Lectures related to Discrete-Time Control Systems: Implementation

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.

Determinantal Point Processes and Extrapolation

Covers determinantal point processes, sine-process, and their extrapolation in different spaces.

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

Signals & Systems I: Sampling and Reconstruction

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

Wireless Receivers: Time and Phase Offset

Covers the impact and compensation of time and phase offset in wireless receivers.

Sampling: Signal Reconstruction and Aliasing

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

Signals, Instruments, and Systems

Explores signals, instruments, and systems, covering ADC, Fourier Transform, sampling, signal reconstruction, aliasing, and anti-alias filters.

Digital Signal Processing: Theory

Covers the theory of digital signal processing, including sampling, transformation methods, digitization, and PID controllers.

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.

Approximate Query Processing: BlinkDB

Introduces BlinkDB, a framework for approximate query processing using sampling techniques.

Ceramics: Powder Characterization

Explores the characterization of powders in ceramics, emphasizing the impact on ceramic properties and the manufacturing process.

Wireless Receivers: Parameter Estimation

Covers parameter estimation in wireless receivers and phase ambiguity in signal modeling.

Generative Models: Boltzmann Machine

Covers generative models, focusing on Boltzmann machines and constrained maximization using Lagrange multipliers.

Implementation of Sampling and Quantization

Covers the generation of signals with noise, sampling, and conversion to digital.

Graph Coloring: Theory and Applications

Covers the theory and applications of graph coloring, focusing on disassortative stochastic block models and planted coloring.

Explicit Stabilised Methods: Applications to Bayesian Inverse Problems

Explores explicit stabilised Runge-Kutta methods and their application to Bayesian inverse problems, covering optimization, sampling, and numerical experiments.

Transfer Functions and Control Algorithms

Explores transfer functions, control algorithms, and system transformations in discrete and analog systems, with practical exercises included.

Generative Models: Self-Attention and Transformers

Covers generative models with a focus on self-attention and transformers, discussing sampling methods and empirical means.

Numerical Control of Dynamic Systems

Covers topics like digital filtering, stability, and discrete systems, including experiments and manipulations.