Signals of need in a cooperatively breeding mammal with mobile offspring

In many bird species with biparental care for young in the nest, hungry chicks beg repeatedly and parents adjust their feeding rate to the call rate of young. Repetitive calling also occurs in fledglings and in some mammals where offspring follow provisioners. It is not yet clear whether, in mobile systems with dispersed young where adults cannot compare the vocal behaviour of all young simultaneously, the calls represent a signal of need. We investigated repetitive begging by cooperatively reared meerkat, Suricata suricatta, pups that foraged with the group. Pups produced two types of begging calls: repeat calls over long periods and high-pitched calls mainly confined to feeding events. Food-deprived pups stayed closer to feeders, and begged for longer and more intensely by calling at a higher rate. Hungry pups increased both the rate of repeat calls, which were given continually, and the number of high-pitched bouts, but adults increased their food allocation only in relation to the rate of repeat calls. Our study indicates that hunger may lead to several changes in vocal behaviour, only some of which may be used by adults to assess need.

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.

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.