool was written in the Microsoft Excel embedded programming language, Visual Basic for Application version 7.1. With the tool it is possible to specify the layout of a radio installation for mobile traffic with different mounted radios and estimate both the sound power level emitted from all these radio units and the sound pressure level at a distance from the radio installation. It is also possible to define new radio units in the tool and save them for later use. The tool was verified by acoustic measurements taken on a test setup at MWL, KTH.<br>Denna studie har två huvudsyften: (1) Att bestämma tillämpligheten av ISO-standarder för beräkning av ljudeffektnivåer som avges av bullerkällor internt på Ericsson, Kista. (2) Att utforma ett mjukvaruverktyg för uppskattning av utsända ljudeffektnivåer från en radioinstallation. För att bestämma tillämpligheten av olika ISO-standarder under designfasen på Ericsson, valdes genomförbara standarder ut och mätningar gjordes på en och samma bullerkälla enligt varje separat standard. Utvärderingen av dessa mätningar gjordes med olika punkter i fokus. • Noggrannhet • Tillgång till utrustning och mätmiljöer • Tidsåtgång • Mätmetodens komplexitet Den ISO-standard som visade sig mest lovande var ISO 3744. Denna standard använder en metod med relativt hög noggrannhet och är ganska lätt att genomföra jämfört med andra standarder som testats. Alla instrument som krävs för att följa denna standard, med undantag för en kalibrerad mikrofon kalibrator, finns på Ericsson, Kista. Standarderna ISO 3747 och ISO 9614-2 kan också vara av intresse om den utrustning som behövs, för att följa standarderna införskaffas.
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Bahrami, Mohsen, and Bane Vasic. "Noise Predictive Information Rate Estimation for TDMR Channels." International Foundation for Telemetering, 2016. http://hdl.handle.net/10150/624263.
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In this paper, we use the forward recursion BCJR algorithm to estimate the symmetric
information rate for Two Dimensional Magnetic Recording (TDMR) channels. In particular, we
consider a TDMR read/write channel whose all components, including recording medium, write
and readback processes are modeled in software. Since the primary source of noise in TDMR
arises from irregularities in the recording medium and leads to highly colored and data-dependent
jitter, the pattern dependent noise predictive (PDNP) algorithm is implemented to improve the
accuracy and performance of SIR estimation. Furthermore, we study the performance gain of
using the PDNP algorithm in SIR estimation through simulations over the Voronoi based media
model for different TDMR channel configurations.
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Benko, Matej. "Hledaní modelů pohybu a jejich parametrů pro identifikaci trajektorie cílů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-445467.
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Táto práca sa zaoberá odstraňovaním šumu, ktorý vzniká z tzv. multilateračných meraní leteckých cieľov. Na tento účel bude využitá najmä teória Bayesovských odhadov. Odvodí sa aposteriórna hustota skutočnej (presnej) polohy lietadla. Spolu s polohou (alebo aj rýchlosťou) lietadla bude odhadovaná tiež geometria trajektórie lietadla, ktorú lietadlo v aktuálnom čase sleduje a tzv. procesný šum, ktorý charakterizuje ako moc sa skutočná trajektória môže od tejto líšiť. Odhad spomínaného procesného šumu je najdôležitejšou časťou tejto práce. Je odvodený prístup maximálnej vierohodnosti a Bayesovský prístup a ďalšie rôzne vylepšenia a úpravy týchto prístupov. Tie zlepšujú odhad pri napr. zmene manévru cieľa alebo riešia problém počiatočnej nepresnosti odhadu maximálnej vierohodnosti. Na záver je ukázaná možnosť kombinácie prístupov, t.j. odhad spolu aj geometrie aj procesného šumu.
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Konaté, Cheick Mohamed. "Enhancing speech coder quality: improved noise estimation for postfilters." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=104578.
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ITU-T G.711.1 is a multirate wideband extension for the well-known ITU-T G.711 pulse code modulation of voice frequencies. The extended system is fully interoperable with the legacy narrowband one. In the case where the legacy G.711 is used to code a speech signal and G.711.1 is used to decode it, quantization noise may be audible. For this situation, the standard proposes an optional postfilter. The application of postfiltering requires an estimation of the quantization noise. The more accurate the estimate of the quantization noise is, the better the performance of the postfilter can be.In this thesis, we propose an improved noise estimator for the postfilter proposed for the G.711.1 codec and assess its performance. The proposed estimator provides a more accurate estimate of the noise with the same computational complexity.<br>ITU-T G.711.1 est une extension multi-débit pour signaux à large-bande de la très répandue norme de compression audio de UIT-T G.711. Cette extension est interoperationelle avec sa version initiale à bande étroite. Lorsque l'ancienne version G.711 est employée pour coder un signal vocal et que G.711.1 est utiliser pour le décoder, le bruit de quantificationpeut être entendu. Pour ce cas, la norme propose un post-filtre optionel. Le post-filtre nécessite l'estimation du bruit de quantification. La précision de l'estimation du bruit de quantification va jouer sur la performance du post-filtre.Dans cette thèse, nous proposons un meilleur estimateur du bruit de quantification pour le post-filtre proposé pour le codec G.711.1 et nous évaluons ses performances. L'estimateur que nous proposons donne une estimation plus précise du bruit de quantification avec la même complexité.
