Signal Processing: Basics and Applications

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Related lectures (32)

Sampling: Signal Reconstruction and Aliasing

Covers the importance of sampling, signal reconstruction, and aliasing in digital representation.

Signals, Instruments, and Systems: System Properties and Transforms

Introduces system properties, Laplace Transform, and analog filters for signal analysis.

Filtering and Sampling of Signals

Explores filtering signals with a moving average filter and the process of sampling, emphasizing the importance of signal reconstruction from samples.

Signals & Systems I: Sampling and Reconstruction

Explores ideal sampling, Fourier transformation, spectral repetition, and analog signal reconstruction.

Discrete Fourier Transform: Introduction

Introduces the discrete Fourier transform, a key tool for digital signal analysis.

Discrete Fourier Transform: Introduction and Sampling

Covers the introduction of discrete Fourier transform and its implications on signal reconstruction.

Sampling and Reconstruction Theory

Covers the concepts of analog, discrete, and digital signals, sampling times, frequencies, and pulses.

Frequency Response Analysis: Linear Systems and Filters

Covers the frequency response of linear systems and filter design principles.

Fourier Transform and Convolution Product in Signal Processing

Explores the Fourier transform, convolution product, and their applications in signal processing.

Fourier Transform: Concepts and Applications

Covers the Fourier transform, its properties, and applications in signal processing and differential equations, demonstrating its importance in mathematical analysis.

Fourier Transform: Basics and Examples

Explains the basics of Fourier transform and demonstrates its application through examples, including periodic functions and Fourier Transform Pairs.

Time Series: Spectral Analysis and Linear Filtering

Explores spectral analysis, aliasing, and linear filtering in time series data.

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