Digital image processing is one of the potential technologies used in precision agriculture to gather information, such as seed emergence, plant health, and phenology from the digital images. Despite its potential, the rate of adoption is slow due to limited accessibility, unsuitability to specific issues, unaffordability, and high technical knowledge requirement from the clientele. Therefore, the development of open source image processing applications that are task-specific, easy-to-use, requiring fewer inputs, and rich with features will be beneficial to the users/farmers for adoption. The Fiji software, an open source free image processing ImageJ platform, was used in this application development study. A collection of four different agricultural field applications were selected to address the existing issues and develop image processing tools by applying novel approaches and simple mathematical principles. First, an automated application, using a digital image and “pixel-march” method, performed multiple radial measurements of sunflower floral components. At least 32 measurements for ray florets and eight for the disc were required statistically for accurate dimensions. Second, the color calibration of digital images addressed the light intensity variations of images using standard calibration chart and derived color calibration matrix from selected color patches. Calibration using just three-color patches: red, green, and blue was sufficient to obtain images of uniform intensity. Third, plant stand count and their spatial distribution from UAS images were determined with an accuracy of ≈96 %, through pixel-profile identification method and plant cluster segmentation. Fourth, the soybean phenological stages from the PhenoCam time-lapse imagery were analyzed and they matched with the manual visual observation. The green leaf index produced the minimum variations from its smoothed curve. The time of image capture and PhenoCam distances had significant effects on the vegetation indices analyzed. A simplified approach using kymograph was developed, which was quick and efficient for phenological observations. Based on the study, these tools can be equally applied to other scenarios, or new user-coded, user-friendly, image processing tools can be developed to address specific requirements. In conclusion, these successful results demonstrated the suitability and possibility of task-specific, open source, digital image processing tools development for agricultural field applications. / United States. Agricultural Research Service / National Institute of Food and Agriculture (U.S.)

A fundamental understanding of ultrafast nonequilibrium dynamical processes in molecular aggregates is crucially important for the design of nanodevices that utilize quantum mechanical effects. However, understanding the coupled electron-phonon dynamics of such high-dimensional systems remains a challenging issue. As a result of the ever-growing computational power that is available, realistic parameterization of model Hamiltonians and implementation of sophisticated quantum dynamics algorithms have become indispensable tools for gaining insight into these processes. The focus of this dissertation is the development and implementation of approximate path integral-based methods to compute the time-evolution as well as linear and nonlinear spectroscopic signals of molecular aggregates following photo-excitation. The developments and applications presented here are geared toward gaining a better understanding of the role that electron-phonon coupling plays in framing ultrafast excitation energy transfer networks in photosynthetic light-harvesting complexes. The ultrafast excitation energy transfer dynamics that occurs upon photo-excitation of a network of electronically coupled chromophores is remarkably sensitive to the strength of electronic coupling as well as the frequencies and coupling strengths that characterize electron-phonon interactions. Based on approximations to the diabatic representation of molecular Hamiltonians, energetic models of condensed phase molecular aggregates can be parameterized from a first principles description. Often times, computational parameterization of these models reveals comparable magnitudes for intermolecular electronic couplings and electron-phonon couplings, negating the applicability of popular perturbative algorithms (such as those based on Forster or Redfield theory) for describing their time-evolution. Moreover, non-perturbative exact methods (e.g. stochastic Schrodinger equations and the Hierarchical Equations of Motion) are generally inefficient for all but a few specific limiting forms of electron-phonon coupling, or make assumptions about autocorrelation timescales of the vibrational environment. Because of the failure of the energetic parameters determined through recent ab initio studies of natural molecular aggregates to abide by the rather restrictive requirements for efficient application of the above-mentioned methods, the development of approximate non-perturbative algorithms for predicting nonequilibrium dynamical properties of such systems is a central theme in this dissertation. Following a general introductory section describing the basic concepts that are fundamental to the remainder of the thesis, the derivation of path integral dynamics methods is presented. These include a cartesian phase space path integral derivation of the truncated Wigner approximation as applied to the Meyer-Miller-Stock-Thoss mapping model for describing vibronic systems as well as a novel derivation of the Partially Linearized Density Matrix algorithm, highlighting its emergence as a leading order approximation to an, in principle, exact expression for the density matrix. An algorithm for computing the nonlinear response function for higher-order optical spectroscopy signals is presented within the framework of the partially linearized density matrix formalism. Time-resolved two-dimensional electronic spectra are computed and compared with exact results as well as standard perturbation theory-based results, highlighting the accuracy and efficiency of the developed method. Additionally, the recently popularized symmetrical quasi-classical method for computing the reduced density matrix dynamics is extended for computing linear optical spectroscopy signals, and compared with results from the partially linearized density matrix treatment. A generalization of the model Hamiltonian form utilized in recent ab initio studies is presented, allowing for direct vibrational energy relaxation due to coupling between intramolecular normal modes and their environment. The consequences of including these interactions within a model Hamiltonian that is inspired by energetic parameters found in studies of a photosynthetic light-harvesting complex are highlighted in the context of density matrix dynamics and time-resolved two-dimensional electronic spectroscopy. The results indicate that this physical process can be utilized as a means of optimizing the efficiency of excitation energy transfer and localization. Inspired by ab initio characterization of model Hamiltonians for molecular aggregates, a new approximate semiclassical propagator for describing the time-evolution of a system consisting of discrete electronic states in the presence of both high-frequency harmonic vibrational modes as well as slow environmental DOFs with arbitrary potentials is presented. Results indicate that this algorithm provides a more accurate description in this parameter regime than standard linearized path integral methods such as the partially linearized density matrix algorithm and the truncated Wigner approximation. Finally, preliminary results of dynamics involving non-perturbative field-matter interactions is presented with emphasis on strategically shaped pulses, field design through optimal control, and non-perturbative pump-probe spectroscopy.

