Visual Reconstruction and Feature Analysis of the Three-Dimensional Surface of Earthworm

This paper demonstrates a method for visual reconstruction and feature analysis of the three-dimensional surface of earthworm in CATIA (Computer Aided Three Dimensional Interactive Application) and IDL (Interactive Data Language). The earthworm, with a relatively simple surface morphology and good capability in reducing soil adhesion and resistance, was selected to study the feasible methods in the visual reconstruction and feature analysis of the three-dimensional surface of living things. The digital measurements of surfaces of the earthworm were carried out using a three-dimensional laser scanner. Point clouds, the scanning digital data of the surface of the earthworm, were processed by screening unwanted data, reconstructing surface and analysing feature in CATIA. In order to get more detail information about the point clouds, IDL, which integrates a powerful, array-oriented language with numerous mathematical analysis and graphical display techniques, was adopted for the visual reconstruction and feature analysis of three- dimensional surface of the earthworm. Importing of point clouds and reconstruction of the surface of earthworm were conducted in CATIA. Analysis feature of the scanning data and reconstructing surface were carried out in IDL, which provides a high level of flexibility to access, analyse and visualize the data using different methods. Polynomial regression equation of the surface of earthworm in the longitudinal plane was derived. In addition, point clouds were more easily displayed and analysed by resizing, rotating and zooming in IDL. Methods and results presented in this paper prove to be potentially useful for analyzing the feature of biological prototype, optimizing the mathematical model and affording deformable physical model to bionic engineering, those works would have great implications to the research of biological coupling theory and technological creation in bionic engineering. Keywords: Visual Reconstruction; Feature Analysis; Three-Dimensional Surface; Earthworm; CATIA; ID

Remote sensing, numerical modelling and ground truthing for analysis of lake water quality and temperature

