Java Digital Signal Processing (DSP) From Ground Up™ from Udemy

What's inside

Learning objectives

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 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
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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 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
Show more
Show less

Syllabus

Introduction

Setting Up

Installingng Java JDK and IDE

Linking up Java JDK and IDE

Downloading gnuplot

Getting started with gnuplot

Plotting signals with gnuplot

Plotting multiple signals in the same window

Signal Statistics and Noise

Nature of a signal

Mean and Standard Deviation

Signal-to-Noise ratio

Coding : Developing the Signal Mean Algorithm

Coding : Developing the Signal Variance Algorithm

Coding : Developing the Signal Standard Deviation algorithm

Coding : The Signal Statistics Class

Coding : Robust Signal Mean Algorithm

Coding : Robust Signal Variance Algorithm

Coding : Robust Signal Standard Deviation Algorithm

Coding : Robust Signal RMS Algorithm

Coding : Robust Signal Maximum Algorithm

Coding : Robust Signal Minimum Algorithm

Quantization and The Sampling Theorem

Quantization

Nyquist Theorem ( Sampling Theorem )

The Passive Low-Pass Filter

The Passive High-Pass Filter

The Modified Sallen-Key Filter

The Bessel, Chebyshev and Butterworth filters

Comparing the performance of the Bessel, Chebyshev and Butterworth filters

Information encoding : Time-domain and frequency-domain encoding

Linear Systems and Superposition

Notice

Signal naming conventions

System Homogeneity

System Additivity

System Shift Invariance

Synthesis and Decomposition

Impulse Decomposition

Step Decomposition

Convolution

Introduction to Convolution

The Delta Function and Impulse Response

The Convolution Kernel

The Convolution Kernel (Part II)

The Output side analysis and the convolution sum equation

Coding : Developing the Convolution algorithm (Part I )

The Identity property of convolution

The Running Sum and First Difference

Coding : Developing the Running Sum algorithm

Coding : Developing the First Difference algorithm

Fourier Transsform

Introduction to Fourier Analysis

Introduction to Discrete Fourier Transform

DFT Basis Functions

Deducing the Inverse DFT

Calculating the Discrete Fourier Transform (DFT)

Coding : Developing the Inverse DFT algorithm (Part I)

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

Symmetry between Time domain and frequency domain -Duality

Polar Notation

Introduction to Spectral Analysis

The Frequency Response

Complex Numbers

The Complex Number System

Polar Representation of Complex Numbers

Euler's Relation

Representation of Sinusoids

Representing Systems

Complex Fourier Transform

Introduction to Complex Fourier Transform

Mathematical Equivalence

The Complex DFT Equation

Comparing Real DFT and Complex DFT

Fast Fourier Transform (FFT)

An Overview of how FFT works.

Understanding the complexity of calculating DFT directly

How the Decimation -in-Time FFT Algorithm works

Digital Filter Design

Introduction to Digital Filters

The Filter Kernel

The Impulse,Step and Frequency response

Understanding the Logarithmic scale and decibels

Information representations of a signal

Time domain parameters

Frequency domain parameters

Designing digital filters using the spectral inversion method

Designing digital filters using the spectral reversal method

Classification of digital filters

Designing Finite Impulse Response FIR) Filters

The Moving Average Filter

The Multiple Pass Moving Average Filter

Coding : Developing the Moving Average Filter

The Recursive Moving Average Filter

Coding : Developing the Recursive Moving Average Filter

Designing Infinite Impulse Response (IIR) Filters

Introduction to Recursive Filters

The Recursion Equation

The Single-Pole Recursive Filter

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Java Digital Signal Processing (DSP) From Ground Up™ with these activities:

Review Complex Numbers

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Solidify your understanding of complex numbers, which are fundamental to understanding the Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT) algorithms covered in the course.

Browse courses on Complex Numbers

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Review the definition of complex numbers.
Practice complex number arithmetic.
Understand polar representation of complex numbers.

Read 'Understanding Digital Signal Processing' by Steven W. Smith

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Supplement your learning with a comprehensive textbook that covers the theoretical underpinnings of DSP, providing a deeper understanding of the algorithms you'll be implementing in Java.

View Digital Signal Processing: A Practical Guide... on Amazon

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Obtain a copy of the book.
Read chapters related to course topics.
Work through examples and exercises.

Implement Convolution in Java

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Reinforce your understanding of convolution by implementing the algorithm in Java, focusing on both code readability and execution speed as emphasized in the course.

