The Experts below are selected from a list of 59337 Experts worldwide ranked by ideXlab platform
Gregory S. Chirikjian - One of the best experts on this subject based on the ideXlab platform.
-
online ultrasound Sensor Calibration using gradient descent on the euclidean group
International Conference on Robotics and Automation, 2014Co-Authors: Martin Kendal Ackerman, Alexis Cheng, Emad M Boctor, Gregory S. ChirikjianAbstract:Ultrasound imaging can be an advantageous imaging modality for image guided surgery. When using ultrasound imaging (or any imaging modality), Calibration is important when more advanced forms of guidance, such as augmented reality systems, are used. There are many different methods of Calibration, but the goal of each is to recover the rigid body transformation relating the pose of the probe to the ultrasound image frame. This paper presents a unified algorithm that can solve the ultrasound Calibration problem for various Calibration methodologies. The algorithm uses gradient descent optimization on the Euclidean Group. It can be used in real time, also serving as a way to update the Calibration parameters on-line. We also show how filtering, based on the theory of invariants, can further improve the online results. Focusing on two specific Calibration methodologies, the AX = XB problem and the BX−1 p problem, we demonstrate the efficacy of the algorithm in both simulation and experimentation.
-
an information theoretic approach to the correspondence free ax xb Sensor Calibration problem
International Conference on Robotics and Automation, 2014Co-Authors: Martin Kendal Ackerman, Alexis Cheng, Gregory S. ChirikjianAbstract:For the case of an exact set of compatible A’s and B’s with known correspondence, the AX=XB problem was solved decades ago. However, in many applications, data streams containing the A’s and B’s will often have different sampling rates or will be asynchronous. For these reasons and the fact that each stream may contain gaps in information, methods that require minimal a priori knowledge of the correspondence between A’s and B’s would be superior to the existing algorithms that require exact correspondence. We present an informationtheoretic algorithm for recovering X from a set of A’s and a set of B’s that does not require a priori knowledge of correspondences. The algorithm views the problem in terms of distributions on the group SE(3), and minimizing the Kullback-Leibler divergence of these distributions with respect to the unknown X . This minimization is performed by an efficient numerical procedure that reliably recovers an unknown X .
-
Sensor Calibration with unknown correspondence solving ax xb using euclidean group invariants
Intelligent Robots and Systems, 2013Co-Authors: Martin Kendal Ackerman, Alexis Cheng, Emad M Boctor, Bernard Shiffman, Gregory S. ChirikjianAbstract:The AX = XB Sensor Calibration problem must often be solved in image guided therapy systems, such as those used in robotic surgical procedures. In this problem, A, X, and B are homogeneous transformations with A and B acquired from Sensor measurements and X being the unknown. It has been known for decades that this problem is solvable for X when a set of exactly measured A's and B's, in a priori correspondence, is given. However, in practical problems, the data streams containing the A' and B's will be asynchronous and may contain gaps (i.e., the correspondence is unknown, or does not exist, for the Sensor measurements) and temporal registration is required. For the AX = XB problem, an exact solution can be found when four independent invariant quantities exist between two pairs of A's and B's. We formally define these invariants, reviewing and elaborating results from classical screw theory. We then illustrate how they can be used, with Sensor data from multiple sources that contain unknown or missing correspondences, to provide a solution for X.
-
a probabilistic solution to the ax xb problem Sensor Calibration without correspondence
International Conference on Geometric Science of Information, 2013Co-Authors: Kendal M Ackerman, Gregory S. ChirikjianAbstract:The “AX=XB” Sensor Calibration problem is ubiquitous in the fields of robotics and computer vision. In this problem A, X, and B are each homogeneous transformations (i.e., rigid-body motions) with A and B given from Sensor measurements, and X is the unknown that is sought. For decades this problem is known to be solvable for X when a set of exactly measured compatible A’s and B’s with known correspondence is given. However, in practical problems, it is often the case that the data streams containing the A’s and B’s will present at different sample rates, they will be asynchronous, and each stream may contain gaps in information. Practical scenarios in which this can happen include hand-eye Calibration and ultrasound image registration. We therefore present a method for calculating the Calibration transformation, X, that works for data without any a priori knowledge of the correspondence between the As and Bs.
R D Saunders - One of the best experts on this subject based on the ideXlab platform.
-
High-heat-flux Sensor Calibration using black-body radiation
Metrologia, 2003Co-Authors: Annageri V. Murthy, Benjamin K. Tsai, R D SaundersAbstract:This paper deals with the radiative Calibration aspects of high-heat-flux Sensors using black-body radiation. In the last two years, several heat-flux Sensors were calibrated up to 50 kW/m 2 using a 25 mm diameter aperture variable-temperature black body and a reference room-temperature electrical-substitution radiometer. Tests on a typical Schmidt-Boelter heat-flux Sensor showed long-term repeatability of Calibration is within 0.6%. Plans for extending the present Calibration capability to 100 kW/m 2 are discussed.
David G Rickerby - One of the best experts on this subject based on the ideXlab platform.
