• Refine Query
  • Source
  • Publication year
  • to
  • Language
  • 48
  • 10
  • 8
  • 4
  • 3
  • 3
  • 2
  • 1
  • 1
  • 1
  • Tagged with
  • 97
  • 97
  • 24
  • 23
  • 19
  • 17
  • 17
  • 16
  • 16
  • 14
  • 13
  • 12
  • 12
  • 10
  • 10
  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

Bayesian M/EEG source localization with possible joint skull conductivity estimation

Costa, Facundo Hernan 02 March 2017 (has links) (PDF)
M/EEG mechanisms allow determining changes in the brain activity, which is useful in diagnosing brain disorders such as epilepsy. They consist of measuring the electric potential at the scalp and the magnetic field around the head. The measurements are related to the underlying brain activity by a linear model that depends on the lead-field matrix. Localizing the sources, or dipoles, of M/EEG measurements consists of inverting this linear model. However, the non-uniqueness of the solution (due to the fundamental law of physics) and the low number of dipoles make the inverse problem ill-posed. Solving such problem requires some sort of regularization to reduce the search space. The literature abounds of methods and techniques to solve this problem, especially with variational approaches. This thesis develops Bayesian methods to solve ill-posed inverse problems, with application to M/EEG. The main idea underlying this work is to constrain sources to be sparse. This hypothesis is valid in many applications such as certain types of epilepsy. We develop different hierarchical models to account for the sparsity of the sources. Theoretically, enforcing sparsity is equivalent to minimizing a cost function penalized by an l0 pseudo norm of the solution. However, since the l0 regularization leads to NP-hard problems, the l1 approximation is usually preferred. Our first contribution consists of combining the two norms in a Bayesian framework, using a Bernoulli-Laplace prior. A Markov chain Monte Carlo (MCMC) algorithm is used to estimate the parameters of the model jointly with the source location and intensity. Comparing the results, in several scenarios, with those obtained with sLoreta and the weighted l1 norm regularization shows interesting performance, at the price of a higher computational complexity. Our Bernoulli-Laplace model solves the source localization problem at one instant of time. However, it is biophysically well-known that the brain activity follows spatiotemporal patterns. Exploiting the temporal dimension is therefore interesting to further constrain the problem. Our second contribution consists of formulating a structured sparsity model to exploit this biophysical phenomenon. Precisely, a multivariate Bernoulli-Laplacian distribution is proposed as an a priori distribution for the dipole locations. A latent variable is introduced to handle the resulting complex posterior and an original Metropolis-Hastings sampling algorithm is developed. The results show that the proposed sampling technique improves significantly the convergence. A comparative analysis of the results is performed between the proposed model, an l21 mixed norm regularization and the Multiple Sparse Priors (MSP) algorithm. Various experiments are conducted with synthetic and real data. Results show that our model has several advantages including a better recovery of the dipole locations. The previous two algorithms consider a fully known leadfield matrix. However, this is seldom the case in practical applications. Instead, this matrix is the result of approximation methods that lead to significant uncertainties. Our third contribution consists of handling the uncertainty of the lead-field matrix. The proposed method consists in expressing this matrix as a function of the skull conductivity using a polynomial matrix interpolation technique. The conductivity is considered as the main source of uncertainty of the lead-field matrix. Our multivariate Bernoulli-Laplacian model is then extended to estimate the skull conductivity jointly with the brain activity. The resulting model is compared to other methods including the techniques of Vallaghé et al and Guttierez et al. Our method provides results of better quality without requiring knowledge of the active dipole positions and is not limited to a single dipole activation.
2

Direction of Arrival Estimation and Localization of Multiple Speech Sources in Enclosed Environments

Swartling, Mikael January 2012 (has links)
Speech communication is gaining in popularity in many different contexts as technology evolves. With the introduction of mobile electronic devices such as cell phones and laptops, and fixed electronic devices such as video and teleconferencing systems, more people are communicating which leads to an increasing demand for new services and better speech quality. Methods to enhance speech recorded by microphones often operate blindly without prior knowledge of the signals. With the addition of multiple microphones to allow for spatial filtering, many blind speech enhancement methods have to operate blindly also in the spatial domain. When attempting to improve the quality of spoken communication it is often necessary to be able to reliably determine the location of the speakers. A dedicated source localization method on top of the speech enhancement methods can assist the speech enhancement method by providing the spatial information about the sources. This thesis addresses the problem of speech-source localization, with a focus on the problem of localization in the presence of multiple concurrent speech sources. The primary work consists of methods to estimate the direction of arrival of multiple concurrent speech sources from an array of sensors and a method to correct the ambiguities when estimating the spatial locations of multiple speech sources from multiple arrays of sensors. The thesis also improves the well-known SRP-based methods with higher-order statistics, and presents an analysis of how the SRP-PHAT performs when the sensor array geometry is not fully calibrated. The thesis is concluded by two envelope-domain-based methods for tonal pattern detection and tonal disturbance detection and cancelation which can be useful to further increase the usability of the proposed localization methods. The main contribution of the thesis is a complete methodology to spatially locate multiple speech sources in enclosed environments. New methods and improvements to the combined solution are presented for the direction-of-arrival estimation, the location estimation and the location ambiguity correction, as well as a sensor array calibration sensitivity analysis.
3

