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In a continuing effort to improve computer system performance, Processor-In-Memory (PIM) architecture has emerged as an alternative solution. PIM architecture incorporates computational units and control logic directly on the memory to provide immediate access to the data. To exploit the potential benefits of PIM, a concept of Co-operative Intelligent Memory (CIM) was developed by the intelligent system group of University of Hertfordshire, based on the previously developed Co-operative Pseudo Intelligent Memory (CPIM). This paper provides an overview on previous works (CPIM, CIM) and realization of CPIM over two scenarios, cumulative successive addition, and non-cumulative successive addition, using Nexar 2004 EDS tool as a design environment to target device (SPARTAN II, XC2S300E-6PQ208C).The performance (speedup) is then measured against an SISD without significant performance acceleration methods to ensure a speedup assessment obtained against base-line architecture.

Identification of authorized train drivers through biometrics is a growing area of interest in locomotive radio remote control systems. The existing technique of password authentication is not very reliable and potentially unauthorized personnel may also operate the system on behalf of the operator. Fingerprint identification system, implemented on PC/104 based real-time systems, can accurately identify the operator. Traditionally, the uniqueness of a fingerprint is determined by the overall pattern of ridges and valleys as well as the local ridge anomalies e.g., a ridge bifurcation or a ridge ending, which are called minutiae points. Designing a reliable automatic fingerprint matching algorithm for minimal platform is quite challenging. In real-time systems, efficiency of the matching algorithm is of utmost importance. To achieve this goal, a prime-feature-based indexing algorithm is proposed in this paper. The technique involves identifying the most prominent feature of the fingerprint and searching only for that feature in the database to expedite the search process. The proposed architect provides efficient matching process and indexing feature for identification is unique.

Knowledge management is a systematic and organizationally specified process and knowledge management system is all those technological components; software, hardware, people and processes supporting knowledge management initiative. These initiatives includes work flow maps, web sites, portals, document/team management system, data warehouses, data mining processes, databases, contact lists, virtual teams, collaboration tools, customer relationship management, applications and news (Davenport and Prusak 1998, Jashapara 2004). Knowledge is not important per se (Agostini et al 2003) instead the process of knowing, learning and creating knowledge is the relevant aspect (Nonaka and Takeuchi 1995). In this paper knowledge representation is presented in 3D style for the understanding and visualization of dynamics of complex social networks by developing a TANetworkTool (Task Analysis Network Tool). The standard or normal representation of a typical social network is through a graph data structure in 2D. The dynamics of larger social networks is so complex some time it becomes difficult to understand the various levels of interactions and dependencies just by mere representation through a tree or graph. Although, many analytical methods provide relationship dependencies, role of different nodes and their importance in the network. In this paper we are presenting a visualization of networks by rotating the network through various dimensions to provide a more realistic view to understand the dynamics of complex social networks and complimenting the analytical results. This representation can also help authorities not necessarily having specific scientific background to understand and perhaps take preventive actions required in certain specific scenarios for example dealing with terrorist/covert networks.

Many methodologies dealing with prediction or simulation of soft tissue deformations on medical image data require preprocessing of the data in order to produce a different shape representation that complies with standard methodologies, such as mass–spring networks, finite element method s (FEM). On the other hand, methodologies working directly on the image space normally do not take into account mechanical behavior of tissues and tend to lack physics foundations driving soft tissue deformations. This chapter presents a method to simulate soft tissue deformations based on coupled concepts from image analysis and mechanics theory. The proposed methodology is based on a robust stochastic approach that takes into account material properties retrieved directly from the image, concepts from continuum mechanics and FEM. The optimization framework is solved within a hierarchical Markov random field (HMRF) which is implemented on the graphics processor unit (GPU See Graphics processing unit ).