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The goal of sparse linear hyperspectral unmixing is to determine a scanty subset of spectral signatures of materials contained in each mixed pixel and to estimate their fractional abundances. This turns into an 0-norm minimization, which is an NP-hard problem. In this paper, we propose a new iterative method, which starts as an 1-norm optimization that is(More)
In this paper, we introduce a new dedicated 256-bit hash function: NESHA-256. The recently contest for hash functions held by NIST, motivates us to design the new hash function which has a parallel structure. Advantages of parallel structures and also using some ideas from the designing procedure of block-cipher-based hash functions strengthen our proposed(More)
Hummingbird is a lightweight encryption algorithm proposed by Engels, Fan, Gong, Hu and Smith at FC′10. Unlike other lightweight cryptographic primitives which can be classified as either block ciphers or stream ciphers, Hummingbird has a hybrid structure of block cipher and stream cipher with 16-bit block size, 256-bit key size, and 80-bit internal(More)
In this paper, we propose an algorithm for a blind sparse signal recovery of the linear system model. This scenario is applicable for various applications in signal processing, telecommunication, machine learning and remote sensing. Precisely, we develop a nonnegative matrix factorization (NMF) algorithm using &#x2113;<sub>p</sub>-norm (for 0 &lt;; p(More)
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