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... Study of contamination at Snake Pond, located southeast of the Camp Edwards training area on Cape Cod ...
Lattice Point Identities and Shannon-Type Sampling demonstrates that significant roots of many recent facets of Shannon's sampling theorem for multivariate signals rest on basic number-theoretic results. This book leads the reader through a research excursion, beginning from the Gaussian circle problem of the early nineteenth century, via the classical Hardy-Landau lattice point identity and the Hardy conjecture of the first half of the twentieth century, and the Shannon sampling theorem (its variants, generalizations and the fascinating stories about the cardinal series) of the second half of the twentieth century. The authors demonstrate how all these facets have resulted in new multivariate extensions of lattice point identities and Shannon-type sampling procedures of high practical applicability, thereby also providing a general reproducing kernel Hilbert space structure of an associated Paley-Wiener theory over (potato-like) bounded regions (cf. the cover illustration of the geoid), as well as the whole Euclidean space. All in all, the context of this book represents the fruits of cross-fertilization of various subjects, namely elliptic partial differential equations, Fourier inversion theory, constructive approximation involving Euler and Poisson summation formulas, inverse problems reflecting the multivariate antenna problem, and aspects of analytic and geometric number theory. Features: New convergence criteria for alternating series in multi-dimensional analysis Self-contained development of lattice point identities of analytic number theory Innovative lattice point approach to Shannon sampling theory Useful for students of multivariate constructive approximation, and indeed anyone interested in the applicability of signal processing to inverse problems.
Everyone who works with forests must measure them, foresters, forestry students, scientists or forest owners. This book summarises modern forest measurement techniques for all those people. It describes how to measure forests, why they are measured and the basis of the science behind the measurements. Trees and forests are large and complex, but even something as difficult as the amount of wood they contain can be measured with quite unsophisticated equipment. This is a book written for all, from professional foresters to the lay person, in fact anyone who needs to measure forests anywhere in the world.
Written for undergraduate geography majors and entry-level graduate students with limited backgrounds in statistical analysis and methods, McGrew and Monroe provide a comprehensive and understandable introduction to statistical methods in a problem-solving framework. Engaging examples and problems are drawn from a variety of topical areas in both human and physical geography and are fully integrated into the text. Without compromising statistical rigor or oversimplifying, the authors stress the importance of written narratives that explain each statistical technique. After introducing basic statistical concepts and terminology, the authors focus on nonspatial and spatial descriptive statistics. They transition to inferential problem solving, including probability, sampling, and estimation, before delving deeper into inferential statistics for geographic problem solving. The final chapters examine the related techniques of correlation and regression. A list of major goals and objectives is included at the end of each chapter, allowing students to monitor their own progress and mastery of geographic statistical materials. An epilogue, offering over 150 geographic situations, gives students a chance to figure out which statistical technique should be used for a particular situation.