Fine Scale Simulation of Fractured Reservoirs: Applications and Comparison

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Top Management Control Functions for Information Systems in Small and Medium Enterprises

This paper analyzes the Top Management Control functions for Information Systems (IS) in Small and Medium Enterprises (SMEs). SMEs extensively rely on information technology resources to enhance their competence in today’s global economy. They should have adequate top management control mechanisms in place for their efficient functioning. Top Management Controls determine how effectively the senior management manages the IS functions in a SME. The major tasks at this level consist of Planning, Organizing, Leading and Controlling functions. A brief introduction to SMEs is given at the beginning followed by the different categories of Top Management Controls. The final section highlights on some good practices to be followed by Top Management to realize the vision for the IS project in SMEs.Charge-Out, Information Systems, IS Plan, Small and Medium Enterprise, Top Management Controls, Zero Based Budgeting

2D shape classification and retrieval

We present a novel correspondence-based technique for efficient shape classification and retrieval. Shape boundaries are described by a set of (ad hoc) equally spaced points – avoiding the need to extract “landmark points”. By formulating the correspondence problem in terms of a simple generative model, we are able to efficiently compute matches that incorporate scale, translation, rotation and reflection invariance. A hierarchical scheme with likelihood cut-off provides additional speed-up. In contrast to many shape descriptors, the concept of a mean (prototype) shape follows naturally in this setting. This enables model based classification, greatly reducing the cost of the testing phase. Equal spacing of points can be defined in terms of either perimeter distance or radial angle. It is shown that combining the two leads to improved classification/retrieval performance.

Reconstructing null-space policies subject to dynamic task constraints in redundant manipulators

We consider the problem of direct policy learning in situations where the policies are only observable through their projections into the null-space of a set of dynamic, non-linear task constraints. We tackle the issue of deriving consistent data for the learning of such policies and make two contributions towards its solution. Firstly, we derive the conditions required to exactly reconstruct null-space policies and suggest a learning strategy based on this derivation. Secondly, we consider the case that the null-space policy is conservative and show that such a policy can be learnt more easily and robustly by learning the underlying potential function and using this as our representation of the policy.