Sparsity-based localization of spatially coherent distributed sources

International audienceIn this paper, the localization of spatially distributed sources is considered. Based on the problem formulation of the De-convolution Approach for the Mapping of Acoustic Sources (DAMAS), a criterion based on a convex optimization under sparsity constraint is proposed to locate the sources. Also an original method is given to recover the angular distributions and the power of the sources. Simulations executed in the scenario of a mixture of distributed and point sources illustrate the validation of the proposed approach compared to other methods

Localisation de sources aéroacoustiques et imagerie à haute résolution

Localization of Coherently Distributed (CD) source presents a challenge in the array signal processing. Our work motivates the localization of aero-acoustic source based on its spatial extension. This challenge is practically ignored in the literature of acoustic imaging field where many applications consist in mapping noisy source to reduce its contribution. The thesis presents the three following contributions. First, we propose a Joint Angle, Distance, Spread and Shape Estimator called JADSSE. The estimation of the so-called spread shape distribution parameter proposed by JADSSE avoids the modeling error due to the required a priori knowledge on the source shape when using classical estimators. Second, we expand the Decoupled DSPE to the near field. This method decouples the Direction of Arrival (DoA) and the range estimation from the spread estimation. Meanwhile, this method prevents the spread estimation for unknown shape distribution. Therefore, we propose the DADSSE to successively estimate the DOA, the range and then the spread and the shape distribution of the source. Third, we generalize the CD model and the JADSSE to consider the bi-dimensional spread of the source. Next, we propose two source power estimation approaches accounting the spatial spread of the source. The proposed methods are tested using a set of experimental data of the Renault wind tunnel application. Results show the presence of new aero-acoustic sources especially the overlapped ones with weak powers. We provide a tool to better map and characterize the aero-acoustic source by estimating the position, spread, power and shape.La localisation de source Distribuée Cohérente (DC) présente un défi du traitement d'antenne. Les contributions de cette thèse s’articulent principalement autour de trois aspects. Premièrement, un estimateur conjoint de l'angle, la distance, la dispersion et la forme de la source appelée JADSSE est proposé pour le cas champ proche. L’estimation d’un paramètre de forme de distribution de la dispersion permet d’éviter des erreurs de modèles sur l’a priori de la forme de la distribution. Deuxièmement, on généralise l'estimateur Decoupled DSPE en champ proche. Cette approche permet de découpler l'estimation de la Direction D’Arrivée (DDA) et de la distance de l'estimation de la dispersion. Afin de permettre l’estimation de la dispersion sans connaître a priori les formes de distribution, on propose le DADSSE qui consiste à estimer successivement la DDA, la distance et ensuite la dispersion et la forme de la distribution de la source. Troisièmement, on généralise le modèle DC avec une dispersion spatiale bidimensionnelle de la source ainsi que l’estimateur JADSSE. Deux approches sont proposées pour l’estimation de la puissance prenant en compte le modèle d’étalement des sources. Les méthodes proposées sont testées sur les données expérimentales de la soufflerie de Renault. Les résultats mettent en évidence des sources aéro-acoustiques proches et de faibles puissances. L’ensemble de ces travaux permet de fournir un outil pour une meilleure cartographie et caractérisation des sources aéro-acoustiques grâce à l’estimation de la position, l'étalement, la puissance et la forme

Position location in wireless MIMO communication systems

Motivation and objectives -- Contributions -- Organization of the thesis -- Wireless communication channels -- Overview of wireless position location systems -- Fundamentals of array signal processing -- Mimo and space-time processing -- Bidirectional mimo channel model -- The system model -- The bidirectional beamforming MIMO channel -- Joint estimation of multipath parameters for Mimo systenms -- The proposed maximum likelihood multipath parameter estimation algorithms -- The proposed subspace-based multipath parameter estimation algorithm -- The cramer-rao lower bound -- Position location of mobile terminal in mimo systems -- The proposed hybrid TDOA/AOA/AOD location method for Mimo systems -- Analysis of the proposed location method for MIMO systems

Advancing Pattern Recognition Techniques for Brain-Computer Interfaces: Optimizing Discriminability, Compactness, and Robustness