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Younan, Nicolas H. "Estimation techniques for parameters of complex exponentials with noise." Ohio : Ohio University, 1988. http://www.ohiolink.edu/etd/view.cgi?ohiou1172004849.
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Eliasson, Björn. "Voice Activity Detection and Noise Estimation for Teleconference Phones." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-108395.
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If communicating via a teleconference phone the desired transmitted signal (speech) needs to be crystal clear so that all participants experience a good communication ability. However, there are many environmental conditions that contaminates the signal with background noise, i.e sounds not of interest for communication purposes, which impedes the ability to communicate due to interfering sounds. Noise can be removed from the signal if it is known and so this work has evaluated different ways of estimating the characteristics of the background noise. Focus was put on using speech detection to define the noise, i.e. the non-speech part of the signal, but other methods not solely reliant on speech detection but rather on characteristics of the noisy speech signal were included. The implemented techniques were compared and evaluated to the current solution utilized by the teleconference phone in two ways, firstly for their speech detection ability and secondly for their ability to correctly estimate the noise characteristics. The evaluation process was based on simulations of the methods' performance in various noise conditions, ranging from harsh to mild environments. It was shown that the proposed method showed improvement over the existing solution, as implemented in this study, in terms of speech detection ability and for the noise estimate it showed improvement in certain conditions. It was also concluded that using the proposed method would enable two sources of noise estimation compared to the current single estimation source and it was suggested to investigate how utilizing two noise estimators could affect the performance.
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Yardibi, Tarik. "Source localization and power estimation in aeroacoustic noise measurements." [Gainesville, Fla.] : University of Florida, 2009. http://purl.fcla.edu/fcla/etd/UFE0024868.
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Chernets, Olga. "The estimation of Arctic ice thickness from ambient noise." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/38188.
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Brookshire, Jonathan David. "Articulated pose estimation via over-parametrization and noise projection." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/87915.
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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 155-161).<br>Outside the factory, robots will often encounter mechanical systems with which they need to interact. The robot may need to open and unload a kitchen dishwasher or move around heavy construction equipment. Many of the mechanical systems encountered can be described as a series of rigid segments connected by joints. The pose of a segment places constraints on adjacent segments because they are mechanically 'connected. When modeling or perceiving the motion of such an articulated system, it is beneficial to make use of these constraints to reduce uncertainty. In this thesis, we examine two aspects of perception related to articulated structures. First, we examine the special case of a single segment and recover the rigid body transformation between two sensors mounted on it. Second, we consider the task of tracking the configuration of a multi-segment structure, given some knowledge of its kinematics. First, we develop an algorithm to recover the rigid body transformation, or extrinsic calibration, between two sensors on a link of a mobile robot. The single link, a degenerate articulated object, is often encountered in practice. The algorithm requires only a set of sensor observations made as the robot moves along a suitable path. Over-parametrization of poses avoids degeneracies and the corresponding Lie algebra enables noise projection to and from the over-parametrized space. We demonstrate and validate the end-to-end calibration procedure, achieving Cramer-Rao Lower Bounds. The parameters are accurate to millimeters and milliradians in the case of planar LIDARs data and about 1 cm and 1 degree for 6-DOF RGB-D cameras. Second, we develop a particle filter to track an articulated object. Unlike most previous work, the algorithm accepts a kinematic description as input and is not specific to a particular object. A potentially incomplete series of observations of the object's links are used to form an on-line estimate of the object's configuration (i.e., the pose of one link and the joint positions). The particle filter does not require a reliable state transition model, since observations are incorporated during particle proposal. Noise is modeled in the observation space, an over-parametrization of the state space, reducing the dependency on the kinematic description. We compare our method to several alternative implementations and demonstrate lower tracking error for fixed observation noise.<br>by Jonathan David Brookshire.<br>Ph. D.
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Tan, Cheng Hock. "Frequency segmented estimation in real-time active noise control." Thesis, University of Southampton, 2003. https://eprints.soton.ac.uk/48109/.
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This thesis addresses three aspects of frequency estimation. First, the problem of accurately estimating frequencies in a multi-tonal signal is discussed with references to many techniques used today. Particularly, frequency estimation methods such as fast Fourier transform (FFT), Multiple Signal Classification (MUSIC), Quinn and Fernandes (Q&F) and a proposed frequency estimation approach called Optimal Frequency Estimation (OFE) are elaborated. This latter technique is proposed as a way to increase the estimation accuracy of tonal frequency components in a complex signal with limited data length. OFE is compared with FFT, MUSIC, a popular multi-tonal frequency estimation method, and the recent Q&F method. Signals with added Gaussian noise are used to investigate its estimation accuracy and variance empirically. The technique is shown to perform well on simulation data. Results demonstrate its superiority in estimating frequencies at signal to noise ratio from 24dB and above. Its computational effort in estimating frequencies is also shown to be efficient as OFE is based on gradient free convergence. Next, OFE is applied to a single input single output (SISO) control system developed recently to investigate its influence. The controller uses online plant estimation and it is extended to multiple input multiple output (MIMO) systems. The online plant estimation and control attenuates unknown disturbance tones through continuous tuning. A stability criterion is derived to demonstrate the need for accurate frequency estimation in the control scheme. The performance of the control algorithm OFE is investigated by computer simulation using an air duct system as an example. Results show that accurate frequency estimation is essential for a successful and stable attenuation. Finally, three hardware test rigs are designed to realize the above schemes for practical digital signal processing (DSP) laboratory experiments. Active sound control is studied on a microphone-speaker array for the SISO case. A novel satellite plate test rig is used to investigate micro-vibrations on board a spacecraft. An L-shaped enclosure modelled after a commercial compressor is constructed with shakers and accelerometers mounted for MIMO control. In conclusion, future work and implementation under more general cases are outlined.