Freshwater accounts for just 2.5% of the earth’s water resources, and its quality and availability are becoming an issue of global concern in the 21st century. Growing human population, over-exploitation of water sources and pressures of global warming mean that both water quantity and quality are affected. In order to effectively manage water quality there is a need for increased monitoring and predictive modelling of freshwater resources. To address these concerns in New Zealand inland waters, an approach which integrates biological and physical sciences is needed. Remote sensing has the potential to allow this integration and vastly increase the temporal and spatial resolution of current monitoring techniques, which typically involve collecting grab-samples. In a complementary way, lake modelling has the potential to enable more effective management of water resources by testing the effectiveness of a range of possible management scenarios prior to implementation. Together, the combination of remote sensing and modelling data allows for improved model initialisation, calibration and validation, which ultimately aid in understanding of complex lake ecosystem processes. This study investigated the use of remote sensing using empirical and semi-analytical algorithms for the retrieval of chlorophyll a (chl a), tripton, suspended minerals (SM), total suspended sediment (SS) and water surface temperature. It demonstrated the use of spatially resolved statistical techniques for comparing satellite estimated and 3-D simulated water quality and temperature. An automated procedure was developed for retrieval of chl a from Landsat Enhanced Thematic Mapper (ETM+) imagery, using 106 satellite images captured from 1999 to 2011. Radiative transfer-based atmospheric correction was applied to images using the Second Simulation of the Satellite in the Solar Spectrum model (6sv). For the estimation of chl a over a time series of images, the use of symbolic regression resulted in a significant improvement in the precision of chl a hindcasts compared with traditional regression equations. Results from this investigation suggest that remote sensing provides a valuable tool to assess temporal and spatial distributions of chl a. Bio-optical models were applied to quantify the physical processes responsible for the relationship between chl a concentrations and subsurface irradiance reflectance used in regression algorithms, allowing the identification of possible sources of error in chl a estimation. While the symbolic regression model was more accurate than traditional empirical models, it was still susceptible to errors in optically complex waters such as Lake Rotorua, due to the effect of variations of SS and CDOM on reflectance. Atmospheric correction of Landsat 7 ETM+ thermal data was carried out for the purpose of retrieval of lake water surface temperature in Rotorua lakes, and Lake Taupo, North Island, New Zealand. Atmospheric correction was repeated using four sources of atmospheric profile data as input to a radiative transfer model, MODerate resolution atmospheric TRANsmission (MODTRAN) v.3.7. The retrieved water temperatures from 14 images between 2007 and 2009 were validated using a high-frequency temperature sensor deployed from a mid-lake monitoring buoy at the water surface of Lake Rotorua. The most accurate temperature estimation for Lake Rotorua was with radiosonde data as an input into MODTRAN, followed by Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2, Atmospheric Infrared Sounder (AIRS) Level 3, and NASA data. Retrieved surface water temperature was used for assessing spatial heterogeneity of surface water temperature simulated with a three-dimensional (3-D) hydrodynamic model (ELCOM) of Lake Rotoehu, located approximately 20 km east of Lake Rotorua. This comparison demonstrated that simulations reproduced the dominant horizontal variations in surface water temperature in the lake. The transport and mixing of a geothermal inflow and basin-scale circulation patterns were inferred from thermal distributions from satellite and model estimations of surface water temperature and a spatially resolved statistical evaluation was used to validate simulations. This study has demonstrated the potential of accurate satellite-based thermal monitoring to validate water surface temperature simulated by 3-D hydrodynamic models. Semi-analytical and empirical algorithms were derived to determine spatial and temporal variations in SS in Lake Ellesmere, South Island, New Zealand, using MODIS band 1. The semi-analytical model and empirical model had a similar level of precision in SS estimation, however, the semi-analytical model has the advantage of being applicable to different satellite sensors, spatial locations, and SS concentration ranges. The estimations of SS concentration (and estimated SM concentration) from the semi-analytical model were used for a spatially resolved validation of simulations of SM derived from ELCOM-CAEDYM. Visual comparisons were compared with spatially-resolved statistical techniques. The spatial statistics derived from the Map Comparison Kit allowed a non-subjective and quantitative method to rank simulation performance on different dates. The visual and statistical comparison between satellite estimated and model simulated SM showed that the model did not perform well in reproducing both basin-scale and fine-scale spatial variation in SM derived from MODIS satellite imagery. Application of the semi-analytical model to estimate SS over the lifetime of the MODIS sensor will greatly extend its spatial and temporal coverage for historical monitoring purposes, and provide a tool to validate SM simulated by 1-D and 3-D models on a daily basis. A bio-optical model was developed to derive chl a, SS concentrations, and coloured dissolved organic matter /detritus absorption at 443 nm, from MODIS Aqua subsurface remote sensing reflectance of Lake Taupo, a large, deep, oligotrophic lake in North Island, New Zealand. The model was optimised using in situ inherent optical properties (IOPs) from the literature. Images were atmospherically corrected using the radiative transfer model 6sv. Application of the bio-optical model using a single chl a-specific absorption spectrum (a*ϕ(λ)) resulted in low correlation between estimated and observed values. Therefore, two different absorption curves were used, based on the seasonal dominance of phytoplankton phyla with differing absorption properties. The application of this model resulted in reasonable agreement between modelled and in situ chl a concentrations. Highest concentrations were observed during winter when Bacillariophytes (diatoms) dominated the phytoplankton assemblage. On 4 and 5 March 2004 an unusually large turbidity current was observed originating from the Tongariro River inflow in the south-east of the lake. In order to resolve fine details of the plume, empirical relationships were developed between MODIS band 1 reflectance (250 m resolution) and SS estimated from MODIS bio-optical features (1 km resolution) were used estimate SS at 250 m resolution. Complex lake circulation patterns were observed including a large clockwise gyre. With the development of this bio-optical model MODIS can potentially be used to remotely sense water quality in near real time, and the relationship developed for B1 SS allows for resolution of fine-scale features such turbidity currents

UAV-Enabled Surface and Subsurface Characterization for Post-Earthquake Geotechnical Reconnaissance