-
end user perspective of low cost Sensors for outdoor air pollution monitoring
Science of The Total Environment, 2017Co-Authors: Prashant Kumar, Francesco Pilla, Andreas N Skouloudis, Silvana Di Sabatino, Ansar Yasar, Carlo Ratti, David G RickerbyAbstract:Low-cost Sensor technology can potentially revolutionise the area of air pollution monitoring by providing high-density spatiotemporal pollution data. Such data can be utilised for supplementing traditional pollution monitoring, improving exposure estimates, and raising community awareness about air pollution. However, data quality remains a major concern that hinders the widespread adoption of low-cost Sensor technology. Unreliable data may mislead unsuspecting users and potentially lead to alarming consequences such as reporting acceptable air pollutant levels when they are above the limits deemed safe for human health. This article provides scientific guidance to the end-users for effectively deploying low-cost Sensors for monitoring air pollution and people's exposure, while ensuring reasonable data quality. We review the performance characteristics of several low-cost particle and gas monitoring Sensors and provide recommendations to end-users for making proper Sensor selection by summarizing the capabilities and limitations of such Sensors. The challenges, best practices, and future outlook for effectively deploying low-cost Sensors, and maintaining data quality are also discussed. For data quality assurance, a two-stage Sensor Calibration process is recommended, which includes laboratory Calibration under controlled conditions by the manufacturer supplemented with routine Calibration checks performed by the end-user under final deployment conditions. For large Sensor networks where routine Calibration checks are impractical, statistical techniques for data quality assurance should be utilised. Further advancements and adoption of sophisticated mathematical and statistical techniques for Sensor Calibration, fault detection, and data quality assurance can indeed help to realise the promised benefits of a low-cost air pollution Sensor network.
Tarkoma Sasu - One of the best experts on this subject based on the ideXlab platform.
-
Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis
2021Co-Authors: Concas Francesco, Mineraud Julien, Lagerspetz Eemil, Varjonen Samu, Liu Xiaoli, Puolamäki Kai, Nurmi Petteri, Tarkoma SasuAbstract:The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality Sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality Sensors, however, presents several challenges: they suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-Calibration can improve the accuracy of low-cost Sensors, particularly with machine-learning-based Calibration, which has shown great promise due to its capability to calibrate Sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost Sensor technologies for air quality monitoring and their Calibration using machine learning techniques. We also identify open research challenges and present directions for future research
-
Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis
'Association for Computing Machinery (ACM)', 2021Co-Authors: Concas Francesco, Mineraud Julien, Lagerspetz Eemil, Varjonen Samu, Liu Xiaoli, Puolamäki Kai, Nurmi Petteri, Tarkoma SasuAbstract:The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality Sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality Sensors, however, presents several challenges: they suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-Calibration can improve the accuracy of low-cost Sensors, particularly with machine-learning-based Calibration, which has shown great promise due to its capability to calibrate Sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost Sensor technologies for air quality monitoring and their Calibration using machine learning techniques. We also identify open research challenges and present directions for future research.Peer reviewe
-
Low-Cost Outdoor Air Quality Monitoring and Sensor Calibration: A Survey and Critical Analysis
2020Co-Authors: Concas Francesco, Lagerspetz Eemil, Varjonen Samu, Liu Xiaoli, Puolamäki Kai, Nurmi Petteri, Mineraud Julie, Tarkoma SasuAbstract:The significance of air pollution and problems associated with it is fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence, environmental monitoring stations typically are deployed sparsely, resulting in limited spatial resolution for measurements. Recently, low-cost air quality Sensors have emerged as an alternative that can improve granularity of monitoring. The use of low-cost air quality Sensors, however, presents several challenges: they suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors such as traffic, weather changes, and human behavior; and their accuracy degrades over time. The accuracy of low-cost Sensors can be improved through periodic re-Calibration with particularly machine learning based Calibration having shown great promise due to its capability to calibrate Sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost Sensor technologies for air quality monitoring, and their Calibration using machine learning techniques. We also identify open research challenges and present directions for future research
Tom E. Diller - One of the best experts on this subject based on the ideXlab platform.
-
in situ high temperature heat flux Sensor Calibration
International Journal of Heat and Mass Transfer, 2010Co-Authors: Clayton A. Pullins, Tom E. DillerAbstract:Abstract Recent advances in heat flux measurement have resulted in the development of a robust thermopile heat flux Sensor intended for use in extreme thermal environments. The High Temperature Heat Flux Sensor (HTHFS) is capable of simultaneously measuring thermopile surface temperature and heat flux at Sensor temperatures up to 1000 °C. The need for high temperature heat flux Calibration of the HTHFS has resulted in the development of a new wide angle radiation Calibration system, which operates with the Sensor at elevated temperatures. The temperature dependence of the Sensor output over the range of 100–900 °C has been successfully characterized with acceptable uncertainty limits. The calibrated HTHFS sensitivity agrees well with a theoretical sensitivity model, suggesting that the primary cause for the Sensor’s output temperature dependence is due to the change in thermal conductivity of the Sensor elements with temperature.