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Mitsogianni, Vivian, and Vivian Mitsogianni@rmit edu au. "white noise PANORAMA: Process-based Architectural Design." RMIT University. Architecture and Design, 2009. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20091218.111942.
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This PhD by project is an examination of process-based architectural design. It offers an examination of one approach to undertaking process-based experimentation in architecture - based on reflection of my own practice and body of work - through which I have been able to consider a complex array of questions and issues that are associated with working in this way. By
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Petrovic, Denis [Verfasser]. "Phase Noise in OFDM : Characterisation, Estimation and Suppression / Denis Petrovic." Aachen : Shaker, 2005. http://d-nb.info/1186589124/34.
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Copeland, Andrew David 1978. "Robust motion estimation in the presence of fixed pattern noise." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/87395.
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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.<br>Includes bibliographical references (p. 41-42).<br>by Andrew David Copeland.<br>M.Eng.
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Loberg, Johannes, and Miranda Gisudden. "Estimation of Noise and Contrast for CTA of the Brain." Thesis, KTH, Skolan för kemi, bioteknologi och hälsa (CBH), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-239916.
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Computed tomography angiography (CTA) of the brain poses challenges on the imaging system; the contrast between blood vessels and surrounding soft tissue is very low, and to render small intricate vessel structures high spatial resolution is needed. Higher precision angiography would facilitate more accurate diagnosis of pathological conditions. The aim of this work was to analyze the factors which contribute to the image quality in cerebrovascular imaging contexts and make a comparison between state-of-the-art energy-integrating and photon counting CT systems. A geometrical model was devised to mimic the conditions of cerebral angiography. Different parameters and detectors were used to reconstruct images of a spherical head phantom. Compton noise was added to several image acquisitions after a Monte Carlo study was used to estimate the scatter to primary ratio (SPR) with a spherical phantom. The images were evaluated qualitatively and quantitatively. A real phantom was scanned with an experimental photon counting detector and compared with the simulated approach. The work resulted in qualitative reconstructed images, a decrease in SPR when introducing air gaps and improved resolution but worsened contrast as a result of smaller detector sizes. The SPR was shown to be higher in cone-beam geometry than fan-beam geometry. Electronic noise present with energy integrating detectors was shown to degrade image quality significantly in low dose imaging, reducing contrast when imaging vascular-like structures. Photon counting detectors without electronic noise could provide greater image quality and better diagnostic information.
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Ghogho, Mounir. "Détection and estimation of signals in multiplicative and additive noise." Toulouse, INPT, 1997. http://www.theses.fr/1997INPT111H.
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L'estimation de sinusoides est un des problemes les plus frequents en traitement du signal. La plupart des methodes existantes sont destinees a l'estimation de sinusoides a amplitude constante dans un bruit additif. Les sources de bruit non additif sont ignorees ou negligees. L'objectif principal de cette these est l'etude de sinusoides en presence de bruits multiplicatif et additif. Un tel model est rencontre dans de nombreuses applications allant du radar, sonar et systemes de telecommunication au traitements d'image et de la parole. La premiere partie de cette these traite le probleme d'estimation de signaux deterministes en presence d'un bruit colore. Nous developpons un algorithme rapide pour le calcul de la matrice d'information de fisher. Des cas particuliers tels que les sinusoides, les sinusoids amorties et les signaux a phase polynomiale sont traites en detail. Les effets de la coloration du bruit sur les performances d'estimation optimales sont etudies. La seconde partie est consacree au probleme d'estimation de sinusoides en presence de bruits multiplicatif et additif. Dans un premier temps, nous explicitons des bornes inferieures des erreurs d'estimation des parametres, et nous proposons une definition pour le rapport signal a bruit. Ensuite, nous derivons les meilleures statistiques cycliques pour la detection et l'estimation. Nous etudions le comportement du lms ale (adaptive line enhancer) quand il est applique a l'extraction de sinusoides noyees dans un bruit multiplicatif colore et un bruit additif blanc. La troisieme partie de la these etudie un modele de bruit multiplicatif rencontre dans les systemes d'imagerie sar et laser. Nous developpons aussi bien des detecteurs optimaux que des detecteurs localement optimaux. Des expressions explicites pour la borne de cramer-rao et des algorithmes pour l'estimation au sens du maximum de vraisemblance sont proposes.
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Kees, Joel Thomas. "Robust Blind Spectral Estimation in the Presence of Impulsive Noise." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/88385.
