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Urban land cover classification has always been crucial due to its ability to link many elements of human and physical environments. Timely, accurate, and detailed knowledge of the urban land cover information derived from remote sensing data is increasingly required among a wide variety of communities. This surge of interest has been predominately driven by the recent innovations in data, technologies, and theories in urban remote sensing. The development of light detection and ranging (LiDAR) systems, especially incorporated with high-resolution camera component, has shown great potential for urban classification. However, the performance of traditional and widely used classification methods is limited in this context, due to image interpretation complexity. On the other hand, random forests (RF), a newly developed machine learning algorithm, is receiving considerable attention in the field of image classification and pattern recognition. Several studies have shown the advantages of RF in land cover classification. However, few have focused on urban areas by fusion of LiDAR data and aerial images. The performance of the RF based feature selection and classification methods for urban areas was explored and compared to other popular feature selection approach and classifiers. Evaluation was based on several criteria: classification accuracy, impact of different training sample size, and computational speed. LiDAR data and aerial imagery with 0.5-m resolution were used to classify four land categories in the study area located in the City of Niagara Falls (ON, Canada). The results clearly demonstrate that the use of RF improved the classification performance in terms of accuracy and speed. Support vector machines (SVM) based and RF based classifiers showed similar accuracies. However, RF based classifiers were much quicker than SVM based methods. Based on the results from this work, it can be concluded that the RF based method holds great potential for recent and future urban land cover classification problem with LiDAR data and aerial images.

Wireless communication systems, aside from path-loss, also suffer from small scale up-and- down variations in the power of the received signal. These fluctuations in the received signal power, commonly referred to as multi-path fading, result in a significant perfor- mance degradation of the system. One way to combat fading is diversity. The idea behind diversity is to provide the receiver with multiple independent copies of the transmitted signal, either in time, frequency or space dimension. In broadcast networks with underlying slow-faded channels, an appropriate option for exploiting diversity is transmit diversity, which deploys several antennas in the transmitter terminal. Based on the amount of available channel state information on the transmitter side, various transmit diversity schemes have been proposed in the literature. Because of certain limitations of broadcast networks, a practical assumption in these networks is to provide no channel state information for the transmitter. In this dissertation, a new scheme is proposed to exploit transmit diversity for broad- cast networks, assuming no channel state information in the transmitter. The main idea of our proposed method is to virtually impose time variations to the underlying slow-faded channels by multiplying quantized pseudo-random phases to data symbols before trans- mission. Using this method, all necessary signal processing can be transferred to the RF front-end of the transmitter, and therefore, the implementation cost is much less than that of alternative approaches. Under the proposed method, the outage probability of the system is analyzed and the corresponding achievable diversity order is calculated. Simulation results show that the performance of our proposed scheme falls slightly below that of the optimum (Alamouti type) approach in the low outage probability region.

Given p independent, symmetric random walks on d-dimensional integer lattice that are the domain of attraction for a stable distribution, we calculate the moderate deviation of the intersection of ranges of the random walks in the case where the walks intersect infinitely often as time goes to infinity. That is to say, we establish a weak law convergence of intersection of ranges to intersection local time of stable processes and use this convergence as a link to establish deviation results.

This study concludes two parts. In the first part, we establish the model of the interaction between two cell populations following the concept of the random-walk, and assume the cell movement is constrained by space limitation primarily. In the other part, the interaction model is deduced from the concept of the flux motion, and the movement is constrained by space limitation, too. Furthermore, we analyze two models to obtain the behavior of two cell populations as time is close to the initial state and far into the future. / This study concludes two parts. In the first part, we establish the model of the interaction between two cell populations following the concept of the random-walk, and assume the cell movement is constrained by space limitation primarily. In the other part, the interaction model is deduced from the concept of the flux motion, and the movement is constrained by space limitation, too. Furthermore, we analyze two models to obtain the behavior of two cell populations as time is close to the initial state and far into the future.

