The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Tshilidzi Marwala - One of the best experts on this subject based on the ideXlab platform.
-
Cyberware Capacity—Platform and Middleware Layers Perspective
Smart Maintenance for Human–Robot Interaction, 2018Co-Authors: Bo Xing, Tshilidzi MarwalaAbstract:In this chapter, we investigate smart maintenance for Cyberware capacity management from the platform and middleware layers viewpoint. We describe the general knowledge of embedded software platform and middleware layers in Sect. 7.1. Then, in order to demonstrate how smart maintenance strategy can be better implemented in such intangible asset management, in particular, platform and middleware layers aspect, one representative research avenue is introduced in Sect. 7.2. Section 7.3 summarises this chapter.
-
Cyberware Capacity—Energy Autonomy Perspective
Smart Maintenance for Human–Robot Interaction, 2018Co-Authors: Bo Xing, Tshilidzi MarwalaAbstract:In this chapter, we investigate smart maintenance for Cyberware capacity management from the energy autonomy viewpoint. We describe the general knowledge of embedded system power management issue in Sect. 9.1. Then, in order to demonstrate how smart maintenance strategy can be better implemented in such intangible asset management, particularly, energy scavenging aspect, one representative research avenue is introduced in Sect. 9.2. Section 9.3 summarises this chapter.
-
Cyberware capacity applications layer perspective
2018Co-Authors: Bo Xing, Tshilidzi MarwalaAbstract:In this chapter, we investigate smart maintenance for Cyberware capacity management from the applications layer viewpoint. We describe the general knowledge of embedded software applications layer in Sect. 8.1. Then, in order to demonstrate how smart maintenance strategy can be better implemented in such intangible asset management, in particular, applications layer aspect, one representative research avenue is introduced in Sect. 8.2. Section 8.3 summarises this chapter.
-
Cyberware capacity energy autonomy perspective
2018Co-Authors: Bo Xing, Tshilidzi MarwalaAbstract:In this chapter, we investigate smart maintenance for Cyberware capacity management from the energy autonomy viewpoint. We describe the general knowledge of embedded system power management issue in Sect. 9.1. Then, in order to demonstrate how smart maintenance strategy can be better implemented in such intangible asset management, particularly, energy scavenging aspect, one representative research avenue is introduced in Sect. 9.2. Section 9.3 summarises this chapter.
-
Cyberware capacity platform and middleware layers perspective
2018Co-Authors: Bo Xing, Tshilidzi MarwalaAbstract:In this chapter, we investigate smart maintenance for Cyberware capacity management from the platform and middleware layers viewpoint. We describe the general knowledge of embedded software platform and middleware layers in Sect. 7.1. Then, in order to demonstrate how smart maintenance strategy can be better implemented in such intangible asset management, in particular, platform and middleware layers aspect, one representative research avenue is introduced in Sect. 7.2. Section 7.3 summarises this chapter.
Bo Xing - One of the best experts on this subject based on the ideXlab platform.
-
Cyberware Capacity—Platform and Middleware Layers Perspective
Smart Maintenance for Human–Robot Interaction, 2018Co-Authors: Bo Xing, Tshilidzi MarwalaAbstract:In this chapter, we investigate smart maintenance for Cyberware capacity management from the platform and middleware layers viewpoint. We describe the general knowledge of embedded software platform and middleware layers in Sect. 7.1. Then, in order to demonstrate how smart maintenance strategy can be better implemented in such intangible asset management, in particular, platform and middleware layers aspect, one representative research avenue is introduced in Sect. 7.2. Section 7.3 summarises this chapter.
-
Cyberware Capacity—Energy Autonomy Perspective
Smart Maintenance for Human–Robot Interaction, 2018Co-Authors: Bo Xing, Tshilidzi MarwalaAbstract:In this chapter, we investigate smart maintenance for Cyberware capacity management from the energy autonomy viewpoint. We describe the general knowledge of embedded system power management issue in Sect. 9.1. Then, in order to demonstrate how smart maintenance strategy can be better implemented in such intangible asset management, particularly, energy scavenging aspect, one representative research avenue is introduced in Sect. 9.2. Section 9.3 summarises this chapter.
