Earth & Space Science

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https://hdl.handle.net/10315/27626

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Open Access
Athermal Optical Phased Array
(2025-04-10) Papakonstantinou, Constantine Athanasios; Lee, Regina S. K.
Optical phased arrays (OPA) are of growing interest in free space communication systems due to the need for high speed communication. Generally, design of OPAs using the Silicon On Insulator (SOI) platform will rely on the thermo-optic (TO) effect to tune the refractive index, thereby controlling the phase in each channel. However this makes the design sensitive to parasitic heat, and thermal cross-talk at scale. Instead of using thermal control, thermal tolerance is introduced by reducing the TO effect, and leveraging the electro-optic (EO) effect in lieu. Metamaterial techniques for the athermal design of optical waveguides and subwavelength structures are presented for the application of designing an OPA feed network. The design of compact and athermal grating antennas suitable for OPA configurations is also explored.
Open Access
Building Footprint Reconstruction from Satellite Imagery Using a Deep Learning Framework with Geometric Regularization
(2025-04-10) Kamran, Muhammad; Sohn, Gunho
Since the launch of Landsat-1 in 1972, Earth observation satellites have significantly evolved, now capturing vast amounts of high-resolution imagery. These satellites continuously transmit data that helps us monitor urban expansion, plan infrastructure, and respond to natural disasters. Satellite imaging plays a crucial role in creating accurate spatial maps, offering detailed insights into the built environment. However, reconstructing maps from satellite images is a complex challenge. The field of computer vision has made impressive progress in object recognition and representation, but accurately modeling buildings as geometric primitives remains difficult. Unlike traditional pixel-based approaches, primitive-based object representation requires understanding the spatial structure and relationships that existing methods often struggle with. In this thesis, we explore four different network architectures aimed at improving our baseline model, R-PolyGCN, by integrating novel modules that enhance building footprint reconstruction. The final chapter presents Decoupled-PolyGCN, our most advanced deep learning model, that leverages Graph Convolution Network (GCN) to enhance building footprint reconstruction. By incorporating geometric regularity, multi-scale feature fusion, and Attraction Field Maps (AFM), the model generates more structured and precise building outlines from a single satellite image. Evaluations on the Wuhan University (WHU) and SpaceNet-2 datasets show that Decoupled-PolyGCN outperforms existing approaches, improving Average Precision (AP) by Average Recall (AR)% and AR by 10%. These improvements enable more accurate and reliable mapping, benefiting applications in urban planning, disaster management, and large-scale spatial analysis.
Open Access
Development of an Orbital LIDAR System for Mars Atmospheric Research
(2025-04-10) Baal, Davin John; Whiteway, James A.
A Monte Carlo model was developed to simulate multiple scattering and depolarization of LIDAR signals in water ice clouds and mineral dust in the atmosphere of Mars. This model was used to test the viability of an orbital LIDAR system for the study of the Martian atmosphere. It was found that a system with the same laser as the Earth-based CALIPSO orbital LIDAR system but with a receiver telescope with half the diameter of CALIPSO's is capable of producing reliable measurements of typical Martian cloud and dust conditions.
Open Access
Mapping and Localization Using LiDAR Fiducial Markers
(2025-04-10) Liu, Yibo; Shan, Jinjun
LiDAR sensors are essential for autonomous systems, yet LiDAR fiducial markers (LFMs) lag behind visual fiducial markers (VFMs) in adoption and utility. Bridging this gap is vital for robotics and computer vision but challenging due to the sparse, unstructured nature of 3D LiDAR data and 2D-focused fiducial marker designs. This dissertation proposes a novel framework for mapping and localization using LFMs is proposed to benefit a variety of real-world applications, including the collection of 3D assets and training data for point cloud registration, 3D map merging, Augmented Reality (AR), and many more. First, an Intensity Image-based LiDAR Fiducial Marker (IFM) system is introduced, using thin, letter-sized markers compatible with VFMs. A detection method locates 3D fiducials from intensity images, enabling LiDAR pose estimation. Second, an enhanced algorithm extends detection to 3D maps, increasing marker range and facilitating tasks like 3D map merging. This method leverages both intensity and geometry, overcoming limitations of geometry-only detection approaches. Third, a new LFM-based mapping and localization method registers unordered, low-overlap point clouds. It employs adaptive threshold detection and a two-level graph framework to solve a maximum a-posteriori (MAP) problem, optimizing point cloud and marker poses. Additionally, the Livox-3DMatch dataset is introduced, improving learning-based multiview point cloud registration methods. Extensive experiments with various LiDAR models in diverse indoor and outdoor scenes demonstrate the effectiveness and superiority of the proposed framework.
