Zobrazeno 1 - 10
of 83
pro vyhledávání: '"Ansari, Naushad"'
Autor:
Rai, Nidhi, Ansari, Naushad, Apoorva, Kumari, Sabitri, Singh, Sneha, Saha, Pajeb, Bisen, Mansi Singh, Pandey-Rai, Shashi
Publikováno v:
In Industrial Crops & Products 15 December 2024 222 Part 4
Publikováno v:
In Journal of the Energy Institute December 2024 117
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Mohamed, Elsayed, Kasem, Ahmed M.M.A., Ghanem, AbdEl-Mageed F.M., Ansari, Naushad, Yadav, Durgesh Singh, Agrawal, Shashi Bhushan
Publikováno v:
In Flora December 2023 309
In this work we propose a technique to remove sparse impulse noise from hyperspectral images. Our algorithm accounts for the spatial redundancy and spectral correlation of such images. The proposed method is based on the recently introduced Blind Com
Externí odkaz:
http://arxiv.org/abs/1912.06630
Autor:
Singh, Priyanka1, Ansari, Naushad1, Mishra, Amit Kumar2, Agrawal, Madhoolika1, Agrawal, Shashi Bhushan1 sbagrawal56@gmail.com
Publikováno v:
Functional Plant Biology. 2024, Vol. 51 Issue 2, p1-13. 13p.
Autor:
Ansari, Naushad, Yadav, Durgesh Singh, Singh, Priyanka, Agrawal, Madhoolika, Agrawal, Shashi Bhushan
Publikováno v:
In Industrial Crops & Products April 2023 194
Autor:
Ansari, Naushad, Gupta, Anubha
Transform learning is being extensively applied in several applications because of its ability to adapt to a class of signals of interest. Often, a transform is learned using a large amount of training data, while only limited data may be available i
Externí odkaz:
http://arxiv.org/abs/1710.10394
Autor:
Ansari, Naushad, Gupta, Anubha
Motivated with the concept of transform learning and the utility of rational wavelet transform in audio and speech processing, this paper proposes Rational Wavelet Transform Learning in Statistical sense (RWLS) for natural images. The proposed RWLS d
Externí odkaz:
http://arxiv.org/abs/1705.00821
Autor:
Ansari, Naushad, Gupta, Anubha
This paper proposes a joint framework wherein lifting-based, separable, image-matched wavelets are estimated from compressively sensed (CS) images and used for the reconstruction of the same. Matched wavelet can be easily designed if full image is av
Externí odkaz:
http://arxiv.org/abs/1702.01970