A tag reading device includes an external interface, an RFID interface, a buffer memory, and a processor. The external interface communicates with a higher-level device. The RFID interface reads tag information from an RFID tag. The buffer memory sequentially records the tag information read from the RFID tag using the RFID interface. The processor transmits the tag information recorded in order in the buffer memory to the higher-level device via the external interface, and updates a transmission start pointer indicating untransmitted tag information in the buffer memory.

For a plurality of sensors, a particular sensor is indicated as a target sensor and the other sensors as input sensors. A regression model is trained using historical data from the plurality of related sensors. The trained regression model is applied to the target sensor to generate a predicted target sensor value. A difference between an actual target sensor value and the predicted target sensor value is calculated. A probability of difference for the calculated difference between the actual target sensor value and the predicted target sensor value is compared against a threshold value.

A data storage device includes a nonvolatile memory device; a control unit configured to generate a descriptor in which works for controlling the nonvolatile memory device are written; a memory control unit configured to provide control signals and write data to the nonvolatile memory device based on the descriptor; and a voltage detector configured to provide a voltage drop signal to the memory control unit in the case where a first operating voltage provided to the memory control unit or a second operating voltage provided to the nonvolatile memory device drops.

Embodiments are described for prognostication of real time operating system (RTOS) health. An example computer-implemented method includes monitoring, for a task from a plurality of tasks being executed by the RTOS, an execution time, an inter-arrival time, and a blocking time. The method further includes computing an RTOS state of health value based on the execution time, the inter-arrival time, and the blocking time of each task from the plurality of tasks. The method further includes, in response to the RTOS state of health value being less than a predetermined threshold, initiating performance of an error handling.

For each setting-file, an apparatus obtains, from first action-records, an occurrence-count value that is associated with the each first action-record and indicates a number of occurrences of a setting-item name identifying each setting-item for the each setting-file, in association with a sequence number assigned to the each first action-record, where each first action-record records an action that is taken in response to a first-incident. When there exists a first setting-file for which an occurrence-pattern generated based on a sequence of occurrence-count values that are each associated with different one of the sequence numbers assigned to the first action-records is similar to a model pattern that is in advance obtained from second action-records for second-incidents occurring due to a setting error of a setting-value that is set to the first setting file, the apparatus stores the first setting-file in association with an identifier identifying the first-incident.

In an embodiment, a method to evaluate radar images includes providing a first raw radar image and a second raw radar image and determining, whether a reliability criterion is fulfilled. The method further includes using a first coordinate and a second coordinate output by a trained neural network as an estimate of a position of an object if the reliability criterion is fulfilled, the trained neural network using the first raw radar image and the second raw radar image as an input. The method further includes using a third coordinate and a fourth coordinate output by another radar processing pipeline as the estimate of the position of the object if the reliability criterion is not fulfilled, the radar processing pipeline using the first raw radar image and the second raw radar image as an input.

A system and method for the distributed analysis of high frequency transaction trace data to constantly categorize incoming transaction data, identify relevant transaction categories, create per-category statistical reference and current data and perform statistical tests to identify transaction categories showing overall statistically relevant performance anomalies. The relevant transaction category detection considers both the relative transaction frequency of categories compared to the overall transaction frequency and the temporal stability of a transaction category over an observation duration. The statistical data generated for the anomaly tests contains next to data describing the overall performance of transactions of a category also data describing the transaction execution context, like the number of concurrently executed transactions or transaction load during an observation period. Anomaly tests consider current and reference execution context data in addition to statistic performance data to determine if detected statistical performance anomalies should be reported.

A method for execution by a computing device of a dispersed storage network (DSN). The method begins with obtaining a plurality of write requests. The method continues where for a write request of the plurality of write requests, the computing device generates a vault identification and a generation number. The method continues where the computing device obtains a rounded timestamp and a capacity factor and generates a temporary object number based on the rounded timestamp and the capacity factor. The method continues where the computing device generates a temporary source name based on the vault identification, the generation number, and the temporary object number. The method continues where the computing device identifies a set of storage units of a plurality of sets of storage units of the DSN based on the temporary source name.

Disclosed are embodiments for automatically resolving faults in a complex network system. Some embodiments monitor one or more of system operational parameter values and message exchanges between network components. A machine learning model detects a fault in the complex network system, and an action is selected based on a cause of the fault. After the action is applied to the complex network system, additional monitoring is performed to either determine the fault has been resolved or additional actions are to be applied to further resolve the fault.

The invention relates to a device and a method (100) for determining a technical incident risk value in an infrastructure (5), said method comprising: a step of receiving (120) performance indicator values, a step of identifying (140) anomalous performance indicators, so as to identify abnormal values, and identifying performance indicators associated with these abnormal values, a step of determining (150) at-risk indicators, comprising an identification of performance indicators of the computing infrastructure that are correlated with the identified anomalous indicators, a step of creating (160) an augmented anomalies vector, comprising the identifiers of the identified anomalous indicators and the identifiers of the determined at-risk indicators, a determination step (170), comprising the comparison of the augmented anomalies vector with predetermined technical incident reference data.