Filtering sensor data is described, for example, where filters conditioned on a local appearance of the signal are predicted by a machine learning system, and used to filter the sensor data. In various examples the sensor data is a stream of noisy video image data and the filtering process denoises the video stream. In various examples the sensor data is a depth image and the filtering process refines the depth image which may then be used for gesture recognition or other purposes. In various examples the sensor data is one dimensional measurement data from an electric motor and the filtering process denoises the measurements. In examples the machine learning system comprises a random decision forest where trees of the forest store filters at their leaves. In examples, the random decision forest is trained using a training objective with a data dependent regularization term.

A mobile computing device for measuring self-awareness of a user includes motion sensors and a processor for displaying instructions on how to conduct a whipping gesture, executing a body awareness assessment including receiving sensor data while the user performs multiple whipping gestures, executing an emotional awareness assessment including receiving sensor data while the user performs multiple whipping gestures, executing a self-assessment including receiving sensor data while the user performs multiple whipping gestures, executing a resiliency awareness assessment including receiving a response from the user to a question and generating a final self-awareness score corresponding to the user's self-awareness based on the body awareness assessment, emotional awareness assessment, self-assessment and resiliency awareness assessment.

A detection report data generation method including acquiring event type information of an electrocardiogram event corresponding to electrocardiogram event data, wherein the event data has one or more pieces of event type information; screening the event data according to signal quality evaluation indexes so as to obtain report conclusion data and report entry data; carrying out quality assessment on an event segment included in the event data according to the signal quality evaluation indexes, and determining a pre-selected sample segment according to a quality assessment result; determining position information of an event heart beat in the pre-selected sample segment, and determining segment interception parameters; carrying out interception processing on the pre-selected sample segment according to the segment interception parameters so as to obtain a typical data segment; generating report graphic data according to the typical data segment; and outputting the entry data, the graphic data and the conclusion data.

A sensory evaluation method for spectral data of mainstream smoke includes: performing a data enhancement on spectral data of mainstream smoke of a plurality of cigarettes; extracting a shallow spectral characteristic from the spectral data of the mainstream smoke of each cigarette; obtaining a shallow sensory quality result of the spectral data of the mainstream smoke of each cigarette based on the spectral data of the mainstream smoke of each cigarette and the shallow spectral characteristic; extracting deep spatial characteristics from the spectral data of the mainstream smoke of each cigarette; obtaining a deep sensory quality result based on the spectral data of the mainstream smoke of each cigarette and the deep spatial characteristics; obtaining a comprehensive sensory quality result according to the shallow sensory quality result and the deep sensory quality result. The sensory evaluation method achieves accurate screening of unknowns in the mainstream smoke.

A gesture classification apparatus and method is disclosed. The apparatus may include a feature extractor configured to extract a plurality of features using a electromyogram (EMG) data group obtained from an EMG signal sensor including a plurality of channels, an artificial neural network including an input layer to which the EMG data group corresponding to the plurality of features is input and an output layer configured to output a preset gesture corresponding to the plurality of features, and a gesture recognizer configured to recognize a gesture performed by a user and corresponding to the extracted features.

Biometric identification using electroencephalogram (EEG) signals is provided. Embodiments are targeted for biometric applications, where an individual can be identified with a precision of over 99%, using sensed brain signals. In particular, a method is described which can extract unique biomarkers from EEG response signals to classify individuals, also referred to as simple visual reaction task-based EEG biometry (SVRTEB). A subject experiences a simple stimulus or task, and a multi-channel EEG response is recorded. Unique biomarkers are extracted from the recorded EEG response (e.g., as periodogram data points corresponding to different frequencies observed in the brain waves, which can be used to identify a person). A novel signal processing approach uses neural network-based architecture to analyze the EEG response and identify the subject. This signal processing architecture can be readily implemented on hardware and provides high accuracy, precision, and recall.

This disclosure relates to method of identifying a gesture from a plurality of gestures using a reservoir based convolutional spiking neural network. A two-dimensional spike streams is received from neuromorphic event camera as an input. The two-dimensional spike streams associated with at least one gestures from a plurality of gestures is preprocessed to obtain plurality of spike frames. The plurality of spike frames is processed by a multi layered convolutional spiking neural network to learn plurality of spatial features from the at least one gesture. A filter block is deactivated from the plurality of filter blocks corresponds to at least one gesture which are not currently being learnt. A spatio-temporal features is obtained by allowing the spike activations from CSNN layer to flow through the reservoir. The spatial feature is classified by classifier from the CSNN layer and the spatio-temporal features from the reservoir to obtain set of prioritized gestures.

A system and method include a memory storing processor executable code for a denoised autoencoder, and one or more processors coupled to the memory to execute the processor executable code to receive raw signal data comprising signal noise, encode, by the denoised autoencoder, the raw signal data by performing a denoising autoencoder operation to produce a latent representation, and decode, by the denoised autoencoder, the latent representation to produce clean signal data reconstructed without the signal noise. A first filter is applied to a signal to emphasize activity within the signal and to produce a first modified signal, a rectifier and a second filter are applied to the first modified signal to smooth areas of the first modified signal with clinical importance and to produce a second modified signal, and high frequency energy zones of the second modified signal are automatically detected using an energy threshold to produce a weights vector.

A device for locating a noise occurring in a steering system includes: a sound receiving unit detecting noise occurring in a steering system; a processing unit inputting data on the noise in the steering system into a neural network model that performs learning in advance and locating a position or a component at which the noise occurs in the steering system, the noise being detected by the sound receiving unit; and a storage unit in which the neural network model that performs the learning in advance is stored.

The present invention provides a classification method for automatically identifying wafer spatial pattern distribution, comprising the following steps: performing statistical analysis to distribution of defects on a wafer, the defects being divided into random defects, repeated defects and cluster defects; performing denoising and signal enhancement to the cluster defects; performing feature extraction to the cluster defects after denoising and signal enhancement; and performing wafer spatial pattern distribution classification to the cluster defects after feature extraction. By performing statistical analysis and neural network training to a great amount of wafer defect distribution, the spatial patterns in defect distribution can be automatically identified, the automatic classification of wafer spatial patterns can be realized, the workload of engineers is effectively reduced and the tracing of the root cause of such spatial pattern is facilitated.