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Write a basic convolution function.
Optimize the function for speed.
Test with different input signals.

Four other activities

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Help others in the course discussion forum

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Reinforce your understanding of DSP concepts by helping other students in the course discussion forum. Explaining concepts to others is a great way to solidify your own knowledge.

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Regularly check the course discussion forum.
Answer questions from other students.
Explain concepts in your own words.

Create a DSP Algorithm Visualization

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Deepen your understanding by creating a visual representation of a DSP algorithm (e.g., FFT, convolution) to illustrate its operation and impact on signals.

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Choose a DSP algorithm to visualize.
Develop a visualization using Java and gnuplot.
Explain the algorithm's steps visually.

Build a Simple Audio Equalizer in Java

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Apply your DSP knowledge by building a practical audio equalizer application in Java, incorporating filter design and frequency analysis techniques learned in the course.

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Design the equalizer's filter bank.
Implement the filters in Java.
Integrate with an audio input/output library.
Test and refine the equalizer's performance.

Read 'Digital Signal Processing: Principles, Algorithms, and Applications' by John G. Proakis and Dimitris G. Manolakis

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Expand your knowledge with a rigorous textbook that provides a deep dive into the mathematical principles underlying DSP, complementing the course's practical approach.

View Digital Signal Processing on Amazon

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Obtain a copy of the book.
Focus on chapters relevant to the course.
Work through the mathematical derivations.

Career center

Learners who complete Java Digital Signal Processing (DSP) From Ground Up™ will develop knowledge and skills that may be useful to these careers:

DSP Engineer

A Digital Signal Processing Engineer designs, develops, and tests signal processing systems. This Java Digital Signal Processing course may be applicable to this role. DSP Engineers use tools like the Discrete Fourier Transform, filter design, and noise suppression to achieve their goals. This course on Java Digital Signal Processing focuses on techniques explained in computer code, that may give DSP Engineers a foundation in practical DSP techniques. The course's coverage of topics like the Fast Fourier Transform and the design of FIR and IIR filters may be particularly relevant to a DSP Engineer.

See salaries and explore the career path for DSP Engineer

Wireless Communications Engineer

Wireless Communications Engineers design and develop wireless communication systems. A Java Digital Signal Processing course may be useful, as Wireless Communications Engineers often use digital signal processing techniques to modulate, demodulate, and encode signals. The course's coverage of topics like the Discrete Fourier Transform, filter design, and spectral analysis may be useful for designing and optimizing wireless communication systems. The course's emphasis on practical techniques and computer code may be helpful for those implementing DSP algorithms in wireless communication devices.

See salaries and explore the career path for Wireless Communications Engineer

Biomedical Engineer

Biomedical Engineers apply engineering principles to solve medical and health-related problems. This Java Digital Signal Processing course could be useful, as Biomedical Engineers often work with signals from the human body. The course's practical techniques may be helpful for those implementing DSP algorithms for biomedical applications. The course's coverage of spectral analysis on ECG signals, noise suppression, and filter design may be particularly useful to a Biomedical Engineer working with physiological signals. The course's emphasis on code may be useful for implementing DSP algorithms in biomedical devices and software.

See salaries and explore the career path for Biomedical Engineer

Embedded Systems Engineer

Embedded Systems Engineers design and develop embedded systems for various applications and a Java Digital Signal Processing course can be helpful. Embedded Systems Engineers often use signal processing techniques to process sensor data and control devices. The course's coverage of topics like filter design, noise suppression, and algorithm optimization may be useful for designing and implementing embedded systems. The course's emphasis on code robustness and execution speed may be helpful for those working with resource-constrained embedded platforms.

See salaries and explore the career path for Embedded Systems Engineer

Audio Engineer

An Audio Engineer manipulates sound using various tools and techniques, and this course on Java Digital Signal Processing may be beneficial to becoming one. Audio Engineers often use digital signal processing to achieve audio quality and address audio issues, and this course can help build a foundation in digital signal processing techniques using Java. This course focuses on practical techniques and computer code, which may be useful to Audio Engineers looking to implement DSP algorithms. The course's coverage of topics like the Discrete Fourier Transform, filter design, and noise suppression may be especially useful.

See salaries and explore the career path for Audio Engineer

Radar Systems Engineer

Radar Systems Engineers design and develop radar systems for various applications. This Java Digital Signal Processing course may be applicable, as Radar Systems Engineers often use signal processing techniques to process radar signals and extract information about targets. The course's coverage of topics like matched filtering, pulse compression, and signal detection may be useful for designing and optimizing radar systems. The course's focus on practical techniques and computer code may be helpful for those implementing DSP algorithms in radar signal processing software.

