The first chapter of this thesis reviews applications of satellite remote sensing and geographical information systems (GIS) in wildlife studies. The simpler uses of remote sensing are for habitat mapping, often using satellite imagery classified for other natural resources. More sophisticated applications incorporate remotely sensed data into a GIS for the digital manipulation of data planes. The most advanced applications are those which use remote sensing and GIS in models predicting habitat quality or population levels. The second chapter reports how brightness values of six Landsat Thematic Mapper (TM) bands were used in multiple linear regressions to predict percent cover of six rangeland components. Regression equations were applied to TM imagery to create cover maps for live shrub, dead and live shrub, sagebrush, forb/grass, forb, and bare ground/rock. Accuracy was assessed at two levels and ranged from 55 to 90%. The third chapter presents results of sage grouse surveys used with satellite data and GIS to assess habitat use patterns. Habitats used by grouse were compared to availability in the landscape for continuous images of rangeland cover variables, for discrete images of rangeland classes, and for habitat diversity values. Overall, results were comparable to those in studies using traditional methods.

The present research is based on the theory that there are two major modes of consciousness, verbal and imaginal, which are controlled by the left and right hemispheres, respectively. This project was concerned with the relationship between these modes of consciousness and the measures employed in this study. The measures employed were an ongoing mentation report (OMR), a paired-associate learning (PAL) task, the Betts vividness of imagery questionnaire, the Gordon test of imagery control, and the visual imagery scale of Imaginal Processes Inventory (IPI). EEG and EOG measures were recorded during the OMR, and lateral eye movement was taken as an indication of activation in the cerebral hemisphere contralateral to the direction of eye movement.

Indiana University-Purdue University Indianapolis (IUPUI) / Floods are one of the worst disasters in the United States. Each year, the government allocates a tremendous amount of manpower and money on flood prevention initiatives. As the first defense line, levees provide protection from temporary flooding (Makhdoom, 2013). These embankments are broadly classified according to the areas they protect, which could either be urban or agricultural levees within floodplains. In the U.S., most of the levees are handled by government agencies such as the U.S. Army Corps of Engineers and the Federal Emergency Management Services. On the other hand, non-levee embankments created by individual farmers (Olson & Morton, 2013) or naturally formed levee-like structures may not be in the government database. The initial purpose of this research was to assist Polis center on the “Mapping of Non-Levee Embankments in the Indiana” project. The non-levee embankments are not certified or engineered levee-like structures. They, therefore, impose lateral constraints on flood flows, reducing the floodplain storage capacity and increasing the flood velocity. These non-levee embankments can cause stream erosion and downstream flooding. Therefore, it is important to know the locations of these features. The first part of the proposed method adapted the Empirical Bayesian theorem and the low pass filter techniques to extract elevated linear features from LiDAR elevation data – Digital Elevation Model (DEM). The second part of the proposed methods combined the Minimum Bounding Circle (MBC) method and the Youden Index to locate the optimal threshold value that can be used to determine whether the extracted features are levee-like structures. The focus of this study is not only limited to artificial levee-like structures, but also takes the natural levees, or any potential levee-like structures into account because this study assumes all embankments play important roles during flood events.

京都大学 / 0048 / 新制・課程博士 / 博士(農学) / 甲第19771号 / 農博第2167号 / 新制||農||1040(附属図書館) / 学位論文||H28||N4987(農学部図書室) / 32807 / 京都大学大学院農学研究科地域環境科学専攻 / (主査)教授 飯田 訓久, 教授 近藤 直, 准教授 中村 公人 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

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Buildings are essential parts to human life, which provide the place to dwell, educate, entertain, etc. However, they are usually vulnerable to earthquakes, and collapsed buildings are the main factor of fatalities and directly impact livelihoods. It is particularly important to quickly and accurately obtain damaged building conditions for further planning rescue. Remote sensing has the ability to quickly capture the information of damaged buildings in a large area, and remote sensing imagery has been used by government organizations, international agencies, and insurance industries for assessing post-event damage. The application of deep learning is encouraged by recent technological developments, enabling the processing of increasing amounts of data in a reasonable time as well as the use of more complex models. In this thesis, deep learning is explored for identifying collapsed buildings using very high-resolution remote sensing imagery after the 2010 Haiti earthquake. In the present study, a simple architecture of convolutional neural network (CNN) model was proposed to evaluate the potential of CNN for extracting features and detecting collapsed buildings using only post-event very high-resolution remote sensing imagery. Three balancing methods were considered to reduce the effect of the imbalance problem for the performance of the CNN, and the results showed that a suitable balancing method should be considered when facing imbalance dataset to retrieve the distribution of collapsed buildings. To improve the classification accuracy, pre- and post-event very high-resolution remote sensing imagery were considered, and a conventional classification method was combined with the CNN. Compared to conventional texture features, deep features learnt from CNNs had better performance for identifying collapsed buildings, and the accuracy was further improved by combing CNN features with random forest classifier. For the limited dataset, a pretrained CNN model was applied to detect collapsed buildings, and the effect of data augmentation was also investigated. The experimental results demonstrated that the pretrained CNN model outperformed the model trained from scratch for identifying collapsed buildings.

