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Undergraduate course on signals and systems(Course-II)
Rating: 4.4 out of 5(38 ratings)
715 students
Last updated 3/2025
English

What you'll learn

  • Transformation techniques in Continuous -time domain
  • Sampling theorem to convert a continuous signal to discrete domain
  • All properties of Transformation techniques that are required to simplify the problem solving
  • All basics required to understand discrete transformation techniques and digital filters

Course content

5 sections98 lectures10h 18m total length
  • L01. Introduction7:39
  • L02. Component and error-Fourier series basics10:55
  • L03 Component Calculation3:49
  • L04 Orthogonal signal space6:27
  • L05 Closed Orthogonal Signal Set6:45
  • L06 Orthogonality in Complex Signals11:01
  • L07 Trigonometric Fourier Series(TFS)-Synthesis8:13
  • L08 TFS Component calculation6:32
  • L09 TFS Summary5:39
  • L10 Compact Trigonometric Fourier series4:52
  • L11 Fourier Spectrum4:28
  • L12 Symmetrical Conditions in TFS-18:24
  • L13 Symmetrical Conditions in TFS-2(Half wave symmetry)11:16
  • L14 Exponential Fourier Series(EFS)-Synthesis6:22
  • L15 EFS Component Calculation9:04
  • L16 EFS- Fourier Spectrum8:17
  • L17 Relation Between EFS & TFS5:28
  • L18 Dirichlet condtions5:38
  • L19 Properties- Time Shifting5:50
  • L20 Properties- Frequency Shifting3:14
  • L21 Properties- Time inversion2:23
  • L22 Properties- Time Scaling Property6:53

    The time scaling property changes the fundamental period to t0/a and the fundamental frequency to a ω0, reflecting compression or expansion; in Fourier series, scaling does not alter the spectrum.

  • L23 Properties- Time Differentiation Property3:27
  • L24 Properties- Linearity3:18
  • L25 Properties- Circular Convolution2:47
  • L26 Properties- Multiplication in Time domain5:54
  • L27 Parsaval's Power Theorem6:35
  • L28 Fourier series for an Impulse Train9:18
  • L29 Rectangular periodic signal- ODD14:29
  • L30 Rectangular periodic signal Fourier spectrum7:11
  • L31 Rectangualar periodic signal synthesis equation5:20
  • L32 Rectangular periodic signal-EVEN8:05
  • L33 Synthesis Equations for even rect signal9:38
  • L34 Summary9:52

Requirements

  • Undergraduate course on Signals & Systems-I
  • Good knowledge in basics of signals, system properties and convolution

Description

This is an undergraduate course on signals and systems. This course is the second part in a series of two courses on basics of signals and systems

For any electrical, electronics, Instrumentation or bio-medical engineering student applying transformation theory to signal processing and system analysis is necessary.

My previous course "undergraduate course on signals and systems-I" is a prerequisite for complete understanding of this course. Or one must have a good knowledge in introductory signals, system properties and Convlution techniques.


Fourier series: Fourier series is a powerful mathematical tool that converts a periodic signal in continuous time domain into frequency domain. Fourier series splits up a periodic signal into infinite harmonically related sinusoidal components or inotherwords by combining infinite harmonically related sinusoidal signals a periodic signal(usually non-sinusoidal) can be synthesized.


Fourier transform: A power-packed mathematical tool that synthesizes aperiodic signals. The Fourier spectrum obtained here is used in analog communication techniques and in Analog filters. Signal processing through an LTI system can be visualized in frequency domain.


Laplace transform: Laplace transform is a simple yet powerful mathematical tool which gives the S-domain representation for a time domain signal. Some of the limitations of Fourier techniques can be overcomed by Laplace transform. Laplace transform is the back-bone of Control systems and Analog network analysis. Transfer function of any system is defined in laplace domain.


Sampling theorem: It is a bridge between continuous-analog signals and discrete-digital signals. Sampling theorem lies the foundation for my next coureses in discrete signal processing.


About Author:

Mr. Udaya Bhaskar is an undergraduate university level faculty and GATE teaching faculty with more than 16 years of teaching experience. His areas of interest are signal processing, semiconductors, digital design and other fundamental subjects of electronics.  He trained thousands of students for GATE and ESE examinations.

Who this course is for:

  • Undergraduate engineering students with Electrical engineering, Electronics engineering, Biomedical engineering, Instrumentation engineering as specialisation
  • Diploma/Polytechnic students with Electronics engineering, Communication engineering, Instrumentation as specialisation
  • GATE and PSU preparing students
  • Any Electronics or communication engineer who wants strengthen signal processing fundamentals