Application of Image inpainting ranges from object removal, photo restoration, scratch removal, and so on. In this thesis, we will propose a modified multi-scale method and learning-based method using artificial neural networks for image inpainting. Multi-scale inpainting method combines image segmentation, contour estimation, and exemplar-based inpainting. The main goal of image segmentation is to separate image to several homogeneous regions outside the target region. After image segmentation, we use contour estimation to estimate curves inside the target region to partition the whole image into several different regions. Then we fill those different regions inside the target region separately by exemplar-based inpainting method. The exemplar-based technique fills the target region via the texture synthesis and filling order of exemplary patches. Exemplary patches are found near target region and the filling order is determined by isophote and densities of exemplary patches. Learning-based inpainting is a novel technique. This technique combines machine learning and the concept of filling order. We use artificial neural networks to learn the structure and texture surrounding the target region. After training, we fill the target region according to the filling order. From our simulation results, very good results can be obtained for removing large-size objects by using the proposed multi-scale method, and for removing medium-size objects of gray images.

An automated signal classifier and a semi-automated signal identifier are designed for collecting the dielectrophoretic signatures of cells flowing through a dielectrophoretic cytometer. In past work, the DEP cytometer signals were manually sorted by going through all recorded signals, which is impractical when analyzing 1000’s of cells per day. In the semi-automated method of collection, signals are automatically identified as events and displayed on the user interface to be accepted or rejected by the user. This approach reduced signal collection time by more than half and produced statistics nearly identical to the manual method. The automated signal classifier based on pattern recognition categorizes detection signals as ‘Accept’ or ‘Reject’. Analyzing large volumes of detection signals is possible in much reduced times and may be approaching real time capability.

An automated signal classifier and a semi-automated signal identifier are designed for collecting the dielectrophoretic signatures of cells flowing through a dielectrophoretic cytometer. In past work, the DEP cytometer signals were manually sorted by going through all recorded signals, which is impractical when analyzing 1000’s of cells per day. In the semi-automated method of collection, signals are automatically identified as events and displayed on the user interface to be accepted or rejected by the user. This approach reduced signal collection time by more than half and produced statistics nearly identical to the manual method. The automated signal classifier based on pattern recognition categorizes detection signals as ‘Accept’ or ‘Reject’. Analyzing large volumes of detection signals is possible in much reduced times and may be approaching real time capability.

Master of Science / Department of Geology / Matthew W. Totten / The Meramec member of Mississippian age is a proficient oil and gas producing formation within the midcontinent region of the United States. It is produced in Kansas, Oklahoma, and Texas. In Kansas, 12% of the state’s petroleum production comes from Mississippian-aged rocks. Bindley Field, located in central west Kansas, has produced 3,669,283 barrels of oil from one facies within the M2 interval of the Meramec formation. This facies is a grain-supported echinoderm/bryozoan dolostone, of variable thickness. Its sporadic occurrence in the subsurface has made exploring Bindley Field and the surrounding area difficult. The challenge in finding oil in this area is in locating a producible zone of this productive facies. Previously, Bindley Field has been the subject of detailed reservoir characterization studies (Ebanks et al., 1977; Johnson, 1990; Johnson, 1994). These studies helped to contribute to a better understanding of Meramecian stratigraphy in Kansas. The Meramec was divided into four major depositional sequences, with some of those sequences nonexistent in the subsurface, due to aerial exposure and erosion post-deposition. The Meramecian units were further separated into parasequence-scale chronostratigraphic units based on marine flooding events. The primary producing interval in Bindley Field is the Meramec 2 interval which consists of seven lithotypes, and is recognized to have six, meter-scale depositional cycles (Johnson, 1990). As production from this interval increased, more information became available about controls on reservoir quality. There are still areas, however, where core data do not exist, and predicting the productive facies remains challenging. The aim of this study is to create a workflow for evaluating the subsurface using regional core and log data from Bindley Field to create a model of the subsurface distribution of the reservoir facies, which could be extended to data poor areas. Geophysical logs (neutron, gamma ray, guard) along with an artificial neural network (ANN), was used to create an accurate prediction of producing intervals within the subsurface. Values are derived from wire line log data and used to develop the ANN definition of facies distribution within Bindley Field. The ANN model was examined for accuracy and precision using core description and well cuttings from wells within Bindley Field and the surrounding area. Correlations were found between the subsurface geometry of the study area, and the production of oil and gas within the study area. An ANN model with an accuracy of 72% was achieved and applied to wells surrounding the Bindley Field, where reservoir intervals have not been as extensively studied. A total of 87 wells in Bindley Field and the surrounding 50 square mile area where applied to the ANN model. The model predicted that the productive facies thickens gradually to the northwest of Bindley Field. Cross sections as well as an isopach map were created using the prediction data from the ANN. Finally, an analysis for the accuracy of the ANN and the predicted facies was created. The productive facies yielded an accuracy value of 77%.

No description available.

