57,770 research outputs found
Human-Machine Interface for Remote Training of Robot Tasks
Regardless of their industrial or research application, the streamlining of
robot operations is limited by the proximity of experienced users to the actual
hardware. Be it massive open online robotics courses, crowd-sourcing of robot
task training, or remote research on massive robot farms for machine learning,
the need to create an apt remote Human-Machine Interface is quite prevalent.
The paper at hand proposes a novel solution to the programming/training of
remote robots employing an intuitive and accurate user-interface which offers
all the benefits of working with real robots without imposing delays and
inefficiency. The system includes: a vision-based 3D hand detection and gesture
recognition subsystem, a simulated digital twin of a robot as visual feedback,
and the "remote" robot learning/executing trajectories using dynamic motion
primitives. Our results indicate that the system is a promising solution to the
problem of remote training of robot tasks.Comment: Accepted in IEEE International Conference on Imaging Systems and
Techniques - IST201
Detection Algorithms for Molecular MIMO
In this paper, we propose a novel design for molecular communication in which
both the transmitter and the receiver have, in a 3-dimensional environment,
multiple bulges (in RF communication this corresponds to antenna). The proposed
system consists of a fluid medium, information molecules, a transmitter, and a
receiver. We simulate the system with a one-shot signal to obtain the channel's
finite impulse response. We then incorporate this result within our
mathematical analysis to determine interference. Molecular communication has a
great need for low complexity, hence, the receiver may have incomplete
information regarding the system and the channel state. Thus, for the cases of
limited information set at the receiver, we propose three detection algorithms,
namely adaptive thresholding, practical zero forcing, and Genie-aided zero
forcing.Comment: 6 pages, 6 figures, 2015 IEEE ICC accepte
Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations
The use of neural networks to directly predict three-dimensional dose
distributions for automatic planning is becoming popular. However, the existing
methods only use patient anatomy as input and assume consistent beam
configuration for all patients in the training database. The purpose of this
work is to develop a more general model that, in addition to patient anatomy,
also considers variable beam configurations, to achieve a more comprehensive
automatic planning with a potentially easier clinical implementation, without
the need of training specific models for different beam settings
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Online Adaptive Radiation Therapy: Implementation of a New Process of Care.
Onboard magnetic resonance imaging (MRI) guided radiotherapy is now clinically available in nine centers in the world. This technology has facilitated the clinical implementation of online adaptive radiotherapy (OART), or the ability to alter the daily treatment plan based on tumor and anatomical changes in real-time while the patient is on the treatment table. However, due to the time sensitive nature of OART, implementation in a large and busy clinic has many potential obstacles as well as patient-related safety considerations. In this work, we have described the implementation of this new process of care in the Department of Radiation Oncology at the University of California, Los Angeles (UCLA). We describe the rationale, the initial challenges such as treatment time considerations, technical issues during the process of re-contouring, re-optimization, quality assurance, as well as our current solutions to overcome these challenges. In addition, we describe the implementation of a coverage system with a physician of the day as well as online planners (physicists or dosimetrists) to oversee each OART treatment with patient-specific 'hand-off' directives from the patient's treating physician. The purpose of this effort is to streamline the process without compromising treatment quality and patient safety. As more MRI-guided radiotherapy programs come online, we hope that our experience can facilitate successful adoption of OART in a way that maximally benefits the patient
Adaptive Energy-aware Scheduling of Dynamic Event Analytics across Edge and Cloud Resources
The growing deployment of sensors as part of Internet of Things (IoT) is
generating thousands of event streams. Complex Event Processing (CEP) queries
offer a useful paradigm for rapid decision-making over such data sources. While
often centralized in the Cloud, the deployment of capable edge devices on the
field motivates the need for cooperative event analytics that span Edge and
Cloud computing. Here, we identify a novel problem of query placement on edge
and Cloud resources for dynamically arriving and departing analytic dataflows.
We define this as an optimization problem to minimize the total makespan for
all event analytics, while meeting energy and compute constraints of the
resources. We propose 4 adaptive heuristics and 3 rebalancing strategies for
such dynamic dataflows, and validate them using detailed simulations for 100 -
1000 edge devices and VMs. The results show that our heuristics offer
O(seconds) planning time, give a valid and high quality solution in all cases,
and reduce the number of query migrations. Furthermore, rebalance strategies
when applied in these heuristics have significantly reduced the makespan by
around 20 - 25%.Comment: 11 pages, 7 figure
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