Printing only part of an MS Word 2010 document

You won't always want to print the whole of your document; here are some useful ways of printing only part of a MS Word 2010 file. For best viewing Download the video.

University of Southampton thesis chapter template for MS Word 2007/2010/2013/2016 for PC and Word 2008/2011/2016 for Apple

The University provides a template for the whole of a thesis but if you wish to construct a thesis by using separate files you can use this file as template for those. This template has mirror margins to account for double sided printing and odd and even page headers. Support materials for using the template are referenced near the start of the file. You will weant to use this in conjunction with the Front Matter http://www.edshare.soton.ac.uk/9405/ and End Matter templates http://www.edshare.soton.ac.uk/11998/.

University of Southampton thesis front matter template for use with MS Word 2007/2010/2013/2016 for PC and 2008/2011/2016 for Apple

The University provides a template for the whole of a thesis but if you wish to construct a thesis by using separate files you can use this file for the introductory section. This file contains all of the sections required (Title Page, Abstract, Table of Contents etc). It also has mirror margins for double sided printing and has different odd and even page headers. Support materials for using the template are referenced near the start of the file. You will want to use this in conjunction with the Chapter http://www.edshare.soton.ac.uk/9403/ and End Matter templates http://www.edshare.soton.ac.uk/11998/

ImageLearn - Decoding Britain's Landscape

Abstract Ordnance Survey, our national mapping organisation, collects vast amounts of high-resolution aerial imagery covering the entirety of the country. Currently, photogrammetrists and surveyors use this to manually capture real-world objects and characteristics for a relatively small number of features. Arguably, the vast archive of imagery that we have obtained portraying the whole of Great Britain is highly underutilised and could be ‘mined’ for much more information. Over the last year the ImageLearn project has investigated the potential of "representation learning" to automatically extract relevant features from aerial imagery. Representation learning is a form of data-mining in which the feature-extractors are learned using machine-learning techniques, rather than being manually defined. At the beginning of the project we conjectured that representations learned could help with processes such as object detection and identification, change detection and social landscape regionalisation of Britain. This seminar will give an overview of the project and highlight some of our research results.