BENEFITS OF NEAR-TERM CLOUD LOCATION FORECASTING FOR LARGE SOLAR PV

As the ‘green’ energy movement continues to gain momentum, photovoltaic generation is becoming an increasingly popular source for new power generation. The primary focus of this paper is to demonstrate the benefits of close-to real-time cloud sensing for Photovoltaic generation. In order to benefit from this close-to real-time data, a source of cloud cover information is necessary. This paper looks into the potential of point insolation sensors to determine overhead cloud coverage. A look into design considerations and economic challenges of implementing such a monitoring system is included. The benefits of cloud location sensing are examined using computer simulations to target important time-scales and options available to plant operators. Finally, the economics of advanced forecasting options will be examined in order to determine the benefit to plant operators

Pollution tracker: finding industrial sources of aerosol emission in satellite imagery

The effects of anthropogenic aerosol, solid or liquid particles suspended in the air, are the biggest contributor to uncertainty in current climate perturbations. Heavy industry sites, such as coal power plants and steel manufacturers, emit large amounts of aerosol in a small area. This makes them ideal places to study aerosol interactions with radiation and clouds. However, existing data sets of heavy industry locations are either not public, or suffer from reporting gaps. Here, we develop a deep learning algorithm to detect unreported industry sites in high-resolution satellite data. For the pipeline to be viable at global scale, we employ a two-step approach. The first step uses 10 m resolution data, which is scanned for potential industry sites, before using 1.2 m resolution images to confirm or reject detections. On held out test data, the models perform well, with the lower resolution one reaching up to 94% accuracy. Deployed to a large test region, the first stage model yields many false positive detections. The second stage, higher resolution model shows promising results at filtering these out, while keeping the true positives. In the deployment area, we find five new heavy industry sites which were not in the training data. This demonstrates that the approach can be used to complement data sets of heavy industry sites

Sequential land cover classification

Land cover classification using remotely sensed data is a critical first step in large-scale environmental monitoring, resource management and regional planning. The classification task is made difficult by severe atmospheric scattering and absorption, seasonal variation, spatial dependence, complex surface dynamics and geometries, and large intra-class variability. Most of the recent research effort in land cover classification has gone into the development of increasingly robust and accurate (and also increasingly complex) classifiers by constructing–often in an ad hoc manner–multispectral, multitemporal, multisource classifiers using modern machine learning techniques such as artificial neural networks, fuzzy-sets, and expert systems. However, the focus has always been (almost exclusively) on increasing the classification accuracy of newly developed classifiers. We would of course like to perform land cover classification (i) as accurately as possible, but also (ii) as quickly as possible. Unfortunately there exists a tradeoff between these two requirements, since the faster we must make a decision, the lower we expect our classification accuracy to be, and conversely, a higher classification accuracy typically requires that we observe more samples (i.e., we must wait longer for a decision). Sequential analysis provides an attractive (indeed an optimal) solution to handling this tradeoff between the classification accuracy and the detection delay–and it is the aim of this study to apply sequential analysis to the land cover classification task. Furthermore, this study deals exclusively with the binary classification of coarse resolution MODIS time series data in the Gauteng region in South Africa, and more specifically, the task of discriminating between residential areas and vegetation is considered.Dissertation (MEng)--University of Pretoria, 2011.Electrical, Electronic and Computer Engineeringunrestricte

Improving estimates and change detection of forest above-ground biomass using statistical methods

