Lectures related to Advanced Filtering in Professor Recruitment List

Explores linear estimation, Wiener filters, and optimal prediction in signal processing.

Explores image filtering techniques, including linear and nonlinear filters, for artifact removal and feature enhancement.

Discrete-Time Stochastic Processes: Wiener Filter

Explores the Wiener filter for discrete-time stochastic processes and its applications.

Linear Prediction and Estimation

Explores linear prediction, optimal filters, random signals, stationarity, autocorrelation, power spectral density, and Fourier transform in signal processing.

Avoiding Implicit Bias in Hiring

Discusses blind auditions, selection criteria, and recommendation letters in hiring to prevent gender bias.

Filter Usage

Covers the usage of filters in the EPFL Service Desk environment and managing group assignments.

Stateful Operations and Materialized Values

Covers stateful operations and materialized values in stream processing.

Advanced Pandas Functions

Covers advanced functions of Pandas, focusing on filtering, labeling, and manipulating dataframes.

Analyze Particles

Covers the process of analyzing particles in images using Fiji software.

Image Processing I: Correlation Measures and Matched Filtering

Explores correlation measures, matched filtering, and feature extraction in image processing.

Kalman Filtering: Applications in Control and Communication Systems

Explores the applications of Kalman Filtering in control and communication systems, focusing on state estimation and channel estimation.

Inference Engines: Resolution and Horn Clauses

Covers inference engines based on resolution, Horn clauses, filtering, and unification in artificial intelligence.

Adaptive Signal Processing: Filtering & Neural Networks

Explores adaptive signal processing, gradient descent, and the LMS algorithm for efficient filtering and neural network training.

Image Processing Techniques

Covers image processing techniques including noise addition, filtering, and image enhancement using various filters and tools.

Digital PID Control: Filtering Noisy Signals

Discusses digital PID controllers, noise filtering in feedback controllers, and the challenges of approximating integral and derivative terms.