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 | Data recovery methods for DNA storage based on fountain codes
To provide data recovery for DNA storage systems, we present a method to automatically reconstruct corrupted or missing data stored in DNA using fountain codes. Our method exploits the relationships between packets encoded with fountain codes to identify and rectify corrupted or lost data.
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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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 | Database Recovery - an overview | ScienceDirect Topics
Database recovery refers to the process of restoring a database to a correct and consistent state after a failure or corruption. It involves recovering data from backups or using transaction logs to undo and redo changes made to the database. AI generated definition based on: Encyclopedia of Information Systems, 2003
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 | A data recovery technique for Redis using internal dictionary structure
NoSQL databases are becoming the center of attraction due to the increasing demand for scalable, flexible and huge data storage requirements, which lack in relational databases. The present study recommends a technique to recover deleted data from Redis database based on internal dictionary structure.
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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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 | A neural tensor decomposition model for high-order sparse data recovery ...
When faced with high missing ratios or sparse observed sets, the recovery results become less ideal [20]. More importantly, the nonlinear information in the data may obscure the low rankness and the model performance may be hindered by the multi-linear hypothesis in the decomposition, making it fail to capture the nonlinear features [21].
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