2,941 research outputs found
Harnessing the Power of AI based Image Generation Model DALLE 2 in Agricultural Settings
This study investigates the potential impact of artificial intelligence (AI)
on the enhancement of visualization processes in the agricultural sector, using
the advanced AI image generator, DALLE 2, developed by OpenAI. By
synergistically utilizing the natural language processing proficiency of
chatGPT and the generative prowess of the DALLE 2 model, which employs a
Generative Adversarial Networks (GANs) framework, our research offers an
innovative method to transform textual descriptors into realistic visual
content. Our rigorously assembled datasets include a broad spectrum of
agricultural elements such as fruits, plants, and scenarios differentiating
crops from weeds, maintained for AI-generated versus original images. The
quality and accuracy of the AI-generated images were evaluated via established
metrics including mean squared error (MSE), peak signal-to-noise ratio (PSNR),
and feature similarity index (FSIM). The results underline the significant role
of the DALLE 2 model in enhancing visualization processes in agriculture,
aiding in more informed decision-making, and improving resource distribution.
The outcomes of this research highlight the imminent rise of an AI-led
transformation in the realm of precision agriculture.Comment: 22 pages, 13 figures, 2 table
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
Self-sustaining Ultra-wideband Positioning System for Event-driven Indoor Localization
Smart and unobtrusive mobile sensor nodes that accurately track their own
position have the potential to augment data collection with location-based
functions. To attain this vision of unobtrusiveness, the sensor nodes must have
a compact form factor and operate over long periods without battery recharging
or replacement. This paper presents a self-sustaining and accurate
ultra-wideband-based indoor location system with conservative infrastructure
overhead. An event-driven sensing approach allows for balancing the limited
energy harvested in indoor conditions with the power consumption of
ultra-wideband transceivers. The presented tag-centralized concept, which
combines heterogeneous system design with embedded processing, minimizes idle
consumption without sacrificing functionality. Despite modest infrastructure
requirements, high localization accuracy is achieved with error-correcting
double-sided two-way ranging and embedded optimal multilateration. Experimental
results demonstrate the benefits of the proposed system: the node achieves a
quiescent current of and operates at while performing
energy harvesting and motion detection. The energy consumption for position
updates, with an accuracy of (2D) in realistic non-line-of-sight
conditions, is . In an asset tracking case study within a
multi-room office space, the achieved accuracy level allows for identifying 36
different desk and storage locations with an accuracy of over . The
system`s long-time self-sustainability has been analyzed over in
multiple indoor lighting situations
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