Lecture

Spatial Sampling: Concepts and Techniques

Related lectures (30)

Covers global relief indicators, altitude models, slope, orientation, curvature, and morphological indices for terrain description.

Sampling: Continuous Spatial Phenomena

Covers different spatial sampling procedures and properties in Geographic Information Systems.

Vector-Vector Interactions

Covers interactions between vector layers in GIS using QGIS tools.

Wireless Receivers: Time and Phase Offset

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

Altitude Models and Derivative Variables

Covers geographic information systems, relief indicators, and digital elevation models.

Spatial Continuous Phenomena, Sampling

Explores sampling procedures for analyzing continuous geographical phenomena and their importance in reducing prediction uncertainty.

Signal processing and vector spaces

Emphasizes the significance of vector spaces in signal processing, offering a unified framework for various signal types and system design.

Practical Sampling and Interpolation

Covers practical sampling, interpolation challenges, spectral representation, and Fourier Transform properties.

Discrete Variables, Geometric Properties

Covers discrete variables in geographic information systems and their geometric properties, including spatial arrangement and autocorrelation.

Determinantal Point Processes and Extrapolation

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

Digital Signal Processing: Theory

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

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.

Graph Coloring: Theory and Applications

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

Deep Learning Modus Operandi

Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.

Natural Language Generation: Decoding & Training

Explores challenges in natural language generation, decoding algorithms, training issues, and reward functions.

Generative Models: Self-Attention and Transformers

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

Frequency Sampling

Explores the frequency sampling method to approximate ideal filters, useful for quick prototyping but lacking fine control over errors.

Digital Elevation Models: Basic Concepts

Covers methods for sampling elevation and measuring elevation using leveling, photogrammetry, and LiDAR in Geographic Information Systems.

Fourier Transform and Sampling

Covers the Fourier transform of sampled signals, reconstruction, and harmonic response.

Generative Models: Boltzmann Machine

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