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A robust sparse least-mean mixture-norm (LMMN) algorithm is proposed, and its performance is appraised in the context of estimating a broadband multi-path wireless channel. The proposed algorithm is implemented via integrating a correntropy-induced metric (CIM) penalty into the conventional LMMN algorithm to modify the basic cost function, which is denoted(More)
Sparse channel estimation has attracted more attention for various broadband wireless communication systems. Square error criterion based adaptive filter algorithms are extensively studied for broadband sparse channel estimations (SCE) such as zero-attracting (ZA) least mean square (ZA-LMS) and reweighting ZA-LMS (RZA-LMS) algorithms. However, these sparse(More)
By virtue of recent developments in machine learning techniques, higher-level information can now to be extracted from big data. To analyze big data, efficient and smart representations of data achieved by using sufficiently fast algorithms, as well as highly accurate results, are important. In this paper, we focus on extracting multiple semantic relations(More)
In this paper, a sparse set-membership proportionate normalized least mean square (SM-PNLMS) algorithm integrated with a correntropy induced metric (CIM) penalty is proposed for acoustic channel estimation and echo cancellation. The CIM is used for constructing a new cost function within the kernel framework. The proposed CIM penalized SM-PNLMS(More)
This paper presents a 3G wireless video surveillance system solution. The system has four cameras collecting real-time information compressed by MPEG-4 inside and outside one bus, then the information will be sent to the monitor center through 3G network. The system corrects video data with LDPC codes, and uses LDAP managing bus and cameras information in(More)
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