title: Ko tōku ingoa ko Ripeka - My name is Ripeka

authors: Waitoki, Waikaremoana

subjects: Wahine

abstract: This book is organised around Ripeka and her family's story. The events described in her story reflect the lives of several wāhine, tāne, rangatahi and tamariki who have been involved in psychological services.

text: Ko tōku ingoa ko Ripeka - My name is Ripeka This book is organised around Ripeka and her family's story. The events described in her story reflect the lives of several wāhine, tāne, rangatahi and tamariki who have been involved in psychological services.

year: 2016

title: Lake Rotokawau: Water quality and sediment study

authors: Pearson, Lisa Kyle

abstract: Lake Rotokawau is a small (0.52 km2), deep (74 m), oligotrophic lake in the Rotorua Lakes District, located approximately 4.1 km east of Lake Rotorua. The lake is privately owned and managed by the local iwi, Ngati Rangiteaorere. During a site visit carried out on the 25 July 2010, surface and bottom water samples and a sediment core were collected, together with a water column profile (CTD) of temperature, dissolved oxygen, fluorescence, conductivity and depth. The CTD cast showed an anoxic hypolimnion and that the lake likely had not mixed during at the time of sampling, in mid-winter. This observation is highly unusual amongst the Te Arawa lakes and suggests that the lake may remain stratified for multiple years. The lack of mixing could be due to several factors including: presence of higher-salinity bottom waters, high water depth to area ratio, and sheltered aspect. The sediment core collected in the central basin revealed the presence of Ruapehu ash (1995-6 eruption) preserved in the upper 5 cm of the sediment record and the Tarawera Tephra (1886 eruption) located at 13 cm below the sediment water interface. The Ruapehu Ash layer is not preserved in sediment from any other Rotorua lakes and has likely been preserved due to the lack of seasonal mixing. The sediment and pore water chemistry are similar to that of the other lakes in the Rotorua region but however multiple sediment cores are necessary to fully assess variability across the whole lake area.
It is recommended that a routine monitoring programme be established so that any improvement or degradation in water quality can be detected and the mixing frequency of the lake determined. The water quality monitoring should include water column profiles of temperature, dissolved oxygen, fluorescence, conductivity and depth, nutrient analysis (ammonium, nitrate, nitrite, phosphate, silicon, total nitrogen and total phosphorus) of surface and bottom waters and water clarity readings (e.g. Secchi disk and light attenuation). Our observations point to the highly unusual nature of Lake Rotokawau in terms of mixing status and indicate a strong need to preserve significant biological and landscape features of this lake which has not been subject to problems with invasive species that characterise several other lakes in this region.

text: Lake Rotokawau: Water quality and sediment study Lake Rotokawau is a small (0.52 km2), deep (74 m), oligotrophic lake in the Rotorua Lakes District, located approximately 4.1 km east of Lake Rotorua. The lake is privately owned and managed by the local iwi, Ngati Rangiteaorere. During a site visit carried out on the 25 July 2010, surface and bottom water samples and a sediment core were collected, together with a water column profile (CTD) of temperature, dissolved oxygen, fluorescence, conductivity and depth. The CTD cast showed an anoxic hypolimnion and that the lake likely had not mixed during at the time of sampling, in mid-winter. This observation is highly unusual amongst the Te Arawa lakes and suggests that the lake may remain stratified for multiple years. The lack of mixing could be due to several factors including: presence of higher-salinity bottom waters, high water depth to area ratio, and sheltered aspect. The sediment core collected in the central basin revealed the presence of Ruapehu ash (1995-6 eruption) preserved in the upper 5 cm of the sediment record and the Tarawera Tephra (1886 eruption) located at 13 cm below the sediment water interface. The Ruapehu Ash layer is not preserved in sediment from any other Rotorua lakes and has likely been preserved due to the lack of seasonal mixing. The sediment and pore water chemistry are similar to that of the other lakes in the Rotorua region but however multiple sediment cores are necessary to fully assess variability across the whole lake area.
It is recommended that a routine monitoring programme be established so that any improvement or degradation in water quality can be detected and the mixing frequency of the lake determined. The water quality monitoring should include water column profiles of temperature, dissolved oxygen, fluorescence, conductivity and depth, nutrient analysis (ammonium, nitrate, nitrite, phosphate, silicon, total nitrogen and total phosphorus) of surface and bottom waters and water clarity readings (e.g. Secchi disk and light attenuation). Our observations point to the highly unusual nature of Lake Rotokawau in terms of mixing status and indicate a strong need to preserve significant biological and landscape features of this lake which has not been subject to problems with invasive species that characterise several other lakes in this region.

year: 2011-07

title: Improving Bags-of-Words model for object categorization

authors: Zhang, Edmond Yiwen

abstract: In the past decade, Bags-of-Words (BOW) models have become popular for the task of object recognition, owing to their good performance and simplicity. Some of the most effective recent methods for computer-based object recognition work by detecting and extracting local image features, before quantizing them according to a codebook rule such as k-means clustering, and classifying these with conventional classifiers such as Support Vector Machines and Naive Bayes.

In this thesis, a Spatial Object Recognition Framework is presented that consists of the four main contributions of the research. 

The first contribution, frequent keypoint pattern discovery, works by combining pairs and triplets of frequent keypoints in order to discover intermediate representations for object classes. Based on the same frequent keypoints principle, algorithms for locating the region-of-interest in training images is then discussed. 
Extensions to the successful Spatial Pyramid Matching scheme, in order to better capture spatial relationships, are then proposed. The pairs frequency histogram and shapes frequency histogram work by capturing more redefined spatial information between local image features.
Finally, alternative techniques to Spatial Pyramid Matching for capturing spatial information are presented. The proposed techniques, variations of binned log-polar histograms, divides the image into grids of different scale and different orientation. Thus captures the distribution of image features both in distance and orientation explicitly.

Evaluations on the framework are focused on several recent and popular datasets, including image retrieval, object recognition, and object categorization. Overall, while the effectiveness of the framework is limited in some of the datasets, the proposed contributions are nevertheless powerful improvements of the BOW model.

text: Improving Bags-of-Words model for object categorization In the past decade, Bags-of-Words (BOW) models have become popular for the task of object recognition, owing to their good performance and simplicity. Some of the most effective recent methods for computer-based object recognition work by detecting and extracting local image features, before quantizing them according to a codebook rule such as k-means clustering, and classifying these with conventional classifiers such as Support Vector Machines and Naive Bayes.

In this thesis, a Spatial Object Recognition Framework is presented that consists of the four main contributions of the research. 

The first contribution, frequent keypoint pattern discovery, works by combining pairs and triplets of frequent keypoints in order to discover intermediate representations for object classes. Based on the same frequent keypoints principle, algorithms for locating the region-of-interest in training images is then discussed. 
Extensions to the successful Spatial Pyramid Matching scheme, in order to better capture spatial relationships, are then proposed. The pairs frequency histogram and shapes frequency histogram work by capturing more redefined spatial information between local image features.
Finally, alternative techniques to Spatial Pyramid Matching for capturing spatial information are presented. The proposed techniques, variations of binned log-polar histograms, divides the image into grids of different scale and different orientation. Thus captures the distribution of image features both in distance and orientation explicitly.

Evaluations on the framework are focused on several recent and popular datasets, including image retrieval, object recognition, and object categorization. Overall, while the effectiveness of the framework is limited in some of the datasets, the proposed contributions are nevertheless powerful improvements of the BOW model.

year: 2013