A finger-worn wearable ring device may include a ring-shaped housing, a printed circuit board, and a sensor module that includes one or more light-emitting components and one or more light-receiving components. The wearable ring device may further include a communication module configured to wirelessly communicate with an application executable on a user device.

Methods and systems for performing machine learning include selecting first and second training data from one or more training sets in one or more databases. Mixed training data is formed by subtracting a value of each data element in the second training data from a value of a corresponding data element in the first training data. A machine learning process is trained using the mixed training to augment data used by the machine learning process.

A method for operating an environment sensor system of a vehicle, in particular of an autonomous motor vehicle, and an environment sensor system. The method includes the steps: producing first image data from a first image of a vehicle environment with the aid of a first sensor, performing a setpoint-actual comparison of the first image data with the second image data, and recognizing an operability of the environment sensor system based on the setpoint-actual comparison, the second image data including standard image data and/or image information from a second image of the vehicle environment, and the operability being recognized with the aid of a first artificial intelligence.

Methods and systems for detecting abnormal application behavior include determining a vector representation of a first syscall graph that is generated by a first application, the vector representation including a representation of a distribution of subgraphs of the first syscall graph. The vector representation of the first syscall graph is compared to one or more second syscall graphs that are generated by respective second applications to determine respective similarity scores. It is determined that the first application is behaving abnormally based on the similarity scores, and a security action is performed responsive to the determination that the first application is behaving abnormally.

Computing devices and methods for reading gauges are disclosed. A gauge reading method includes capturing image data corresponding to a captured image of one or more gauges, detecting one or more gauges in the captured image, cropping a detected gauge in the captured image to provide a use image including the detected gauge, and classifying the detected gauge to correlate the detected gauge with a template image. The gauge reading method also includes attempting to perform feature detection rectification on the use image to produce a rectified image of the detected gauge, performing template matching rectification on the use image to produce the rectified image responsive to a failure to perform the feature detection rectification, and estimating a gauge reading responsive to the rectified image. A computing device may implement at least a portion of a gauge reading method.

A system and method for risk object identification via causal inference that includes receiving at least one image of a driving scene of an ego vehicle and analyzing the at least one image to detect and track dynamic objects within the driving scene of the ego vehicle. The system and method also include implementing a mask to remove each of the dynamic objects captured within the at least one image. The system and method further include analyzing a level of change associated with a driving behavior with respect to a removal of each of the dynamic objects. At least one dynamic object is identified as a risk object that has a highest level of influence with respect to the driving behavior.

The present application provides an image detection method performed by a server. The method includes: intercepting a first image and a second image at a preset time interval from a video stream; performing pixel matching on the first image and the second image to obtain a value of total matching pixels between the first image and the second image; performing picture content detection on the second image in response to determining that the value of total matching pixels between the first image and the second image satisfies a preset matching condition based on the value of total matching pixels; and determining that the video stream is abnormal in response to determining that no picture content is in the second image by the picture content detection. In this way, an image recognition manner can be used to perform detection on image pictures of the video stream at the preset time interval.

An embodiment includes training a first convolutional neural network (CNN) using a plurality of training images to generate first and second trained CNNs, and then adding an interface layer to the second trained CNN. The embodiment processes a first and second images in a sequence of images using the first trained CNN to generate a first and second result vectors. The embodiment also processes the second image using the second trained CNN and sensor data input to the interface layer to generate a third result vector. The embodiment modifies the sensor data using a compensation value. The embodiment compares the third result vector to the second result vector to generate an error value, and then calculates a modified compensation value using the error value. The embodiment then generates a sensor-compensated trained CNN based on the second trained CNN with the modified compensation value.

A fully-automated computer-implemented system and method for generating a road network map from a remote sensing (RS) image in which the classification accuracy is satisfactory combines moving vehicle detection with spectral classification to overcome the limitations of each. Moving vehicle detections from an RS image are used as seeds to extract and characterize image-specific spectral roadway signatures from the same RS image. The RS image is then searched and the signatures matched against the scene to grow a road network map. The entire process can be performed using the radiance measurements of the scene without having to perform the complicated geometric and atmospheric conversions, thus improving computational efficiency, the accuracy of moving vehicle detection (location, speed, heading) and ultimately classification accuracy.

A method for human-object interaction detection includes receiving an image. A set of features are extracted from multiple positions of the image. One or more human-object pairs may be predicted based on the extracted set of features. A human-object interaction may be determined based on a set of candidate interactions and the predicted human-object pairs.