The present invention relates to a system for determining a posture of an operator of a device that transmits vibrations to the operator, wherein the system comprises at least one vibration sensor for measuring vibrations at at least one location on the body of the operator and/or the device, and a posture determining means for detecting one or more characteristics of the measured vibrations assigned to a predetermined posture and for determining the predetermined posture as the posture of the operator when the characteristic is detected.

The analyzing apparatus: generates first internal data; converts a position of first feature data in a feature space, based on the first internal data and a second learning parameter; reallocates, based on a result of first conversion and the first feature data, the first feature data to a position obtained through the conversion in the feature space; calculates a predicted value of a hazard function of analysis time in a case where the first feature data is given, based on a result of reallocation and a third learning parameter; optimizes the first to third learning parameters, based on a response variable and a first predicted value; generates second internal data, based on second feature data and the optimized first learning parameter; converts a position of the second feature data in the feature space, based on the second internal data and the optimized second learning parameter; and calculates importance data.

A system includes a lighting module, a processing module, and photovoltaic units. Each of the photovoltaic units receives light reflected off a body portion which is illuminated by light from the lighting module, and converts light energy of the reflected light into electricity. The processing module stores modes each of which specifies a code set. When one of the modes is selected, the processing module activates the lighting module to emit light based on the code set specified by the mode thus selected. The processing module converts electrical quantities measured individually for the photovoltaic units into respective code parameters, and composes an identification code using the code parameters.

A method for determining a defection probability includes receiving user data. The method also includes determining, from the user data, a set of features characterizing a likelihood of the user to change from the initial service channel to an alternative service channel. The method further includes generating an input encoding for the set of features. The method additionally includes determining, using a predictive model, a probability that indicates how likely the user is to change from the initial service channel, wherein the predictive model is trained to receive, as input, the input encoding and to generate, as output, a respective probability that indicates how likely a respective user is to change from the initial service channel to the alternative service channel. The method also includes determining whether the probability from the predictive model satisfies a defection threshold and selectively generating a remedial action based on the probability.

The present disclosure provides radio-frequency (RF) systems that can detect the presence of RF signals received by the system, as well as determine characteristics such as the operating frequency of RF signals, the type of RF source that transmitted each RF signal, and/or the location of each RF source with high precision and sensitivity while using low cost, scalable electronics that are versatile enough for deployment in a variety of environments. Such systems can employ a network of RF sensors that can coordinate in response to communication with a computer to perform any such detection and/or determination using trained models executed onboard the RF sensors and/or the computer. RF signals may have unique characteristics when received at one or more RF sensors that may be detected using trained models described herein, even in high noise or non-line of sight (LOS) environments and with low cost, low resolution RF receiver hardware.

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.

Systems and methods for disability simulations and accessibility evaluations of content is disclosed. A disclosed system runs using an information loss determination engine via a processor, for a given disability, at least one simulation to simulate how a content is experienced by a user having such disability. The system computes information loss based on comparison of the simulated content with desired original content. Further, the system transmits data packets indicative of a content optimization strategy that is determined based on the determined information loss.

Methods, apparatus, and processor-readable storage media for incorporating data into search engines using deep learning mechanisms are provided herein. An example computer-implemented method includes extracting one or more features from a search query by applying one or more machine learning algorithms to the search query; generating one or more word vectors by applying at least one deep learning technique to the one or more extracted features; mapping the one or more generated word vectors to one or more words from a corpus of data by implementing at least one deep similarity network; and outputting one or more results in response to the search query, wherein the one or more results are based at least in part on the one or more words from the corpus to which the one or more generated word vectors were mapped.

The present disclosure relates to systems, methods, and non-transitory computer readable media for generating shift-resilient neural network outputs based on utilizing a dense pooling layer, a low-pass filter layer, and a downsampling layer of a neural network. For example, the disclosed systems can generate a pooled feature map utilizing a dense pooling layer to densely pool feature values extracted from an input. The disclosed systems can further apply a low-pass filter to the pooled feature map to generate a shift-adaptive feature map. In addition, the disclosed systems can downsample the shift-adaptive feature map utilizing a downsampling layer. Based on the downsampled, shift-adaptive feature map, the disclosed systems can generate shift-resilient neural network outputs such as digital image classifications.

The present disclosure describes systems and methods that include calculating, via a reinforcement learning agent (RLA) controller, a plurality of state-action values based on sensor data representing an observed state, wherein the RLA controller utilizes a deep neural network (DNN) and generating, via a fuzzy controller, a plurality of linear models mapping the plurality of state-action values to the sensor data.