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 | Analysis of false lock in Mueller-Muller clock and data recovery system ...
Another view suggests that data correlation is the key contributor [ [9], [10]]. In this work, we provide a comprehensive analysis of MMPD false lock and introduce an enhanced mitigation strategy, validated via simulations. Section 2 investigates the false-lock mechanism and presents an improved phase detection strategy.
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 | Distributed neural tensor completion for network monitoring data recovery
Abstract Network monitoring data is usually incomplete, accurate and fast recovery of missing data is of great significance for practical applications. The tensor-based nonlinear methods have attracted recent attentions with their capability of capturing complex interactions among data for more accurate recovery.
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 | IRTF: A new tensor factorization for irregular multidimensional data ...
By integrating the proposed IRTF and TV-CST, we establish an irregular multidimensional data recovery model. From (d), we can observe that our method preserves the color and the edge of the spatial-irregular multidimensional data as compared with traditional tensor factorization with preprocessing.
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 | Tensor completion via joint reweighted tensor
Tensor, as a generalization of vector and matrix, is a powerful tool for processing multi-dimensional data and plays a critical role in different fields of scientific computing, like color image and video inpainting [1], [2], [3], hyperspectral data recovery and classification [4], [5], [6], [7], and seismic data reconstruction [8].
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 | Data Recovery - an overview | ScienceDirect Topics
Data recovery strategies include hot sites, spare or underutilized servers, the use of noncritical servers, duplicate data centers, replacement agreements, and transferring operations to other locations.
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 | Lost data recovery for structural vibration data based on improved U ...
Verification was conducted on single-channel and multi-channel data from practical engineering of large-span bridges by comparing the recovery levels in the time and frequency domains. Different missing ratios are set, a mask matrix is used to construct random lost data, and the proposed model is used to reconstruct the lost data.
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