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A short review of signals and systems, convolution, discrete-time Fourier transform, and the z-transform.
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Theory on random processes and their importance in modeling complicated signals.
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Linear and time-invariant systems
Linear and time-invariant (LTI) systems are a particularly important class of systems. They're the systems for which convolution holds. We review their most important properties and how to analyze them.
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The mathmatical formulation of sampling and reconstruction.
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How to change the sampling rate of digital signals.
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How to model quantization and its effects on digital systems.
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How to implement digital filters based on their difference equation.
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Quantization in filter structures
Effect of fix-point operations in digital filter, how to analyze it, and how to minimize its effects.
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Filter design of infinite impulse response (IIR) and finite impulse response (FIR) filters.
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Introdcution to adpative signal processing using the least mean squares (LMS) algorithm.
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The discrete Fourier transform (DFT) and its properties. This lecture also covers fast algorithms to compute the DFT known as fast Fourier transform (FFT).
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Analysis of signals in the frequency domain using the DFT and FFT.
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How to estiamte the power spectrum density (PSD) of signals.
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Introduction to parametric signal processing and how to use systems to represent complicated signals.
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Recap of the most important concepts covered throughout the course.
Homework assignments and their solution can be found in this folder.