Mukund A. Sanglikar

Particle swarm optimisation based Diophantine equation solver

The paper introduces particle swarm optimisation as a viable strategy to find numerical solution of Diophantine equation, for which there exists no general method of finding solutions. The proposed methodology uses a population of integer particles. The candidate solutions in the feasible space are optimised to have better positions through particle best and global best positions. The methodology, which follows fully connected neighbourhood topology, can offer many solutions of such equations.

Evolution Induced Secondary Immunity: An Artificial Immune System Based Intrusion Detection System

The analogy between immune systems and intrusion detection systems encourage the use of artificial immune systems for anomaly detection in computer networks. This paper describes a technique of applying artificial immune system along with genetic algorithm to develop an intrusion detection system. Far from developing primary immune response, as most of the related works do, it attempts to evolve this primary immune response to a secondary immune response using the concept of memory cells prevalent in natural immune systems. A genetic algorithm using genetic operators- selection, cloning, crossover and mutation- facilitates this. Memory cells formed enable faster detection of already encountered attacks. These memory cells, being highly random in nature, are dependent on the evolution of the detectors and guarantee greater immunity from anomalies and attacks. The fact that the whole procedure is enveloped in the concepts of approximate binding and memory cells of lightweight of natural immune systems makes this system reliable, robust and quick responding.

Modelling rolling ball blends for computer aided geometric design

Abstract Blend surfaces in solid modelling systems demand their explicit definitions, capable of supplying every detail needed in further processing. This paper developes a mathematical model of the blend generated by a ball shaped cutter, called the rolling ball blend. A discriminating feature of this work is that the two surfaces to be blended, as well as the blend surface, are represented in parametric form. The blend equations have been formulated using differential geometry and their solutions are analyzed for various types of surfaces to be blended. The method provides closed form analytic solutions for most of the surfaces which are common in current solid modellers. More importantly, the method aims at bringing parametric patches into the realm of solid modelling by providing an explicit surface definition for a rolling ball blend in parametric form.

Parametric blends for shape modeling

Blends are surfaces that arise in manufacturing and do not usually appear on blue-prints. Methods are presented for modelling them in CAD/CAM systems. Modelling of two types of blends for parametric surfaces is considered; rolling sphere blends and general cross-section blends. The blend surface design task is split into two subtasks: identification of the normal cross section as well as the path followed by it. The path is identified as the intersection of the offsets of the given two surfaces. The offsetting technique establishes a natural parametric correspondence between the two blend boundary curves. The design of the shape of the cross section curve is left to the user. This provides the flexibility demanded by the application at hand.<<ETX>>

Finding numerical solutions of diophantine equations using ant colony optimization

The paper attempts to find numerical solutions of Diophantine equations, a challenging problem as there are no general methods to find solutions of such equations. It uses the metaphor of foraging habits of real ants. The ant colony optimization based procedure starts with randomly assigned locations to a fixed number of artificial ants. Depending upon the quality of these positions, ants deposit pheromone at the nodes. A successor node is selected from the topological neighbourhood of each of the nodes based on this stochastic pheromone deposit. If an ant bumps into an already encountered node, the pheromone is updated correspondingly. A suitably defined pheromone evaporation strategy guarantees that premature convergence does not take place. The experimental results, which compares with those of other machine intelligence techniques, validate the effectiveness of the proposed method.

An Analysis of Effort Variance in Software Maintenance Projects

Quantitative project management, understanding process variations and improving overall process capability, are fundamental aspects of process improvements and are now strongly propagated by all best-practice models of process improvement. Organizations are moving to the next level of quantitative management where empirical methods are used to establish process predictability, thus enabling better project planning and management. In this paper we use empirical methods to analyze Effort Variance in software maintenance projects. The Effort Variance model established was used to identify process improvements and baseline performance.

Using Decision Structures for Policy Analysis in Software Product-line Evolution – A Case Study

Project management decisions are the primary basis for project success (or failure). Mostly, such decisions are based on an intuitive understanding of the underlying software engineering and management process and have a likelihood of being misjudged. Our problem domain is product-line evolution. We model the dynamics of the process by incorporating feedback loops appropriate to two decision structures: staffing policy, and the forces of growth associated with long-term software evolution. The model is executable and supports project managers to assess the long-term effects of possible actions. Our work also corroborates results from earlier studies of E-type systems, in particular the FEAST project and the rules for software evolution, planning and management.