System monitors and methods of monitoring a system are disclosed. In one arrangement a system monitor predicts a future state of a system. A data receiving unit receives system data representing a set of one or more measurements performed on the system. A first statistical model is fitted to the system data. The first statistical model is compared to each of a plurality of dictionary entries in a database. Each dictionary entry comprises a second statistical model. The second statistical model is of the same general class as the first statistical model and obtained by fitting the second statistical model to data representing a set of one or more previous measurements performed on a system of the same type as the system being monitored and having a known subsequent state. A prediction of a future state of the system being monitored is output based on the comparison. The first statistical model and the second statistical model are each a stochastic process or approximation to a stochastic process.

A method for allowing or disallowing control of a motion tracking system by means of voice, comprising: digitally processing sound detected by each microphone of the at least one microphone; digitally computing SNR by computing both first energy of a voice signal in the detected sound and second energy of noise in the detected sound; digitally processing electromagnetic waves captured by each antenna of the at least one antenna so as to detect data packets transmitted to the computing apparatus by each sensor of the plurality of sensors, each data packet including RSSI of a respective sensor; digitally computing distance between each sensor and the computing apparatus based on the RSSI of the data packets received from the respective sensor; digitally computing a percentage of sensors of the plurality of sensors having at least one of: a distance exceeding a predetermined maximum distance threshold, and a change in distance exceeding a predetermined maximum changing distance threshold; and digitally setting allowance or disallowance of voice control based on both the SNR computed and the percentage of sensors computed.

The group determination means 13 determines a group of item names from a set of item names of unclassified items. The display control means 14 displays individual item names belonging to the group, and displays a plurality of candidates of a classification name for each item represented by each item name belonging to the group in a user-specifiable manner. When one of the plurality of candidates of the classification name is specified by a user, the classification determination means 16 determines that each item represented by each item name belonging to the group is classified by the classification name specified by the user.

A material data collection system allows capturing of material data. For example, the material data collection system may include digital image data for materials. The material data collection system may ensure that captured digital image data is properly aligned, so that material data may be easily recalled for later use, while maintaining the proper alignment for the captured digital image. The material data collection system may include using a capture guide, to provide cues on how to orient a mobile device used with the material data collection system.

A computing system is configured to derive insights related to asset operation and present these insights via a GUI. To these ends, the computing system (a) receives data related to the operation of assets, (b) based on this data, derives a plurality of insights related to the operation of at least a subset of the assets, (c) from the insights, defines a given subset of insights to be presented to a user, (d) defines at least one aggregated insight representative of one or more individual insights in the given subset of insights that are related to a common underlying problem, and (e) causes the user's client station to display a visualization of the given subset of insights including (i) an insights pane that provides a high-level overview of the subset of insights and (ii) a details pane that provides additional details regarding a selected one of the subset of insights.

A method for wireless communication performed by a head-mounted user equipment (UE), the method includes determining a first spatial relationship between an eye of a human user of the head-mounted UE and physical transmission and reception ports of the head-mounted UE; based on the first spatial relationship, determining a second spatial relationship between a plurality of radio frequency (RF) beam directions of the head-mounted UE and the eye of the human user; selecting a first RF beam direction from among the plurality of RF beam directions based at least in part on the second spatial relationship with respect to the first RF beam direction; and transmitting or receiving RF radiation using a first RF beam conforming to the first RF beam direction.

Introduced here are health management platforms able to monitor changes in the health state of a subject based on the context of digital activities performed by, or involving, the subject. Initially, a health management platform can identify a physiological response by examining physiological data associated with a subject. Then, the health management platform can identify a stimulus presented by an electronic device that provoked the physiological response by examining contextual data associated with the subject. The contextual data may be in the form of a screenshot of a computer program in use by the subject during the physiological response. In some embodiments, the health management platform prompts the subject to specify whether the physiological response is a positive physiological response that resulted in an upward shift in health or a negative physiological response that resulted in a downward shift in health.

The present invention relates to non-invasive method and system for characterising and certifying cognitive activities by detecting gaseous substances emitted by an organism, by means of the respiration, perspiration, and/or secretion, and changes measureable by sensors during said cognitive activities. Substance detection makes it possible to characterise the olfactory signal in order to determine and certify whether or not a cognitive activity has occurred and to classify said signals into different categories of cognitive activities.

A method for identifying by mass spectrometry an unknown microorganism subgroup among a set of reference subgroups, including a step of constructing one knowledgebase and one classifying model per associated subgroup on the basis of the acquisition of at least one set of learning spectra of microorganisms identified as belonging to the subgroups of a group and including: constructing an adjusting model allowing mass-to-charge offsets of the acquired spectra to be corrected on the basis of reference masses-to-charges that are common to the various subgroups; adjusting the masses-to-charges of all of the lists of peaks of the learning spectra and constructing one classifying model per subgroup and the associated knowledgebase on the basis of the adjusted learning spectra.

A processor-implemented method of generating feature data includes: receiving an input image; generating, based on a pixel value of the input image, at least one low-bit image having a number of bits per pixel lower than a number of bits per pixel of the input image; and generating, using at least one neural network, feature data corresponding to the input image from the at least one low-bit image.