Assessment of the environmental impacts of an agricultural production system requires information on both soil water quality and solute flux. Passive Capillary Samplers (PCAPS), which sample water from the vadose zone using fiber glass wicks, have shown potential to provide both flux and solute concentration in unsaturated zone sampling but have not been tested under long-term, natural, rainfall conditions. The objectives of this study are to (1) evaluate PCAPS operation under non-steady, natural rain and irrigation fed conditions, (2) determine the samplers ability to estimate recharge, and (3) estimate the loss of nutrients resulting from agricultural production. 32 PCAPS and 78 suction cup samplers were installed below the root zone at 16 commercial fields in Lane County, Oregon. PCAPS' were installed in positions using ground penetrating radar such that PCAPS' were placed in homogeneous or concave profile locations. Two PCAPS and six suction cups were installed at each site. Rain gages and TDR probes were installed at eight of the 16 sites. These data were used to develop a mass balance for each of the eight special study sites. Comparison to mass balance data indicates that the PCAPS flux measurements were within 10% of the mass balance estimated recharge. Surface runoff of potential drainage water during periods of high rainfall was a point of concern for estimated recharge discrepancies because runoff was not measured. The saturated hydraulic conductivity was shown to be the most influential design parameter for matching wick and soil types. On the other hand, the incident flux, rather than conductivity, determined the ultimate ground water recharge. PCAPS collection was found to be significantly correlated (average R��=0.75) to the mass balance monthly estimated recharge. To estimate the mean monthly recharge at each site with a 30% bound on the mean and 95% confidence level, 20 PCAPS would be required at each site. PCAPS were found to be superior to suction cup samplers for estimating ground water recharge concentrations because PCAPS were able to sample both flux and resident concentrations. Mint and row crop, organic and inorganic, production systems contributed to the largest adverse environmental impacts with average recharge concentrations for mint and row crop of 24 mg L����� and 28 mg L�����, respectively. Orchard and blueberry production systems had little impact with their seasonal concentrations averaging below the EPA water quality standard. Amounts of percolation were key in determining which management systems were inefficiently operated. Over-irrigation during the summer lead to increased losses of nitrogen for the mint production systems in the summer as well as the winter. Over-fertilization was important for creating significant differences in seasonal mass losses of nitrogen from row crop production systems. Overall, the PCAPS estimated nitrogen loss was 12% lower than that calculated using a simplified nitrogen mass balance approach. Best management practice suggestions concerning irrigation, fertilization and cover cropping were provided as a direct result of the findings of the project. With technical support and increase in concern over nitrate contamination, farmers should be able to control leaching losses without the use of quotas or allotments. / Graduation date: 1995

Many traffic accidents are caused by driver unawareness. This includes fatigue, drowsiness and distraction. In this thesis two systems are described that could be used to decrease the number of accidents. In the first part of this thesis a system using long-term information to warn drivers suffering from fatigue is developed. Three different versions with different criteria are evaluated. The systems are shown to handle more then 60% of the cases correctly. The second part of this thesis examines the possibilities of developing a warning system based on the predicted time-to-lane crossing, TLC. A basic TLC model is implemented and evaluated. For short time periods before lane crossing this may offer adequate accuracy. However the accuracy is not good enough for the model to be used in a TLC based warning system to warn the driver of imminent lane departure.

