Abstract: Disclosed is an operation method of a memory controller controlling a memory device. The operation method includes loading raw data from the memory device, performing a 0th error correction operation on the raw data, generating first flip data by performing a first bit flip operation on the raw data based on a correction history table, when an error of the raw data is not corrected through the 0th error correction operation, and performing a first error correction operation on the first flip data, and the correction history table includes correction addresses indicating locations of one or more memory cells in the memory device, which are associated with previously corrected error bits.

Abstract: Disclosed is a storage device, the device comprising, a nonvolatile memory device configured to store data; and a storage controller configured to perform logging for a selected memory area of the nonvolatile memory device in response to a specific event based on receiving a telemetry request from a host, and transmit log data collected by the logging in response to the telemetry request, the storage controller including a logging module configured to sample distribution data of a voltage range corresponding to the event type during the logging and provide the sampled distribution data as the log data.

Abstract: Systems and methods are provided for improving MRI data acquisition efficiency while providing more detailed information with high resolution and isotropic resolution without gaps. Improved data acquisition efficiency may be achieved by implementing a machine learning algorithm with a hardware processor and a memory to estimate imperfections in fast imaging sequences, such as a multi-shot echo planar imaging (MS-EPI) sequence. These imperfections, such as patient motion, physiological noise, and phase variations, may be difficult to model or otherwise estimate using standard physics-based reconstructions.

Abstract: Systems and methods are provided for improving MRI data acquisition efficiency while providing more detailed information with high resolution and isotropic resolution without gaps. Improved data acquisition efficiency may be achieved by implementing a machine learning algorithm with a hardware processor and a memory to estimate imperfections in fast imaging sequences, such as a multi-shot echo planar imaging (MS-EPI) sequence. These imperfections, such as patient motion, physiological noise, and phase variations, may be difficult to model or otherwise estimate using standard physics-based reconstructions.