Forests store approximately as much carbon as is in the atmosphere, with potential to take in or release carbon rapidly based on growth, climate change and human disturbance. Above-ground biomass (AGB) is the largest carbon pool in most forest systems, and the quickest to change following disturbance. Quantifying AGB on a global scale and being able to reliably map how it is changing, is therefore required for tackling climate change by targeting and monitoring policies. AGB can be mapped using remote sensing and machine learning methods, but such maps have high uncertainties, and simply subtracting one from another does not give a reliable indication of changes. To improve the quantification of AGB changes it is necessary to add advanced statistical methodology to existing machine learning and remote sensing methods. This review discusses the areas in which techniques used in statistical research could positively impact AGB quantification. Nine global or continental AGB maps, and a further eight local AGB maps, were investigated in detail to understand the limitations of techniques currently used. It was found that both modelling and validation of maps lacked spatial consideration. Spatial cross validation or other sampling methods, which specifically account for the spatial nature of this data, are important to introduce into AGB map validation. Modelling techniques which capture the spatial nature should also be used. For example, spatial random effects can be included in various forms of hierarchical statistical models. These can be estimated using frequentist or Bayesian inference. Strategies including hierarchical modelling, Bayesian inference, and simulation methods can also be applied to improve uncertainty estimation. Additionally, if these uncertainties are visualised using pixelation or contour maps this could improve interpretation. Improved uncertainty, which is commonly between 30% and 40%, is in addition needed to produce accurate change maps which will benefit policy decisions, policy implementation, and our understanding of the carbon cycle

Development of gas detection systems based on microstructured optical fibres

Estágio realizado no INESC e orientado pelo Prof. Doutor Luís Alberto de Almeida FerreiraTese de mestrado integrado. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 200

Novel channel sensing and access strategies in opportunistic spectrum access networks

textTraditionally radio spectrum was considered a commodity to be allocated in a fixed and centralized manner, but now the technical community and the regulators approach it as a shared resource that can be flexibly and intelligently shared between competing entities. In this thesis we focus on novel strategies to sense and access the radio spectrum within the framework of Opportunistic Spectrum Access via Cognitive Radio Networks (CRNs). In the first part we develop novel transmit opportunity detection methods that effectively exploit the gray space present in packet based networks. Our methods proactively detect the maximum safe transmit power that does not significantly affect the primary network nodes via an implicit feedback mechanism from the Primary network to the Secondary network. A novel use of packet interarrival duration is developed to robustly perform change detection in the primary network's Quality of Service. The methods are validated on real world IEEE 802.11 WLANs. In the second part we study the inferential use of Goodness-of-Fit tests for spectrum sensing applications. We provide the first comprehensive framework for decision fusion of an ensemble of goodness-of-fit tests through use of p-values. Also, we introduce a generalized Phi-divergence statistic to formulate goodness-of-fit tests that are tunable via a single parameter. We show that under uncertainty in the noise statistics or non-Gaussianity in the noise, the performance of such non-parametric tests is significantly superior to that of conventional spectrum sensing methods. Additionally, we describe a collaborative spatially separated version of the test for robust combining of tests in a distributed spectrum sensing setting. In the third part we develop the sequential energy detection problem for spectrum sensing and formulate a novel Sequential Energy Detector. Through extensive simulations we demonstrate that our doubly hierarchical sequential testing architecture delivers a significant throughput improvement of 2 to 6 times over the fixed sample size test while maintaining equivalent operating characteristics as measured by the Probabilities of Detection and False Alarm. We also demonstrate the throughput gains for a case study of sensing ATSC television signals in IEEE 802.22 systems.Electrical and Computer Engineerin

Crop Phenotyping of Sorghum bicolors Physiological Response to Salt-Affected Soils Using TLS and GPR Remote Sensing Technologies in Nevada Drylands

