For introductory courses (freshman and sophomore courses) in Digital Signal Processing and Signals and Systems. Text may be used before the student has taken a course in circuits. DSP First and it's accompanying digital assets are the result of more than 20 years of work that originated from, and was guided by, the premise that signal processing is the best starting point for the study of electrical and computer engineering. The "DSP First" approach introduces the use of mathematics as the language for thinking about engineering problems, lays the groundwork for subsequent courses, and gives students hands-on experiences with MATLAB.  The Second Edition features three new chapters on the Fourier Series, Discrete-Time Fourier Transform, and the The Discrete Fourier Transform as well as updated labs, visual demos, an update to the existing chapters, and hundreds of new homework problems and solutions.

Table of Contents:
Introduction    1-1 Mathematical Representation of Signals   1-2 Mathematical Representation of Systems        1-3       Systems as Building Blocks 1-4       The Next Step Sinusoids           2-1       Tuning Fork Experiment    2-2 Review of Sine and Cosine Functions 2-3Sinusoidal Signals 2-3.1Relation of Frequency to Period 2-3.2  Phase and Time Shift 2-4 Sampling and Plotting Sinusoids 2-5Complex Exponentials and Phasors 2-5.1 Review of Complex Numbers 2-5.2 Complex Exponential Signals 2-5.3  The Rotating Phasor Interpretation 2-5.4  Inverse Euler Formulas Phasor Addition 2-6Phasor Addition 2-6.1  Addition of Complex Numbers 2-6.2  Phasor Addition Rule 2-6.3  Phasor Addition Rule: Example 2-6.4  MATLAB Demo of Phasors 2-6.5  Summary of the Phasor Addition Rule Physics of the Tuning Fork 2-7.1  Equations from Laws of Physics 2-7.2  General Solution to the Differential Equation 2-7.3  Listening to Tones 2-8Time Signals: More Than Formulas Summary and Links Problems Spectrum Representation   3-1 The Spectrum of a Sum of Sinusoids 3-1.1  Notation Change 3-1.2  Graphical Plot of the Spectrum 3-1.3  Analysis vs. Synthesis Sinusoidal Amplitude Modulation 3-2.1  Multiplication of Sinusoids 3-2.2  Beat Note Waveform 3-2.3  Amplitude Modulation 3-2.4  AM Spectrum 3-2.5  The Concept of Bandwidth Operations on the Spectrum 3-3.1  Scaling or Adding a Constant 3-3.2  Adding Signals 3-3.3  Time-Shifting x.t/ Multiplies ak by a Complex Exponential 3-3.4  Differentiating x.t/ Multiplies ak by .j 2nfk/ 3-3.5  Frequency Shifting Periodic Waveforms 3-4.1  Synthetic Vowel 3-4.3  Example of a Non-periodic Signal Fourier Series 3-5.1  Fourier Series: Analysis 3-5.2  Analysis of a Full-Wave Rectified Sine Wave 3-5.3  Spectrum of the FWRS Fourier Series 3-5.3.1  DC Value of Fourier Series 3-5.3.2  Finite Synthesis of a Full-Wave Rectified Sine Time—Frequency Spectrum 3-6.1  Stepped Frequency 3-6.2  Spectrogram Analysis Frequency Modulation: Chirp Signals 3-7.1  Chirp or Linearly Swept Frequency 3-7.2  A Closer Look at Instantaneous Frequency Summary and Links Problems Fourier Series Fourier Series Derivation 4-1.1  Fourier Integral Derivation Examples of Fourier Analysis 4-2.1  The Pulse Wave 4-2.1.1  Spectrum of a Pulse Wave 4-2.1.2  Finite Synthesis of a Pulse Wave 4-2.2  Triangle Wave 4-2.2.1  Spectrum of a Triangle Wave 4-2.2.2  Finite Synthesis of a Triangle Wave 4-2.3  Half-Wave Rectified Sine 4-2.3.1  Finite Synthesis of a Half-Wave Rectified Sine Operations on Fourier Series 4-3.1  Scaling or Adding a Constant 4-3.2  Adding Signals 4-3.3  Time-Scaling 4-3.4  Time-Shifting x.t/ Multiplies ak by a Complex Exponential 4-3.5  Differentiating x.t/ Multiplies ak by .j!0k/ 4-3.6  Multiply x.t/ by Sinusoid Average Power, Convergence, and Optimality 4-4.1  Derivation of Parseval’s Theorem 4-4.2  Convergence of Fourier Synthesis 4-4.3  Minimum Mean-Square Approximation Pulsed-Doppler Radar Waveform 4-5.1  Measuring Range and Velocity Problems Sampling and Aliasing   Sampling 5-1.1  Sampling Sinusoidal Signals 5-1.2  