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This paper discusses a highly interdisciplinary course offered to students during the Spring 2007 semester: Design of Intelligent Spacecraft. The course integrates concepts from mathematics, physics, engineering and computer scien...
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This paper discusses a highly interdisciplinary course offered to students during the Spring 2007 semester: Design of Intelligent Spacecraft. The course integrates concepts from mathematics, physics, engineering and computer science for the purpose of educating 4th year undergraduate and introductory masters-level students on the design of intelligent spacecraft. Course content is divided into two pedagogically separate parts: The historical development of physical models, including mathematical models for celestial mechanics and thermodynamics. Application of these models for creating intelligent spacecraft, i.e., applications of these models to pattern recognition, computer vision, and image processing. The first section introduces physical mathematical models which, in the second section of the course, are re-visited to allow for model-based design. In part (1), a new tact is taken for teaching the historical development of mathematics and physics that shapes the scientific view of the world today. Lectures seek to emphasize the rationale behind scientific thought through the variety of personalities that have defined it best characterized by the phrase: All science was new at some point. Specific classical topics include celestial mechanics and thermodynamics which are introduced using excerpts from original works of the scientists that defined and revolutionized our understandings of these fields. Some scientists considered are Aristotle, Tycho, Kepler, Newton, Euler, Bernoulli, Fourier and other scientists relevant to course topics. Where possible, original manuscripts were provided and clarified by reformulating the work in modern terminology and mathematical notation. Historical content is complemented with discussion on contemporary space missions relevant to the discussion topic. For example, historical discussions on the discoveries of Cassini or Galileo includes discussions on the recent Cassini-Huygens mission to Saturn. Further, these discussions include mission spacecraft type, its relevant design considerations and mission objectives. Discussion of mission objectives serve to highlight current boundaries of scientific knowledge and how specific space missions seek to understand topics at these boundaries.
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摘要 :
This paper discusses a highly interdisciplinary course offered to students during the Spring 2007 semester: Design of Intelligent Spacecraft. The course integrates concepts from mathematics, physics, engineering and computer scien...
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This paper discusses a highly interdisciplinary course offered to students during the Spring 2007 semester: Design of Intelligent Spacecraft. The course integrates concepts from mathematics, physics, engineering and computer science for the purpose of educating 4th year undergraduate and introductory masters-level students on the design of intelligent spacecraft. Course content is divided into two pedagogically separate parts: The historical development of physical models, including mathematical models for celestial mechanics and thermodynamics. Application of these models for creating intelligent spacecraft, i.e., applications of these models to pattern recognition, computer vision, and image processing. The first section introduces physical mathematical models which, in the second section of the course, are re-visited to allow for model-based design. In part (1), a new tact is taken for teaching the historical development of mathematics and physics that shapes the scientific view of the world today. Lectures seek to emphasize the rationale behind scientific thought through the variety of personalities that have defined it best characterized by the phrase: All science was new at some point. Specific classical topics include celestial mechanics and thermodynamics which are introduced using excerpts from original works of the scientists that defined and revolutionized our understandings of these fields. Some scientists considered are Aristotle, Tycho, Kepler, Newton, Euler, Bernoulli, Fourier and other scientists relevant to course topics. Where possible, original manuscripts were provided and clarified by reformulating the work in modern terminology and mathematical notation. Historical content is complemented with discussion on contemporary space missions relevant to the discussion topic. For example, historical discussions on the discoveries of Cassini or Galileo includes discussions on the recent Cassini-Huygens mission to Saturn. Further, these discussions include mission spacecraft type, its relevant design considerations and mission objectives. Discussion of mission objectives serve to highlight current boundaries of scientific knowledge and how specific space missions seek to understand topics at these boundaries.
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This paper investigates the problem of planning a minimum-length tour for a three-dimensional Dubins airplane model to visually inspect a series of targets located on the ground or exterior surface of objects in an urban environme...
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This paper investigates the problem of planning a minimum-length tour for a three-dimensional Dubins airplane model to visually inspect a series of targets located on the ground or exterior surface of objects in an urban environment. Objects are 2.5D extruded polygons representing buildings or other structures. A visibility volume defines the set of admissible (occlusion-free) viewing locations for each target that satisfy feasible airspace and imaging constraints. The Dubins traveling salesperson problem with neighborhoods (DTSPN) is extended to three dimensions with visibility volumes that are approximated by triangular meshes. Four sampling algorithms are proposed for sampling vehicle configurations within each visibility volume to define vertices of the underlying DTSPN. Additionally, a heuristic approach is proposed to improve computation time by approximating edge costs of the 3D Dubins airplane with a lower bound that is used to solve for a sequence of viewing locations. The viewing locations are then assigned pitch and heading angles based on their relative geometry. The proposed sampling methods and heuristics are compared through a Monte-Carlo experiment that simulates view planning tours over a realistic urban environment.
