Learn More
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)
A training procedure that adapts the weights of a trained layered perceptron artificial neural network to training data originating from a slowly varying nonstationary process is proposed. The resulting adaptively trained neural network (ATNN), based on nonlinear programming techniques, is shown to adapt to new training data that are in conflict with(More)
Permanent implantation of radioactive seeds is a viable and effective therapeutic option widely used today for early stage prostate cancer. In order to perform intraoperative dosimetry the seed locations must be determined accurately with high efficiency. However, the task of seed segmentation is often hampered by the wide range of signal-to-noise ratios(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)