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Robust nonparametric spectral estimation includes generating an accurate estimate of the Power Spectral Density (PSD) for a given set of data while trying to minimize the bias due to data outliers. Robust nonparametric spectral estimation is applied in the domain of electrical communications and digital signal processing when a PSD estimate of the electromagnetic spectrum is desired (often for the goal of signal detection), and when the spectrum is also contaminated by Impulsive Noise (IN). Power Line Communication (PLC) is an example of a communication environment where IN is a concern because power lines were not designed with the intent to transmit communication signals. There are many different noise models used to statistically model different types of IN, but one popular model that has been used for PLC and various other applications is called the Middleton Class A model, and this model is extensively used in this thesis. The performances of two different nonparametric spectral estimation methods are analyzed in IN: the Welch method and the multitaper method. These estimators work well under the common assumption that the receiver noise is characterized by Additive White Gaussian Noise (AWGN). However, the performance degrades for both of these estimators when they are used for signal detection in IN environments. In this thesis basic robust estimation theory is used to modify the Welch and multitaper methods in order to increase their robustness, and it is shown that the signal detection capabilities in IN is improved when using the modified robust estimators.<br>Master of Science<br>One application of blind spectral estimation is blind signal detection. Unlike a car radio, where the radio is specifically designed to receive AM and PM radio waves, sometimes it is useful for a radio to be able to detect the presence of transmitted signals whose characteristics are not known ahead of time. Cognitive radio is one application where this capability is useful. Often signal detection is inhibited by Additive White Gaussian Noise (AWGN). This is analogous to trying to hear a friend speak (signal detection) in a room full of people talking (background AWGN). However, some noise environments are more impulsive in nature. Using the previous analogy, the background noise could be loud banging caused by machinery; the noise will not be as constant as the chatter of the crowd, but it will be much louder. When power lines are used as a medium for electromagnetic communication (instead of just sending power), it is called Power Line Communication (PLC), and PLC is a good example of a system where the noise environment is impulsive. In this thesis, methods used for blind spectral estimation are modified to work reliably (or robustly) for impulsive noise environments.
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Nasr, Amr. "A Noise Estimation Scheme for Blind Spectrum Sensing Using EMD." Thesis, University of New Hampshire, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10274375.
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<p> The scarcity of spectral resources in wireless communications, due to a fixed frequency allocation policy, is a strong limitation to the increasing demand for higher data rates. One solution is to use underutilized spectrum. Cognitive Radio (CR) technologies identify transmission opportunities in unused channels and avoid interfering with primary users. The key enabling technology is the Spectrum Sensing (SS). Different SS techniques exist, but techniques that do not require knowledge of the signals (non-coherent) are preferred. Noise estimation plays an essential role in enhancing the performance of non-coherent spectrum sensors such as energy detectors. In this thesis, we present an energy detector based on the behavior of Empirical Mode Decomposition (EMD) towards vacant channels (noise-dominant). The energy trend from the EMD processed signal is used to determine the occupancy of a given band of interest. The performance of the proposed EMD-based detector is evaluated for different noise levels and sample sizes. Further, a comparison is carried out with conventional spectrum sensing techniques to validate the efficacy of the proposed detector and the results revealed that it outperforms the other sensing methods. </p><p>
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Gustavsson, Jan-Olof. "Estimation in non-gaussian noise and classification of welding signals." Licentiate thesis, Luleå tekniska universitet, Signaler och system, 1991. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-26514.
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Iscar, Vergara Jorge. "Channel and Noise Variance Estimation for Future 5G Cellular Networks." FIU Digital Commons, 2016. http://digitalcommons.fiu.edu/etd/3026.
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Future fifth generation (5G) cellular networks have to cope with the expected ten-fold increase in mobile data traffic between 2015 and 2021. To achieve this goal, new technologies are being considered, including massive multiple-input multiple-output (MIMO) systems and millimeter-wave (mmWave) communications. Massive MIMO involves the use of large antenna array sizes at the base station, while mmWave communications employ frequencies between 30 and 300 GHz. In this thesis we study the impact of these technologies on the performance of channel estimators.
Our results show that the characteristics of the propagation channel at mmWave frequencies improve the channel estimation performance in comparison with current, low frequency-based, cellular networks. Furthermore, we demonstrate the existence of an optimal angular spread of the multipath clusters, which can be used to maximize the capacity of mmWave networks. We also propose efficient noise variance estimators, which can be employed as an input to existing channel estimators.
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Ceylan, Ciwan. "Conditional Noise-Contrastive Estimation : With Application to Natural Image Statistics." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-213847.
