The identification and classification of space objects that cannot be imaged in the normal way with telescopes is an important but difficult problem for tracking thousands of objects, including satellites, rocket bodies, debris, and asteroids, in orbit around the earth.
After feature extraction, the classification of space-time code can be considered as a pattern recognition problem.
Because there is no good way of measuring similarities between chemical reactions, there is a barrier to the development of global classifications of "metabolic space" and subsequent studies comparable to those done for protein sequences and structures.
The provided values of indoor space characteristics Cs and Ls may be used in a wide range of technological building applications, including comparisons and classifications of indoor spaces, design and selection of construction materials and furnishing as well as the investigation of effects from electric equipment, windows or doors opening, short-time ventilations, brief stay of visitors, etc.
We developed a novel classification of green space types, based on the utility of each space (usable or non-usable), and found different socioeconomic gradients in exposure to usable and total green space.
This enables the classification of parameter space into regions where qualitatively different steady-state and dynamic phenomena are observed.
We characterize the domain by presenting a classification of different space models that appear in the Geographical Information Systems GISS) literature, as well as of some of the problems addressed by map-centered applications.
Spatial land cover data sets for Kaunas city were obtained from the municipality, and were processed using ArcGIS 10 software to produce the classification of green space exposure.
Classification of protein space into functional families is useful for annotating uncharacterized sequences by inheriting annotations from characterized sequences in the family the query sequence matches best.
Using a geographical information system (GIS) we developed a classification of green space for small areas across New Zealand that distinguished between usable and non-usable areas.
