Lectures related to Nonlinear filter

Image Filtering: Basics and Techniques

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

Kalman Filter: Linearized vs Extended

Discusses the limitations of a linearized Kalman filter and introduces the extended Kalman filter.

Time Series: Spectral Analysis and Linear Filtering

Explores spectral analysis, aliasing, and linear filtering in time series data.

Kalman Filter: Linearized vs Extended

Explores the linearized and extended Kalman Filters, illustrating their application in nonlinear systems and the estimation of unknown parameters.

Kalman Filter: Introduction

Introduces the Kalman Filter, a method to estimate system state from noisy measurements.

Image Filtering: Summary

Covers the fundamental principles of image filtering in biomedical analysis.

Convolution of Distributions and Differential Equations

Explores the convolution of tempered distributions and solving differential equations using fundamental solutions.

Signal Processing: Image Restoration and Linear Prediction

Explores image restoration and signal prediction using linear filters in signal processing.

The convolution theorem

Explores the convolution theorem, DTFT reconstruction, frequency response effects, and signal building.

Fourier Transform and Convolution Product in Signal Processing

Explores the Fourier transform, convolution product, and their applications in signal processing.

Image Processing: Smoothing and Filtering

Covers image smoothing, noise reduction, and segmentation techniques using filters and transforms.

Linear Estimation and Prediction

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

Linear Prediction and Estimation

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

Adaptive Filtering: LMS Algorithm

Covers adaptive filtering using the LMS algorithm for immobile recording scenarios, focusing on practical implementation in MATLAB.

Signal Processing: Linear Filters and Estimation

Covers applying concepts to build linear filters and perform estimation.

Digital Filters: Implementation and Analysis

Explores digital filters' implementation, cyclic convolution, FFT-based filtering, and the importance of filtering in signal processing.

Neural Signals and Signal Processing

Explores neural signal processing for brain-computer interfaces, including decoding techniques like Kalman filters and spike sorting.

Image Processing I: Filter Design and Boundary Conditions

Explores filter design, boundary conditions, Fourier-domain filtering, and useful smoothing filters for image processing.

Linear Phase Filters: Step Response and Characteristics

Explores linear phase filters, rise time, Gaussian filters, and rational transfer functions.

Linear time-invariant filters

Explains the concept of linear time-invariant filters in signal processing and the requirements for their operation.