An information processing device includes: an operation determining unit that determines whether or not a function input operation performed by the user is an operation intended by the user, by determining whether or not the function input operation performed by the user is a repetition of a predetermined operation pattern, on the basis of a plurality of pieces of function operation information corresponding to the function input operation among a plurality of pieces of operation information obtained by an operation obtaining unit, the function input operation being an input operation for allowing a function of a device to be performed; and a notification output unit that outputs, when the operation determining unit determines that the function input operation performed by the user is not an operation intended by the user, notification information indicating that the user has performed the function input operation by repeating the predetermined operation pattern.

A method, system, and apparatus for fault detection in a microprocessor-based system uses a serial data communication protocol for communications between a peripheral device and a controller. Peripheral device interface circuitry is adapted to intermittently receive input serial data frames from the controller using the serial communication protocol and to intermittently send output serial data frames to the controller using the serial communication protocol. Each output serial data frame includes one or more status bits representing communication status data and one or more data bits representing peripheral device data. The status bits and the data bits are serially followed by at least one fault bit that indicates whether a fault is detected during sending of the output serial data frame.

The present disclosure relates to system(s) and method(s) for verifying data loading requirements of an avionics unit. The system receives a request for data loading. The request comprises file data, and data loading requirements associated with the avionics unit. Further, the system obtains target file from a repository based on an analysis of the request. The system further generates valid data set and invalid data set in the target file based on an analysis of the data loading requirements. Upon generation, the system verifies predefined data loading requirements of the avionics unit using the invalid data set from the target file.

An apparatus and a method for optimizing a satellite system considering a hard error stability and a soft error stability are disclosed. The satellite system optimizing method which considers a hard error stability and a soft error stability according to an exemplary embodiment of the present disclosure includes acquiring hardware information of a processor which is loaded in the satellite system; acquiring workload information including a task which is performed by the processor; establishing a scheduling policy for the task based on the hardware information and the workload information; and quantifying a soft error stability and a hard error stability in accordance with the scheduling policy.

An automatic processing system, a system on chip and a method for monitoring a processing module are described herein. The automatic driving processing system comprises: an automatic driving processing module, configured for receiving an input data stream and processing the input data stream based on a deep learning model so as to generate a processing result; a fault detection module, configured for generating a control signal and a fault detection stimulating data stream, and receiving the processing result from the automatic driving processing module; and a multi-way selection module, configured for receiving an automatic driving data stream as well as the control signal and the fault detection stimulating data stream, and selectively outputting the automatic driving data stream or the fault detection stimulating data stream to the automatic driving processing module based on the control signal, as an input data stream.

A system for detecting and responding to an anomaly in a chaotic environment, comprising one or more autonomous agent devices and a central server comprising a processor and non-transitory memory. The memory stores instructions that cause the processor to receive a first set of sensor readings from one or more remote electronic sensors, during a first time window, the sensor readings recording pseudo-Brownian change in one or more variables in the chaotic environment; determine, based on the first set of sensor readings, an expected range of the one or more variables during a second time window after the first time w window; receive a second set of sensor readings from the one or more remote electronic sensors during the second time window recording change in the one or more variables: determine, based on the second set of sensor readings, that one variable of the one or more variables is not within the expected range; and cause the one or more autonomous agent devices to attempt to mitigate a potential harm indicated by the one variable being outside of the expected range.

A system, method and computer program product for operating a low-voltage Internet-of-Things sensor device. The method includes sensing of the temperature dependence at each voltage condition in addition to the actual temperature and voltage. A programmed machine learning model uses the information to decide when it is appropriate to test the device functionality and use the results of different tests to determine when the system should run synchronously or asynchronously through a machine learning predictive algorithm. Based on said one or more sensed operating conditions, the system uses the model to detect a mode of operation of said IoT device indicating IoT device meets an expected level of performance, or a mode indicating said IoT device is not operating according to the expected level of performance. Based on the detected operating condition, the IoT device automatically adapts its operation to ensure a desired level of IoT sensor device performance.

The present invention relates to methods and systems for allocating platform resources in a vehicle for development, evaluation, and/or testing of ADS features. The method comprises storing, during a time period, sensor data indicative of a surrounding environment of the vehicle in a data storage device of the vehicle, and obtaining data indicative of a set of platform constraints of the vehicle, a set of requirements for each of a plurality of ADS features, a priority scheme for the plurality of ADS features, and a current scene or scenario in the surrounding environment of the vehicle. Furthermore, the method comprises generating, based on the platform constraints, the set of requirements, the priority scheme and the current scene or scenario, an arbitration signal indicative of a resource allocation of the platform of the vehicle to at least one of the plurality of ADS features.

An information processing apparatus, an information processing method, and a non-transitory recording medium. The information processing apparatus determines an external device with maximum capacity among a plurality of external devices with non-volatile storage media for storing log information, changes an amount of log information according to a capacity of the external device with the maximum capacity, and outputs the log information to the external device with the maximum capacity.

Distributed wearable and non-wearable devices, controllers and methods for processing information from a plurality of devices are provided. A distributed system includes a plurality of devices distributed in an environment. Each device has at least a communication capability for interchanging information with others of the devices and/or with a communication system. Each of at least some of the devices has one or more sensors for acquiring sensor data related to the environment proximate to the device. At least one of the communication system or one or more of the devices is configured as a controller configured to: select a subset of devices from among the plurality of devices, receive information based on the acquired sensor data of the selected subset, and combine the received information from the selected subset to determine a characteristic of the environment proximate to one or more of the devices.