The focus of this research is concerned with performing forecasting in technoeconomic contexts using a set of certain novel artificial neural networks (ANNs). Relevant efforts in general, entail the task of quantitatively estimating the details about the likelihood of future events (or unknown outcomes/effects) based on past and current information on the observed events (or known causes). Commensurate with the scope and objectives of the research, the specific topics addressed are as follows: A review on various methods adopted in technoeconomic forecasting and identified are econometric projections that can be used for forecasting via artificial neural network (ANN)-based simulations Developing and testing a compatible version of ANN designed to support a dynamic sigmoidal (squashing) function that morphs to the stochastical trends of the ANN input. As such, the network architecture gets pruned for reduced complexity across the span of iterative training schedule leading to the realization of a constructive artificial neural-network (CANN). Formulating a training schedule on an ANN with sparsely-sampled data via sparsity removal with cardinality enhancement procedure (through Nyquist sampling) and invoking statistical bootstrapping technique of resampling applied on the cardinality-improved subset so as to obtain an enhanced number of pseudoreplicates required as an adequate ensemble for robust training of the test ANN: The training and prediction exercises on the test ANN corresponds to optimally elucidating output predictions in the context of the technoeconomics framework of the power generation considered Prescribing a cone-of-error to alleviate over- or under-predictions toward prudently interpreting the results obtained; and, squeezing the cone-of-error to get a final cone-of-forecast rendering the forecast estimation/inference to be more precise Designing an ANN-based fuzzy inference engine (FIE) to ascertain the ex ante forecast details based on sparse sets of ex post data gathered in technoeconomic contexts - Involved thereof a novel method of .fusing fuzzy considerations and data sparsity.Lastly, summarizing the results with essential conclusions and identifying possible research items for future efforts identified as open-questions. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

An ocean turbine extarcts the kinetic energy from ocean currents to generate electricity. Machine Condition Monitoring (MCM) / Prognostic Health Monitoring (PHM) systems allow for self-checking and automated fault detection, and are integral in the construction of a highly reliable ocean turbine. MCM/PHM systems enable real time health assessment, prognostics and advisory generation by interpreting data from sensors installed on the machine being monitored. To effectively utilize sensor readings for determining the health of individual components, macro-components and the overall system, these measurements must somehow be combined or integrated to form a holistic picture. The process used to perform this combination is called data fusion. Data mining and machine learning techniques allow for the analysis of these sensor signals, any maintenance history and other available information (like expert knowledge) to automate decision making and other such processes within MCM/PHM systems. ... This dissertation proposes an MCM/PHM software architecture employing those techniques which were determined from the experiments to be ideal for this application. Our work also offers a data fusion framework applicable to ocean machinery MCM/PHM. Finally, it presents a software tool for monitoring ocean turbines and other submerged vessels, implemented according to industry standards. / by Janell A. Duhaney. / Thesis (Ph.D.)--Florida Atlantic University, 2012. / Includes bibliography. / Mode of access: World Wide Web. / System requirements: Adobe Reader.

Zhou, Shengli. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 133-140). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.

Zhang, Hankui. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 99-118). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese.

