This paper investigates multimedia streaming over wireless local area networks. Physical layer sigmoid analytical models are presented for 802.11a/g and for 2x3 MIMO 802.11n MIMO-based systems are presented. Performance results in a wireless LAN environment are presented for traffic using UDP and TCP transport mechanisms. Packet losses are observed in WLAN environments which affects the overall throughput available. Possibilities for performance improvements with the use of 802.11e and MIMO technologies are discussed. System platform architecture performance issues for wireless video conferencing between Intel PXA27x processor-based handheld platforms are presented and results with retry-limit adaptation are also presented.
This paper presents neural network models for storing terminating and cyclic temporal sequences of patterns under synchronous, sequential and asynchronous dynamics. We use fully interconnected neural networks with asymmetric weight connections for synchronous and sequential dynamics and a layered neural network with feedback for asynchronous dynamics. The network were successfully implemented and the number of patterns that could be stored and recalled was approximately 12% of the size of the patterns in the network.
This paper deals with the classification of pen gestures using the learning vector quantization algorithm, a supervised learning technique. Both single stroke and multi stroke gestures are considered. The slope information from the strokes is extensively preprocessed before classification. The preprocessing and the classification algorithms chosen help to obtain very high rates of gesture classification. This is especially true in the multi stroke case. The recognition of the pen gestures is independent of their position, orientation, and size.