Browsing by Author "Costa Sousa, Mario"

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    3D Sketching and Collaborative Design with Napkin Sketch-The Video
    (2011-04-19T16:37:51Z) Xin, Min; Sharlin, Ehud; Costa Sousa, Mario
    This is a video presentation of our work on Napkin Sketch, a new 3D Sketching and Collaborative Design tool. The video presentation is based on Min Xin's M.Sc. defense talk. Computer-supported 3D design tools have become increasingly popular and abundant because they offer easy editing, efficient content management, extensive sharing, and rich rendering capabilities. However, many of these tools are focused on generating high quality, visually appealing, and detailed models of baked ideas but often seem to fail in effectively supporting the intricate process and environment which help to create and nurture these ideas in the early design stages. Inspired by the simple yet rich interactions afforded by traditional design tools such as pencil, paper, or napkin in supporting the creative process of the early design stages, this thesis attempts to capture their essential qualities like portability, flexibility, fluidity, expressiveness, ambiguity, and sociability in Napkin Sketch, a computer supported tool which enables 3D sketching and collaborative design. Concepts such as tangible interaction and freeform interaction are explored and applied to create a sketching experience which leverages users' innate ability to physically interact with tools, media, and collaborators and provides freedom to suggest ideas and invite changes without having to commit prematurely. The contributions of the thesis are centered around Napkin Sketch which include a hardware platform that enables users to tangibly explore the 3D design space and manipulate the sketching media, a complementary software platform that facilitates the creation of 3D sketches while maintaining the familiar paradigm of sketching on a flat physical surface, a collaborative sketching environment that supports ad hoc co-located collaboration via multiple instances of the system, and three design critiques that provide preliminary assessment of the potential effectiveness of Napkin Sketch as a useful tool for supporting creativity in the early design stages. This video report highlights the main points of the our project.
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    The 3D Tractus: A Three-Dimensional Drawing Board
    (2005-08-26) Lapides, Paul; Sharlin, Ehud; Costa Sousa, Mario; Streit, Lisa
    We present the 3D Tractus: a simple and inexpensive system for interaction and exploration of three-dimensional (3D) data. The device is based on a traditional drawing board-like mechanical structure that can be easily moved up and down while its surface height is being tracked using a simple sensor. Users interact with a tablet or tablet PC that rests on the surface while simultaneously changing its height. The result is direct mapping of virtual and physical spaces allowing intuitive 3D interaction and data exploration. The 3D Tractus allows us to investigate novel 3D interaction techniques based on sketching and drawing as well as intuitive visual indicators and GUI layouts. The 3D Tractus' simple design concept can be easily adapted to other tabletop systems and the simple nature of the physical interaction allows the design of various exciting applications. We detail here the design and development of the 3D Tractus hardware and software as well as preliminary evaluation of a 3D drawing and sketching application realized using the new tabletop interface.
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    A Methodology for Autonomous Navigation and Mapping in an Unknown Unstructured Dynamic Indoor Environment
    (2017) Mohamed, Haytham Alaa Eldin Abdalla; El-Sheimy, Naser; Sesay, Abu-Bakarr B; Elhabiby, Mohamed; Noureldin, Aboelmagd; Costa Sousa, Mario; El-Rabbany, Ahmed
    Unmanned aerial vehicles (UAVs) became an effective technology for indoor search and rescue operations, providing real-time mapping of the environment, locating victims, and determining the hard-hit areas after a natural disaster. Typically, most of the indoor missions’ environments could be unknown, unstructured, and/or dynamic. Therefore, navigation of UAVs in such environments is addressed by Simultaneous Localization and Mapping approach (SLAM) in either local or global scan matching approaches. SLAM approaches that utilize laser rangefinders depend on a scan matching method of the successive scans. The local approaches suffer from high time consumption due to iterative fashion of the scan matching method. Moreover, point-to-point scan matching is prone to bad data association process. Thus, a preceding initialization step is proposed before the local approach. This step aims to increase the convergence probability and to decrease the time consumption by limiting the number of iterations needed to reach convergence. However, the local approach still