A VLSI sensory-motor architecture for an obstacle avoidance task in an unstructured environment

Obstacle avoidance is a difficult task for autonomous robots. To overcome limitations of traditional computer vision systems, some robots have made use of efficient VLSI sensory-motor systems. However, because the processing in these systems is at the pixel level, it is difficult to achieve algorithms that can deal with real-world, unstructured environments. To make these VLSI sensory-motor systems more widely applicable, new architectures and strategies are needed. This thesis presents an architecture for a VLSI sensory-motor system designed for obstacle avoidance by a mobile robot in an unstructured environment. Drawing inspiration from biology and behavior-based robotics, the development of the architecture is guided by an emphasis on the requirements of an obstacle avoidance behavior for a mobile robot. The architecture incorporates features which enable it to deal with unstructured environments. A special foveation and weighting scheme are used to facilitate the detection of real-world objects. The sensory and motor maps of the system are aligned to relate features in the visual field to a left and a right control signal. The effectiveness of the architecture is demonstrated through computer simulation. A model of a sensory-motor system based on the architecture is used to create a realistic looking virtual environment simulator. Simulation results show that such a system is capable of efficient obstacle avoidance in an unstructured environment, while using only a small number of simple operations connected hierarchically, potentially leading to an implementation with small pixel

A space-variant architecture for active visual target tracking

An active visual target tracking system is an automatic feedback control system that can track a moving target by controlling the movement of a camera or sensor array. This kind of system is often used in applications such as automatic surveillance and human-computer interaction. The design of an effective target tracking system is challenging because the system should be able to precisely detect the fine movements of a target while still being able to detect a large range of target velocities. Achieving this in a computationally efficient manner is difficult with a conventional system architecture. This thesis presents an architecture for an active visual target tracking system based on the idea of space-variant motion detection. In general, space-variant imaging involves the use of a non-uniform distribution of sensing elements across a sensor array, similar to how the photoreceptors in the human eye are not evenly distributed. In the proposed architecture, space-variant imaging is used to design an array of elementary motion detectors (EMDs). The EMDs are tuned in such a way as to make it possible to detect motion both precisely and over a wide range of velocities in a computationally efficient manner. The increased ranges are achieved without additional computational costs beyond the basic mechanism of motion detection. The technique is general in that it can be used with different motion detection mechanisms and the overall space-variant structure can be varied to suit a particular application. The design of a tracking system based on a space-variant motion detection array is a difficult task. This thesis presents a method of analysis and design for such a tracking system. The method of analysis consists of superimposing a phase-plane plot of the continuous-time dynamics of the tracking system onto a map of the detection capabilities of the array of EMDs. With the help of this 'sensory-motor' plot, a simple optimization algorithm is used to design a tracking system to meet particular objectives for settling time, steady-state error and overshoot. Several simulations demonstrate the effectiveness of the method. A complete active vision system is implemented and a set of target tracking experiments are performed. Experimental results support the effectiveness of the approac

Diaphragmatic hernia in the south-west of England.

A retrospective anatomical, family, and epidemiological study was made of 143 patients (81 female and 62 male) with diaphragmatic hernia who were born in the south-west of England between 1943 and 1974. Thirty-nine cases were stillborn. Seventy-five per cent of patients had a left-sided diaphragmatic defect, 22% had a right-sided defect, and 3% had a bilateral defect. Fifty per cent of the patients had other congenital malformations, most frequently of the nervous system. No maternal age or birth order effect was noted. Cases of diaphragmatic hernia without other malformations had in general a normal fetal growth rate. Eight per cent of the cases were illegitimate. There were two pairs of twins discordant for diaphragmatic hernia, one pair being dizygotic and the other monozygotic. In no case of diaphragmatic hernia was there a relative affected with a diaphragmatic hernia. The most common type of diaphragmatic defect was a posterolateral hernia (92%), followed in frequency by an eventration of the diaphragm (5%), the least common defect being a retrocostosternal hernia (2%). Diaphragmatic hernia appears to be aetiologically as well as anatomically heterogeneous. In this series there were two cases of trisomy 18, one case of trisomy 21, one case trisomic for a small part of chromosome 20, and two cases with the Pierre Robin syndrome. It seems likely that diaphragmatic hernia is a non-specific consequence of several teratological processes

BAAD: a biomass and allometry database for woody plants\ud \ud \ud

Understanding how plants are constructed—i.e., how key size dimensions and the amount of mass invested in different tissues varies among individuals—is essential for modeling plant growth, carbon stocks, and energy fluxes in the terrestrial biosphere. Allocation patterns can differ through ontogeny, but also among coexisting species and among species adapted to different environments. While a variety of models dealing with biomass allocation exist, we lack a synthetic understanding of the underlying processes. This is partly due to the lack of suitable data sets for validating and parameterizing models. To that end, we present the Biomass And Allometry Database (BAAD) for woody plants. The BAAD contains 259 634 measurements collected in 176 different studies, from 21 084 individuals across 678 species. Most of these data come from existing publications. However, raw data were rarely made public at the time of publication. Thus, the BAAD contains data from different studies, transformed into standard units and variable names. The transformations were achieved using a common workflow for all raw data files. Other features that distinguish the BAAD are: (i) measurements were for individual plants rather than stand averages; (ii) individuals spanning a range of sizes were measured; (iii) plants from 0.01–100 m in height were included; and (iv) biomass was estimated directly, i.e., not indirectly via allometric equations (except in very large trees where biomass was estimated from detailed sub-sampling). We included both wild and artificially grown plants. The data set contains the following size metrics: total leaf area; area of stem cross-section including sapwood, heartwood, and bark; height of plant and crown base, crown area, and surface area; and the dry mass of leaf, stem, branches, sapwood, heartwood, bark, coarse roots, and fine root tissues. We also report other properties of individuals (age, leaf size, leaf mass per area, wood density, nitrogen content of leaves and wood), as well as information about the growing environment (location, light, experimental treatment, vegetation type) where available. It is our hope that making these data available will improve our ability to understand plant growth, ecosystem dynamics, and carbon cycling in the world's vegetation