Java Digital Signal Processing (DSP) From Ground Up™

Java Digital Signal Processing (DSP) From Ground Up™, available at $49.99, has an average rating of 4.05, with 103 lectures, based on 50 reviews, and has 457 subscribers.

You will learn about Build a Complete DSP Library in Java Develop the Convolution Kernel algorithm in Java Develop the Discrete Fourier Transform (DFT) algorithm in Java Master Efficient DSP algorithm techniques such as Loop Unrolling and MAC in Java Develop the Discrete Fourier Transform (DFT) algorithm in Java Develop the Inverse Discrete Fourier Transform (IDFT) algorithm in Java Develop the Fast Fourier Transform (FFT) algorithm in Java Perform spectral analysis on ECG signals in Java Design and develop Windowed-Sinc filters in Java Design and develop Finite Impulse Response (FIR) filters in Java Design and develop Infinite Impulse Response (IIR) filters in Java Develop the Moving Average filter algorithm in Java Develop the Recursive Moving Average filter algorithm in Java Be able to build Bessel, Chebyshev and Butterworth filters Understand all about Linear Systems and their characteristics Understand how to synthesize and decompose signals Plot signals with gnuplot Give a lecture on Digital Signal Processing (DSP) Suppress noise in signals This course is ideal for individuals who are If you are an absolute beginner to signal processing, then take this course. or I you are already familiar with the theory of dsp then take this course to learn how to translate the mathematical equations into code or If you are a java developer take this course to learn how to build robust DSP algorithms in java It is particularly useful for If you are an absolute beginner to signal processing, then take this course. or I you are already familiar with the theory of dsp then take this course to learn how to translate the mathematical equations into code or If you are a java developer take this course to learn how to build robust DSP algorithms in java.

Enroll now: Java Digital Signal Processing (DSP) From Ground Up™

Summary

Title: Java Digital Signal Processing (DSP) From Ground Up™

Price: $49.99

Average Rating: 4.05

Number of Lectures: 103

Number of Published Lectures: 102

Number of Curriculum Items: 103

Number of Published Curriculum Objects: 102

Original Price: $84.99

Quality Status: approved

Status: Live

What You Will Learn

Build a Complete DSP Library in Java
Develop the Convolution Kernel algorithm in Java
Develop the Discrete Fourier Transform (DFT) algorithm in Java
Master Efficient DSP algorithm techniques such as Loop Unrolling and MAC in Java
Develop the Discrete Fourier Transform (DFT) algorithm in Java
Develop the Inverse Discrete Fourier Transform (IDFT) algorithm in Java
Develop the Fast Fourier Transform (FFT) algorithm in Java
Perform spectral analysis on ECG signals in Java
Design and develop Windowed-Sinc filters in Java
Design and develop Finite Impulse Response (FIR) filters in Java
Design and develop Infinite Impulse Response (IIR) filters in Java
Develop the Moving Average filter algorithm in Java
Develop the Recursive Moving Average filter algorithm in Java
Be able to build Bessel, Chebyshev and Butterworth filters
Understand all about Linear Systems and their characteristics
Understand how to synthesize and decompose signals
Plot signals with gnuplot
Give a lecture on Digital Signal Processing (DSP)
Suppress noise in signals

Who Should Attend

If you are an absolute beginner to signal processing, then take this course.
I you are already familiar with the theory of dsp then take this course to learn how to translate the mathematical equations into code
If you are a java developer take this course to learn how to build robust DSP algorithms in java

Target Audiences

If you are an absolute beginner to signal processing, then take this course.
I you are already familiar with the theory of dsp then take this course to learn how to translate the mathematical equations into code
If you are a java developer take this course to learn how to build robust DSP algorithms in java

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 and computer code, 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 Java programming language.

With each dsp topic we shall develop two versions of the same algorithm. One version shall be focused on code readable and the other version shall focus on robustness and execution speed- we shall employ programming techniques such loop unrolling and Multiply- Accumulate (MAC) to accomplish this.

