The Weekly Messenger, Vol.2 No.39- July 16, 1813

A weekly newspaper that was published every Friday from 1811 to 1815.

Portsmouth Oracle, 18 June 1814 (vol. 25, no. 39) - Newspaper

Volumes of interest were published between 1812 and 1815 with articles about the War of 1812.

Boston Gazette, 12 July 1813 (vol. 39, no. 6) - Newspaper

Volumes of interest were published between 1812 and 1815 with articles about the War of 1812. Issue for July 12, 1813 includes news from the War of 1812 at Sackets Harbour, Albany, and Fort George (relating to the Battle of Beaver Dams)

Boston Gazette, 13 December 1813 (vol. 39, no. 50) - Newspaper

Volumes of interest were published between 1812 and 1815 with articles about the War of 1812. Issue for Dec. 13, 1813: much of the front page & a portion of page 2 are taken up with the "President's Message" which is signed by him in type at its conclusion: James Madison. Obviously much of the focus of his speech is with the on-going War of 1812 including mention of some significant battles such as Lake Erie, etc.

The War, 15 March 1814 (vol. 2, no. 39, whole no. 91) - Newspaper

Volumes of interest were published between 1812 and 1815 with articles about the War of 1812.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

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.