suffers from accumulated errors. Hector SLAM algorithm, as a global approach, suffers from getting trapped in local minima because of the employed gradient ascent. Hence, the multi-resolution map representation is utilized to avoid getting trapped in local minima. However, this approach increases the time consumption and the memory requirements of the process. Thus, a preceding initialization step is proposed before the Hector SLAM algorithm. This step aims to reduce the process time consumption and decrease the multi-resolution map representation into a single level with small grid cell size. However, the scan matching process of the Hector SLAM algorithm still suffers from accumulated errors. Therefore, a low-cost novel method for 2D real-time laser scan matching based on reference key frame is proposed. The proposed method is a hybrid scan matching technique comprised of feature-to-feature and point-to-point approaches, using single laser scan rangefinder, and optical flow sensors. Unlike the local and global approaches, the proposed algorithm aims at mitigating errors accumulation using the key frame technique, which is inspired from video streaming broadcast process. The SLAM approach is implemented using a UAV. In this scenario, the UAV can translate and rotate around all its axes. Consequently, navigating in 3D environments often requires 3D representation of the environments which usually suffers from memory and computational costs. Thus, an efficient 3D SLAM approach is proposed using multiple 2D point cloud slices. Furthermore, for autonomous exploration, the UAV should be able to mimic humans and take decisions according to the surrounding situations. Hence, the vehicle must be able to detect a proper destination and generate an appropriate path to that destination. Since the time constraint is a key factor for most indoor search and rescue operations, an efficient exploration algorithm is proposed to maximize the visited area and minimizing the risk on the generated path. In conclusion, to validate and evaluate the proposed algorithm, the mapping performance and time consumption of the proposed algorithm are compared with the Hector SLAM, ICP, and feature-to-feature registration such as corners, in static and dynamic environments. The performance of the proposed algorithm exhibits promising navigational and mapping results and very short computational time; the transformation parameters between each two successive scans are estimated in approximately 9 milliseconds, that indicates the potential use of the new proposed algorithm with real-time systems.
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    Analyzing Twitter Data for Emergency Management
    (2018-05-23) Marbouti, Mahshid; Maurer, Frank; Braun, John; Willett, Wesley; Far, Behrouz Homayoun; Costa Sousa, Mario
    Social media is an important part of our lives. It is hard to ignore the role of social media in our everyday lives and during disastrous events. During emergencies, emergency personnel need to make strategic decisions in a short amount of time, coordinate actions and prioritize tasks. Social media can be a powerful source of information that comes directly from the public; it can reflect public sentiment, needs, and questions. In this research, I performed an interview study to find the use cases and challenges that emergency-related organizations encounter when dealing with social media. The findings reveal the needs of practitioners for designing social media monitoring tools to help them find the information they need. One of the main challenges for practitioners is that commercial tools are not designed for emergency response, and academic approaches do not consider their requirements. This dissertation brings insight into the design of expert-informed machine learning solutions for identifying relevant information from social media by following a human-centered design approach. By actively being involved with emergency practitioners throughout three years, I designed, developed, and evaluated a social media monitoring tool for emergency response. The evaluation results show the effectiveness of bringing analysts into the classification loop to train and get feedback to machine learning classifiers. It also shows that analysts would like to combine the training tasks with their response tasks. Another aspect of this research is exploring the significance of various categories of features and machine learning algorithms and automatically identifying situational awareness information in different emergency event datasets. Results show that significant features vary across different events which indicates that training should happen during the event.
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    Applications of Interactive Topographic Maps: Tangibility with Improved Spatial Awareness and Readability
    (2019-07-02) Li, Hao; Sharlin, Ehud; Costa Sousa, Mario; Takashima, Kazuki; Chen, Zhangxing; Figueroa, Pablo; Willett, Wesley J.