Major earthquakes continue to cause significant damage to infrastructure systems and the loss of life (e.g. 2016 Kaikoura, New Zealand; 2016 Muisne, Ecuador; 2015 Gorkha, Nepal). Following an earthquake, costly human-led reconnaissance studies are conducted to document structural or geotechnical damage and to collect perishable field data. Such efforts are faced with many daunting challenges including safety, resource limitations, and inaccessibility of sites. Unmanned Aerial Vehicles (UAV) represent a transformative tool for mitigating the effects of these challenges and generating spatially distributed and overall higher quality data compared to current manual approaches. UAVs enable multi-sensor data collection and offer a computational decision-making platform that could significantly influence post-earthquake reconnaissance approaches. As demonstrated in this research, UAVs can be used to document earthquake-affected geosystems by creating 3D geometric models of target sites, generate 2D and 3D imagery outputs to perform geomechanical assessments of exposed rock masses, and characterize subsurface field conditions using techniques such as in situ seismic surface wave testing. UAV-camera systems were used to collect images of geotechnical sites to model their 3D geometry using Structure-from-Motion (SfM). Key examples of lessons learned from applying UAV-based SfM to reconnaissance of earthquake-affected sites are presented. The results of 3D modeling and the input imagery were used to assess the mechanical properties of landslides and rock masses. An automatic and semi-automatic 2D fracture detection method was developed and integrated with a 3D, SfM, imaging framework. A UAV was then integrated with seismic surface wave testing to estimate the shear wave velocity of the subsurface materials, which is a critical input parameter in seismic response of geosystems. The UAV was outfitted with a payload release system to autonomously deliver an impulsive seismic source to the ground surface for multichannel analysis of surface waves (MASW) tests. The UAV was found to offer a mobile but higher-energy source than conventional seismic surface wave techniques and is the foundational component for developing the framework for fully-autonomous in situ shear wave velocity profiling.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145793/1/wwgreen_1.pd

A Map-algebra-inspired Approach for Interacting With Wireless Sensor Networks, Cyber-physical Systems or Internet of Things

The typical approach for consuming data from wireless sensor networks (WSN) and Internet of Things (IoT) has been to send data back to central servers for processing and analysis. This thesis develops an alternative strategy for processing and acting on data directly in the environment referred to as Active embedded Map Algebra (AeMA). Active refers to the near real time production of data, and embedded refers to the architecture of distributed embedded sensor nodes. Network macroprogramming, a style of programming adopted for wireless sensor networks and IoT, addresses the challenges of coordinating the behavior of multiple connected devices through a high-level programming model. Several macroprogramming models have been proposed, but none to date has adopted a comprehensive spatial model. This thesis takes the unique approach of adapting the well-known Map Algebra model from Geographic Information Science to extend the functionality of WSN/IoT and the opportunities for user interaction with WSN/IoT. As an inherently spatial model, the Map Algebra-inspired metaphor supports the types of computation desired from a network of geographically dispersed WSN nodes. The AeMA data model aligns with the conceptual model of GIS layers and specific layer operations from Map Algebra. A declarative query and network tasking language, based on Map Algebra operations, provides the basis for operations and interactions. The model adds functionality to calculate and store time series and specific temporal summary-type composite objects as an extension to traditional Map Algebra. The AeMA encodes Map Algebra-inspired operations into an extensible Virtual Machine Runtime system, called MARS (Map Algebra Runtime System) that supports Map Algebra in an efficient and extensible way. Map algebra-like operations are performed in a distributed manner. Data do not leave the network but are analyzed and consumed in place. As a consequence, collected information is available in-situ to drive local actions. The conceptual model and tasking language are designed to direct nodes as active entities, able to perform some actions on their environment. This Map Algebra inspired network macroprogramming model has many potential applications for spatially deployed WSN/IoT networks. In particular the thesis notes its utility for precision agriculture applications

COBE's search for structure in the Big Bang

The launch of Cosmic Background Explorer (COBE) and the definition of Earth Observing System (EOS) are two of the major events at NASA-Goddard. The three experiments contained in COBE (Differential Microwave Radiometer (DMR), Far Infrared Absolute Spectrophotometer (FIRAS), and Diffuse Infrared Background Experiment (DIRBE)) are very important in measuring the big bang. DMR measures the isotropy of the cosmic background (direction of the radiation). FIRAS looks at the spectrum over the whole sky, searching for deviations, and DIRBE operates in the infrared part of the spectrum gathering evidence of the earliest galaxy formation. By special techniques, the radiation coming from the solar system will be distinguished from that of extragalactic origin. Unique graphics will be used to represent the temperature of the emitting material. A cosmic event will be modeled of such importance that it will affect cosmological theory for generations to come. EOS will monitor changes in the Earth's geophysics during a whole solar color cycle

