A control algorithm for autonomous optimization of extracellular recordings

This paper develops a control algorithm that can autonomously position an electrode so as to find and then maintain an optimal extracellular recording position. The algorithm was developed and tested in a two-neuron computational model representative of the cells found in cerebral cortex. The algorithm is based on a stochastic optimization of a suitably defined signal quality metric and is shown capable of finding the optimal recording position along representative sampling directions, as well as maintaining the optimal signal quality in the face of modeled tissue movements. The application of the algorithm to acute neurophysiological recording experiments and its potential implications to chronic recording electrode arrays are discussed

Suivi Multi-Locuteurs avec des Informations Audio-Visuelles pour la Perception des Robots

Robot perception plays a crucial role in human-robot interaction (HRI). Perception system provides the robot information of the surroundings and enables the robot to give feedbacks. In a conversational scenario, a group of people may chat in front of the robot and move freely. In such situations, robots are expected to understand where are the people, who are speaking, or what are they talking about. This thesis concentrates on answering the first two questions, namely speaker tracking and diarization. We use different modalities of the robot’s perception system to achieve the goal. Like seeing and hearing for a human-being, audio and visual information are the critical cues for a robot in a conversational scenario. The advancement of computer vision and audio processing of the last decade has revolutionized the robot perception abilities. In this thesis, we have the following contributions: we first develop a variational Bayesian framework for tracking multiple objects. The variational Bayesian framework gives closed-form tractable problem solutions, which makes the tracking process efficient. The framework is first applied to visual multiple-person tracking. Birth and death process are built jointly with the framework to deal with the varying number of the people in the scene. Furthermore, we exploit the complementarity of vision and robot motorinformation. On the one hand, the robot’s active motion can be integrated into the visual tracking system to stabilize the tracking. On the other hand, visual information can be used to perform motor servoing. Moreover, audio and visual information are then combined in the variational framework, to estimate the smooth trajectories of speaking people, and to infer the acoustic status of a person- speaking or silent. In addition, we employ the model to acoustic-only speaker localization and tracking. Online dereverberation techniques are first applied then followed by the tracking system. Finally, a variant of the acoustic speaker tracking model based on von-Mises distribution is proposed, which is specifically adapted to directional data. All the proposed methods are validated on datasets according to applications.La perception des robots joue un rôle crucial dans l’interaction homme-robot (HRI). Le système de perception fournit les informations au robot sur l’environnement, ce qui permet au robot de réagir en consequence. Dans un scénario de conversation, un groupe de personnes peut discuter devant le robot et se déplacer librement. Dans de telles situations, les robots sont censés comprendre où sont les gens, ceux qui parlent et de quoi ils parlent. Cette thèse se concentre sur les deux premières questions, à savoir le suivi et la diarisation des locuteurs. Nous utilisons différentes modalités du système de perception du robot pour remplir cet objectif. Comme pour l’humain, l’ouie et la vue sont essentielles pour un robot dans un scénario de conversation. Les progrès de la vision par ordinateur et du traitement audio de la dernière décennie ont révolutionné les capacités de perception des robots. Dans cette thèse, nous développons les contributions suivantes : nous développons d’abord un cadre variationnel bayésien pour suivre plusieurs objets. Le cadre bayésien variationnel fournit des solutions explicites, rendant le processus de suivi très efficace. Cette approche est d’abord appliqué au suivi visuel de plusieurs personnes. Les processus de créations et de destructions sont en adéquation avecle modèle probabiliste proposé pour traiter un nombre variable de personnes. De plus, nous exploitons la complémentarité de la vision et des informations du moteur du robot : d’une part, le mouvement actif du robot peut être intégré au système de suivi visuel pour le stabiliser ; d’autre part, les informations visuelles peuvent être utilisées pour effectuer l’asservissement du moteur. Par la suite, les informations audio et visuelles sont combinées dans le modèle variationnel, pour lisser les trajectoires et déduire le statut acoustique d’une personne : parlant ou silencieux. Pour experimenter un scenario où l’informationvisuelle est absente, nous essayons le modèle pour la localisation et le suivi des locuteurs basé sur l’information acoustique uniquement. Les techniques de déréverbération sont d’abord appliquées, dont le résultat est fourni au système de suivi. Enfin, une variante du modèle de suivi des locuteurs basée sur la distribution de von-Mises est proposée, celle-ci étant plus adaptée aux données directionnelles. Toutes les méthodes proposées sont validées sur des bases de données specifiques à chaque application

