Graeme R. Cole

Detection mechanisms in L-, M-, and S-cone contrast space

Detection thresholds were obtained for a 2 degrees Gaussian-blurred spot flashed for 200 ms on an 8.9 degrees white adapting field of 1070 trolands. The spots contrast was represented in an L-, M-, and S-cone contrast space. Detection thresholds were obtained for many vectors close to specific but theoretically important planes within this space. A three-dimensional surface was fitted to the data generated by the probability summation of three mechanisms, each a weighted sum of cone contrasts. The fit revealed a red-green chromatic mechanism driven by delta L/L--delta M/M with no S-cone input that was 1 order of magnitude more sensitive than the two other mechanisms. The latter consisted of a luminance mechanism with little S-cone input and a blue-yellow chromatic mechanism with the S cone opposed to L and M cones.

Computation of cone contrasts for color vision research

Cone-contrast coordinates have proved useful for representing transient test stimuli used in color vision experiments. These representations automatically take into account the spectral absorption of cones and any Weberian adaptation to steady fields of light. Under these conditions, they also can be interpreted as being an approximation to the incremental cone response. A detailed methodology is presented for making appropriate measurements and calculations of cons contrasts for any light source, especially color monitors.

Texture segregation with luminance and chromatic contrast

Preattentive texture discrimination was investigated using low spatial frequency texture elements. The contrast between the texture elements and the background was either purely luminance or purely chromatic, or some combination of both these types of contrast. The threshold to discriminate correctly the location of a different textured region was obtained from each subject, as was each subjects threshold to detect the elements of the texture. Using the ratio of texture to element detection as a measure of the effectiveness of texture discrimination, little difference could be found between the perception of luminance or chromatic texture. However, there were large and significant variations among subjects with otherwise normal colour vision.

Estimation of linear detection mechanisms for stimuli of medium spatial frequency

Detection thresholds were obtained for a circularly-symmetric Gabor profile and Craik-Cornsweet profiles presented on a large white adapting field. These stimuli possessed peak spatial power between 1 and 6 c/deg. Their contrast was represented in an L, M and S cone contrast space. Detection thresholds were obtained for many vectors close to specific but theoretically important planes within this space. These data were fitted with a model comprising independent mechanisms, each a weighted sum of cone contrasts. The fit revealed a chromatic mechanism driven by delta L/L-delta M/M with no S cone input. Within cone contrast space, this mechanism was more sensitive than both a luminance mechanism with little S cone input but considerable variation in relative L to M cone input, and a blue-yellow chromatic mechanism.

A biologically inspired four legged walking robot

This paper presents the design and implementation of a biologically inspired four legged walking robot, with a certain level of comparable complexities and similarities to its biological counterpart. A four-phase walking strategy inspired from four legged animals has been proposed and implemented in the robot. Four parallel Subsumption Architectures and a simple Central Pattern Generator are used in the robot for physical implementation. Experimental results demonstrate that the robot employs the proposed walking strategy and can successfully carry out its walking behaviours under various experimental terrain conditions, such as flat ground, incline, decline and uneven ground.

A modular laboratory for process control and process engineering

Abstract A modular laboratory to support process control and process engineering teaching has been designed and commissioned. The Instrumentation and Control Laboratory in the School of Engineering, Murdoch University, is unique in that it contains no fixed experiments. Rather, an experiment is constructed for a particular teaching concept from the range of process modules available. After use, the experiment is disconnected, and its components used for other experiments or returned to a store. This modular approach has allowed great flexibility in the construction of experiments to meet current learning objectives, while proving very cost effective. The reuse of equipment has avoided duplication and provided a much higher usage factor, allowing a wider menu of experiments for less investment. The other unique aspect of the laboratory is its centralised data acquisition and control system in which every measurement and control signal is linked to a central server and then made available across a network.

A neural network shortest path algorithm for routing in packet-switched communication networks

This paper presents a Hopfield (1986) neural network that solves the routing problem in communication network. It uses mean field annealing to eliminate the constraint terms in the energy function. Since there are no penalty parameters this approach should avoid the problems of scaling. Computer simulations of the neural network algorithm have shown that it can find optimal or near-optimal valid routes for all origin-destination pairs in a fourteen node communication network.

Linear colour mechanisms obtained from detection thresholds in cone contrast space

We have obtained thresholds for detection of a 2° diameter Gaussian blurred (σ = 0.25°) spot flashed for 200 ms on an 8.9° diam. white adapting field (1070 td). The contrast of the spot was represented in L, M and S cone contrast space. For three specific but theoretically important planes within this space, we obtained complete detection contours for three normal observers, and partial detection contours for a fourth observer. The contours were assumed to be determined by the probability summation of three mechanisms, each of which constituted a linear combination of cone contrast inputs. The quantitative weightings of cone inputs into the three best fitting mechanisms revealed the following: a luminance mechanism with little S cone input, a red-green chromatic mechanism driven by L-M with no S cone input and a blue-yellow chromatic mechanism with the S cone opposed to L and M cones. Fitting the data with four or five mechanisms did not significantly increase the goodness-of-fit.

Demonstrating remote area power supply systems on the World Wide Web

One of the primary advantages of the internet is its ability to enable flexible, 24-h access to up-to-date information, irrespective of the geographic location of the user. It is thus rapidly becoming an important tool for the dissemination of information on a global scale. In order to take advantage of the increased accessibility available through the internet, the Murdoch University Energy Research Institute has developed an internet-based version of its physical renewable energy remote area power supply (RAPS) demonstration system. In this way, the physical site, which aims to inform the community about the cost, performance and reliability of renewable energy systems by enabling them to visit a practical operating RAPS system, is available to a much larger audience. The internet-based site, WebRAPS, contains a number of features including case studies of the three RAPS systems at the physical site, specifications of the components used in the systems and general information about the renewable energy system technologies. It also displays historical and real-time data from a monitoring system integrated with the largest of the physical RAPS systems. Visitors to the internet site are able to see how the RAPS system is operating, almost as well as if they were present at the physical site itself. This paper demonstrates the features of the WebRAPS site and discusses how it was developed.

Using the Hopfield neural network with mean field annealing to solve the shortest path problem in a communication network

The performance of the Hopfield neural network with mean field annealing for finding solutions to the shortest path problem in a communication network is investigated. The neural network uses mean field annealing to eliminate the constraint terms in the energy function. Unlike other systems which use penalty constraint terms there is no need to tune constraint parameters (this tuning has been found to be difficult and problem specific). Also, we avoid the need to pre-determine the minimum number of hops corresponding to the optimal route. We have very encouraging simulation results for the nine node grid network and fourteen node NFSNET-backbone network but have found that the neural network has difficulty finding valid routes when many hops are required to get from the source to destination.