The main objective of this thesis is to evaluate accuracy and precision of the machine vision system used to calibrate a prepared environment foroptical navigation. Rotationally independent optimized colour reference labels (symbols) creates an environment. Any number of symbols can be used. A symbol carries 8–bit (0 to 255) information, which can be designed for different values by using Matlab algorithms. An optical navigation system enters into the environment and captures thesymbols. The symbols are then decoded to determine the geographical positions of the symbols from reference position of the system by using Matlab algorithms. Then, the system is moved to a known position and the same set of symbols are captured, decoded and located. The process is repeated for several positions of the system to find precision and accuracy. Finally, the results are analysed.

This thesis discusses recent advances in precision calculations of quantum chromodynamics and their application to the Large Hadron Collider (LHC) physics program and beyond. The first half of the thesis is dedicated to the study of vector boson fusion Higgs (VBF) production; fully differential at the next-to-next-to-leading order level (NNLO), and inclusively at next-to-next-to-next-to-leading order (N³LO). Both calculations are performed in the structure function approximation, where the VBF process is treated as a double deep inelastic scattering. For the differential calculation a new subtraction method, "projection-to-Born", is introduced and applied. We study VBF production in a number of scenarios relevant for the LHC and for Future Circular Colliders (FCC). We find NNLO corrections after typical cuts of 5-6% while differential distributions show corrections of up to 10-12% for some standard observables. For the inclusive calculation we find N³LO corrections at the order of 1-2‰. The second half of the thesis presents recent results on the matching of fixed order calculations with parton showers. We first present the POsitive Weight Hardest Emission Generator (POWHEG) method for matching next-to-leading order (NLO) calculations with parton showers. We then proceed to apply it to the case of vector boson fusion ZZjj production and discuss the results for scenarios relevant for the LHC and a possible FCC. In order to present the matching of a NNLO calculation with a parton shower, we next discuss the Multi-Scale Improved NLO (MiNLO) procedure. By applying a reweighting procedure to MiNLO improved Drell-Yan production, we obtain a generator which is NNLO accurate when integrated over all radiation while providing a fully exclusive description of the final state phase space. We compare the calculation to dedicated next-to-next-to-leading logarithm resummations and find very good agreement. The generator is also found to be in good agreement with 7 and 8 TeV LHC data.

Doctor of Philosophy / Department of Agronomy / V. P. Vara Prasad / Crop assessment and monitoring is important to crop management both at crop production level and research plot level, such as high-throughput phenotyping in breeding programs. Optical sensors based agricultural applications have been around for decades and have soared over the past ten years because of the potential of some new technologies to be low-cost, accessible, and high resolution for crop remote sensing which can help to improve crop management to maintain producers’ income and diminish environmental degradation. The overall objective of this study was to develop methods and compare the different optical sensors in crop assessment and monitoring at different scales and perspectives. At crop production level, we reviewed the current status of different optical sensors used in precision crop production including satellite-based, manned aerial vehicle (MAV)-based, unmanned aircraft system (UAS)-based, and vehicle-based active or passive optical sensors. These types of sensors were compared thoroughly on their specification, data collection efficiency, data availability, applications and limitation, economics, and adoption. At research plot level, four winter wheat experiments were conducted to compare three optical sensors (a Canon T4i® modified color infrared (CIR) camera, a MicaSense RedEdge® multispectral imager and a Holland Scientific® RapidScan CS-45® hand-held active optical sensor (AOS)) based high-throughput phenotyping for in-season biomass estimation, canopy estimation, and grain yield prediction in winter wheat across eleven Feekes stages from 3 through 11.3. The results showed that the vegetation indices (VIs) derived from the Canon T4i CIR camera and the RedEdge multispectral camera were highly correlated and can equally estimate winter wheat in-season biomass between Feekes 3 and 11.1 with the optimum point at booting stage and can predict grain yield as early as Feekes 7. Compared to passive sensors, the RapidScan AOS was less powerful and less temporally stable for biomass estimation and yield prediction. Precise canopy height maps were generated from a CMOS sensor camera and a multispectral imager although the accuracy could still be improved. Besides, an image processing workflow and a radiometric calibration method were developed for UAS based imagery data as bi-products in this project. At temporal dimension, a wheat phenology model based on weather data and field contextual information was developed to predict the starting date of three key growth stages (Feekes 4, 7, and 9), which are critical for N management. The model could be applied to new data within the state of Kansas to optimize the date for optical sensor (such as UAS) data collection and save random or unnecessary field trips. Sensor data collected at these stages could then be plugged into pre-built biomass estimation models (mentioned in the last paragraph) to estimate the productivity variability within 20% relative error.

