title: The LIDS Research Project: appendage to usability study report (1/2002)

authors: Thomson, Kirsten

subjects: computer science

abstract: This report is a follow on to an earlier report (titled:  Usability Study Report (1/2002), dated 1 July 2002) that presented the University of Waikato Usability Laboratory’s (Usability Laboratory) analysis of the Large Interactive Display Screen (LIDS) technologies as developed by the LIDS Research Project.

text: The LIDS Research Project: appendage to usability study report (1/2002) This report is a follow on to an earlier report (titled:  Usability Study Report (1/2002), dated 1 July 2002) that presented the University of Waikato Usability Laboratory’s (Usability Laboratory) analysis of the Large Interactive Display Screen (LIDS) technologies as developed by the LIDS Research Project.

year: 2002-07

title: Augmenting NIR spectra in deep regression to improve calibration

authors: Wohlers, Mark W.

subjects: computer science

abstract: Deep learning, particularly with convolutional neural networks, shows promise in modelling near-infrared spectroscopy (NIRS), but the lack of robust generalisation across instruments often affects performance in practice. Here, we investigate a method to increase the robustness of this approach. The proposed method involves using a simple data augmentation technique during the training process. The performance of convolutional neural network regression is compared to partial least squares regression (PLSR) using kiwifruit data collected from multiple handheld devices over three seasons and mango data collected from a single device over four seasons. The results suggest that data augmentation for NIR spectra can prevent overfitting. In particular, augmenting the training data to mimic spectra collected over multiple devices results in a neural network model with improved performance over PLSR.

text: Augmenting NIR spectra in deep regression to improve calibration Deep learning, particularly with convolutional neural networks, shows promise in modelling near-infrared spectroscopy (NIRS), but the lack of robust generalisation across instruments often affects performance in practice. Here, we investigate a method to increase the robustness of this approach. The proposed method involves using a simple data augmentation technique during the training process. The performance of convolutional neural network regression is compared to partial least squares regression (PLSR) using kiwifruit data collected from multiple handheld devices over three seasons and mango data collected from a single device over four seasons. The results suggest that data augmentation for NIR spectra can prevent overfitting. In particular, augmenting the training data to mimic spectra collected over multiple devices results in a neural network model with improved performance over PLSR.

year: 2023

title: Maori perspectives of the environment: A review of spatial databases

authors: Whangapirita, Laura

subjects: Maori

abstract: As part of our broader brief to gather, summarise, analyse and distribute information held by Environment Waikato on Maori and the environment, we identified and analysed a number of spatial datasets managed by the Information Resource Group. This report has been prepared to contribute towards developing a resource fro Environment Waikato’s Strategic Plan Review Teams.

text: Maori perspectives of the environment: A review of spatial databases As part of our broader brief to gather, summarise, analyse and distribute information held by Environment Waikato on Maori and the environment, we identified and analysed a number of spatial datasets managed by the Information Resource Group. This report has been prepared to contribute towards developing a resource fro Environment Waikato’s Strategic Plan Review Teams.

year: 2003-11