Abstract: A remote control system detects a directional signal at an alerting sensor package; activates an appliance to accept a control signal in response to the directional signal; accepts the control signal at the appliance from a muscle sensor package; and adjusts operation of the appliance in response to the control signal.

Abstract: A method includes analyzing a preview image made by an image sensor when operating in a preview image mode to determine if the preview image contains some portion that corresponds to information that is considered not subject to being imaged. The method further includes, if it is determined that the preview image does comprise some image portion that corresponds to information that is considered not subject to being imaged, disabling the image sensor from capturing an image that would correspond to the preview image. Analyzing can include accessing a data storage that contains representations of information that is considered not subject to being imaged for comparison with image features extracted from the preview image, where the data storage is at least one of contained in a device that also contains the image sensor, or is located remotely from the device and can be accessed via a wireless connection.

Abstract: Methods of fabricating a probe are described. In an example, a structure may be formed on a surface of a substrate. The structure may include the probe, a hinge, and an anchor arranged linearly, where an angle is formed between the probe and the hinge. The hinge may be positioned between the probe and the anchor, and the structure may be parallel to the substrate. An amount of solder may be deposited on an area of the structure that spans from a portion of the probe to a portion of the anchor, and across the hinge. The deposited solder may be reshaped by an execution of a solder reflow process. The reshape of the deposited solder may cause the probe to rotate about the hinge in order to reduce the angle between the probe and the hinge.

Abstract: A Static Random Access Memory (SRAM) device in a binary neural network is provided. The SRAM device includes an SRAM inference engine having an SRAM computation architecture with a forward path that include multiple SRAM cells. The multiple SRAM cells are configured to form a chain of SRAM cells such that an output of a given one of the multiple SRAM cells is an input to a following one of the multiple SRAM cells. The SRAM computation architecture is configured to compute a prediction from an input.

Abstract: A random number signal generator used for performing dropout or weight initialization for a node in a neural network. The random number signal generator includes a transistor which generates a random noise signal. The transistor includes a substrate, source and drain regions formed in the substrate, a first insulating layer formed over a channel of the transistor, a first trapping layer formed over the first insulating layer, a second insulating layer formed over the first trapping layer, and a second trapping layer formed over the second insulating layer. One or more traps in the first and second trapping layers are configured to capture or release one or more carriers flowing through the channel region. The random noise signal is generated as a function of one or more carrier being captured or released by the one or more traps.

Abstract: Methods and systems for activity monitoring include capturing an infrared image of an environment that comprises at least one patient being monitored and at least one infrared-emitting tag. A relationship between the patient being monitored and the at least one infrared-emitting tag is determined. An activity conducted by the patient being monitored is determined based on the relationship between the patient being monitored and the at least one infrared-emitting tag. A course of treatment for the patient being monitored is adjusted based on the determined activity.

Abstract: A computer-implemented method, computer program product, and system for determination of critical parts and component correlations in a circuit using a correlation graph and centrality analysis including; receiving a circuit layout portion of a larger circuit layout, converting the circuit layout portion into a correlation graph representing components as nodes and connecting wires as edges, determining, using ground truth and Naïve Bayes to determine correlation weighting, scaling the correlation graph to represent the larger circuit, and presenting the larger correlation graph on a graphical user interface (GUI).

Abstract: Methods and systems for activity monitoring include capturing an infrared image of an environment that comprises at least one patient being monitored and at least one infrared-emitting tag. A relationship between the patient being monitored and the at least one infrared-emitting tag is determined. An activity conducted by the patient being monitored is determined based on the relationship between the patient being monitored and the at least one infrared-emitting tag. A course of treatment for the patient being monitored is adjusted based on the determined activity.

Abstract: A thermal tag for activity monitoring. The thermal tag includes a base layer having a plurality of metal lines to provide a conductive path, and a pattern layer having one or more infrared emitting features positioned over portions of the conductive path, wherein at least one infrared emitting feature couples to the conductive path to emit a predetermined infrared pattern in accordance with nearby activity.

Abstract: A thermal tag for activity monitoring. The thermal tag includes a base layer having a plurality of metal lines to provide a conductive path, and a pattern layer having one or more infrared emitting features positioned over portions of the conductive path, wherein at least one infrared emitting feature couples to the conductive path to emit a predetermined infrared pattern in accordance with nearby activity.

