Cotton (Gossypium hirsutum L.) and corn (Zea mays L.) spectral reflectance holds promise for deriving variable rate N (VRN) treatments calibrated with red-edge inflection (REI) type vegetation indices (VIs). The objectives of this study were to define the relationships between two commercially available sensors and the suitable VIs used to predict N status. Field trials were conducted during the 2012-2013 growing seasons using fixed and variable N rates in cotton ranging from 33.6-134.4 kg N ha-1 and fixed N rates in corn ranging from 0.0 to 268.8 kg N ha-1. Leaf N concentration, SPAD chlorophyll and crop yield were analyzed for their relation to fertilizer N treatment. Sensor effects were significant and red-edge VIs most strongly correlated to N status. A theoretical ENDVI index was derived from the research dataset as an improvement and alternative to the Guyot’s Red Edge Inflection and Simplified Canopy Chlorophyll Content Index (SI).

Approximately 37 m (120 ft) of core was studied with the objective of evaluating and documenting the development and loss of porosity in the dry Mobil McElroy-1 well (Lower Cretaceous Aptian – Albian Sligo Formation). Core slabs were described and thin section samples, taken every 1.5 m (5 ft), were stained and analyzed under standard petrographic, cathode luminescence, confocal and scanning electron microscopes. The main conclusion is that average porosity significantly reduced with depth. Carbon and oxygen isotope values obtained for 20 samples show that the main pore-occluding diagenetic environment was meteoric with most samples having relatively low delta18O (-3.1%o to -6.7%o V- PDB) values. Early through late stage medium (1 mm – 3 mm) to large (> 3 mm) calcite and nonerroan dolomite jointly contributed to more than 10% of primary porosity loss. This study significantly contributes to the understanding of the Sligo Formation and promotes development of natural gas resources.

Cotton (Gossypium hirsutum L.) reflectance has potential to drive variable rate N (VRN) applications, but more precise definitions of relationships between sensor-observed reflectance, plant height, and N status are necessary. The objectives of this study were to define effectiveness and relationships between three commercially available sensors, and examine relationships of wavelengths and indices obtained by a spectrometer to plant height and N status. Field trials were conducted during 2008-2010 growing seasons at Mississippi State, MS. Fertilizer N rates ranged from 0-135 kg N ha-1 to establish growth differences. Sensor effects were significant, but sensors monitoring Normalized Difference Vegetation Index (NDVI) failed to correlate well with early-season N status. Wavelengths and indices utilizing the red-edge correlated most strongly with N status. Both Guyot’s Red Edge Index (REI) and Canopy Chlorophyll Content Index (I) correlated consistently with N status independent of biomass status early enough in the growing season to drive VRN.

Spatial variations in soil properties can directly affect Nitrogen status of corn (Zea mays L.) and decrease efficiency of uniform fertilizer N applications. The objective of this study was to assess the spatial variations of soil properties and measure the effect on corn Nitrogen status through canopy reflectance. Field trials were conducted in 2010 and 2011 on a producer’s field west of Yazoo City, MS that contained high in field variability. Soil physical and chemical properties all exhibited moderate to high spatial dependency during both years of this study. Vegetative indices were derived from canopy reflectance values and indices utilizing the red-edge were the strongest and most consistent descriptors of tissue N percent and whole plant N uptake. The Canopy Chlorophyll Content Index (I) shows the greatest potential of assessing variations of corn Nitrogen status among the indices tested.

Processing hyperspectral image data can be computationally expensive and difficult to employ for real-time applications due to its extensive spatial and spectral information. Further, applications in which computational resources may be limited can be hindered by the volume of data that is common with airborne hyperspectral image data. This paper proposes utilizing band selection to down-select the number of spectral bands to consider for a given classification task such that classification can be done at the edge. Specifically, we consider the following state of the art band selection techniques: Fast Volume-Gradient-based Band Selection (VGBS), Improved Sparse Subspace Clustering (ISSC), Maximum-Variance Principal Component Analysis (MVPCA), and Normalized Cut Optimal Clustering MVPCA (NC-OC-MVPCA), to investigate their feasibility at identifying discriminative bands such that classification performance is not drastically hindered. This would greatly benefit applications where time-sensitive solutions are needed to ensure optimal outcomes. In this research, an NVIDIA AGX Xavier module is used as the edge device to run trained models on as a simulated deployed unmanned aerial system. Performance of the proposed approach is measured in terms of classification accuracy and run time.

