| 1. |
- A. Mouris, Boules, et al.
(author)
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A Dual-Polarized Multi-Antenna Structure for Simultaneous Transmission of Wireless Information and Power
- 2019
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In: 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, APSURSI 2019 - Proceedings. - : IEEE. ; , s. 1805-1806
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Conference paper (peer-reviewed)abstract
- In this paper, a dual-polarized multi-antenna structure is designed at 2.45 GHz with the goal of allowing simultaneous transmission of wireless information and power. Differential feeding was used to minimize the mutual coupling due to radiation leakage in addition to a mushroom-type EBG structure for suppressing the surface waves. Simulation results for the proposed structure show a mutual coupling level lower than -40 dB between the information transmitting antenna and the power transmitting antennas for both polarizations. The isolation level between the antennas is improved by at least 22 dB and 14 dB for the E-plane and H-plane coupling, respectively.
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| 2. |
- Aagaard, Annabeth, et al.
(author)
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Creating and capturing sustainable value through business model and service innovation
- 2018
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Conference paper (peer-reviewed)abstract
- A number of researchers and practitioners emphasize the potentials of creating value through sustainable business models. However, little attention has been paid as to how sustainable value is created and implemented into the organization and how sustainable value is perceived by the customers. This research gap is explored empirically through a case study of active facilitation and implementation of sustainable business models as experience internally and externally among two hotels’ employees and customers. The findings reveal different key control mechanisms in sustainable value creation and value capture through sustainable business models and sustainable service innovation. The managerial implications of creating and implementing sustainable business models in ways that are perceived sustainable by customer, stress the need for employee engagement, customer involvement, and targeted and personal communication educating internal and external sustainability ambassadors.
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| 3. |
- AAl Abdulsalam, Abdulrahman, et al.
(author)
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UtahBMI at SemEval-2016 Task 12 : Extracting Temporal Information from Clinical Text
- 2016
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In: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016). - : Association for Computational Linguistics. ; , s. 1256-1262
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Conference paper (peer-reviewed)abstract
- The 2016 Clinical TempEval continued the 2015 shared task on temporal information extraction with a new evaluation test set. Our team, UtahBMI, participated in all subtasks using machine learning approaches with ClearTK (LIBLINEAR), CRF++ and CRFsuite packages. Our experiments show that CRF-based classifiers yield, in general, higher recall for multi-word spans, while SVM-based classifiers are better at predicting correct attributes of TIMEX3. In addition, we show that an ensemble-based approach for TIMEX3 could yield improved results. Our team achieved competitive results in each subtask with an F1 75.4% for TIMEX3, F1 89.2% for EVENT, F1 84.4% for event relations with document time (DocTimeRel), and F1 51.1% for narrative container (CONTAINS) relations.
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| 6. |
- Aarno, Daniel, et al.
(author)
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Adaptive virtual fixtures for machine-assisted teleoperation tasks
- 2005
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In: 2005 IEEE International Conference on Robotics and Automation (ICRA), Vols 1-4. - 078038914X ; , s. 1139-1144
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Conference paper (peer-reviewed)abstract
- It has been demonstrated in a number of robotic areas how the use of virtual fixtures improves task performance both in terms of execution time and overall precision, [1]. However, the fixtures are typically inflexible, resulting in a degraded performance in cases of unexpected obstacles or incorrect fixture models. In this paper, we propose the use of adaptive virtual fixtures that enable us to cope with the above problems. A teleoperative or human machine collaborative setting is assumed with the core idea of dividing the task, that the operator is executing, into several subtasks. The operator may remain in each of these subtasks as long as necessary and switch freely between them. Hence, rather than executing a predefined plan, the operator has the ability to avoid unforeseen obstacles and deviate from the model. In our system, the probability that the user is following a certain trajectory (subtask) is estimated and used to automatically adjusts the compliance. Thus, an on-line decision of how to fixture the movement is provided.
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| 7. |
- Aarno, Daniel, et al.
(author)
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Artificial potential biased probabilistic roadmap method
- 2004
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In: 2004 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1- 5, PROCEEDINGS. - 0780382323 ; , s. 461-466
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Conference paper (peer-reviewed)abstract
- Probabilistic roadmap methods (PRMs) have been successfully used to solve difficult path planning problems but their efficiency is limited when the free space contains narrow passages through which the robot must pass. This paper presents a new sampling scheme that aims to increase the probability of finding paths through narrow passages. Here, a biased sampling scheme is used to increase the distribution of nodes in narrow regions of the free space. A partial computation of the artificial potential field is used to bias the distribution of nodes.
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| 8. |
- Aarno, Daniel, et al.
(author)
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Constrained path planning and task-consistent path adaptation for mobile manipulators
- 2005
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In: 2005 12th International Conference on Advanced Robotics. - 0780391772 ; , s. 268-273
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Conference paper (peer-reviewed)abstract
- This paper presents our ongoing research in the design of a versatile service robot capable of operating in a home or office environment. Ideas presented here cover architectural issues and possible applications for such a robot system with focus on tasks requiring constrained end-effector motions. Two key components of such system is a path planner and a reactive behavior capable of force relaxation and path adaptation. These components are presented in detail along with an overview of the software architecture they fit into.
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| 9. |
- Aarno, Daniel, et al.
(author)
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Early reactive grasping with second order 3D feature relations
- 2008
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In: Recent Progress In Robotics: Viable Robotic Service To Human. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 9783540767282 ; , s. 91-105
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Conference paper (peer-reviewed)abstract
- One of the main challenges in the field of robotics is to make robots ubiquitous. To intelligently interact with the world, such robots need to understand the environment and situations around them and react appropriately, they need context-awareness. But how to equip robots with capabilities of gathering and interpreting the necessary information for novel tasks through interaction with the environment and by providing some minimal knowledge in advance? This has been a longterm question and one of the main drives in the field of cognitive system development. The main idea behind the work presented in this paper is that the robot should, like a human infant, learn about objects by interacting with them, forming representations of the objects and their categories that are grounded in its embodiment. For this purpose, we study an early learning of object grasping process where the agent, based on a set of innate reflexes and knowledge about its embodiment. We stress out that this is not the work on grasping, it is a system that interacts with the environment based on relations of 3D visual features generated trough a stereo vision system. We show how geometry, appearance and spatial relations between the features can guide early reactive grasping which can later on be used in a more purposive manner when interacting with the environment.
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| 10. |
- Aarno, Daniel, et al.
(author)
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Layered HMM for motion intention recognition
- 2006
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In: 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vols 1-12. - NEW YORK : IEEE. - 9781424402588 ; , s. 5130-5135
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Conference paper (peer-reviewed)abstract
- Acquiring, representing and modeling human skins is one of the key research areas in teleoperation, programming. by-demonstration and human-machine collaborative settings. One of the common approaches is to divide the task that the operator is executing into several subtasks in order to provide manageable modeling. In this paper we consider the use of a Layered Hidden Markov Model (LHMM) to model human skills. We evaluate a gestem classifier that classifies motions into basic action-primitives, or gestems. The gestem classifiers are then used in a LHMM to model a simulated teleoperated task. We investigate the online and offline classilication performance with respect to noise, number of gestems, type of HAIM and the available number of training sequences. We also apply the LHMM to data recorded during the execution of a trajectory-tracking task in 2D and 3D with a robotic manipulator in order to give qualitative as well as quantitative results for the proposed approach. The results indicate that the LHMM is suitable for modeling teleoperative trajectory-tracking tasks and that the difference in classification performance between one and multi dimensional HMMs for gestem classification is small. It can also be seen that the LHMM is robust w.r.t misclassifications in the underlying gestem classifiers.
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