A wearable device for tracking swim activities of a user is provided. The wearable device may include one or more sensors configured to generate sensor data, and based on the sensor data, the wearable device may determine swim metrics such as swim stroke count, swim stroke type, swim lap count, and swim speed. The determined swim metrics may be filtered based on one or more swim periods during which the user is likely to have been swimming. The wearable device may determine such swim periods based on the sensor data and/or the determined swim metrics.

Performance testing of an athlete uses a gate which provides status indications for the athlete during the test and a sensor which detects movement of the athlete from a start position. A controller communicates with the gate and the sensor to conduct the test. The controller receives athlete movement data and determines first movement characteristics of the athlete at the start position.

A system for time series analysis using attention models is disclosed. The system may capture dependencies across different variables through input embedding and may map the order of a sample appearance to a randomized lookup table via positional encoding. The system may capture capturing dependencies within a single sequence through a self-attention mechanism and determine a range of dependency to consider for each position being analyzed. The system may obtain an attention weighting to other positions in the sequence through computation of an inner product and utilize the attention weighting to acquire a vector representation for a position and mask the sequence to enable causality. The system may employ a dense interpolation technique for encoding partial temporal ordering to obtain a single vector representation and a linear layer to obtain logits from the single vector representation. The system may use a type dependent final prediction layer.

A system to combine inertial-based measurements and optical-based measurements via a Kalman-type filter. For example, a sensor module uses an inertial measurement unit to generate first positions and first orientations of the sensor module at a first time interval during a first period of time containing multiple of the first time interval. At least one camera is used to capture images of the sensor module at a second time interval, larger than the first time interval, during the first period of time containing multiple of the second interval. Second positions and second orientations of the sensor module during the first period of time are computed from the images. The filter receives the first positions, the first orientations, the second positions, and the second orientations to generate estimates of position and orientation of the sensor module at a time interval no smaller than the first time interval.

Example implementations involve fault detection and isolation in industrial networks through defining a component as a combination of measurements and parameters and define an industrial network as a set of components connected with different degrees of connections (weights). Faults in industrial network are defined as unpermitted changes in component parameters. Further, the fault detection and isolation in industrial networks are formulated as a node classification problem in graph theory. Example implementations detect and isolate faults in industrial networks through 1) uploading/learning network structure, 2) detecting component communities in the network, 3) extracting features for each community, 4) using the extracted features for each community to detect and isolate faults, 5) at each time step, based on the faulty components provide maintenance recommendation for the network.

Aspects of the invention include a method including collecting, by a processor, physiological data from a user in an environment and a sound waveform from the user's environment. The method detects and labels as a potential annoyance, by the processor, a set of potential annoyance data based on the collected physiological data and the sound waveform. The method decomposes, by the processor, the sound waveform into a first sound waveform segment associated with the set of potential annoyance data and a second sound waveform segment not associated with the set of potential annoyance data. The method predicts, by the processor, that the potential annoyance is an actual annoyance. The method filters and modifies, by the processor, the first sound waveform segment associated with the actual annoyance and provides, by the processor, the second sound waveform segment not associated with the actual annoyance to the user.

Methods and systems fort operating one or more light sources to project adversarial patterns generated to disorient a machine learning based detection system, comprising generating one or more adversarial patterns configured to disorient the machine learning based detection system and operating one or more light sources configured to project one or more of the adversarial pattern(s) in association with the targeted object in order to disorient the machine learning based detection system.

Described herein are systems and methods for fault tolerance in a network cloud environment. In accordance with various embodiments, the present disclosure provides an improved fault tolerance solution, and improvement in the fault tolerance of systems, by way of failure prediction, or prediction of when an underlying infrastructure will fail, and using the predictions to counteract the failure by spinning up or otherwise providing new component pieces to compensate for the failure.

The present application provides a method and apparatus for detecting an ECG signal and an electronic device. According to an example of the method, an ECG signal with a set time length is segmented to obtain a first set number of single heartbeats; feature data corresponding to each of the first set number of single heartbeats is determined to obtain a first set number of feature data; and a pathological category of the ECG signal with the set time length is determined based on the ECG signal with the set time length and the first set number of feature data.

The present invention relates to a system for analysing volatile organic compounds (VOCs) in soil comprising an apparatus and a soil VOC sensor strip, wherein the apparatus comprises a sampling chamber for receiving soil, a sensor strip aperture in the sampling chamber for positioning the sensor strip in fluid communication with the sampling chamber, a power source and an electrical resistance detector, wherein the sensor strip comprises a flexible substrate with a first surface and an array of semiconductor polymer sensors arranged on the first surface, wherein each of the semiconductor polymer sensors comprises a pair of electrodes, wherein the pair of electrodes comprises a first electrode and a second electrode, wherein a semiconductor polymer is disposed between the first electrode and the second electrode, and wherein the sensor strip is electrically connectable to the power source and the electrical resistance detector.