Department of Agriculture : restructuring will impact farm service agencies' automation plans and programs : statement for the record by JayEtta Z. Hecker, Director, Resources, Community, and Economic Development Information Systems, Information Management and Technology Division, before the Subcommittee on Wheat, Soybeans, and Feed Grains, Committee on Agriculture, House of Representatives /

"July 30, 1992."

Automation, machine-readable records, and archival administration : an annotated bibliography /

Includes indexes.

Human aspects of library automation : helping staff and patrons cope /

Includes bibliographical references and index.

Automation; economic implications and impact upon collective bargaining.

Includes bibliographies.

Stock markets : information vendors need SEC oversight to control automation risks : report to the Chairman, Securities and Exhange Commission /

"B-246404"--P. 1.

Program development plan : automation of field operations and services /

On spine: AFOS program development plan.

Progress report, Automation Committee, formed under agreements of September 1, 1959 between Armour and Company and United Packinghouse Food and Allied Workers, AFL-CIO, Amalgamated Meat Cutters, and Butcher Workmen of North America, AFL-CIO.

Appendix (p. 28-29): Text of the "Automation" section of agreement ... dated August 31, 1959.

A modified particle filter for simultaneous robot localization and landmark tracking in an indoor environment

This paper presents the implementation of a modified particle filter for vision-based simultaneous localization and mapping of an autonomous robot in a structured indoor environment. Through this method, artificial landmarks such as multi-coloured cylinders can be tracked with a camera mounted on the robot, and the position of the robot can be estimated at the same time. Experimental results in simulation and in real environments show that this approach has advantages over the extended Kalman filter with ambiguous data association and various levels of odometric noise.

Hippocampal models for simultaneous localisation and mapping on an autonomous robot

To navigate successfully in a novel environment a robot needs to be able to Simultaneously Localize And Map (SLAM) its surroundings. The most successful solutions to this problem so far have involved probabilistic algorithms, but there has been much promising work involving systems based on the workings of part of the rodent brain known as the hippocampus. In this paper we present a biologically plausible system called RatSLAM that uses competitive attractor networks to carry out SLAM in a probabilistic manner. The system can effectively perform parameter self-calibration and SLAM in onedimension. Tests in two dimensional environments revealed the inability of the RatSLAM system to maintain multiple pose hypotheses in the face of ambiguous visual input. These results support recent rat experimentation that suggest current competitive attractor models are not a complete solution to the hippocampal modelling problem.

Representation and learning of visual information for pose recognition

Recovering position from sensor information is an important problem in mobile robotics, known as localisation. Localisation requires a map or some other description of the environment to provide the robot with a context to interpret sensor data. The mobile robot system under discussion is using an artificial neural representation of position. Building a geometrical map of the environment with a single camera and artificial neural networks is difficult. Instead it would be simpler to learn position as a function of the visual input. Usually when learning images, an intermediate representation is employed. An appropriate starting point for biologically plausible image representation is the complex cells of the visual cortex, which have invariance properties that appear useful for localisation. The effectiveness for localisation of two different complex cell models are evaluated. Finally the ability of a simple neural network with single shot learning to recognise these representations and localise a robot is examined.

Helicopter automation using a low-cost sensing system

This paper details the design of an autonomous helicopter control system using a low cost sensor suite. Control is maintained using simple nested PID loops. Aircraft attitude, velocity, and height is estimated using an in-house designed IMU and vision system. Information is combined using complimentary filtering. The aircraft is shown to be stabilised and responding to high level demands on all axes, including heading, height, lateral velocity and longitudinal velocity.