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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.
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