Reprint

Sensor Signal and Information Processing II

Edited by
August 2020
418 pages
  • ISBN978-3-03928-270-8 (Paperback)
  • ISBN978-3-03928-271-5 (PDF)

This book is a reprint of the Special Issue Sensor Signal and Information Processing II that was published in

Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Summary

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 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 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 topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing.

Format
  • Paperback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
alpha-divergence; Kullback–Leibler divergence; non-linear filtering; exponential family distribution; fault diagnosis; feature extraction; self-adaptive spectrum analysis; bearing; uncooperative sensor signal processing; MIMO radar; fractional Fourier transform; fractional autocorrelation interpolation; convolutional neural network; spatial pyramid pooling; fault diagnosis; bearing; wavelet transform; neuromorphic systems; event-based sensors; dynamic vision sensor; bioinspired event filtering; FPGA implementation; spike-based; event data reduction; image reconstruction; nullspace measurement matrix; regularized least squares problem; smoothed L0-norm; sparse signal recovery; UBSS; weighted function; TDLAS; signal processing; gas sensor; denoise; interference fringe; background correction; electrocardiogram; QRS complex; fiducial point; polygonal approximation; dynamic programming; QT-database; MIT-BIH arrhythmia database; earth-rock dam; 3D visualization; deformation monitoring; terrestrial laser scanning (TLS); NURBS; fault diagnosis; bearing; SVD; VMD; adaptive Multiclass Mahalanobis Taguchi System; wideband OFDM-LFM signal; switched-element system; fractional autocorrelation; DOA estimation; ultrasonic flaw echo enhancement; empirical mode decomposition; sample entropy; Otsu’s method for thresholding; flaw echo separation; early gear pitting fault diagnosis; vibration signals; SAE; GBRBM; ultrasonic phased array; scheduling algorithm; multi-group sensors; FPGA; DOA estimation; direction-dependent mutual coupling; time-frequency distribution; self-calibration; manipulator; model independent method; collision detection; collision identification; vibration analysis; artificial neural network; computer vision; 3D reconstruction; point cloud; bearings-only tracking; clutter; variational Bayesian; Shifted Rayleigh Filter; compressed sensing; low rank matrix; stochastic gradient; weak selection method; reliability verification strategy; reconstruction performance; TDoA; weighted least squares; firefly algorithm; hybrid-FA; non-destructive testing; current mapping; digital micromirror device; compressed sensing; data compression; wireless sensor networks; energy consumption; demosaicing; debayering; color filter array; image interpolation; image reconstruction; phishing detection; cyber security; deep learning; sensors; phase arrays; signal processing; image processing; 3D processing; deep learning; machine learning; compressive sensing; wireless communication