Lectures related to Aliasing for Arbitrary Spectra

Sampling: DT-time processing of CT signals

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

Covers the concept of sampling, the sampling theorem, signal reconstruction, and the conversion of analogue signals to digital signals.

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Stochastic Models for Communications: Introduction

Introduces mathematical tools for communication systems and data science, focusing on stochastic processes and preparing students for advanced courses.

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Signal Processing: Basic Definitions and Signal Typology

Covers the basic definitions and typology of signals, including analog and digital signals.

Neural Signals and Signal Processing

Explores neuronal signals, brain organization, measurement techniques, and MRI principles.

Signal Processing: Sampling and Reconstruction

Covers the concepts of quantization, coding, and sampling in signal processing.

Smart Sensors for the IoT: ADCs Fundamentals

Explores ADCs fundamentals for IoT smart sensors, covering quantization, non-idealities, and topologies.

Signal Processing: Properties and Norms

Explores the properties and norms of discrete signals in signal processing.