A sports training aid comprising a body unit, attachable to a person's body or the person's sports implement, is provided with a positioning sensor module; a feedback stimulator; and a processor. The sports training aid is configured to provide instantaneous feedback on motion faults of a studied sports motion, and the body unit is intended to be attached to a person's body (or a person's sports implement) at a representative location, the location being bound to travel a path representative of the studied sports motion, and the positioning sensor module comprises acceleration sensors and gyro sensors. The processor is configured to determine a still position corresponding to an event wherein the body unit is determined to be still, to keep track of the sensor module's movements relative to the still position, and to activate the feedback stimulator in real time, upon detection of a sports motion fault.

A system that measures a swing of equipment (such as a bat or golf club) with inertial sensors, and analyzes sensor data to create a rotational profile. Swing analysis may use a two-lever model, with a body lever from the center of rotation to the hands, and an equipment lever from the hands to the sweet spot of the equipment. The rotational profile may include graphs of rates of change of the angle of the body lever and of the relative angle between the body lever and the equipment lever, and a graph of the centripetal acceleration of the equipment. These three graphs may provide insight into players' relative performance. The timing and sequencing of swing stages may be analyzed by partitioning the swing into four phases: load, accelerate, peak, and transfer. Swing metrics may be calculated from the centripetal acceleration curve and the equipment/body rotation rate curves.

A method selectively activates a resource to accommodate an advanced emotion. A supervisor computer receives a first piece of content, and then applies an emotion classifier to the first piece of content in order to create a first concept/emotion/sentiment/time tuple. The supervisor computer creates a second concept/emotion/sentiment/time tuple for a second piece of content, and compares the first and second tuples. If the concept in the first piece of content matches the concept in the second piece of content but that at least one of the emotion, sentiment, and time of the first piece of content does not match the emotion, sentiment, and time of the second piece of content, the supervisor computer determines that the emotion of the second piece of content is an advanced emotion that is not expressed by the first or second pieces of content, and activates a resource that accommodates the advanced emotion.

Described herein are compositions, methods, and techniques to generate a cancer signature and uses thereof. The cancer signature can be used to determine a cancer progression risk of a subject based upon expression levels of genes of a progression gene signature in a sample. The methods can be used to predict a prognosis, to select an appropriate treatment regimen, to identify or screen for an agent effective against a cancer, or a combination thereof. Computer implemented methods and systems that implement those methods are also provided. This abstract is intended as a scanning tool for purposes of searching in the particular art and is not intended to be limiting of the present disclosure.

A class label of a three-dimensional point cloud can be identified with high performance. The key point choice unit 22 extracts a key point cloud 35 including three-dimensional points efficiently representing features of an object and a non-key point cloud 37. A inference unit 24 takes, as representative points, a plurality of points selected by down-sampling from each of the key point cloud 35 and the non-key point cloud 37, extracts, with respect to each of the representative points, a feature of each representative point from coordinates and the feature of the representative point and coordinates and features of neighboring points positioned near the representative point. The inference unit 24 extracts features of a plurality of new representative points from the coordinates and the features of the plurality of representative points, coordinates and features of a plurality of three-dimensional points before sampling which are the new representative points, and coordinates and features of neighboring points positioned near the new representative points. The inference unit 24 derives a class label from the coordinates and features of the plurality of representative points, or the coordinates and features of the plurality of new representative points, and outputs the class label.

Techniques for audio tagging of an object of interest are provided. An object of interest within a field of view of a first video camera may be identified at a first time. At least one audio tag representing a first sound created by the object of interest may be generated and associated with the object of interest. At a second time later than the first and at a second video camera, a second sound generated by an unidentified object that is not in the field of view of the second video camera may be detected. An audio tag representing the second tag may be generated. It may be determined that the object of interest and the unidentified object of interest are the same when the audio tag representing the first sound and the second sound are the same.

A method and electronic device for determining relevant signals in radar signal processing. The electronic device includes a radar transceiver, a memory, and a processor. The processor is configured to cause the electronic device to obtain, via the radar transceiver of the electronic device, radar measurements for one or more modes in a set of modes; process the radar measurements to obtain a set of radar images; identify relevant signals in the set of radar images based on signal determination criteria for an application; and perform the application using only the relevant signals.

A highlight learning technique is provided to detect and identify highlights in sports videos. A set of event models are calculated from low-level frame information of the sports videos to identify recurring events within the videos. The event models are used to characterize videos by detecting events within the videos and using the detected events to generate an event vector. The event vector is used to train a classifier to identify the videos as highlight or non-highlight.

A method and apparatus of detecting incipient corrosion on surfaces of an object. The method comprising immersing the object into an electrolyte, and detecting by electrochemical techniques the presence of corrosion on the surfaces of the object based on current originating from redox reaction of iron.

A vibration-based authentication method for an access control system includes: collecting vibration signals generated by a built-in vibration motor in an authentication device; filtering, denoising, and performing endpoint segmentation on the collected vibration signals, and extracting vibration signals containing effective touch; performing an alignment on the segmented vibration signals; performing a fast Fourier transform on the aligned vibration signals to obtain frequency-domain data, extracting frequency-domain features obtained after alignment and features obtained before alignment to construct a training data set, and storing the training data set in a database of the authentication device; using a new unlock signal generated when a user touches the authentication device as test data, and processing the test data to obtain test data containing effective touch; and matching and classifying the test data containing effective touch with the training data set by using a machine learning classification model, to obtain an authentication result.