This book presents comprehensive experimental, numerical, and theoretical research on projectile impact analysis, such as the rigid projectile penetration/perforation of concrete and metallic targets, and shaped-charge-formed projectile and jet penetrations. Concrete and metal materials are widely used in protective structures in both civil engineering and armored vehicles, such as military fortifications, underground shelters, infantry fighting vehicles, and tanks, which are designed to withstand intentional or accidental impact loadings caused by projectiles and fragments, and the responses of these targets under projectile impact have been a topic of discussion for several decades. Written for researchers and engineers working in the fields of protective structures and high-speed penetration mechanics, the book is also a valuable reference for senior undergraduate and postgraduate students majoring in defense engineering, terminal ballistics and other related fields.

Automatic speech recognition (ASR) is a forefront of technology and research today. The effectiveness of ASR depends upon the accurate and quick classification of phonemes, which are the basic building blocks of speech. To derive such a classifier for phoneme classification in the context of ASR is the subject of my MASc thesis at the University of Waterloo carried out in between April 2011 and July 2012 under the supervision of Professor Fakhreddine Karray. Drawing upon several recent research topics applied to this area, such as discriminative learning and locally adaptive metrics, a novel classifier referred to as the discriminative locally-adaptive nearest centroid classifier (DLANC). DLANC is structurally simple, very quick to train on even very large sets of data, and it also produces very good classification results on standard TIMIT data. This book describes the DLANC classifier in detail, including its background and how it is derived. A detailed comparison between the DLANC classifier and several other existing classifiers for phoneme classification are made on standard TIMIT data. Numerous illustrations and diagrams make many theoretical points easy to understand.