Abstract:
Embodiments include a device comprising a pipe having at least one section that spans between a first end and a second end of the pipe. The pipe has a cylindrical cross-section. The device comprises a receptacle positioned in the pipe a first distance from the first end and a second distance from the second end. The receptacle receives an electronic device having microphones that are to be calibrated and secures the microphones a third distance inside an inside surface of the pipe. The device comprises an adapter connected to the first end. The adapter connects a loudspeaker to the pipe. The pipe controls an acoustic energy experienced by the plurality of microphones so that each microphone of the plurality of microphones receives equivalent acoustic energy.
Abstract:
Embodiments relate generally to electrical and electronic hardware, computer software, wired and wireless network communications, and computing devices, and, in particular, to antenna structures and formation methods for a wearable pod and/or device implementing a touch-sensitive interface in a metal pod cover. According to an embodiment, formation of a wearable pod includes selecting an antenna having a first surface area that extends beyond a second surface area associated with an attachment portion a cradle for a wearable pod. The method also includes forming an under-anchor portion composed of an interface material configured to bind to the cradle and to an elastomer, and disposing the antenna on a surface of the under-anchor portion at a distance from the second surface area associated with the attachment portion. Also, the method can include forming an over-anchor portion.
Abstract:
Device-based activity classification using predictive feature analysis is described, including evaluating an indicator associated with a predictive feature, identifying an application, using the name, to be performed, and invoking the application, the application being configured to interpret the indicator to determine an operation to perform at one or more levels of a protocol stack using data generated from evaluating a signal detected by a sensor, the sensor being coupled to a wearable device, and the application being configured to perform the operation using other data generated from evaluating another signal detected by another sensor, the another sensor being substantially different than the sensor.
Abstract:
Device-based activity classification using predictive feature analysis is described, including receiving a signal from a sensor coupled to a device, the sensor being configured to detect the signal over a time period and to detect motion, evaluating the signal to generate data, the data being used to indicate motion, the data being further evaluated to select a classifier based on whether the motion is detected, activating another sensor coupled to the device, the another sensor being configured to detect another signal that is substantially different than the signal, the another signal being used to generate other data associated with whether the motion is detected, invoking the classifier, the classifier being configured to evaluate a predictive feature to identify a type associated with whether the motion is detected, the predictive feature invoking an application configured to determine the type and a state using a feature interpreter, and processing the data using the application and the feature interpreter to generate information associated with a biological state associated with whether the motion is detected, the information being configured to display on an interface associated with the device.