Saline and sodic soils are major abiotic stressors on the production of flood-irrigated crops in drylands. We conducted a crop phenotyping, remote sensing study on five genotypes of sorghum [Sorghum bicolor (L.) Moench], a drought and salt-tolerant crop, to assist in the molecular breeding of salt-tolerant cultivars. A control plot and a spatially heterogeneous saline-sodic plot (treatment plot) were established in collaboration with Dr. Yerka, Mr. Alfredo Delgado, Dr. Washington-Allen, the Nevada Agricultural Experiment Station (NAES) and the United States Department of Agriculture’s Plant Materials Center (USDA-PMC) in Fallon, Nevada. This location is representative of the variable salinity/sodicity conditions typical of Northern Nevada soils and associated belowground biomass dynamics in drylands. We generated pre- and post-harvest soil attribute maps of the treatment plot using spatial interpolation, we expected individual genotypes to be affected differently by the gradient of various soil constituents. We hypothesized that above- and belowground three-dimensional structural phenology of the five genotypes would be differently affected across the salinity gradient in the treatment plot relative to the control plot. Additionally, we hypothesized that the GPR signal return would vary with the salinity gradient. Finally, we expected an increase in belowground biomass, relative to the control plot, in response to salt-stress as an adaptation to drought. The phenology of coarse-root depth and three-dimensional structure from pre-planting to harvest was non-invasively measured 15 times using a real-time kinematic (RTK) GPS-mounted IDS GeoRadar dual channel (400MHz and 900MHz) ground penetrating radar (GPR) system. Plant height and three-dimensional structural phenology of the five varieties were mapped using a FARO Focus3D X 330 terrestrial laser scanner (TLS). We found differences in above- and belowground three-dimensional structural phenology across the five genotypes in response to the salinity and sodicity gradient. Of the five genotypes in this study, only four emerged in the treatment plot, where Richardson Seed’s Ultra-Early Hybrid performed best under the gradient of salinity and sodicity with the highest rate of emergence (68%), the highest rate of panicle production (4.1 panicles per row), and the greatest panicle volume (67.2%) relative to the control plot. Furthermore, we found that the GPR return signal was not able to detect root mass in the highly saline-sodic soil, however, I was able to detect root mass phenology in the control plot. GPR return signal was not linear in response to the salinity gradient, however, a signal pattern emerged from the different salinity ranges suggesting a gradient response. This study shows the efficacy of the use of these technologies in crop phenotyping and precision agriculture. Future work may improve TLS derived data processing efficiency by developing methods for automating the detection of phenotypic traits (e.g., panicles, leaf area index, number of individual plants). These methods likely will include machine learning algorithms, allometric equations for biomass calculations, and use of drone-mounted LiDAR to reduce occlusion. The use of GPR in the salt-affected soils of this study was not able to definitively identify root mass, however, its use in soil composition for salts and other constituents is indeed promising. Further testing of GPR’s non-detect threshold in salt-affected soils and its ability to quantify individual soil constituents has potential to be highly valuable to the field of soil science and precision agriculture. Furthermore, this study was able to detect a root mass response using GPR, future work may focus on differentiating genotypic variation in root phenology

Control of transport dynamics in overlay networks

Transport control is an important factor in the performance of Internet protocols, particularly in the next generation network applications involving computational steering, interactive visualization, instrument control, and transfer of large data sets. The widely deployed Transport Control Protocol is inadequate for these tasks due to its performance drawbacks. The purpose of this dissertation is to conduct a rigorous analytical study on the design and performance of transport protocols, and systematically develop a new class of protocols to overcome the limitations of current methods. Various sources of randomness exist in network performance measurements due to the stochastic nature of network traffic. We propose a new class of transport protocols that explicitly accounts for the randomness based on dynamic stochastic approximation methods. These protocols use congestion window and idle time to dynamically control the source rate to achieve transport objectives. We conduct statistical analyses to determine the main effects of these two control parameters and their interaction effects. The application of stochastic approximation methods enables us to show the analytical stability of the transport protocols and avoid pre-selecting the flow and congestion control parameters. These new protocols are successfully applied to transport control for both goodput stabilization and maximization. The experimental results show the superior performance compared to current methods particularly for Internet applications. To effectively deploy these protocols over the Internet, we develop an overlay network, which resides at the application level to provide data transmission service using User Datagram Protocol. The overlay network, together with the new protocols based on User Datagram Protocol, provides an effective environment for implementing transport control using application-level modules. We also study problems in overlay networks such as path bandwidth estimation and multiple quickest path computation. In wireless networks, most packet losses are caused by physical signal losses and do not necessarily indicate network congestion. Furthermore, the physical link connectivity in ad-hoc networks deployed in unstructured areas is unpredictable. We develop the Connectivity-Through-Time protocols that exploit the node movements to deliver data under dynamic connectivity. We integrate this protocol into overlay networks and present experimental results using network to support a team of mobile robots