-
Cyberware capacity applications layer perspective
2018Co-Authors: Bo Xing, Tshilidzi MarwalaAbstract:In this chapter, we investigate smart maintenance for Cyberware capacity management from the applications layer viewpoint. We describe the general knowledge of embedded software applications layer in Sect. 8.1. Then, in order to demonstrate how smart maintenance strategy can be better implemented in such intangible asset management, in particular, applications layer aspect, one representative research avenue is introduced in Sect. 8.2. Section 8.3 summarises this chapter.
-
Cyberware capacity energy autonomy perspective
2018Co-Authors: Bo Xing, Tshilidzi MarwalaAbstract:In this chapter, we investigate smart maintenance for Cyberware capacity management from the energy autonomy viewpoint. We describe the general knowledge of embedded system power management issue in Sect. 9.1. Then, in order to demonstrate how smart maintenance strategy can be better implemented in such intangible asset management, particularly, energy scavenging aspect, one representative research avenue is introduced in Sect. 9.2. Section 9.3 summarises this chapter.
-
Cyberware capacity platform and middleware layers perspective
2018Co-Authors: Bo Xing, Tshilidzi MarwalaAbstract:In this chapter, we investigate smart maintenance for Cyberware capacity management from the platform and middleware layers viewpoint. We describe the general knowledge of embedded software platform and middleware layers in Sect. 7.1. Then, in order to demonstrate how smart maintenance strategy can be better implemented in such intangible asset management, in particular, platform and middleware layers aspect, one representative research avenue is introduced in Sect. 7.2. Section 7.3 summarises this chapter.
A. G. Paranhos - One of the best experts on this subject based on the ideXlab platform.
-
1D and 3D anthropometric data application on public transport vehicle layout and on oil and gas laboratories work environment design
Work, 2012Co-Authors: F. C H Pastura, C. P. Guimarães, M. C P Zamberlan, G. L. Cid, V. S. Santos, A. G. Paranhos, R. T. Cobbe, K. T. Cobbe, Peter Streit, D. S. BatistaAbstract:The goal of this paper is to present 1D and 3D anthropometric data applied to two distinct design situations: one related to the interior layout of a public transport vehicle and another one related to oil and gas laboratories work environment design. On this study, the 1D anthropometric data were extracted from the Brazilian anthropometric database developed by INT and the 3D anthropometric data were obtained using a Cyberware 3D whole body scanner. A second purpose of this paper is to present the 3D human scanning data as a tool that can help designers on decision making. [ABSTRACT FROM AUTHOR]
-
DHM in human-centered product design: a case-study on public transport vehicle.
Work, 2012Co-Authors: Venétia Santos, G. L. Cid, Carla Patrícia Guimarães, G.a.n. Franca, A. G. ParanhosAbstract:The goal of this paper is to present the advantages on the use of 3D Digital Human Models (DHM) on the design of public transport vehicles. In this case, the subjects were scanned using the WBX Cyberware 3D Whole Body Scanner, with functional and daily postures according to the use of public transportation and some especial cases, such as a mother with her offspring or a business man with his valise, so the volume of the person would be taken in consideration. A data collection was created to simulate several situations of the daily use of the vehicle.
Maureen Stone - One of the best experts on this subject based on the ideXlab platform.
-
ACCV (2) - A hierarchical method for 3d rigid motion estimation
Computer Vision – ACCV 2006, 2006Co-Authors: Thitiwan Srinark, Chandra Kambhamettu, Maureen StoneAbstract:We propose a hierarchical method for 3D rigid motion estimation between two 3D data sets of objects represented by triangular meshes. Multiresolution surfaces are generated from the original surface of each object. These surfaces are decomposed into small patches based on estimated geodesic distance and curvature information. In our method, segment-to-segment matching to recover rigid motions at each resolution level of surfaces is performed. Motion results from low resolution surface matching are propagated to higher resolution surface matching in order to generate a spatial constraint for similar segment selection. Our approach can recover 3D rigid motion of both rigid body and nonrigid body (with partial rigid areas). The method was tested to estimate rigid motions of 3D data obtained by the Cyberware scanner.
-
A Hierarchical Method for 3D Rigid Motion Estimation
Lecture Notes in Computer Science, 2006Co-Authors: Thitiwan Srinark, Chandra Kambhamettu, Maureen StoneAbstract:We propose a hierarchical method for 3D rigid motion estimation between two 3D data sets of objects represented by triangular meshes. Multiresolution surfaces are generated from the original surface of each object. These surfaces are decomposed into small patches based on estimated geodesic distance and curvature information. In our method, segment-to-segment matching to recover rigid motions at each resolution level of surfaces is performed. Motion results from low resolution surface matching are propagated to higher resolution surface matching in order to generate a spatial constraint for similar segment selection. Our approach can recover 3D rigid motion of both rigid body and non-rigid body (with partial rigid areas). The method was tested to estimate rigid motions of 3D data obtained by the Cyberware scanner.