No description available.

High-pass filtering has been shown to be a promising method for edge detection in optical scanning holography. By using a circular function as a pupil for the system, the radius of the circle can be varied to block out different ranges of frequencies. Implementing this system in simulation yields an interesting result, however. As the radius increases, a singular edge can split off into two edges instead. To understand the specific conditions under which this split occurs, Airy pattern filtering and single-sided filtering were implemented to analyze the results from the original high-pass simulation. These methods were tested with different input objects to assess any common patterns. Ultimately, no definitive answer was found, as Airy pattern filtering resulted in inconsistent results across different input objects, and single-sided filtering does not completely isolate the edge. Nonetheless, the documented results may aid a future understanding of this phenomenon. / Master of Science / Holograms are three-dimensional recordings of an object, reminiscent of how a photograph records a two-dimensional image of an object. Detecting edges in images and the reconstructed images from holograms can help us identify objects within the recorded image or hologram. In computer vision, common edge detection techniques involve analyzing the image's spatial frequency, or changes in relative intensity over space. One such technique is high-pass filtering, in which lower spatial frequencies are blocked out. High-pass filtering can also be applied to holographic imaging systems. However, when applying high-pass filtering to a holographic system, detected edges can split into two as higher frequencies are filtered out. This thesis examines the conditions for why this split-edge phenomenon occurs by modifying the original recorded object and the filtering mechanism, then analyzing the resultant holograms. While the results did not give a conclusive answer, they have been documented for the purpose of further research.

Over the past few years, Deep Learning (DL), a promising technology leading the next generation of intelligent environments, has attracted significant attention and has been intensively utilized in various fields in the fourth industrial revolution era. The applications of Deep Learning in the area of Mobile Edge Computing (MEC) have achieved remarkable outcomes. Among several functionalities of MEC, the service migration frameworks have been proposed to overcome the shortcomings of the traditional methodologies in supporting high-mobility users with real-time responses. The service migration in MEC is a complex optimization problem that considers several dynamic environmental factors to make an optimal decision on whether, when, and where to migrate. In line with the trend, various service migration frameworks based on a variety of optimization algorithms have been proposed to overcome the limitations of the traditional methodologies. However, it is required to devise a more sophisticated and realistic model by solving the computational complexity and improving the inefficiency of existing frameworks. Therefore, an efficient service migration mechanism that is able to capture the environmental variables comprehensively is required. In this thesis, we propose an enhanced service migration model to address user proximity issues. We first introduce innovative service migration models for single-user and multi-user to overcome the users’ proximity issue while enforcing the service execution efficiency. Secondly, We formulate the service migration process as a complicated optimization problem and utilize Deep Reinforcement Learning (DRL) to estimate the optimal policy to minimize the migration cost, transaction cost, and consumed energy jointly. Lastly, we compare the proposed models with existing migration methodologies through analytical simulations from various aspects. The numerical results demonstrate that the proposed models can estimate the optimal policy despite the computational complexity caused by the dynamic environment and high-mobility users.

State and specifically location estimation is a core concept in automation and is a well-researched field. One such estimation technique is Moving Horizon Estimation (MHE). In this thesis, the MHE variant single-shooting estimation will estimate the location and velocity of a moving object. The moving object is equipped with an  Inertial Measuring Unit (IMU)  measuring acceleration and angular velocity. This thesis will explore pre-integrating the IMU measurement on the device attached to the moving object and using them in another device running the MHE. The acceleration and angular velocity measurements are measured in the local frame of reference of the moving object, rotating the measurement to a global frame of reference requires a known rotation of the tracked object, finding this rotation is also a task in this thesis. This thesis found the presented theory ill-equipped to estimate the object's state without an angle measurement, this thesis assumed any such measurement is made from a magnetometer but the solution presented is not biased towards any other method of measuring angles. With the addition of an angel measurement, the estimation performs at a decimeter-level precision for location.

