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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.

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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 Kernel algorithm 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, Chebyshev and Butterworth filters, 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.

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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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Syllabus

Introduction
Setting Up
Installingng Java JDK and IDE
Linking up Java JDK and IDE
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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

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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
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
Show steps
  • 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
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.
Show steps
  • Obtain a copy of the book.
  • Read chapters related to course topics.
  • Work through examples and exercises.
Implement Convolution in Java
Reinforce your understanding of convolution by implementing the algorithm in Java, focusing on both code readability and execution speed as emphasized in the course.
Show steps
  • Write a basic convolution function.
  • Optimize the function for speed.
  • Test with different input signals.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Help others in the course discussion forum
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.
Show steps
  • Regularly check the course discussion forum.
  • Answer questions from other students.
  • Explain concepts in your own words.
Create a DSP Algorithm Visualization
Deepen your understanding by creating a visual representation of a DSP algorithm (e.g., FFT, convolution) to illustrate its operation and impact on signals.
Show steps
  • 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
Apply your DSP knowledge by building a practical audio equalizer application in Java, incorporating filter design and frequency analysis techniques learned in the course.
Show steps
  • 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
Expand your knowledge with a rigorous textbook that provides a deep dive into the mathematical principles underlying DSP, complementing the course's practical approach.
Show steps
  • 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Java Digital Signal Processing (DSP) From Ground Up™.
Provides a comprehensive overview of DSP concepts, making it an excellent reference for the course. It covers topics such as convolution, Fourier transforms, and filter design in detail. It is particularly useful for students who want a deeper understanding of the mathematical foundations of DSP. This book is commonly used as a textbook at academic institutions.
Classic and comprehensive textbook on digital signal processing. It covers a wide range of topics, including discrete-time signals and systems, z-transform, Fourier analysis, filter design, and multirate signal processing. While it is more mathematically rigorous than the course, it provides a solid theoretical foundation for understanding DSP concepts. This book is commonly used as a textbook at academic institutions.

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