Abstract: Disclosed embodiments include wearable devices and techniques for detecting swimming activities, classifying user motion, detecting water submersion, and monitoring performance during swimming activities. By accurately and promptly detecting swimming activities and automatically distinguishing between different swimming stroke type performed during a swimming activity, the disclosure enables wearable devices to accurately calculate user performance information when users forget to start and/or stop recording swimming activities. In various embodiments, swimming activity detection techniques may improve the selectivity of motion based methods of identifying swimming activities identification by confirming motion analysis with water immersion and pressure data analysis that detects when the wearable device is submerged in water.

Abstract: A storage apparatus includes a plurality of memory cells and storage circuitry. The storage circuitry is configured to store a mapping that maps sets of readout bit-flip counts to respective predefined impairment profiles. The impairment profiles specify two or more severity levels of respective impairment types, including read disturb, retention and endurance. Each of the bit-flip counts includes a one-to-zero error count or a zero-to-one error count. The storage circuitry is configured to read data from a group of the memory cells using given readout parameters, to evaluate an actual set of bit-flip counts corresponding to the read data, to classify the group of the memory cells to a respective impairment profile by mapping the actual set of the bit-flip counts using the mapping, and to adapt the readout parameters based on the impairment profile to which the group of the memory cells was classified.

Abstract: A memory system includes an interface and storage circuitry. The interface is configured to communicate with a plurality of memory cells that store data by setting the memory cells to analog voltages representative of respective storage values. The storage circuitry is configured to receive data for storage, to measure a temperature at a time of programming the received data, and, to program the received data to the memory cells using a first programming scheme when the measured temperature falls within a predefined normal temperature range, and otherwise to program the received data to the memory cells using a second programming scheme having a lower net storage utilization than the first programming scheme.

Abstract: A memory system includes an interface and storage circuitry. The interface is configured to communicate with a plurality of memory cells that store data by setting the memory cells to analog voltages representative of respective storage values. The storage circuitry is configured to receive data for storage, to measure a temperature at a time of programming the received data, and, to program the received data to the memory cells using a first programming scheme when the measured temperature falls within a predefined normal temperature range, and otherwise to program the received data to the memory cells using a second programming scheme having a lower net storage utilization than the first programming scheme.

Abstract: Embodiments herein relate to using a convolutional neural network (CNN) for time-of-flight estimation in a wireless communication system. A wireless device may receive, from a remote device, wireless communications including a first transmission time value associated with the transmission of the wireless communications. The wireless device may perform a coarse time-of-arrival (TOA) estimation on the wireless communications received from the remote device. The coarse TOA estimation may be used to generate an estimated impulse response, which may be input to a CNN associated with the wireless device to calculate a line-of-sight estimate. The wireless device may determine a range between the wireless device and the remote device based on the transmission time value and the line-of-sight estimate.

Abstract: A storage apparatus includes a plurality of memory cells and storage circuitry. The storage circuitry is configured to store a mapping that maps sets of readout bit-flip counts to respective predefined impairment profiles. The impairment profiles specify two or more severity levels of respective impairment types, including read disturb, retention and endurance. Each of the bit-flip counts includes a one-to-zero error count or a zero-to-one error count. The storage circuitry is configured to read data from a group of the memory cells using given readout parameters, to evaluate an actual set of bit-flip counts corresponding to the read data, to classify the group of the memory cells to a respective impairment profile by mapping the actual set of the bit-flip counts using the mapping, and to adapt the readout parameters based on the impairment profile to which the group of the memory cells was classified.

Abstract: A storage apparatus includes a plurality of memory cells and storage circuitry. The storage circuitry is configured to store a mapping that maps sets of readout bit-flip counts to respective predefined impairment profiles. The impairment profiles specify two or more severity levels of respective impairment types, including read disturb, retention and endurance. Each of the bit-flip counts includes a one-to-zero error count or a zero-to-one error count. The storage circuitry is configured to read data from a group of the memory cells using given readout parameters, to evaluate an actual set of bit-flip counts corresponding to the read data, to classify the group of the memory cells to a respective impairment profile by mapping the actual set of the bit-flip counts using the mapping, and to adapt the readout parameters based on the impairment profile to which the group of the memory cells was classified.