4 resultados para 1995_01300042 TM-63 4302803
em Brock University, Canada
Resumo:
The Weekly Register, also known as Niles Weekly Register, was a weekly periodical edited by Hezekiah Niles (1777-1839) and published in Baltimore Maryland. Volumes of interest were published between 1811 (Vol. 1, No. 1, September 7, 1811) to 1814 (Vol. 5, No. 26, February 26, 1814). These volumes focus primarily on 19th century politics and government in the United States of America. Niles edited and published the Weekly Register until 1836, making it one of the most widely-circulated magazines in the United States. The popularity also made Niles into one of the most influential journalists of his day. Devoted primarily to politics, Niles' Weekly Register is considered an important source for the history of the period. The Register also recorded current economics, technology, science, medicine, geography, archaeology, the weather, and stories of human interest. This issue is part of a bound book titled the Weekly Register 1812-1814. Pages are divided as follows: November 14, 1812- Pages 161-174 January 22, 1814- Pages 337-352 July 30, 1814- Pages 361-376 Look for other issues of the Weekly Register within the website.
Resumo:
Volumes of interest were published between 1812 and 1815 with articles about the War of 1812.
Resumo:
Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.