    Traditional flat topographic maps are difficult to understand due to the distortion and compromise of the 3-dimensional (3D) spatial representation when it is folded into lower-dimension media (e.g. 2D). During the process, the x-y coordinate of a location can be captured but its physical elevation must be transformed using some visualization techniques, resulting in noticeable cognitive effort in comprehending the original geometric and geographic properties of the original terrain. In this manuscript-based dissertation, I present a collection of my past publications that aim to increase the readability of topographic maps by restoring the original spatiality of the terrain - including the elevations - with a physical map representation and then superimpose additional data visualization on top of it. In this way, the entire terrain topology is kept in a scaled physical representation, allowing users to view it with natural human perceptions. Additionally, user gestures can be tracked in real-time as a sketch-based input to allow novel dynamic interaction of the map interface and data manipulation of the spatial information. Through the chapters, I present the aforementioned concept, named interactive topographic interface, along with a few applications of it in different academic and industrial environments. I also report the design and results of a user study that compares the interface with traditional flat topographic maps. In the long-term, I hope that research mentioned in this dissertation inspires future interactive physical cartography to not only improve map comprehension but also facilitate better spatial and situational awareness over the map interface, resulting in an evolved map usefulness.
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    Balanced Multiresolution for Symmetric/Antisymmetric Filters
    (2014-05-13) Hasan, Mahmudul; Samavati, Faramarz; Costa Sousa, Mario
    Given a set of symmetric/antisymmetric filter vectors containing only regular multiresolution filters, the method we present in this article can establish a balanced multiresolution scheme for images, allowing their balanced decomposition and subsequent perfect reconstruction without the use of any extraordinary boundary filters. We define balanced multiresolution such that it allows balanced decomposition i.e. decomposition of a high-resolution image into a low-resolution image and corresponding details of equal size. Such a balanced decomposition makes on-demand reconstruction of regions of interest efficient in both computational load and implementation aspects. We find this balanced decomposition and perfect reconstruction based on an appropriate combination of symmetric/antisymmetric extensions near the image and detail boundaries. In our method, exploiting such extensions correlates to performing sample (pixel/voxel) split operations. Our general approach is demonstrated for some commonly used symmetric /antisymmetric multiresolution filters. We also show the application of such a balanced multiresolution scheme in real-time focus+context visualization.
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    Beyond Pixels: Illustration with Vector Graphics
    (2006-02-14) Isenberg, Tobias; Brennecke, Angela; Costa Sousa, Mario; Carpendale, Sheelagh
    This report presents a novel vector rendering pipeline that allows us to easily break the pixel barrier and create high-quality illustrations. Recently, most graphic research has been directed towards rendering pixel images that appear realistic. In contrast, we investigate the generation of vector graphic illustrations using non-photorealistic techniques such as line rendering and Gooch shading. By combining vector output from both shading and line rendering of 3D models we create high-quality illustrations that can directly be used in print reproduction. Our approach uses a vector graphic pipeline that tracks multiple attributes of strokes and uses them for stylization. This allows to have multiple layers of line rendering such as different stroke types or visible and hidden parts of strokes, each treated differently according to specific stylization rules. Using high quality vector graphics (as opposed to pixel renditions) for representing illustration is essential, in particular, in the print reproduction process. Foremost, vector graphics can be reproduced at any desired resolution; they do not suffer from the resolution dependence of pixel images. In addition, only vector graphics can capture fine details accurately while maintaining a reasonable file size. Finally, vector graphics do not need to be half-toned when printed as long as spot colors are used. Even if some layers of the image use, e. g., shading, only those parts of the vector graphic need to be half-toned that actually do not make use of the available spot colors. Thus, we can combine both shading and line layers without compromising print quality.
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    Congestion Control in Software-Defined Networks: A Simulation Study
    (2019-11) Gholizadeh, Reza; Williamson, Carey L.; Ghaderi, Majid; Costa Sousa, Mario
    Congestion is an underlying reason for performance degradation in computer networks. Current TCP congestion control has no information about the network. Hence, it increases the sending window to overflow the bottleneck link buffer, and backs off when packet drops are detected. Software-Defined Networking SDN is a new paradigm, which provides information about the network. In this thesis, we propose a novel centralized congestion control scheme for SDN. Our solution exploits the information provided by the SDN controller to prevent formation of persistent queues in bottleneck links. Also, we introduce an SDN Simulation Tool developed in Java, which facilitates simulation experiments. We used our tool to evaluate the proposed solution. The preliminary results shows the potential scalability and flexibility of the protocol.
