W e present a data compression system u s i n g a neural network based a r c h i t e c t u r e t h a t w i l l accept a whole scene o f data, compress i t f o r storage or transmission, r e t r i e v e i t and r e c o n s t r u c t t h e o r i g i n a l scene. The a r c h i t e c t u r e o f t h e network i s a feed forward f o u r layer network. The network i s t r a i n e d by u s i n g a nested t r a i n i n g algorithm. I n a recent paper we had presented l i m i t e d r e s u l t s on t h e performance o f t h i s system. I n t h i s paper we present more d e t
This paper develops and evaluates the performance of an advanced multiple access protocol for transmission of full complement of multimedia signals consisting of various combinations of voice, video, data, text and images over wireless networks. The protocol is called Advanced Multiple Access Protocol for Multimedia Transmission (AMAPMT) and is to be used in the Data Link Layer of the protocol stack. The principle of operation of the protocol is presented in a number of logical flow charts. The protocol grants permission to transmit to a source on the basis of a priority scheme that takes into account a timeto-live (TTL) parameter of all the transactions, selectable priorities assigned to all the sources and relevant channel state information (CSI) in this order. Performance of the protocol is evaluated in terms of quality of service parameters like packet loss ratio (PLR), mean packet transfer delay (MPTD
This paper presents the adaptation and performance evaluation of the Wilcoxon and Mann -Whitney nonparametric detectors for point targets in infrared clutter backgrounds.These detectors are designed to detect a level shift in a set of M samples containing interference which has an incompletely known distribution. We present a transformation based on the Walsh transformation which allows us to apply the Wilcoxon or Mann -Whitney detectors to the case of a target of small extent imbedded in a large number of background samples.We include the formulation of the two nonparametric detectors along with analytical approximations that can be used to calculate performance predictions in terms of probability of detection and false alarm.Last, we present simulation results comparing the nonparametric detectors with an adaptive linear detector.Performance comparisons are shown for simulations against scenes consisting of recorded infrared clutter video as well as a synthetic white noise scene.
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