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Nanomaterials via Single-Source Precursors: Synthesis, Processing and Applications presents recent results and overviews of synthesis, processing, characterization and applications of advanced materials for energy, electronics, biomedicine, sensors and aerospace. A variety of processing methods (vapor, liquid and solid-state) are covered, along with materials, including metals, oxides, semiconductor, sulfides, selenides, nitrides, and carbon-based materials. Production of quantum dots, nanoparticles, thin films and composites are described by a collection of international experts. Given the ability to customize the phase, morphology, and properties of target materials, this "rational approach" to synthesis and processing is a disruptive technology for electronic, energy, structural and biomedical (nano)materials and devices. The use of single-source chemical precursors for materials processing technology allows for intimate elemental mixing and hence production of complex materials at temperatures well below traditional physical methods and those involving direct combination of elements. The use of lower temperatures enables thin-film deposition on lightweight polymer substrates and reduces damage to complex devices structures such as used in power, electronics and sensors.

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Developing increasingly computationally-efficient codes for communication and compression has been the major goal of information and coding theory. There have been significant advances towards this goal in the last couple of decades with the emergence of turbo codes, sparse-graph codes, and polar codes. The world has seen faster network speeds and greater storage due to many of these developments. A new class of codes, Sparse Regression Codes (SPARCs), are a promising class of codes for achieving the Shannon limits of a communication channel. This monograph presents a unified and comprehensive overview of sparse regression codes, covering theory, algorithms, and practical implementation aspects. Written by the world's recognized experts in the field it describes the use of SPARCs for efficient communication over AWGN channels, for lossy compression and multi-terminal communication. Researchers and students in modern communication and network systems will find Sparse Regression Codes an essential resource in understanding these new techniques that will have a significant impact on such systems in the years to come.