585 research outputs found
Dense and long-term monitoring of Earth surface processes with passive RFID -- a review
Billions of Radio-Frequency Identification (RFID) passive tags are produced
yearly to identify goods remotely. New research and business applications are
continuously arising, including recently localization and sensing to monitor
earth surface processes. Indeed, passive tags can cost 10 to 100 times less
than wireless sensors networks and require little maintenance, facilitating
years-long monitoring with ten's to thousands of tags. This study reviews the
existing and potential applications of RFID in geosciences. The most mature
application today is the study of coarse sediment transport in rivers or
coastal environments, using tags placed into pebbles. More recently, tag
localization was used to monitor landslide displacement, with a centimetric
accuracy. Sensing tags were used to detect a displacement threshold on unstable
rocks, to monitor the soil moisture or temperature, and to monitor the snowpack
temperature and snow water equivalent. RFID sensors, available today, could
monitor other parameters, such as the vibration of structures, the tilt of
unstable boulders, the strain of a material, or the salinity of water. Key
challenges for using RFID monitoring more broadly in geosciences include the
use of ground and aerial vehicles to collect data or localize tags, the
increase in reading range and duration, the ability to use tags placed under
ground, snow, water or vegetation, and the optimization of economical and
environmental cost. As a pattern, passive RFID could fill a gap between
wireless sensor networks and manual measurements, to collect data efficiently
over large areas, during several years, at high spatial density and moderate
cost.Comment: Invited paper for Earth Science Reviews. 50 pages without references.
31 figures. 8 table
Generative AI for Unmanned Vehicle Swarms: Challenges, Applications and Opportunities
With recent advances in artificial intelligence (AI) and robotics, unmanned
vehicle swarms have received great attention from both academia and industry
due to their potential to provide services that are difficult and dangerous to
perform by humans. However, learning and coordinating movements and actions for
a large number of unmanned vehicles in complex and dynamic environments
introduce significant challenges to conventional AI methods. Generative AI
(GAI), with its capabilities in complex data feature extraction,
transformation, and enhancement, offers great potential in solving these
challenges of unmanned vehicle swarms. For that, this paper aims to provide a
comprehensive survey on applications, challenges, and opportunities of GAI in
unmanned vehicle swarms. Specifically, we first present an overview of unmanned
vehicles and unmanned vehicle swarms as well as their use cases and existing
issues. Then, an in-depth background of various GAI techniques together with
their capabilities in enhancing unmanned vehicle swarms are provided. After
that, we present a comprehensive review on the applications and challenges of
GAI in unmanned vehicle swarms with various insights and discussions. Finally,
we highlight open issues of GAI in unmanned vehicle swarms and discuss
potential research directions.Comment: 23 page
Design of Smart Open Parking Using Background Subtraction in the IoT Architecture
The Internet of Things (IoT) has evolved and penetrated to our live since the end of the last century. Nowadays, many devices for any purpose are connected through the Internet. A smart node, in smart campus environment, can detect an availability of an open parking space by calculating the vehicle that enters or outs from the space. The node applies a background subtraction method, which is deployed in IoT architecture. The Gaussian Mixture Model (GMM) is utilized to determine foreground and background image, in order to detect a moving object at an open area. Furthermore, the node can discriminate the type of vehicle with a high accuracy. The result of vehicle type classification is transmitted by the node through the Internet, and then it is saved to the data server. We observe the designed system succeeds delivering a good performance in terms of average accuracy determining car and motorcycle are 93.47% and 91.73%, respectively
Topology Recoverability Prediction for Ad-Hoc Robot Networks: A Data-Driven Fault-Tolerant Approach
Faults occurring in ad-hoc robot networks may fatally perturb their
topologies leading to disconnection of subsets of those networks. Optimal
topology synthesis is generally resource-intensive and time-consuming to be
done in real time for large ad-hoc robot networks. One should only perform
topology re-computations if the probability of topology recoverability after
the occurrence of any fault surpasses that of its irrecoverability. We
formulate this problem as a binary classification problem. Then, we develop a
two-pathway data-driven model based on Bayesian Gaussian mixture models that
predicts the solution to a typical problem by two different pre-fault and
post-fault prediction pathways. The results, obtained by the integration of the
predictions of those pathways, clearly indicate the success of our model in
solving the topology (ir)recoverability prediction problem compared to the best
of current strategies found in the literature
SUSTAINABLE ENERGY HARVESTING TECHNOLOGIES – PAST, PRESENT AND FUTURE
Chapter 8: Energy Harvesting Technologies:
Thick-Film Piezoelectric Microgenerato
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