Search results for ""Author Kai Wang""

A practical introduction to state-of-the-art freeform optics design for LED packages and applications By affording designers the freedom to create complex, aspherical optical surfaces with minimal or no aberrations, freeform design transcends the constraints imposed by hundreds of years of optics design and fabrication. Combining unprecedented design freedom with precise light irradiation control, freeform optics design is also revolutionizing the design and manufacture of high quality LED lighting. The first and only book of its kind, Freeform Optics for LED Packages and Applications helps put readers at the forefront of the freeform optics revolution. Designed to function as both an authoritative review of the current state of the industry and a practical introduction to advanced optical design for LED lighting, this book makes learning and mastering freeform optics skills simpler and easier than ever before with: Real-world examples and case studies systematically describing an array of algorithms and designs—from new freeform algorithms to design methods to advanced optical designs Coding for all freeform optics algorithms covered—makes it easier and more convenient to start developing points of freeform optics and construct lenses or reflectors, right away Case studies of a range of products, including designs for a freeform optics LED bulb, an LED spotlight, LED street lights, an LED BLU, and many more Freeform Optics for LED Packages and Applications is must-reading for optical design engineers and LED researchers, as well as advanced-level students with an interest in LED lighting. It is also an indispensable working resource design practitioners within the LED lighting industry.

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This SpringerBrief provides the first systematic review of the existing works of cohesive subgraph search (CSS) over large heterogeneous information networks (HINs). It also covers the research breakthroughs of this area, including models, algorithms and comparison studies in recent years. This SpringerBrief offers a list of promising future research directions of performing CSS over large HINs.The authors first classify the existing works of CSS over HINs according to the classic cohesiveness metrics such as core, truss, clique, connectivity, density, etc., and then extensively review the specific models and their corresponding search solutions in each group. Note that since the bipartite network is a special case of HINs, all the models developed for general HINs can be directly applied to bipartite networks, but the models customized for bipartite networks may not be easily extended for other general HINs due to their restricted settings. The authors also analyze and compare these cohesive subgraph models (CSMs) and solutions systematically. Specifically, the authors compare different groups of CSMs and analyze both their similarities and differences, from multiple perspectives such as cohesiveness constraints, shared properties, and computational efficiency. Then, for the CSMs in each group, the authors further analyze and compare their model properties and high-level algorithm ideas.This SpringerBrief targets researchers, professors, engineers and graduate students, who are working in the areas of graph data management and graph mining. Undergraduate students who are majoring in computer science, databases, data and knowledge engineering, and data science will also want to read this SpringerBrief.

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