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Block loss and propagation error due to cell loss or missing packet information during the transmission over lossy networks can cause severe degradation of block and predictive-based video coding. Herein, new fast spatial and temporal methods are presented for block loss recovery. In the spatial algorithm, missing block recovery and edge extention are(More)
An approach is presented for query-based neural network learning. A layered perceptron partially trained for binary classification is considered. The single-output neuron is trained to be either a zero or a one. A test decision is made by thresholding the output at, for example, one-half. The set of inputs that produce an output of one-half forms the(More)
The generalization performance of feedforward layered perceptrons can, in many cases, be improved either by smoothing the target via convolution, regularizing the training error with a smoothing constraint, decreasing the gain (i.e., slope) of the sigmoid nonlinearities, or adding noise (i.e., jitter) to the input training data, In certain important cases,(More)
—One of the most important considerations in applying neural networks to power system security assessment is the proper selection of training features. Modern interconnected power systems often consist of thousands of pieces of equipment each of which may have an affect on the security of the system. Neural networks have shown great promise for their(More)
Simple rules, when executed by individual agents in a large group, or swarm, can lead to complex behaviors that are often difficult or impossible to predict knowing only the rules. However, aggregate behavior is not always unpredictable-even for swarm models said to be beyond analysis. For the class of swarming algorithms examined herein, we analytically(More)
We consider the problem of maximizing the time-to-first-failure (TTFF), defined as the time till the first node in the network runs out of battery energy, in energy constrained broadcast wireless networks. We show that the TTFF criterion , by itself, fails to provide the " ideally optimum " mul-ticast tree and propose a composite weighted objective function(More)
In this paper, we address the minimum power broadcast problem in wireless networks. Assuming nodes are equipped with omni-directional antennas, the inherently broadcast nature of wireless networks can be exploited to compute power efficient routing trees. We propose a 2-stage cluster-merge algorithm for computing minimum power broadcast trees. The cluster(More)
Wireless multicast/broadcast sessions, unlike wired networks , inherently reaches several nodes with a single transmission. For omnidirectional wireless broadcast to a node, all nodes closer will also be reached. An algorithm for constructing the minimum power tree in wireless networks was first proposed by Wieselthier et al.. The broadcast incremental(More)