See salaries and explore the career path for Radar Systems Engineer

Control Systems Engineer

Control Systems Engineers design and develop control systems for various applications. This Java Digital Signal Processing course may be valuable, as Control Systems Engineers often use signal processing techniques to analyze system behavior. The course's coverage of topics like the Discrete Fourier Transform, filter design, and system analysis may be useful for designing and tuning control systems. The course's focus on practical techniques and computer code may be helpful for those implementing DSP algorithms in control systems.

See salaries and explore the career path for Control Systems Engineer

Acoustic Consultant

Acoustic Consultants assess and mitigate noise and vibration issues. This Java Digital Signal Processing course may be applicable, as Acoustic Consultants often use signal processing techniques to analyze sound and vibration data. The course's coverage of topics like the Discrete Fourier Transform, filter design, and noise suppression may be useful for designing acoustic treatments and mitigating noise pollution. The course's focus on practical techniques and computer code may be helpful for those implementing DSP algorithms in acoustic modeling software.

See salaries and explore the career path for Acoustic Consultant

Machine Learning Engineer

Machine Learning Engineers develop and implement machine learning algorithms and systems. This Java Digital Signal Processing course may be helpful, as Machine Learning Engineers often use signal processing techniques to preprocess data and extract features. The course's coverage of topics like the Discrete Fourier Transform, filter design, and spectral analysis may be useful for building machine learning models for signal data. The course's hands-on approach to learning may be helpful for Machine Learning Engineers looking to implement DSP algorithms in their models.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists analyze and interpret complex digital data, and this course in Java Digital Signal Processing can prepare them for this role. Data Scientists may benefit from the course’s coverage of topics like the Discrete Fourier Transform, the Fast Fourier Transform, and filter design because those are applicable in data analysis. The hands-on approach to learning implemented in the course may be useful for Data Scientists looking to apply DSP techniques to their work. The inclusion of real-world applications, such as spectral analysis on ECG signals, may be valuable.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts interpret data to identify trends and patterns. This Java Digital Signal Processing course may be insightful, as Data Analysts may use signal processing techniques to extract meaningful information from time-series data. The course's coverage of topics like the Discrete Fourier Transform, filter design, and statistical analysis may be useful for analyzing and interpreting data in various domains. The course's focus on practical techniques and coding in Java may be helpful for those implementing DSP algorithms in data analysis tools.

See salaries and explore the career path for Data Analyst

Robotics Engineer

Robotics Engineers design, build, and program robots. This Java Digital Signal Processing course can provide a background in signal processing. They use signal processing techniques to process sensor data and control robot movements. The course's coverage of topics like the Discrete Fourier Transform, filter design, and noise suppression may be useful for designing robot control systems and processing sensor signals. The course's hands-on approach to learning may be helpful for Robotics Engineers looking to implement DSP algorithms on robot hardware.

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Software Engineer

Software Engineers are involved in designing, developing, testing, and evaluating software. While a broad field, this Java Digital Signal Processing course can give Software Engineers a background in digital signal processing. Software Engineers may find it helpful to learn how to implement DSP algorithms in Java, as is taught in this course. A Software Engineer may find the course's focus on code readability, robustness, and execution speed techniques useful. This course may be applicable to Software Engineers working on projects requiring signal processing.

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Image Processing Engineer

Image Processing Engineers develop algorithms and systems for processing and analyzing images. Though the course is focused on digital signals, many of the same techniques apply to image processing, and this Java Digital Signal Processing course could be beneficial. This course's coverage of topics like the Discrete Fourier Transform, filter design, and noise reduction may be useful for designing and optimizing image processing algorithms. The course's hands-on approach to learning may be helpful for Image Processing Engineers.

See salaries and explore the career path for Image Processing Engineer

Financial Engineer

Financial Engineers use quantitative methods to analyze financial markets and manage risk. This Java Digital Signal Processing course may be beneficial, as Financial Engineers often use signal processing techniques to analyze time-series data and identify patterns. The course's coverage of topics like the Discrete Fourier Transform, filter design, and statistical signal processing may be useful for building models. The course's focus on practical techniques and computer code may be helpful for those implementing DSP algorithms in financial modeling software. An advanced degree is typically required for this role.

See salaries and explore the career path for Financial Engineer

Java Digital Signal Processing (DSP) From Ground Up™

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