Application of the EM algorithm for the multitarget/multisensor tracking problem

Caption title.Includes bibliographical references (p. 27-29).Supported by the Laboratory for Information and Decision Systems, Massachusetts Institute of Technology. DAAH04-95-1-0103Karl J. Molnar, James W. Modestino

Conditional Posterior Cramer-Rao Lower Bound and Distributed Target Tracking in Sensor Networks

Sequential Bayesian estimation is the process of recursively estimating the state of a dynamical system observed in the presence of noise. Posterior Cramer-Rao lower bound (PCRLB) sets a performance limit onany Bayesian estimator for the given dynamical system. The PCRLBdoes not fully utilize the existing measurement information to give anindication of the mean squared error (MSE) of the estimator in the future. In many practical applications, we are more concerned with the value of the bound in the future than in the past. PCRLB is an offline bound, because it averages out the very useful measurement information, which makes it an off-line bound determined only by the system dynamical model, system measurement model and the prior knowledge of the system state at the initial time. This dissertation studies the sequential Bayesian estimation problem and then introduces the notation of conditional PCRLB, which utilizes the existing measurement information up to the current time, and sets the limit on the MSE of any Bayesian estimators at the next time step. This work has two emphases: firstly, we give the mathematically rigorous formulation of the conditional PCRLB as well as the approximate recursive version of conditional PCRLB for nonlinear, possibly non-Gaussian dynamical systems. Secondly, we apply particle filter techniques to compute the numerical values of the conditional PCRLB approximately, which overcomes the integration problems introduced by nonlinear/non-Gaussian systems. Further, we explore several possible applications of the proposed bound to find algorithms that provide improved performance. The primary problem of interest is the sensor selection problem for target tracking in sensor networks. Comparisons are also made between the performance of sensor selection algorithm based on the proposed bound and the existing approaches, such as information driven, nearest neighbor, and PCRLB with renewal strategy, to demonstrate the superior performances of the proposed approach. This dissertation also presents a bandwidth-efficient algorithm for tracking a target in sensor networks using distributed particle filters. This algorithm distributes the computation burden for target tracking over the sensor nodes. Each sensor node transmits a compressed local tracking result to the fusion center by a modified expectationmaximization (EM) algorithm to save the communication bandwidth. The fusion center incorporates the compressed tracking results to give the estimate of the target state. Finally, the target tracking problem in heterogeneous sensor networks is investigated extensively. Extended Kalman Filter and particle filter techniques are implemented and compared for tracking a maneuvering

EM Algorithm for Multiple Wideband Source Localization

A computationally efficient algorithm using the expectation-maximization (EM) algorithm for multiple wideband source localization in the near field of a sensor array/area is addressed in this thesis. Our idea is to decompose the observed sensor data, which is a superimposition of multiple sources, into the individual components in the frequency domain and then estimate the corresponding location parameters associated with each component separately. Instead of the conventional alternating projection (AP) method, we propose to adopt the EM algorithm in this work; our new method involves two steps, namely Expectation (E-step) and Maximization (M-step). In the E-step, the individual incident source waveforms are estimated. Then, in the M-step, the maximum likelihood estimates of the source location parameters are obtained. These two steps are executed iteratively and alternatively until the pre-defined convergence is reached. The computational complexity comparison between our proposed EM algorithm and the existing AP scheme is investigated. It is shown through Monte Carlo simulations that the computational complexity of the proposed EM algorithm is significantly lower than that of the existing AP algorithm

Bibliographic Review on Distributed Kalman Filtering

In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area