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Sparse unmixing of hyperspectral data is an important technique which aims at estimating the fractional abundances of endmembers (pure spectral components). It is well known that enforcing sparseness becomes a necessary process in sparse unmixing methods. To better exploit the sparsity in hyperspectral imagery, a double reweighted sparse unmixing algorithm has been proposed. However, it focusses on...
Classification is one of the most important analysis techniques for hyperspectral image analysis. Sparse representation is an extremely powerful tool for this purpose, but the high computational complexity of sparse representation-based classification techniques limits their application in time-critical scenarios. To improve the efficiency and performance of sparse representation classification techniques...
Information-centric networking (ICN) is designed to decouple contents from hosts at the network layer, using in-network caching as a key feature to improve the overall performance. However, the en-route caching strategy used in many ICN implementations generally yields redundancies in the cached contents across different routers. There are also some recent works focusing on cache optimization by respectively...
The autocorrelation sidelobe peak (ASP) levels of discrete frequency-coding waveform with fixed frequency pulses (DFCW_FF) is very large. It is about 0.21 or -13.2dB. Replacing the fixed frequency pulses with linear FM pulses (LFM) can lower the ASP. We investigate theambiguity function (AF) and cross-AF for DFCW with arbitrary frequency firing order. It shows that DFCW_LFM have different AF expression...
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