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Unnormalised parametric models are an important class of probabilistic models which are difficult to estimate. The models are important since they occur in many different areas of application, e.g. in modelling of natural images, natural language and associative memory. However, standard maximum likelihood estimation is not applicable to unnormalised models, so alternative methods are required. Noise-contrastive estimation (NCE) has been proposed as an effective estimation method for unnormalised models. The basic idea is to transform the unsupervised estimation problem into a supervised classification problem. The parameters of the unnormalised model are learned by training the model to differentiate the given data samples from generated noise samples. However, the choice of the noise distribution has been left open to the user, and as the performance of the estimation may be sensitive to this choice, it is desirable for it to be automated. In this thesis, the ambiguity in the choice of the noise distribution is addressed by presenting the previously unpublished conditional noise-contrastive estimation (CNCE) method. Like NCE, CNCE estimates unnormalised models by classifying data and noise samples. However, the choice of noise distribution is partly automated via the use of a conditional noise distribution that is dependent on the data. In addition to introducing the core theory for CNCE, the method is empirically validated on data and models where the ground truth is known. Furthermore, CNCE is applied to natural image data to show its applicability in a realistic application.<br>Icke-normaliserade parametriska modeller utgör en viktig klass av svåruppskattade statistiska modeller. Dessa modeller är viktiga eftersom de uppträder inom många olika tillämpningsområden, t.ex. vid modellering av bilder, tal och skrift och associativt minne. Dessa modeller är svåruppskattade eftersom den vanliga maximum likelihood-metoden inte är tillämpbar på icke-normaliserade modeller. Noise-contrastive estimation (NCE) har föreslagits som en effektiv metod för uppskattning av icke-normaliserade modeller. Grundidén är att transformera det icke-handledda uppskattningsproblemet till ett handlett klassificeringsproblem. Den icke-normaliserade modellens parametrar blir inlärda genom att träna modellen på att skilja det givna dataprovet från ett genererat brusprov. Dock har valet av brusdistribution lämnats öppet för användaren. Eftersom uppskattningens prestanda är känslig gentemot det här valet är det önskvärt att få det automatiserat. I det här examensarbetet behandlas valet av brusdistribution genom att presentera den tidigare opublicerade metoden conditional noise-contrastive estimation (CNCE). Liksom NCE uppskattar CNCE icke-normaliserade modeller via klassificering av data- och brusprov. I det här fallet är emellertid brusdistributionen delvis automatiserad genom att använda en betingad brusdistribution som är beroende på dataprovet. Förutom att introducera kärnteorin för CNCE valideras även metoden med hjälp av data och modeller vars genererande parametrar är kända. Vidare appliceras CNCE på bilddata för att demonstrera dess tillämpbarhet.
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Jin, Xiaodan. "Poisson Approximation to Image Sensor Noise." University of Dayton / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1292306911.
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Pickering, John David. "Process value estimation at Pacific Bell." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1995. http://handle.dtic.mil/100.2/ADA305649.
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Rogge-Solti, Andreas. "Probabilistic Estimation of Unobserved Process Events." Phd thesis, Universität Potsdam, 2014. http://opus.kobv.de/ubp/volltexte/2014/7042/.
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Organizations try to gain competitive advantages, and to increase customer satisfaction. To ensure the quality and efficiency of their business processes, they perform business process management. An important part of process management that happens on the daily operational level is process controlling. A prerequisite of controlling is process monitoring, i.e., keeping track of the performed activities in running process instances. Only by process monitoring can business analysts detect delays and react to deviations from the expected or guaranteed performance of a process instance. To enable monitoring, process events need to be collected from the process environment.
When a business process is orchestrated by a process execution engine, monitoring is available for all orchestrated process activities. Many business processes, however, do not lend themselves to automatic orchestration, e.g., because of required freedom of action. This situation is often encountered in hospitals, where most business processes are manually enacted. Hence, in practice it is often inefficient or infeasible to document and monitor every process activity. Additionally, manual process execution and documentation is prone to errors, e.g., documentation of activities can be forgotten. Thus, organizations face the challenge of process events that occur, but are not observed by the monitoring environment. These unobserved process events can serve as basis for operational process decisions, even without exact knowledge of when they happened or when they will happen. An exemplary decision is whether to invest more resources to manage timely completion of a case, anticipating that the process end event will occur too late.
This thesis offers means to reason about unobserved process events in a probabilistic way. We address decisive questions of process managers (e.g., "when will the case be finished?", or "when did we perform the activity that we forgot to document?") in this thesis. As main contribution, we introduce an advanced probabilistic model to business process management that is based on a stochastic variant of Petri nets. We present a holistic approach to use the model effectively along the business process lifecycle. Therefore, we provide techniques to discover such models from historical observations, to predict the termination time of processes, and to ensure quality by missing data management. We propose mechanisms to optimize configuration for monitoring and prediction, i.e., to offer guidance in selecting important activities to monitor. An implementation is provided as a proof of concept. For evaluation, we compare the accuracy of the approach with that of state-of-the-art approaches using real process data of a hospital. Additionally, we show its more general applicability in other domains by applying the approach on process data from logistics and finance.<br>Unternehmen versuchen Wettbewerbsvorteile zu gewinnen und die Kundenzufriedenheit zu erhöhen. Um die Qualität und die Effizienz ihrer Prozesse zu gewährleisten, wenden Unternehmen Geschäftsprozessmanagement an. Hierbei spielt die Prozesskontrolle im täglichen Betrieb eine wichtige Rolle. Prozesskontrolle wird durch Prozessmonitoring ermöglicht, d.h. durch die Überwachung des Prozessfortschritts laufender Prozessinstanzen. So können Verzögerungen entdeckt und es kann entsprechend reagiert werden, um Prozesse wie erwartet und termingerecht beenden zu können. Um Prozessmonitoring zu ermöglichen, müssen prozessrelevante Ereignisse aus der Prozessumgebung gesammelt und ausgewertet werden.
