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Blockchain System Implementation
Published in Shaun Aghili, The Auditor's Guide to Blockchain Technology, 2023
Bharghava Sai Nakkina, Deepthi Gudapati, Naga Venkat Palaparthy, Sai Sreenath Sadupally
Memory correction is a common memory repair technique. In most cases, error correcting code (ECC) is used to rectify corrupted memory. The recalibration of the system is important in a post-incident environment; this includes checking if the computer’s data storage is n-bit corrupted. Data corruption occurs due to unintentional single-bit errors caused during reading/writing data from the memory. An error correction code can be generated and stored on a blockchain. On a blockchain, this code can be used to detect and correct errors. If data are corrupted, blockchains can be used for data correction [9].
Data generation, collection, analysis, and preprocessing
Published in Madhusree Kundu, Palash Kumar Kundu, Seshu Kumar Damarla, Chemometric Monitoring: Product Quality Assessment, Process Fault Detection, and Applications, 2017
Madhusree Kundu, Palash Kumar Kundu, Seshu Kumar Damarla
Data pretreatment is a major concern in data-based application/algorithm development, and it includes outlier detection, data reconciliation, data smoothing, and application of transforms on data (if required for a specific application). Data corruption may be caused by failures in sensors or transmission lines, process equipment malfunctions, erroneous recording of measurement and analysis of results, or external disturbances. These faults would cause the data to have spikes, jumps, or excessive oscillations. The general strategy is to detect data that are not likely in conformity with other measurements/information (outlier detection) and to substitute these data with estimated values that are in agreement with other data/process-related information (data reconciliation). Sometimes data are transformed, which allows the data to provide more information that is not available in their original form (transform). Transformation and transform are often used interchangeably, though incorrectly. Data are often transformed for better interpretability. The logarithmic, square root, and multiplicative transformations are widely used transformations, where the data dimension and domain remain unchanged, unlike data realizing various transforms.
Performance of Soft Viterbi Decoder enhanced with Non-Transmittable Codewords for storage media
Published in Cogent Engineering, 2018
Kilavo Hassan, Kisangiri Michael, Salehe I. Mrutu
There is a big challenge behind the error correction for the storage media due to higher demand of digital data of which most of them are stored on storage media. The demand for storage media devices is increasingly vast (Coughlin & Handy, 2008). Large file sizes requirement for high resolution and multi-camera images are among the reason for increasing demand of storage devices (Coughlin, 2015). Ensuring data reliability and quality of data from the storage media is one of the big challenges (Peters, Rabinowitz, & Jacobs, 2006). The demand for storage media increases every day and it is estimated that over 90% of all information and data produced in the world are stored on hard disk drives (Pinheiro, Weber, & Barroso, 2007). Majority of the people are not aware and interested in improving the Forward Error Correction codes for storage media; rather they are interested in improving backup systems and data recovery software (Hassan, Michael, & Mrutu, 2015). To prevent errors from causing data corruption in storage media, data can be protected with error correction codes. The Viterbi decoder was introduced by Andrew J. Viterbi in 1967 (Mousa, Taman, & Mahmoud, 2015; Sargent, Bimbot, & Vincent, 2011). Since then the researchers are tirelessly working to expand his work by finding better Viterbi decoder (Andruszkiewicz, Davis, & Lleo, 2014; Takano, Ishikawa, & Nakamura, 2015). The Viterbi algorithm allows a random number of the most possible sequences to be enumerated. It can be used to efficiently calculate the most likely path of the hidden state process (Cartea & Jaimungal, 2013; Titsias, Holmes, & Yau, 2016). Channel coding techniques in storage media are used to make the transmitted data robust against any impairment. The data in a storage media get corrupted by noise and can be recovered by using channel coding techniques (Cover & Thomas, 2012). The encoding technique can be either systematic encoding or non-systematic encoding. The comparison between channel codes can be done by looking on different metrics such as coding accuracy, coding efficiency and coding gain. The coding accuracy means a channel is strong and can usually recover corrupted data. The accuracy can be compared on how close the recovered data match with the original data which is measured by Bit Error Rate (BER) probabilities (Jiang, 2010). Coding efficiency means the code has relatively a small number of encoder bits per data symbol and this is defined in terms of code rate which is given by R = K/N, where K is an input symbol to each encoder and N is an output symbol from each encoder. Decreasing the redundant bits decreases the number of error per symbol that can correct/detect errors. Coding gain is the measure of the difference between signal to noise ratio (SNR) level between coded system and uncoded system that require reaching the same bit error rate (BER) levels.