Design of e-textiles for acoutsic applications

Shenoy, Ravi Rangnath 05 November 2003 (has links)
The concept of replacing threads with flexible wires and sensors in a fabric to provide an underlying platform for integrating electronic components is known as e-textiles. This concept can be used to design applications involving different types of electronic components including sensors, digital signal processors, microcontrollers, color-changing fibers, and power sources. The adaptability of the textiles to the needs of the individual and the functionality of electronics can be integrated to provide unobtrusive, robust, and inexpensive clothing with novel features. This thesis focuses on the design of e-textiles for acoustic signal processing applications. This research examines challenges encountered when developing e-textile applications involving distributed arrays of microphones. A framework for designing such applications is presented. The design process and the performance analysis of two e-textiles, a large-scale beamforming fabric and a speech-processing vest, are presented. / Master of Science
4

An Assurance Metric and Robustness Evaluation of a Low-cost Acoustic Beamformer for Source Localization

Coleman, Thomas Christopher 26 July 2018 (has links)
A rise in interest for service robotic rovers produces a need for a low-cost method for source localization in order for a prospective robotic unit to engage with a human operator. This study examines the use of the LMS algorithm for constructing a beamformer using an optimized Weiner filter solution for this source localization application and evaluates the robustness of a developed characterization method for assuring that a proper approximation for the desired signal is achieved. The method presented in this paper encompasses using a filter and sum method in which the sums are generated for a selected set of filter angles, and this set of sums are compared and characterized to produce a selection for an approximate arrival angle from the sound source to the microphone array. These filters are adaptively trained offline using a generated desired signal chirp to represent the average human whistle and a training data set for each of the four possible room configurations. This method was tested to determine if a selected filter configuration could still produce viable outputs for scenarios in which the testing room had been changed, whether noise was injected into the testing environment, if two or three microphones were used in testing process, and whether the filter angles are aligned with the arrival angles of the signal. Results on the robustness of the adaptive LMS beamformer are presented. Limitations of the system performance are discussed and possible solutions for results that have undesired performance are given in future work. / Master of Science
5

Underwater source localization with a generalized likelihood ratio processor

Conn, Rebecca M. January 1994 (has links)
No description available.
6

Autonomous Localization of 1/R² Sources Using an Aerial Platform

Brewer, Eric Thomas 20 January 2010 (has links)
Unmanned vehicles are often used in time-critical missions such as reconnaissance or search and rescue. To this end, this thesis provides autonomous localization and mapping tools for 1/R² sources. A "1/R²" source is one in which the received intensity of the source is inversely proportional to the square of the distance from the source. An autonomous localization algorithm is developed which utilizes a particle swarm particle ltering method to recursively estimate the location of a source. To implement the localization algorithm experimentally, a command interface with Virginia Tech's autonomous helicopter was developed. The interface accepts state information from the helicopter, and returns command inputs to drive the helicopter autonomously to the source. To make the use of the system more intuitive, a graphical user interface was developed which provides localization functionality as well as a waypoint navigation outer-loop controller for the helicopter. This assists in positioning the helicopter and returning it home after the the algorithm is completed. An autonomous mapping mission with a radioactive source is presented, along with a localization experiment utilizing simulated sensor readings. This work is the rst phase of an on-going project at the Unmanned Systems Lab. Accordingly, this thesis also provides a framework for its continuation in the next phase of the project. / Master of Science
7