Open Access
Application of GNSS Precise Point Positioning to Low-Cost Hardware for cm-level Positioning
(2025-04-10) Basnet, Pragati; Bisnath, Sunil B.
Precise Point Positioning (PPP) offers high-precision GNSS positioning solutions. The advent of low-cost hardware provides an affordable alternative to costly geodetic-grade hardware, broadening the accessibility of high-precision positioning across many applications. However, this hardware produces measurements with higher noise levels, reduced multipath suppression, and lower carrier-to-noise density ratios (C/N0), restricting its ability to achieve cm-level accuracy. This study addresses these limitations by developing a novel C/N0-based empirical observation weighting model to accompany the signal characteristics of low-cost hardware. This model enhances positioning accuracy by emphasizing high-quality signals above a nominal C/N0 threshold and down-weighting observations below it. The proposed model reduces float to carrier-phase integer ambiguity resolution (fixed) convergence time by 71% for 5 cm and 38% for 2.5 cm horizontal error thresholds for the static dataset tested, demonstrating the potential of low-cost GNSS devices as viable, high-precision positioning solutions.
Open Access
Resident Space Object Light Curve Classification & Space Situational Awareness Sensitivity and Simulation Studies
(2025-04-10) Qashoa, Randa; Lee, Regina S. K.
The number of objects being launched into space is rapidly increasing, emphasizing the critical importance of detecting, characterizing, and tracking these objects—an area of focus known as Space Situational Awareness (SSA). These Resident Space Objects (RSOs) include satellites (both active and inactive), rocket bodies and debris. Knowing the type of object near our satellites of interest is very important as it gives satellite operators the knowledge needed to accurately plan maneuvers to keep our orbits safe. This dissertation explores three main contributions within the field of SSA. The first is a light curve classifier which uses Machine Learning (ML) to classify Low Earth Orbit (LEO) RSOs into stable satellites, tumbling satellites and rocket bodies. Multiple approaches were tested but the method with the highest accuracy is a Barlow Twins network which has a 75% accuracy for two minute light curves and a 97% accuracy for five minute light curves. The classification is used to characterize the motion of objects, which operators can use in combination with real images to determine the risk of collision and to perform effective maneuvers. The second contribution is regarding SSA mission planning. A sensitivity analysis was conducted to determine the best camera to use for observing co-orbiting RSOs within 250 km of the observer. The analysis includes exploring the location of potential targets in the Field-Of-View (FOV) of the observer as well as the Signal-to-Noise Ratio (SNR) of different targets. A similar analysis to the one presented in this dissertation has been performed for the Redwing microsatellite mission. Lastly, RSO image prediction simulations are tested for use in SSA. This dissertation demonstrated the implementation of an anti-sun pointing orientation for prediction simulations with validation from real images. Predicted images were used to determine targets for observation which were then validated following the downlink of the images.
Open Access
Image Processing for Stratospheric Based Space Situtational Awareness (SSA)
(2025-04-10) Suthakar, Vithurshan; Lee, Regina S.K.
This research explores the use of a stratospheric platform imager for advancing Space Situational Awareness (SSA). The primary goal was to develop and validate Resident Space Objects (RSO) detection algorithms using the RSONAR dataset, consisting of wide field-of-view imagery. RSO Detection methods were tested on 429 images, achieving F1 scores between 68% and 88%. Additionally, the potential of a dual-purpose star tracker for SSA was validated, analyzing over 27,000 images to assess astrometric and photometric properties of RSOs. Further, 544 RSO streaks were characterized based on parameters such as length, signal-to-noise ratio, and orientation. The development of RSONAR II, a next-generation camera system, allowed for capturing over 65,000 images at varying resolutions, and its optical performance was compared across two imaging systems. This study provides a comprehensive evaluation of wide field-of-view imagery for SSA and presents advancements in dual-purpose star tracker systems for future missions.