Mobile computation offloading (MCO) is a way of improving mobile device (MD) performance by offloading certain task executions to a more resourceful edge server (ES), rather than running them locally on the MD. This thesis first considers the problem of assigning the wireless communication bandwidth and the ES capacity needed for this remote task execution, so that task completion time constraints are satisfied. The objective is to minimize the average power consumption of the MDs, subject to a cost budget constraint on communication and computation resources. The thesis includes contributions for both soft and hard task completion deadline constraints. The soft deadline case aims to create assignments so that the probability of task completion time deadline violation does not exceed a given violation threshold. In the hard deadline case, it creates resource assignments where task completion time deadlines are always satisfied. The problems are first formulated as mixed integer nonlinear programs. Approximate solutions are then obtained by decomposing the problems into a collection of convex subproblems that can be efficiently solved. Results are presented that demonstrate the quality of the proposed solutions, which can achieve near optimum performance over a wide range of system parameters. The thesis then introduces algorithms for static task class partitioning in MCO. The objective is to partition a given set of task classes into two sets that are either executed locally or those classes that are permitted to contend for remote ES execution. The goal is to find the task class partition that gives the minimum mean MD power consumption subject to task completion deadlines. The thesis generates these partitions for both soft and hard task completion deadlines. Two variations of the problem are considered. The first assumes that the wireless and computational capacities are given and the second generates both capacity assignments subject to an additional resource cost budget constraint. Two class ordering methods are introduced, one based on a task latency criterion, and another that first sorts and groups classes based on a mean power consumption criterion and then orders the task classes within each group based on a task completion time criterion. A variety of simulation results are presented that demonstrate the excellent performance of the proposed solutions. The thesis then considers the use of digital twins (DTs) to offload physical system (PS) activity. Each DT periodically communicates with its PS, and uses these updates to implement features that reflect the real behaviour of the device. A given feature can be implemented using different models that create the feature with differing levels of system accuracy. The objective is to maximize the minimum feature accuracy for the requested features by making appropriate model selections subject to wireless channel and ES resource availability. The model selection problem is first formulated as an NP-complete integer program. It is then decomposed into multiple subproblems, each consisting of a modified Knapsack problem. A polynomial-time approximation algorithm is proposed using dynamic programming to solve it efficiently, by violating its constraints by at most a given factor. A generalization of the model selection problem is then given and the thesis proposes an approximation algorithm using dependent rounding to solve it efficiently with guaranteed constraint violations. A variety of simulation results are presented that demonstrate the excellent performance of the proposed solutions. / Thesis / Doctor of Philosophy (PhD) / Mobile devices (MDs) such as smartphones are currently used to run a wide variety of application tasks. An alternative to local task execution is to arrange for some MD tasks to be run on a remote non-mobile edge server (ES). This is referred to as mobile computation offloading (MCO). The work in this thesis studies two important facets of the MCO problem. 1. The first considers the joint effects of communication and computational resource assignment on task completion times. This work optimizes task offloading decisions, subject to task completion time requirements and the cost that one is willing to incur when designing the network. Procedures are proposed whose objective is to minimize average mobile device power consumption, subject to these cost constraints. 2. The second considers the use of digital twins (DTs) as a way of implementing mobile computation offloading. A DT implements features that describe its physical system (PS) using models that are hosted at the ES. A model selection problem is studied, where multiple DTs share the execution services at a common ES. The objective is to optimize the feature accuracy obtained by DTs subject to the communication and computation resource availability. The thesis proposes different approximation and decomposition methods that solve these problems efficiently.