Signals and Systems II: Statistical Properties and Signal Representation

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Signals & Systems II: Vector Analogy and Discrete Signals

Explores the link between DTFT, Z-transforms, and Fourier transforms, vector analogy, and discrete signals.

Sampling and Reconstruction Theory

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

Fourier Transform: Concepts and Applications

Covers the Fourier transform, its properties, applications in signal processing, and differential equations, emphasizing the concept of derivatives becoming multiplications in the frequency domain.

Signal Processing Fundamentals

Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.

Signals and Systems: Sampling Theorem and Applications

Discusses the sampling theorem and its applications in signal processing.

Signals and Systems: Convolution and Fourier Transform

Covers convolution and Fourier transforms in linear time-invariant systems.

Detection & Estimation

Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.

Signals & Systems II: Sampling and Signal Representation

Explores sampling, signal representation, point transformations, and signal vector spaces in the context of signals and systems.

Fourier Transform and Sampling

Covers the Fourier transform of sampled signals, reconstruction, and harmonic response.

Sampling: DT-time processing of CT signals

Covers the importance of sampling in signal processing, including the sampling theorem and signal reconstruction.