[100% Off] Signals And Systems: From Fundamentals To Fourier &Amp; Laplace
Master system properties, convolution, Laplace and Fourier analysis, and signal modeling with real-world examples.
What you’ll learn
- Key properties of systems: linearity
- time invariance
- causality
- and stability
- Common signal types: unit step
- delta
- sinusoids
- exponentials
- and more
- Signal operations: scaling
- shifting
- even/odd decomposition
- and energy/power
- Convolution and its role in analyzing LTI systems
- Laplace transforms and their application in system analysis
- Inverse Laplace transforms and pole-zero analysis
- Modeling and analyzing a DC motor using Laplace techniques
- Fourier series and Fourier transforms for frequency-domain analysis
- AM modulation
- sampling theorem
- and signal reconstruction
Requirements
- Basic knowledge of calculus and linear algebra
- Familiarity with differential equations is helpful but not required
Description
Understanding signals and systems is essential for anyone pursuing a career in electrical engineering, computer engineering, or signal processing. These concepts form the theoretical backbone of modern technologies—from communication systems and control engineering to audio processing, robotics, and embedded systems.
But for many students, signals and systems can feel abstract and mathematically intense. That’s where this course comes in.
“Signals and Systems Masterclass: From Fundamentals to Fourier and Laplace” is a comprehensive, student-friendly course designed to make complex topics accessible and practical. This course walks you through the essential building blocks of signals and systems with clarity, structure, and real-world relevance.
You’ll begin by exploring the core properties of systems—linearity, time invariance, causality, and stability—before diving into the variety of signals used in engineering, such as unit step, delta, sinusoidal, exponential, and periodic signals. You’ll learn how to manipulate and classify signals using operations like scaling, shifting, and decomposition into even and odd components.
From there, you’ll master convolution, a cornerstone of system analysis, and understand how it reveals the behavior of Linear Time-Invariant (LTI) systems. You’ll then move into the Laplace transform, learning how to analyze systems in the s-domain, assess stability using pole-zero plots, and apply inverse transforms to return to the time domain.
The course also includes a practical case study: modeling a DC motor using Laplace transforms to analyze its response to inputs and initial conditions—bridging theory with engineering application.
Finally, you’ll explore Fourier analysis, learning how to represent signals in the frequency domain using Fourier series and Fourier transforms. You’ll apply these tools to analyze periodic signals, understand modulation, and grasp the sampling theorem, which underpins digital signal processing.
Each topic is supported by step-by-step examples, visual explanations, and real-world applications to ensure deep understanding and long-term retention.
By the end of this course, you’ll not only understand the theory behind signals and systems—you’ll be able to apply it confidently in engineering contexts, academic exams, and future studies in digital signal processing, control systems, and beyond.
