Flying insects are able to manoeuvre through complex environments with remarkable ease and accuracy despite their simple visual system. Physiological evidence suggests that flight control is primarily guided by a small system of neurons tuned to very specific types of complex motion. This system is a promising model for bio-inspired approaches to low-cost artificial motion analysis systems, such as collision avoidance devices. A number of models of motion detection have been proposed, with the basic model being the Reichardt Correlator. Electrophysiological data suggest a variety of non-linear elaborations, which include compressive non-linearities and adaptive feedback of local motion detector outputs. In this paper we review a number of computational models for motion detection from the point of view of ease of implementation in low cost VLSI technology. We summarise the features of biological motion analysis systems that are important for the design of real-time artificial motion analysis systems. Then we report on recent progress in bio-inspired analog VLSI chips that capture properties of biological neural computation.
In the context of a military operation, even if the intended actions, the geographic location, and the capabilities of the opposition are known, there are still some critical uncertainties that could have a major impact on the effectiveness of a given set of capabilities. These uncertainties include unpredictable events and the response alternatives that are available to the command and control elements of the capability set. They greatly complicate any a priori mathematical description. In a forecasting approach, the most likely future might be chosen and a solution sought that is optimal for that case. With scenario analysis, futures are proposed on the basis of critical uncertainties and the option that is most robust is chosen. We use scenario analysis but our approach is different in that we focus on the complexity and use the coupling between scenarios and options to create information on ideal options. The approach makes use of both soft and hard operations research methods, with subject matter expertise being used to define plausible responses to scenarios. In each scenario, uncertainty affects only a subset of the system-inherent variables and the variables that describe system-environment interactions. It is this scenario-specific reduction of variables that makes the problem mathematically tractable. The process we define is significantly different to existing scenario analysis processes, so we have named it adversarial scenario analysis. It can be used in conjunction with other methods, including recent improvements to the scenario analysis process. To illustrate the approach, we undertake a tactical level scenario analysis for a logistics problem that is defined by a network, expected throughputs to end users, the transport capacity available, the infrastructure at the nodes and the capacities of roads, stocks etc. The throughput capacity, e.g. the effectiveness, of the system relies on all of these variables and on the couplings between them. The system is initially in equilibrium for a given level of demand. However, different, and simpler, solutions emerge as the balance of couplings and the importance of variables change. The scenarios describe such changes in conditions. For each scenario it was possible to define measures that describe the differences between options. As with agent-based distillations, the solution is essentially qualitative and exploratory, bringing awareness of possible future difficulties and of the capabilities that are necessary if we are to deal successfully with those difficulties.