The disclosure provides an adaptive inversion method of Internet-of-things environmental parameters based on an RFID multi-feature fusion sensing model, including the following steps. Space-medium-interference is proposed as an overall concept, from the multipath propagation mechanism of electromagnetic waves, the electromagnetic wave transmission mechanism is considered. Combining with the joint characteristics of the generalized time domain, frequency domain, energy domain, and spatial domain, a global signal transfer function of RFID sensing is analyzed and derived to complete extraction of RFID sensing main features. A multi-feature fusion sensing model is established, an algebraic relationship between multi-feature fusion parameters and an experimental result is used to give an error functional between a measured data and a forward simulation data, and newly-added sensing information is applied to an environment spatio-temporal changeable adaptive element iteration to form an Internet-of-things environmental parameter adaptive inversion and provide a basis for deployment of RFID in complex Internet-of-things scenes.

A system for training a neurome that emulates a brain of a user comprises a non-invasive brain interface assembly configured for detecting neural activity of the user in response to analog instances of a plurality of stimuli peripherally input into the brain of the user from at least one source of content, memory configured for storing a neurome configured for outputting a plurality of determined brain states of an avatar in response to inputs of the digital instances of the plurality of stimuli, and a neurome training processor configured for determining a plurality of brain states of the user based on the detected neural activity of the user, and modifying the neurome based on the plurality of determined brain states of the user and the plurality of determined brain states of the avatar.

The present disclosure relates to user authentication using a contactless payment instrument. The contactless payment instrument includes a contactless chip and a gesture control module. The user makes one or more gestures at an access device during a card-present transaction scenario and the gestures are validated and the authentication status is determined by matching the gesture made by the user at the time of the transaction with one or more predefined gestures. If the authentication is successful, the payment instrument transmits the payment information required for conducting the transaction.

The presently disclosed subject matter provides methods and compositions for treating myeloid disorders (e.g., acute myeloid leukemia (AML)). It relates to immunoresponsive cells bearing antigen recognizing receptors (e.g., chimeric antigen receptors (CARs)) targeting AML-specific antigens.

A method for monitoring an individual engaged in an athletic activity includes detecting movement of the individual at a first time, using a sensor module coupled to the individual, determining that the movement of the individual corresponds to a predetermined activation movement, entering an active state of the sensor module in response to the determination that the movement of the individual corresponds to the predetermined activation movement, and detecting movement of the individual at a second time, using the sensor module in the active state.

Provided is an analysis system including: an analysis unit including a classifier that performs classification of an event type on input time-series data; a display information generation unit that generates first display information used for displaying, out of the time-series data, first time-series data in which association of an event type is undecided and which is classified by the classifier as a first event type corresponding to a state where a target event is occurring, second time-series data associated with the first event type, and third time-series data associated with a second event type corresponding to a state where the target event is not occurring; and an input unit that accepts first input regarding association of an event type with the first time-series data.

Illustrative embodiments of the present disclosure relate to a method, a wearable device, an electronic device, and a computer program product for monitoring a user. The method includes verifying an identity of the user by analyzing user data related to the user and obtained by a wearable device, the user data including at least a first image of a part of the user's body. The method further includes monitoring a relative position of the wearable device with respect to the user based on sensor data obtained by the wearable device if the verification on the identity of the user succeeds; monitoring a surrounding environment of the user based on a second image of the surrounding environment obtained by the wearable device; and monitoring behaviors of the user based at least in part on the monitored relative position and the monitored surrounding environment.

A method for controlling a plant includes using a neural network modeling technique to calculate a neural network prediction based on plant input data, using a second modeling technique to calculate a second prediction based on the plant input data, and determining whether to use (1) the neural network prediction without the second prediction, (2) the second prediction without the neural network prediction, or (3) both the neural network prediction and the second prediction by comparing a location of the plant input data in a multi-dimensional modeling space to one or more thresholds. The method includes generating a combined prediction using one or both of the neural network prediction and the second prediction in accordance with a result of the determining and controlling the plant using the combined prediction.

A system for detecting anomalies in metric data provided by one or more customers according to an embodiment includes at least one processor and at least one memory comprising a plurality of instructions stored thereon that, in response to execution by the at least one processor, causes the system to receive metric data indicative of a plurality of time-series based observations for a particular customer metric, to define, based on the metric data, a plurality of parameters to characterize one or more spheres each configured to capture a number of time-series based observations for the particular customer metric, and to generate, based on the plurality of parameters, the one or more spheres to determine coverage of the metric data within the one or more spheres and detect one or more anomalies in the metric data.

An equipment failure diagnosis apparatus, an equipment failure diagnosis method, a smart factory system, and an application agent configured to generate a virtual abnormal signal based on a normal signal data stored in the database, determine whether an equipment signal is an abnormal signal based on a virtual abnormal signal data for the virtual abnormal signal, and output a determination result information. Accordingly, the present disclosure can quickly and accurately diagnose a failure of equipment in a factory, even under conditions where labeling data is not present or insufficient.