Arkansas Tech University
- Page ID
- 96159
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)- Discrete-Time Signal Processing
- Front Matter
- 1: Introduction to Digital Signal Processing
- 2: The Sampling Theorem
- 3: Amplitude Quantization
- 4: Discrete -Time Signals and Systems
- 5: Z-Transform and Discrete Time System Design
- 6: Discrete Time Fourier Series (DTFS)
- 7: Discrete -Time Fourier Transform (DTFT)
- 8: Discrete Fourier Transforms (DFT)
- 9: DFT - Computational Complexity
- 10: Fast Fourier Transform (FFT)
- 11: Spectrograms
- 12: Discrete-Time Systems
- 13: Discrete-Time Systems in the Time-Domain
- 14: Discrete -Time Systems in the Frequency Domain
- 15: Filtering in the Frequency Domain
- 16: Efficiency of Frequency - Domain Filtering
- 17: Discrete -Time Filtering of Analog Signals
- 18: Digital Signal Processing Problems
- Back Matter
- Engineering Modeling and Analysis with Python
- This hands-on textbook provides a practical and accessible introduction to the fundamental concepts of engineering modeling and analysis, leveraging the power of the Python programming language. Readers will learn the basics of Python syntax, data types (like numbers, strings, lists, and dictionaries), control flow (conditionals and loops), functions, and how to effectively use its extensive libraries.
- Front Matter
- 1: Introduction to Engineering Modeling and Analysis with Python
- 2: Variables, Expressions, and Statements
- 3: Conditional Execution
- 4: Functions
- 5: Iterations
- 6: Strings
- 7: Files
- 8: Lists
- 9: Dictionaries
- 10: Tuples
- 11: Libraries
- 12: Model Building and Regression
- 13: Statistics, Probability, and Interpolation
- 14: Systems of Linear Equations
- 15: Dynamic Systems
- 16: Regular Expressions
- 17: Object-Oriented Programming
- Back Matter
- Engineering Modeling and Analysis with Python, 2E
- An open-access textbook designed to equip undergraduate engineering students with core computational and analytical skills using the Python ecosystem. It transitions from fundamental Python programming concepts, such as data structures, control flow, file I/O, and object-oriented programming into practical applications with libraries like NumPy, SciPy, Matplotlib, Pandas, and SymPy. Dynamic systems, solve linear algebra equations, and visualize engineering data.
- Front Matter
- 1: Introduction to Engineering Modeling and Analysis with Python
- 2: Variables, Expressions, and Statements
- 3: Lists
- 4: Tuples
- 5: Dictionaries
- 6: Files
- 7: Libraries
- 8: Functions
- 9: Conditional Execution
- 10: Loops and Iterations
- 11: Strings
- 12: Statistics, Probability, and Interpolation
- 13: Model Building and Regression
- 14: Systems of Linear Equations
- 15: Dynamic Systems
- 16: Regular Expressions
- 17: Object-Oriented Programming
- 18: Exercises
- Back Matter

