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What you'll get

  • Job Credibility
  • Certification Valid for Life
  • Live Classes
  • Certificate of Completion

Exam details

  • Mode of Exam : Online
  • Duration : 1 Hour
  • Multiple Choice Questions are asked
  • No. of Questions are asked : 50
  • Passing Marks : 25 (50%)
  • There is no negative marking

Digital signal processing is the branch of engineering that deals with improving and optimizing digital signals so that the performance can enhance. This is an online course to provide fundamental information on digital signal concepts. We will start learning from the ground level and will dive into deep topics step by step. This course allows you to get complete fundamental information of digital signal processing and after the course, you will not only have the theoretical knowledge about this subject but also have practical and real-life examples to people its technical tasks. 

This online Certificate in Digital Signal Processing certificate course is going to help you to understand the digital signal so that to work in related profile independently. this will build up the complete foundation that will encourage you to go advanced in this field by pursuing higher education. Or if you do not want to get any higher education after this course, then this is also sufficient to get a suitable job that matches you.

You will be learning in this online digital signal processing course

  • You will understand the theory part of the curriculum first.
  • Then will learn about the nature of discrete-time signals
  • after then you have vector and vector space topics
  • you will also learn how to do Fourier analysis

By the end of the course, you will know about Design FIR and IIR filters, tables, and standard transform. You will also have an understanding of the DT system, error correction coding, and much more. 

Course Content

Total: 186 lectures
  • The Roots of DSP
  • Telecommunications
  • Audio Processing
  • Echo Location
  • Imaging Processing
  • Signal and Graph Terminology
  • Mean and Standard Deviation
  • Signal vs. Underlying Process
  • The Histogram, Pmf and Pdf
  • The Normal Distribution
  • Digital Noise Generation
  • Precision and Accuracy
  • Quantization
  • The Sampling Theorem
  • Digital-to-Analog Conversion
  • Analog Filters for Data Conversion
  • Selecting the Antialias Filter
  • Multirate Data Conversion
  • Single Bit Data Conversion
  • Computer Numbers
  • Fixed Point (Integers)
  • Floating Point (Real Numbers)
  • Number Precision
  • Execution Speed: Program Language
  • Execution Speed: Hardware
  • Execution Speed: Programming Tips
  • Signals and Systems
  • Requirements for Linearity
  • Static Linearity and Sinusoidal Fidelity
  • Examples of Linear and Nonlinear Systems
  • Special Properties of Linearity
  • Superposition: the Foundation of DSP
  • Common Decompositions
  • Alternatives to Linearity
  • The Delta Function and Impulse Response
  • Convolution
  • The Input Side Algorithm
  • The Output Side Algorithm
  • The Sum of Weighted Inputs
  • Common Impulse Responses
  • Mathematical Properties
  • Correlation
  • Speed
  • The Family of Fourier Transforms
  • Notation and Format of the real DFT
  • The Frequency Domain's Independent Variable
  • DFT Basis Functions
  • Synthesis, Calculating the Inverse DFT
  • Analysis, Calculating the DFT
  • Duality
  • Polar Notation
  • Polar Nuisances
  • Spectral Analysis of Signals
  • Frequency Response of Systems
  • Convolution via the Frequency Domain
  • Linearity of the Fourier Transform
  • Characteristics of the Phase
  • Periodic Nature of the DFT
  • Compression and Expansion, Multirate methods
  • Multiplying Signals (Amplitude Modulation)
  • The Discrete Time Fourier Transform
  • Parseval's Relation
  • Delta Function Pairs
  • The Sinc Function
  • Other Transform Pairs
  • Gibbs Effect
  • Harmonics
  • Chirp Signals
  • Real DFT Using the Complex DFT
  • How the FFT Works
  • FFT Programs
  • Speed and Precision Comparisons
  • Further Speed Increases
  • The Delta Function
  • Convolution
  • The Fourier Transform
  • The Fourier Series
  • Filter Basics
  • How Information is Represented in Signals
  • Time Domain Parameters
  • Frequency Domain Parameters
  • High-Pass, Band-Pass and Band-Reject Filters
  • Filter Classification
  • Implementation by Convolution
  • Noise Reduction vs. Step Response
  • Frequency Response
  • Relatives of the Moving Average Filter
  • Recursive Implementation
  • Strategy of the Windowed-Sinc
  • Designing the Filter
  • Examples of Windowed-Sinc Filters
  • Pushing it to the Limit
  • Arbitrary Frequency Response
  • Deconvolution
  • Optimal Filters
  • FFT Convolution
  • The Overlap-Add Method
  • FFT Convolution
  • Speed Improvements
  • The Recursive Method
  • Single Pole Recursive Filters
  • Narrow-band Filters
  • Phase Response
  • Using Integers
  • The Chebyshev and Butterworth Responses
  • Designing the Filter
  • Step Response Overshoot
  • Stability
  • Match #1: Analog vs. Digital Filters
  • Match #2: Windowed-Sinc vs. Chebyshev
  • Match #3: Moving Average vs. Single Pole
  • Human Hearing
  • Timbre
  • Sound Quality vs. Data Rate
  • High Fidelity Audio
  • Companding
  • Speech Synthesis and Recognition
  • Nonlinear Audio Processing
  • Digital Image Structure
  • Cameras and Eyes
  • Television Video Signals
  • Other Image Acquisition and Display
  • Brightness and Contrast Adjustments
  • Grayscale Transforms
  • Warping
  • Convolution 397
  • 3×3 Edge Modification
  • Convolution by Separability
  • Example of a Large PSF: Illumination Flattening
  • Fourier Image Analysis
  • FFT Convolution
  • A Closer Look at Image Convolution
  • Spatial Resolution
  • Sample Spacing and Sampling Aperture
  • Signal-to-Noise Ratio
  • Morphological Image Processing
  • Computed Tomography
  • Target Detection
  • Neural Network Architecture
  • Why Does it Work?
  • Training the Neural Network
  • Evaluating the Results
  • Recursive Filter Design
  • Data Compression Strategies
  • Run-Length Encoding
  • Huffman Encoding
  • Delta Encoding
  • LZW Compression
  • JPEG (Transform Compression)
  • MPEG
  • How DSPs are different
  • Circular Buffering
  • Architecture of the Digital Signal Processor
  • Fixed versus Floating Point
  • C versus Assembly
  • How Fast are DSPs?
  • The Digital Signal Processor Market
  • The ADSP-2106x family
  • The SHARC EZ-KIT Lite
  • Design Example: An FIR Audio Filter
  • Analog Measurements on a DSP System
  • Another Look at Fixed versus Floating Point
  • Advanced Software Tools
  • The Complex Number System
  • Polar Notation
  • Using Complex Numbers by Substitution
  • Complex Representation of Sinusoids
  • Complex Representation of Systems
  • Electrical Circuit Analysis
  • The Real DFT
  • Mathematical Equivalence
  • The Complex DFT
  • The Family of Fourier Transforms
  • Why the Complex Fourier Transform is Used
  • The Nature of the s-Domain
  • Strategy of the Laplace Transform
  • Analysis of Electric Circuits
  • The Importance of Poles and Zeros
  • Filter Design in the s-Domain
  • The Nature of the z-Domain
  • Analysis of Recursive Systems
  • Cascade and Parallel Stages
  • Spectral Inversion
  • Gain Changes
  • Chebyshev-Butterworth Filter Design
  • The Best and Worst of DSP

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