Digital Signal Processing (DSP) From Ground Up™ in C

Digital Signal Processing (DSP) From Ground Up& in C, available at $59.99, has an average rating of 4.14, with 92 lectures, based on 758 reviews, and has 5345 subscribers.

You will learn about Be able to develop the Convolution Kernel algorithm in C Be able able to develop the Discrete Fourier Transform (DFT) algorithm in C Be able to develop the Inverse Discrete Fourier Transform (IDFT) algorithm in C Be able to develop the Fast Fourier Transform (FFT) algorithm in C Be able to perform spectral analysis on ECG signals in C Be able to design and develop Windowed-Sinc filters in C Be able to design and develop Finite Impulse Response (FIR) filters in C Be able to design and develop Infinite Impulse Response (IIR) filters in C Be able to develop the FFT Convolution algorithm in C Be able to develop the First Difference algorithm in C Be able to develop the Running Sum algorithm in C Be able to develop the Moving Average filter algorithm in C Be able to develop the Recursive Moving Average filter algorithm in C Be able to develop signal statistical algorithms in C Be able to build passive Low-pass and High-pass filters Be able to build Modified Sallen-Key filters Be able to build Bessel, Chebyshev and Butterworth filters Understand all about Linear Systems and their characteristics Understand how to synthesize and decompose signals Understand the relationship between the delta function and the Impulse response Be able to plot signals with gnuplot Be able to give a lecture on Digital Signal Processing (DSP) Be able to suppress noise in signals This course is ideal for individuals who are Engineering Students or C Developers or Embedded Systems Engineers or Computer Engineering students or Hobbyists or Embedded Systems Instructors It is particularly useful for Engineering Students or C Developers or Embedded Systems Engineers or Computer Engineering students or Hobbyists or Embedded Systems Instructors.

Enroll now: Digital Signal Processing (DSP) From Ground Up& in C

Summary

Title: Digital Signal Processing (DSP) From Ground Up& in C

Price: $59.99

Average Rating: 4.14

Number of Lectures: 92

Number of Published Lectures: 92

Number of Curriculum Items: 92

Number of Published Curriculum Objects: 92

Original Price: $119.99

Quality Status: approved

Status: Live

What You Will Learn

Be able to develop the Convolution Kernel algorithm in C

Be able able to develop the Discrete Fourier Transform (DFT) algorithm in C

Be able to develop the Inverse Discrete Fourier Transform (IDFT) algorithm in C

Be able to develop the Fast Fourier Transform (FFT) algorithm in C

Be able to perform spectral analysis on ECG signals in C

Be able to design and develop Windowed-Sinc filters in C

Be able to design and develop Finite Impulse Response (FIR) filters in C

Be able to design and develop Infinite Impulse Response (IIR) filters in C

Be able to develop the FFT Convolution algorithm in C

Be able to develop the First Difference algorithm in C

Be able to develop the Running Sum algorithm in C

Be able to develop the Moving Average filter algorithm in C

Be able to develop the Recursive Moving Average filter algorithm in C

Be able to develop signal statistical algorithms in C

Be able to build passive Low-pass and High-pass filters

Be able to build Modified Sallen-Key filters

Be able to build Bessel, Chebyshev and Butterworth filters

Understand all about Linear Systems and their characteristics

Understand how to synthesize and decompose signals

Understand the relationship between the delta function and the Impulse response

Be able to plot signals with gnuplot

Be able to give a lecture on Digital Signal Processing (DSP)

Be able to suppress noise in signals

Who Should Attend

Engineering Students

C Developers

Embedded Systems Engineers

Computer Engineering students

Hobbyists

Embedded Systems Instructors

Target Audiences

Engineering Students

C Developers

Embedded Systems Engineers

Computer Engineering students

Hobbyists

Embedded Systems Instructors

With a programming based approach, this course is designed to give you a solid foundation in the most useful aspects of Digital Signal Processing (DSP) in an engaging and easy to follow way. The goal of this course is to present practical techniques while avoiding? obstacles of abstract mathematical theories. To achieve this goal, the DSP techniques are explained in plain language, not simply proven to be true through mathematical derivations.

Still keeping it simple, this course comes in different programming languages and hardware architectures so that students can put the techniques to practice using a programming language or hardware architecture? of their choice. This version of the course uses the C programming language.

By the end of this course you should be able develop the Convolution Kernelalgorithm in C, develop the Discrete Fourier Transform (DFT) algorithm in C, develop the Inverse Discrete Fourier Transform (IDFT) algorithm in C, design and develop Finite Impulse Response (FIR) filters in C, design and develop Infinite Impulse Response (IIR) filters in C, develop Windowed-Sinc filters in C, build Modified Sallen-Key filters,? build Bessel, Chebyshevand Butterworthfilters, develop the Fast Fourier Transform (FFT) algorithm in C , even give a lecture on DSP and so much more. Please take a look at the full course curriculum.