Open Access
Advancements in the GRACE and GRACE-FO Gradiometer Mode
(2025-04-10) Pandit, Nikeet Prashant; Pagiatakis, Spiros
Gravity gradients have not been globally measured since 2013. Accordingly, we advance GRACE gradiometer mode (GM) to enable GRACE, GRACE-FO, and future gravity missions to be used in a gravity gradient mode. The benefit for the geosciences is that gravity and gravity gradients contain complementary information about Earth’s structure. Based on the seminal contribution from Peidou (2020), we develop a novel configuration for GM that views any single gravimeter satellite as a ‘gradiometer’. The new GRACE-C ‘gradiometer’ gradients more clearly delineate global tectonic plate boundaries in the Himalayas and North Africa regions, the Aleutian trench, the Java trench, and the Peru-Chile trench. Over Canada, the effect of glacial isostatic adjustment is apparent. We also observe signals resembling terrestrial water storage changes in Africa, among others. Overall, we highlight the usefulness of GM gradients for a wide variety of geoscience applications. We then develop a software package to produce GM gradients.
Open Access
Resident Space Object Tracking for Space Situational Awareness
(2024-11-07) Kunalakantha, Perushan; Lee, Regina S. K.
This research presents several contributions aimed at improving the current state of Space Situational Awareness (SSA) using optical imagery. The first component involved the development of an image capture algorithm which was successfully used to acquire over 90000 optical images from the stratosphere, with hundreds of visually verified Resident Space Objects (RSOs). The second component involved the development of a novel RSO tracking algorithm which was able to detect 87% of the RSOs in an 878-image dataset at least once. The third component proposed an automated annotation framework and corresponding four-tool annotation suite to develop a 500-image dataset to train and test another RSO tracking algorithm. The final component involved the demonstration of a dual-purpose payload, performing RSO detection alongside an existing Attitude Determination (AD) algorithm which detected 11 unique RSOs in real-time during another stratospheric balloon mission.
Open Access
New Generation Stochastic Data-driven Calibration of the Accelerometers and Modelling of the Non-gravitational Accelerations in GRACE missions
(2024-11-07) Tzamali, Evangelia Myrto; Pagiatakis, Spiros
The Gravity Recovery and Climate Experiment (GRACE) and its successor, GRACE Follow-On (GRACE-FO), have significantly advanced our understanding of Earth's gravity field and its temporal changes by measuring subtle gravity variations caused by mass redistribution on and beneath Earth's surface. A key component of these missions is the use of geodetic-quality GPS receivers for precise orbit determination and 3D accelerometers, which measure non-gravitational forces like atmospheric drag and solar radiation pressure. However, challenges in using accelerometers must be addressed to ensure data and modeling accuracy. A primary challenge is the calibration of accelerometers, as inaccuracies can lead to significant errors in gravity field modeling and thermospheric density estimates of up to 10%, depending on the levels of solar activity. This study introduces a calibration method using matched filter techniques applied to accelerometer measurements and GPS-derived accelerations. The estimated calibration parameters show high stability with values close to 1, as expected from the ultra-sensitive electrostatic space accelerometers and proposed by other studies. Error assessment of GRACE and future mass change missions must consider accelerometer measurements as stochastic quantities with realistic error estimates. This involves identifying and quantifying measurement errors stemming from instrument noise, thermal effects, correlations and transient effects. This study generates a stochastic (weighted) 1B accelerometer dataset to investigate systematic errors from various sources like geomagnetic storms, temperature changes, and terminator crossings. Contrastingly to the official accuracy which is 〖0.1 nm/s〗^2 for the along-track and radial direction and 〖1 nm/s〗^2 in the cross-track, the proposed dataset being clean from spikes has an accuracy of 〖 10^(-3) nm/s〗^2. Accurate modeling of non-gravitational forces is essential for isolating gravitational signals and estimating drag, which is crucial for determining thermospheric densities. This study proposes a data-driven model for the dominant forces of solar and thermal radiation pressure and drag on the GRACE-C satellite, using the new 1B dataset produced in this study. A comprehensive analysis of residuals which are up to 〖2 nm/s〗^2 in the along-track, 〖0.5 nm/s〗^2 in the cross-track and up to 〖5 nm/s〗^2 in the radial direction, reveals disturbances with latitudinal dependency, especially during high geomagnetic activity.
