Browsing by Author "Shahbazi, Mozhdeh M."
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- ItemOpen AccessConceptual Design Framework for Transitional VTOL Aircraft with Application to Highly-Maneuverable UAVs(2019-08-30) Abdelrahman, Ashraf Mohamed Kamal Mahmoud; Ramírez-Serrano, Alejandro; Johansen, Craig T.; Morton, Chris R.; Shahbazi, Mozhdeh M.; Laliberté, Jeremy F.Transitional Vertical Take-off and Landing Aircraft (TVA) are systems capable of flying as Fixed-Wing (FW) aircraft and rotorcraft as well as transition between these flight modes. Responding to the technology advancement and impetus by the emerged mission needs, TVA have recently gained much interest in the aviation industry and many current/future aircraft are required/envisioned to have both the FW and rotorcraft capabilities in diverse potential applications. However, consolidating the characteristics of FW aircraft and rotorcraft increases the challenges when designing aircraft for which solutions currently do not exist. The number of Design Requirements (DRs) needed to be achieved and the number of contradictory Design Parameters (DPs) involved in the design process, further complicates the design process of TVA compared with traditional design methods for either FW aircraft or rotorcraft. Despite the maturity in the field of design for conventional FW aircraft and rotorcraft, considerable design work, techniques, and methodologies need to be specifically developed to tackle the challenges that exist in TVA design. Generally, the earlier design steps are the most important within any aircraft design and development process as significant decisions/calculations about the aircraft configuration are made with a somewhat limited knowledge about the aircraft. This thesis discusses the challenges/difficulties associated with the early design steps of TVA and introduces a newly developed conceptual design framework to tackle them. First, a systematic concept development methodology is developed with all necessary mathematical formulations and complementary benchmarks that integrates well-known methods/tools in a novel way suitable for TVA concept development. The proposed approach allows managing multiple conflicting criteria and coupled decisions. Furthermore, the methodology enables efficient exploration of a very large design space with different alternatives and complex design hierarchies to generate the most relevant aircraft configurations responding to a set of DRs and selecting the one that best meets the requirements. The proposed approach allows designers to examine more alternatives than what is feasible with traditional design methods and prevents designers from either choosing poor concepts due to the lack of experience or overlooking valuable ones. Second, a generalized formal sizing methodology for TVA is developed by modifying several assumptions typically made when using the available and well-known FW and rotorcraft performance equations. From such an approach, a new set of equations is developed to enable the simultaneous calculation of the adequate sizing parameters such that TVA satisfy the DRs in all of the three flight modes (i.e., FW, transition, and rotorcraft). In order to demonstrate and validate the capabilities and adaptation of the developed framework, the approach is applied to the conceptual design of an advanced unmanned highly-maneuverable TVA having challenging DRs (e.g., requirement to perform maneuvers not possible by traditional aircraft like pitch-hover and transition to FW flight mode at any attitude). The obtained results revealed that the proposed framework can be applied to TVA conceptual design with a reasonable level of confidence in its accuracy. The formulations and tools developed reduce the time typically needed to develop aircraft concepts and increase the chances to generate a final aircraft with high performance meeting the initial DRs.
- ItemOpen AccessConcrete Damage Inspection by Classification of Terrestrial Laser Scanner Point Clouds(2020-05-05) Hadavandsiri, Zahra; Lichti, Derek D.; Shahbazi, Mozhdeh M.; Dawson, Peter C.Concrete structures endure damage and deterioration when subjected to human activities and natural hazards. Early detection of damage on concrete structures is vital to counter the side effects deriving from damage and to allow timely maintenance procedures. This thesis presents a novel approach for high-precision detection of damage on concrete surfaces using terrestrial laser scanner point clouds (PCs). At first, an unsupervised approach is developed that utilizes a robust version of principal component analysis (PCA) classification in order to distinguish between structural damage and outliers present in the data. Numerical simulations are conducted to develop a systematic point-wise defect classifier that automatically diagnoses the location of surface damage on the investigated region. The developed method examined on two real datasets, demonstrate the validity of the proposed systematic framework for reliable detection of damage of any type which makes roughness as small as 1 cm or larger on the surface of concrete structures captured with any laser-scanning PC with a minimum spatial resolution of 5 mm point spacing. At second, a supervised approach is developed that employs the outcome of the primary unsupervised classifier in order to accurately annotate the training data without the need for manual labeling. One flume of an aqueduct dataset was used for training the system. This machine learning-based model relies on a support vector machine (SVM) algorithm to train a point-wise defect classifier for locating the concrete damage. This yields an average classification precision and F1-score of 97.33% showing the potential of using machine learning for concrete damage detection. The performance of the prediction model was evaluated on three real datasets. The prediction model can successfully mirror the high performance of the unsupervised method used in the training process. In addition, by exploiting a more extensive variety of geometric features and skipping the intensive computation of the robust PCA, it outperforms the unsupervised classifier in terms of model performance and computational efficiency, respectively. Consequently, the properly trained machine learning system provides reliable diagnosis of the health conditions of large concrete structures that are not computationally feasible to be inspected by the primary unsupervised classifier.