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    Designing NeuroSimVR: A Stereoscopic Virtual Reality Spine Surgery Simulator
    (2017-11-01) Mostafa, Ahmed E.; Ryu, Won Hyung A.; Chan, Sonny; Takashima, Kazuki; Kopp, Gail; Costa Sousa, Mario; Sharlin, Ehud
    This paper contributes NeuroSimVR, a stereoscopic virtual reality spine surgery simulator that allows novice surgeons to learn and practice a spinal pedicle screw insertion (PSI) procedure using simplified interaction capabilities and 3D haptic user interfaces. By collaborating with medical experts and following an iterative approach, we provide characterization of the PSI task, and derive requirements for applying this procedure in a 3D immersive interactive simulation system. We describe how these requirements were realized in our NeuroSimVR prototype, and outline the educational benefits of our 3D interactive system for training the PSI procedure. We conclude the paper with the results of a preliminary evaluation of NeuroSimVR and reflect on our interface benefits and limitations.
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    "Designing Video Games with Social, Physical, and Authorship Gameplay
    (2012-02-15T18:13:31Z) Lapides, Paul; Sharlin, Ehud; Costa Sousa, Mario
    "Today's video games have undergone many changes that have turned them from niche hobbies into ubiquitous and popular activities. Modern games now utilize physical interaction as the primary modality for controlling the game, they have become more casual and social activities, and they have enabled players to express their creativity through the authorship of content. In this thesis, we investigate the use of physical interaction, socialization, and authorship of content in the design of video games. We have created a design framework that can be used as a guide for designing new games and have designed three games to investigate the successes and limitations of each of the three themes: Film Karaoke, Joke's On You, and Social Comics. Through our evaluation of Social Comics, we have come to conclude that physicality, sociability, and authorship are powerful design themes for enhancing the player's experience and for creating new forms of gameplay."
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    Enhanced UAV Navigation Under Challenging Conditions
    (2019-07-23) Zahran, Shady Abd El-Kader; El-Sheimy, Naser; Sesay, Abu B.; Moussa, Adel M.; Noureldin, Aboelmagd; Costa Sousa, Mario; Detchev, Ivan D.; Toth, Charles K.
    During the past decade, there has been an enormous increase in the applications utilizing fully autonomous Unmanned Aerial Vehicles (UAVs). Initially these applications were mostly restricted to military fields like border surveillance, troops enforcement, combat, target, and decoy. More recently they have been extended to many civilian applications, such as firefighting, traffic monitoring, and commercial UAVs. This wide variety of applications makes the UAVs autonomous navigation a challenging task. The UAVs mainly depend on the integration of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS) to navigate autonomously but during the lack or unavailability of the GNSS signals, the UAVs lose their ability to navigate autonomously, due to the massive drift exhibited by the INS when working in dead reckoning mode. GNSS signals may be unavailable due to jamming, spoofing, blockage, or multipath. So, another aiding system must be integrated with the INS, to yield a reliable navigation solution that makes the UAV capable of fulfilling its assigned tasks in a wider range of working conditions. Different sensors and methods were utilized to aid the navigation solution during GNSS outage. Light Detection and Ranging sensor (LiDAR) is one of the main sensors integrated with INS during these periods. The main drawback in the use of LiDAR with small and micro UAVs, is the weight, size, power consumption, and high computational cost. Camera is also considered as a viable solution to aid in navigation during the GNSS outage periods, as it is characterized by features that make it suitable for small and micro UAVs like light weight, small size, and low power consumption, also it provides rich information like features and colors. Visual information provided by a camera can be used for 3D surrounding construction, obstacle avoidance, and position estimation. One of the algorithms mostly used for exploiting camera in UAV navigation is Visual Odometry (VO), which depends on the extraction of the vehicle velocity from matching features of successive images. One of the biggest problems facing the use of a single camera is the loss of scale information. Although this issue can usually be resolved by using stereo/multiple cameras, such a solution is not so effective for small and micro UAVs, because its accuracy mainly depends on the base distance between the cameras, which is necessarily limited in such cases. Regardless of using single/multiple cameras, this sensor performance is still greatly sensitive to certain environmental factors, like light variation or featureless scenes. Radar