The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Marcello Pellicciari - One of the best experts on this subject based on the ideXlab platform.
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Real-time 3D features reconstruction through monocular vision
International Journal on Interactive Design and Manufacturing (IJIDeM), 2010Co-Authors: Alfredo Liverani, Francesco Leali, Marcello PellicciariAbstract:A fast and interactive implementation for camera pose registration and 3D point reconstruction over a physical surface is described in this paper. The method (called SRE—Smart Reverse Engineering) extracts from a continuous image streaming, provided by a single camera moving around a real object, a point cloud and the camera’s spatial trajectory. The whole per frame procedure follows three steps: camera Calibration, camera registration, bundle adjustment and 3D point calculation. Camera Calibration Task was performed using a traditional approach based on 2-D structured pattern, while the Optical Flow approach and the Lucas-Kanade algorithm was adopted for feature detection and tracking. Camera registration problem was then solved thanks to the Essential Matrix definition. Finally a fast Bundle Adjustment was performed through the Levenberg-Marquardt algorithm to achieve the best trade-off between 3D structure and camera variations. Exploiting a PC and a commercial webcam, an experimental validation was done in order to verify precision in 3D data reconstruction and speed. Practical tests helped also to tune up several optimization parameters used to improve efficiency of most CPU time consuming algorithms, like Optical Flow and Bundle Adjustment. The method showed robust results in 3D reconstruction and very good performance in real-time applications.
Alfredo Liverani - One of the best experts on this subject based on the ideXlab platform.
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Real-time 3D features reconstruction through monocular vision
International Journal on Interactive Design and Manufacturing (IJIDeM), 2010Co-Authors: Alfredo Liverani, Francesco Leali, Marcello PellicciariAbstract:A fast and interactive implementation for camera pose registration and 3D point reconstruction over a physical surface is described in this paper. The method (called SRE—Smart Reverse Engineering) extracts from a continuous image streaming, provided by a single camera moving around a real object, a point cloud and the camera’s spatial trajectory. The whole per frame procedure follows three steps: camera Calibration, camera registration, bundle adjustment and 3D point calculation. Camera Calibration Task was performed using a traditional approach based on 2-D structured pattern, while the Optical Flow approach and the Lucas-Kanade algorithm was adopted for feature detection and tracking. Camera registration problem was then solved thanks to the Essential Matrix definition. Finally a fast Bundle Adjustment was performed through the Levenberg-Marquardt algorithm to achieve the best trade-off between 3D structure and camera variations. Exploiting a PC and a commercial webcam, an experimental validation was done in order to verify precision in 3D data reconstruction and speed. Practical tests helped also to tune up several optimization parameters used to improve efficiency of most CPU time consuming algorithms, like Optical Flow and Bundle Adjustment. The method showed robust results in 3D reconstruction and very good performance in real-time applications.
Scott A Banks - One of the best experts on this subject based on the ideXlab platform.
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sensitivity based Calibration technique for patient specific intra operative planning of knee replacement
Orthopaedic Proceedings, 2018Co-Authors: Nicholas Dunbar, Scott A BanksAbstract:Intraoperative planning of knee replacement components, targeting a desired functional outcome, requires a calibrated patient-specific model of the patient's soft-tissue anatomy and mechanics. Previously, a surgical technique was demonstrated for measuring knee joint kinematics and kinetics consistent with modern navigation systems in conjunction with the development of a patient-customizable knee model. A data efficient approach for the model Calibration Task was achieved utilizing the sensitivity of the model to simulated clinical hand manipulations of the knee joint requiring 85% less computations.For this numerical investigation a simplified knee joint model, based on the OpenKnee repository, consisting of bone (rigid), cruciate ligaments (single-bundle, nonlinear spring), collateral ligaments (multiple nonlinear springs), articular cartilage (rigid, pressure-over-closure relationship), and combined capsule/meniscus (linear springs) was created using a custom Matlab (MathWorks)-Abaqus (Dassault System...