Sofern eine Prozessausführungsengine die Orchestrierung von Geschäftsprozessen übernimmt, kann jede Prozessaktivität überwacht werden. Aber viele Geschäftsprozesse eignen sich nicht für automatisierte Orchestrierung, da sie z.B. besonders viel Handlungsfreiheit erfordern. Dies ist in Krankenhäusern der Fall, in denen Geschäftsprozesse oft manuell durchgeführt werden. Daher ist es meist umständlich oder unmöglich, jeden Prozessfortschritt zu erfassen. Zudem ist händische Prozessausführung und -dokumentation fehleranfällig, so wird z.B. manchmal vergessen zu dokumentieren. Eine Herausforderung für Unternehmen ist, dass manche Prozessereignisse nicht im Prozessmonitoring erfasst werden. Solch unbeobachtete Prozessereignisse können jedoch als Entscheidungsgrundlage dienen, selbst wenn kein exaktes Wissen über den Zeitpunkt ihres Auftretens vorliegt. Zum Beispiel ist bei der Prozesskontrolle zu entscheiden, ob zusätzliche Ressourcen eingesetzt werden sollen, wenn eine Verspätung angenommen wird.
Diese Arbeit stellt einen probabilistischen Ansatz für den Umgang mit unbeobachteten Prozessereignissen vor. Dabei werden entscheidende Fragen von Prozessmanagern beantwortet (z.B. "Wann werden wir den Fall beenden?", oder "Wann wurde die Aktivität ausgeführt, die nicht dokumentiert wurde?"). Der Hauptbeitrag der Arbeit ist die Einführung eines erweiterten probabilistischen Modells ins Geschäftsprozessmanagement, das auf stochastischen Petri Netzen basiert. Dabei wird ein ganzheitlicher Ansatz zur Unterstützung der einzelnen Phasen des Geschäftsprozesslebenszyklus verfolgt. Es werden Techniken zum Lernen des probabilistischen Modells, zum Vorhersagen des Zeitpunkts des Prozessendes, zum Qualitätsmanagement von Dokumentationen durch Erkennung fehlender Einträge, und zur Optimierung von Monitoringkonfigurationen bereitgestellt. Letztere dient zur Auswahl von relevanten Stellen im Prozess, die beobachtet werden sollten. Diese Techniken wurden in einer quelloffenen prototypischen Anwendung implementiert. Zur Evaluierung wird der Ansatz mit existierenden Alternativen an echten Prozessdaten eines Krankenhauses gemessen. Die generelle Anwendbarkeit in weiteren Domänen wird examplarisch an Prozessdaten aus der Logistik und dem Finanzwesen gezeigt.
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Srinivasan, Balaji Vasan. "Gaussian process regression for model estimation." College Park, Md.: University of Maryland, 2008. http://hdl.handle.net/1903/8962.
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Thesis (M.S.) -- University of Maryland, College Park, 2008.<br>Thesis research directed by: Dept. of Electrical and Computer Engineering E. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Lau, Wing-yi, and 劉穎兒. "New recursive parameter estimation algorithms in impulsive noise environment with application to frequency estimation and systemidentification." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37595866.
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Soltane, Marius. "Statistique asymptotique de certaines séries chronologiques à mémoire." Thesis, Le Mans, 2020. http://cyberdoc-int.univ-lemans.fr/Theses/2020/2020LEMA1027.pdf.
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Cette thèse est dévolue à la statistique inférentielle asymptotique de différents modèles chronologiques dirigés par un bruit comportant de la mémoire. Dans ces modèles, l'estimateur des moindres carrés n'est pas consistant et nous considérons d'autres estimateurs. Nous commençons par étudier les propriétés asymptotiques presquesûres de l'estimateur du maximum de vraisemblance du coefficient d'autorégression dans un processus autorégressif dirigé par un bruit gaussien stationnaire. Nous présentons ensuite une procédure statistique afin de détecter un changement de régime au sein de ce modèle en s'inspirant du cas classique dirigé par un bruit blanc fort. Nous abordons ensuite un modèle autorégressif où les coefficients sont aléatoires et possèdent une courte mémoire. Là encore l'estimateur des moindres carrés n'est pas consistant et nouscorrigeons l'estimation afin d'estimer correctement les paramètres du modèle. Pour finir nous étudions un nouvel estimateur joint de l'exposant de Hurst et de la variance dans un bruit gaussien fractionnaire observé à haute fréquence dont les qualités sont comparables au maximum de vraisemblance<br>This thesis is devoted to asymptotic inferenre of differents chronological models driven by a noise with memory. In these models, the least squares estimator is not consistent and we consider other estimators. We begin by studying the almost-sureasymptotic properties of the maximum likelihood estimator of the autoregressive coefficient in an autoregressive process drivenby a stationary Gaussian noise. We then present a statistical procedure in order to detect a change of regime within this model,taking inspiration from the classic case driven by a strong white noise. Then we consider an autoregressive model where the coefficients are random and have a short memory. Here again, the least squares estimator is not consistent and we correct the previous statistic in order to correctly estimate the parameters of the model. Finally we study a new joint estimator of the Hurst exponent and the variance in a fractional Gaussian noise observed at high frequency whose qualities are comparable to the maximum likelihood estimator
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Shrestha, Deep. "Impulsive noise cancellation and channel estimation in power line communication systems." Doctoral thesis, Universitat Politècnica de Catalunya, 2019. http://hdl.handle.net/10803/667520.
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Power line communication (PLC) is considered as the most viable enabler of the smart grid. PLC exploits the power line infrastructure for data transmission and provides an economical communication backbone to support the requirements of smart grid applications. Though PLC brings a lot of benefits to the smart grid implementation, impairments such as frequency selective attenuation of the high-frequency communication signal, the presence of impulsive noise (IN) and the narrowband interference (NBI) from closely operating wireless communication systems, make the power line a hostile environament for reliable data transmission. Hence, the main objective of this dissertation is to design signal processing algorithms that are specifically tailored to overcome the inevitable impairments in the power line environment.