Open Access
On the Impact of Land Use & Landcover Change on GHG Emissions using Advanced Remote Sensing Technology
(2024-11-07) Ituen, Ima-Obong; Hu, Baoxin
There is an interesting effect of the changing climate: it has been observed in Northern Ontario that the warming weather has lengthened the growing season. Consequently, there has been increased land use conversion from natural forests to agriculture in Northern Ontario to capitalise on the new economic opportunities resulting from longer growing seasons. However, the long-term effects of land conversion on soil carbon, nutrients, and greenhouse gas emissions (GHGs) are unknown. This research study leveraged advanced remote sensing technology to detect changes in land cover and land use. A major problem addressed in this work was the lack of landcover classification data which is current, extends over the entire study area, and is of high spatial and temporal resolution with which to analyse the study region properly. This study proposed a new change detection framework which uses the best available data for landcover classification and disturbance information, even in the event of scarce input or training data. Thus, an analyst can progressively adapt the available data sources to present a more accurate assessment of the disturbances. More accurate input data for the carbon models was shown in this study to decrease carbon and GHG estimates by between 15% and 32%. The study concludes with tools that an everyday user can utilize to examine their region. The applications can run for free on their devices, permitting users to discover the landcover changes in their region, and estimate forest loss or gain. The applications could serve as a bridge to discussions concerning the areas that are best suited to concentrate carbon sequestration efforts.
Open Access
Deep Learning-Enhanced Autonomous Aerial and Ground Robotics Using UWB and Lidar in GNSS-Denied Environments
(2024-11-07) Arjmandi, Zahra; Sohn, Gunho; Armenakis, Costas
Over the last decade, advancements in Unmanned Aerial Vehicle (UAV) technology and Artificial Intelligence (AI) have led to significant improvements in navigation and positioning, yet widespread adoption remains limited due to challenges in integrating various technologies and ensuring reliable real-time data processing. This thesis addresses these issues by developing a comprehensive framework that merges advanced data collection platforms, deep learning algorithms, and novel fusion methods to enhance UAV positioning accuracy and reliability. A central contribution of this research is the creation of the Q-Drone Ultra-Wideband (UWB) benchmark dataset. This dataset, generated from a UAV equipped with five UWB sensors across five diverse environments (indoor, outdoor, and semi-outdoor) over a distance of 4 km, provides a standardized benchmark for testing UAV positioning systems. It enables researchers to develop and validate algorithms under varied conditions, supporting advancements in UAV navigation and positioning research. The thesis also introduces an incremental smoothing approach, integrating high-rate and low-rate UWB measurements with inertial data within a unified pose graph framework. This method, using an "add-after-eliminating" strategy, reduces Mean Absolute Error (MAE) by 0.2 meters compared to baseline multilateration methods and achieves a 0.3-meter MAE reduction compared to two-factor pose graph methods. Further, the DeepCovPG framework is developed, combining a Variational Autoencoder (VAE) with a Long Short-Term Memory (LSTM) network to predict and incorporate dynamic covariances into the pose graph. This approach results in a 48% reduction in Root Mean Square Error (RMSE) and a 51% reduction in Range Covariance RMSE, with notable improvements of 0.41 meters in tunnels and 0.23 meters in fields. The framework also achieves a 26% reduction in multilateration RMSE and a 32% reduction in multilateration Covariance RMSE. Additionally, the thesis explores Light Detection and Ranging (LiDAR)-based positioning and proposes the INAF fusion method. This method dynamically selects relevant information from geometric and AI-based odometry techniques, improving accuracy by 3.90% over direct fusion methods and 0.25% over attention-based fusion methods. The INAF fusion method demonstrates enhanced adaptability to various driving conditions, improving accuracy in both straight and dynamic environments.