土地利用/覆盖变化是地球上最重要的景观之一，同全球环境变化高度相关。通过对全球变化的整体模拟以及综合评价研究，可以了解全球气候变化运行机制以及人地关系。同时，全球尺度的土地利用/覆盖变化及其驱动机制研究，将揭示人类在全球气候变化机制中所起的作用，使人类更好地适应全球环境的变化。目前全球尺度的土地利用/覆盖研究大多是基于现有的五种欧洲或美国开发的全球地表覆盖产品，这些产品在一定程度上满足了全球变化研究的基本要求。但是，仍然存在一些不足之处，如统一的分类系统，精度低，产品之间的不一致以及低时效性等，使得这些产品并不适合全球环境变化的对比研究，也不能满足建立更高的精度和更可靠的全球气候变化模型的要求。因此，开发高分辨率，实时的地表覆盖产品，已成为当前全球变化研究的紧迫需要。 / 目前，遥感影像已广泛被用于制作全球地表覆盖产品，但由于传感器的技术要求和资金预算的限制，影像的空间和时间分辨率不能满足更高精度和可靠的全球变化研究需要。鉴于此，迫切需要我们研究和开发更加先进的卫星影像处理方法和地表覆盖产品的生产技术，为全球变化研究提供高精度和高可靠性的地表覆盖产品。 / 因此，为了提供更多的时间和更高空间分辨率的卫星影像以及地表覆盖产品，以更好地开展全球变化研究。本文主要从技术层面上，研究利用多源遥感影像的优点，生成高分辨率和多时相的卫星合成影像，并在此基础上发展了卫星数据融合理论和方法。本文研究中，传统的光谱空间数据融合理论将被回顾和充分讨论，考虑到卫星影像的多时相特征，传统的数据融合理论在时间维度得到扩展，本文将提出新的时空数据融合方法，并应用于植被监测和土地利用制图。 / 通过对融合理论及相关方法的系统学习，本文对各种融合方法进行了系统的回顾与总结，比如基于HIS变换图像融合方法 ，基于小波变换的图像融合方法，时空自适应反射融合模型（STARFM）等，并从遥感应用的角度，提出各种方法的优缺点。结合本文的研究目标，以下为本论文的主要研究内容。 / (1)数据融合相关理论将得到系统的研究和总结，包括各种融合模型及其应用，如基于IHS变换，PCA变换，或者小波分析的数据融合方法，等等。同时，结合具体应用归纳并总结了这些方法的优缺点。 / (2)由于传统数据融合方法依赖于空间及光谱信息，很难处理多源影像数据所蕴含的时空变化信息。因此，本文中，传统数据融合理论和方法在考虑到时间信息后得到改善和扩展。本文通过结合高空间分辨率Landsat数据和高时间分辨率MODIS数据为例，提出两种不同的时空数据融合方法。实验结果也表明，他们适合于处理多时空数据集成, 并能够满足全球变化研究对高质量数据的需要。 / (3)时空数据融合建模中的主要问题有两个，第一个问题是不同数据源之间具有不一致性，如不同卫星数据具有不同的地表反射率以及不同的数据可靠性。第二个是地表覆盖的季节性或者土地利用变化规则在空间和时间的维度具有不确定性，尤其是在复杂地区。考虑这些问题，本文在基于时间和空间自适应反射融合模型（STARFM）的基础上，提出一种新的改进模型，结果表明，它将比原有模型更为有效和更为准确的生成高分辨率合成影像数据。 / (4)混合像元问题是处理卫星数据中的一个常见问题。对于多源卫星数据来说，一个低分辨率图像像素区域将包含多个高分辨率图像像素。因此，不同数据源所获得的遥感数据将会因为混合像元问题从而影响到地表反射率数据在空间尺度上的差异，并影响到最终的融合精度。为了解决时空多源数据融合中的混合像元问题，本文将提出一种改进的基于附加条件的混合像元解缠的时空数据融合方法，实验结果表明它是适合植被监测应用，特别是具有先验土地覆盖图的地区。 / (5)在时空数据融合方法产生的一系列高分辨率合成影像的基础上，时空马尔可夫随机场分类方法被提出并用于研制生产高分辨率土地覆盖产品，该方法利用影像的时空上下文信息。这种方法提供了新的策略去制作土地覆盖产品 ，在缺乏高分辨率影像的地区。实验结果表明，它的精度是可以接受的，可以为缺乏高分辨率数据地区提供高品质的土地覆盖产品。 / Land use/cover change is one of the most important landscapes on the earth and it is highly related to global environmental change, based on which an overall simulation and comprehensive evaluation of global change research can be achieved for understanding the global change mechanism and the linkages between the human and natural environments. Moreover, study of global-scale land use/cover change and its driving mechanism will reveal the human role in global change mechanisms and processes for human adaptation to global environmental change. Most of the current global-scale land use/cover research is based on the existing five land cover products that have been developed by Europe and the US, and these indeed meet the basic requirements for the global change research to some extent. However, certain shortcomings still exist, such as their unified classification system, low accuracy, poor inconsistency, weak timeliness, etc., so, it is impossible to take the comparative global environmental change research as a basis for building more highly accurate and more reliable global change models, and it is urgent and necessary to develop a high-resolution, and up-to-date land cover product for global change research. / Currently, remote sensing imagery has been widely used for generating global land cover products, but due to certain physical and budget limitations related to the sensors, their spatial and temporal resolution are too low to attain more accurate and more reliable global change research. In this situation, there is an urgent need to study and develop a more advanced satellite image processing method and land cover producing techniques to generate higher resolution images and land cover products for global change research. / Accordingly, in order to provide more multi-temporal, high-resolution images and land cover products for global change research, this research mainly focuses on the technical level, of using both advantages of satellite images from different sources to generate high-resolution, multi-temporal images and develop satellite data fusion theory and methods. In this research, the traditional data fusion theory will be fully discussed and an improved scheme will be produced, taking into consideration the temporal information from satellite images at different times. Consequently, the spatial and temporal data fusion method will be proposed and applied to the monitoring of vegetation growth and land cover mapping. / Through conducting a comprehensive study of the related theories and methods related to data fusion, various methods are systematically reviewed and summarized, such as HIS transformation image fusion, Wavelet transform image fusion, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), etc. The advantages and disadvantages of these methods are highlighted according to their specific applications in the field of remote sensing. Based on my research target, the following are the main contents of this thesis: / (1) Data fusion theory will be systematically studied and summarized, including various fusion models and specific applications, such as IHS transformation, PCA transformation, Wavelet analysis based data fusion, etc. Furthermore, their advantages and disadvantages are pointed out in relation to specific applications. / (2) As traditional data fusion methods rely on spatial information and it is hard to deal with multi-source data fusion with temporal variation, therefore, the traditional data fusion theory and methods will be improved by a consideration of temporal information. Accordingly, some spatial