Abstract: A method for human movement classification includes a plurality of radio frequency (RF) antennas to expose a body surface of a human user to an E-field, at least a part of the human user positioned within a near-field region relative to the RF antennas. The plurality of RF antennas are scanned to determine ground-relative changes in electrical conditions. A differential-scanning subset of RF antennas are selected, including two or more RF antennas for which the changes in electrical conditions exceed a threshold. For each RF antenna in the differential-scanning subset, a set of antenna-relative changes in electrical conditions are determined by comparing the RF antenna to one or more other RF antennas in the differential-scanning subset as a plurality of pairs. Based at least in part on the ground-relative changes in electrical conditions and the antenna-relative changes in electrical conditions, a movement performed by the human user is classified.

Abstract: A mechanism for adjusting the data rate of data to be transmitted over a data channel based on anticipated changes in the communication channel, rather than based on the current state of the communication channel. This is done by accessing real time environment context data obtained from sensor data generated by one or more sensors of a sensor device, and then predicting a future capacity of a communication channel with a subject head-mounted device based on the accessed real time environment context data. The appropriate data rate is then determined based on this predicted future channel capacity rather than the current channel capacity. The data rate of data that is initiated towards the communication channel is then adjusted based on the determined data rate in anticipation of the predicted future capacity of the communication channel.

Abstract: A mechanism for repeatedly adjusting communication with a subject head-mounted device based on a changing real time environment of the subject head-mounted device. By utilizing information about the environment context in which the head-mounted device exists, the optimal parameters may be more quickly determined and with less power. The environment context may be generated from sensors on the head-mounted device itself, or from a proximate sensor device. Thus, the communication properties (such as which protocol to use and what parameters) may be quickly determined in time to be useful to maintain a good connection despite movement of the head-mounted device, and despite the connection being dropped and reestablished. Furthermore, limited battery power is more judiciously utilized.

Abstract: A computing system configured to determine a time, a frequency, or a volume that time-critical data are to arrive and generate an uplink scheduling request based on the determined time, frequency, or volume. The uplink scheduling request is then sent to a base station, requesting an uplink grant. The computing system then receives the uplink grant from the base station over the radio interface. The uplink grant contains scheduling information associated with transmission of the time-critical data. In response to arrival of the time-critical data, the computing system then transmits the time-critical data over the radio interface based on the uplink grant with no or little latency.

Abstract: A mechanism for adjusting the data rate of data to be transmitted over a data channel based on anticipated changes in the communication channel, rather than based on the current state of the communication channel. This is done by accessing real time environment context data obtained from sensor data generated by one or more sensors of a sensor device, and then predicting a future capacity of a communication channel with a subject head-mounted device based on the accessed real time environment context data. The appropriate data rate is then determined based on this predicted future channel capacity rather than the current channel capacity. The data rate of data that is initiated towards the communication channel is then adjusted based on the determined data rate in anticipation of the predicted future capacity of the communication channel.

Abstract: A method for human movement classification includes a plurality of radio frequency (RF) antennas to expose a body surface of a human user to an E-field, at least a part of the human user positioned within a near-field region relative to the RF antennas. The plurality of RF antennas are scanned to determine ground-relative changes in electrical conditions. A differential-scanning subset of RF antennas are selected, including two or more RF antennas for which the changes in electrical conditions exceed a threshold. For each RF antenna in the differential-scanning subset, a set of antenna-relative changes in electrical conditions are determined by comparing the RF antenna to one or more other RF antennas in the differential-scanning subset as a plurality of pairs. Based at least in part on the ground-relative changes in electrical conditions and the antenna-relative changes in electrical conditions, a movement performed by the human user is classified.

Abstract: A self-encrypting drive (SED) comprises an SED controller and a nonvolatile storage medium (NVSM) responsive to the SED controller. The SED controller enables the SED to perform operations comprising: (a) receiving an encrypted media encryption key (eMEK) for a client; (b) decrypting the eMEK into an unencrypted media encryption key (MEK); (c) receiving a write request from the client, wherein the write request includes data to be stored and a key tag value associated with the MEK; (d) using the key tag value to select the MEK for the write request; (e) using the MEK for the write request to encrypt the data from the client; and (f) storing the encrypted data in a region of the NVSM allocated to the client. Other embodiments are described and claimed.

Abstract: A self-encrypting drive (SED) comprises an SED controller and a nonvolatile storage medium (NVSM) responsive to the SED controller. The SED controller enables the SED to perform operations comprising: (a) receiving an encrypted media encryption key (eMEK) for a client; (b) decrypting the eMEK into an unencrypted media encryption key (MEK); (c) receiving a write request from the client, wherein the write request includes data to be stored and a key tag value associated with the MEK; (d) using the key tag value to select the MEK for the write request; (e) using the MEK for the write request to encrypt the data from the client; and (f) storing the encrypted data in a region of the NVSM allocated to the client. Other embodiments are described and claimed.