title: An Algorithm for the explicit evaluation of GL(n, R) Kolsterman sums

authors: Broughan, Kevin A.

subjects: Kloosterman sum

abstract: An algorithm for the explicit evaluation of Kloosterman sums for GL(n, R) for n ≥2 and an implementation in the Mathematics package GL(n) pack are described

text: An Algorithm for the explicit evaluation of GL(n, R) Kolsterman sums An algorithm for the explicit evaluation of Kloosterman sums for GL(n, R) for n ≥2 and an implementation in the Mathematics package GL(n) pack are described

year: 2009

title: Studies of the coefficient of variation of the magnitude of EEG signals

authors: Round, W. Howell

subjects: EEG

abstract: An analysis of the variation in magnitude of EEG signals in various frequency bands of anesthetized patients and normal sleeping volunteers was carried out. The coefficient of variation (CoV), i.e. the standard deviation/mean, within 10 second epochs was found to be quite constant throughout the whole of the EEG recordings and was typically about 0.46. This was found to be the case for both the patients and the volunteers. Histograms of the magnitudes indicated that the magnitudes are distributed as f(x)=βxe(-αx2) functions. However a CoV of 0.46 is consistent with f(x)=βxe(-αx3) functions. The non-stationary nature of the EEG is such that it is likely that while over short periods the EEG magnitudes are distributed as f(x)=βxe(-αx3) functions, variations of α over time mean that in the long term the EEG magnitudes are distributed as f(x)=βxe(-αx2) functions.

text: Studies of the coefficient of variation of the magnitude of EEG signals An analysis of the variation in magnitude of EEG signals in various frequency bands of anesthetized patients and normal sleeping volunteers was carried out. The coefficient of variation (CoV), i.e. the standard deviation/mean, within 10 second epochs was found to be quite constant throughout the whole of the EEG recordings and was typically about 0.46. This was found to be the case for both the patients and the volunteers. Histograms of the magnitudes indicated that the magnitudes are distributed as f(x)=βxe(-αx2) functions. However a CoV of 0.46 is consistent with f(x)=βxe(-αx3) functions. The non-stationary nature of the EEG is such that it is likely that while over short periods the EEG magnitudes are distributed as f(x)=βxe(-αx3) functions, variations of α over time mean that in the long term the EEG magnitudes are distributed as f(x)=βxe(-αx2) functions.

year: 2001

title: An AR memory app based on Māori mnemonic aids

authors: Taia, Ivy

subjects: computer science

abstract: Traumatic Brain Injury (TBI) is a world-wide “silent epidemic” causing a variety of memory issues for those affected. Although several software approaches aim to aid memory recollection, they are inadequate for many TBI survivors and none address aspects of cultural awareness in New Zealand. We explored design concepts of traditional Māori cultural tools / techniques and how they helped Māori people recall past information. This paper describes our research and development of Maumahara Papahou, a mobile app that uses Augmented Reality features to create a digital memory treasure box based on the concepts of Māori mnemonic aids.

text: An AR memory app based on Māori mnemonic aids Traumatic Brain Injury (TBI) is a world-wide “silent epidemic” causing a variety of memory issues for those affected. Although several software approaches aim to aid memory recollection, they are inadequate for many TBI survivors and none address aspects of cultural awareness in New Zealand. We explored design concepts of traditional Māori cultural tools / techniques and how they helped Māori people recall past information. This paper describes our research and development of Maumahara Papahou, a mobile app that uses Augmented Reality features to create a digital memory treasure box based on the concepts of Māori mnemonic aids.

year: 2018