6,511 research outputs found
Visualizing Sensor Network Coverage with Location Uncertainty
We present an interactive visualization system for exploring the coverage in
sensor networks with uncertain sensor locations. We consider a simple case of
uncertainty where the location of each sensor is confined to a discrete number
of points sampled uniformly at random from a region with a fixed radius.
Employing techniques from topological data analysis, we model and visualize
network coverage by quantifying the uncertainty defined on its simplicial
complex representations. We demonstrate the capabilities and effectiveness of
our tool via the exploration of randomly distributed sensor networks
Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation
Joint segmentation and classification of fine-grained actions is important
for applications of human-robot interaction, video surveillance, and human
skill evaluation. However, despite substantial recent progress in large-scale
action classification, the performance of state-of-the-art fine-grained action
recognition approaches remains low. We propose a model for action segmentation
which combines low-level spatiotemporal features with a high-level segmental
classifier. Our spatiotemporal CNN is comprised of a spatial component that
uses convolutional filters to capture information about objects and their
relationships, and a temporal component that uses large 1D convolutional
filters to capture information about how object relationships change across
time. These features are used in tandem with a semi-Markov model that models
transitions from one action to another. We introduce an efficient constrained
segmental inference algorithm for this model that is orders of magnitude faster
than the current approach. We highlight the effectiveness of our Segmental
Spatiotemporal CNN on cooking and surgical action datasets for which we observe
substantially improved performance relative to recent baseline methods.Comment: Updated from the ECCV 2016 version. We fixed an important
mathematical error and made the section on segmental inference cleare
Communicating Uncertainty and Risk in Air Quality Maps
Environmental sensors provide crucial data for understanding our
surroundings. For example, air quality maps based on sensor readings help users
make decisions to mitigate the effects of pollution on their health. Standard
maps show readings from individual sensors or colored contours indicating
estimated pollution levels. However, showing a single estimate may conceal
uncertainty and lead to underestimation of risk, while showing sensor data
yields varied interpretations. We present several visualizations of uncertainty
in air quality maps, including a frequency-framing "dotmap" and small
multiples, and we compare them with standard contour and sensor-based maps. In
a user study, we find that including uncertainty in maps has a significant
effect on how much users would choose to reduce physical activity, and that
people make more cautious decisions when using uncertainty-aware maps.
Additionally, we analyze think-aloud transcriptions from the experiment to
understand more about how the representation of uncertainty influences people's
decision-making. Our results suggest ways to design maps of sensor data that
can encourage certain types of reasoning, yield more consistent responses, and
convey risk better than standard maps
Software Defined Radio Localization using 802.11-style Communications
This major qualifying project implements a simple indoor localization system using software defined radio. Both time of arrival and received signal strength methods are used by an array of wireless receivers to trilaterate a cooperative transmitter. The implemented system builds upon an IEEE 802.11b-like communications platform implemented in GNU Radio. Our results indicate substantial room for improvement, particularly in the acquisition of time data. This project contributes a starting point for ongoing research in indoor localization, both through our literature review and system implementation
Robots that Anticipate Pain: Anticipating Physical Perturbations from Visual Cues through Deep Predictive Models
abstract: To ensure system integrity, robots need to proactively avoid any unwanted physical perturbation that may cause damage to the underlying hardware. In this thesis work, we investigate a machine learning approach that allows robots to anticipate impending physical perturbations from perceptual cues. In contrast to other approaches that require knowledge about sources of perturbation to be encoded before deployment, our method is based on experiential learning. Robots learn to associate visual cues with subsequent physical perturbations and contacts. In turn, these extracted visual cues are then used to predict potential future perturbations acting on the robot. To this end, we introduce a novel deep network architecture which combines multiple sub- networks for dealing with robot dynamics and perceptual input from the environment. We present a self-supervised approach for training the system that does not require any labeling of training data. Extensive experiments in a human-robot interaction task show that a robot can learn to predict physical contact by a human interaction partner without any prior information or labeling. Furthermore, the network is able to successfully predict physical contact from either depth stream input or traditional video input or using both modalities as input.Dissertation/ThesisMasters Thesis Computer Science 201
Seafloor characterization using airborne hyperspectral co-registration procedures independent from attitude and positioning sensors
The advance of remote-sensing technology and data-storage capabilities has progressed in the last decade to commercial multi-sensor data collection. There is a constant need to characterize, quantify and monitor the coastal areas for habitat research and coastal management. In this paper, we present work on seafloor characterization that uses hyperspectral imagery (HSI). The HSI data allows the operator to extend seafloor characterization from multibeam backscatter towards land and thus creates a seamless ocean-to-land characterization of the littoral zone
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