Thermochromic dyes and sunlight activating systems - An alternative means to induce colour change.

The general purpose of this work is to investigate the potential of a mobile phone to capture soil colour images and process them, returning the corresponding Munsell colour coordi- nates from the digital RGB captured images, and also estimate the tristimulus values from the same images. A mobile phone HTC Desire HD, which runs Android 2.2, has been used to take and process images of a Munsell Soil Colour Chart under fixed illumination conditions. To obtain tristimulus values of each sample a Konica Minolta CS2000d spectroradiometer has been used under the same conditions. Penrose’s pseudoinverse method has been used to compute relationship between RGB coordinates from digital images and tristimulus values. Once the model has been computed it was implemented in the mobile phone. Results of this calibration show that more than 90% of the samples used in the calibration (238 chips) were measured by our mobile phone application with accuracy below 2.03 CIELAB units and a mean correlation coefficient equal to 0.9972. In case of Munsell models mean correlation coefficient is equal to 0.9407. This points to the idea that a conventional mobile device can be used to determine the colour of a soil under controlled illumination conditions.

A Comparison of Geo-tagging in Mobile Internet Browsing Applications on iOS and Android.

Nowadays there is almost no crime committed without a trace of digital evidence, and since the advanced functionality of mobile devices today can be exploited to assist in crime, the need for mobile forensics is imperative. Many of the mobile applications available today, including internet browsers, will request the user’s permission to access their current location when in use. This geolocation data is subsequently stored and managed by that application's underlying database files. If recovered from a device during a forensic investigation, such GPS evidence and track points could hold major evidentiary value for a case. The aim of this paper is to examine and compare to what extent geolocation data is available from the iOS and Android operating systems. We focus particularly on geolocation data recovered from internet browsing applications, comparing the native Safari and Browser apps with Google Chrome, downloaded on to both platforms. All browsers were used over a period of several days at various locations to generate comparable test data for analysis. Results show considerable differences not only in the storage locations and formats, but also in the amount of geolocation data stored by different browsers and on different operating systems.