Dynamic gridmaps: comparing building techniques

Mobile robots need to represent obstacles in their surroundings, even moving ones, to make right movement decisions. For higher autonomy the robot should automatically build such representation from its sensory input. This paper compares the dynamic character of several gridmap building techniques: probabilistic, fuzzy, theory of evidence and histogramic. Two criteria are defined to rank such dynamism in the representation: time to show a new obstacle and time to show a new hole. The update rules for first three such techniques hold associative property which confers them static character, inconvenient for dynamic environments. Major contribution of this paper is the introduction of two new approaches are presented to improve the perception of mobile obstacles: one uses a differential equation to update the map and another uses majority voting in a limited memory per cell. Their dynamisms are also evaluated and the results presented

Dynamic gridmaps: comparing building techniques

P. 5-22Mobile robots need to represent obstacles in their surroundings, even moving ones, to make right movement decisions. For higher autonomy the robot should automatically build such representation from its sensory input. This paper compares the dynamic character of several gridmap building techniques: probabilistic, fuzzy, theory of evidence and histogramic. Two criteria are defined to rank such dynamism in the representation: time to show a new obstacle and time to show a new hole. The update rules for first three such techniques hold associative property which confers them static character, inconvenient for dynamic environments. Major contribution of this paper is the introduction of two new approaches are presented to improve the perception of mobile obstacles: one uses a differential equation to update the map and another uses majority voting in a limited memory per cell. Their dynamisms are also evaluated and the results presentedS

Throughput of a Cognitive Radio Network under Congestion Constraints: A Network-Level Study

In this paper we analyze a cognitive radio network with one primary and one secondary transmitter, in which the primary transmitter has bursty arrivals while the secondary node is assumed to be saturated (i.e. always has a packet waiting to be transmitted). The secondary node transmits in a cognitive way such that it does not impede the performance of the primary node. We assume that the receivers have multipacket reception (MPR) capabilities and that the secondary node can take advantage of the MPR capability by transmitting simultaneously with the primary under certain conditions. We obtain analytical expressions for the stationary distribution of the primary node queue and we also provide conditions for its stability. Finally, we provide expressions for the aggregate throughput of the network as well as for the throughput at the secondary node.Comment: Presented at CROWNCOM 201

A decision support simulation model for bed management in healthcare

In order to provide access to care in a timely manner, it is necessary to effectively manage the allocation of limited resources such as beds. Bed management is key to the effective delivery of high-quality and low-cost healthcare. An efficient utilization of beds requires a detailed understanding of the hospital\u27s operational behavior. It is necessary to understand the behavior of a hospital in order to make necessary adjustments to its resources, and policies, which can improve patient\u27s access to care. The aim of this research was to develop a discrete event simulation to assist in planning and staff scheduling decisions. Each department\u27s performance measures were taken into consideration separately to understand and quantify the behavior of individual departments, and the hospital system as a whole. Several scenarios were analyzed to determine the impact on reducing the number of patients waiting in queue, waiting time for patients, and length of stay of patients. From the results, the departments that have long queues of patients, waiting times, and lengths of stay are detailed to predict how the hospital reacts to patient flow --Abstract, page iv

Frequency Modulated Continuous Waveform Radar for Collision Prevention in Large Vehicles

The drivers of large vehicles can have very limited visibility, which contributes to poor situation awareness and an increased risk of collision with other agents. This thesis is focused on the development of reliable sensing for this close proximity problem in large vehicles operating in harsh environmental conditions. It emphasises the use of in-depth knowledge of a sensor’s physics and performance characteristics to develop effective mathematical models for use in different mapping algorithms. An analysis of the close proximity problem and the demands it poses on sensing technologies is presented. This guides the design and modelling process for a frequency modulated continuous waveform (FMCW) radar sensor for use in solving the close proximity problem. Radar offers better all-weather performance than other sensing modalities, but its measurement structure is more complex and often degraded by noise and clutter. The commonly used constant false alarm rate (CFAR) threshold approach performs poorly in applications with frequent extended targets and a short measurement vector, as is the case here. Therefore, a static detection threshold is calculated using measurements of clutter made using the radar, allowing clutter measurements to be filtered out in known environments. The detection threshold is used to develop a heuristic sensor model for occupancy grid mapping. This results in a more reliable representation of the environment than is achieved using the detection threshold alone. A Gaussian mixture extended Kalman probability hypothesis density filter (GM-EK-PHD) is implemented to allow mapping in dynamic environments using the FMCW radar. These methods are used to produce maps of the environment that can be displayed to the driver of a large vehicle to better avoid collisions. The concepts developed in this thesis are validated using simulated and real data from a low-cost 24GHz FMCW radar developed at the Australian Centre for Field Robotics at the University of Sydney