Open Access
Sub-metre Positioning with Smartphone Global Navigation Satellite System Measurements in User Environments
(2024-11-07) Hu, Jiahuan; Bisnath, Sunil
The ubiquity of smartphones catalyzes various smartphone-based Internet of Things (IoT) applications, among which smartphone positioning that uses Global Navigation Satellite System (GNSS) observations to provide user position plays an important role. The noisy smartphone GNSS measurements from signal obstructed environments and embedded antennas prevent users from obtaining reliable and accurate positioning solutions. With a correct understanding of the measurement characteristics and advanced positioning techniques such as Real-Time Kinematic (RTK), Precise Point Positioning (PPP), and PPP-RTK, this dissertation aims to achieve sub-metre- or decimetre-level positioning accuracy with smartphone GNSS measurements in realistic environments. In this dissertation, a novel smartphone range error derivation method is proposed, as well as an improved cycle slip (CS) detection method. New stochastic modelling and measurement outlier detection methods are developed. And smartphone ambiguity resolution (AR) is conducted with a newly proposed candidate selecting strategy. Smartphone measurements exhibit significantly higher noise levels as compared to those from geodetic hardware, in which the range errors are more correlated with the carrier-to-noise density ratio (C/N0). Moreover, range errors are environment-dependent, and they behave differently when, e.g., the smartphone is mounted on the vehicle roof versus the dashboard. Considering that the conventional Doppler CS detection method is sometimes not applicable due to inconsistent time-differenced carrier phase (TDCP) and Doppler clocks, a novel modified Doppler method is proposed and validated with simulated cycle slips. With proposed Doppler cycle slip detection method, all simulated cycle slip combinations are detected. In terms of quality control methods, the improved stochastic model and novel outlier detection method outperform the conventional C/N0-based weighting and a fixed residual rejection threshold, showing a percentage improvement from 38% to 54% for PPP horizontal positioning errors within 1 metre. A novel smartphone RTK wide-lane (WL) partial AR strategy is proposed, for which the static results show an improvement of 83% in horizontal when WL AR is conducted compared to the underlying float solution, and the positioning accuracies can reach 6.8, 2.9, and 11.5 cm in E, N, and U, respectively. The proposed algorithm is validated with kinematic datasets collected in real-life environments, in which the time series of horizontal positioning errors exhibit less variation than the float solutions, represented by smaller positioning errors. Moreover, with fixing WL ambiguities, the horizontal positioning performance has improvements ranging from several centimetres to up to 8 decimetres depending on the related environments. the biggest improvement of 0.79 m is noticed for 95th percentile horizontal positioning errors under suburban environments. Improvements on the above-mentioned aspects show potential on achieving decimetre-level positioning performance with smartphone embedded antennas in realistic environments.
Open Access
Analyzing Mars' Polar Anomalies: Computer Vision Techniques for Seasonal Changes and Polar Dynamics
(2024-11-07) Acharya, Pruthviraj Jaydipsinh; Smith, Isaac B.
This dissertation offers a detailed analysis of seasonal ice cap dynamics and surface anomalies on Mars, utilizing autonomous tracking techniques with polar stereographic images from the Mars Color Imager (MARCI) spanning multiple Mars Years (MY). This dissertation investigates the recession of the Northern Polar Seasonal Cap (NPSC) from MY 29 to MY 35. Employing Python for automation, this analysis tracks the recession with high temporal fidelity, uncovering intraseasonal variability in the recession rate in addition to significant interannual variability. This variability is coincident with specific events influenced by off-polar winds and Global Dust Storm (GDS) events. The chapter notably examines the divergent effects of GDS events on the size of the NPSC and its recession rates, emphasizing the influence of storm timing and duration. Additionally, the dissertation explores the recession of the Southern Polar Seasonal Cap (SPSC) from MY 28 to MY 31, characterized by significant discontinuity and variability in recession rates. This study highlights the impacts of the GDS events on SPSC dynamics, demonstrating accelerated sublimation rates and a reduction in cap size after the storm onset. Finally, the dissertation delves into seasonal phenomena known as Cold and Bright Anomalies (CABAs) and Warm and Dark Anomalies (WADAs) on the North Polar Residual Cap (NPRC). Extensive analysis from MY 29 to MY 35 examines the evolution of temperature and albedo at anomaly sites, revealing a strong correlation with local topography and atmospheric conditions, including katabatic winds and transient eddies. Collectively, this dissertation provides a nuanced understanding of Martian polar dynamics, offering insights into the interactions between atmospheric phenomena and surface conditions. The adoption of automated tracking technologies significantly enhances the precision and efficiency of these analyses, contributing to our broader understanding of Martian climatology and its seasonal cycles.
