A new multicompartmental reaction-diffusion modeling method links transient membrane attachment of E. coli MinE to E-ring formation

Many important cellular processes are regulated by reaction-diffusion (RD) of molecules that takes place both in the cytoplasm and on the membrane. To model and analyze such multicompartmental processes, we developed a lattice-based Monte Carlo method, Spatiocyte that supports RD in volume and surface compartments at single molecule resolution. Stochasticity in RD and the excluded volume effect brought by intracellular molecular crowding, both of which can significantly affect RD and thus, cellular processes, are also supported. We verified the method by comparing simulation results of diffusion, irreversible and reversible reactions with the predicted analytical and best available numerical solutions. Moreover, to directly compare the localization patterns of molecules in fluorescence microscopy images with simulation, we devised a visualization method that mimics the microphotography process by showing the trajectory of simulated molecules averaged according to the camera exposure time. In the rod-shaped bacterium _Escherichia coli_, the division site is suppressed at the cell poles by periodic pole-to-pole oscillations of the Min proteins (MinC, MinD and MinE) arising from carefully orchestrated RD in both cytoplasm and membrane compartments. Using Spatiocyte we could model and reproduce the _in vivo_ MinDE localization dynamics by accounting for the established properties of MinE. Our results suggest that the MinE ring, which is essential in preventing polar septation, is largely composed of MinE that is transiently attached to the membrane independently after recruited by MinD. Overall, Spatiocyte allows simulation and visualization of complex spatial and reaction-diffusion mediated cellular processes in volumes and surfaces. As we showed, it can potentially provide mechanistic insights otherwise difficult to obtain experimentally

Sensory processing and world modeling for an active ranging device

In this project, we studied world modeling and sensory processing for laser range data. World Model data representation and operation were defined. Sensory processing algorithms for point processing and linear feature detection were designed and implemented. The interface between world modeling and sensory processing in the Servo and Primitive levels was investigated and implemented. In the primitive level, linear features detectors for edges were also implemented, analyzed and compared. The existing world model representations is surveyed. Also presented is the design and implementation of the Y-frame model, a hierarchical world model. The interfaces between the world model module and the sensory processing module are discussed as well as the linear feature detectors that were designed and implemented

Scene Mapping and Understanding by Robotic Vision

The first mechanical Automaton concept was found in a Chinese text written in the 3rd century BC, while Computer Vision was born in the late 1960s. Therefore, visual perception applied to machines (i.e. the Machine Vision) is a young and exciting alliance. When robots came in, the new field of Robotic Vision was born, and these terms began to be erroneously interchanged. In short, we can say that Machine Vision is an engineering domain, which concern the industrial use of Vision. The Robotic Vision, instead, is a research field that tries to incorporate robotics aspects in computer vision algorithms. Visual Servoing, for example, is one of the problems that cannot be solved by computer vision only. Accordingly, a large part of this work deals with boosting popular Computer Vision techniques by exploiting robotics: e.g. the use of kinematics to localize a vision sensor, mounted as the robot end-effector. The remainder of this work is dedicated to the counterparty, i.e. the use of computer vision to solve real robotic problems like grasping objects or navigate avoiding obstacles. Will be presented a brief survey about mapping data structures most widely used in robotics along with SkiMap, a novel sparse data structure created both for robotic mapping and as a general purpose 3D spatial index. Thus, several approaches to implement Object Detection and Manipulation, by exploiting the aforementioned mapping strategies, will be proposed, along with a completely new Machine Teaching facility in order to simply the training procedure of modern Deep Learning networks

Deep Learning based 3D Segmentation: A Survey

3D object segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving, robotics, augmented reality and medical image analysis. It has received significant attention from the computer vision, graphics and machine learning communities. Traditionally, 3D segmentation was performed with hand-crafted features and engineered methods which failed to achieve acceptable accuracy and could not generalize to large-scale data. Driven by their great success in 2D computer vision, deep learning techniques have recently become the tool of choice for 3D segmentation tasks as well. This has led to an influx of a large number of methods in the literature that have been evaluated on different benchmark datasets. This paper provides a comprehensive survey of recent progress in deep learning based 3D segmentation covering over 150 papers. It summarizes the most commonly used pipelines, discusses their highlights and shortcomings, and analyzes the competitive results of these segmentation methods. Based on the analysis, it also provides promising research directions for the future.Comment: Under review of ACM Computing Surveys, 36 pages, 10 tables, 9 figure

Study of random porous morphologies by means of statistical analysis and computer simulations of fluid dynamics