Esta dissertação descreve os métodos, medidas e análises feitas para otimizar a rede de comunicação de dados do Instituto de Física de São Carlos. As tecnologias e protocolos utilizados na rede são apresentados. Especial atenção é dada a análise do desempenho de VLANs utilizando inicialmente o protocolo LANE no núcleo ATM da rede. Neste caso a rede é composta de switches ATM e ATM-Ethernet. Medidas comparativas foram realizadas com a utilização da tecnologia Fast Ethernet no backbone, que possui uma capacidade de transmissão relativamente próxima da ATM de 155Mbps. Melhores resultados obtidos com a implementação de sub-redes maiores, reduzindo em um numero menor de sub-redes, são discutidos e apresentados. Análises estatísticas baseadas apenas no tempo de resposta da rede são apresentados para avaliar o desempenho das alterações efetuadas nas configurações da rede. Mesmo que o tráfego tenha aumentado muito durante esses anos e que vários serviços tenha sido agregados a esta rede, foi possível adequar o desempenho as novas necessidades beneficiando-se da evolução tecnológica que os equipamentos de rede de dados trouxeram ao IFSC. / This work describes the methods, measures and analyses performed aiming to optimize the data communication network from the Physics Institute of Sao Carlos as well as the technologies and protocols used in the network recently. Special care is given to the analysis of the VLANs performance using, initially, the LANE protocol over ATM which has its core based on pure ATM and ATM-Ethernet switches. Comparative measures had been carried out using a backbone working on a Fast Ethernet technology, which seems to have a very close transmission rate from the ATM 155Mbps. This work also discusses best results acquired with the implementation of larger networks reducing the number of subnetworks, statistical analyses based on time delay of the network in order to evaluate the performance of the changes made on its configuration. Even though the traffic from the Institute has been increased over these years and several services have been added to the network, it was completely possible to adapt the performance to the needs, using the benefits of the technological evolution which the network equipments had brought to the Institute.

Indiana University-Purdue University Indianapolis (IUPUI) / In the past decades, the rapid innovation of technology has greatly affected the automotive industry. However, every innovation has always been paired with safety risks that needs to be quickly addressed. This is where Petri nets (PNs) have come into the picture and have been used to model complex systems for different purposes, such as production management, traffic flow estimation and the introduction of new car features collectively known as, Adaptive Driver Assistance Systems (ADAS). Since most of these systems include both discrete and continuous dynamics, the Hybrid Petri net (HPN) model is an essential tool to model these. The objective of this thesis is to develop, analyze and simulate a lane keeping support system using an HPN model. Chapter 1 includes a brief summary of the specific ADAS used, lane departure warning and lane keeping assist systems and then related work on PNs is mentioned. Chapter 2 provides a background on Petri nets. In chapter 3, we develop a discrete PN model first, then we integrate continuous dynamics to extend it to a HPN model that combines the functionalities of the two independent ADAS systems. Several scenarios are introduced to explain the expected model behavior. Chapter 4 presents the analysis and simulation results obtained on the final model. Chapter 5 provides a summary for the work done and discusses future work.

The lane-keeping system in autonomous vehicles (AV) or even as a part of the advanced driving assistant system (ADAS) is known as one of the primary options of AVs and ADAS. The developed lane-keeping systems work on either computer vision or deep learning algorithms for their lane detection section. However, even the strongest image processing units or the robust deep learning algorithms for lane detection have inaccuracies during lane detection under certain conditions. The source of these inaccuracies could be rainy or foggy weather, high contrast shades of buildings and objects on-street, or faded lines. Since the lane detection unit of these systems is responsible for controlling the steering, even a momentary loss of lane detection accuracy could result in an accident or failure. As mentioned, different lane detection algorithms have been presented based on computer vision and deep learning during the last few years, and each one has pros and cons. Each model may have a better performance in some situations and fail in others. For example, deep learning-based methods are vulnerable to new samples. In this research, multiple models of lane detection are evaluated and used together to implement a robust lane detection algorithm. The purpose of this research is to develop an estimator-based Multiple Model Adaptive Estimation (MMAE) algorithm on the lane-keeping system to improve the robustness of the lane detection system. To verify the performance of the implemented algorithm, the AirSim simulation environment was used. The test simulation vehicle was equipped with one front camera and one back camera used to implement the proposed algorithm. The front camera images are used for detecting the lane and the offset of the vehicle and center point of the lane. The rear camera, which offered better performance in lane detection, was used as an estimator for calculating the uncertainty of each model. The simulation results showed that combining two implemented models with MMAE performed robustly even in those case studies where one of the models failed. The proposed algorithm was able to detect the failures of either of the models and then switch to another good working model to improve the robustness of the lane detection system. However, the proposed algorithm had some limitations; it can be improved by replacing PID controller with an MPC controller in future studies. In addition, in the presented algorithm, two computer vision-based algorithms were used; however, adding a deep learning-based model could improve the performance of the proposed MMAE. To have a robust deep learning-based model, it is suggested to train the network based on AirSim output images. Otherwise, the network will not work accurately due to the differences in the camera's location, camera configuration, colors, and contrast.