Steven Paquette - One of the best experts on this subject based on the ideXlab platform.
-
Three-Dimensional Image Capture and Applications - Comparison of Cyberware PX and PS 3D human head scanners
Three-Dimensional Image Capture and Applications 2008, 2008Co-Authors: Jeremy Carson, Brian D. Corner, Eric Crockett, Steven PaquetteAbstract:A common limitation of laser line three-Dimensional (3D) scanners is the inability to scan objects with surfaces that are either parallel to the laser line or that self-occlude. Filling in missing areas adds some unwanted inaccuracy to the 3D model. Capturing the human head with a Cyberware PS Head Scanner is an example of obtaining a model where the incomplete areas are difficult to fill accurately. The PS scanner uses a single vertical laser line to illuminate the head and is unable to capture data at top of the head, where the line of sight is tangent to the surface, and under the chin, an area occluded by the chin when the subject looks straight forward. The Cyberware PX Scanner was developed to obtain this missing 3D head data. The PX scanner uses two cameras offset at different angles to provide a more detailed head scan that captures surfaces missed by the PS scanner. The PX scanner cameras also use new technology to obtain color maps that are of higher resolution than the PS Scanner. The two scanners were compared in terms of amount of surface captured (surface area and volume) and the quality of head measurements when compared to direct measurements obtained through standard anthropometry methods. Relative to the PS scanner, the PX head scans were more complete and provided the full set of head measurements, but actual measurement values, when available from both scanners, were about the same.
-
comparison of Cyberware px and ps 3d human head scanners
electronic imaging, 2008Co-Authors: Jeremy Carson, Brian D. Corner, Eric Crockett, Steven PaquetteAbstract:A common limitation of laser line three-Dimensional (3D) scanners is the inability to scan objects with surfaces that are either parallel to the laser line or that self-occlude. Filling in missing areas adds some unwanted inaccuracy to the 3D model. Capturing the human head with a Cyberware PS Head Scanner is an example of obtaining a model where the incomplete areas are difficult to fill accurately. The PS scanner uses a single vertical laser line to illuminate the head and is unable to capture data at top of the head, where the line of sight is tangent to the surface, and under the chin, an area occluded by the chin when the subject looks straight forward. The Cyberware PX Scanner was developed to obtain this missing 3D head data. The PX scanner uses two cameras offset at different angles to provide a more detailed head scan that captures surfaces missed by the PS scanner. The PX scanner cameras also use new technology to obtain color maps that are of higher resolution than the PS Scanner. The two scanners were compared in terms of amount of surface captured (surface area and volume) and the quality of head measurements when compared to direct measurements obtained through standard anthropometry methods. Relative to the PS scanner, the PX head scans were more complete and provided the full set of head measurements, but actual measurement values, when available from both scanners, were about the same.
-
Three-Dimensional Image Capture and Applications - Automatic reconstruction of the top of the head from Cyberware PS head scan data
Three-Dimensional Image Capture and Applications IV, 2001Co-Authors: Brian D. Corner, Steven PaquetteAbstract:Most current 3-D head scanners cannot capture a complete surface of the head due to limitation in view. As a postprocessing aid, we developed an automated method for approximating the top of the head surface. The top-of-head surface is usually the largest void area in a 360-degree head scan such as these obtained with a Cyberware PS head scanner. In this paper, we describe a two-step B-spline curve/surface approximation process to reconstruct the top ofhead from raw data set.
-
automatic reconstruction of the top of the head from Cyberware ps head scan data
Proceedings of SPIE, 2001Co-Authors: Brian D. Corner, Steven PaquetteAbstract:Most current 3-D head scanners cannot capture a complete surface of the head due to limitation in view. As a postprocessing aid, we developed an automated method for approximating the top of the head surface. The top-of-head surface is usually the largest void area in a 360-degree head scan such as these obtained with a Cyberware PS head scanner. In this paper, we describe a two-step B-spline curve/surface approximation process to reconstruct the top ofhead from raw data set.