Abstract: A computer implemented method includes capturing, via a camera, one or more digital images of a face of a person representative of blood circulation of the person, collecting context information via one or more processors corresponding to the person contemporaneously with the capturing of the one or more digital images, labeling, via a trained individual health model executing on the one or more processors, the one or more digital images based on the blood circulation represented in the image and the collected context information via the trained individual health model that has been trained on prior such digital images and context information; and analyzing, via the one or more processors, the one or more labeled digital images to generate a health index of the person.

Abstract: Method and apparatus for monitoring eating behavior are disclosed. A wearable device including an imaging sensor, an electromyography (EMG) sensor, and a processing unit is configured to collect EMG data via the EMG sensor, transmit the EMG data to a computing device, receive a control signal to capture one or more images via the imaging sensor, capture, one or more images, and transmit the one or more images. A computing device is disclosed including a processor and a memory containing a machine learning model and computer program code that, when executed, performs an operation. The operation includes receiving EMG data, processing the EMG data as input to a trained machine learning model, transmitting the control signal to capture one or more images via the imaging sensor, receiving the one or more images, and determining nutritional content of the food or beverage using the one or more images.

Abstract: A thermal tag includes a power source to generate current along a resistive path and a plurality of infrared-emitting features positioned along the resistive path to emit infrared light along an infrared-emitting pattern, wherein the plurality of infrared-emitting features include one or more infrared-emitting stripes on a first surface of the thermal tag defining a pattern region and a plurality of infrared-emitting dots within a portion of the pattern region to provide a thermal-diffusion resistant pattern for activity monitoring.

Abstract: One or more biological signals are obtained. The one or more biological signals are converted to one or more graph structures. Correlation between two or more of the biological signals are determined using the one or more graph structures. One or more changes in the one or more graph structures within a time window are recorded. A signal graph model is generated based on the recorded changes.

Abstract: Identity recognition is achieved using user's living signatures, such as a heartbeat signal, from multiple devices. A user requests identity recognition for using a first registered device, such as a mobile device. The request is provided to a cloud. When the cloud confirms the mobile device is a registered device, the cloud directs the first registered device to upload the user living signature to the cloud. The cloud also sends a request to a user second registered device to upload the user living signature from the second registered device to the cloud. The user second registered device may be a headband monitor, watch, Google glasses, or the like capable of monitoring and uploading the user living signature to the cloud. The cloud compares both living signature signals from the first registered device and from the second registered device. If the signals are matched, the user identity is confirmed. If the signals are not matched, the user identity is not confirmed.

Abstract: Method and apparatus for optimizing a convolutional neural network (CNN). A respective measure of importance is calculated for each of a plurality of elements within a CNN. A first one of the measures of importance is calculated by back propagating a second one of the measures of importance through the CNN. One or more of the plurality of elements is pruned from the CNN, based on the calculated measures of importance.

Abstract: A Resilient Distributed Dataset (RDD) processing method, system, and computer program product include sending a different portion including a global identifier and different values of a message from a receiver to a sender and at a receiver side, an already-processed message is extracted from an RDD object repository corresponding to the global identifier of the different portion and the incoming differential portion is applied to the already-processed message

Abstract: Wireless communication is established between electronic devices by an initiating device transmitting a wireless communication request to a peripheral device; the initiating device detecting a visible electromagnetic pattern displayed on the peripheral device in response to the wireless communication request; the initiating device decoding the visible electromagnetic pattern to generate a passcode; and the initiating device echoing the passcode to the peripheral device to authenticate the wireless communication request without user intervention.

Abstract: A remote control system detects a directional signal at an alerting sensor package; activates an appliance to accept a control signal in response to the directional signal; accepts the control signal at the appliance from a muscle sensor package; and adjusts operation of the appliance in response to the control signal

Abstract: At a mobile electronic device, first sensor data is obtained from at least one wearable muscle sensor, suggesting that location tracking will imminently be desirable. A first indication that an external locator signal is not usable is also obtained. Responsive to the first sensor data and the indication, second sensor data, from at least one wearable muscle sensor, indicative of motion of a subject wearing the at least one muscle sensor, is recorded. A second indication that the external locator signal is now usable is obtained. Responsive to the second indication, the external locator signal is used to determine a current location of the mobile electronic device; and the second sensor data is sued to backtrack from the current location of the mobile electronic device to indicate a location of the mobile electronic device at a time when the recording of the second sensor data was initiated.