Fei Wei

Qiang Zhang

An overview of the recent developments and prospects in this highly topical area, covering the synthesis, characterization, properties and applications of hierarchical nanostructured materials. The book concentrates on those materials relevant for research and development in the fields of energy, biomedicine and environmental protection, with a strong focus on 3D materials based on nanocarbons, mesoporous silicates, hydroxides, core-shell particles and helical nanostructures. Thanks to its clear concept and application-oriented approach, this is an essential reference for experienced researchers and newcomers to the field alike.

Advanced Hierarchical Nanostructured Materials

Baoxin Li

The last few years have witnessed fast development on dictionary learning approaches for a set of visual computing tasks, largely due to their utilization in developing new techniques based on sparse representation. Compared with conventional techniques employing manually defined dictionaries, such as Fourier Transform and Wavelet Transform, dictionary learning aims at obtaining a dictionary adaptively from the data so as to support optimal sparse representation of the data. In contrast to conventional clustering algorithms like K-means, where a data point is associated with only one cluster center, in a dictionary-based representation, a data point can be associated with a small set of dictionary atoms. Thus, dictionary learning provides a more flexible representation of data and may have the potential to capture more relevant features from the original feature space of the data. One of the early algorithms for dictionary learning is K-SVD. In recent years, many variations/extensions of K-SVD and other new algorithms have been proposed, with some aiming at adding discriminative capability to the dictionary, and some attempting to model the relationship of multiple dictionaries. One prominent application of dictionary learning is in the general field of visual computing, where long-standing challenges have seen promising new solutions based on sparse representation with learned dictionaries. With a timely review of recent advances of dictionary learning in visual computing, covering the most recent literature with an emphasis on papers after 2008, this book provides a systematic presentation of the general methodologies, specific algorithms, and examples of applications for those who wish to have a quick start on this subject.

Dictionary Learning in Visual Computing

Dabang Jiang

Xiaoguang Yang

Huopo Chen

Claudio Fabian Szlafsztein

Climate Change and Associated Impacts

Bin Jiang

Yafang Han

Collection of selected, peer reviewed papers from the Chinese Materials Congress 2014 (CMC 2014), July 4-7, 2014, Chengdu, China. The 145 papers are grouped as follows: Symposium L: Metal Matrix and Ceramic Matrix Composites; Symposium M: High Temperature/Ultra-High Temperature Structural Materials, Surface and Coating Engineering; Symposium N: High Entropy Alloys and Special High Temperature Material; Symposium O: Advanced Magnesium Alloys and Application; Symposium P: Superalloys; Symposium Q: Powder Metallurgy; Symposium R: Steel

High Performance Structural Material

Qiang Zhang

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