Gaining knowledge about methods of memory enhancement is important because it allows us to develop effective strategies and interventions to increase memory performance, optimize cognitive function, and potentially alleviate memory-related challenges such as age-related cognitive decline or memory disorders. This thesis aims to examine the influence of age on self-reported mental imagery (MI) experiences and investigate the potential effects of age and MI on memory training performance using the Method of Loci (MoL). The thesis utilizes data collected from a mobile application from a previous research project conducted at Umeå University. The main findings of this thesis indicate that older adults spent more time on encoding and retrieval tasks, suggesting a decline in cognitive processing speed. However, there was no significant decline in MI ability with age, challenging previous research and suggesting that MI ability may remain relatively stable throughout the lifespan. The findings also suggest that there was no significant correlation between higher MI ability and performance in MoL training. Participants rating themselves as having weaker MI were often achieving high levels in the MoL-training, indicating the potential utilization of alternative cognitive strategies than MI. Spending more time on encoding and retrieval tasks was associated with better performance in MoL training. The findings suggest that age-related declines in processing speed may not necessarily hinder individuals' ability to utilize MoL effectively. However, the findings challenge the idea that MI is such a decisive factor in MoL training and raise questions about which other mechanisms work together for a successful result.

With the development of small satellites it has become easier and cheaper to deploy satellites for earth observation from space. While optical sensors capture high-resolution data, this data is traditionally sent to earth for analysis which puts a high constraint on the data link and increases the time for making data based decisions. This thesis explores the possibilities of deploying an AI model in small satellites for detecting objects in satellite imagery and therefore reduce the amount of data that needs to be transmitted. The neural network model YOLOv8 was trained on the xView and DIOR dataset and evaluated in a hardware restricted execution environment. The model achieved a mAP50 of 0.66 and could process satellite images at a speed of 309m2/s.

Bakgrund: Subacromiellt smärtsyndrom och rotatorcuffrelaterade besvär behandlas vanligtvis med rehabilitering i from av fysisk träning. Forskning visar mer och mer på positiv effekt av motorsimulering (MI) vid både träning och rehabilitering i from av bland annat ökad styrka och rörlighet. Området är dock inte tillräckligt utforskat och det saknas forskning på just SAPS och rotatorcuffproblematik.Syfte: Syftet med studien var att undersöka om ett tillägg av mental träning genom MI vid rehabilitering av SAPS eller rotatorcuffrelaterade besvär kan bidra till ytterligare effekt i form av minskad smärta och förbättrad upplevd funktionsnivå.Metod: Studien utfördes som en experimentell studie med interventionsgrupp (n=10) som utförde fysisk rehabilitering och MI samt referensgrupp (n=11) som utförde fysisk rehabilitering under 8 veckor. Upplevd funktion och smärta undersöktes med WORC och NRS vid rehabiliteringsperiodens start, efter 4 veckor och efter 8 veckor. Data analyserades med Mann-Whitney U test och ANOVA repeated measures. WORC analyserades i sin helhet samt dess delområden och smärtan utifrån NRS och WORC:s två frågor om smärtaResultat: Det noterades inga signifikanta skillnader mellan grupperna vid någon tidpunkt. För interventionsgruppen förelåg en signifikant förbättring på WORC:s totala poäng samt delområdena Fysisk, Arbete och Livsstil samt för smärtskattning VAS skarp smärta. För referensgruppen förelåg en signifikant förbättring för WORC:s delområden Fysiska, Arbete och Känslor samt NRS och smärtskattning VAS skarp smärta.Slutsats: Resultatet visar tendenser till att MI kan ha effekt som komplement till fysisk rehabilitering vid SAPS eller rotatorcuffsrelaterade besvär vad gäller upplevd funktion men inte för smärtminskning. Detta innebär att MI skulle kunna vara ett alternativ vid behandling av dessa besvär. Denna studie genomfördes som en pilotstudie med ett relativt lågt deltagarantal vilket gör att resultaten bör tolkas med försiktighet.