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This paper investigates the problem of planning a minimum-length tour for a three-dimensional Dubins airplane model to visually inspect a series of targets located on the ground or exterior surface of objects in an urban environme...
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This paper investigates the problem of planning a minimum-length tour for a three-dimensional Dubins airplane model to visually inspect a series of targets located on the ground or exterior surface of objects in an urban environment. Objects are 2.5D extruded polygons representing buildings or other structures. A visibility volume defines the set of admissible (occlusion-free) viewing locations for each target that satisfy feasible airspace and imaging constraints. The Dubins traveling salesperson problem with neighborhoods (DTSPN) is extended to three dimensions with visibility volumes that are approximated by triangular meshes. Four sampling algorithms are proposed for sampling vehicle configurations within each visibility volume to define vertices of the underlying DTSPN. Additionally, a heuristic approach is proposed to improve computation time by approximating edge costs of the 3D Dubins airplane with a lower bound that is used to solve for a sequence of viewing locations. The viewing locations are then assigned pitch and heading angles based on their relative geometry. The proposed sampling methods and heuristics are compared through a Monte-Carlo experiment that simulates view planning tours over a realistic urban environment.
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We propose the width-resolution mutual learning method (MutualNet) to train a network that is executable at dynamic resource constraints to achieve adaptive accuracy-efficiency trade-offs at runtime. Our method trains a cohort of ...
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We propose the width-resolution mutual learning method (MutualNet) to train a network that is executable at dynamic resource constraints to achieve adaptive accuracy-efficiency trade-offs at runtime. Our method trains a cohort of sub-networks with different widths (i.e., number of channels in a layer) using different input resolutions to mutually learn multi-scale representations for each sub-network. It achieves consistently better ImageNet top-1 accuracy over the state-of-the-art adaptive network US-Net under different computation constraints, and outperforms the best compound scaled MobileNet in EfficientNet by 1.5%. The superiority of our method is also validated on COCO object detection and instance segmentation as well as transfer learning.
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We propose the width-resolution mutual learning method (MutualNet) to train a network that is executable at dynamic resource constraints to achieve adaptive accuracy-efficiency trade-offs at runtime. Our method trains a cohort of ...
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We propose the width-resolution mutual learning method (MutualNet) to train a network that is executable at dynamic resource constraints to achieve adaptive accuracy-efficiency trade-offs at runtime. Our method trains a cohort of sub-networks with different widths (i.e., number of channels in a layer) using different input resolutions to mutually learn multi-scale representations for each sub-network. It achieves consistently better ImageNet top-1 accuracy over the state-of-the-art adaptive network US-Net under different computation constraints, and outperforms the best compound scaled MobileNet in EfficientNet by 1.5%. The superiority of our method is also validated on COCO object detection and instance segmentation as well as transfer learning. Surprisingly, the training strategy of MutualNet can also boost the performance of a single network, which substantially outperforms the powerful AutoAugmentation in both efficiency (GPU search hours: 15000 vs. 0) and accuracy (ImageNet: 77.6% vs. 78.6%). Code is available at https://github.com/taoyang1122/MutualNet.
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A heretofore unsolved problem of great archaeological importance is the automatic assembly of pots made on a wheel from the hundreds (or thousands) of sherds found at an excavation site. An approach is presented to the automatic e...
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A heretofore unsolved problem of great archaeological importance is the automatic assembly of pots made on a wheel from the hundreds (or thousands) of sherds found at an excavation site. An approach is presented to the automatic estimation of mathematical models of such pots from 3D measurements of sherds. The overall approach is formulated and described and some detail is provided on the elements of the procedure. The end result is a representation suitable for comparisons, geometric feature extraction, visualization and digital archiving. Matching of fragments and aligning them geometrically is based on matching break-curves (curves on a pot surface separating fragments), estimated axes and profile curves for individual fragments and groups of matched fragments, and a number of features of groups of break-curves. Pot assembly is a bottom-up maximum likelihood performance-based search. In our case, associated with subassemblies of fragments is a loglikelihood which is a sum of energy functions. Experiments are illustrated on pots which were broken for the purpose, and on sherds from an archaeological dig located in Petra, Jordan. The addressed problem and solution can be considered as problems in "geometric learning" and in "perceptual grouping," where subgroups of pot fragments at a site location are to be assembled into individual virtual pots and other fragments are to be discarded as clutter.