sensor is much more superior to cameras or LiDAR, due to its immunity to environmental changes. Previously Radar’s weight, size, and power consumption were not adequate for small/micro UAVs. Ultra-Wide Band (UWB) devices have been recently considered in several UAV aided navigation works. Although their great potential to aid the UAV during loss of GNSS signals (absolute positions), due to their limited power, they can only be used in small areas (like indoors). In order to use them outdoors, a huge infrastructure is required, in addition to the availability of beacons with known positions in the flight area is not always guaranteed. So other methods or approaches that do not cost the UAV additional size, weight, cost, or power consumption is essential to achieve a robust and accurate navigation system that is able to fulfill different tasks in all circumstances with acceptable performance. First approach taken during the research is the Vehicle Dynamic Model (VDM) enhanced navigation system. The VDM is the relation between the actuators and vehicle states (accelerations and rotations). We accommodate such approach because it will not cost the UAV any additional weight, size, power, or cost. The main drawback of accommodating this approach is the requirement for a special equipment to estimate the mathematical model parameter of the UAV. Any perturbations during this modelling process will make this approach unable to aid the UAV during GNSS signals outage. In order to avoid such drawback a Machine Learning (ML) approach is adopted to take advantage of the available data from previous flights. Although this VDM- machine learning approach greatly enhanced the navigation solution compared to low-cost INS solution, but the model formed with the aid of the ML approach is specific for this UAV with this configuration, this leads to the second accommodated approach, which is enhanced UAV navigation using micro-Radar as a way to find a more robust aiding approach that’s suitable for most UAVs. Radar as a sensor is more immune to environmental changes (e.g. rain, fog, light conditions), but its weight, size, and power requirement makes it not suitable for such small UAVs. Nowadays micro Radars is available due to the advancement in technologies, which makes it a good candidate to aid small UAVs navigation system during GNSS signals outage. However, low-cost Radars come with more challenges with regards to the extraction of useful information, due to its poor performance, and the existence of large amount of clutters especially at low altitudes. Typical radar targets extractions algorithms are not suitable, especially in our case where the Radar is mounted on board of the UAV, so there is no significant difference between the targets and the background. In order to efficiently extract the targets and estimate the UAV’s velocity a computer vision-based approach is accommodated instead of the typical Radar approaches, our approach consider the output of the radar (range and velocity) as image. This approach greatly enhances the navigation solution during six minutes of complete GNSS signal outage, which reached a 2D RMSE of 5.81 m compared to INS RMSE which reached hundreds of meters. Although micro-Radar system greatly enhance the navigation solution but it’s power requirement, weight, and cost are still a burden on small UAVs. So, the Radar based navigation is followed by other approach that respect all the UAVs limitation and efficiently estimate the UAVs velocity. This final approach is based on manipulating the typical use of two sensors (Hall-effect magnetic sensor, and Mass-flow meter). Both sensors are used to efficiently estimate the velocity of a drone while respecting small drones’ limitations. By utilizing these two sensors the navigation solution is greatly enhanced during GNSS signal outage compared to low-cost INS. These two sensors specifications facilitate the ability to be merged with other sensors like camera or LiDAR to enhance the navigation solution even more.
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    Evaluating User Preferences for Augmented Reality Interactions for the Internet of Things
    (2019-12-20) Chopra, Shreya; Maurer, Frank; Wong, Nelson; Costa Sousa, Mario
    This thesis investigates how users want to control IoT devices in their homes with headset AR. Gestural and voice controls are both suitable methods of input for headset AR. However, there is a lack of end-user input in the design of such gestures and voice commands: especially in comparative terms. An elicitation study is performed with 16 participants to gather their preferred voice commands and gestures for a set of referents. The contribution is an analysis of 784 inputs (392 gestures and 392 voice), a resulting gesture set and command types, comparative method preferences, a novel method to analyze voice command input (called voice command pattern template), as well as design recommendations based on observations and interviews. These recommendations serve as guides for future designers and implementors of such voice commands and gestures.
