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Abstract

This paper presents a portable system and method for recognizing the 26 hand shapes of the American Sign Language alphabet, using a novel glove-like device. Two additional signs, 'space', and 'enter' are added to the alphabet to allow the user to form words or phrases and send them to a speech synthesizer. Since the hand shape for a letter varies from one signer to another, this is a 28-class pattern recognition system. A three-level hierarchical classifier divides the problem into "dispatchers" and "recognizers." After reducing pattern dimension from ten to three, the projection of class distributions onto horizontal planes makes it possible to apply simple linear discrimination in 2D, and Bayes' Rule in those cases where classes had features with overlapped distributions. Twenty-one out of 26 letters were recognized with 100% accuracy; the worst case, letter U, achieved 78%.

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Today's Internet provides only a single Class of Service (CoS) to all of its services (e.g., Multimedia, Data, Audio), namely the best-effort service. This service does not make any guarantees on the Quality of Service (QoS) an application receives. The continuous demands for QoS guarantees have led to the introduction of various network-layer mechanisms that provide some end-to-end QoS assurance including MPLS, Integrated Service Model (IntServ), and Differentiated Service Model (DiffServ). However, the QoS mapping between the network-layer and the application-layer is still a challenge and needs to be addressed. To guarantee and end-to-end QoS, it is essential to map QoS parameters between these layers. A framework for investigating the mapping of the packet loss rate as an important QoS parameter is presented here. The network QoS performance characteristic for the loss parameter is mapped from lower to upper layer in a quantifiable way.