17,051 research outputs found
Real-time Optimal Resource Allocation for Embedded UAV Communication Systems
We consider device-to-device (D2D) wireless information and power transfer
systems using an unmanned aerial vehicle (UAV) as a relay-assisted node. As the
energy capacity and flight time of UAVs is limited, a significant issue in
deploying UAV is to manage energy consumption in real-time application, which
is proportional to the UAV transmit power. To tackle this important issue, we
develop a real-time resource allocation algorithm for maximizing the energy
efficiency by jointly optimizing the energy-harvesting time and power control
for the considered (D2D) communication embedded with UAV. We demonstrate the
effectiveness of the proposed algorithms as running time for solving them can
be conducted in milliseconds.Comment: 11 pages, 5 figures, 1 table. This paper is accepted for publication
on IEEE Wireless Communications Letter
A new root-knot nematode, Meloidogyne moensi n. sp. (Nematoda : Meloidogynidae), parasitizing Robusta coffee from Western Highlands, Vietnam
A new root-knot nematode, parasitizing Robusta coffee in Dak Lak Province, Western Highlands of Vietnam, is described as Meloidogyne moensi n. sp. Morphological and molecular analyses demonstrated that this species differs clearly from other previously described root-knot nematodes. Morphologically, the new species is characterized by a swollen body of females with a small posterior protuberance that elongated from ovoid to saccate; perineal patterns with smooth striae, continuous and low dorsal arch; lateral lines marked as a faint space or linear depression at junction of the dorsal and ventral striate; distinct phasmids; perivulval region free of striae; visible and wide tail terminus surrounding by concentric circles of striae; medial lips of females in dumbbell-shaped and slightly raised above lateral lips; female stylet is normally straight with posteriorly sloping stylet knobs; lip region of second stage juvenile (J2) is not annulated; medial lips and labial disc of J2 formed dumbbell shape; lateral lips are large and triangular; tail of J2 is conoid with rounded unstriated tail tip; distinct phasmids and hyaline; dilated rectum. Meloidogyne moensi n. sp. is most similar to M. africana, M. ottersoni by prominent posterior protuberance. Results of molecular analysis of rDNA sequences including the D2-D3 expansion regions of 28S rDNA, COI, and partial COII/16S rRNA of mitochondrial DNA support for the new species status
Advancing Wound Filling Extraction on 3D Faces: A Auto-Segmentation and Wound Face Regeneration Approach
Facial wound segmentation plays a crucial role in preoperative planning and
optimizing patient outcomes in various medical applications. In this paper, we
propose an efficient approach for automating 3D facial wound segmentation using
a two-stream graph convolutional network. Our method leverages the Cir3D-FaIR
dataset and addresses the challenge of data imbalance through extensive
experimentation with different loss functions. To achieve accurate
segmentation, we conducted thorough experiments and selected a high-performing
model from the trained models. The selected model demonstrates exceptional
segmentation performance for complex 3D facial wounds. Furthermore, based on
the segmentation model, we propose an improved approach for extracting 3D
facial wound fillers and compare it to the results of the previous study. Our
method achieved a remarkable accuracy of 0.9999986\% on the test suite,
surpassing the performance of the previous method. From this result, we use 3D
printing technology to illustrate the shape of the wound filling. The outcomes
of this study have significant implications for physicians involved in
preoperative planning and intervention design. By automating facial wound
segmentation and improving the accuracy of wound-filling extraction, our
approach can assist in carefully assessing and optimizing interventions,
leading to enhanced patient outcomes. Additionally, it contributes to advancing
facial reconstruction techniques by utilizing machine learning and 3D
bioprinting for printing skin tissue implants. Our source code is available at
\url{https://github.com/SIMOGroup/WoundFilling3D}
Application of Self-Supervised Learning to MICA Model for Reconstructing Imperfect 3D Facial Structures
In this study, we emphasize the integration of a pre-trained MICA model with
an imperfect face dataset, employing a self-supervised learning approach. We
present an innovative method for regenerating flawed facial structures,
yielding 3D printable outputs that effectively support physicians in their
patient treatment process. Our results highlight the model's capacity for
concealing scars and achieving comprehensive facial reconstructions without
discernible scarring. By capitalizing on pre-trained models and necessitating
only a few hours of supplementary training, our methodology adeptly devises an
optimal model for reconstructing damaged and imperfect facial features.
Harnessing contemporary 3D printing technology, we institute a standardized
protocol for fabricating realistic, camouflaging mask models for patients in a
laboratory environment
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