Compressed sensing and finite rate of innovation for efficient data acquisition of quantitative acoustic microscopy images

La microscopie acoustique quantitative (MAQ) est une modalité d'imagerie bien établie qui donne accès à des cartes paramétriques 2D représentatives des propriétés mécaniques des tissus à une échelle microscopique. Dans la plupart des études sur MAQ, l'échantillons est scanné ligne par ligne (avec un pas de 2µm) à l'aide d'un transducteur à 250 MHz. Ce type d'acquisition permet d'obtenir un cube de données RF 3D, avec deux dimensions spatiales et une dimension temporelle. Chaque signal RF correspondant à une position spatiale dans l'échantillon permet d'estimer des paramètres acoustiques comme par exemple la vitesse du son ou l'impédance. Le temps d'acquisition en MAQ est directement proportionnel à la taille de l'échantillon et peut aller de quelques minutes à quelques dizaines de minutes. Afin d'assurer des conditions d'acquisition stables et étant donnée la sensibilité des échantillons à ces conditions, diminuer le temps d'acquisition est un des grand défis en MAQ. Afin de relever ce défi, ce travail de thèse propose plusieurs solutions basées sur l'échantillonnage compressé (EC) et la théories des signaux ayant un faible nombre de degré de liberté (finite rate of innovation - FRI, en anglais). Le principe de l'EC repose sur la parcimonie des données, sur l'échantillonnage incohérent de celles-ci et sur les algorithmes d'optimisation numérique. Dans cette thèse, les phénomènes physiques derrière la MAQ sont exploités afin de créer des modèles adaptés aux contraintes de l'EC et de la FRI. Plus particulièrement, ce travail propose plusieurs pistes d'application de l'EC en MAQ : un schéma d'acquisition spatiale innovant, un algorithme de reconstruction d'images exploitant les statistiques des coefficients en ondelettes des images paramétriques, un modèle FRI adapté aux signaux RF et un schéma d'acquisition compressée dans le domaine temporel.Quantitative acoustic microscopy (QAM) is a well-accepted modality for forming 2D parameter maps making use of mechanical properties of soft tissues at microscopic scales. In leading edge QAM studies, the sample is raster-scanned (spatial step size of 2µm) using a 250 MHz transducer resulting in a 3D RF data cube, and each RF signal for each spatial location is processed to obtain acoustic parameters, e.g., speed of sound or acoustic impedance. The scanning time directly depends on the sample size and can range from few minutes to tens of minutes. In order to maintain constant experimental conditions for the sensitive thin sectioned samples, the scanning time is an important practical issue. To deal with the current challenge, we propose the novel approach inspired by compressed sensing (CS) and finite rate of innovation (FRI). The success of CS relies on the sparsity of data under consideration, incoherent measurement and optimization technique. On the other hand, the idea behind FRI is supported by a signal model fully characterized as a limited number of parameters. From this perspective, taking into account the physics leading to data acquisition of QAM system, the QAM data can be regarded as an adequate application amenable to the state of the art technologies aforementioned. However, when it comes to the mechanical structure of QAM system which does not support canonical CS measurement manners on the one hand, and the compositions of the RF signal model unsuitable to existing FRI schemes on the other hand, the advanced frameworks are still not perfect methods to resolve the problems that we are facing. In this thesis, to overcome the limitations, a novel sensing framework for CS is presented in spatial domain: a recently proposed approximate message passing (AMP) algorithm is adapted to account for the underlying data statistics of samples sparsely collected by proposed scanning patterns. In time domain, as an approach for achieving an accurate recovery from a small set of samples of QAM RF signals, we employ sum of sincs (SoS) sampling kernel and autoregressive (AR) model estimator. The spiral scanning manner, introduced as an applicable sensing technique to QAM system, contributed to the significant reduction of the number of spatial samples when reconstructing speed of sound images of a human lymph node. Furthermore, the scanning time was also hugely saved due to the merit of the mechanical movement of the proposed sensing pattern. Together with the achievement in spatial domain, the introduction of SoS kernel and AR estimator responsible for an innovation rate sampling and a parameter estimation respectively led to dramatic reduction of the required number of samples per RF signal compared to a conventional approach. Finally, we showed that both data acquisition frameworks based on the CS and FRI can be combined into a single spatio-temporal solution to maximize the benefits stated above

Sensor-based autonomous pipeline monitoring robotic system

The field of robotics applications continues to advance. This dissertation addresses the computational challenges of robotic applications and translations of actions using sensors. One of the most challenging fields for robotics applications is pipeline-based applications which have become an indispensable part of life. Proactive monitoring and frequent inspections are critical in maintaining pipeline health. However, these tasks are highly expensive using traditional maintenance systems, knowing that pipeline systems can be largely deployed in an inaccessible and hazardous environment. Thus, we propose a novel cost effective, scalable, customizable, and autonomous sensor-based robotic system, called SPRAM System (Sensor-based Autonomous Pipeline Monitoring Robotic System). It combines robot agent based technologies with sensing technologies for efficiently locating health related events and allows active and corrective monitoring and maintenance of the pipelines. The SPRAM System integrates RFID systems with mobile sensors and autonomous robots. While the mobile sensor motion is based on the fluid transported by the pipeline, the fixed sensors provide event and mobile sensor location information and contribute efficiently to the study of health history of the pipeline. In addition, it permits a good tracking of the mobile sensors. Using the output of event analysis, a robot agent gets command from the controlling system, travels inside the pipelines for detailed inspection and repairing of the reported incidents (e.g., damage, leakage, or corrosion). The key innovations of the proposed system are 3-fold: (a) the system can apply to a large variety of pipeline systems; (b) the solution provided is cost effective since it uses low cost powerless fixed sensors that can be setup while the pipeline system is operating; (c) the robot is autonomous and the localization technique allows controllable errors. In this dissertation, some simulation experiments described along with prototyping activities demonstrate the feasibility of the proposed system

Design of a Fully Autonomous Mobile Pipeline Exploration Robot (FAMPER)

Pipelines have been an integral part of our constructions for many centuries. However, need to be maintained, and the cost of maintenance continues to increase. Robots have been considered as an attractive alternative, and many different types of pipeline robots have been proposed in the past. Unfortunately many of them work under only very restricted environments such as customized pipelines, often have no vertical mobility, or can traverse through only a simple pipeline structure due to wired control. This thesis presents the design and implementation of a robot based on novel idea we call “caterpillar navigational mechanism”. A Fully Autonomous Mobile Pipeline Exploration Robot (FAMPER), for exploring pipeline structures autonomously has been built and its performance has been evaluated. We present the design of a robot based on wall-pressed caterpillar type for not only horizontal, but also vertical mobility in pipeline elements such as straight pipelines, elbows and branches, and its autonomous navigational system providing useful information for pipeline maintenance. FAMPER has been designed for 6 inch sewer pipes, which are predominantly used in urban constructions. The proposed design enables FAMPER to display formidable mobility and controllability in most of the existing structure of pipeline, and provides a spacious body for housing various electronic devices. Specifically, FAMPER is equipped with several sensors, and a high performance processor for autonomous navigation. We have performed experiments to evaluate the effectiveness of our architecture and we present here a discussion of the performed results