Many optical processing systems rely critically on the availability of high performance, electrically-addressed spatial light modulators. Ferroelectric liquid crystal over silicon is an attractive spatial light modulator technology because it combines two well matched technologies. Ferroelectric liquid crystal modulating materials exhibit fast switching times with low operating voltages, while very large scale silicon integrated circuits offer high-frequency, low power operation, and versatile functionality. This thesis describes the design and characterisation of the SBS256 - a general purpose 256 x 256 pixel ferroelectric liquid crystal over silicon spatial light modulator that incorporates a static-RAM latch and an exclusive-OR gate at each pixel. The static-RAM latch provides robust data storage under high read-beam intensities, while the exclusive-OR gate permits the liquid crystal layer to be fully and efficiently charge balanced. The SBS256 spatial light modulator operates in a binary mode. However, many applications, including helmet-mounted displays and optoelectronic implementations of artificial neural networks, require devices with some level of grey-scale capability. The 2 kHz frame rate of the device, permits temporal multiplexing to be used as a means of generating discrete grey-scale in real-time. A second integrated circuit design is also presented. This prototype neuraldetector backplane consists of a 4 x 4 array of optical-in, electronic-out processing units. These can sample the temporally multiplexed grey-scale generated by the SBS256. The neurons implement the post-synaptic summing and thresholding function, and can respond to both positive and negative activations - a requirement of many artificial neural network models.

With increasing demands on limited water resources and unavailability of suitable dam sites, it is essential that available storage works be carefully planned and efficiently operated to meet the present and future water needs.This research report presents an attempt to: i) use Artificial Neural Networks (ANN) for the simulation of the Crocodile water resource system located in the Mpumalanga province of South Africa and ii) use the model to assess to what extent Kwena dam, the only major dam in the system could meet the required 0.9m3/s cross border flow to Mozambique. The modelling was confined to the low flow periods when the Kwena dam releases are significant. The form of ANN model developed in this study is the standard error backpropagation run on a daily time scale. It is comprised of 32 inputs being four irrigation abstractions at Montrose, Tenbosch, Riverside and Karino; current and average daily rainfall totals for the previous 4 days at the respective rainfall stations; average daily temperature at Karino and Nelspruit; daily releases from Kwena dam; daily streamflow from the tributaries of Kaap, Elands and Sand rivers and the previous day’s flow at Tenbosch. The single output was the current day’s flow at Tenbosch. To investigate the extent to which the 0.9m3/s flow requirement into Mozambique could be met, data from a representative dry year and four release scenarios were used. The scenarios assumed that Kwena dam was 100%, 75%, 50% and 25% full at the beginning of the year. It was found as expected that increasing Kwena releases improved the cross border flows but the improvement in providing the 0.9m3/s cross border flow was minimal. For the scenario when the dam is initially full, the requirement was met with an improvement of 11% over the observed flows.

Secret sharing is a fundamental notion for secure cryptographic design. In a secret sharing scheme, a set of participants shares a secret among them such that only pre-specified subsets of these shares can get together to recover the secret. This dissertation introduces a neural network approach to solve the problem of secret sharing for any given access structure. Other approaches have been used to solve this problem. However, the yet known approaches result in exponential increase in the amount of data that every participant need to keep. This amount is measured by the secret sharing scheme information rate. This work is intended to solve the problem with better information rate.

An electric power system consists of the generating stations, the transmission lines, and the distribution systems. Transmission lines are the connecting links between the generating stations and the distribution systems. With the rapid growth of economy and technology, the demand for large blocks of power, power quality and increased reliability suggested the interconnection of neighboring systems. Transmission lines are elements of a network which connects the generating plants to the distribution systems, and could extend hundreds of miles . Because of the long distances traversed by transmission lines over open area, they tend to fade by natural and artificial calamity imposed on the power system. It maybe easy to discover the fault with sufficient information in the populous region. When fault occurs in the remote region, it is difficult to identify the outage location. An efficient and reliable technique is thus desirable to resolve the problem. This dissertation presents the fault location for high voltage lines with Artificial Neural Network( ANN ) method. Beside the fault location, this research also improve the problem further by considering the fault resistance. The fault resistance may not remain the same due to the variation of environmental factors. The fault location may involve errors owing to the fault resistance. An algorithms has been developed in this dissertation to calculate fault resistance and revise the ANN training data for three-phase fault, double line-to-ground fault, single line-to-ground fault, and line-to-line fault. To verify the effectiveness of the method, practical transmission lines were used for tests. The results proved that the method could be used to identify the fault location effectively and help dispatchers determine a reference distance.

­^¤å´£­n¡G A utility function model of individual credit card holder based on their spending behavior is constructed in this research. An accumulation of the individual utility of three different risk attitudes of cardholders may be useful for promoting the profits of credit card business for banks. Due to the privacy of cardholders and the lack of real data, a questionnaire sampling is used to collect data for this study. A result of this experimental study indicates that credit card holders with a different sex, age, level of education, asset condition, seniority, and occupation have different risk tendency. Based on 249 effective samples in this research, credit card holders who belong to females, teenagers, relatively low educated, without real estate, middle seniority, and relatively volatile occupation are more risk seeking. Relatively risk seeking credit card holders have the tendency to make use of their revolving credit and to borrow cash or to buy financial products with their credit cards. For those with three different risk attitudes, their default of credit card loans are not significantly different. The finding indicates relatively risk seeking cardholders may contribute more profits to the credit card business for banks. A risk attitude classification model built by artificial neural network has also been developed. The model may assist banks' administrators using their applicants' demographics to distinguish their risk attitude for approving an appropriate credit limit for a cardholder's expenditure to promote the total credit card profit for banks.