The Signal Processing Foundations series of 16 lessons begins with the philosophy of the field. Next you will discover the basic notation and terminology. Signal Processing Foundations also introduces methods for describing the interaction between signals and signal-processing systems. Understanding the philosophy of signal processing will help you later follow the context and rationale for different signal processing methods. Signal processing has developed its own language for clearly communicating important concepts; Signal Processing Foundations will teach you cornerstone vocabulary of the field. You will also be introduced to several mathematical tools for relating the input to the output of signal-processing systems. Different tools provide different perspectives on the interaction and have different roles in signal processing.
- Signals Everywhere - Free Lesson
- Ever-Present Noise - Free Lesson
- Models, Math, and Real-World Signals - Free Lesson
- Four Signal-Processing Themes - Free Lesson
- Building Signals with Blocks: Basis Expansions - Free Lesson
- Signals: The Basics - Free Lesson
- Sinusoidal Signals - Free Lesson
- Sinusoidal Signals Examples - Free Lesson
- Complex Sinusoids - Free Lesson
- Exponential, Step, and Impulse Signals - Free Lesson
- Introduction to Linear, Time-Invariant Systems - Free Lesson
- Introduction to Difference Equation System Descriptions - Free Lesson
- Impulse Response Descriptions for LTI Systems - Free Lesson
- Frequency Response Descriptions for LTI Systems - Free Lesson
- Introduction to the System Function and System Poles and Zeros - Free Lesson
- The Four Fourier Representations - Free Lesson
- Summary Problems for Foundations - Free Lesson
Time Domain LTI Systems
This series of lessons builds on several of the concepts introduced in the Foundations series. It concerns time-domain descriptions for the characteristics of linear, time-invariant (LTI) systems. You will learn the origins and properties of convolution for describing LTI systems in terms of the impulse response and a procedure for evaluating convolution. You will also learn how differential and difference equations are used to represent LTI systems and what they reveal about system behavior. If you have no prior experience with LTI systems, then this series is designed to efficiently teach you the knowledge you need for future study.
- Impulse Response and LTI Systems - Part II
- Graphical Evaluation of Discrete-Time Convolution
- Graphical Evaluation of Continuous-Time Convolution
- Difference Equations: Solving System Responses with Stored Energy
- Characteristics of Systems Described by Difference Equations - Free Lesson
- Differential Equations: Solving System Responses with Stored Energy
- Characteristics of Systems Described by Differential Equations
- Two-Dimensional Signal Processing: Discrete Space
- Problems for Time Domain LTI Systems
Fourier Series and Transforms
This series of lessons reviews the basics of Fourier transforms and series. You will learn the details of how to represent signals in the frequency domain and the properties of Fourier representations. You will also gain understanding how to use Fourier methods to analyze interactions between signals and systems. This series is designed to efficiently teach you the knowledge you need to use and understand Fourier methods in signal processing.
- The Fourier Series: Continuous-Time Periodic Signals
- Square Wave Fourier Series and the Sinc Function
- Fourier Series Properties
- The Fourier Transform: Linking Time and Frequency Domains - Free Lesson
- Properties of the Fourier Transform
- The Discrete-Time Fourier Transform - Free Lesson
- Discrete-Time Fourier Transform Properties
- Fourier Transforms and Discrete-Time Fourier Transforms for Periodic Signals
- Frequency-Domain Descriptions for Continuous-Time Linear Time-Invariant Systems
- Frequency-Domain Descriptions for Discrete-Time Linear Time-Invariant Systems
- Two-Dimensional Signal Processing: Continuous Space
Sampling and Reconstruction
All signals in the physical world, e.g., light, sound, seismic waves, and so on, have continuous independent variables. These signals must be sampled to convert them to a sequence of numerical values prior to computer-based signal processing. The Sampling and Reconstruction series of 15 lessons introduces you to the requirements on sampling in order to ensure a unique representation. You will learn to use the Fourier transform as a tool for analyzing the effect of sampling in the frequency domain. Much of the series will teach you practical issues associated with sampling and techniques for addressing them, including anti-aliasing, oversampling, anti-imaging, upsampling and downsampling. Finally, you will learn how to model the apparent noise that is introduced when representing the amplitude of each sample with a finite number of bits.