- ItemOpen AccessEmpirical Models for Estimating Significant Wave Height Using RADARSAT-2 Data(2019-01-08) Ma, Meng; Collins, Michael J.; Collins; Wang, Xin; Shahbazi, Mozhdeh M.The images captured by Synthetic Aperture Radar (SAR) are useful for retrieving ocean wave parameters. The objective of this study is to establish empirical algorithms to estimate significant wave height (Hs) from RADARSAT-2 (RS2) fine-quad (FQ) beam mode images. This is the first time to retrieve Hs by utilizing around 1,400 collocated samples over the eastern and western North America. The fitting procedures are based on linear regression and neural network. The models are validated against to buoy observations. Unlike the most related work, this study explores the effects of incidence angle and polarization on the estimation of Hs. I report five accuracy metrics and introduce a novel cost function to assess and compare the models' performance. Finally, the proposed two types of models both show a good agreement with buoy observations. The RMSE and R could reach to 0.26m and 0.97, respectively.
- ItemOpen AccessGeometric modelling and calibration of a spherical camera imaging system(2020-04-16) Lichti, Derek D.; Jarron, David; Tredoux, Wynand; Shahbazi, Mozhdeh M.; Radovanovic, RobertThe Ladybug5 is an integrated, multi-camera system that features a near-spherical field of view. It is commonly deployed on mobile mapping systems to collect imagery for 3D reality capture. This paper describes an approach for the geometric modelling and self-calibration of this system. The collinearity equations of the pinhole camera model are augmented with five radial lens distortion terms to correct the severe barrel distortion. Weighted relative orientation stability constraints are added to the self-calibrating bundle adjustment solution to enforce the angular and positional stability between the Ladybug5’s six cameras. Results are presented from two calibration data-sets and an independent data-set for accuracy assessment. It is demonstrated that centimetre-level 3D reconstruction accuracy can be achieved with the proposed approach. Moreover, the effectiveness of the lens distortion modelling is demonstrated. Image-space precision and object-space accuracy are improved by 92% and 93%, respectively, relative to a two-term model. The high correlations between lens distortion coefficients were not found to be detrimental to the solution. The mechanical stability of the system was assessed by comparing calibrations taken before and after ten months of routine camera system use. The results suggest sub-pixel interior orientation stability and millimetre-level relative orientation stability. Analyses of accuracy and parameter correlation demonstrate that a slightly-relaxed weighting strategy is preferred to tightly-enforced relative orientation stability constraints.