and temporal data fusion methods will be proposed, in which both high-resolution & low-temporary imagery and low-resolution & high-temporary imagery are incorporated. Our experiments also show that they are suitable for dealing with multi-temporal data integration and generating high-resolution, multi-temporal images for global change research. / (3) There are two main issues related to spatial and temporal data fusion theory. The first is that there are inconsistencies in different images, such as the different levels of land surface reflectance and different degrees of reliability of multi-source satellite data. The second is the rule of phonological variation/land cover variation in both the spatial and temporal dimensions, particularly in areas with heterogeneous landscapes. When considering these issues, an improved STARFM (spatial and temporal adaptive reflectance fusion model) is proposed, based on the original model, and the preliminary results show that it is more efficient and accurate in generating high-resolution land surface imagery than its predecessor. / (4) Mixed pixels is a common issue in relation to satellite data processing, as one pixel in a coarse resolution image will constitute several pixels in a high-resolution image of the same size, so different levels of land surface reflectance will be acquired from multi-source satellite data because of the mixed pixel effect on the coarse resolution data, and the final accuracy of the fused result will be affected if these data are subjected to data fusion. In order to solve the mixed pixel issue in multi-source data fusion, an improved spatial and temporal data fusion approach, based on the constraint unmixing technique, was developed in this thesis. The experimental results show that it is well-suited to the phenological monitoring task when a prior land cover map is available. / (5) Based on the high-resolution reflectance images generated from spatial and temporal fusion, a spatial and temporal classification method based on the spatial and temporal Markov random field was developed to produce a high-resolution land cover product, in which both spatial and temporal contextual information are included within the classification scheme. This method provides a new strategy for generating high-resolution land cover products in the area without high-resolution images at a certain time, and the experimental results show that it is acceptable and suitable for generating high quality land cover products in areas for which there is a lack of high-resolution data. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Xu, Yong. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 151-158). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / ABSTRACT --- p.II / Acknowledgement --- p.VII / Contents --- p.VIII / List of Figures --- p.X / List of Tables --- p.XII / Abbreviations --- p.XIV / Chapter CHAPTER 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Research objectives and significance --- p.5 / Chapter 1.3 --- Research issues --- p.11 / Chapter 1.4 --- Research framework and methodology --- p.13 / Chapter 1.5 --- Organization of thesis --- p.16 / Chapter CHAPTER 2 --- Review of the Existing Image Fusion Methods --- p.19 / Chapter 2.1 --- Overview --- p.19 / Chapter 2.2 --- The multi-source image fusion method --- p.24 / Chapter 2.3 --- The multi-temporal, multi-source image fusion method --- p.29 / Chapter 2.4 --- Details of STARFM --- p.35 / Chapter 2.5 --- Accuracy of the assessment of the image fusion method --- p.41 / Chapter 2.6 --- Summary and discussion --- p.44 / Chapter CHAPTER 3 --- An Improved Spatial and Temporal Adaptive Reflectance Data Fusion Model --- p.47 / Chapter 3.1 --- Introduction --- p.48 / Chapter 3.2 --- Theoretical basis of the spatial and temporal reflectance data fusion model --- p.49 / Chapter 3.3 --- An improved spatial and temporal reflectance data fusion model --- p.57 / Chapter 3.4 --- Experiments with simulated data --- p.60 / Chapter 3.5 --- Experiments with actual data from the BOREAS and PANYU study areas --- p.67 / Chapter 3.6 --- Summary and discussion --- p.76 / Chapter CHAPTER 4 --- Spatial and Temporal Data Fusion Method Using the Constrained Unmixing Approach --- p.78 / Chapter 4.1 --- Introduction --- p.78 / Chapter 4.2 --- Methodology --- p.80 / Chapter 4.3 --- Experiments with simulated data --- p.86 / Chapter 4.4 --- Experiments with actual data --- p.90 / Chapter 4.5 --- Applications for NDVI and Land Surface Reflectance Monitoring --- p.96 / Chapter 4.6 --- Summary and conclusions --- p.105 / Chapter CHAPTER 5 --- Spatial and Temporal Classification of Synthetic Satellite Imagery: Land Cover Mapping and Accuracy Validation --- p.107 / Chapter 5.1 --- Introduction --- p.107 / Chapter 5.2 --- Study sites and data sources --- p.109 / Chapter 5.3 --- Methodology --- p.113 / Chapter 5.4 --- Synthetic Data Generation at the HARV and PANYU Study Areas --- p.119 / Chapter 5.5 --- Land Cover Mapping with Synthetic Data --- p.133 / Chapter 5.6 --- Summary and discussion --- p.142 / Chapter CHAPTER 6 --- Summary and Conclusions --- p.144 / Chapter 6.1 --- Summary --- p.144 / Chapter 6.2 --- Contributions --- p.147 / Chapter 6.3 --- Recommendations for further research --- p.149 / REFERENCES --- p.151