First, we propose a novel IN mitigation scheme for PLC systems. The proposed scheme actively estimates the locations of IN samples and eliminates the effect of IN only from the contaminated samples of the received signal. By doing so, the typical problem encountered while mitigating the IN is avoided by using passive IN power suppression algorithms, where samples besides the ones containing the IN are also affected creating additional distortion in the received signal.
Apart from the IN, the PLC transmission is also impaired by NBI. Exploiting the duality of the problem where the IN is impulsive in the time domain and the NBI is impulsive in the frequency domain, an extended IN mitigation algorithm is proposed in order to accurately estimate and effectively cancel both impairments from the received signal. The numerical validation of the proposed schemes shows improved BER performance of PLC systems in the presence of IN and NBI.
Secondly, we pay attention to the problem of channel estimation in the power line environment. The presence of IN makes channel estimation challenging for PLC systems. To accurately estimate the channel, two maximumlikelihood (ML) channel estimators for PLC systems are proposed in this thesis.
Both ML estimators exploit the estimated IN samples to determine the channel coefficients. Among the proposed channel estimators, one treats the estimated IN as a deterministic quantity, and the other assumes that the estimated IN is a random quantity. The performance of both estimators is analyzed and numerically evaluated to show the superiority of the proposed estimators in comparison to conventional channel estimation strategies in the presence of IN. Furthermore, between the two proposed estimators, the one that is based on the random approach outperforms the deterministic one in all typical PLC scenarios. However, the deterministic approach based estimator can perform consistent channel estimation regardless of the IN behavior with less computational effort and becomes an efficient channel estimation strategy in situations where high computational complexity cannot be afforded.
Finally, we propose two ML algorithms to perform a precise IN support detection. The proposed algorithms perform a greedy search of the samples in the received signal that are contaminated by IN. To design such algorithms, statistics defined for deterministic and random ML channel estimators are exploited and two multiple hypothesis tests are built according to Bonferroni and Benjamini and Hochberg design criteria. Among the proposed estimators, the random ML-based approach outperforms the deterministic ML-based approach while detecting the IN support in typical power line environment.
Hence, this thesis studies the power line environment for reliable data transmission to support smart grid. The proposed signal processing schemes are
robust and allow PLC systems to effectively overcome the major impairments in an active electrical network.The efficient mitigation of IN and NBI and accurate estimation of channel enhances the applicability of PLC to support critical applications that are envisioned for the future electrical power grid.<br>La comunicación a través de líneas de transmisión eléctricas (PLC) se considera uno de los habilitadores principales de la red eléctrica inteligente (smart grid). PLC explota la infraestructura de la red eléctrica para la transmisión de datos y proporciona una red troncal de comunicación económica para poder cumplir con los requisitos de las aplicaciones para smart grids. Si bien la tecnología PLC aporta muchos beneficios a la implementación de la smart grid, los impedimentos, como la atenuación selectiva en frecuencia de la señal de comunicación, la presencia de ruido impulsivo (IN) y las interferencias de banda estrecha (NBI) de los sistemas de comunicación inalámbrica de operación cercana, hacen que la red eléctrica sea un entorno hostil para la transmisión fiable de datos. En este contexto, el objetivo principal de esta tesis es diseñar algoritmos de procesado de señal que estén específicamente diseñados para superar los impedimentos inevitables en el entorno de la red eléctrica como son IN y NBI. Primeramente, proponemos un nuevo esquema de mitigación de IN en sistemas PLC. El esquema propuesto estima activamente las ubicaciones de las muestras de IN y elimina el efecto de IN solo en las muestras contaminadas de la señal recibida. Al hacerlo, el problema típico que se encuentra al mitigar el IN con técnicas tradicionales (donde también se ven afectadas otras muestras que contienen la IN, creando una distorsión adicional en la señal recibida) se puede evitar con la consiguiente mejora del rendimiento. Aparte de IN, los sistemas PLC también se ven afectados por el NBI. Aprovechando la dualidad del problema (el IN es impulsivo en el dominio del tiempo y el NBI es impulsivo en el dominio de la frecuencia), se propone un algoritmo de mitigación de IN ampliado para estimar con precisión y cancelar efectivamente ambas degradaciones de la señal recibida. La validación numérica de los esquemas propuestos muestra un mejor rendimiento en términos de tasa de error de bit (BER) en sistemas PLC con presencia de IN y NBI. En segundo lugar, prestamos atención al problema de la estimación de canal en entornos PLC. La presencia de IN hace que la estimación de canal sea un desafío para los sistemas PLC futuros. En esta tesis, se proponen dos estimadores de canal para sistemas PLC de máxima verosimilitud (ML) para sistemas PLC. Ambos estimadores ML explotan las muestras IN estimadas para determinar los coeficientes del canal. Entre los estimadores de canal propuestos, uno trata la IN estimada como una cantidad determinista, y la otra asume que la IN estimada es una cantidad aleatoria. El rendimiento de ambos estimadores se analiza y se evalúa numéricamente para mostrar la superioridad de los estimadores propuestos en comparación con las estrategias de estimación de canales convencionales en presencia de IN. Además, entre los dos estimadores propuestos, el que se basa en el enfoque aleatorio supera el determinista en escenarios PLC típicos. Sin embargo, el estimador basado en el enfoque determinista puede llevar a cabo una estimación de canal consistente independientemente del comportamiento de la IN con menos esfuerzo computacional y se convierte en una estrategia de estimación de canal eficiente en situaciones donde no es posible disponer de una alta complejidad computacional
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Sepehri, Arsalan. "Estimation and reduction of background noise from MRI blood flow images." Thesis, Imperial College London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.326246.