This thesis presents an investigation of porous media by means of simulation techniques and morphological analysis. As a basis for the investigation throughout this work, we use three- dimensional (3D) images of porous structures obtained by imaging techniques, in particular, fo- cused ion beam scanning electron microscopy (FIB-SEM) and confocal laser scanning microscopy (CLSM) for macroporous space, and scanning transmission electron microscopy (STEM) to re- solve mesopores. A set of different morphological methods (chord length distribution (CLD), medial axis analysis (MAA), estimations of geometric, branch and diffusive tortuosities) are applied to capture averaged descriptors of the reconstructed porous samples. Because fluid dy- namics is inherent in many applications of porous media, several techniques are deployed to simulate the fluid dynamics in the reconstructions of porous media. This work includes four chapters that cover three different topics associated with the investigation of fluid dynamics in porous media. Each chapter also represents a separate journal publication. In the first chapter, we perform hydrodynamic dispersion simulations to study the morphology- flow relationship in physical reconstructions of particulate beds as well as in computer-generated packings of monosized spheres. A combination of lattice-Boltzmann and random-walk parti- cle tracking (RWPT) methods were utilized to simulate the flow and mass transport, respec- tively. Based on mean chord length μ and standard deviation σ estimated for CLD, we present a morphological descriptor, σ/μ, that can predict the longitudinal dispersion coefficient for any morphological configuration of packed beds. In the second chapter, we introduce the overall hindrance factor expression, H(λ), that de- scribes transport limitations in mesoporous space of random silica monoliths in dependence of λ, the ratio of solute size to mean pore size. The presented H(λ) is obtained through diffusion simulations of finite-size tracers applying the RWPT technique in three reconstructions of mor- phologically similar porous silica. The expression can also be utilized to assess the hindered diffusion coefficient for any material with similar morphology. In the third chapter, we adopt the lattice-gas mean field density functional theory (MFDFT) to virtually reproduce adsorption/desorption processes in a mesopore network of one of the monoliths from the second chapter. We demonstrate a good qualitative agreement of simulated boundary curves with experimental isotherms with an H2 hysteresis loop obtained for nitrogen at 77 K. We also use 3D images of the phase distribution that can be provided by MFDFT for any relative pressure value in the range 0 < p/p0 ≤ 1 to reveal the relation between hysteresis and phase distribution. In the fourth chapter, we continue using the concept of exploration of phase distribution and perform MFDFT modeling in physically reconstructed geometrical models of two ordered (SBA-15, KIT-6) and one random mesoporous silicas. We conduct a short parametric study of the MFDFT model to find optimal agreement with experimental isotherms in the hysteresis region. We also present simulated boundary curves for both ordered structures with a clear H1 hysteresis loop and for the disordered material with type H2(a) hysteresis. The phase distribution analysis as well as the shape of scanning curves reveal a highly heterogeneous morphology of the random silica. Hence, pore blocking and cavitation phenomena are identified and analyzed

3D Scene Reconstruction with Micro-Aerial Vehicles and Mobile Devices

Scene reconstruction is the process of building an accurate geometric model of one\u27s environment from sensor data. We explore the problem of real-time, large-scale 3D scene reconstruction in indoor environments using small laser range-finders and low-cost RGB-D (color plus depth) cameras. We focus on computationally-constrained platforms such as micro-aerial vehicles (MAVs) and mobile devices. These platforms present a set of fundamental challenges - estimating the state and trajectory of the device as it moves within its environment and utilizing lightweight, dynamic data structures to hold the representation of the reconstructed scene. The system needs to be computationally and memory-efficient, so that it can run in real time, onboard the platform. In this work, we present three scene reconstruction systems. The first system uses a laser range-finder and operates onboard a quadrotor MAV. We address the issues of autonomous control, state estimation, path-planning, and teleoperation. We propose the multi-volume occupancy grid (MVOG) - a novel data structure for building 3D maps from laser data, which provides a compact, probabilistic scene representation. The second system uses an RGB-D camera to recover the 6-DoF trajectory of the platform by aligning sparse features observed in the current RGB-D image against a model of previously seen features. We discuss our work on camera calibration and the depth measurement model. We apply the system onboard an MAV to produce occupancy-based 3D maps, which we utilize for path-planning. Finally, we present our contributions to a scene reconstruction system for mobile devices with built-in depth sensing and motion-tracking capabilities. We demonstrate reconstructing and rendering a global mesh on the fly, using only the mobile device\u27s CPU, in very large (300 square meter) scenes, at a resolutions of 2-3cm. To achieve this, we divide the scene into spatial volumes indexed by a hash map. Each volume contains the truncated signed distance function for that area of space, as well as the mesh segment derived from the distance function. This approach allows us to focus computational and memory resources only in areas of the scene which are currently observed, as well as leverage parallelization techniques for multi-core processing