Computational Acoustic Beamforming of Noise Source on Wind Turbine Airfoil

Li, Chi Shing January 2014 (has links)
A new method, Computational Acoustic Beamforming, is proposed in this thesis. This novel numerical sound source localization methodology combines the advantages of the Computational Fluid Dynamics (CFD) simulation and experimental acoustic beamforming, which enable this method to take directivity of sound source emission into account while maintaining a relatively low cost. This method can also aid the optimization of beamforming algorithm and microphone array design. In addition, it makes sound source prediction of large structures in the low frequency range possible. Three modules, CFD, Computational Aeroacoustics (CAA) and acoustic beamforming, are incorporated in this proposed method. This thesis adopts an open source commercial software OpenFOAM for the flow field simulation with the Improved Delayed Detached Eddy Simulation (IDDES) turbulence model. The CAA calculation is conducted by an in-house code using impermeable Ffowcs-Williams and Hawkings (FW-H) equation for static sound source. The acoustic beamforming is performed by an in-house Delay and Sum (DAS) beamformer code with several different microphone array designs. Each module has been validated with currently available experimental data and numerical results. A flow over NACA 0012 airfoil case was chosen as a demonstration case for the new method. The aerodynamics and aeroacoustics results are shown and compared with the experimental measurements. A relatively good agreement has been achieved which gives the confidence of using this newly proposed method in sound source localization applications.
8

Blind Received Signal Strength Difference Based Source Localization with System Parameter Error and Sensor Position Uncertainty

Lohrasbipeydeh, Hannan 27 August 2014 (has links)
Passive source localization in wireless sensor networks (WSNs) is an important field of research with numerous applications in signal processing and wireless communications. One purpose of a WSN is to determine the position of a signal emitted from a source. This position is estimated based on received noisy measurements from sensors (anchor nodes) that are distributed over a geographical area. In most cases, the sensor positions are assumed to be known exactly, which is not always reasonable. Even if the sensor positions are measured initially, they can change over time. Due to the sensitivity of source location estimation accuracy with respect to the a priori sensor position information, the source location estimates obtained can vary significantly regardless of the localization method used. Therefore, the sensor position uncertainty should be considered to obtain accurate estimates. Among the many localization approaches, signal strength based methods have the advantages of low cost and simple implementation. The received signal energy mainly depends on the transmitted power and path loss exponent which are often unknown in practical scenarios. In this dissertation, three received signal strength difference (RSSD) based methods are presented to localize a source with unknown transmit power. A nonlinear RSSD-based model is formulated for systems perturbed by noise. First, an effective low complexity constrained weighted least squares (CWLS) technique in the presence of sensor uncertainty is derived to obtain a least squares initial estimate (LSIE) of the source location. Then, this estimate is improved using a computationally efficient Newton method. The Cramer-Rao lower bound (CRLB) is derived to determine the effect of sensor location uncertainties on the source location estimate. Results are presented which show that the proposed method achieves the CRLB when the signal to noise ratio (SNR) is sufficiently high. Least squares (LS) based methods are typically used to obtain the location estimate that minimizes the data vector error instead of directly minimizing the unknown parameter estimation error. This can result in poor performance, particularly in noisy environments, due to bias and variance in the location estimate. Thus, an efficient two stage estimator is proposed here. First, a minimax optimization problem is developed to minimize the mean square error (MSE) of the proposed RSSD-based model. Then semidefinite relaxation is employed to transform this nonconvex and nonlinear problem into a convex optimization problem. This can be solved e ciently to obtain the optimal solution of the corresponding semidefinite programming (SDP) problem. Performance results are presented which con rm the e ciency of the proposed method which achieves the CRLB. Finally, an extended total least squares (ETLS) method is developed for blind localization which considers perturbations in the system parameters as well as the constraints imposed by the relation between the observation matrix and data vector. The corresponding nonlinear and nonconvex RSSD-based localization problem is then transformed to an ETLS problem with fewer constraints. This is transformed to a convex semidefinite programming (SDP) problem using relaxation. The proposed ETLS-SDP method is extended to the case with an unknown path loss exponent. The mean squared error (MSE) and corresponding CRLB are derived as performance benchmarks. Performance results are presented which show that the RSSD-based ETLS-SDP method attains the CRLB for a sufficiently large SNR. / Graduate / 0544 / lohrasbi@uvic.ca
9

Géolocalisation de sources radio-électriques : stratégies, algorithmes et performances / Geographical localization of de radiotransmitters : strategies, algorithms and performances