The thesis consists of two coherent projects. The first project presents the results of evaluating salinity tolerance in barley using growth curve analysis where different growth trajectories are observed within barley families. The study of salinity tolerance in plants is crucial to understanding plant growth and productivity. Because fully-automated smarthouses with conveyor systems allow non-destructive and high-throughput phenotyping of large number of plants, it is now possible to apply advanced statistical tools to analyze daily measurements and to study salinity tolerance. To compare different growth patterns of barley variates, we use functional data analysis techniques to analyze the daily projected shoot areas. In particular, we apply the curve registration method to align all the curves from the same barley family in order to summarize the family-wise features. We also illustrate how to use statistical modeling to account for spatial variation in microclimate in smarthouses and for temporal variation across runs, which is crucial for identifying traits of the barley variates. In our analysis, we show that the concentrations of sodium and potassium in leaves are negatively correlated, and their interactions are associated with the degree of salinity tolerance. The second project studies change-points detection methods in extremes when multiple time series data are available. Motived by the scientific question of whether the chances to experience extreme weather are different in different seasons of a year, we develop a change-points detection model to study changes in extremes or in the tail of a distribution. Most of existing models identify seasons from multiple yearly time series assuming a season or a change-point location remains exactly the same across years. In this work, we propose a random effect model that allows the change-point to vary from year to year, following a given distribution. Both parametric and nonparametric methods are developed for detecting single and multiple change-points, and their performance is compared by simulation studies. The proposed method is illustrated using sea surface temperature data and the tail distributions before and after the change-point from two models, with and without random effects are compared.

京都大学 / 0048 / 新制・課程博士 / 博士(経済学) / 甲第22227号 / 経博第615号 / 新制||経||293(附属図書館) / 京都大学大学院経済学研究科経済学専攻 / (主査)教授 関口 格, 教授 原 千秋, 准教授 NEWTON Jonathan Charles Scott / 学位規則第4条第1項該当 / Doctor of Economics / Kyoto University / DGAM

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第22576号 / 情博第713号 / 新制||情||122(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 吉川 正俊, 教授 鹿島 久嗣, 教授 田島 敬史 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

Hovering is an attempt to navigate the contemporary digital environment through traditional means. The imagery created function as paintings, photographs, prints, and drawings. They explore and raise questions about visual perception and sensory awareness, as well as highlighting the modular limitations of the pixelated image and the illusion of reality in the digital landscape.

The objective of this Master's thesis is to find, using genetic algorithm (GA), an optimal profile for lubricating gap of a thrust bearing of a turbocharger. Compared to the analytical profile, the optimal profile is expected to have minimized friction for an equivalent load capacity. Friction minimization is one way to increase the efficiency of the thrust bearing; it reduces the friction losses in the bearing. An initial problem was given: a thrust bearing with Load capacity 1000 N, inner and outer radii of 30mm and 60mm respectively, rotor speed of 45000 rpm and angle of running surface of $0.5^0$. Lubricant properties were also provided for the initial problem: oil density of $ 840 kg/m^3$, dynamic viscosity $(\eta)$ of 0.01 Pa.s With this data, the numerical solution of the Reynolds equation was computed using MATLAB. To obtain more information, the minimum lubricating gap thickness was also computed using MATLAB. With this information, the shape of the analytical profile, and its characteristics were found. The analytical profile was then used a guide to create a general profile. The general profile thus obtained is then optimized using GA. The characteristics of the generated profile is then computed and compared to that of the analytical profile.

This thesis deals with creating a software guitar looper in a visual programming language called Pure Data and also building a specific remote controller for it. The remote cont- roller will be based on a MIDI protocol. As a part of the software, there will be a special aligning algorithm, that will take care of precise alignment of the created loop on beat in the way that no other intrusive rhythmical or tempo related elements are present.

This master’s thesis deals with phase-shifting transformers (PST) and coordination of electrically close devices in an interconnected region. Theoretical research is performed in the work, describing methods of power control, function and design of PST, as well as actual methods of PST coordination in the rest of the world. Based on the research there is an optimalisation algorithm designed in MATLAB, which computes recommended tap petting of PST based on the network configuration. In the second part of this thesis there are described components of this algorithm and also presented the results of three demonstrative scenarios. Algorithm is able to solve minor problems in the network; more complex problems are beyond its solving ability. For an improved functionality there is a necessity to further expand this algorithm by a network redispatch solving algorithm.

The aim of this master thesis is optimization of the rotor in the terms of efficiency. An analysis of the effects of rotor parameters is performed by RMXPRT. A genetic algorithm is created to find the optimal solution. Solutions are evaluated and adjusted according to the results. Measurements on the actual machine are performed and evaluated to verify the functionality of the simulation models.