Open Access
Examining Particulate Matter Emissions from Vehicular Traffic in an Urban Environment
(2024-11-07) Reet, Rhythm; Gordon, Mark
This research investigates the complexities of particulate matter (PM) emissions in urban environments, with a focus on non-exhaust sources like brake and tire wear, primarily caused by vehicular traffic in an urban area. By examining the variance in PM size, specifically particles sizes between 50 nm and 1000 nm, the study assesses the impact of traffic volume and patterns on PM emissions in Toronto, Ontario. The approach used is based on vertical fluxes measured with eddy-covariance, counting vehicles and estimation of the footprint. In this study variations in vehicle traffic were linked to fluctuations in particle concentrations and turbulent fluxes. Higher vehicle rates in weekday evenings did not correspond with increased particle numbers, whereas weekday mornings experienced higher concentrations, possibly due to overnight pollutant accumulation and the rising atmospheric boundary layer. Larger particles were more likely to originate from background sources than the road itself, particularly in areas affected by stop-and-go traffic. Over a specific week in March, however, road emissions significantly contributed to particle concentrations, a deviation from the norm. Weekends presented consistent particle deposition, suggesting that roads act more as sinks than sources, especially during periods with fewer vehicles. The complexities of urban particulate dynamics were highlighted, indicating that larger particles are prone to settle on the road surface, and changes in traffic patterns can transform typical emission sources into particle sinks. This study lays the groundwork for future research, emphasizing the need for detailed traffic data to better understand emission sources and implications for urban air quality and public health.
Open Access
Modeling trace elements over Athabasca oil sands region in Alberta, Canada using WRF-Chem
(2024-11-07) Hao, Jingliang; Chen, Yongsheng
The Athabasca Oil Sands Region (AOSR) in northern Alberta, Canada serves as a significant source of trace elements. In this study, the Weather Research and Forecasting model coupled with chemistry (WRF-Chem) is modified to predict the transport and deposition of eight elements (Al, Ca, Fe, K, Mn, Si, Ti, and Zn) in the AOSR in 2016 and 2017. The model has a good performance on the air temperature, wind at surface, and precipitation. The model-measurement percentage differences in the annual concentrations of the eight elements at three sites are in the range of -6.9% to 76% at AMS1, -48% to 72% at AMS17, and -165% to 5.8% at AMS18. Modeled annual concentrations and atmospheric deposition of individual element range from 0.016 to 2.67 µg m-3 and 2.62 to 385 mg m-2yr-1 in the central region of the oil sands industry, respectively. Modeled element concentrations and deposition show a rapid decline by around three orders of magnitude from the central region to the remote region in a distance of around 150 km. The modeled total concentrations of the eight elements at three sites are overestimated by 82% in the cold season but underestimated by 38% in the warm season. In the first sensitivity test, the annual emission is reallocated to 30% in the cold season and 70% in the warm season. This leads to a reduction in the bias for the modeled total concentrations of eight elements from 45% to 13% in the cold season and from 45% to 24% in the warm season. In the second sensitivity test, the original dry&wet deposition schemes in WRF-Chem are replaced by other schemes. The modeled annual total dry and wet depositions of all elements are decreased by 56% and increased by 33%, respectively. The total dry&wet deposition is decreased by 31%.
Open Access
Design and Additive Manufacture of a High-speed Piezoelectrically Actuated Compliant Mesoscale Parallel Robot
(2024-11-07) Tabak, Ariel; Orszulik, Ryan
This work presents the design of an additively manufactured mesoscale parallel robot that is actuated by piezoelectric bimorphs through a compliant transmission mechanism. The piezoelectric bimorph actuators are modeled via finite element and experimentally tested to verify their performance. A transmission mechanism is designed using finite element analysis to determine the critical parameters for the conversion of the piezoelectric actuators small linear displacements to large rotational motion. Multiple transmissions are fabricated using additive manufacturing and are tested experimentally. The transmission mechanism is then used to create a six-bar parallel robot that is driven by the piezoelectric bimorphs, which is simulated and tested experimentally. The planar parallel robot is 37.88 by 20.54 millimeters in size and has a workspace of 14.36 by 8.66 millimeters for a total area of 65.6 millimeters squared. The robot is capable of following trajectories within the workspace at a frequency of up to 10 Hz.