The Concept of Aliasing 5-1.3  Spectrum of a Discrete-Time Signal 5-1.4  The Sampling Theorem 5-1.5  Ideal Reconstruction Spectrum View of Sampling and Reconstruction 5-2.1  Spectrum of a Discrete-Time Signal Obtained by Sampling 5-2.2  Over-Sampling 5-2.3  Aliasing Due to Under-Sampling 5-2.4  Folding Due to Under-Sampling 5-2.5  Maximum Reconstructed Frequency Strobe Demonstration 5-3.1  Spectrum Interpretation Discrete-to-Continuous Conversion 5-4.1  Interpolation with Pulses 5-4.2  Zero-Order Hold Interpolation 5-4.3  Linear Interpolation 5-4.4  Cubic Spline Interpolation 5-4.5  Over-Sampling Aids Interpolation 5-4.6  Ideal Bandlimited Interpolation The Sampling Theorem Summary and Links Problems FIR Filters          6-1 Discrete-Time Systems 6-2 The Running-Average Filter 6-3 The General FIR Filter 6-3.1  An Illustration of FIR Filtering The Unit Impulse Response and Convolution 6-4.1  Unit Impulse Sequence 6-4.2  Unit Impulse Response Sequence 6-4.2.1  The Unit-Delay System 6-4.3  FIR Filters and Convolution 6-4.3.1  Computing the Output of a Convolution 6-4.3.2  The Length of a Convolution 6-4.3.3  Convolution in MATLAB 6-4.3.4  Polynomial Multiplication in MATLAB 6-4.3.5  Filtering the Unit-Step Signal 6-4.3.6  Convolution is Commutative 6-4.3.7  MATLAB GUI for Convolution Implementation of FIR Filters 6-5.1  Building Blocks 6-5.1.1  Multiplier 6-5.1.2  Adder 6-5.1.3  Unit Delay 6-5.2  Block Diagrams 6-5.2.1  Other Block Diagrams 6-5.2.2  Internal Hardware Details Linear Time-Invariant (LTI) Systems 6-6.1  Time Invariance 6-6.2  Linearity 6-6.3  The FIR Case Convolution and LTI Systems 6-7.1  Derivation of the Convolution Sum 6-7.2  Some Properties of LTI Systems Cascaded LTI Systems Example of FIR Filtering Summary and Links Problems Frequency Response of FIR Filters 7-1 Sinusoidal Response of FIR Systems 7-2 Superposition and the Frequency Response 7-3 Steady-State and Transient Response 7-4 Properties of the Frequency Response 7-4.1  Relation to Impulse Response and Difference Equation 7-4.2  Periodicity of H.ej !O / 7-4.3  Conjugate Symmetry Graphical Representation of the Frequency Response 7-5.1  Delay System 7-5.2  First-Difference System 7-5.3  A Simple Lowpass Filter Cascaded LTI Systems Running-Sum Filtering 7-7.1  Plotting the Frequency Response 7-7.2  Cascade of Magnitude and Phase 7-7.3  Frequency Response of Running Averager 7-7.4  Experiment: Smoothing an Image Filtering Sampled Continuous-Time Signals 7-8.1  Example: Lowpass Averager 7-8.2  Interpretation of Delay Summary and Links Problems The Discrete-Time Fourier Transform DTFT: Discrete-Time Fourier Transform 8-1.1  The Discrete-Time Fourier Transform 8-1.1.1  DTFT of a Shifted Impulse Sequence 8-1.1.2  Linearity of the DTFT 8-1.1.3  Uniqueness of the DTFT 8-1.1.4  DTFT of a Pulse 8-1.1.5  DTFT of a Right-Sided Exponential Sequence 8-1.1.6  Existence of the DTFT 8-1.2  The Inverse DTFT 8-1.2.1  Bandlimited DTFT 8-1.2.2  Inverse DTFT for the Right-Sided Exponential 8-1.3  The DTFT is the Spectrum Properties of the DTFT 8-2.1  The Linearity Property 8-2.2  The Time-Delay Property 8-2.3  The Frequency-Shift Property 8-2.3.1  DTFT of a Complex Exponential 8-2.3.2  DTFT of a Real Cosine Signal 8-2.4  Convolution and the DTFT 8-2.4.1  Filtering is Convolution 8-2.5  Energy Spectrum and the Autocorrelation Function 8-2.5.1  Autocorrelation Function Ideal Filters 8-3.1  Ideal Lowpass Filter 8-3.2  Ideal Highpass Filter 8-3.3  Ideal Bandpass Filter Practical FIR Filters 8-4.1  Windowing 8-4.2  Filter Design 8-4.2.1  Window the Ideal Impulse Response 8-4.2.2  Frequency Response of Practical Filters 8-4.2.3  Passband Defined for the Frequency Response 8-4.2.4  Stopband Defined for the Frequency Response 8-4.2.5  Transition Zone of the LPF 8-4.2.6  Summary of Filter Specifications 8-4.3  GUI for Filter Design Table of Fourier Transform Properties and Pairs Summary and Links Problems The Discrete Fourier Transform               Discrete