Development of a Novel Media-independent Communication Theology for Accessing Local & Web-based Data: Case Study with Robotic Subsystems

Realizing media independence in today’s communication system remains an open problem by and large. Information retrieval, mostly through the Internet, is becoming the most demanding feature in technological progress and this web-based data access should ideally be in user-selective form. While blind-folded access of data through the World Wide Web is quite streamlined, the counter-half of the facet, namely, seamless access of information database pertaining to a specific end-device, e.g. robotic systems, is still in a formative stage. This paradigm of access as well as systematic query-based retrieval of data, related to the physical enddevice is very crucial in designing the Internet-based network control of the same in real-time. Moreover, this control of the end-device is directly linked up to the characteristics of three coupled metrics, namely, ‘multiple databases’, ‘multiple servers’ and ‘multiple inputs’ (to each server). This triad, viz. database-input-server (DIS) plays a significant role in overall performance of the system, the background details of which is still very sketchy in global research community. This work addresses the technical issues associated with this theology, with specific reference to formalism of a customized DIS considering real-time delay analysis. The present paper delineates the developmental paradigms of novel multi-input multioutput communication semantics for retrieving web-based information from physical devices, namely, two representative robotic sub-systems in a coherent and homogeneous mode. The developed protocol can be entrusted for use in real-time in a complete user-friendly manner

A Linear Combination of Heuristics Approach to Spatial Sampling Hyperspectral Data for Target Tracking

Persistent surveillance of the battlespace results in better battlespace awareness which aids in obtaining air superiority, winning battles, and saving friendly lives. Although hyperspectral imagery (HSI) data has proven useful for discriminating targets, it presents many challenges as a useful tool in persistent surveillance. A new sensor under development has the potential of overcoming these challenges and transforming our persistent surveillance capability by providing HSI data for a limited number of pixels and grayscale video for the remainder. The challenge of exploiting this new sensor is determining where the HSI data in the sensor\u27s field of view will be the most useful. The approach taken is to use a utility function with components of equal dispersion, periodic poling, missed measurements, and predictive probability of association error (PPAE). The relative importance or optimal weighting of the different types of TOI is accomplished by a genetic algorithm using a multi-objective problem formulation. Experiments show using the utility function with equal weighting results in superior target tracking compared to any individual component by itself, and the equal weighting in close to the optimal solution. The new sensor is successfully exploited resulting in improved persistent surveillance

Learning Vector Quantization for Multimodal Data

Hammer B, Strickert M, Villmann T. Learning Vector Quantization for Multimodal Data. In: Dorronsoro JR, ed. Proc. International Conf. on Artificial Neural Networks (ICANN). Lecture Notes in Computer Science, 2415. Berlin: Springer Verlag; 2002: 370-376

Learning Vector Quantization for Multimodal Data

Learning vector quantization (LVQ) as proposed by Kohonen is a simple and intuitive, though very successful prototype-based clustering algorithm