Indiana University-Purdue University Indianapolis (IUPUI) / The lane-keeping system in autonomous vehicles (AV) or even as a part of the advanced driving assistant system (ADAS) is known as one of the primary options of AVs and ADAS. The developed lane-keeping systems work on either computer vision or deep learning algorithms for their lane detection section. However, even the strongest image processing units or the robust deep learning algorithms for lane detection have inaccuracies during lane detection under certain conditions. The source of these inaccuracies could be rainy or foggy weather, high contrast shades of buildings and objects on-street, or faded lines. Since the lane detection unit of these systems is responsible for controlling the steering, even a momentary loss of lane detection accuracy could result in an accident or failure. As mentioned, different lane detection algorithms have been presented based on computer vision and deep learning during the last few years, and each one has pros and cons. Each model may have a better performance in some situations and fail in others. For example, deep learning-based methods are vulnerable to new samples. In this research, multiple models of lane detection are evaluated and used together to implement a robust lane detection algorithm. The purpose of this research is to develop an estimator-based Multiple Model Adaptive Estimation (MMAE) algorithm on the lane-keeping system to improve the robustness of the lane detection system. To verify the performance of the implemented algorithm, the AirSim simulation environment was used. The test simulation vehicle was equipped with one front camera and one back camera used to implement the proposed algorithm. The front camera images are used for detecting the lane and the offset of the vehicle and center point of the lane. The rear camera, which offered better performance in lane detection, was used as an estimator for calculating the uncertainty of each model. The simulation results showed that combining two implemented models with MMAE performed robustly even in those case studies where one of the models failed. The proposed algorithm was able to detect the failures of either of the models and then switch to another good working model to improve the robustness of the lane detection system. However, the proposed algorithm had some limitations; it can be improved by replacing PID controller with an MPC controller in future studies. In addition, in the presented algorithm, two computer vision-based algorithms were used; however, adding a deep learning-based model could improve the performance of the proposed MMAE. To have a robust deep learning-based model, it is suggested to train the network based on AirSim output images. Otherwise, the network will not work accurately due to the differences in the camera's location, camera configuration, colors, and contrast.

