Through the clock's workings

Through the Clock’s Workings is a world first: a remixed and remixable anthology of literature.----- Prominent Australian authors have written new short stories and released them under a Creative Commons Attribution Non-Commercial ShareAlike licence. What that means is you can remix the stories, but only if you acknowledge the author, the remix is not for commercial use, and your new work is available for others to remix. The authors’ stories were made available on our website and new and emerging writers were invited to create their own remixes to be posted on the website and considered for publication in the print anthology alongside the original stories.----- The result is a world first: a remixed and remixable anthology of literature. Buy your copy now from the Sydney University Press eStore or download the electronic version.----- So how do you use a remixable anthology? Simple.----- Step 1 - Read. Thumb your way through the pages at will. Find the stories you love, the ones you hate, the ones that could be better.----- Step 2 - Re/create. Each story is yours to share and to remix. Use only one paragraph or character or just make subtle changes. Change the genre, alter its formal or stylistic characteristics, or revise its message. Use as little or as much as you like - as long as it works.----- Step 3 - Share. Be part of a growing community of literature remixing. Email your remix to us and start sharing. The entire anthology can be remixed - the original stories, the remixes, and even the fonts.----- Through the Clock’s Workings is Read&Write!

Digit recognition using trispectral features

Features derived from the trispectra of DFT magnitude slices are used for multi-font digit recognition. These features are insensitive to translation, rotation, or scaling of the input. They are also robust to noise. Classification accuracy tests were conducted on a common data base of 256× 256 pixel bilevel images of digits in 9 fonts. Randomly rotated and translated noisy versions were used for training and testing. The results indicate that the trispectral features are better than moment invariants and affine moment invariants. They achieve a classification accuracy of 95% compared to about 81% for Hu's (1962) moment invariants and 39% for the Flusser and Suk (1994) affine moment invariants on the same data in the presence of 1% impulse noise using a 1-NN classifier. For comparison, a multilayer perceptron with no normalization for rotations and translations yields 34% accuracy on 16× 16 pixel low-pass filtered and decimated versions of the same data.