S
Sos S. Agaian
Researcher at City University of New York
Publications - 582
Citations - 10193
Sos S. Agaian is an academic researcher from City University of New York. The author has contributed to research in topics: Image processing & Computer science. The author has an hindex of 38, co-authored 532 publications receiving 8216 citations. Previous affiliations of Sos S. Agaian include College of Staten Island & University of Texas System.
Papers
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Proceedings ArticleDOI
A fast, efficiency-preserving system for simultaneous compression & encryption
TL;DR: Methods for embedment of stream cipher rules into compressive Elias-type entropy coders are presented and a novel method is proposed which exploits the compression process to hide cipherstream information in the case of a known plaintext attack.
Proceedings ArticleDOI
Weather removal with a lightweight quaternion Chebyshev neural network
Vladimir Frants,Sos S. Agaian +1 more
Posted Content
A comprehensive review of Binary Neural Network.
Chunyu Yuan,Sos S. Agaian +1 more
TL;DR: Binary Neural Network (BNN) as discussed by the authors is an extreme application of convolutional neural network (CNN) parameter quantization, which uses 1-bit activations and weights.
Proceedings ArticleDOI
Implementation of Digital Electronic Arithmetic and its application
TL;DR: A hardware implementation of the parametric image-processing framework that will accurately process images and speed up computation for addition, subtraction, and multiplication and the design of arithmetic circuits including parallel counters, adders and multipliers based in two high performance threshold logic gate implementations that are developed.
Journal ArticleDOI
Distinctions between Choroidal Neovascularization and Age Macular Degeneration in Ocular Disease Predictions via Multi-Size Kernels ξcho-Weighted Median Patterns
TL;DR: In this paper , the authors developed an automated quantification and classification system for CNV in neovascular age-related macular degeneration using OCT angiography images. But this method is not suitable for image texture descriptors.