Droughts, exacerbated by anthropogenic climate change, threaten plants through hydraulic failure. This hydraulic failure is caused by the formation of embolisms which block water flow in a plant's xylem conduits. By tracking these failures over time, vulnerability curves (VCs) can be created. The creation of these curves is laborious and time consuming. This study seeks to automate the creation of these curves. In particular, it seeks to automate the optical vulnerability (OV) method of determining hydraulic failure. To do this, embolisms need to be segmented across a sequence of images. Three fully convolutional models were considered for this task, namely U-Net, U-Net (ResNet34), and W-Net. The sample consisted of four unique leaves, each with its own sequence of images. Using these leaves, three experiments were conducted. They considered whether a leaf could generalise across samples from the same leaf, across different leaves of the same species, and across different species. The results were assessed on two levels; the first considered the results of the segmentation, and the second considered how well VCs could be constructed. Across the three experiments, the highest test precision-recall AUCs achieved were 81%, 45%, and 40%. W-Net performed the worst across the models, while U-Net and U-Net (ResNet-34) performed similarly to one another. VC reconstruction was assessed using two metrics. The first is Normalised Root Mean Square Error. The second is the difference in Ψ50 values between the true VC and the predicted VC, where Ψ50 is a physiological value of interest. This study found that the shape of the VCs could be reconstructed well if the model was able to recall a portion of embolisms across all images which had embolisms. Moreover, it found that some images may be more important than others due to a non-linear mapping between time and water potential. VC reconstruction was satisfactory, except for the third experiment. This study demonstrates that, in certain scenarios, automation of the OV method is attainable. To support the ubiquitous use and development of the work done in this study, a website was created to document the code base. In addition, this website contains instructions on how to interact with the code base. For more information please visit: https://plant-network-segmentation.readthedocs.io/.

Policy makers and the government rely heavily on survey data when making policyrelated decisions. Survey data is labour intensive, costly and time consuming, hence it cannot be frequently or extensively collected. The main aim of this research is to demonstrate how Convolutional Neural Network (CNN) coupled with statistical regression modelling can be used to estimate poverty from aerial images supplemented with national household survey data. This provides a more frequent and automated method for updating data that can be used for policy making. This aerial poverty estimation approach is executed in two phases; aerial classification and detection phase and poverty modelling phase. The aerial classification and detection phase use CNN to perform settlement typology classification of the aerial images into three broad geotype classes namely; urban, rural and farm. This is then followed by object detection to detect three broad dwelling type classes in the aerial images namely; brick house, traditional house, and informal settlement. Mask Region-based Convolutional Neural Network (Mask R-CNN) model with a resnet101 CNN backbone model is used to perform this task. The second phase, poverty modelling phase, involves using NIDS data to compute the poverty measure Sen-Shorrocks-Thon (SST) index. This is followed by using regression models to model the poverty measure using aggregated results from the aerial classification and detection phase. The study area for this research is Kwa-Zulu Natal (KZN), South Africa. However, this approach can be extended to other provinces in South Africa, by retraining the models on data associated with the location in question.

The presence of non-linear shape variation in 3D data is known to influence the reliability of linear statistical shape models (SSM). This problem is regularly acknowledged, but disregarded, as it is assumed that linear models are able to adequately approximate such non-linearities. Model reliability is crucial for medical imaging and computer vision tasks; however, prior to modelling, the non-linearity in the data is not often considered. The study provides a framework to identify the presence of non-linearity in using principal component analysis (PCA) and autoencoders (AE) shape modelling methods. The data identified to have linear and non-linear shape variations is used to compare two sophisticated techniques: linear Gaussian process morphable models (GPMM) and non-linear variational autoencoders (VAE). Their model performance is measured using generalisation, specificity and computational efficiency in training. The research showed that, given limited computational power, GPMMs managed to achieve improved relative generalisation performance compared to VAEs, in the presence of non-linear shape variation by at least a factor of six. However, the non-linear VAEs, despite the simplistic training scheme, presented improved specificity generative performance of at least 18% for both datasets.

International Telemetering Conference Proceedings / October 26-29, 1992 / Town and Country Hotel and Convention Center, San Diego, California / An distributed architecture for high-speed digital and analog instrumentation is discussed. This architecture supports the collection, formatting, recording of both conventional telemetry (analog & PCM) and high-speed digital data. Remotely located instrumentation data interface units provide data acquision close to the data sources. The remotely located units are connected via high-bandwidth fiber optic links to a central formatting and recording unit. Data is recorded on digital rotary head recorders. Graphic workstations provide visual data displays for test control and monitoring. This system was developed to handle the high-speed data acquision requirements of advanced avionics sensor and seeker systems, however, it provides the basis for many other applications.

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The Study of Microstructure Analysis of Pb/Sn And Sn/Ag/Cu Solder Ball in BGA Package.

The effect of employee retention to corporate, it can be easily reflected by lower the rate of turnover. The higher turnover rate will cause the damage of corporate, so how to retain employee properly becomes the important topic for corporate. Some employees will choose to leave the damage company but some will choose to stay. The study is focus on the employees of damage company, according to the promotion and advanced study opportunity divided into four types: Own promotion & Own advanced study opportunity, No promotion & No advanced study opportunity, Own promotion & No advanced study opportunity, No promotion & Own advanced study opportunity. By using the interview method to discuss the main retention reasons of employees. The result finds out the prefer ranking of the four types are:Own promotion & No advanced study , No promotion & No advanced study ,Own promotion & Own advanced study , No promotion & Own advanced study. The two types within the four categories are needed to deeply discuss, the reason conclude as below: 1.Own promotion & No advanced study:(1)Has good communication with supervisor (2)Excellent of work performance (3)Already owned high education background (ex. Master). 2.No promotion & Own advanced study:(1)The major has no relation with work (2) No promotion vacancy (3)Bad communication with supervisor (4)Worse of work performance (5)No convinced from colleagues.

Univ., Diss.--Bochum, 2006. / Enth. Zsfassung in engl. und dt. Sprache.