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Semiconductor Physical Electronics, Second Edition, provides comprehensive coverage of fundamental semiconductor physics that is essential to an understanding of the physical and operational principles of a wide variety of semiconductor electronic and optoelectronic devices. This text presents a unified and balanced treatment of the physics, characterization, and applications of semiconductor materials and devices for physicists and material scientists who need further exposure to semiconductor and photonic devices, and for device engineers who need additional background on the underlying physical principles. This updated and revised second edition reflects advances in semicondutor technologies over the past decade, including many new semiconductor devices that have emerged and entered into the marketplace. It is suitable for graduate students in electrical engineering, materials science, physics, and chemical engineering, and as a general reference for processing and device engineers working in the semicondictor industry. .
Semiconductors. --- Solid state physics. --- Physics --- Solids --- Crystalline semiconductors --- Semi-conductors --- Semiconducting materials --- Semiconductor devices --- Crystals --- Electrical engineering --- Electronics --- Solid state electronics --- Materials --- Computer engineering. --- Optical materials. --- Chemistry, inorganic. --- Optics, Lasers, Photonics, Optical Devices. --- Electrical Engineering. --- Solid State Physics. --- Spectroscopy and Microscopy. --- Optical and Electronic Materials. --- Inorganic Chemistry. --- Inorganic chemistry --- Chemistry --- Inorganic compounds --- Optics --- Computers --- Design and construction --- Lasers. --- Photonics. --- Electrical engineering. --- Spectroscopy. --- Microscopy. --- Electronic materials. --- Inorganic chemistry. --- Analysis, Spectrum --- Spectra --- Spectrochemical analysis --- Spectrochemistry --- Spectrometry --- Spectroscopy --- Chemistry, Analytic --- Interferometry --- Radiation --- Wave-motion, Theory of --- Absorption spectra --- Light --- Spectroscope --- Electric engineering --- Engineering --- New optics --- Light amplification by stimulated emission of radiation --- Masers, Optical --- Optical masers --- Light amplifiers --- Light sources --- Optoelectronic devices --- Nonlinear optics --- Optical parametric oscillators --- Electronic materials --- Analysis, Microscopic --- Light microscopy --- Micrographic analysis --- Microscope and microscopy --- Microscopic analysis --- Optical microscopy --- Qualitative --- Analytical chemistry
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This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary. Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.
Knowledge representation (Information theory) --- Big data. --- Data sets, Large --- Large data sets --- Representation of knowledge (Information theory) --- Computer science. --- Data mining. --- Artificial intelligence. --- Image processing. --- Pattern recognition. --- Computer Science. --- Data Mining and Knowledge Discovery. --- Artificial Intelligence (incl. Robotics). --- Pattern Recognition. --- Image Processing and Computer Vision. --- Artificial intelligence --- Information theory --- Data sets --- Optical pattern recognition. --- Computer vision. --- Artificial Intelligence. --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Machine vision --- Vision, Computer --- Image processing --- Pattern recognition systems --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Optical data processing. --- Optical computing --- Visual data processing --- Integrated optics --- Photonics --- Computers --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Optical equipment
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Electronics and optics of solids --- Solid state physics --- Inorganic chemistry --- Electronics --- Computer. Automation --- informatica --- spectroscopie --- anorganische chemie --- elektronica --- fysica --- transistoren --- halfgeleiders --- fysicochemie --- optica
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This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace, and dictionary. Leveraging the theory of low-rank and sparse modeling, the authors develop robust feature representations under various learning paradigms, including unsupervised learning, supervised learning, semi-supervised learning, multi-view learning, transfer learning, and deep learning. Robust Representations for Data Analytics covers a wide range of applications in the research fields of big data, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision.
Mathematical statistics --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- computervisie --- patroonherkenning --- beeldverwerking --- factoranalyse --- datamining --- big data --- deep learning --- KI (kunstmatige intelligentie) --- data acquisition --- optica
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Semiconductors --- Semiconductors --- Silicon-on-insulator technology --- Design and construction --- Electric properties
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