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

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Setting Up

Lecture 1: Installingng Java JDK and IDE

Lecture 2: Linking up Java JDK and IDE

Lecture 3: Downloading gnuplot

Chapter 3: Getting started with gnuplot

Lecture 1: Plotting signals with gnuplot

Lecture 2: Plotting multiple signals in the same window

Chapter 4: Signal Statistics and Noise

Lecture 1: Nature of a signal

Lecture 2: Mean and Standard Deviation

Lecture 3: Signal-to-Noise ratio

Lecture 4: Coding : Developing the Signal Mean Algorithm

Lecture 5: Coding : Developing the Signal Variance Algorithm

Lecture 6: Coding : Developing the Signal Standard Deviation algorithm

Lecture 7: Coding : The Signal Statistics Class

Lecture 8: Coding : Robust Signal Mean Algorithm

Lecture 9: Coding : Robust Signal Variance Algorithm

Lecture 10: Coding : Robust Signal Standard Deviation Algorithm

Lecture 11: Coding : Robust Signal RMS Algorithm

Lecture 12: Coding : Robust Signal Maximum Algorithm

Lecture 13: Coding : Robust Signal Minimum Algorithm

Chapter 5: Quantization and The Sampling Theorem

Lecture 1: Quantization

Lecture 2: Nyquist Theorem ( Sampling Theorem )

Lecture 3: The Passive Low-Pass Filter

Lecture 4: The Passive High-Pass Filter

Lecture 5: The Modified Sallen-Key Filter

Lecture 6: The Bessel, Chebyshev and Butterworth filters

Lecture 7: Comparing the performance of the Bessel, Chebyshev and Butterworth filters

Lecture 8: Information encoding : Time-domain and frequency-domain encoding

Chapter 6: Linear Systems and Superposition

Lecture 1: Notice

Lecture 2: Signal naming conventions

Lecture 3: System Homogeneity

Lecture 4: System Additivity

Lecture 5: System Shift Invariance

Lecture 6: Synthesis and Decomposition

Lecture 7: Impulse Decomposition

Lecture 8: Step Decomposition

Chapter 7: Convolution

Lecture 1: Introduction to Convolution

Lecture 2: The Delta Function and Impulse Response

Lecture 3: The Convolution Kernel

Lecture 4: The Convolution Kernel (Part II)

Lecture 5: The Output side analysis and the convolution sum equation

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

Lecture 7: The Identity property of convolution

Lecture 8: The Running Sum and First Difference

Lecture 9: Coding : Developing the Running Sum algorithm

Lecture 10: Coding : Developing the First Difference algorithm

Chapter 8: Fourier Transsform

Lecture 1: Introduction to Fourier Analysis

Lecture 2: Introduction to Discrete Fourier Transform

Lecture 3: DFT Basis Functions

Lecture 4: Deducing the Inverse DFT

Lecture 5: Calculating the Discrete Fourier Transform (DFT)

Lecture 6: Coding : Developing the Inverse DFT algorithm (Part I)

Lecture 7: Coding : Computing the DFT and Inverse DFT of an ECG signal

Lecture 8: Symmetry between Time domain and frequency domain -Duality

Lecture 9: Polar Notation

Lecture 10: Introduction to Spectral Analysis

Lecture 11: The Frequency Response

Chapter 9: Complex Numbers

Lecture 1: The Complex Number System

Lecture 2: Polar Representation of Complex Numbers

Lecture 3: Euler's Relation

Lecture 4: Representation of Sinusoids

Lecture 5: Representing Systems

Chapter 10: 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

Chapter 11: 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 12: 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 13: Designing Finite Impulse Response FIR) Filters

Lecture 1: The Moving Average Filter

Lecture 2: The Multiple Pass Moving Average Filter

Lecture 3: Coding : Developing the Moving Average Filter

Lecture 4: The Recursive Moving Average Filter

Lecture 5: Coding : Developing the Recursive Moving Average Filter

Chapter 14: Designing Infinite Impulse Response (IIR) Filters

Lecture 1: Introduction to Recursive Filters

Lecture 2: The Recursion Equation

Lecture 3: The Single-Pole Recursive Filter

Instructors

PyTribe .
Practical Python Mastery for Everyone

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 10 votes
4 stars: 15 votes
5 stars: 23 votes

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