Introduction to Signals and Filters

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Covers examples of signal processing, analog signal processing, continuous amplitude modulation, image processing, compression, micro-systems, and medical electronics.

Signals & Systems I: Introduction to Communication Systems

Covers the basics of signals and systems in communication, including modulation, medical imaging, Fourier analysis, and biological systems.

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 and Systems: Sampling Theorem and Applications

Discusses the sampling theorem and its applications in signal processing.

Principles of Digital Communication

Covers the principles of digital communication, focusing on the Nyquist Sampling Theorem and signal space dimension.

Conclusions on Module II

Concludes Module II by presenting two theories on optimal signal representation.

Data Compression: Sparse Signals and Data Recording

Explores data compression through signal sparsity, questioning the need for recording vast amounts of data.

Compressive Sensing

Explores compressive sensing theory and hardware implementations for signal reconstruction.

Optimization of Neuroprosthetic Systems

Explores the optimization of neuroprosthetic systems, including sensory feedback restoration and neural stimulation strategies.

Introduction: Course Structure and Fundamentals of Computing

Explores the role of Computing in society and the basics of computing, algorithms, communication systems, and computer security.