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    Evaluating User Preferences for Interaction for Single User Cross Reality Transition of 3D Virtual Objects
    (2023-04-30) Wang, Nanjia; Maurer, Frank; Costa Sousa, Mario; Kowch, Eugene
    This thesis investigates how single users want to transfer 3D virtual objects between different spaces along Milgram’s Reality-Virtuality Continuum(RVC) during a Cross Reality (CR) session. Cross-reality is a newly emerged research field that studies the transition and current usage of multiple systems along RVC. Studies have been conducted to investigate the interactions that are efficient and preferred by the user on spaces along RVC. However, single-user CR virtual object transition is an unexplored interaction space. Thus there is no established interaction the user prefers so researchers and developers can refer to it when implementing CR applications. An elicitation study was conducted with 20 participants to gather their preferred interaction for a set of referents. The contribution is an analysis of 1200 inputs that lead to a design recommendation based on observations and interviews. These recommendations will serve as guidelines for researchers and developers when developing CR applications that involve the transition of 3D virtual objects.
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    Exploring Convolutional Neural Networks and Transfer Learning for Oil Sands Drill Core Image Analysis
    (2021-08-24) Anzum, Fahim; Costa Sousa, Mario; Alim, Usman; John Jacobson Jr., Michael; Osvaldo Trad, Daniel; Zhao, Richard
    An accurate permeability estimate is crucial for effectively characterizing the McMurray oil sands for in situ recovery. Such an estimate is critical to inform the best locations for placing wells and pads and accurately forecast future oil production rates. This fact is becoming significantly important as in situ development moves to areas of increasingly complex geology. The traditional methods of estimating permeability largely do not work well in oil sands because of the core disturbance or the fact that the core is filled with immobile bitumen. Moreover, it is expensive to get physical samples from many different depths at many wells, and the experiments carried out in the labs to measure permeability sometimes are not representative. However, permeability can be estimated from different parameters such as mean grain size (MGS), median grain size, and particle size distribution (PSD). This thesis investigates how convolutional neural networks (CNNs) and transfer learning perform when estimating MGS from the oil sands drill core photos. Three preliminary approaches are explored for classifying core photos based on the facies, including (1) the application of transfer learning on the pre-trained VGG-16 CNN model, (2) fine-tuning a few top layers of VGG-16, and (3) the combination of VGG-16 and traditional machine learning (ML) algorithms. Experimental results achieved by these classification models reveal opportunities to extend these approaches for predicting MGS from core photos. Therefore, the three approaches are then investigated using a library of core photographs with known MGS calculated from PSD to see which one works best. Experimental results exhibit good performance in estimating MGS from core photos using the explored approaches. Overall, the investigation supports that the application of CNNs, and transfer learning is feasible in different oil sands drill core image analysis workflows and more advanced research outcomes can be achieved by further exploration of these techniques in the oil sands research domain.
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    Flying Frustum: A Spatial Interface for Enhancing Human- UAV Awareness
    (2015-06-09) Li, Nico; Cartwright, Stephen; Shekhar Nittala, Aditya; Sharlin, Ehud; Costa Sousa, Mario
    We present Flying Frustum, a 3D spatial interface that enables control of semi-autonomous UAVs (Unmanned Aerial Vehicles) using pen interaction on a physical model of the terrain, and that spatially situates the information streaming from the UAVs onto the physical model. Our interface is based on a 3D printout of the terrain, which allows the operator to enter goals and paths to the UAV by drawing them directly on the physical model. In turn, the UAV’s streaming reconnaissance information is superimposed on the 3D printout as a view frustum, which is situated according to the UAV’s position and orientation on the actual terrain. We argue that Flying Frustum’s 3D spatially situated interaction can potentially help improve human-UAV awareness, allow a better operators-to-UAV ratio, and enhance the overall situational awareness. We motivate our design approach for Flying Frustum, discuss previous related work in CSCW and HRI, present our current prototype using both handheld and headset augmented reality interfaces, reflect on Flying Frustum’s strengths and weaknesses, and discuss our plans for future evaluation and prototype improvements.