- Introduction to Sampling and Reconstruction
- Aliasing and the Sampling Theorem Simplified - Free Lesson
- Fourier Transform Interpretation of Sampling - Free Lesson
- Reconstruction and the Sampling Theorem
- Reconstruction and the Sampling Theorem Examples
- Two-Dimensional Sampling Theorem
- Equivalent Analog Filtering
- Practical Sampling: Anti-Aliasing Filters
- Practical Reconstruction: The Zero-Order Hold
- Practical Digital Filtering and Oversampling
- Oversampling Example
- Downsampling: Reducing the Sampling Rate
- Upsampling: Increasing the Sampling Rate
- Analog to Digital Conversion: Quantization and Coding
- Analysis of Quantization Error
The DFT and Applications
The discrete Fourier transform or DFT is the frequency domain workhorse of signal processing. It is the only Fourier representation that can be evaluated with a computer. In this series of 13 lessons you will learn how the DFT is related to the discrete-time Fourier transform, and how the DFT can be used to approximate the Fourier transform. You will learn the principles behind the fast Fourier transform algorithm for efficiently computing the DFT. You will also learn the principles of circular convolution and how to implement filtering using the DFT. This series is essential for everyone interested in spectral analysis of data or any computational Fourier analysis application.
- Discrete Fourier Transform: Sampling the Discrete-Time Fourier Transform - Free Lesson
- Important Discrete Fourier Transform Properties
- Fast Fourier Transform (FFT) Algorithm - Free Lesson
- Introduction to Circular Convolution and Filtering with the Discrete Fourier Transform
- Circular Convolution Property of the Discrete Fourier Transform
- Filtering with the Discrete Fourier Transform
- The Discrete Fourier Transform Approximation to the Fourier Transform
- The Effect of Windowing on the Discrete Fourier Transform Approximation to the Fourier Transform
- Windows and the Discrete-Time Fourier Transform: Trading Resolution for Dynamic Range
- An Example of Approximating the Fourier Transform with the Discrete Fourier Transform
- The Short-Time Fourier Transform and the Spectrogram
- A Matrix Interpretation of the Discrete Fourier Transform
- A Matrix Interpretation of the Fast Fourier Transform Algorithm
The -transform is an important signal-processing tool for analyzing the interaction between signals and systems. A significant advantage of the -transform over the discrete-time Fourier transform is that the -transform exists for many signals that do not have a discrete-time Fourier transform. Thus, it is a more general analysis tool. In this series of 13 lessons you will learn how to work with the -transform and use it to characterize signal processing systems. You will learn how the poles and zeros of a system tell us whether the system can be both stable and causal, and whether it has a stable and causal inverse system. You will also learn how the pole and zero locations of a system give us insight into the nature of its frequency and impulse response. The insights gained with the -transform are particularly useful for designing frequency-selective filters.
- Minimum-Phase and All-Pass Systems
- Frequency Response Magnitude and Poles and Zeros
- Impulse Response and Poles and Zeros
- Inversion of the -Transform: Partial Fraction Expansion
- Properties of the -Transform
- -Transform Analysis of LTI Systems
- Stability and Causality of LTI Systems Described by Difference Equations
- Inverse Systems for LTI Systems Described by Difference Equations
- Introduction to the -Transform
- The Region of Convergence for the -Transform
- Poles and Zeros of the -Transform - Free Lesson
- Properties of the Region of Convergence
- Inversion of the -Transform via Power Series Expansion
Intro to Filter Design
This series of four lessons sets the context for the subsequent two series on designing frequency selective filters. You will learn the different types of frequency selective filters and the difference between infinite and finite impulse response (IIR and FIR) filters. You will also learn how group delay characterizes the phase distortion introduced by a filter and how to implement zero-phase filters when all the data to be filtered is already stored. This series will provide insight that helps you master IIR and FIR filter design in the next two series.