- ItemOpen AccessGeometric Primitives in MLS Point Clouds Processing(2020-04-14) Xia, Shaobo; Wang, Ruisheng; Lichti, Derek D.; Shahbazi, Mozhdeh M.; Gao, Yang; Kang, ZhizhongMobile Light Detection and Ranging (LiDAR), as an active remote sensing system, has become an accessible street-level mapping technology in the last decade due to its ability to collect accurate and dense 3D point clouds efficiently. Although tremendous effort has been made to LiDAR data processing, there still exist many problems in everyday tasks ( e.g., segmentation and detection). In this thesis, the LiDAR data processing is re-visited from a geometric-primitive perspective, with the hope that existing problems can be partly solved or even well addressed by tapping the potential of geometric primitives. A survey on geometric primitive extraction, regularization and their applications is presented for the first time. In this review, geometric primitives that consist of a group of discrete points are categorized into two classes: shape primitives (e.g., planes) and structure primitives (e.g., edges). The rest of this thesis focuses on geometric primitives in mobile LiDAR data processing. A fast 3D edge extraction method which consists of finding and linking edge candidates is proposed and tested in large-scale scenes. Given extracted edge clusters, a new facade separation method for mobile LiDAR point clouds is developed, based on which connected facades are separated into facade instances for the first time. To explore the potential of plane primitives in mobile LiDAR data processing, a novel instance-level building detection method based on plane primitives extracted from original point clouds is proposed. After that, a new point cloud segmentation algorithm that succeeds in separating buildings and vegetations is presented. The main contribution lies in using plane priors to improve segmentation accuracy. For line primitives, a new extraction method is presented in this thesis, which can extract multiple primitives simultaneously from projected point clouds. Based on extracted line segments, a graph-based method is presented to construct 2D building footprints. Last but not least, this thesis also introduces the energy-based ``hypothesis and selection" (HS) framework to object detection and segmentation in LiDAR point clouds for the first time. The adapted frameworks are proved to be flexible and effective according to extensive experiments in different applications.
- ItemOpen AccessLocation Estimation and Trajectory Prediction for Collision Risk Assessment in Connected Vehicle Environment(2019-09-18) Afkhami Goli, Sepideh; Far, Behrouz H.; Fapojuwo, Abraham O.; Shahbazi, Mozhdeh M.; Krishnamurthy, DiwakarSafety systems in intelligent and autonomous vehicles rely heavily on the accuracy of localization and location prediction of nearby road users. Current vehicular systems use a variety of sensors to perceive the environment. Cameras, proximity and ranging sensors are the most common types of devices used for this purpose. The main limitation of onboard sensors is the partial perception of the surrounding environment due to occlusions, limited field of view, or resolution and range restrictions. Wireless vehicular communication offers new opportunities for safety applications via information sharing and extending the perception of a car, beyond the limitations of its onboard sensors. This thesis first explores the problem of fusing multiple sources of location information, including the sensor data and information shared via Vehicle-to-Vehicle (V2V) communication to improve localization accuracy. Using sensor data adds more challenges as it is usually noisy, mixed with clutter and false alarms. To address these challenges, the problem is formulated in Random Finite Set (RFS) statistics and solved via the Probability Hypothesis Density (PHD) filter. Second, this thesis investigates the location prediction problem in the connected vehicle environment. A data-driven framework is proposed to learn motion patterns from historical trajectory data via Gaussian Process Regression (GPR) and share this information among vehicles. In this framework, a vehicle leverages GPR models alongside sensory location data to predict the positions of nearby cars. Third, to improve the accuracy of both location estimation and prediction, a new multi-target Bayesian filtering algorithm is proposed that incorporates the GPR models in the Multi-source Multi-target Bayesian filters. Simulations based on real-world data and comparisons to similar algorithms from the state-of-the-art demonstrate the performance of the proposed methods. The results show about 30% improvement in estimating and predicting the location of surrounding vehicles for seconds in advance, fulfilling the requirements for a real-time collision risk assessment system.
