NUST Institutions Library Catalogue Search for 'kw,wrdl: (su-br:"Remote sensing.")'

Deep mining challenges : international mining forum 2009 /

[S.l.] : Taylor & Francis, 2009 . 234 p. ; 25 cm.. 0415804280 (hardcover) | 9780415804288 (hardcover)

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Unmanned Aerial Vehicles /

By Valavanis, Kimon P.. 2011 9789400711099

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Uncertainty in remote sensing and GIS /

Hoboken, NJ : J. Wiley, 2002 . xviii, 307 p., [4] p. of plates : 26 cm.. 0470844086 (alk. paper)

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Antennas for all applications /

By Kraus, John Daniel,. New York : McGraw-Hill, 2002 . xviii, 938 p. : 24 cm.. 0071122400 (International ed.) | 0072321032 (alk. paper)

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Antennas and wave propogation /

By D Kraus, John . New Delhi Tata McGraw Hill 2010 . xxiii,906p. 9780070671553

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Image processing for remote sensing-(E-BOOK)

Boca Raton, FL : CRC Press/Taylor & Francis Group, 2008 . 380 s. 1-4200-6664-1 | 978-1-4200-6664-7

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Biomedical Engineering (E-Book)

By Carlos Alexandre Barros de Mello. India In-Teh 2009 . x,658 P;

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International mining forum 2006, new technological solutions in underground mining : proceedings of the 7th international mining forum, cracow - szczyrk - wieliczka, poland, february 2006 /

[S.l.] : Taylor & Francis, 2006 . 191 p. ; 26 cm.. 0415401178 | 9780415401173

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Atmospheres in the solar system : comparative aeronomy /

Washington, D.C. : American Geophysical Union, 2002 . ix, 388 p. : 28 cm.. 0875909892

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Ground and surface water hydrology /

By Mays, Larry W.. Hoboken, N.J. : Wiley, 2012 . xxii, 617 p. : 26 cm.. 9780470169872 (hardback) | 0470169877 (hardback)

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Modern hydrology and sustainable water development /

By Gupta, Sushil K.. Chichester, West Sussex, UK ; | Hoboken, NJ : Wiley-Blackwell, 2011 . xx, 437 p., [12] p. of plates : 26 cm.. 9781405171243 (cloth) | 1405171243 (cloth)

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Regional satellite oceanography /

By Victorov, S. V.. London ; | Bristol, PA : Taylor & Francis, 1996 . xiv, 306 p. 26 cm..

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Landscapes of preindustrial urbanism /

. xi, 314 pages : , "Dumbarton Oaks Colloquium on the History of Landscape Architecture XLI"--Page preceding title page. | "Volume based on papers presented at the symposium "Landscapes of Pre-Industrial Cities," held at the Dumbarton Oaks Research Library and Collection, Washington, D.C., on May 5-6, 2017."--Title page verso 27 cm. 9780884024712

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Improving Frequency-Aware Image Restoration Techniques for Real-World Degradations /

By Ayub, Nooh . . 74p. , Image quality often deteriorates under challenging weather conditions such as haze and snow, which create significant barriers for vision-driven intelligent systems, including autonomous vehicles, real-time surveillance, and remote sensing applications. To address this pressing challenge, we propose a comprehensive deep learning framework capable of simultaneously handling both image dehazing and desnowing in a unified manner. At the core of our design lies the NAFXT block—an advanced extension of NAFNet—that integrates Dynamic Tanh (DyT) normalization in place of the traditional layer normalization. Notably, DyT normalization is introduced for the first time in this context as a more effective alternative, offering superior adaptability compared to standard layer normalization within convolutional neural network (CNN) architectures, particularly the NAFNet framework. While layer normalization primarily ensures stable training and faster convergence, DyT normalization enhances the model’s ability to capture richer and more expressive feature representations. As a result, the proposed framework achieves better restoration quality while requiring fewer training epochs than several cutting-edge methods. In addition, we incorporate the Multi-Branch Dynamic Selective Frequency (MDSF) and Multi-Branch Compact Selective Frequency (MCSF) modules, originally introduced in FSNet, to promote frequency-aware global feature learning across multiple scales. The network further adopts a multi-input and multi-output encoder–decoder structure, where both NAFXT and residual blocks are systematically integrated at every stage to progressively refine the restored outputs. Extensive experiments performed on three widely recognized benchmark datasets confirm that our method consistently surpasses state-of-the-art approaches, achieving higher quantitative scores and delivering visually sharper, clearer, and more natural results in both dehazing and desnowing tasks. 30cm.

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Development of New Content Based Image Retrieval Techniques /

By Sukhia, Komal Nain . Rawalpindi, MCS (NUST), 2020 . xv, 118 p

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