Bøger af Vinod Patil

Radio Frequency Identification (RFID) is an automated data-capture technology that can be used to electronically identify, track and store information on groups of products or individual items. Industry and public interest in RFID technology took a major leap in 2003 when WalMart mandated its largest 100 suppliers to commence using RFID tags on shipped items at the pallet level by 2005. The literature highlighted that the individual tag prices are expected to fall to 5 cents per unit threshold by 2008. This anticipated price should be a catalyst to increase the adoption and diffusion of RFID technology in a number of applications. Libraries are fast growing adopters of RFID as the technology promises to relieve repetitive strain injury, speed patron self check, reduce pilferage and provide accurate and timely inventory management. It has been suggested that library RFID applications may be the first major deployment of item level tagging. The library provides the panorama to study the operational issues of RFID tagging at the item level.

An Artificial Neural Network (ANN) is an information-processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well. Neural network simulations appear to be a recent development. However, this field was established before the advent of computers, and has survived several eras. Many important advances have been boosted by the use of inexpensive computer emulations.