Zobrazeno 1 - 10
of 3 120
pro vyhledávání: '"A. Kayan"'
Publikováno v:
Tropical Animal Science Journal, Vol 47, Iss 1 (2024)
The effects of meat pH on muscle fiber characteristics, cortisol level, and Tenascin C (TNC) gene expression were examined. The muscle samples (n=100) were randomly collected from the Longissimus thoracis et lumborum (LTL) to determine meat pH at 24
Externí odkaz:
https://doaj.org/article/64e6e95252674178bc92a3a1a3310bdc
Building a foundation model for 3D vision is a complex challenge that remains unsolved. Towards that goal, it is important to understand the 3D reasoning capabilities of current models as well as identify the gaps between these models and humans. The
Externí odkaz:
http://arxiv.org/abs/2410.10799
Autor:
A. Kayan, S. Theerawatanasirikul, P. Lekcharoensuk, C. Boonkaewwan, A. Kaewkot, M. Chanaksorn, C. Tantikositruj, A. Gunawan
Publikováno v:
Tropical Animal Science Journal, Vol 45, Iss 2 (2022)
An experiment was conducted to study the association and expression of JHDM1A gene as a candidate gene for meat quality. The polymorphism was genotyped by the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) using restric
Externí odkaz:
https://doaj.org/article/74b7ee3f5d3f4018b959f68a5b4f731e
Autor:
Raistrick, Alexander, Mei, Lingjie, Kayan, Karhan, Yan, David, Zuo, Yiming, Han, Beining, Wen, Hongyu, Parakh, Meenal, Alexandropoulos, Stamatis, Lipson, Lahav, Ma, Zeyu, Deng, Jia
We introduce Infinigen Indoors, a Blender-based procedural generator of photorealistic indoor scenes. It builds upon the existing Infinigen system, which focuses on natural scenes, but expands its coverage to indoor scenes by introducing a diverse li
Externí odkaz:
http://arxiv.org/abs/2406.11824
Video and wearable sensor data provide complementary information about human movement. Video provides a holistic understanding of the entire body in the world while wearable sensors provide high-resolution measurements of specific body segments. A ro
Externí odkaz:
http://arxiv.org/abs/2405.17368
Autor:
Cotton, R. James, Peiffer, J. D., Shah, Kunal, DeLillo, Allison, Cimorelli, Anthony, Anarwala, Shawana, Abdou, Kayan, Karakostas, Tasos
Publikováno v:
Ambient Inteligence for Healthcare workshop at MICCAI 2023
Markerless motion capture (MMC) is revolutionizing gait analysis in clinical settings by making it more accessible, raising the question of how to extract the most clinically meaningful information from gait data. In multiple fields ranging from imag
Externí odkaz:
http://arxiv.org/abs/2307.16321
Autor:
Raistrick, Alexander, Lipson, Lahav, Ma, Zeyu, Mei, Lingjie, Wang, Mingzhe, Zuo, Yiming, Kayan, Karhan, Wen, Hongyu, Han, Beining, Wang, Yihan, Newell, Alejandro, Law, Hei, Goyal, Ankit, Yang, Kaiyu, Deng, Jia
We introduce Infinigen, a procedural generator of photorealistic 3D scenes of the natural world. Infinigen is entirely procedural: every asset, from shape to texture, is generated from scratch via randomized mathematical rules, using no external sour
Externí odkaz:
http://arxiv.org/abs/2306.09310
The rapid and accurate detection of COVID-19 cases is critical for timely treatment and preventing the spread of the disease. In this study, a two-stage feature extraction framework using eight state-of-the-art pre-trained deep Convolutional Neural N
Externí odkaz:
http://arxiv.org/abs/2304.10677
Autor:
Cotton, R. James, DeLillo, Allison, Cimorelli, Anthony, Shah, Kunal, Peiffer, J. D., Anarwala, Shawana, Abdou, Kayan, Karakostas, Tasos
Markerless motion capture using computer vision and human pose estimation (HPE) has the potential to expand access to precise movement analysis. This could greatly benefit rehabilitation by enabling more accurate tracking of outcomes and providing mo
Externí odkaz:
http://arxiv.org/abs/2303.10654
By using low-cost microcontrollers and TinyML, we investigate the feasibility of detecting potential early warning signs of domestic violence and other anti-social behaviors within the home. We created a machine learning model to determine if a door
Externí odkaz:
http://arxiv.org/abs/2210.02642