A Mobile Multimedia Technology to Aid Those with Alzheimer's Disease

A first-generation, mobile, video-based reminder system offers memory support to those afflicted with mild-stage Alzheimer's disease.

New Retrodirective Antenna Techniques for Mobile Terminal Applications

This paper presents a new architecture together with practical results for a high performance analogue retrodirective array architecture with the following significant advantages: (1) It is able to constructively combine signals on receive, as well as on transmit, a feature not seen before on this type of array, (2) It is capable of operating with real life communication received signal levels as low as -120dBm. This work opens the way for fully co-operating Retrodirective arrays for use on un-stabilized co-operating mobile platforms where maximum S/N simultaneously on receive and on retransmit is automatically guaranteed.

"Producers and Consumers in EU E-Commerce Law by John Dickie, Oxford: Hart Publishing, 2005 (ISBN 1-841-13454-6)"

Book Review

An efficient feature selection method for mobile devices with application to activity recognition

This paper presents a feature selection method for data classification, which combines a model-based variable selection technique and a fast two-stage subset selection algorithm. The relationship between a specified (and complete) set of candidate features and the class label is modelled using a non-linear full regression model which is linear-in-the-parameters. The performance of a sub-model measured by the sum of the squared-errors (SSE) is used to score the informativeness of the subset of features involved in the sub-model. The two-stage subset selection algorithm approaches a solution sub-model with the SSE being locally minimized. The features involved in the solution sub-model are selected as inputs to support vector machines (SVMs) for classification. The memory requirement of this algorithm is independent of the number of training patterns. This property makes this method suitable for applications executed in mobile devices where physical RAM memory is very limited. An application was developed for activity recognition, which implements the proposed feature selection algorithm and an SVM training procedure. Experiments are carried out with the application running on a PDA for human activity recognition using accelerometer data. A comparison with an information gain based feature selection method demonstrates the effectiveness and efficiency of the proposed algorithm.