Fourier Transform (DFT) 9-1.1  The Inverse DFT 9-1.2  DFT Pairs from the DTFT 9-1.2.1  DFT of Shifted Impulse 9-1.2.2  DFT of Complex Exponential 9-1.3  Computing the DFT 9-1.4  Matrix Form of the DFT and IDFT Properties of the DFT 9-2.1  DFT Periodicity for XŒk] 9-2.2  Negative Frequencies and the DFT 9-2.3  Conjugate Symmetry of the DFT 9-2.3.1  Ambiguity at XŒN=2] 9-2.4  Frequency Domain Sampling and Interpolation 9-2.5  DFT of a Real Cosine Signal Inherent Periodicity of xŒn] in the DFT 9-3.1  DFT Periodicity for xŒn] 9-3.2  The Time Delay Property for the DFT 9-3.2.1  Zero Padding 9-3.3  The Convolution Property for the DFT Table of Discrete Fourier Transform Properties and Pairs Spectrum Analysis of Discrete Periodic Signals 9-5.1  Periodic Discrete-time Signal: Fourier Series 9-5.2  Sampling Bandlimited Periodic Signals 9-5.3  Spectrum Analysis of Periodic Signals Windows 9-6.0.1  DTFT of Windows The Spectrogram 9-7.1  An Illustrative Example 9-7.2  Time-Dependent DFT 9-7.3  The Spectrogram Display 9-7.4  Interpretation of the Spectrogram 9-7.4.1  Frequency Resolution 9-7.5  Spectrograms in MATLAB The Fast Fourier Transform (FFT) 9-8.1  Derivation of the FFT 9-8.1.1  FFT Operation Count Summary and Links Problems z-Transforms   Definition of the z-Transform Basic z-Transform Properties 10-2.1  Linearity Property of the z-Transform 10-2.2  Time-Delay Property of the z-Transform 10-2.3  A General z-Transform Formula The z-Transform and Linear Systems 10-3.1  Unit-Delay System 10-3.2  z-1 Notation in Block Diagrams 10-3.3   The z-Transform of an FIR Filter 10-3.4   z-Transform of the Impulse Response 10-3.5  Roots of a z-transform Polynomial Convolution and the z-Transform 10-4.1  Cascading Systems 10-4.2  Factoring z-Polynomials 10-4.3  Deconvolution Relationship Between the z-Domain and the !O -Domain 10-5.1   The z-Plane and the Unit Circle The Zeros and Poles of H.z/ 10-6.1  Pole-Zero Plot 10-6.2   Significance of the Zeros of H.z/ 10-6.3  Nulling Filters 10-6.4  Graphical Relation Between z and !O 10-6.5  Three-Domain Movies Simple Filters 10-7.1   Generalize the L-Point Running-Sum Filter 10-7.2  A Complex Bandpass Filter 10-7.3  A Bandpass Filter with Real Coefficients Practical Bandpass Filter Design Properties of Linear-Phase Filters 10-9.1  The Linear-Phase Condition 10-9.2  Locations of the Zeros of FIR Linear-Phase Systems Summary and Links Problems IIR Filters           The General IIR Difference Equation Time-Domain Response 11-2.1  Linearity and Time Invariance of IIR Filters 11-2.2  Impulse Response of a First-Order IIR System 11-2.3  Response to Finite-Length Inputs 11-2.4  Step Response of a First-Order Recursive System System Function of an IIR Filter 11-3.1  The General First-Order Case 11-3.2  H.z/ from the Impulse Response 11-3.3  The z-Transform Method The System Function and Block-Diagram Structures 11-4.1  Direct Form I Structure 11-4.2  Direct Form II Structure 11-4.3  The Transposed Form Structure Poles and Zeros 11-5.1  Roots in MATLAB 11-5.2  Poles or Zeros at z D 0 or 1 11-5.3  Output Response from Pole Location Stability of IIR Systems 11-6.1  The Region of Convergence and Stability Frequency Response of an IIR Filter 11-7.1  Frequency Response using MATLAB 11-7.2  Three-Dimensional Plot of a System Function Three Domains The Inverse z-Transform and Some Applications 11-9.1  Revisiting the Step Response of a First-Order System 11-9.2  A General Procedure for Inverse z-Transformation Steady-State Response and Stability Second-Order Filters 11-11.1 z-Transform of Second-Order Filters 11-11.2 Structures for Second-Order IIR Systems 11-11.3 Poles and Zeros 11-11.4 Impulse Response of a Second-Order IIR System 11-11.4.1  Distinct Real Poles 11-11.5 Complex Poles Frequency Response of Second-Order IIR Filter 11-12.1 Frequency Response via MATLAB 11-12.23-dB Bandwidth 11-12.3 Three-Dimensional Plot of System Functions Example of an IIR Lowpass Filter Summary and Links Problems