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    FLYING FRUSTUM: A Spatial Interface for Enhancing Human-UAV Awareness: The Video
    (2016-01-18) Li, Nico; Cartwright, Stephen; Shekhar Nittala, Aditya; Sharlin, Ehud; Costa Sousa, Mario
    We present Flying Frustum, a 3D spatial interface that enables control of semi-autonomous UAV (Unmanned Aerial Vehicles) using pen interaction on a physical model of the terrain, and that spatially situates the information streaming from the UAVs onto the physical model. Our interface is based on a 3D printout of the terrain, which allows the operator to enter goals and paths to the UAV by drawing them directly on the physical model. In turn, the UAV’s streaming reconnaissance information is superimposed on the 3D printout as a view frustum, which is situated on the physical model according to the UAV’s location on the actual terrain. We argue that Flying Frustum’s 3D spatially situated interaction can help improve human- UAV awareness, allow a better operators-to-UAV ratio, and enhance the overall situational awareness. In this video we illustrate the design and demonstrate the proof-of-concept system of Flying Frustum.
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    Global Sensitivity Analysis for Covering Reservoir Geological and Flow Uncertainty
    (2017-12-21) Karami Moghadam, Ali; Chen, Zhangxing (John); Costa Sousa, Mario; Mackay, Eric J; Dimitrov, Vassil Simeonov; Alim, Usman
    Understanding sources of uncertainty has a major impact in the reservoir management design and significantly influences the operations decision-making. Integrating all sources of flow and geological uncertainty is very important to quantify the production uncertainty and to make optimal decisions in reservoir development. However, this task is computationally very intensive and the current methods used by the industry are not robust enough to capture the full complexity of the problem. To address this, this research focuses on identifying sources of input uncertainty that significantly influence reservoir response and decision making. Some sources of input uncertainty are significant by themselves. Others are significant through their interactions. Yet others are not significant at all. This information offers great insight as well as computational gains that reservoir engineers can exploit towards better utilizing their knowledge when making reservoir decisions for the company assets. For this purpose, this research provides promising sensitivity analysis frameworks that are suitable for tackling complex multi-dimensional models within the reservoir modelling workflow and overcome the drawbacks of the commonly used approaches. The first section of this research study introduces a screening method that can successfully categorize the uncertain parameters in terms of their significance to the reservoir response with a low computational cost. This method is followed by a more sophisticated approach that is able to quantify the contribution of each input parameter to the variability of the model responses as well as the existing interactions among the parameters. The relation between the accuracy of the results and the choice of experiment design is discussed in this section. In order to overcome the high cost of computation intrinsic to this method, Single-Layer and Multi-Layer Neural Network surrogate models are successfully employed and integrated with the method. In the next chapter, another approach based on classifying the response/decision variables into a limited set of discrete classes is discussed. This approach quantifies the sensitivity to parameters and parameter interactions, and incorporates the possibility that the interactions can be asymmetric for complex reservoir modeling. The discussed approaches are demonstrated and validated with multiple well known sensitivity analysis test functions and real field case studies.
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    Interacting with Microseismic Visualizations
    (2013-01-23) Mostafa, Ahmed; Sharlin, Ehud; Greenberg, Saul; Costa Sousa, Mario; Brazil, Emilio
    Microseismic visualization systems present complex 3D data of small seismic events within oil reservoirs to allow experts to explore and interact with that data. Yet existing systems suffer several problems: 3D spatial navigation and orientation is difficult, and selecting 3D data is challenging due to the problems of occlusion and lack of depth perception. Our work mitigates these problems by applying both proxemic interactions and a spatial input device to simplify how experts navigate through the visualization, and a painting metaphor to simplify how they select that information.
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    LifeBrush: An Illustrative Simulation Canvas for the Biological Mesoscale
    (2020-01) Davison, Timothy; Jacob, Christian; Samavati, Faramarz Famil; Costa Sousa, Mario; Olson, Arthur; Willett, Wesley J.; Herzog, W.