In dieser Dissertation formulieren wir drei zentrale Zielkriterien zur systematischen Weiterentwicklung der Mustererkennung moderner Brain-Computer Interfaces (BCIs). Darauf aufbauend wird ein Rahmenwerk zur Mustererkennung von BCIs entwickelt, das die drei Zielkriterien durch einen neuen Optimierungsalgorithmus vereint. Darüber hinaus zeigen wir die erfolgreiche Umsetzung unseres Ansatzes für zwei innovative BCI Paradigmen, für die es bisher keine etablierte Mustererkennungsmethodik gibt

Data assimilation for conductance-based neuronal models

This dissertation illustrates the use of data assimilation algorithms to estimate unobserved variables and unknown parameters of conductance-based neuronal models. Modern data assimilation (DA) techniques are widely used in climate science and weather prediction, but have only recently begun to be applied in neuroscience. The two main classes of DA techniques are sequential methods and variational methods. Throughout this work, twin experiments, where the data is synthetically generated from output of the model, are used to validate use of these techniques for conductance-based models observing only the voltage trace. In Chapter 1, these techniques are described in detail and the estimation problem for conductance-based neuron models is derived. In Chapter 2, these techniques are applied to a minimal conductance-based model, the Morris-Lecar model. This model exhibits qualitatively different types of neuronal excitability due to changes in the underlying bifurcation structure and it is shown that the DA methods can identify parameter sets that produce the correct bifurcation structure even with initial parameter guesses that correspond to a different excitability regime. This demonstrates the ability of DA techniques to perform nonlinear state and parameter estimation, and introduces the geometric structure of inferred models as a novel qualitative measure of estimation success. Chapter 3 extends the ideas of variational data assimilation to include a control term to relax the problem further in a process that is referred to as nudging from the geoscience community. The nudged 4D-Var is applied to twin experiments from a more complex, Hodgkin-Huxley-type two-compartment model for various time-sampling strategies. This controlled 4D-Var with nonuniform time-samplings is then applied to voltage traces from current-clamp recordings of suprachiasmatic nucleus neurons in diurnal rodents to improve upon our understanding of the driving forces in circadian (~24) rhythms of electrical activity. In Chapter 4 the complementary strengths of 4D-Var and UKF are leveraged to create a two-stage algorithm that uses 4D-Var to estimate fast timescale parameters and UKF for slow timescale parameters. This coupled approach is applied to data from a conductance-based model of neuronal bursting with distinctive slow and fast time-scales present in the dynamics. In Chapter 5, the ideas of identifiability and sensitivity are introduced. The Morris-Lecar model and a subset of its parameters are shown to be identifiable through the use of numerical techniques. Chapter 6 frames the selection of stimulus waveforms to inject into neurons during patch-clamp recordings as an optimal experimental design problem. Results on the optimal stimulus waveforms for improving the identifiability of parameters for a Hodgkin-Huxley-type model are presented. Chapter 7 shows the preliminary application of data assimilation for voltage-clamp, rather than current-clamp, data and expands on voltage-clamp principles to formulate a reduced assimilation problem driven by the observed voltage. Concluding thoughts are given in Chapter 8

Advances in Bioengineering

The technological approach and the high level of innovation make bioengineering extremely dynamic and this forces researchers to continuous updating. It involves the publication of the results of the latest scientific research. This book covers a wide range of aspects and issues related to advances in bioengineering research with a particular focus on innovative technologies and applications. The book consists of 13 scientific contributions divided in four sections: Materials Science; Biosensors. Electronics and Telemetry; Light Therapy; Computing and Analysis Techniques

Understanding quantitative DCE-MRI of the breast : towards meaningful clinical application