Banca: Ricardo Antônio Ferreira Rodrigues / Banca: Salatiér Buzetti / Banca: Paulo Leonel Libardi / Banca: Carlos Roberto Espindola / Banca: Edson Eiji Matsura / Resumo: Por se tratar de um cultivo perene, a pupunheira se torna bastante exigente quanto aos atributos físico-químicos do solo, refletindo de forma mais imediata ao efeito causado pelo manejo que é adotado. O objetivo do presente trabalho foi identificar por meio de análises lineares, geoestatísticas e multivariadas os atributos do solo que melhor expliquem a variabilidade linear e espacial da cultura da pupunha em Registro, SP. Em 2014, foi analisado os componentes produtivos da cultura da pupunha em função de alguns atributos físicos e químicos de um Cambissolo eutrófico. Foi demarcada a malha geoestatística, com 54 pontos amostrais, numa área de 1 ha. Quanto ao ponto de vista linear, diâmetro do caule e altura da planta foi explicada pelo modelo potencial direto e exponencial, o que foi altamente significativa, devido à resistência mecânica à penetração. A classificação em grupos foi feita por três métodos: método de agrupamentos hierárquico, método não-hierárquico k-means e análise de componentes principais. Assim, pode se concluir que a análise multivariada por meio da análise de componentes principais permitiu identificar dois grupos que explicam 67,8 % da variabilidade total dos dados, onde estes grupos são constituídos pelos atributos físicos densidade do solo, porosidade total, umidade gravimétrica e resistência mecânica à penetração que se mostraram com maior poder de explicação da variação do diâmetro do caule da pupunha. Já para os atributos químicos a classificação em grupos foi feita por três métodos: método de agrupamentos hierárquico, método não-hierárquico k-means e análise de componentes principais. Pode se concluir que a análise multivariada por meio da análise de componentes principais permitiu identificar dois grupos que explicam 56,62 % da variabilidade total dos dados, onde estes grupos são constituídos pelos atributos químicos matéria orgânica, potássio, magnésio.. / Abstract: Because it is a perennial crop, peach palm becomes quite demanding as physico-chemical soil properties, reflecting more immediately the effect caused by the management that is adopted. The objective of this study was to identify through linear analysis, geostatistics and multivariate soil attributes that best explain the linear and spatial variability of the culture of peach palm in Registro, SP. In 2014, was analyzed productive components of the culture of peach palm due to some physical and chemical attributes of a eutrophic Cambisol. It was demarcated the geostatistical grid with 54 sampling points in an area of 1 ha. As the linear point of view, stem diameter, and plant height was explained by the potential direct and exponential model, which was highly significant due to the mechanical resistance. Classification into groups was made by three methods: method of hierarchical clusters, non-hierarchical k-means method and principal component analysis. Thus, it can be concluded that the multivariate analysis using principal component analysis allowed us to identify two groups that explain 67.8% of the total variability of the data, where these groups are made up of the physical attributes of the soil density, total porosity, humidity and gravimetric penetration resistance that demonstrated greater explanatory power of the variation of peach palm stem diameter. As for the chemical attributes the classification in groups was made by three methods: method of hierarchical clusters, non-hierarchical k-means method and principal component analysis. It can be concluded that the multivariate analysis using principal component analysis allowed us to identify two groups that explain 56.62% of the total variability of the data, where these groups are made up of the chemical attributes organic matter, potassium, magnesium, aluminum, exchange capacity cation and aluminum saturation stood out as main components that demonstrated greater explanatory power...

Precision Farming is by far the most exciting new agricultural technology developed during the past decade, and although technology transfer is especially difficult in agriculture for a number of reasons, this technology has survived its initial stages of implementation. Historically field boundaries were often along natural soil boundaries, leading to small fields, which were treated homogenously. As agricultural machinery was developed and grew ever larger, fields were often combined to allow for more efficient cultivation. As result, fields with varying properties were created resulting in inefficiencies. Precision Farming was developed to overcome this problem. In this paper some results of initial research undertaken in South Africa under a variety of circumstances will be shown. / Dissertation (MEng)--University of Pretoria, 2005. / Civil Engineering / Unrestricted

This study investigated the effects of a precision teaching package on response rates of children with autism. Prior to both experiments a preference assessment was conducted to identify high preference activities for each participant. Experiment 1 investigated whether response rates would shift as a function of rate-contingent consequences during an academic task. Different activities were associated with different rates of responding. The experimental package of 1 minute timings, rate contingent consequences, and charting was successful in increasing the rates of responding when the most highly preferred activity was associated with high rates of responding. When the contingencies were switched and the most highly preferred activity was contingent on lower rates of responding, the participant's responding did not decrease. Experiment 2 was an attempt to replicate the results of Experiment 1 using a multiple baseline across tasks. The experimental package was not successful in increasing the rate of responding.

Indiana University-Purdue University Indianapolis (IUPUI) / Currently, there are many types of Automatic Guided Vehicles (AGVs) in different industries. Typically their job is to move raw materials or parts around a manufacturing facility, and they can be very accurate by following the guides from wires in the floor, magnets, laser, or vision. However, currently AGVs only work indoors. Therefore, the purpose of this thesis is to discuss the implementation of the outdoor AGV. An outdoor AGV has much more constraints than an indoor one. The environment indoors can be easily controlled while the outdoor cannot because there could be such problems as rough outdoor surfaces, no pre-set guiding wires or magnets, vision blocking by dust, and so on. The solution, which will be introduced in this paper, to achieve the outdoor AGV is laser guidance. In addition, a buffer will be installed to stabilize the cargo or others working devices, to prevent them from the shaking due to the rough outdoor surfaces. To be more specific, a prototype will be built to simulate the working of a seeder. In agriculture, it is very important to plant corns in a straight line, not only to increase the absorption of sunlight and ventilation, but also to reduce the work of irrigation, fertilizing, and harvest. Furthermore, to achieve unmanned agriculture, a corn field with straight lines will also be a good condition for other agriculture robots.

No description available.

To overcome some limitations of manual insect sampling in soybeans, an unmanned aerial vehicle (UAV) sampling platform was developed that could collect insects in a sweep net attached to the bottom of a UAV. Before this UAV sampling platform can be used to make management decisions, correlations with manual sweep net and/or drop cloth sampling methods are needed. This will allow action thresholds for the various pests to be calculated for the UAV sampling platform. To make the correlations, 87 soybean fields were sampled during 2020 and 2021 with each of 4 sampling methods, a UAV travelling 50-m, the same UAV travelling 25-m, 25 manual sweeps with sweep net, and a 1.5-row-m sample on a drop cloth. Data were compiled for 12 insect pests of soybeans in 5 families. Significant positive correlations between all sampling methods showed that all methods were useful for sampling all the insects of interest.

No description available.