Abstract: Apparatus and methods for an extensible robotic device with artificial intelligence and receptive to training controls. In one implementation, a modular robotic system that allows a user to fully select the architecture and capability set of their robotic device is disclosed. The user may add/remove modules as their respective functions are required/obviated. In addition, the artificial intelligence is based on a neuronal network (e.g., spiking neural network), and a behavioral control structure that allows a user to train a robotic device in manner conceptually similar to the mode in which one goes about training a domesticated animal such as a dog or cat (e.g., a positive/negative feedback training paradigm) is used. The trainable behavior control structure is based on the artificial neural network, which simulates the neural/synaptic activity of the brain of a living organism.

Abstract: Apparatus and methods for an extensible robotic device with artificial intelligence and receptive to training controls. In one implementation, a modular robotic system that allows a user to fully select the architecture and capability set of their robotic device is disclosed. The user may add/remove modules as their respective functions are required/obviated. In addition, the artificial intelligence is based on a neuronal network (e.g., spiking neural network), and a behavioral control structure that allows a user to train a robotic device in manner conceptually similar to the mode in which one goes about training a domesticated animal such as a dog or cat (e.g., a positive/negative feedback training paradigm) is used. The trainable behavior control structure is based on the artificial neural network, which simulates the neural/synaptic activity of the brain of a living organism.

Abstract: Apparatus and methods for an extensible robotic device with artificial intelligence and receptive to training controls. In one implementation, a modular robotic system that allows a user to fully select the architecture and capability set of their robotic device is disclosed. The user may add/remove modules as their respective functions are required/obviated. In addition, the artificial intelligence is based on a neuronal network (e.g., spiking neural network), and a behavioral control structure that allows a user to train a robotic device in manner conceptually similar to the mode in which one goes about training a domesticated animal such as a dog or cat (e.g., a positive/negative feedback training paradigm) is used. The trainable behavior control structure is based on the artificial neural network, which simulates the neural/synaptic activity of the brain of a living organism.

Abstract: A multi-panel electronic device and method are disclosed. In a particular embodiment, a method includes receiving first acceleration data from a first sensor coupled to a first portion of an electronic device. The method further includes receiving second acceleration data from a second sensor coupled to a second portion of the electronic device, where a position of the first portion is movable with respect to a position of the second portion. The method further includes determining a configuration of the electronic device at least partially based on the first acceleration data and the second acceleration data.

Abstract: A method for synchronization of data over multiple panels is provided. A communications apparatus that synchronizes data across multiple displays is provided. A computer program product, comprising a computer-readable medium that synchronizes video data across multiple displays is provided. At least one processor configured to synchronize data across multiple panels is provided. The video data can be sent between the multiple panels or displays at different rates to facilitate synchronization of the data. Double buffering at each panel can allow data to be written to a first buffer and at substantially the same time data is extracted from a second buffer and written to a display.

Abstract: Methods, systems, and devices are described for collecting physiological data using a configurable biopotential array. The array is embedded on a surface area of a handheld device. The array includes a number of electrode tiles. The electrodes include biosensors to collect the physiological data of a user. The electrodes are polled to detect contact with the user's skin. Electrodes in contact with the skin are electrically coupled to form an active electrode area. The coupled electrodes collect the physiological data relating to the user via the biosensors. The electrodes are decoupled after contact with the user's skin is terminated. The physiological data is analyzed and an emotional state or health state of the user is determined from the analyzed data.

Abstract: Techniques for designing a sensor module that may be inserted into a container, for example, a box, for tracking RFID-labeled items enclosed in the container. In an exemplary embodiment, at least two planar surfaces are adjoined along a linear interface. Each of the planar surfaces supports a planar antenna whose radiation pattern may cover the volume of the container. A battery-powered tracking unit is attached to at least one of the planar surfaces to drive the planar antennas. In an exemplary embodiment, the planar antennas may be driven in succession to avoid collisions between the RFID communication signals, and for better RFID coverage of the items enclosed in the container.

Abstract: Methods, systems, and devices are described for collecting physiological data using a configurable biopotential array. The array is embedded on a surface area of a handheld device. The array includes a number of electrode tiles. The electrodes include biosensors to collect the physiological data of a user. The electrodes are polled to detect contact with the user's skin. Electrodes in contact with the skin are electrically coupled to form an active electrode area. The coupled electrodes collect the physiological data relating to the user via the biosensors. The electrodes are decoupled after contact with the user's skin is terminated. The physiological data is analyzed and an emotional state or health state of the user is determined from the analyzed data.

Abstract: A method for synchronization of data over multiple panels is provided. A communications apparatus that synchronizes data across multiple displays is provided. A computer program product, comprising a computer-readable medium that synchronizes video data across multiple displays is provided. At least one processor configured to synchronize data across multiple panels is provided. The video data can be sent between the multiple panels or displays at different rates to facilitate synchronization of the data. Double buffering at each panel can allow data to be written to a first buffer and at substantially the same time data is extracted from a second buffer and written to a display.

Abstract: A method for use by a touch screen device includes detecting a first touch screen gesture at a first display surface of an electronic device, detecting a second touch screen gesture at a second display surface of the electronic device, and discerning that the first touch screen gesture and the second touch screen gesture are representative of a single command affecting a display on the first and second display surfaces.

Abstract: A multi-panel electronic device and method are disclosed. In a particular embodiment, a method includes receiving first acceleration data from a first sensor coupled to a first portion of an electronic device. The method further includes receiving second acceleration data from a second sensor coupled to a second portion of the electronic device, where a position of the first portion is movable with respect to a position of the second portion. The method further includes determining a configuration of the electronic device at least partially based on the first acceleration data and the second acceleration data.