Zobrazeno 1 - 10
of 176
pro vyhledávání: '"A. Bijamov"'
Autor:
Morningstar, Warren, Bijamov, Alex, Duvarney, Chris, Friedman, Luke, Kalibhat, Neha, Liu, Luyang, Mansfield, Philip, Rojas-Gomez, Renan, Singhal, Karan, Green, Bradley, Prakash, Sushant
We study the relative effects of data augmentations, pretraining algorithms, and model architectures in Self-Supervised Learning (SSL). While the recent literature in this space leaves the impression that the pretraining algorithm is of critical impo
Externí odkaz:
http://arxiv.org/abs/2403.05726
Autor:
Kalibhat, Neha, Morningstar, Warren, Bijamov, Alex, Liu, Luyang, Singhal, Karan, Mansfield, Philip
Self-Supervised Learning (SSL) enables training performant models using limited labeled data. One of the pillars underlying vision SSL is the use of data augmentations/perturbations of the input which do not significantly alter its semantic content.
Externí odkaz:
http://arxiv.org/abs/2312.02205
Autor:
Rojas-Gomez, Renan A., Singhal, Karan, Etemad, Ali, Bijamov, Alex, Morningstar, Warren R., Mansfield, Philip Andrew
Existing data augmentation in self-supervised learning, while diverse, fails to preserve the inherent structure of natural images. This results in distorted augmented samples with compromised semantic information, ultimately impacting downstream perf
Externí odkaz:
http://arxiv.org/abs/2312.01187
Autor:
Fridon Shubitidze, K. O'Neill, Juan Pablo Fernández, Alex Bijamov, Irma Shamatava, David Karkashadze, Benjamin E. Barrowes
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 52:4658-4670
We introduce a fast and accurate numerical technique for the solution of electromagnetic induction sensing problems called the orthonormalized volume magnetic source model. The model assumes that the secondary magnetic field measured by a sensor orig
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6524958b3d01406e87644980b5679e88
https://doi.org/10.1109/tgrs.2013.2283346
https://doi.org/10.1109/tgrs.2013.2283346
Camp Butner Live-Site UXO Classification Using Hierarchical Clustering and Gaussian Mixture Modeling
Autor:
Alex Bijamov, Irma Shamatava, Kevin O'Neill, Fridon Shubitidze, Benjamin E. Barrowes, Juan Pablo Fernández
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 52:5218-5229
We demonstrate in detail a semisupervised scheme to classify unexploded ordnance (UXO) by using as an example the data collected with a time-domain electromagnetic towed array detection system during a live-site blind test conducted at the former Cam
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cfa8cccc7966b7cb99c1143c4f0e9466
https://doi.org/10.1109/tgrs.2013.2287510
https://doi.org/10.1109/tgrs.2013.2287510
Publikováno v:
Journal of Applied Physics; Nov2010, Vol. 108 Issue 10, p104701, 9p, 1 Diagram, 12 Graphs
Publikováno v:
International Journal of Infrared and Millimeter Waves. 29:1172-1185
The need for a highly efficient numerical simulation platform for designing photonic band gap (PBG) structures is outlined in the context of various functional device topologies. In this paper we therefore introduce the Method of Auxiliary Sources (M
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::64ba3d371032ed9b7e7bf26746bf047d
https://doi.org/10.1007/s10762-008-9413-2
https://doi.org/10.1007/s10762-008-9413-2
Autor:
Juan Pablo Fernández, Kevin O'Neill, Alex Bijamov, Fridon Shubitidze, Daniel A. Steinhurst, Irma Shamatava, Benjamin E. Barrowes
Publikováno v:
SPIE Proceedings.
Current electromagnetic induction (EMI) sensors of the kind used to discriminate buried unexploded orndance (UXO) can detect targets down to a depth limited by the geometric size of the transmitter (Tx) coils, the amplitudes of the transmitting curre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::30f41c81c25657edd91e566c51b198c6
https://doi.org/10.1117/12.919588
https://doi.org/10.1117/12.919588
Autor:
B. E. Barrowes, Fridon Shubitidze, Juan Pablo Fernández, A. Luperon, Kevin O'Neill, Alex Bijamov, Irma Shamatava
Publikováno v:
SPIE Proceedings.
ESTCP live-site UXO classification results are presented for cued data collected with two advanced EMI instruments, the cart-based 2 × 2 3D TEMTADS array and the Man Portable Vector (MPV) handheld sensor, at the former Camp Beale in California. Ther
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9118c14b7d8f19531e6eef3706d08590
https://doi.org/10.1117/12.919549
https://doi.org/10.1117/12.919549