17,263 research outputs found
A multi-modal event detection system for river and coastal marine monitoring applications
Abstract—This work is investigating the use of a multi-modal
sensor network where visual sensors such as cameras and
satellite imagers, along with context information can be used to complement and enhance the usefulness of a traditional in-situ sensor network in measuring and tracking some feature of a river or coastal location. This paper focuses on our work in relation to the use of an off the shelf camera as part of a multi-modal sensor network for monitoring a river environment. It outlines our results in relation to the estimation of water level using a visual sensor. It also outlines the benefits of a multi-modal sensor network for marine environmental monitoring and how this can lead to a smarter, more efficient sensing network
Damage identification in structural health monitoring: a brief review from its implementation to the Use of data-driven applications
The damage identification process provides relevant information about the current state of a structure under inspection, and it can be approached from two different points of view. The first approach uses data-driven algorithms, which are usually associated with the collection of data using sensors. Data are subsequently processed and analyzed. The second approach uses models to analyze information about the structure. In the latter case, the overall performance of the approach is associated with the accuracy of the model and the information that is used to define it. Although both approaches are widely used, data-driven algorithms are preferred in most cases because they afford the ability to analyze data acquired from sensors and to provide a real-time solution for decision making; however, these approaches involve high-performance processors due to the high computational cost. As a contribution to the researchers working with data-driven algorithms and applications, this work presents a brief review of data-driven algorithms for damage identification in structural health-monitoring applications. This review covers damage detection, localization, classification, extension, and prognosis, as well as the development of smart structures. The literature is systematically reviewed according to the natural steps of a structural health-monitoring system. This review also includes information on the types of sensors used as well as on the development of data-driven algorithms for damage identification.Peer ReviewedPostprint (published version
Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology
INE/AUTC 10.0
Robust Deep Multi-Modal Sensor Fusion using Fusion Weight Regularization and Target Learning
Sensor fusion has wide applications in many domains including health care and
autonomous systems. While the advent of deep learning has enabled promising
multi-modal fusion of high-level features and end-to-end sensor fusion
solutions, existing deep learning based sensor fusion techniques including deep
gating architectures are not always resilient, leading to the issue of fusion
weight inconsistency. We propose deep multi-modal sensor fusion architectures
with enhanced robustness particularly under the presence of sensor failures. At
the core of our gating architectures are fusion weight regularization and
fusion target learning operating on auxiliary unimodal sensing networks
appended to the main fusion model. The proposed regularized gating
architectures outperform the existing deep learning architectures with and
without gating under both clean and corrupted sensory inputs resulted from
sensor failures. The demonstrated improvements are particularly pronounced when
one or more multiple sensory modalities are corrupted.Comment: 8 page
Biosignal and context monitoring: Distributed multimedia applications of body area networks in healthcare
We are investigating the use of Body Area Networks (BANs), wearable sensors and wireless communications for measuring, processing, transmission, interpretation and display of biosignals. The goal is to provide telemonitoring and teletreatment services for patients. The remote health professional can view a multimedia display which includes graphical and numerical representation of patients’ biosignals. Addition of feedback-control enables teletreatment services; teletreatment can be delivered to the patient via multiple modalities including tactile, text, auditory and visual. We describe the health BAN and a generic mobile health service platform and two context aware applications. The epilepsy application illustrates processing and interpretation of multi-source, multimedia BAN data. The chronic pain application illustrates multi-modal feedback and treatment, with patients able to view their own biosignals on their handheld device
Control limitations from distributed sensing: theory and Extremely Large Telescope application
We investigate performance bounds for feedback control of distributed plants
where the controller can be centralized (i.e. it has access to measurements
from the whole plant), but sensors only measure differences between neighboring
subsystem outputs. Such "distributed sensing" can be a technological necessity
in applications where system size exceeds accuracy requirements by many orders
of magnitude. We formulate how distributed sensing generally limits feedback
performance robust to measurement noise and to model uncertainty, without
assuming any controller restrictions (among others, no "distributed control"
restriction). A major practical consequence is the necessity to cut down
integral action on some modes. We particularize the results to spatially
invariant systems and finally illustrate implications of our developments for
stabilizing the segmented primary mirror of the European Extremely Large
Telescope.Comment: submitted to Automatic
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