This book is a step-by-step tutorial on how to design a low-power, high-resolution (not less than 12 bit), and high-speed (not less than 200 MSps) integrated CMOS analog-to-digital (AD) converter, to respond to the challenge from the rapid growth of IoT. The discussion includes design techniques on both the system level and the circuit block level. In the architecture level, the power-efficient pipelined AD converter, the hybrid AD converter and the time-interleaved AD converter are described. In the circuit block level, the reference voltage buffer, the opamp, the comparator, and the calibration are presented. Readers designing low-power and high-performance AD converters won’t want to miss this invaluable reference. Provides an in-depth introduction to the newest design techniques for the power-efficient, high-resolution (not less than 12 bit), and high-speed (not less than 200 MSps) AD converter; Presents three types of power-efficient architectures of the high-resolution and high-speed AD converter; Discusses the relevant circuit blocks (i.e., the reference voltage buffer, the opamp, and the comparator) in two aspects, relaxing the requirements and improving the performance.
This book presents the theories and methods for geology intelligent interpretation based on deep learning and remote sensing technologies. The main research subjects of this book include lithology and mineral abundance. This book focuses on the following five aspects: 1. Construction of geology remote sensing datasets from multi-level (pixel-level, scene-level, semantic segmentation-level, prior knowledge-assisted, transfer learning dataset), which are the basis of geology interpretation based on deep learning. 2. Research on lithology scene classification based on deep learning, prior knowledge, and remote sensing. 3. Research on lithology semantic segmentation based on deep learning and remote sensing. 4. Research on lithology classification based on transfer learning and remote sensing. 5. Research on inversion of mineral abundance based on the sparse unmixing theory and hyperspectral remote sensing. The book is intended for undergraduate and graduate students who are interested in geology, remote sensing, and artificial intelligence. It is also used as a reference book for scientific and technological personnel of geological exploration.
This book examines the theory and methods of remote sensing intelligent interpretation based on deep learning. Based on geological and environmental effects on mines, this book constructs a set of systematic mine remote sensing datasets focusing on the multi-level task with the system of “target detection→scene classification→semantic segmentation." Taking China’s Hubei Province as an example, this book focuses on the following four aspects: 1. Development of a multiscale remote sensing dataset of the mining area, including mine target remote sensing dataset, mine (including non-mine areas) remote sensing scene dataset, and semantic segmentation remote sensing dataset of mining land cover. The three datasets are the basis of intelligent interpretation based on deep learning. 2. Research on mine target remote sensing detection method based on deep learning. 3. Research on remote sensing scene classification method of mine and non-mine areas based on deep learning. 4. Research on the fine-scale classification method of mining land cover based on semantic segmentation. The book is a valuable reference both for scholars, practitioners and as well as graduate students who are interested in mining environment research.
This book examines the theory and methods of remote sensing intelligent interpretation based on deep learning. Based on geological and environmental effects on mines, this book constructs a set of systematic mine remote sensing datasets focusing on the multi-level task with the system of “target detection→scene classification→semantic segmentation." Taking China’s Hubei Province as an example, this book focuses on the following four aspects: 1. Development of a multiscale remote sensing dataset of the mining area, including mine target remote sensing dataset, mine (including non-mine areas) remote sensing scene dataset, and semantic segmentation remote sensing dataset of mining land cover. The three datasets are the basis of intelligent interpretation based on deep learning. 2. Research on mine target remote sensing detection method based on deep learning. 3. Research on remote sensing scene classification method of mine and non-mine areas based on deep learning. 4. Research on the fine-scale classification method of mining land cover based on semantic segmentation. The book is a valuable reference both for scholars, practitioners and as well as graduate students who are interested in mining environment research.
This book presents the theories and methods for geology intelligent interpretation based on deep learning and remote sensing technologies. The main research subjects of this book include lithology and mineral abundance. This book focuses on the following five aspects: 1. Construction of geology remote sensing datasets from multi-level (pixel-level, scene-level, semantic segmentation-level, prior knowledge-assisted, transfer learning dataset), which are the basis of geology interpretation based on deep learning. 2. Research on lithology scene classification based on deep learning, prior knowledge, and remote sensing. 3. Research on lithology semantic segmentation based on deep learning and remote sensing. 4. Research on lithology classification based on transfer learning and remote sensing. 5. Research on inversion of mineral abundance based on the sparse unmixing theory and hyperspectral remote sensing. The book is intended for undergraduate and graduate students who are interested in geology, remote sensing, and artificial intelligence. It is also used as a reference book for scientific and technological personnel of geological exploration.
This volume brings together articles on the mathematical aspects of life sciences, astrophysics, and nonlinear wave problems. It covers theoretical problems associated with the nervous system, drosophila embryos, protein folding, biopolymers, protoplanetary disks and extrasolar planets, gaseous disks, spiral galaxies, dark matter dynamics, star formation, solitary waves, photonics, and nonlinear light propagation in periodic media. The contributions are written for a general audience, and the authors have included references for further reading.
This book comprehensively utilizes the new generation of artificial intelligence and remote sensing science and technology to systematically carry out researches on high-precision recognition, monitoring, analysis, and assessment of geological disasters by using different technologies of "ground, airspace, and space-based systems" and different scales of "target-semantic-region". The main contents include: 1) Intelligent interpretation theory and methods of geological disasters, 2) Intelligent analysis of landslide based on long-term ground monitoring data, 3) Intelligent analysis of landslide evolution based on optical satellite remote sensing data, 4) Deep learning-based remote sensing detection of landslide, 5) Intelligent assessment methods of landslide susceptibility, 6) Intelligent recognition of ground figure based on airspace-based remote sensing data. The book is of interest to graduate student, scientific, and technological personnel who work in the area of geological disasters, natural hazards, remote sensing, and artificial intelligence.
This book is a rigorous, unified account of the fundamental principles of the density-functional theory of the electronic structure of matter and its applications to atoms and molecules. Containing a detailed discussion of the chemical potential and its derivatives, it provides an understanding of the concepts of electronegativity, hardness and softness, and chemical reactivity. Both the Hohenberg-Kohn-Sham and the Levy-Lieb derivations of the basic theorems are presented, and extensive references to the literature are included. Two introductory chapters and several appendices provide all the background material necessary beyond a knowledge of elementary quantum theory. The book is intended for physicists, chemists, and advanced students in chemistry.
This book is a step-by-step tutorial on how to design a low-power, high-resolution (not less than 12 bit), and high-speed (not less than 200 MSps) integrated CMOS analog-to-digital (AD) converter, to respond to the challenge from the rapid growth of IoT. The discussion includes design techniques on both the system level and the circuit block level. In the architecture level, the power-efficient pipelined AD converter, the hybrid AD converter and the time-interleaved AD converter are described. In the circuit block level, the reference voltage buffer, the opamp, the comparator, and the calibration are presented. Readers designing low-power and high-performance AD converters won’t want to miss this invaluable reference. Provides an in-depth introduction to the newest design techniques for the power-efficient, high-resolution (not less than 12 bit), and high-speed (not less than 200 MSps) AD converter; Presents three types of power-efficient architectures of the high-resolution and high-speed AD converter; Discusses the relevant circuit blocks (i.e., the reference voltage buffer, the opamp, and the comparator) in two aspects, relaxing the requirements and improving the performance.
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