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In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem-solving. These algorithms have the capacity to generalize and discover knowledge for themselves and to learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves the mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topics range from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspired filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensor signal processing.

History of engineering & technology --- geometric calibration --- long- and short-period errors --- equivalent bias angles --- sparse recovery --- linear array push-broom sensor --- deep learning --- signal detection --- modulation classification --- the single shot multibox detector networks --- the multi-inputs convolutional neural networks --- medical image registration --- similarity measure --- non-rigid transformation --- computational efficiency --- registration accuracy --- signal denoising --- singular value decomposition --- Akaike information criterion --- reaction wheel --- micro-vibration --- permutation entropy (PE) --- weighted-permutation entropy (W-PE) --- reverse permutation entropy (RPE) --- reverse dispersion entropy (RDE) --- time series analysis --- complexity --- sensor signal --- tensor principal component pursuit --- stable recovery --- tensor SVD --- ADMM --- kalman filter --- nonlinear autoregressive --- neural network --- noise filtering --- multiple-input multiple-output (MIMO) --- frequency-hopping code --- dual-function radar-communications --- information embedding --- mutual information (mi) --- waveform optimization --- spectroscopy --- compressed sensing --- inverse problems --- dictionary learning --- image registration --- large deformation --- weakly supervised --- high-order cumulant --- cyclic spectrum --- decision tree–support vector machine --- wind turbine --- gearbox fault --- cosine loss --- long short-term memory network --- indoor localization --- CSI --- fingerprinting --- Bayesian tracking --- image reconstruction --- computed tomography --- nonlocal total variation --- sparse-view CT --- low-dose CT --- proximal splitting --- row-action --- brain CT image --- audio signal processing --- sound event classification --- nonnegative matric factorization --- blind signal separation --- support vector machines --- brain-computer interface --- motor imagery --- machine learning --- internet of things --- pianists --- surface inspection --- aluminum ingot --- mask gradient response --- Difference of Gaussian --- inception-v3 --- EEG --- sleep stage --- wavelet packet --- state space model --- image captioning --- three-dimensional (3D) vision --- human-robot interaction --- Laplacian scores --- data reduction --- sensors --- Internet of Things (IoT) --- LoRaWAN --- n/a --- decision tree-support vector machine

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem-solving. These algorithms have the capacity to generalize and discover knowledge for themselves and to learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves the mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topics range from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspired filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensor signal processing.

geometric calibration --- long- and short-period errors --- equivalent bias angles --- sparse recovery --- linear array push-broom sensor --- deep learning --- signal detection --- modulation classification --- the single shot multibox detector networks --- the multi-inputs convolutional neural networks --- medical image registration --- similarity measure --- non-rigid transformation --- computational efficiency --- registration accuracy --- signal denoising --- singular value decomposition --- Akaike information criterion --- reaction wheel --- micro-vibration --- permutation entropy (PE) --- weighted-permutation entropy (W-PE) --- reverse permutation entropy (RPE) --- reverse dispersion entropy (RDE) --- time series analysis --- complexity --- sensor signal --- tensor principal component pursuit --- stable recovery --- tensor SVD --- ADMM --- kalman filter --- nonlinear autoregressive --- neural network --- noise filtering --- multiple-input multiple-output (MIMO) --- frequency-hopping code --- dual-function radar-communications --- information embedding --- mutual information (mi) --- waveform optimization --- spectroscopy --- compressed sensing --- inverse problems --- dictionary learning --- image registration --- large deformation --- weakly supervised --- high-order cumulant --- cyclic spectrum --- decision tree–support vector machine --- wind turbine --- gearbox fault --- cosine loss --- long short-term memory network --- indoor localization --- CSI --- fingerprinting --- Bayesian tracking --- image reconstruction --- computed tomography --- nonlocal total variation --- sparse-view CT --- low-dose CT --- proximal splitting --- row-action --- brain CT image --- audio signal processing --- sound event classification --- nonnegative matric factorization --- blind signal separation --- support vector machines --- brain-computer interface --- motor imagery --- machine learning --- internet of things --- pianists --- surface inspection --- aluminum ingot --- mask gradient response --- Difference of Gaussian --- inception-v3 --- EEG --- sleep stage --- wavelet packet --- state space model --- image captioning --- three-dimensional (3D) vision --- human-robot interaction --- Laplacian scores --- data reduction --- sensors --- Internet of Things (IoT) --- LoRaWAN --- n/a --- decision tree-support vector machine