A functional similarity is described between cells of an occupancy grid for robot sonar, and integrate-and-re neurons of an artiicial neural net. Using this analogy, a new grid-based mapping system for robot sonar is described, which makes use of the neural concepts of receptive elds and recurrent connections. The performance of the new network is compared to that of a previous Bayesian grid-ba...

Instance level video object segmentation is an important technique for video editing and compression. To capture the temporal coherence, in this paper, we develop MaskRNN, a recurrent neural net approach which fuses in each frame the output of two deep nets for each object instance — a binary segmentation net providing a mask and a localization net providing a bounding box. Due to the recurrent...

Routing is one of the basic parts of a message passing multiprocessor system. The routing procedure has a great impact on the efficiency of a system. Neural algorithms that are currently in use for computer networks require a large number of neurons. If a specific topology of a multiprocessor network is considered, the number of neurons can be reduced. In this paper a new recurrent neural ne...

in recent decades artificial neural networks (anns) have shown great ability in modeling and forecasting non-linear and non-stationary time series and in most of the cases especially in prediction of phenomena have showed very good performance. this paper presents the application of artificial neural networks to predict drought in yazd meteorological station. in this research, different archite...

Segmentation is one of the most common methods for analyzing and processing medical images, assisting doctors in making accurate diagnoses by providing detailed information about required body part. However, segmenting images presents a number challenges, including need professionals to be trained, fact that it time-consuming prone errors. As result, appears an automated image segmentation syst...

The application of Deep Neural Networks for ranking in search engines may obviate the need for the extensive feature engineering common to current learning-to-rank methods. However, we show that combining simple relevance matching features like BM25 with existing Deep Neural Net models often substantially improves the accuracy of these models, indicating that they do not capture essential local...

In this paper we show that programming languages are implementable on neural nets, namely, neural nets can be designed to solve any (computable) high level programming task. Constructions like the one that follows can also be used to built large scale neural nets that integrate learning and control structures. We use a very simple model of analog recurrent neural nets and a number-theoretic app...

Neural networks provide an alternative method of building models of human performance. They can learn behavior from examples, reducing the need for many identical repetitions and intensive analysis. A properly trained net can be very robust in its response to a novel stimulus. This opens the door to modeling performance in the presence of an interactive stimulus. Neural networks provide the pos...