Course Curriculum

Chapter 1: Set up

Lecture 1: Setting up an Integrated Development Environment (IDE)

Lecture 2: Overview of CodeBlocks

Lecture 3: Downloading gnuplot

Lecture 4: Installing gnuplot

Lecture 5: Overview of gnuplot

Chapter 2: Getting started with gnuplot

Lecture 1: Plotting signals with gnuplot

Lecture 2: Plotting multiple signals in the same window

Chapter 3: Signal Statistics and Noise

Lecture 1: Signal Statistics and Noise

Lecture 2: Mean and Standard Deviation

Lecture 3: Coding : Developing the Signal Mean algorithm

Lecture 4: Coding : Computing the Signal Mean

Lecture 5: Coding : Developing the Signal Variance algorithm

Lecture 6: Coding : Developing the Signal Standard Deviation algorithm

Chapter 4: Quantization and The Sampling Theorem

Lecture 1: Nyquist Theorem ( Sampling Theorem )

Lecture 2: The Passive Low-Pass Filter

Lecture 3: The Passive High-Pass Filter

Lecture 4: The Active Filter

Lecture 5: The Bessel, Chebyshev and Butterworth filters

Chapter 5: Linear Systems and Superposition

Lecture 1: Notice

Lecture 2: Introduction to Linear Systems

Lecture 3: Understanding Superposition

Lecture 4: Impulse and Step Decomposition

Chapter 6: Convolution

Lecture 1: Introduction to Convolution

Lecture 2: The Convolution Operation

Lecture 3: Examinging the Output of Convolution

Lecture 4: The Convolution Sum Equation

Lecture 5: A Closer look at the Delta function

Lecture 6: Coding : Developing the Convolution algorithm (Part I )

Lecture 7: Coding : Developing the Convolution algorithm (Part I I)

Lecture 8: Coding : Developing the Convolution algorithm (Part III)

Lecture 9: Coding : Developing the Convolution algorithm (Part IV)

Lecture 10: The Running Sum and First Difference

Lecture 11: Coding : Developing the Running Sum algorithm

Chapter 7: Fourier Transsform

Lecture 1: Introduction to Fourier Analysis

Lecture 2: The DFT Engine

Lecture 3: Understanding Forward and Inverse DFT

Lecture 4: Code : Developing the DFT algorithm (Part I)

Lecture 5: Code : Developing the DFT algorithm (Part II)

Lecture 6: Code : Developing the DFT algorithm (Part III)

Lecture 7: Coding : Developing the Inverse DFT algorithm (Part I)

Lecture 8: Coding : Developing the Inverse DFT algorithm (Part II)

Lecture 9: Coding : Developing the Inverse DFT algorithm (Part III)

Lecture 10: Coding : Computing the DFT and IDFT of an ECG signal (Part I)

Lecture 11: Coding : Computing the DFT and IDFT of an ECG signal (Part II)

Lecture 12: Coding : Identifying the frequencies present in the DFT plot

Lecture 13: Symmetry between Time domain and frequency domain -Duality

Lecture 14: Polar Notation

Lecture 15: Coding : Rectangular notation to the polar notation ( Part I)

Lecture 16: Coding : Rectangular notation to the polar notation ( Part II)

Chapter 8: Complex Numbers

Lecture 1: The Complex Number System

Lecture 2: Polar Representation of Complex Numbers

Lecture 3: Eulers Relation

Lecture 4: Representation of Sinusoids

Lecture 5: Representing Systems

Chapter 9: Complex Fourier Transform

Lecture 1: Introduction to Complex Fourier Transform

Lecture 2: Mathematical Equivalence

Lecture 3: The Complex DFT Equation

Lecture 4: Comparing Real DFT and Complex DFT

Lecture 5: Coding : Developing the Complex DFT equation (Part I)

Lecture 6: Coding : Developing the Complex DFT equation (Part II )

Chapter 10: Fast Fourier Transform (FFT)

Lecture 1: An Overview of how FFT works.

Lecture 2: Understanding the complexity of calculating DFT directly

Lecture 3: How the Decimation -in-Time FFT Algorithm works

Chapter 11: Digital Filter Design

Lecture 1: Introduction to Digital Filters

Lecture 2: The Filter Kernel

Lecture 3: The Impulse,Step and Frequency response

Lecture 4: Understanding the Logarithmic scale and decibels

Lecture 5: Information representations of a signal

Lecture 6: Time domain parameters

Lecture 7: Frequency domain parameters

Lecture 8: Designing digital filters using the spectral inversion method

Lecture 9: Designing digital filters using the spectral reversal method

Lecture 10: Classification of digital filters

Chapter 12: Designing Finite Impulse Response FIR) Filters

Lecture 1: The Moving Average Filter

Lecture 2: The Multiple Pass Moving Average Filter

Lecture 3: The Recursive Moving Average Filter

Chapter 13: Designing Infinite Impulse Response (IIR) Filters

Lecture 1: Introduction to Recursive Filters

Lecture 2: The Recursion Equation

Lecture 3: The Single-Pole Recursive Filter

Lecture 4: Digital Chebyshev Filters

Chapter 14: Designing Windowed-Sinc Filters

Lecture 1: Introduction to Windowed-Sinc Filters

Lecture 2: The Sinc Function and the Truncated Sinc Filter

Lecture 3: The Blackman window

Lecture 4: The Hamming and Blackman window equations

Lecture 5: Designing the Windowed Sinc filter

Lecture 6: Coding : Developing the Low-pass Windowed-Sinc Filter Algorithm (Part I)

Instructors

Israel Gbati
Embedded Firmware Engineer

BHM Engineering Academy
21st Century Engineering Academy

Rating Distribution

1 stars: 15 votes

2 stars: 39 votes

3 stars: 141 votes

4 stars: 236 votes

5 stars: 327 votes

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