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Signals, Instrumentation, Control, and Machine Learning cover
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This book stems from a unique and a highly effective approach to introducing signal processing, instrumentation, diagnostics, filtering, control, system integration, and machine learning.

It presents the interactive industrial grade software testbed of mold oscillator that captures the distortion induced by beam resonance and uses this testbed as a virtual lab to generate input-output data records that permit unravelling complex system behavior, enhancing signal processing, modeling, and simulation background, and testing controller designs.

All topics are presented in a visually rich and mathematically well supported, but not analytically overburdened format. By incorporating software testbed into homework and project assignments, the narrative guides a reader in an easily followed step-by-step fashion towards finding the mold oscillator disturbance removal solution currently used in the actual steel production, while covering the key signal processing, control, system integration, and machine learning concepts.

The presentation is extensively class-tested and refined though the six-year usage of the book material in a required engineering course at the University of Illinois at Urbana-Champaign.

Sample Chapter(s)
Preface
Chapter 1: Case Study and Course Overview

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Contents:

  • Dedication
  • Preface
  • Case Study and Course Overview
  • Introduction to Signals
  • First Look at Signal Processing, Filtering, and Instrumentation
  • Sampling Basics, Harmonic Signals, and Signal Spectrum
  • Function Projection and Fourier Series
  • Linear System Characteristics, Fourier Transform and Introduction to Filters
  • CT, DT and Digital Filter Design and Implementation
  • Introduction to Discrete-Continuous Spectral Analysis
  • Introduction to Control Systems: Basic Control Actions and Basic Controller Design
  • Introduction to Nonstationary Signal Analysis and Machine Learning
  • Appendices:
    • Useful Mathematical Formulas
    • System Classification
    • Basics of Random Signals
    • Fourier Transform
    • Laplace Transform
    • Basic Types of Sensors and Actuators
    • Euler-Bernoulli and Timoshenko Beam Models and Their Use in Software Testbed Development
    • Software Testbed Matlab Programs
  • Bibliography
  • Index

Readership: Researchers, professionals, academics, undergraduate and graduate students in mechanical engineering, electrical & electronic engineering, systems engineering and industrial engineering.