Systems and methods for identifying golf equipment. The system may include one or more performance tracking devices, such as an optical sensor system or a radar sensor system for tracking at least one of a golf club swing or a golf ball flight. The system also may include at least one processor and memory storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations. The operations include receiving current dynamic input for a golf shot from a golfer and current static input for the golfer. The operations also include executing a trained machine-learning model based on the received current dynamic input and current static input to generate predicted golf club properties and/or predicted golf ball properties for the golfer. The predicted golf club properties and/or predicted golf ball properties are displayed on a connected display.

Head-mounted augmented reality (AR) devices can track pose of a wearer's head or pose of a hand-held user input device to enable wearer interaction in a three-dimensional AR environment. A pose sensor (e.g., an inertial measurement unit) in the user input device can provide data on pose (e.g., position or orientation) of the user input device. An electromagnetic (EM) tracking system can also provide pose data. For example, the handheld user input device can include an EM emitter that generates an EM field, and the head-mounted AR device can include an EM sensor that senses the EM field. The AR device can combine the output of the pose sensor and the EM tracking system to reduce drift in the estimated pose of the user input device or to transform the pose into a world coordinate system used by the AR device. The AR device can utilize a Kalman filter to combine the output of the pose sensor and the EM tracking system.

Systems, methods and computer-readable media are provided for identifying organisms for production. The identification is based upon determining one or more outlier detection parameters for identifying outliers (e.g., outlier wells, strains, plates holding organisms) from a data set of organism performance metrics. A prediction engine may identify one or more candidate outliers based upon a first set of outlier detection parameters (e.g., outlier detection threshold), and determine probability metrics that represent likelihoods that candidate outliers belong to an outlier class. Based on those metrics, some of the outliers may be excluded from consideration in predicting organism performance for the purpose of selecting organisms for production.

The present disclosure provides a subway operating state prediction method and apparatus, an electronic device and a storage medium, and relates to technical fields such as intelligent transportation and artificial intelligence. A specific implementation solution involves: acquiring air pressure information of a mobile terminal used by a user on a subway at respective moments in a time window; and predicting an operating state of the subway in the time window based on the air pressure information at the moments in the time window.

A system for analysis of user gait and to provide correction in form of haptic and visual feedback. This system comprises a motion and force sensors and a haptic actuator embedded in the user shoe insoles in communication with a smart-phone based analysis application, configured to calculate motion and orientation of the user feet in relation to the value, location and distribution of ground reaction forces measured by sensors located in the shoe insoles and after analysis of said forces and motion, to provide haptic feedback to the user foot instructing about the location (and timing) of pressure the user must apply to achieve an optimal gait.

A pattern recognition device comprising: a coupled network of damped, nonlinear, dynamic elements configured to generate an output response in response to at least one environmental condition, wherein each element has an associated multi-stable potential energy function that defines multiple energy states of an individual element, and wherein the elements are tuned such that environmental noise triggers stochastic resonance between energy levels of at least two elements; a processor configured to monitor the output response over time and to determine a probability that the pattern recognition device is in a given state based on the monitored output response; and detecting a pattern in the at least one environmental condition based on the probability.

A method, computer system, and computer program product are provided for detecting drift in predictive models for network devices and traffic. A plurality of streams of time-series telemetry data are obtained, the time-series telemetry data generated by network devices of a data network. The plurality of streams are analyzed to identify a subset of streams, wherein each stream of the subset of streams includes telemetry data that is substantially empirically distributed. The subset of streams of time-series data are analyzed to identify a change point. In response to identifying the change point, additional time-series data is obtained from one or more streams of the plurality of streams of time-series telemetry data. A predictive model is trained using the additional time-series data to update the predictive model and provide a trained predictive model.

A biometric biochemical analysis system includes a user interface module to provide instructions for collecting and handling biochemical sampling and processing related to biometric data gathering as well as capturing biometric data using digital data capturing devices. The user interface module and display are integrated with analysis and communications portions of the biometric biochemical analysis system to provide a portable system for multi-portion data collecting, storage, verification, and analysis.

A network device includes a transceiver configured to concurrently transmit signals and receive signals within a single frequency band resulting in radio-frequency signal interference. The device includes an analog canceler configured to mitigate the signal interference. The device includes a neural network that receives data that describes characteristics of the signal interference and provides coefficients for the analog canceler as outputs. The neural network-generated coefficients are applied to the analog canceler which uses them to cancel the signal interference.

A system for motion analysis includes a sensing device positioned at a knee joint of a bicycle rider, and an electronic device in communication with the sensing device. The electronic device is configured to receive, from the sensing device, a series of pieces of sensed data representing orientations of the sensing device at different times, to determine a motion trajectory of a knee of the rider based on the received pieces of sensed data, and to generate an estimation result regarding correctness of a riding posture of the rider based on the motion trajectory.