In most industrialized countries breast cancer will affect one out of eight women during her lifetime. In the USA, after continuously increasing for more than two decades, incidence rates are slowly decreasing since 2001. Since 1990, death rates from breast cancer have steadily decreased in women, which is attributed to both earlier detection and improved treatment. Still, it is second only to lung cancer as a cause of cancer death in women. In this work we set out to improve early detection of breast cancer via quantitative analysis of magnetic resonance images (MRI). Screening and diagnosis of breast cancer are generally performed using X-ray mammography, possibly in conjunction with ultrasonography. However, MRI is becoming an important modality for screening of women at high-risk due to for instance hereditary gene mutations, as a problem-solving tool in case of indecisive mammographic and / or ultrasonic imaging, and for anti-cancer therapy assessment. In this work, we focused on MR imaging of the breast. More specifically, the dynamic contrast-enhanced (DCE) part of the protocol was highlighted, as well as radiological assessment of DCE-MRI data. The T_1-weighted (T_1: longitudinal relaxation time, a tissue property) signal-versus-time curve that can be extracted from the DCE-MRI series that is acquired at the time of and after injection of a T_1-shortening (shorter T_1 results in higher signal) contrast agent, is usually visually assessed by the radiologist. For example, a fast initial rise to the peak (1-2 minutes post injection) followed by loss of signal within a time frame of about 5-6 minutes is a sign for malignancy, whereas a curve showing persistent (slow) uptake within the same time frame is a sign for benignity. This difference in contrast agent uptake pattern is related to physiological changes in tumorous tissue that for instance result in a stronger uptake of the contrast agent. However, this descriptive way of curve type classification is based on clinical statistics, not on knowledge about tumor physiology. We investigated pharmacokinetic modeling as a quantitative image analysis tool. Pharmacokinetics describes what happens to a substance (e.g. drug or contrast agent) after it has been administered to a living organism. This includes the mechanisms of absorption and distribution. The terms in which these mechanisms are described are physiological and can therefore provide parameters describing the functioning of the tissue. This physiological aspect makes it an attractive approach to investigate (aberrant) tissue functioning. In addition, this type of analysis excludes confounding factors due to inter- and intra-patient differences in the systemic blood circulation, as well as differences in the injection protocol. In this work, we discussed the physiological basis and details of different types of pharmacokinetic models, with the focus on compartmental models. Practical implications such as obtaining an arterial input function and model parameter estimation were taken into account as well. A simulation study of the data-imposed limitations – in terms of temporal resolution and noise properties – on the complexity of pharmacokinetic models led to the insight that only one of the tested models, the basic Tofts model, is applicable to DCE-MRI data of the breast. For the basic Tofts model we further investigated the aspect of temporal resolution, because a typical diagnostic DCE-MRI scan of the breast is acquired at a rate of about 1 image volume every minute; whereas pharmacokinetic modeling usually requires a sampling time of less than 10 s. For this experiment we developed a new downsampling method using high-temporal-resolution raw k-space data to simulate what uptake curves would have looked like if they were acquired at lower temporal resolutions. We made use of preclinical animal data. With this data we demonstrated that the limit of 10 s can be stretched to about 1 min if the arterial input function (AIF, the input to the pharmacokinetic model) is inversely derived from a healthy reference tissue, instead of measured in an artery or taken from the literature. An important precondition for the application of pharmacokinetic modeling is knowledge of the relationship between the acquired DCE-MRI signal and the actual concentration of the contrast agent in the tissue. This relationship is not trivial because with MRI we measure the indirect effect of the contrast agent on water protons. To establish this relationship via calculation of T_1 (t), we investigated both a theoretical and an empirical approach, making use of an in-house (University of Chicago) developed reference object that is scanned concurrently with the patient. The use of the calibration object can shorten the scan duration (an empirical approach requires less additional scans than an approach using a model of the acquisition technique), and can demonstrate if theoretical approaches are valid. Moreover we produced concentration images and estimated tissue proton density, also making use of the calibration object. Finally, via pharmacokinetic modeling and other MRI-derived measures we partly revealed the actions of a novel therapeutic in a preclinical study. In particular, the anti-tumor activity of a single dose of liposomal prednisolone phosphate was investigated, which is an anti-inflammatory drug that has demonstrated tumor growth inhibition. The work presented in this thesis contributes to a meaningful clinical application and interpretation of quantitative DCE-MRI of the breast

