The increasing use of powder metallurgy techniques to make an almost infinite variety of materials and products places greater emphasis on utilization of sophisticated experimental techniques. Usually research and development efforts initiate the use of newly developed equipment and analytical procedures. Indeed, the contents of this book are strongly linked to research endeavors, in both the academic and industrials worlds. However, this volume can serve a much needed function in industrial applied powder metallurgy. Although many researchÂ ers will find the contents of great value, the technical personnel more involved with production, quality control, customer services and product design now have at their dispoÂ sal a means to learn about the potential uses of several very important techniques. With today's "knowledge explosion" the present set of papers greatly facilitates the comprehension and adoption of new procedures. If powder metallurgy is to continue its rapid rate of growth in virtually all segments of industry, then the transition of modern equipment and procedures from tools of research and development laboratories to everyday plant operations and applications must be hastened. The editors hope that this volume aids in this process, as well as assisting students and researchers by providing a ready source of up-to-date useful information.
<i>The Handbook of High-Performance Virtual Teams</i> is an essential resource for leaders, virtual team members, and work group leaders. The editors provide a proved framework based on five principles for working collaboratively across boundaries of time, space, and culture. Written by experts in the field, the contributors offer practical suggestions and tools for virtual team who need to assess their current level of effectiveness and develop strategies for improvement. This important resource also contains an array of illustrative cases as well as practical tools for designing, implementing, and maintaining effective virtual work.
This book presents practical optimization techniques used in image processing and computer vision problems. Ill-posed problems are introduced and used as examples to show how each type of problem is related to typical image processing and computer vision problems. Unconstrained optimization gives the best solution based on numerical minimization of a single, scalar-valued objective function or cost function. Unconstrained optimization problems have been intensively studied, and many algorithms and tools have been developed to solve them. Most practical optimization problems, however, arise with a set of constraints. Typical examples of constraints include: (i) pre-specified pixel intensity range, (ii) smoothness or correlation with neighboring information, (iii) existence on a certain contour of lines or curves, and (iv) given statistical or spectral characteristics of the solution. Regularized optimization is a special method used to solve a class of constrained optimization problems. The term regularization refers to the transformation of an objective function with constraints into a different objective function, automatically reflecting constraints in the unconstrained minimization process. Because of its simplicity and efficiency, regularized optimization has many application areas, such as image restoration, image reconstruction, optical flow estimation, etc.
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