Robot localization is one of the most important subjects in the Robotics science. It is an interesting and complicated topic. There are many algorithms to solve the problem of localization. Each localization system has its own set of features, and based on them, a solution will be chosen. In my thesis, I want to present a solution to find the best estimate for a robot position in certain space for which a map is available. The thesis started with an elementary introduction to the probability and the Gaussian theories. Simple and advanced practical examples are presented to illustrate each concept related to localization. Extended Kalman Filter is chosen to be the main algorithm to find the best estimate of the robot position. It was presented through two chapters with many examples. All these examples were simulated in Matlab in this thesis in order to give the readers and future students a clear and complete introduction to Kalman Filter. Fortunately, I applied this algorithm on a robot that I have built its base from scratch. MCECS-Bot was a project started in Winter 2012 and it was assigned to me from my adviser, Dr. Marek Perkowski. This robot consists of the base with four Mecanum wheels, the waist based on four linear actuators, an arm, neck and head. The base is equipped with many sensors, which are bumper switches, encoders, sonars, LRF and Kinect. Additional devices can provide extra information as backup sensors, which are a tablet and a camera. The ultimate goal of this thesis is to have the MCECS-Bot as an open source system accessed by many future classes, capstone projects and graduate thesis students for education purposes. A well-known MRPT software system was used to present the results of the Extended Kalman Filter (EKF). These results are simply the robot positions estimated by EKF. They are demonstrated on the base floor of the FAB building of PSU. In parallel, simulated results to all different solutions derived in this thesis are presented using Matlab. A future students will have a ready platform and a good start to continue developing this system.