Unfocused ultrasound waves for manipulating and imaging microbubbles

With unfocused plane/diverging ultrasound waves, the capability of simultaneous sampling on each element of an array transducer has spawned a branch known as high-frame-rate (HFR) ultrasound imaging, whose frame rate can be two orders of magnitude faster than traditional imaging systems. Microbubbles are micron-sized spheres with a heavy gas core that is stabilized by a shell made of lipids, polymers, proteins, or surfactants. They are excellent ultrasound scatters and have been used as ultrasound contrast agents, and more recently researched as a mechanism for targeted drug delivery. With the Ultrasound Array Research Platform II (UARP II), the objective of this thesis was to develop and advance several techniques for manipulating and imaging microbubbles using unfocused ultrasound waves. These techniques were achieved by combining custom transmit/receiving sequencing and advanced signal processing algorithms, holding promise for enhanced diagnostic and therapeutic applications of microbubbles. A method for locally accumulating microbubbles with fast image guidance was first presented. A linear array transducer performed trapping of microbubble populations interleaved with plane wave imaging, through the use of a composite ultrasound pulse sequence. This technique could enhance image-guided targeted drug delivery using microbubbles. A key component of targeted drug delivery using liposome-loaded microbubbles and ultrasound is the ability to track these drug vehicles to guide payload release locally. As a uniquely identifiable emission from microbubbles, the subharmonic signal is of interest for this purpose. The feasibility of subharmonic plane wave imaging of liposome-loaded microbubbles was then proved. The improved subharmonic sensitivity especially at depth compared to their counterpart of bare (unloaded) microbubbles was confirmed. Following plane wave imaging, the combination of diverging ultrasound waves and microbubbles was investigated. The image formation techniques using coherent summation of diverging waves are susceptible to tissue and microbubble motion artefacts, resulting in poor image quality. A correlation-based 2-D motion estimation algorithm was then proposed to perform motion compensation for HFR contrast-enhanced echocardiography (CEE). A triplex cardiac imaging technique, consisting of B mode, contrast mode and 2-D vector flow imaging with a frame rate of 250 Hz was presented. It was shown that the efficacy of coherent diverging wave imaging of the heart is reliant on carefully designed motion compensation algorithms capable of correcting for incoherence between steered diverging-wave transmissions. Finally, comparisons were made between the correlation-based method and one established image registration method for motion compensation. Results show that the proposed correlation-based method outperformed the image registration model for motion compensation in HFR CEE, with the improved image contrast ratio and visibility of geometrical borders both in vitro and in vivo

Visual Processing and Latent Representations in Biological and Artificial Neural Networks

The human visual system performs the impressive task of converting light arriving at the retina into a useful representation that allows us to make sense of the visual environment. We can navigate easily in the three-dimensional world and recognize objects and their properties, even if they appear from different angles and under different lighting conditions. Artificial systems can also perform well on a variety of complex visual tasks. While they may not be as robust and versatile as their biological counterpart, they have surprising capabilities that are rapidly improving. Studying the two types of systems can help us understand what computations enable the transformation of low-level sensory data into an abstract representation. To this end, this dissertation follows three different pathways. First, we analyze aspects of human perception. The focus is on the perception in the peripheral visual field and the relation to texture perception. Our work builds on a texture model that is based on the features of a deep neural network. We start by expanding the model to the temporal domain to capture dynamic textures such as flames or water. Next, we use psychophysical methods to investigate quantitatively whether humans can distinguish natural textures from samples that were generated by a texture model. Finally, we study images that cover the entire visual field and test whether matching the local summary statistics can produce metameric images independent of the image content. Second, we compare the visual perception of humans and machines. We conduct three case studies that focus on the capabilities of artificial neural networks and the potential occurrence of biological phenomena in machine vision. We find that comparative studies are not always straightforward and propose a checklist on how to improve the robustness of the conclusions that we draw from such studies. Third, we address a fundamental discrepancy between human and machine vision. One major strength of biological vision is its robustness to changes in the appearance of image content. For example, for unusual scenarios, such as a cow on a beach, the recognition performance of humans remains high. This ability is lacking in many artificial systems. We discuss on a conceptual level how to robustly disentangle attributes that are correlated during training, and test this on a number of datasets

Performance analysis of distributed source parameter estimator (DSPE) in the presence of modeling errors due to the spatial distributions of sources

International audienceIn this paper, the direction of arrival (DOA) localization of spatially distributed sources impinging on a sensor array is considered. The performance of the Distributed Source Parameter Estimator (DSPE) is studied in the presence of model errors due to the angular distribution shapes of the sources. Taking into account the coherently distributed source model proposed in Valaee et al.[1], we propose a definition of angular dispersion which makes DSPE robust to the angular distribution shapes of sources, and establish closed-form expressions of the DOA estimation bias and mean square error (MSE) due to both the model errors and the effects of a finite number of snapshots. The analytical results are validated by numerical simulations and allow to analyze the performance of DSPE for coherently distributed sources. The results also show the advantage of DSPE for the localization of spatially distributed sources even if the source angular distribution shape is not exactly known