A process is provided for the management of environmental data for tobacco production. The process includes inserting at least one sensor into a collection of tobacco leaves and connecting one or more of the sensors to a communication node for collecting data. A DNCP server is adapted to receive at least one communication from a communication node to identify a data gateway among a plurality of available gateways. Environmental data is transmitted from the sensor and communication node to the gateway for transmission to a server.

Data received from an in-situ monitoring system includes, for each scan of a sensor, a plurality of measured signal values for a plurality of different locations on a layer. A thickness of a polishing pad is determined based on the data from the in-situ monitoring system. For each scan, a portion of the measured signal values are adjusted based on the thickness of the polishing pad. For each scan of the plurality of scans and each location of the plurality of different locations, a value is generated representing a thickness of the layer at the location. This includes processing the adjusted signal values using one or more processors configured by machine learning. A polishing endpoint is detected or a polishing parameter is modified based on the values representing the thicknesses at the plurality of different locations.

Data received from an in-situ monitoring system includes, for each scan of a sensor, a plurality of measured signal values for a plurality of different locations on a layer. A thickness of a polishing pad is determined based on the data from the in-situ monitoring system. For each scan, a portion of the measured signal values are adjusted based on the thickness of the polishing pad. For each scan of the plurality of scans and each location of the plurality of different locations, a value is generated representing a thickness of the layer at the location. This includes processing the adjusted signal values using one or more processors configured by machine learning. A polishing endpoint is detected or a polishing parameter is modified based on the values representing the thicknesses at the plurality of different locations.

A system for community monitoring is disclosed. The system includes a community processing subsystem. The community processing subsystem includes an entity control module, configured to control each of plurality of tasks corresponding to one of a plurality of entities. The community processing subsystem also includes a task monitoring module, configured to monitor each of the plurality of tasks corresponding to one of the plurality of entities by entity associated techniques. The community processing subsystem also includes an information exchange module, configured to exchange information within the plurality of entities. A community memory subsystem is configured to store details regarding monitored plurality of tasks corresponding to one of the plurality of entities in real time. Present disclosure enables proper monitoring of every possible entity in a society through a common platform.

An industrial asset may have a plurality of monitoring nodes, each monitoring node generating a series of monitoring node values over time representing current operation of the industrial asset. An abnormality detection computer may determine that an abnormal monitoring node is currently being attacked or experiencing a fault. An autonomous, resilient estimator may continuously execute an adaptive learning process to create or update virtual sensor models for that monitoring node. Responsive to an indication that a monitoring node is currently being attacked or experiencing a fault, a level of neutralization may be automatically determined. The autonomous, resilient estimator may then be dynamically reconfigured to estimate a series of virtual node values based on information from normal monitoring nodes, appropriate virtual sensor models, and the determined level of neutralization. The series of monitoring node values from the abnormal monitoring node or nodes may then be replaced with the virtual node values.

A control system configured for safe input, visualization and/or communication for controlling a machine, includes a display device configured to display at least one symbol, an optical input device configured to detect, from the display device, at least a portion of the symbol, a redundant communication system configured to send the symbol, and a control unit configured to send the symbol and a test sequence to the display device, receive the symbol and the test sequence from the display device, test at least the test sequence, and, if the check sequence is correctly received, generate a redundant encoding of the symbol and send the redundantly encoded symbol over a redundant network.

A non-transitory computer-readable medium includes instructions that, when executed by processing circuitry, are configured to cause the processing circuitry to receive, from first sensors, first sensory datasets associated with an industrial automation system, receive, from second sensors, second sensory datasets associated with a machine configured to perform mechanical operations, determine a position of the machine relative to the industrial automation system based on the first sensory datasets and the second sensory datasets, determine output representative data associated with the industrial automation system based on the first sensory datasets and the second sensory datasets and in accordance with the position of the machine relative to the industrial automation system, instruct an extended reality device to present the output representative data, determine movement of components of the machine, and instruct the extended reality device to present feedback based on the movement of the components.

A non-transitory computer-readable medium includes instructions that, when executed by processing circuitry, are configured to cause the processing circuitry to receive, from first sensors, first sensory datasets associated with an industrial automation system, receive, from second sensors, second sensory datasets associated with a machine configured to perform mechanical operations, determine a position of the machine relative to the industrial automation system based on the first sensory datasets and the second sensory datasets, determine output representative data associated with the industrial automation system based on the first sensory datasets and the second sensory datasets and in accordance with the position of the machine relative to the industrial automation system, instruct an extended reality device to present the output representative data, determine movement of components of the machine, and instruct the extended reality device to present feedback based on the movement of the components.

A data processing system, including a cyclic correlation establishing module, a data pattern establishing module, and a data pattern alignment module, is provided. The cyclic correlation establishing module receives a plurality of first sensor data, obtained from a first sensor operation performed on processing devices, and receives a table of processing steps and cyclic procedures. The cyclic correlation establishing module obtains a data correlation of the first sensor data according to the number of sample points in a data cycle of the first sensor data and the table to correct the first sensor data. The data pattern establishing module obtains a plurality of first data pattern features from the first sensor data. The data pattern alignment module aligns a plurality of second sensor data obtained from a second sensor operation performed on the processing devices with the first sensor data according to the first data pattern features.

A data processing system, including a cyclic correlation establishing module, a data pattern establishing module, and a data pattern alignment module, is provided. The cyclic correlation establishing module receives a plurality of first sensor data, obtained from a first sensor operation performed on processing devices, and receives a table of processing steps and cyclic procedures. The cyclic correlation establishing module obtains a data correlation of the first sensor data according to the number of sample points in a data cycle of the first sensor data and the table to correct the first sensor data. The data pattern establishing module obtains a plurality of first data pattern features from the first sensor data. The data pattern alignment module aligns a plurality of second sensor data obtained from a second sensor operation performed on the processing devices with the first sensor data according to the first data pattern features.