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A heretofore unsolved problem of great archaeological importance is the automatic assembly of pots made on a wheel from the hundreds (or thousands) of sherds found at an excavation site. An approach is presented to the automatic e...
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A heretofore unsolved problem of great archaeological importance is the automatic assembly of pots made on a wheel from the hundreds (or thousands) of sherds found at an excavation site. An approach is presented to the automatic estimation of mathematical models of such pots from 3D measurements of sherds. The overall approach is formulated and described and some detail is provided on the elements of the procedure. The end result is a representation suitable for comparisons, geometric feature extraction, visualization and digital archiving. Matching of fragments and aligning them geometrically is based on matching break-curves (curves on a pot surface separating fragments), estimated axes and profile curves for individual fragments and groups of matched fragments, and a number of features of groups of break-curves. Pot assembly is a bottom-up maximum likelihood performance-based search. In our case, associated with subassemblies of fragments is a loglikelihood which is a sum of energy functions. Experiments are illustrated on pots which were broken for the purpose, and on sherds from an archaeological dig located in Petra, Jordan. The addressed problem and solution can be considered as problems in "geometric learning" and in "perceptual grouping," where subgroups of pot fragments at a site location are to be assembled into individual virtual pots and other fragments are to be discarded as clutter.
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This paper describes a novel embedded system capable of estimating 3D positions of surfaces viewed by a stereoscopic rig consisting of a pair of calibrated cameras. Novel theoretical and technical aspects of the system are tied to...
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This paper describes a novel embedded system capable of estimating 3D positions of surfaces viewed by a stereoscopic rig consisting of a pair of calibrated cameras. Novel theoretical and technical aspects of the system are tied to two aspects of the design that deviate from typical stereoscopic reconstruction systems: (1) incorporation of an 10x zoom lens (Rainbow-H10x8.5) and (2) implementation of the system on an embedded system. The system components include a DSP running μClinux, an embedded version of the Linux operating system, and an FPGA. The DSP orchestrates data flow within the system and performs complex computational tasks and the FPGA provides an interface to the system devices which consist of a CMOS camera pair and a pair of servo motors which rotate (pan) each camera. Calibration of the camera pair is accomplished using a collection of stereo images that view a common chess board calibration pattern for a set of pre-defined zoom positions. Calibration settings for an arbitrary zoom setting are estimated by interpolation of the camera parameters. A low-computational cost method for dense stereo matching is used to compute depth disparities for the stereo image pairs. Surface reconstruction is accomplished by classical triangulation of the matched points from the depth disparities. This article includes our methods and results for the following problems: (1) automatic computation of the focus and exposure settings for the lens and camera sensor, (2) calibration of the system for various zoom settings and (3) stereo reconstruction results for several free form objects.
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This paper describes a novel embedded system capable of estimating 3D positions of surfaces viewed by a stereoscopic rig consisting of a pair of calibrated cameras. Novel theoretical and technical aspects of the system are tied to...
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This paper describes a novel embedded system capable of estimating 3D positions of surfaces viewed by a stereoscopic rig consisting of a pair of calibrated cameras. Novel theoretical and technical aspects of the system are tied to two aspects of the design that deviate from typical stereoscopic reconstruction systems: (1) incorporation of an lOx zoom lens (Rainbow-H 10x8.5) and (2) implementation of the system on an embedded system. The system components include a DSP running μClinux, an embedded version of the Linux operating system, and an FPGA. The DSP orchestrates data flow within the system and performs complex computational tasks and the FPGA provides an interface to the system devices which consist of a CMOS camera pair and a pair of servo motors which rotate (pan) each camera. Calibration of the camera pair is accomplished using a collection of stereo images that view a common chess board calibration pattern for a set of pre-defined zoom positions. Calibration settings for an arbitrary zoom setting are estimated by interpolation of the camera parameters. A low-computational cost method for dense stereo matching is used to compute depth disparities for the stereo image pairs. Surface reconstruction is accomplished by classical triangulation of the matched points from the depth disparities. This article includes our methods and results for the following problems: (1) automatic computation of the focus and exposure settings for the lens and camera sensor, (2) calibration of the system for various zoom settings and (3) stereo reconstruction results for several free form objects.
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