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Dong, Li. "Noise level estimation from single image based on natural image statistics." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3952094.
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Wang, Kefei. "Parameter Estimation of a High Frequency Cascode Low Noise Amplifier Model." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/1053.
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"A Low Noise Amplifier (LNA) is an important building block in the RF receiver chain. Typically the LNA should provide acceptable gain and high linearity while maintaining low noise and power consumption. To optimize these conflicting goals the so-called Cascode topology is widely used in industry. Here the gain cell is comprised of two transistors, one in common-source and the other in common gate configuration. Cascode has a number of competitive advantages over other topologies such as high output impedance that shields the input device from voltage variations at the output, good reverse isolation resulting in improved stability, and acceptable input matching. Moreover, the topology features excellent frequency characteristics. Unfortunately, a Cascode design is expensive to deploy in RF systems and it requires more careful tuning and matching. Since the design relies on many circuit components, optimization methods are generally difficult to implement and often inaccurate in their predictions. To overcome these problems, this thesis proposes a modeling environment within the Advanced Design Systems (ADS) simulator that utilized DC and RF measurements in an effort to characterize each transistor separately. The model creates an easy-to-apply design approach capable of predicting the most important circuit components of the Cascode topology. The validity of the method is tested in ADS with a realistic p-HEMT library device. The comparison between model prediction and the realistic device involves both standard transistor parameters and high-frequency parasitic effects. "
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Skeen, Matthew E. (Matthew Edward). "Maximum likelihood estimation of fractional Brownian motion and Markov noise parameters." Thesis, Massachusetts Institute of Technology, 1991. http://hdl.handle.net/1721.1/42527.
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Lee, Junehee 1969. "Blind noise estimation and compensation for improved characterization of multivariable processes." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/86437.
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Fang, Yue. "Volatility modeling and estimation of high-frequency data with Gaussian noise." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/11041.
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Hao, Wei-Da. "Waveform Estimation with Jitter Noise by Pseudo Symmetrical Probability Density Function." PDXScholar, 1993. https://pdxscholar.library.pdx.edu/open_access_etds/4587.
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A new method for solving jitter noise in estimating high frequency waveform is proposed. It reduces the bias of the estimation in those points where all the other methods fail to achieve. It provides preliminary models for estimating percentiles in Normal, Exponential probability density function. Based on the model for Normal probability density function, a model for any probability density function is derived. The resulting percentiles, in turn, are used as estimates for the amplitude of the waveform. Simulation results show us with satisfactory accuracy.
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Lau, Wing-yi. "New recursive parameter estimation algorithms in impulsive noise environment with application to frequency estimation and system identification." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37595866.
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Loustaunau, Matthieu. "Industrial process error estimation by machine learning." Thesis, KTH, Numerisk analys, NA, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-178069.
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Performing a set-up on a complex machine may be difficult. This problem arises frequently for the industry, especially when the relation between input and output data cannot be defined precisely. Heavy methods of optimization may be used to perform a set-up. This master thesis investigate the possibility to use a machine learning approach on a specific machine. We study the structure of the relation between input and output data. We show the variations are smooth. We define a set of tests to evaluate future models. We design and test several models on simulation data, and select the best one. We design a strategy to use data in the best possible way. The selected model is then tested on actual data in order to be optimized.<br>Att justera ett komplext maskineri kan vara mödosamt. Detta problem uppkommer ofta i industrin när relationen mellan in- och utdata inte kan definieras. Tunga optimeringsmetoder kan användas för justeringprocessen. Dennauppsats undersöker möjligheten att använda en s.k. machine learning approach med ett specifikt maskineri. Vi studerar datastrukturen och relationen mellan in- och utdata. Vi visar på att variationer är släta. Vi definierar en rad av tester för att värdera framtida modeller. Vi formger och testar flera modeller med simulerade data och välja den bästa. Vi designar en strategi för att använda data på bästa sätt, för att sedan testa den utvalda modellen på verklig data för att optimeras.
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Zoghi, Masrour. "Regret bounds for Gaussian process bandits without observation noise." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/42865.
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This thesis presents some statistical refinements of the bandits approach presented in [11] in the situation where there is no observation noise. We give an improved bound on the cumulative regret of the samples chosen by an algorithm that is related (though not identical) to the UCB algorithm of [11] in a complementary setting. Given a function f on a domain D ⊆ R^d , sampled from a Gaussian process with an anisotropic kernel that is four times differentiable at 0, and a lattice L ⊆ D, we show that if the points in L are chosen for sampling using our branch-and-bound algorithm, the regret asymptotically decreases according to O(e^{τt/(ln t)^{d/4}}) with high probability,
where t is the number of observations carried out so far and τ is a constant that depends on the objective function.