Underwater simulation and mapping using imaging sonar through ray theory and Hilbert maps

Mapping, sometimes as part of a SLAM system, is an active topic of research and has remarkable solutions using laser scanners, but most of the underwater mapping is focused on 2D maps, treating the environment as a floor plant, or on 2.5D maps of the seafloor. The reason for the problematic of underwater mapping originates in its sensor, i.e. sonars. In contrast to lasers (LIDARs), sonars are unprecise high-noise sensors. Besides its noise, imaging sonars have a wide sound beam effectuating a volumetric measurement. The first part of this dissertation develops an underwater simulator for highfrequency single-beam imaging sonars capable of replicating multipath, directional gain and typical noise effects on arbitrary environments. The simulation relies on a ray theory based method and explanations of how this theory follows from first principles under short-wavelegnth assumption are provided. In the second part of this dissertation, the simulator is combined to a continous map algorithm based on Hilbert Maps. Hilbert maps arise as a machine learning technique over Hilbert spaces, using features maps, applied to the mapping context. The embedding of a sonar response in such a map is a contribution. A qualitative comparison between the simulator ground truth and the reconstucted map reveal Hilbert maps as a promising technique to noisy sensor mapping and, also, indicates some hard to distinguish characteristics of the surroundings, e.g. corners and non smooth features.O mapeamento, às vezes como parte de um sistema SLAM, é um tema de pesquisa ativo e tem soluções notáveis usando scanners a laser, mas a maioria do mapeamento subaquático é focada em mapas 2D, que tratam o ambiente como uma planta, ou mapas 2.5D do fundo do mar. A razão para a dificuldade do mapeamento subaquático origina-se no seu sensor, i.e. sonares. Em contraste com lasers (LIDARs), os sonares são sensores imprecisos e com alto nível de ruído. Além do seu ruído, os sonares do tipo imaging têm um feixe sonoro muito amplo e, com isso, efetuam uma medição volumétrica, ou seja, sobre todo um volume. Na primeira parte dessa dissertação se desenvolve um simulador para sonares do tipo imaging de feixo único de alta frequência capaz de replicar os efeitos típicos de multicaminho, ganho direcional e ruído de fundo em ambientes arbitrários. O simulador implementa um método baseado na teoria geométrica de raios, com todo seu desenvolvimento partindo da acústica subaquática. Na segunda parte dessa dissertação, o simulador é incorporado em um algoritmo de reconstrução de mapas contínuos baseado em Hilbert Maps. Hilbert Maps surge como uma técnica de aprendizado de máquina sobre espaços de Hilbert, usando mapas de características, aplicadas ao contexto de mapeamento. A incorporação de uma resposta de sonar em um tal mapa é uma contribuição desse trabalho. Uma comparação qualitativa entre o ambiente de referência fornecido ao simulador e o mapa reconstruído pela técnica proposta, revela Hilbert Maps como uma técnica promissora para mapeamento atráves de sensores ruidosos e, também, aponta para algumas características do ambiente difíceis de se distinguir, e.g. cantos e regiões não suaves

Building Proteins in a Day: Efficient 3D Molecular Reconstruction

Discovering the 3D atomic structure of molecules such as proteins and viruses is a fundamental research problem in biology and medicine. Electron Cryomicroscopy (Cryo-EM) is a promising vision-based technique for structure estimation which attempts to reconstruct 3D structures from 2D images. This paper addresses the challenging problem of 3D reconstruction from 2D Cryo-EM images. A new framework for estimation is introduced which relies on modern stochastic optimization techniques to scale to large datasets. We also introduce a novel technique which reduces the cost of evaluating the objective function during optimization by over five orders or magnitude. The net result is an approach capable of estimating 3D molecular structure from large scale datasets in about a day on a single workstation.Comment: To be presented at IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 201