The clearing of unexploded ordnance (UXO) is a deadly and time consuming process. The U.S. Government is currently spending millions of dollars to remove UXO's from bases that are closing around the world. Existing methods for detecting UXO's only inform the clearing team that a piece of metal is present, rather than the type of metal, either UXO, shrapnel, or garbage. A lot of time and money is spent digging up every piece of metal detected. This thesis presents the use of artificial neural networks to determine the type of UXO that is detected. A multi layered feed forward neural network using the back propagation training algorithm was developed using the language Lisp. The network was trained to recognize five pieces of ammunition. Results from the research show that four out of five pieces of ammunition from the test set were identified with an accuracy of .99 out of 1.0. The network also correctly identified that a tin can was not one of the five pieces of ammunition.

The chapters in this volume were presented at the July–August 2008 NATO Advanced Study Institute on Unexploded Ordnance Detection and Mitigation. The conference was held at the beautiful Il Ciocco resort near Lucca, in the glorious Tuscany region of northern Italy. For the ninth time we gathered at this idyllic spot to explore and extend the reciprocity between mathematics and engineering. The dynamic interaction between world-renowned scientists from the usually disparate communities of pure mathematicians and applied scientists which occurred at our eight previous ASI’s continued at this meeting. The detection and neutralization of unexploded ordnance (UXO) has been of major concern for very many decades; at least since the First World war. UXO continues to be the subject of intensive research in many ?elds of science, incl- ing mathematics, signal processing (mainly radar and sonar) and chemistry. While today’s headlines emphasize the mayhem resulting from the placement of imp- vised explosive devices (IEDs), humanitarian landmine clearing continues to draw signi?cant global attention as well. In many countries of the world, landmines threaten the population and hinder reconstruction and fast, ef?cient utilization of large areas of the mined land in the aftermath of military con?icts.

The three-volume set LNCS 6675, 6676 and 6677 constitutes the refereed proceedings of the 8th International Symposium on Neural Networks, ISNN 2011, held in Guilin, China, in May/June 2011. The total of 215 papers presented in all three volumes were carefully reviewed and selected from 651 submissions. The contributions are structured in topical sections on computational neuroscience and cognitive science; neurodynamics and complex systems; stability and convergence analysis; neural network models; supervised learning and unsupervised learning; kernel methods and support vector machines; mixture models and clustering; visual perception and pattern recognition; motion, tracking and object recognition; natural scene analysis and speech recognition; neuromorphic hardware, fuzzy neural networks and robotics; multi-agent systems and adaptive dynamic programming; reinforcement learning and decision making; action and motor control; adaptive and hybrid intelligent systems; neuroinformatics and bioinformatics; information retrieval; data mining and knowledge discovery; and natural language processing.

Ultra-Wideband Short-Pulse Electromagnetics 6 was held at theAmerican Electromagnetics 2002 conference June 3-7, 2002 at the U.S.Naval Academy in Annapolis, Maryland. Topics include: UWB RadarSystems; UWB Antennas; Scattering; Pulsed Power; Short-PulseMeasurement Techniques; Time-Domain Computation Techniques; Time-Domain Signal Processing; UWB Polarimetry; UWB Sensing ofTerrain; Wavelets & Multi-Resolution Algorithms; Target Detection &Discrimination; Propagation; Underground & Subsurface Propagation; Electromagnetic Theory; New Canonical Problems, Benchmark Solutions; Signal Processing.

A refreshing, up-to-date exploration of the latest developments in near-surface techniques, for advanced-undergraduate and graduate students, and professionals.

This book was primarily written for an audience that has heard about neural networks or has had some experience with the algorithms, but would like to gain a deeper understanding of the fundamental material. For those that already have a solid grasp of how to create a neural network application, this work can provide a wide range of examples of nuances in network design, data set design, testing strategy, and error analysis.Computational, rather than artificial, modifiers are used for neural networks in this book to make a distinction between networks that are implemented in hardware and those that are implemented in software. The term artificial neural network covers any implementation that is inorganic and is the most general term. Computational neural networks are only implemented in software but represent the vast majority of applications.While this book cannot provide a blue print for every conceivable geophysics application, it does outline a basic approach that has been used successfully.