Open Access
Image Processing Techniques for Space Situational Awareness
(2024-07-22) Stewart, Michael Ian; Lee, Regina
With a renewed interest in space, it is paramount to understand the space environment; an act commonly referred to as Space Situational Awareness (SSA). Tracking and performing measurements on nearby orbiting objects; known as RSOs helps us better understand SSA. Image processing is a significant step in improving SSA capabilities, aiding in space object detection, tracking, and characterization. This thesis addresses gaps in RSO image processing, with a focus placed on developing specialized pipelines for the production of photometric measurements of Geosynchronous Equatorial Orbit (GEO) RSOs and the James Webb Space Telescope (JWST). Despite challenges like the unique Earth-Sun L2 Orbit of JWST and high noise issues, this study successfully produced calibrated photometric light curves of three targeted RSOs, advancing our understanding of photometric SSA methodologies.
Open Access
BIM based Energy Consumption Estimation using Data-driven model
(2024-07-18) Kiavarz Moghaddam, Hamid; Jadidi, Mojgan
Building Information Modeling (BIM) is undergoing rapid technological evolution in the building construction industry. Recently, employing BIM as a building 3D digital model in Building Energy Consumption Estimation (BECE) has gained momentum because of the enriched geometric and semantic information. Indeed, indoor BECE notably depends on the semantics, geometry (building elements and shapes), and topology information of the building's elements to recognize the spaces in a building with high energy demand. However, despite extensive studies on applying the BIM and Industry Foundation Classes (IFC) as an open standard data model for BIM in BECE analysis, employing the full potential of the BIM remains poor due to its data model complexity and incompatibility with BECE data-driven algorithms. There is a significant lack of building energy modeling in using the detailed geometry, semantic, and 3D topology information in BECE data-driven models. The objective of this dissertation is to develop an innovative and comprehensive framework called space-based precise building energy consumption estimation using BIM. In this research, a framework is developed to convert the IFC model into a space-based graph, including the geometry, semantic, and topology information on the proposed graph nodes and edges. The graph is compatible with the machine learning algorithm. A graph-based classification algorithm is suggested in this research to find critical spaces in the building for energy consumption. This research proposed a prescriptive model by integrating building energy simulation with optimization techniques, using BIM data and a Genetic Algorithm (GA) to develop a prescriptive model for indoor building design. The study focuses on space-based BECE analysis, leveraging BIM interoperability to recommend optimal solutions. The proposed model employs the value engineering method to balance energy consumption, functionality, and cost, providing engineers and designers with insights to optimize building performance effectively. This approach enhances energy efficiency and offers substantial design optimization solutions, bridging the gap between energy prediction and practical application in the architectural, engineering, and construction (AEC) industry. The outcomes of this study are conducive to contemporary data-driven models in BIM and indoor BECE analysis. This provides a comprehensive perspective on both present and prospective requirements for BIM in the estimation of building energy consumption. The study integrates various sectors, including architecture, construction, machine learning, ad 3D geospatial analysis, aiming to derive comprehensive and optimal solutions. Furthermore, it underscores the necessity for future multidisciplinary research by unfolding existing gaps and limitations.
Open Access
Neural Network Based Sliding Mode Control for Robotic Manipulator
(2024-07-18) Gomes Carmo, Ingredy Gabriela; Shan, Jinjun
Robotic manipulators are used in many applications. However, robotic arms are complex systems due to external disturbances, perturbations, and their coupled non-linear dynamics. This thesis aims to propose a robust control strategy for autonomous robotic manipulation. First, the trajectory tracking problem was introduced and an approach to overcome this issue using a sliding mode controller combined with a neural network is proposed. Then, the proposed approach is compared to classical and modern control methods including controllers from the literature to demonstrate the performance of the proposed controller. The proposed controller was then integrated with a grasp detection algorithm for an autonomous manipulation application. Simulations and hardware experiments were conducted to validate the performance of the proposed method.