Various embodiments relate to network capabilities. Devices of a network can have different capabilities. The network can provide artificial intelligence (AI) enabled, machine learning (ML) enabled, deep learning (DL) enabled networked access to these capabilities. The capabilities can share a common AI/ML/DL-enabled open layer-based net-centric logical protocol architecture. Also, different features can be achieved through different layers. As an example, AI enabled access can be achieved through the application layer, ML enabled access and DL enabled access can be achieved through the presentation layer and the session layer, and network access is achieved through the transport layer, the network layer, the link layer, and the physical layer.

A system, method and apparatus for detecting facial expressions according to EMG signals.

Methods, apparatus and systems for enhanced wireless monitoring of vital signs are described. In one example, a described system comprises: a transmitter configured to transmit a wireless signal through a wireless channel of a venue; a receiver configured to receive the wireless signal through the wireless channel; and a processor. The received wireless signal differs from the transmitted wireless signal due to the wireless channel that is impacted by a periodic motion of a vital sign of an object in the venue. The processor is configured for: obtaining a time series of channel information (CI) of the wireless channel based on the received wireless signal, computing a two dimensional (2D) decomposition of the time series of CI (TSCI), enhancing the 2D decomposition, and monitoring the periodic motion of the vital sign based on the enhanced 2D decomposition.

Methods and systems are disclosed for generating optimal data predictions in time series data signals based on empirically-optimized model selection, noise filtering, and window size selection using machine learning models. For example, the system may receive a first subset of time series data. The system may receive a prediction horizon. The system may generate a feature input based on the first subset of time series data and the prediction horizon. The system may input the feature input into a machine learning model, wherein the machine learning model includes multiple components. The system may receive an output from the machine learning model. The system may generate for display, on a user interface, a prediction for the first subset of time series data at the prediction horizon based on the output.

A gated truncated readout system for position sensitive or imaging detectors that improves resolution over traditional readout systems. The readout system includes two or more amplifiers that receive a multichannel output analog data from the detector. Analog gates control circuitry, included in the readout circuit, receives the signals from the amplifiers, determines a fractional value of the sum-integral of the signals, and enables analog gates operation around an area of interest, disabling all other channels where noise dominates the signal value and thereby improving interpolation accuracy of the signals centroid position and the detector resolution. Filtered signals are transmitted to a centroid interpolation signal processing device for computation of the centroid position. As a result disabling all channels where noise dominates the signal value, the gated truncated readout system provides better accuracy improved detector resolution.

Methods, apparatus and systems for radio-based sleep tracking are described. In one example, a described system comprises: a transmitter configured to transmit a first wireless signal through a wireless multipath channel in a venue; a receiver configured to receive a second wireless signal through the wireless multipath channel, wherein the second wireless signal differs from the first wireless signal due to the wireless multipath channel which is impacted by a sleeping motion of an object in the venue; and a processor. The processor is configured for: obtaining a time series of channel information (TSCI) of the wireless multipath channel based on the second wireless signal, wherein each channel information (CI) of the TSCI comprises N1 components, wherein N1 is a positive integer larger than one, computing N1 component-wise analytics each associated with one of the N1 components of the TSCI, identifying N2 largest component-wise analytics among the N1 component-wise analytics, wherein N2 is a positive integer less than N1 computing at least one first motion statistics based on the N2 largest component-wise analytics of the TSCI, and monitoring the sleeping motion of the object based on the at least one first motion statistics.

A method is provided for hyperspectral inversion of a phosphorus content of rubber tree leaves. The method includes: acquiring hyperspectral data of to-be-detected rubber tree leaves; extracting key wavelengths of the rubber tree leaves according to the hyperspectral data and a pre-established wavelength extraction model, where the key wavelengths are related to the phosphorus content of the rubber tree leaves, and the pre-established wavelength extraction model is obtained by learning and training hyperspectral sample data and sample phosphorus content data pairs in a pre-established sample database by adopting a competitive adaptive reweighted sampling (CARS) algorithm and a successive projection algorithm (SPA); and inputting the key wavelengths into a pre-established phosphorus content prediction model to calculate the phosphorus content of the to-be-detected rubber tree leaves. Moreover, the CARS algorithm and the SPA are comprehensively applied to extract the key wavelengths closely related to the phosphorus content of the rubber tree leaves.

A method for diagnosing and predicting operation conditions of large-scale equipment based on feature fusion and conversion, including: collecting a vibration signal of each operating condition of the equipment, and establishing an original vibration acceleration data set of the vibration signal; performing noise reduction on the original vibration acceleration data set, and calculating a time domain parameter; performing EMD on a de-noised vibration acceleration and calculating a frequency domain parameter; constructing a training sample data set through the time domain parameter and the frequency domain parameter; establishing a GBDT model, and inputting the training sample data set into the GBDT model; extracting a leaf node number set from a trained GBDT model; performing one-hot encoding on the leaf node number set to obtain a sparse matrix; and inputting the sparse matrix into a factorization machine to obtain a prediction result.

A system, method and apparatus for detecting facial expressions according to EMG signals.

An electronic device includes: a housing including a front plate oriented in a first direction and a rear plate oriented in a second direction; a display visible through at least a portion of the front plate; and a biometric sensor module located between the front plate and the rear plate. The biometric sensor module may include: a cover glass oriented in the second direction; at least one light source configured to emit light to the outside; a light detector disposed adjacent to the at least one light source, and configured to receive reflected light corresponding to light emitted from the light source; a light guide disposed between the light detector and the cover glass, and configured to provide a path of the reflected light received by the light detector; and a circuit board electrically connected to the at least one light source and the light detector.