This thesis presents the unified bias analysis of subspace-based DOA estimation algorithms in terms of physical parameters such as source separation, signal coherence, number of senors and snapshots. The analysis reveals the direct relationship between the performance of the DOA algorithms and signal measurement conditions. Insights into different algorithms are provided. Based upon previous first-order subspace perturbations, second-order subspace perturbations are developed which provide basis for bias analysis and unification. Simulations verifying the theoretical bias analysis are presented.

Bibliography: p. 231-232. / xvi, 232 p. : ill. ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / The aim of this thesis is detecting and tracking objects at large ranges, when no target features are visible, with imaging type sensors. A system which estimates the optical flow of the scene in a parallel architecture is developed. The architecture is similar to that of an artifical neural network. / Thesis (Ph.D.)--University of Adelaide, Dept. of Electrical and Electronic Engineering, 1994

The probability hypothesis density (PHD), popularized by Ronald Mahler, presents a novel and theoretically-rigorous approach to multitarget, multisensor tracking. Based on random set theory, the PHD is the first moment of a point process of a random track set, and it can be propagated by Bayesian prediction and observation equations to form a multitarget, multisensor tracking filter. The advantage of the PHD filter lies in its ability to estimate automatically the expected number of targets present, to fuse easily different kinds of data observations, and to locate targets without performing any explicit report-to-track association. We apply a particle-filter implementation of the PHD filter to realistic multitarget, multisensor tracking using passive coherent location (PCL) systems that exploit illuminators of opportunity such as FM radio stations. The objective of this dissertation is to enhance the usefulness of the PHD particle filter for multitarget, multisensor tracking, in general, and within the context of PCL, in particular. This involves a number of thrusts, including: 1) devising intelligent proposal densities for particle placement, 2) devising a peak-extraction algorithm for extracting information from the PHD, 3) incorporating realistic probabilities of detection and signal-to-noise ratios (including multipath effects) to model realistic PCL scenarios, 4) using range, Doppler, and direction of arrival (DOA) observations to test the target detection and data fusion capabilities of the PHD filter, and 5) clarifying the concepts behind FISST and the PHD to make them more accessible to the practicing engineer. A goal of this dissertation is to serve as a tutorial for anyone interested in becoming familiar with the probability hypothesis density and associated PHD particle filter. It is hoped that, after reading this thesis, the reader will have gained a clearer understanding of the PHD and the functionality and effectiveness of the PHD particle filter.

In this study, the performances of the optical sensors and instruments carried on both ground-based and airborne platforms were evaluated for monitoring crop growing status, detecting the vegetation response to aerial applied herbicides, and identifying crop nitrogen status. Geostatistical analysis on remotely sensed data was conducted to investigate spatial structure of crop canopy normalized difference vegetation index and multispectral imagery. A computerized crop monitoring system was developed that combined sensors and instruments that measured crop structure and spectral data with a global positioning system. The integrated crop monitoring system was able to collect real-time, multi-source, multi-form, and crop related data simultaneously as the tractor-mounted system moved through the field. This study firstly used remotely sensed data to evaluate glyphosate efficacy on weeds applied with conventional and emerging aerial spray nozzles. A weedy field was In this study, the performances of the optical sensors and instruments carried on both ground-based and airborne platforms were evaluated for monitoring crop growing status, detecting the vegetation response to aerial applied herbicides, and identifying crop nitrogen status. Geostatistical analysis on remotely sensed data was conducted to investigate spatial structure of crop canopy normalized difference vegetation index and multispectral imagery. A computerized crop monitoring system was developed that combined sensors and instruments that measured crop structure and spectral data with a global positioning system. The integrated crop monitoring system was able to collect real-time, multi-source, multi-form, and crop related data simultaneously as the tractor-mounted system moved through the field. This study firstly used remotely sensed data to evaluate glyphosate efficacy on weeds applied with conventional and emerging aerial spray nozzles. A weedy field was set up in three blocks and four aerial spray technology treatments were tested. Spectral reflectance measurements were taken using ground-based sensors from all the plots at 1, 8, and 17 days after treatment. The results indicated that the differences among the treatments could be detected with spectral data. This study could provide applicators with guidance equipment configurations that can result in herbicide savings and optimized applications in other crops. The main focus of this research was to apply sensor fusion technology to ground-based and airborne imagery data. Experimental plots cropped with cotton and soybean plants were set up with different nitrogen application rates. The multispectral imagery was acquired by an airborne imaging system over crop field; at the same period, leaf chlorophyll content and spectral reflectance measurements were gathered with chlorophyll meter and spectroradiometer at canopy level on the ground, respectively. Statistical analyses were applied on the data from individual sensor for discrimination with respect to the nitrogen treatment levels. Multisensor data fusion was performed at data level. The results showed that the data fusion of airborne imagery with ground-based data were capable of improving the performance of remote sensing data on detection of crop nitrogen status. The method may be extended to other types of data, and data fusion can be performed at feature or decision level.