Digital Signal Processing

Engineering

2023-11-28

Lessons List

L1 Introduction to signals sampling basics nyquist theorem examples

L5 Introduction course overview applications signal conversion

L3 Review of signals continuous vs discrete vs digital sampling quantization

L2 Basic signals unit step exponentials sinusoids frequency concepts

L4 Basic operations on signals time shifting reversal addition scaling

L9 Classification of signals sequences causal finite infinite sinusoid sequences

L7 Discrete time sinusoid periodicity frequency fourier series

Review

L6 Summarizing week 1 signals operations periodicity in discrete time

L8 Problem solving 1 impulse step symmetry periodic sequences

L11 Time scaling downsampling upsampling rate conversion

L12 Introduction to systems memory linearity time invariance

L13 Linear time invariant system the convolution sum impulse response

L19 Problem solving 2 signal types sinusoids scaling

L14 Problem solving 3 upsampling downsampling periodicity analysis

L15 Problem solving 4 even odd signals signal products linearity

L16 Problem solving 5 linearity time invariance convolution sum

L20 Causal systems impulse response moving average filters

L17 Energy power signals partial energy power classification

L18 Stability of a system boundedness summability bibo stability

L22 Linear constant coefficient difference equation explained

L21 From time domain to frequency domain discrete time fourier transform DTFT

L31 Convolution analytical method exponential signals causality

L24 Interconnection of linear time invariant lti systems convolution impulse response

L23 Numericals on energy power of signals bounded input bounded output BIBO Stability

L25 Sampling nyquist aliasing band limited signals spoke wheel

L26 Convolution with impulse Signal recap sampling intro examples

L27 Reconstruction nyquist theorem sampling spectrum filters

L28 Oversampling matters filter design spectral copies

L32 Problem solving on sampling 3

L30 Problem solving on sampling – 1 nyquist Rate sinc sliasing

L29 Problem solving on sampling 2 nyquist rate explained with examples

L33 Mathematical framework of sampling spectrum replication reconstruction

L34 Reconstruction filter

L35 Undersampling example aliasing signal reconstruction explained

L36 Quantization

L40 Discrete time fourier transform inverse discrete time fourier transform spectral analysis

L38 DTFT examples step by step discrete time fourier transform in signal processing

L37 Symmetry properties of dtft real imaginary conjugate components

L39 Symmetry properties of dtft when the discrete time signal is real

L42 Example moving average filter discrete time fourier transform frequency phase analysis

L41 Existence of dtft conditions for convergence in signal processing

L43 Special cases for existence of dtft periodic finite energy dirichlet signals

L44 Fourier transform theorems properties linearity shifting time reversal explained

L47 Fourier transform theorems properties convolution parseval s theorem

L45 Fourier transform theorems differentiation in frequency domain multiplication property

L46 Problem solving discrete time fourier transform properties phase parseval s theorem example

L48 Problem solving on properties of discrete time fourier transform parseval shifting

L50 Ideal filters discrete time fourier transform DTFT low high pass band pass band stop

L49 Linear constant coefficient difference equation LCCDE frequency response

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Vishwa.N

Good

2024-04-19

Feri Dwi Febrianto

Nice

2024-03-16

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GOOD

2024-03-03

Eng. James Suburya

A good beginning to Digital signal processing

2024-01-14

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2023-12-18

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awesome

2023-12-17

SOJITH M

Good lesson

2023-12-13

ALHAJ ABDULRAHMAN ALTAYEB JALLY

Good work

2023-12-12

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2023-12-12

Chandra Kant Yadav

nice lecture

2023-12-08

Hane rajesh narayan

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2023-12-05

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Digital Signal course in hindi, In this course you'll delve into the world of digital signals, exploring their representation, processing, and applications in modern technology.

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Digital Signal Processing

Engineering

L1 Introduction to signals sampling basics nyquist theorem examples

L5 Introduction course overview applications signal conversion

L3 Review of signals continuous vs discrete vs digital sampling quantization

L2 Basic signals unit step exponentials sinusoids frequency concepts

L4 Basic operations on signals time shifting reversal addition scaling

L9 Classification of signals sequences causal finite infinite sinusoid sequences

L7 Discrete time sinusoid periodicity frequency fourier series

Review

L6 Summarizing week 1 signals operations periodicity in discrete time

L8 Problem solving 1 impulse step symmetry periodic sequences

L11 Time scaling downsampling upsampling rate conversion

L12 Introduction to systems memory linearity time invariance

L13 Linear time invariant system the convolution sum impulse response

L19 Problem solving 2 signal types sinusoids scaling

L14 Problem solving 3 upsampling downsampling periodicity analysis

L15 Problem solving 4 even odd signals signal products linearity

L16 Problem solving 5 linearity time invariance convolution sum

L20 Causal systems impulse response moving average filters

L17 Energy power signals partial energy power classification

L18 Stability of a system boundedness summability bibo stability

L22 Linear constant coefficient difference equation explained

L21 From time domain to frequency domain discrete time fourier transform DTFT

L31 Convolution analytical method exponential signals causality

L24 Interconnection of linear time invariant lti systems convolution impulse response

L23 Numericals on energy power of signals bounded input bounded output BIBO Stability

L25 Sampling nyquist aliasing band limited signals spoke wheel

L26 Convolution with impulse Signal recap sampling intro examples

L27 Reconstruction nyquist theorem sampling spectrum filters

L28 Oversampling matters filter design spectral copies

L32 Problem solving on sampling 3

L30 Problem solving on sampling – 1 nyquist Rate sinc sliasing

L29 Problem solving on sampling 2 nyquist rate explained with examples

L33 Mathematical framework of sampling spectrum replication reconstruction

L34 Reconstruction filter

L35 Undersampling example aliasing signal reconstruction explained

L36 Quantization

L40 Discrete time fourier transform inverse discrete time fourier transform spectral analysis

L38 DTFT examples step by step discrete time fourier transform in signal processing

L37 Symmetry properties of dtft real imaginary conjugate components

L39 Symmetry properties of dtft when the discrete time signal is real

L42 Example moving average filter discrete time fourier transform frequency phase analysis

L41 Existence of dtft conditions for convergence in signal processing

L43 Special cases for existence of dtft periodic finite energy dirichlet signals

L44 Fourier transform theorems properties linearity shifting time reversal explained

L47 Fourier transform theorems properties convolution parseval s theorem

L45 Fourier transform theorems differentiation in frequency domain multiplication property

L46 Problem solving discrete time fourier transform properties phase parseval s theorem example

L48 Problem solving on properties of discrete time fourier transform parseval shifting

L50 Ideal filters discrete time fourier transform DTFT low high pass band pass band stop

L49 Linear constant coefficient difference equation LCCDE frequency response

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