    At the mesoscale, molecular machines assemble structure and orchestrate the processes of life. It is a chaotic and alien world whose scale makes communicating scientific findings a daunting challenge. Scientific illustrators confront the challenges with static illustration and video animations. With \lifeBrush{}, a virtual reality tool, we bring those static illustrations to life as interactive illustrative simulations. \lifeBrush{} is an illustrative simulation canvas, for sketching, simulating and visualizing the biological mesoscale. Like an artists paint palette, we design molecular arrangements, self-assembly rules, and generative procedures in an interactive palette. Then, we use our palette and generative algorithms to sketch illustrative simulations. We developed a novel realtime algorithm for painting and synthesizing element arrangements from a palette into virtual worlds. Our synthesis algorithm has applications for virtual world construction, and for interactively constructing illustrative mesoscale simulations. We synthesize networks of interconnected proteins filaments with sketch-based swarm grammars. To model macromolecular self-assembly, and to interactively construct macromolecular structures with our sketch-based system, we propose a realtime physics approximation based on spatial rules designed in the palette. We use our system to sketch and simulate cytoskeletal filaments in our illustrative simulation canvas. With our system, we structurally recreate, simulate and step inside some famous illustrations by the structural biologist David Goodsell.
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    Model Calibration and Performance Optimization Using Multiple-Point Geostatistics and Machine Learning Techniques
    (2020-09-14) Khani, Hojjat; Chen, Zhangxing (John); Costa Sousa, Mario; Nghiem, Long; Tahmasebi, Pejman; Maurer, Frank; Mackay, Eric J.
    The overall objective of reservoir modeling is to reduce the uncertainty of production forecasts by including all available data into the model. Most importantly, the model must be able to match the historical production while preserving geological data. In this work, some new techniques are developed to model the impact of geological uncertainty on history matching and optimization problems. Most of traditional history matching and optimization methods are based on some forms of trial and error schemes. These methods are not usually designed to include and/or preserve the geological continuity of the facies. The Probability Perturbation Method (PPM) is a pixel-based data integration method, which employs the power of multiple-point geostatistics (MPS), to match production data while being constrained to other reservoir data including prior geological information. Using the PPM, one can systematically perturb a spatial distribution of reservoir properties while maintaining the pattern and consistency of the geological information. One of the shortcomings of the original PPM is that there is no straightforward approach to intelligently explore the space of uncertainty (the variability between realizations). In this work, a cluster-aided probability perturbation method (CAPPM) is developed, which can efficiently search the space of uncertainty by clustering the generated realizations. This method allows us to navigate through realizations and approximate the uncertainty bounds with fewer iterations and, consequently, less computational cost. In addition, a new segmentation method, which does not require flow simulation, is presented that allows different parts of the reservoir to be perturbed separately. This method improves the robustness, efficiency, and the convergence speed of the PPM. Three examples with synthetic, realistic and real training images are used to illustrate and validate the developed techniques for various types of reservoirs with different levels of geological complexities.Additionally, this work extends the applicability of MPS techniques for modelling some critical heterogeneities (i.e., complex fracture networks) present in unconventional tight and shale reservoirs. This extension allows us to apply the developed cluster-aided workflow for history matching the production data from multiple-fractured horizontal wells in future studies. Therefore, it is attempted to model complex fracture networks around primary hydraulic fractures using multiple-point geostatistical algorithms. Secondary probability maps, which can be derived from microseismic events, are also included in the modelling process to account for the variability in the extent of the induced and existing fracture networks within a stimulated reservoir volume near the primary hydraulic fractures. A sensitivity study is performed to understand the effect of different parameters on the well flow performance given different fracture network models. Optimization of subsurface flow processes can result in more economical projects. However, ignoring the uncertainty of geological realizations in the optimization process can lead to suboptimal outcomes that can be considerably different from the actual optimal solutions. A robust optimization workflow for the SAGD (steam assisted gravity drainage) process is presented that considers the geological uncertainties by optimizing over a subset of representative realizations obtained from three model ranking techniques. The first method is by applying the base case well locations and operating constraints to all the realizations and running simulations. The realizations are then ordered according to their resulting Net Present Values (NPVs). In the second ranking method, the clustering of a low-dimensional cumulative steam-oil ratio is employed as the feature vector for the SAGD process. Finally, the third method relies on kernel-clustering of permeability realizations using multidimensional scaling and similarity metrics. Although the best solution is obtained by the second ranking approach using the simulation results, the third method is more appealing as it provides competitive results using the static data only.