DO YOU WANT TO LEARN FROM BASICS ?
DO YOU WANT TO LEARN MORE NUMERICALS ?
DO YOU WANT TO ANALYZE CONCEPTS WITH BEAUTIFUL EXAMPLE PROBLEMS ?
DO YOU WANT TO CHECK HOW MUCH CONTENT YOU HAVE GRASPED AFTER EACH LECTURE ?
DO YOU WANT TO PRACTICE MORE ASSIGNMENTS ?
DO YOU WANT TO SOLVE QUIZ QUESTIONS AFTER LEARNING EVERY TOPIC ?
THEN MY DEAR STUDENTS THIS COURSE IS THE ONE STOP SOLUTION FOR ALL THE ABOVE QUESTIONS.
“Signals and systems” is the basis of all control and signal processing engineering. It will allow you to take a real world machine, process (the system) and create a mathematical model, at which we apply stimuli and analyze it's response (stimuli and response being signals).
Examples of systems that manipulate signals are speech recognition, video streaming, cellular networks and medical scans such as MRI. The disciplines of signal and image processing are concerned with the analysis and synthesis of signals and their interaction with systems.
Students will
Be able to describe signals mathematically
Understand mathematical description and representation of continuous and discrete time signals
Be familiar with commonly used signals such as the unit step, ramp, impulse function, sinusoidal signals and complex exponential
Understand how to perform mathematical operations on signals
Be able to classify signals as continuous-time Vs. discrete-time, periodic Vs. non-periodic, energy signal Vs. power signal, odd Vs. even, causal Vs. non- causal signals
Understand system properties - linearity, time in variance, presence or absence of memory, causality, bounded-input bounded-output stability and invertibility
Be able to perform the process of convolution between signals and understand its implication for analysis of linear time-invariant systems. Understand the notion of an impulse response
Development of the mathematical skills to solve problems involving convolution
Understand and resolve the signals in frequency domain using Fourier series and Fourier transforms Further, be able to use the properties of the Fourier transform to compute the Fourier transform (and its inverse) for a broader class of signals
Understand the limitations of Fourier transform and need for Laplace transform and develop the ability to analyze the system in s- domain
Apply the Laplace transform and Z- transform for analyze of continuous-time and discrete-time signals and system