In recent years, natural, man-made and technological disasters have been increasing in magnitude and frequency of occurrence. Terrorist attacks have increased after the September 11, 2001. Some authorities suggest that global warming is partly the blame for the increase in frequency of natural disasters, such as the series of hurricanes in the early-2000's. Furthermore, there has been noticeable growth in population within many metropolitan areas not only in the US but also worldwide. These and other facts motivate the need for better emergency evacuation route planning (EERP) approaches in order to minimize the loss of human lives and property. This research considers aspects of evacuation routing never before considered in research and, more importantly, in practice. Previous EERP models only either consider unidirectional evacuee flow from the source of a hazard to destinations of safety or unidirectional emergency first responder flow to the hazard source. However, in real-life emergency situations, these heterogeneous, incompatible flows occur simultaneously over a bi-directional capacitated lane-based travel network, especially in unanticipated emergencies. By incompatible, it is meant that the two different flows cannot occupy a given lane and merge or crossing point in the travel network at the same time. In addition, in large-scale evacuations, travel lane normal flow directions can be reversed dynamically to their contraflow directions depending upon the degree of the emergency. These characteristics provide the basis for this investigation. This research considers the multiple flow EERP problem where the network travel lanes can be reconfigured using contraflow lane reversals. The first flow is vehicular flow of evacuees from the source of a hazard to destinations of safety, and the second flow is the emergency first responders to the hazard source. After presenting a review of the work related to the multiple flow EERP problem, mathematical formulations are proposed for three variations of the EERP problem where the objective for each problem is to identify an evacuation plan (i.e., a flow schedule and network contraflow lane configuration) that minimizes network clearance time. Before the proposed formulations, the evacuation problem that considers only the flow of evacuees out of the network, which is viewed as a maximum flow problem, is formulated as an integer linear program. Then, the first proposed model formulation, which addresses the problem that considers the flow of evacuees under contraflow conditions, is presented. Next, the proposed formulation is expanded to consider the flow of evacuees and responders through the network but under normal flow conditions. Lastly, the two-flow problem of evacuees and responders under contraflow conditions is formulated. Using real-world population and travel network data, the EERP problems are each solved to optimality; however, the time required to solve the problems increases exponentially as the problem grows in size and complexity. Due to the intractable nature of the problems as the size of the network increases, a genetic-based heuristic solution procedure that generates evacuation network configurations of reasonable quality is proposed. The proposed heuristic solution approach generates evacuation plans in the order of minutes, which is desirable in emergency situations and needed to allow for immediate evacuation routing plan dissemination and implementation in the targeted areas.

A road lane detection and tracking algorithm is developed, especially tailored to run on high-performance heterogeneous hardware like GPUs and FPGAs in autonomous road vehicles. The algorithm was initially developed in C/C++ and was ported to OpenCL which supports computation on heterogeneous hardware.A novel road lane detection algorithm is proposed using random sampling of particles modeled as straight lines. Weights are assigned to these particles based on their location in the gradient image. To improve the computation efficiency of the lane detection algorithm, lane tracking is introduced in the form of a Particle Filter. Creation of the particles in lane detection step and prediction, measurement updates in lane tracking step are computed parellelly on GPU/FPGA using OpenCL code, while the rest of the code runs on a host CPU. The software was tested on two GPUs - NVIDIA GeForce GTX 660 Ti &amp; NVIDIA GeForce GTX 285 and an FPGA - Altera Stratix-V, which gave a computational frame rate of up to 104 Hz, 79 Hz and 27 Hz respectively. The code was tested on video streams from five different datasets with different scenarios of varying lighting conditions on the road, strong shadows and the presence of light to moderate traffic and was found to be robust in all the situations for detecting a single lane. / <p>Validerat; 20140128 (global_studentproject_submitter)</p>

The objective of this dissertation is to develop a Multi-Camera Lane Departure Warning (MCLDW) system and a framework to evaluate it. A Lane Departure Warning (LDW) system is a safety feature that is included in a few luxury automobiles. Using a single camera, it performs the task of informing the driver if a lane change is imminent. The core component of an LDW system is a lane detector, whose objective is to find lane markers on the road. Therefore, we start this dissertation by explaining the requirements of an ideal lane detector, and then present several algorithmic implementations that meet these requirements. After selecting the best implementation, we present the MCLDW methodology. Using a multi-camera setup, MCLDW system combines the detected lane marker information from each camera's view to estimate the immediate distance between the vehicle and the lane marker, and signals a warning if this distance is under a certain threshold. Next, we introduce a procedure to create ground truth and a database of videos which serve as the framework for evaluation. Ground truth is created using an efficient procedure called Time-Slicing that allows the user to quickly annotate the true locations of the lane markers in each frame of the videos. Subsequently, we describe the details of a database of driving videos that has been put together to help establish a benchmark for evaluating existing lane detectors and LDW systems. Finally, we conclude the dissertation by citing the contributions of the research and discussing the avenues for future work.

No description available.