- ItemOpen AccessA Model-based, Optimal Design System for Terrestrial Laser Scanning Networks in Complex Sites(2019-08-29) Jia, Fengman; Lichti, Derek D.; O'Keefe, Kyle P. G.; Wang, Ruisheng; Shahbazi, Mozhdeh M.; Lindenbergh, Roderik C.With the rapid increase of terrestrial laser scanner (TLS) applications, especially for the high-accuracy modelling of large-volume, complex objects, a design system is required to provide the optimal solutions for both scanner and target placement, so that the project requirements in terms of coverage, precision, economy and reliability can be met. In this thesis, a model-based, optimal design system for terrestrial laser scanning networks in complex sites is proposed. First, a hierarchical TLS viewpoint planning strategy driven by an improved optimization method is developed to solve the optimal scanner placement problem. The main contribution of the proposed method is to improve the efficiency in design without jeopardizing the optimality of the solution, compared with the traditional method with the extensive search strategy. In addition, the target placement for registration, which draws limited attention in the existing research, is determined by optimizing the target arrangement criterion, and the number of target locations is minimized by accepting the close to optimal target arrangement. Finally, the quality of the design, including the sensitivity of the object coverage to viewpoint placement and the precision of the point cloud are provided. The proposed methods were verified by the relatively small network first and then applied on two building complexes located on the University of Calgary campus. The design for scanner placement was compared with the “brute force” strategy in terms of the optimality of the solutions and runtime. The results showed that the proposed strategy provided scanning networks with a compatible quality but a significantly improved efficiency in design. The number of target locations necessary for registration from the proposed system was surprisingly small, considering the volume and complexity of the networks. Through the quality assessments, the sensitivity of the object coverage to the scanner placement indicated where users might need to consider viewpoint densification, and the point cloud precision indicated if the network design could meet the project requirements.
- ItemOpen AccessMulti-Algorithm Evaluation of Ship Detection Accuracy Using Synthetic Aperture Radar for the Radarsat Constellation Mission(2018-03-23) Mantey, Victoria Dawn; Collins, Michael J.; Kim, Jeong Woo; Shahbazi, Mozhdeh M.This study investigates a 2-stage ship detection algorithm for the SAR Radarsat Constellation Mission. The three beam modes on the RCM with the most potential for ship detection are tested using four different dual polarizations: two linear and two compact polarizations. The first stage is an intensity-based detection; five different implementations of the detection are investigated, using a combination of the likelihood ratio test and constant false alarm rate algorithms. The second stage of the detection algorithm is a decomposition stage which estimates the proportion of single-bounce, double-bounce and volume backscatter from each pixel and identifies ship pixels based on their scattering mechanisms. The combination of the intensity-based and decomposition algorithms fails to identify and remove sidelobe detections from the scenes. Therefore, a study of three possible sidelobe removal algorithms is undertaken to attempt to remove these false detections from the results. The best option identified is a refined decomposition threshold, developed in this work, to use the volume scattering information to improve the removal of sidelobe pixels. The end results are promising with the majority of extra detections removed from the scenes for all three RCM beam modes tested and fewer than one verified target per scene missed by the detections. The compact polarizations outperformed the linear polarizations for both the intensity-based and decomposition algorithms.
- ItemOpen AccessMulti-camera panoramic imaging system calibration(2019-01) Jarron, David; Lichti, Derek D.; Shahbazi, Mozhdeh M.; Radovanovic, Robert S.A mobile mapping system (MMS) is a three-dimensional reality capture system that collects georeferenced spatial data with integrated navigation and imaging sensors from a moving vehicle. Several imaging subsystems can be found on board an MMS, such as panoramic camera systems and LiDAR sensors. The data collected from a panoramic imaging system must be accurately georeferenced and the sensors must be rigorously calibrated to ensure accurate registration of images to the point clouds collected by the LiDAR sensors, and to ensure panoramic images are generated seamlessly. The panoramic imaging system studied in this work is the Ladybug5 (by FLIR Integrated Imaging Solutions), which is a spherical camera system comprised of six individual wide-angle cameras. Having accurate estimates of the interior and relative orientation parameters of these cameras is essential for integrating the camera system with other sensors in the MMS to generate georeferenced spatial data. However, field experience has shown that factory-provided calibrations may be insufficiently accurate for high-precision applications. An investigation of the geometric calibration of the Ladybug 5 system was conducted in a dedicated indoor calibration facility at the University of Calgary: an 11 m x 11 m x 4 m field comprising 291 signalized photogrammetry targets. Multiple free-network, self-calibrating bundle adjustments were performed using different sets of constraints to model several systematic error sources. Weighted constraints were included in the adjustment to enforce the stability of the six relative orientation parameters between image pairs, and separate colour channel adjustments were used to compensate for chromatic aberrations. The overall fit of observations to the calibration model as measured by the root mean square error of the image point residuals was at the level of 0.3-0.4 pixels. Mean object point precision was at the 0.3 mm level. Rectified and ortho-rectified panoramas were also generated to verify the calibrations precision and observe how adjustments with constraints effect panorama generation.
