Abstract:
In embodiments, apparatuses, methods and storage media (transitory and non-transitory) are described that receive sensor data from one or more sensor devices that depict a user gesture in three dimensional space, determine a flight path based at least in part on the sensor data, and store the flight path in memory for use to control operation of a drone. Other embodiments may be described and/or claimed.
Abstract:
Computer-readable storage media, apparatus and method associated with storing a copy of local data in a historical data store, among other embodiments, are disclosed herein. In embodiments, one or more computer-readable storage media may contain instructions which when executed by a computing device may provide access of local data to one or more applications on the computing device for contemporaneous processing by the one or more applications. The local data may be associated, at least in part, with one or more sensors of the computing device. In some embodiments, a copy of the local data may be transmitted to a remote historical data store where it may be categorized and correlated with data from computing devices associated with one or more other users for further processing.
Abstract:
Apparatuses, systems, media and methods may provide for environment actuation by one or more augmented reality elements. A location module may determine a location of one or more networked devices in a real space and/or establish a location of the one or more augmented reality elements in a virtual space, which may be mapped to the real space. A coordinator module may coordinate a virtual action in the virtual space of the one or more augmented reality elements with an actuation event by the one or more networked devices in the real space. The actuation event may correspond to the virtual action in the virtual space and be discernible in the real space.
Abstract:
Technologies for remotely controlling a separate computing device includes a wearable computing device to receive sensor data from an optical sensor of the wearable computing device. The sensor data comprises data is indicative of a skin surface of a forearm of a user of the wearable computing device. The wearable computing device generates control data based on the received sensor data. The generated control data is transmitted to the separate computing device. In some embodiments, an x-coordinate is generated based on detection of longitudinal movement of the wearable computing device relative to the skin surface of the forearm of the user and a y-coordinate is generated based on detection of rotational movement of the wearable computing device relative to the skin surface of the forearm of the user.
Abstract:
System and techniques for user input via elastic deformation of a material are described herein. The morphology of an elastic material may be observed with a sensor. The observations may include a first and a second morphological sample of the elastic material. The first and second morphological samples may be compared against each other to ascertain a variance. The variance may be filtered to produce an output. The output may be translated into a user input parameter. A device action corresponding to the user input parameter may be invoked.
Abstract:
One particular example includes a system, comprising a processor and a memory to store instructions that when executed by the processor performs operations, comprising displaying a map of a first area on a display; receiving an instruction to designate a second area on the map to be avoided; displaying the second area to be avoided on the map; and generating a route from a first point to a second point, where the route does not go through the second area to be avoided.
Abstract:
A method may include calculating a first distance between first and second devices and determining a direction of a movement of the first device. The method may further include calculating a second distance between the first and second devices after the movement of the first device and determining the relative position of the first device with respect to the second device based on the direction of the movement, the first distance, and the second distance.
Abstract:
One particular example includes a system, comprising a processor and a memory to store instructions that when executed by the processor performs operations, comprising displaying a map of a first area on a display; receiving an instruction to designate a second area on the map to be avoided; displaying the second area to be avoided on the map; and generating a route from a first point to a second point, where the route does not go through the second area to be avoided.
Abstract:
A neural network model for anomaly detection may include convolutional blocks with different spatial scales. The model may be trained with training data, which may be normal data that lacks anomaly. The convolutional blocks may generate embedding features having different spatial scales. A distance between each embedding feature and a corresponding model embedding may be determined. The distances for the embedding features may be accumulated for determining a loss of the model. The model may be trained based on the loss. An accuracy of the trained model may be tested with testing data that has verified anomaly. One or more convolutional blocks may be selected from all the convolutional blocks in the model, e.g., based on the spatial scales of the convolutional blocks and the spatial scale of data on which anomaly detection is to be performed. The selected convolutional block(s) may be used to detect anomaly in the data.
Abstract:
Systems and methods for multi-modal user device authentication are disclosed. An example electronic device includes a first sensor, a microphone, a first camera, and a confidence analyzer to authenticate a subject as the authorized user in response to a user presence detection analyzer detecting a presence of the subject and one or more of (a) an audio data analyzer detecting a voice of an authorized user or (b) an image data analyzer detecting a feature of the authorized user. The example electronic device includes a processor to cause the electronic device to move from a first power state to a second power state in response to the confidence analyzer authenticating the user as the authorized user. The electronic device is to consume a greater amount of power in the second power state than the first power state.