Practical Exercises to Explain the Topographic Map

Relational Generative Topographic Map

The generative topographic mapping (GTM) has been proposed as a statistical model to represent high dimensional data by means of a sparse lattice of points in latent space, such that visualization, compression, and data inspection become possible. Original GTM is restricted to Euclidean data points in a vector space. Often, data are not explicitly embedded in a Euclidean vector space, rather pa...

Exercises in Practical Physiology

Maximum Likelihood Topographic Map Formation

We introduce a new unsupervised learning algorithm for kernel-based topographic map formation of heteroscedastic gaussian mixtures that allows for a unified account of distortion error (vector quantization), log-likelihood, and Kullback-Leibler divergence.

Texture based recognition of topographic map symbols

This paper introduces a method that is able to assign the symbols of vectorized maps into polygon attributes. It reduces the required storage space of the database by removing the corresponding polygons from the vectorized data model. This procedure is presented with optimized pattern matching on the raster image source of the map, where the symbols are handled as special textures. This method ...

Practical Exercises for Learning to Construct NMR/MRI Probe Circuits

Nine sets of laboratory exercises are designed to acquaint the student with many aspects of NMR/MRI probe resonant circuits. These include demonstrations of stray inductance and capacitance, matching a resonant circuit to 50 V, inductive coupling, doubly resonant circuits, loading effects of tissue, and radiation from the probe circuit. Many of the exercises are for the bench, but some are perf...