- ItemOpen AccessMulti-Criteria Multi-Participant Automated Negotiation: Belief Propagation-based Proposal Preparation and Real Time Opponent Learning(2019-05) Eshragh, Faezeh; Far, Behrouz H.; Shahbazi, Mozhdeh M.; Liang, Steve H. L.; Heckbert, ScottAutomated negotiation has received considerable attention in the past few decades as a computer tool for modeling human negotiations. The aim of automated negotiation is capturing the model of interactions during the negotiation process and improving the efficiency and quality of real-world negotiations. Due to the complexity of negotiations, there are many challenges in modelling different aspects of this process. One of the important types of negotiation, which is the focus of this thesis, is multi-issue multi-participant argumentation-based negotiation. In such negotiations, several participants with different viewpoints and perspectives negotiate over several criteria. The involved parties in these negotiations exchange proposals (a set of values assigned to negotiation issues) and receive their opponents’ evaluation of the offered proposals as well as possible arguments. The primary goal of the negotiation is finding a solution that can satisfy all the involved parties. However, in multi-issue multi-participant negotiations, finding such a solution can be quite challenging because: 1- participants have different and, sometimes, conflicting preferences about the negotiation issues; and 2- these preferences are not usually revealed to others. The higher the number of negotiation issues (i.e., the dimensions of the search space for a satisfactory solution), the higher the number of unknown preferences and therefore, the harder to reach an agreement. Therefore, the negotiation process can take a long time before approaching a possible agreement. The current thesis studies two critical aspects of automated negotiation: proposal preparation and opponent modelling. The order of the offered proposals in consecutive rounds of the negotiation directly impacts the pace of reaching an agreement. Therefore, selecting the right proposal for each round based on the interactions in the previous rounds is the key to effective negotiation. In this thesis, a novel proposal-preparation solution is proposed. It represents the negotiation issues and participants’ preferences via a graphical model and applies belief propagation to optimize this graph, the output of which is a proposal to offer to the participants. The thesis also discusses the problem of unknown preferences of the participants in this negotiation context. A recursive Bayesian filtering algorithm is proposed to learn/estimate the preferences of the opponents only through the limited information they exchange as the negotiation proceeds. The proposed approaches are then applied to two case studies to investigate their impact on the efficiency of the negotiation process. The experimental results show that using the presented proposal preparation and opponent modelling techniques, the efficiency of the negotiation process is increased by up to 85% in both case studies.
- ItemOpen AccessWide-angle Lens Camera Calibration using Automatic Target Recognition(2020-05-15) Jarron, David Mackenzie; Lichti, Derek D.; Shahbazi, Mozhdeh M.; O'Keefe, Kyle P. G.; Detchev, Ivan D.The focus of this thesis is the calibration and integration of the Ladybug5 multi-camera system into a Mobile Mapping System. To calibrate this system an efficient and accurate automatic target recognition methodology that could work with a multi-camera system was needed. This automatic target recognition methodology was developed and works by projecting the known coordinates of the surveyed calibration targets into the camera frame through a series of simulated or measured orientations and matching to signalized targets already detected in the image to a very high degree of accuracy. Through calibration of this system and rigorous modelling of its intrinsic properties, it became apparent that there was ambiguity in the research field about the most precise projection model to use for wide angle lens cameras and camera systems. A series of camera calibrations were carried out on two wide angle camera systems. Both camera systems exhibit properties that make them difficult to classify as either a central perspective camera or as a fisheye camera. Calibrations were performed on both camera systems using both central perspective and fisheye projection models. The calibrations that utilized a fisheye projection model estimated calibration parameters that more closely fit the observations. Finally, the calibration of the Ladybug5 as a multi-camera system, utilizing ROP stability constraints was performed to rectify issues relating to issues with the panoramic image generation of the Ladybug5. These panoramic images are important for point cloud coloration, and other aspects of multi-camera system integration with mobile mapping systems. It was determined that the calibration of the Ladybug5 using relative orientation stability constraints allowed for the generation of more seamless panoramic images, allowing the camera to better integrate with mobile mapping systems.