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sensitivity based Calibration technique for patient specific intra operative planning of knee replacement
Journal of Bone and Joint Surgery-british Volume, 2017Co-Authors: Nicholas Dunbar, Scott A BanksAbstract:Intraoperative planning of knee replacement components, targeting a desired functional outcome, requires a calibrated patient-specific model of the patient's soft-tissue anatomy and mechanics. Previously, a surgical technique was demonstrated for measuring knee joint kinematics and kinetics consistent with modern navigation systems in conjunction with the development of a patient-customizable knee model. A data efficient approach for the model Calibration Task was achieved utilizing the sensitivity of the model to simulated clinical hand manipulations of the knee joint requiring 85% less computations. For this numerical investigation a simplified knee joint model, based on the OpenKnee repository, consisting of bone (rigid), cruciate ligaments (single-bundle, nonlinear spring), collateral ligaments (multiple nonlinear springs), articular cartilage (rigid, pressure-over-closure relationship), and combined capsule/meniscus (linear springs) was created using a custom Matlab (MathWorks)-Abaqus (Dassault Systemes) implicit finite element modeling framework (Figure 1). A sensitivity analysis was performed by applying constant loading along the anterior-posterior, medial-lateral, varus-valgus, and internal-external directions (30 N for forces and 3 Nm for moments) while perturbing each customizable parameter positively and negatively by 1 mm at 0, 25, 50, 75 and 100 degrees of flexion. A constant load of 150 N was maintained in compression. The change in static endpoint position was measured relative to the respective position without perturbation. Sensitivity results were then arranged by load direction and principal component analysis was subsequently performed (Table 1). First a single optimization Task was simulated including all model parameters and all loading sequences with the goal of minimizing the kinematic differences between the reference model and a perturbed model (Figure 2). Second, a piecewise optimization Task was designed using only the sensitive parameters for a spanning set of loads for the same perturbed model. Parameters 3 and 4 were tuned using internal and external endpoints. Then parameters 1 and 5 were tuned using the anterior endpoints. Similarly, parameters 2 and 7 were tuned using the posterior endpoints. Finally, parameter 8 was tuned using the varus endpoints. All loadings were observed to be insensitive to parameter 6 (ACL-Y). The number of model evaluations required were 2520 and 390 for the single and piecewise optimizations, respectively. The single simulation Task recovered all parameters within 0.57 mm on average compared to 0.64 mm on average for the piecewise Task. Kinematic errors due to the Calibration technique were within 0.001 mm and 0.18 deg compared to 0.001 mm and 0.04 deg. Computational cost for the optimization Task required to calibrate a patient-specific knee model was reduced while maintaining clinically relevant accuracy. This model reduction approach will further enable the rapid adoption of the technology for intraoperative planning of knee replacement components based on targeted functional outcomes.
Michael Perryman - One of the best experts on this subject based on the ideXlab platform.
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The FAST Hipparcos data reduction consortium - Overview of the reduction software
Astronomy and Astrophysics, 1992Co-Authors: J. Kovalevsky, J. L. Falin, J. L. Pieplu, P. L. Bernacca, F. Donati, M. Froeschle, I. Galligani, Francois Mignard, B. Morando, Michael PerrymanAbstract:The main features of the data analysis methods adopted by FAST in the Calibration and processing of the Hipparcos data are discussed. The structure of the data reduction software is examined, and the actual processing and evaluation are presented. Attention is given to the first look Task, the Calibration Task, data preparation, attitude determination, the great circle reduction, synthesis treatment, iterations, data management and command software, and follow-up and evaluation activities.
Debbah Mérouane - One of the best experts on this subject based on the ideXlab platform.
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Deep Learning for UL/DL Channel Calibration in Generic Massive MIMO Systems
HAL CCSD, 2019Co-Authors: Huang Chongwen, Alexandropoulos George, Zappone Alessio, Yuen Chau, Debbah MérouaneAbstract:International audienceOne of the fundamental challenges to realize massive multiple-input multiple-output communications is the accurate acquisition of channel state information for a plurality of users at the base station. This is usually accomplished in the UpLink direction profiting from the Time Division Duplexing mode. In practical base station transceivers, there exist inevitably non-linear hardware components, like signal amplifiers and various analog filters, which complicates the Calibration Task. To deal with this challenge, we design a deep neural network for channel Calibration between the uplink and downlink directions. During the initial training phase, the deep neural network is trained from both uplink and downlink channel measurements. We then leverage the trained deep neural network with the instantaneously estimated uplink channel to calibrate the downlink one, which is not observable during the uplink transmission phase. Our numerical results confirm the merits of the proposed approach, and show that it can achieve performance comparable to conventional approaches, like the Agros method and methods based on least squares, that however assume linear hardware behavior models. More importantly, considering generic nonlin-ear relationships between the uplink and downlink channels, it is demonstrated that our deep neural network approach exhibits robust performance, even when the number of training sequences is limited
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Deep Learning for UL/DL Channel Calibration in Generic Massive MIMO Systems
2019Co-Authors: Huang Chongwen, Zappone Alessio, Yuen Chau, Alexandropoulos, George C., Debbah MérouaneAbstract:One of the fundamental challenges to realize massive Multiple-Input Multiple-Output (MIMO) communications is the accurate acquisition of channel state information for a plurality of users at the base station. This is usually accomplished in the UpLink (UL) direction profiting from the time division duplexing mode. In practical base station transceivers, there exist inevitably nonlinear hardware components, like signal amplifiers and various analog filters, which complicates the Calibration Task. To deal with this challenge, we design a deep neural network for channel Calibration between the UL and DownLink (DL) directions. During the initial training phase, the deep neural network is trained from both UL and DL channel measurements. We then leverage the trained deep neural network with the instantaneously estimated UL channel to calibrate the DL one, which is not observable during the UL transmission phase. Our numerical results confirm the merits of the proposed approach, and show that it can achieve performance comparable to conventional approaches, like the Agros method and methods based on least squares, that however assume linear hardware behavior models. More importantly, considering generic nonlinear relationships between the UL and DL channels, it is demonstrated that our deep neural network approach exhibits robust performance, even when the number of training sequences is limited.Comment: 6-pages, accepted by ICC WC Symposium 201