IIR Filter Design
Infinite impulse response or IIR filters are designed using well-established designs for continuous-time filters. In this series of eight lessons you will learn the characteristics of the four widely used types of IIR filters and the principles of converting a continuous-time prototype filter to a discrete-time filter that satisfies your design specifications. In practice the steps of the design process are normally performed using a software package such as MATLAB. You will learn the rationale behind and limitations of IIR filter design methodology. Examples of both good and poor quality filter designs are provided so you can recognize when your design is effective and when it is problematic.
- IIR Filter Design Procedure
- Analog Filters Used for IIR Filter Design
- Continuous-Time Butterworth Filters
- Continuous-Time Chebyshev and Elliptic Filters
- Frequency Transformations for Continuous-Time Systems
- The Bilinear Transform
- IIR Filter Examples Designed Using MATLAB - Free Lesson
- Poor IIR Filter Designs: Don't Make These Mistakes
FIR Filter Design
Finite impulse response or FIR filters have the advantage of always being stable, even for very high orders, and can be designed to introduce no phase distortion. In this series of six lessons you will learn about the conditions for an FIR filter to introduce no phase distortion and three different methods for FIR filter design. You will learn about the intuitive, able-to-be-performed-with-pencil-and-paper window design method. You will also learn about the minimax optimal Parks-McClellan computer-based filter design method. The third method uses the technique of frequency sampling to obtain designs with arbitrary magnitude and phase response.
Random Signal Characterization
The ability to deal with uncertainty in the characteristics of signals is a very important part of advanced signal processing methods. This series of seven lessons introduces you to tools from probability for describing signals that are modeled as having random characteristics. You will learn about auto- and cross-correlation for describing random signals in the time domain, and power spectra, cross spectra, and coherence for describing random signals in the frequency domain. You will also learn how to represent random signals as the output of a linear time-invariant system with white noise input using autoregressive, moving average, and autoregressive moving average models.
Basis Representations of Signals
Representing signals as a weighted sum (or integral) of certain basis signals is a powerful signal-processing tool. It is the very essence of Fourier transforms. In this series of seven lessons you will learn the general form of basis representations. You will also learn about wavelets as an alternative basis expansion to the sinusoids of Fourier methods. You will also learn about principal component analysis, a method for choosing efficient bases for random data.
Estimation of Power Spectra and Coherence
This series of eight lessons addresses the signal-processing problem of estimating properties of a random signal from measurements. Five of the eight lessons concern estimation of frequency domain characteristics such as the power spectrum and coherence. You will learn about the periodogram and why averaging is necessary to obtain acceptable estimates of the power spectral density. You will learn how the averaged periodogram or Welch's method reduces the variance of the periodogram estimator at the expense of resolution loss. In the final two lessons you will learn about maximum likelihood estimation as a general tool for estimating unknown parameters in a random signal.
Introduction to Signal Estimation and Detection Theory
This series of six lessons introduces you to the principles of signal estimation and signal detection or hypothesis testing. You will the maximum likelihood criterion for estimation and how to classify different types of hypothesis tests and the metrics used to characterize the performance of detectors such as the probability of correct detection and the receiver operating characteristic or ROC. You will learn about the likelihood ratio, which is the optimal test of simple binary hypotheses. There are no known optimal tests for more general testing scenarios, so you will learn about the generalized likelihood ratio as a principled approach for obtaining a good test.
MMSE Filtering and Least-Squares Problems
This set of lessons covers a wide ranging set of signal-processing methods for minimum mean-squared error filtering and other applications of least squares problems occurring in estimation and imaging applications. This includes adaptive filtering methods such as the least-mean-square (LMS) algorithm.