Signals and Systems

Name: Signals and Systems
Rating: 3.9 (5 reviews)

Signals and Systems, Fundamentals

Created byAssoc. Prof. Yücel Çimtay

Last updated 8/2026

English

English [Auto],

What you'll learn

fundamentals of signal analysis
signals and systems
Linear and Time Invariant Systems
Fourier Transform
Filtering
Sampling
Z-Transform

Course content

1 section • 33 lectures • 13h 47m total length

Signals and Systems24:49
Sinusoids-I20:00
Sinusoids-II20:00
Sinusoids-III25:07
Special Signals, System Properties-I15:00
Special Signals, System Properties-II15:00
Special Signals, System Properties-III11:03
System Properties32:08
Linear, Time-Invariant (LTI) Systems Ia20:00
Linear, Time-Invariant (LTI) Systems Ib19:59
Linear, Time-Invariant (LTI) Systems Ic25:25
Linear, Time-Invariant (LTI) Systems II a19:56
Linear, Time-Invariant (LTI) Systems II b20:00
Explore memoryless and invertible properties of linear time-invariant systems, analyze impulse response, causality, and stability, and relate continuous and discrete time representations.
Linear, Time-Invariant (LTI) Systems II c13:57
Linear, Time-Invariant (LTI) Systems III a19:00
Linear, Time-Invariant (LTI) Systems III b20:29
This lecture shows how differentiator and integrator LTI systems, via delta inputs, unit step or ramp, through impulse responses and convolution, yield higher order derivatives or running integrals when cascaded.
Differential and Difference Equations-I20:00
Differential and Difference Equations-II20:00
Understand discrete time linear time invariant, causal systems via nth order difference equations, deriving impulse response h(n) and expressing the solution as particular plus homogeneous parts with stability when |a|<1.
Differential and Difference Equations-III17:46
Demonstrate nth order difference and differential equations via block diagrams, using delay and integral elements; compare direct form one and two in LTI, causal systems, and memory-friendly reductions.
Continuous Time Fourier Series-I20:00
Learn how to decompose continuous-time signals in LTI systems using the Fourier series, expressing inputs as complex exponentials and computing outputs via convolution and Fourier coefficients for periodic signals.
Continuous Time Fourier Series-II20:00
Explore the continuous-time Fourier series, derive Fourier series coefficients a_k from x(t) using the analysis equation, and reconstruct x(t) with the synthesis equation, illustrated by odd and even square-wave examples.
Continuous Time Fourier Series-III17:39
Continuous Time Fourier Transform (CTFT) Ia20:00
Learn to synthesize signals from Fourier series coefficients and analyze them with the analysis equation, then extend to non-periodic signals by repeating the period to infinity, yielding x(omega) as envelope.
Continuous Time Fourier Transform (CTFT) Ib18:49
Continuous Time Fourier Transform (CTFT) Ic18:48
Continuous Time Fourier Transform (CTFT) IIa27:00
Continuous Time Fourier Transform (CTFT) IIb27:24
Discrete Time Fourier Transform (DTFT) I1:05:46
Discrete Time Fourier Transform (DTFT) II38:14
Filtering56:20
Sampling38:51
Z-Transform I39:11
Z-Transform II39:35

Requirements

Linear Algebra

Description

Signals and Systems is an introduction to analog and digital signal processing, a topic that forms an integral part of engineering systems in many diverse areas, including seismic data processing, communications, speech processing, image processing, defense electronics, consumer electronics, and consumer products.

The course presents and integrates the basic concepts for both continuous-time and discrete-time signals and systems. Signal and system representations are developed for both time and frequency domains. These representations are related through the Fourier transform and its generalizations, which are explored in detail. Filtering and filter design, modulation, and sampling for both analog and digital systems, as well as exposition and demonstration of the basic concepts of feedback systems for both analog and digital systems, are discussed and illustrated.

Both for pedagogical reasons and as a reflection of the nature of modern signal processing systems, the concepts associated with continuous-time and with discrete-time signals and systems are treated together in a closely coordinated way. Among other things, this approach emphasizes both the similarities and the differences in the two classes of systems. Developing this video course has been an extremely enjoyable and rewarding experience. I hope that you also find it enjoyable, stimulating, and rewarding.

The lecture notes used in this course is taken from MIT OpenCourseWare, Signals and Systems Course.

Who this course is for:

University students
Electrical Engineers
Lecturers
Computer science students