This article examines methods to efficiently estimate the mean response in a linear model with an unknown error distribution under the assumption that the responses are missing at random. We show how the asymptotic variance is affected by the estimator of the regression parameter and by the imputation method. To estimate the regression parameter the Ordinary Least Squares method is efficient only if the error distribution happens to be normal. If the errors are not normal, then we propose a One Step Improvement estimator or a Maximum Empirical Likelihood estimator to estimate the parameter efficiently. In order to investigate the impact that imputation has on estimation of the mean response, we compare the Listwise Deletion method and the Propensity Score method (which do not use imputation at all), and two imputation methods. We show that Listwise Deletion and the Propensity Score method are inefficient. Partial Imputation, where only the missing responses are imputed, is compared to Full Imputation, where both missing and non-missing responses are imputed. Our results show that in general Full Imputation is better than Partial Imputation. However, when the regression parameter is estimated very poorly, then Partial Imputation will outperform Full Imputation. The efficient estimator for the mean response is the Full Imputation estimator that uses an efficient estimator of the parameter.

School-based problem-solving consultation is an indirect problem-solving process where the consultant works directly with the teacher in order to solve a current work problem of the teacher. The focus of school-based problem-solving consultation was to remediate a current difficult; however, during school-based problem-solving consultation, the teacher developed coping skills that improved his/her ability to handle future problems. Although the subject of several previous syntheses of the literature attesting to its promise, the current state of school-based problem consultation effectiveness was not known. This study sought to update the school-based problem-solving consultation effectiveness literature as measured by conducting a meta-analysis spanning the years 1986 to 2009. A secondary goal was to identify variables that functioned as moderators. Following procedures advocated by Lipsey and Wilson in 2001, 19 studies were identified producing 205 effect sizes. However, these effect sizes were not calculated independently. Instead, the effect sizes from each study were averaged in order to form a mean effect size per study. The mean effects were then averaged to form the omnibus mean effect size. The omnibus mean effect size from the 19 studies was g = 0.42, with a range of -0.01 to 1.52 demonstrating a medium-sized effect. This effect size was more modest in magnitude when compared to the previous school-based problem-solving consultation meta-analyses; however, the results indicated that school-based problem-solving consultation positively impacted client-level outcomes. With the exception of grade level, moderator analyses produced little information in terms of statistical differences between and among categories for “teacher type of class, consultant type, school type, referral source, referral reason, consultation model, comparison group, intervention type, design quality, outcome measured, and data type. For grade level, students in the “Other/Not Specified” category benefited most from school-based problem-solving consultation when compared to the “Elementary (K-6)” category. In addition to examining the omnibus mean effect size and potential moderators, limitations and implications for practice and future research were discussed.

Thesis (Ph.D.)--University of Hawaii at Manoa, 1994. / Includes bibliographical references (leaves 212-216). / Microfiche. / xiii, 216 leaves, bound ill. 29 cm

Wireless communication systems, aside from path-loss, also suffer from small scale up-and- down variations in the power of the received signal. These fluctuations in the received signal power, commonly referred to as multi-path fading, result in a significant perfor- mance degradation of the system. One way to combat fading is diversity. The idea behind diversity is to provide the receiver with multiple independent copies of the transmitted signal, either in time, frequency or space dimension. In broadcast networks with underlying slow-faded channels, an appropriate option for exploiting diversity is transmit diversity, which deploys several antennas in the transmitter terminal. Based on the amount of available channel state information on the transmitter side, various transmit diversity schemes have been proposed in the literature. Because of certain limitations of broadcast networks, a practical assumption in these networks is to provide no channel state information for the transmitter. In this dissertation, a new scheme is proposed to exploit transmit diversity for broad- cast networks, assuming no channel state information in the transmitter. The main idea of our proposed method is to virtually impose time variations to the underlying slow-faded channels by multiplying quantized pseudo-random phases to data symbols before trans- mission. Using this method, all necessary signal processing can be transferred to the RF front-end of the transmitter, and therefore, the implementation cost is much less than that of alternative approaches. Under the proposed method, the outage probability of the system is analyzed and the corresponding achievable diversity order is calculated. Simulation results show that the performance of our proposed scheme falls slightly below that of the optimum (Alamouti type) approach in the low outage probability region.