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It is essential for these methods to have optimum precision and accuracy.
As a result, the optimum precision is closely related to the signal quality of NAND Flash memory.
The presented experimental results show that optimum precision selection is very important for low-energy soft-decision-based decoding of NAND Flash memory.
So it is important to find some kind of "optimum" precision, which minimizes the design complexity while keeping the highest as possible quality of the compressed image.
The DFT-based measurements only have optimum precision when used with periodic stimuli, whereas the LMS does not require periodicity of signals.
We analyze this relation quantitatively, and also propose a method that can find the optimum precision using the iteration count of an LDPC decoder.
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We studied the optimum output precision of NAND Flash memory for low-energy soft-decision-based error correction.
Under optimum conditions, precision, linearity (correlation coefficient, r2 = 0.9966 over the concentration range of 10 1000 ng mL−1 CB), detection limit (4.9 ng mL−1), enrichment factor (175), relative recovery (97%) had been obtained.
This value was determined as the optimum for precision based on previous studies evaluating NLP applications to extract symptom data in mental health.
Under the optimum conditions, the precision of the method has been investigated.
The achievements may serve as an important guide for selecting the reasonable processing parameters of splitting spinning based on different aluminum alloys, and are very significant for optimum design and precision control of the splitting spinning process.
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