Bosse, Jonathan 24 January 2012 (has links)
Cette thèse porte sur la géolocalisation de sources radio-électriques dans le cadre du traitement d'antenne, c'est-à-dire l'estimation de la position, dans le plan ou l'espace, de sources incidentes à divers réseaux multicapteurs (stations de base). Il s'agit de concevoir des algorithmes estimant au mieux la position d'un ensemble de sources et de caractériser les limites théoriques, en termes d'erreur quadratique moyenne, des approches envisagées pour résoudre le problème de géolocalisation. Nous nous plaçons dans un contexte passif, sans a priori sur les signaux émis. De manière classique, la position des sources est souvent estimée à l'aide de paramètres intermédiaires (angles d'arrivée, temps d'arrivée, fréquences d'arrivée ...) estimés localement sur chacune des stations de base dans un premier temps. Ces paramètres intermédiaires sont ensuite transmis à une unité centrale de traitement qui réalise l'étape de localisation dans un second temps. On parle parfois d'approche en deux étapes. Cette solution décentralisée est par nature sous-optimale. Une approche optimale du problème de localisation consiste à estimer directement la position des sources à l'aide de l'ensemble des signaux reçus par les stations et transmis directement à l'unité centrale de traitement. Il convient alors de réaliser la localisation à l'aide d'une approche centralisée ne comportant qu'une seule étape : la position des sources étant directement estimée à partir de l'ensemble des signaux disponibles à l'unité centrale de traitement. Le problème à résoudre dépend directement de la position des sources et non plus de paramètres intermédiaires. Cette approche du problème de localisation offre de nouvelles perspectives quant à la conception de nouveaux algorithmes et pose la question de son intérêt théorique en termes d'amélioration des performances de localisation. Dans cette thèse, nous examinons l'intérêt constitué par l'exploitation simultanée des signaux de toutes les stations de base afin de réaliser la localisation. Nous nous attachons dans un premier temps à caractériser en termes d'erreur quadratique moyenne et de borne de Cramer-Rao les approches centralisées et décentralisées pour la localisation dans un contexte de signaux bande étroite sur l'ensemble du réseau de stations. Ensuite, pour le cas plus général de signaux large bande sur l'ensemble du réseau de stations, nous proposons une approche basée sur un traitement spatio-temporel. Nous montrons son intérêt comparativement à l'état de l'art et aux performances optimales théoriques qui font elles-mêmes l'objet d'une partie des travaux exposés dans cette thèse. Un algorithme de géolocalisation en contexte de multitrajets est également proposé dans cette thèse. / This thesis deal with the geographical positioning of multiple radio transmitters thanks to array processing techniques. The estimation of the position is achieved thanks to multiple sensor base stations. We aim to design estimators of the sources position and to characterize the fundamental limits of localization strategies in terms of root-mean-square-error in a passive signal context (no prior information on the transmitted signals). Traditionally, the geographical positioning is achieved by means of a two steps procedure. In the first step, intermediate location parameters (angles of arrival, times of arrival ...) are locally estimated, in a decentralized processing on each station. Then, the location is achieved in a central processing unit thanks to all the transmitted parameters in the second step. This strategy is obviously suboptimal. An optimal solution of the geographical localization problem rather consists in estimating the position of the sources in a centralized manner at the central processing unit, assuming that all base stations are able to transfer all their signals to the central processing unit. Then the localization can be achieved in a one step procedure. The problem now depends on the position of the sources directly and not on intermediate parameters. This approach appears to be very interesting but the characterization of their fundamental limits is still an open question. In this thesis we examine the advantages of the centralized one step procedure compared to the traditional decentralized two-step procedure. First, we study the case of narrowband signals on the station network that offers a relevant theoretical framework to compare the performance of centralized and decentralized localization scheme. Then, we propose an alternative to the existing techniques in the more general wideband signal context based on a spatio-temporal approach. The comparison of existing techniques and the new ones to optimal performance is also part of the work reported in this thesis. A multistage geographical positioning technique is also provided for the multipaths propagation context.
10

Acoustic Source Localization in an Anisotropic Plate Without Knowing its Material Properties

Park, Won Hyun, Park, Won Hyun January 2016 (has links)
Acoustic source localization (ASL) is pinpointing an acoustic source. ASL can reveal the point of impact of a foreign object or the point of crack initiation in a structure. ASL is necessary for continuous health monitoring of a structure. ASL in an anisotropic plate is a challenging task. This dissertation aims to investigate techniques that are currently being used to precisely determine an acoustic source location in an anisotropic plate without knowing its material properties. A new technique is developed and presented here to overcome the existing shortcomings of the acoustic source localization in anisotropic plates. It is done by changing the analysis perspective from the angular dependent group velocity of the wave and its straight line propagation to the wave front shapes and their geometric properties when a non-circular wave front is generated. Especially, 'rhombic wave front' and 'elliptical wave front' are dealt with because they are readily observed in highly anisotropic composite plates. Once each proposed technique meets the requirements of measurement, four sensor clusters in three different quadrants (recorded by 12 sensors) for the rhombus and at least three sensor clusters (recorded by 9 sensors) for the ellipse, accurate Acoustic Source Localization is obtained. It has been successfully demonstrated in the numerical simulations. In addition, a series of experimental tests demonstrate reliable and robust prediction performance of the developed new acoustic source localization technique.

Page generated in 0.1732 seconds