Undergraduate and Graduate Course Descriptions, 2023 Spring

Wright State University undergraduate and graduate course descriptions from Spring 2023

Planet Earth 2011

The failure of the UN climate change summit in Copenhagen in December 2009 to effectively reach a global agreement on emission reduction targets, led many within the developing world to view this as a reversal of the Kyoto Protocol and an attempt by the developed nations to shirk out of their responsibility for climate change. The issue of global warming has been at the top of the political agenda for a number of years and has become even more pressing with the rapid industrialization taking place in China and India. This book looks at the effects of climate change throughout different regions of the world and discusses to what extent cleantech and environmental initiatives such as the destruction of fluorinated greenhouse gases, biofuels, and the role of plant breeding and biotechnology. The book concludes with an insight into the socio-religious impact that global warming has, citing Christianity and Islam

Visible and Near Infrared imaging spectroscopy and the exploration of small scale hydrothermally altered and hydrated environments on Earth and Mars

The use of Visible and Near Infrared (VNIR) imaging spectroscopy is a cornerstone of planetary exploration. This work shall present an investigation into the limitations of scale, both spectral and spatial, in the utility of VNIR images for identifying small scale hydrothermal and potential hydrated environments on Mars, and regions of the Earth that can serve as martian analogues. Such settings represent possible habitable environments; important locations for astrobiological research. The ESA/Roscosmos ExoMars rover PanCam captures spectrally coarse but spatially high resolution VNIR images. This instrument is still in development and the first field trial of an emulator fitted with the final set of geological filters is presented here. Efficient image analysis techniques are explored and the ability to accurately characterise a hydrothermally altered region using PanCam data products is established. The CRISM orbital instrument has been returning hyperspectral VNIR images with an 18 m2 pixel resolution since 2006. The extraction of sub-pixel information from CRISM pixels using Spectral Mixture Analysis (SMA) algorithms is explored. Using synthetic datasets a full SMA pipeline consisting of publically available Matlab algorithms and optimised for investigation of mineralogically complex hydrothermal suites is developed for the first time. This is validated using data from Námafjall in Iceland, the region used to field trial the PanCam prototype. The pipeline is applied to CRISM images covering four regions on Mars identified as having potentially undergone hydrothermal alteration in their past. A second novel use of SMA to extract a unique spectral signature for the potentially hydrated Recurring Slope Lineae features on Mars is presented. The specific methodology presented shows promise and future improvements are suggested. The importance of combining different scales of data and recognising their limitations is discussed based on the results presented and ways in which to take the results presented in this thesis forward are given

A GIS-Multicriteria Approach to Analyzing Noise and Visual Impacts of Wind Farms

Land-use conflicts in facility siting can trigger public opposition in communities. A negative public perception, such as the Not-in-my-backyard (NIMBY) attitude, is a planning issue that is strongly associated with some types of siting decisions. After the Feed-in-Tariff (FIT) program through the Green Energy Act was introduced in Ontario in 2009, a large number of wind farm developments were proposed and implemented. Public concerns regarding the noise and aesthetic impacts of wind turbines have created public resistance and caused project delays. More importantly, the wind farm siting decision making process is a top-down process, which overrides the power of municipalities and ignores public concerns towards wind farms. In this thesis, a Geographic Information System (GIS)-based multi-criteria decision analysis (MCDA) siting approach has been developed, which is capable of representing the potential noise and visual impacts caused by wind turbines in a wind farm siting process. After identifying a sample of feasible sites in Southern Ontario, the noise and visual impact assessment approaches were applied to estimate the affected-population by wind farm sites. The changes of suitability levels within each feasible site can be determined after the integration of noise and visual criteria with the common siting criteria, which include physical, environmental, planning and economic factors. This siting approach is generalizable, which means it can be applied to other facility developments that have potential noise and visual impacts to the public. The results illustrate the spatial changes of suitability level before and after introducing the noise and visual criteria into the siting process. Planners and decision makers could potentially apply this siting approach to address public concerns in the future wind farm siting decisions

The MGX framework for microbial community analysis

Jaenicke S. The MGX framework for microbial community analysis. Bielefeld: Universität Bielefeld; 2020