This disclosure concerns a medical device designed for delivering a medical fluid or designed for controlling delivery of a medical fluid, and to a method of operating such a medical device. The medical device comprises a user interface associated with an electronic circuit connected to a first port and to a second port of a controller. The electronic circuit enables the controller to detect actuation of the user interface. The controller is configured to execute the following sequence of steps: first step: configure the first port as an output port and to apply a first signal to the first port; second step: to acquire a second signal from the second port; and third step: to determine on the basis of the second signal if a short circuit has occurred and to generate a short circuit alert signal if applicable.

A system-on-chip includes a memory, an error injection controller, an injection logic circuit, and an error detection circuit. The error injection controller is configured to generate and transmit error data, and at least one of read and write access requests associated with the memory to the injection logic circuit. The injection logic circuit is configured to access the memory based on at least one of the read and write access requests to execute at least one of read and write operations. The injection logic circuit is further configured to inject an error in at least one of first data and second data to generate at least one of erroneous first data and erroneous second data, respectively. The error detection circuit is configured to detect an error in at least one of the erroneous first data and the erroneous second data to generate an error signal.

An error-correction code memory includes memory locations for storing data. The memory is programmed to store one or more intentionally invalid words. Testing of an error correction circuit for the memory is performed by accessing the one or more intentionally invalid words and performing an error detection and error correction operation.

An error management system can include register sets associated with an error reaction. The test errors are injected in functional signals based on activation of multiple bits in one of the register sets. When the functional signals with the injected test errors are received by the error management system, multiple bits in the other register set are activated. The error management system generates an activated indication signal when a number of the activated bits in one register set matches a number of activated bits in the other register set. When the indication signal is activated, the error management system generates a reaction signal indicative of the error reaction. Thus, the error management system generates a single reaction signal in response to the injected test errors requiring the same reaction.

A verifying method for an error checking and correcting (ECC) circuit of a static random-access memory (SRAM) includes inputting original data into an error-correcting-and-coding procedure to output first data; obtaining second data according to an error-injecting mask; performing a bit operation on the first data and the second data to obtain third data; writing the third data into a test target area in the SRAM as fourth data; reading the fourth data from the test target area; inputting the fourth data into an error-correcting-and-decoding procedure to output fifth data and an error message; and obtaining a verification result according to the fifth data, the original data, the error message, and the second data.

Systems and methods for selecting an optimal error recovery procedure for correcting a read error in a solid-state drive are provided. A machine learning model is trained to forecast which error recovery procedure of a plurality of error recovery procedures is most likely to achieve a predetermined goal given a state of a solid-state drive. The predetermined goal is based on at least one of a read latency and a failure rate of the solid-state drive. A current state of the solid-state drive is determined. An error recovery procedure is selected from among the plurality of error recovery procedures by inputting the current state of the solid-state drive into the trained machine learning model, thereby triggering the trained machine learning model to output the selected error recovery procedure. The selected error recovery procedure is executed to recover data from the solid-state drive.

A Recovery Maturity Index 1 (RMM) is used to determine whether a particular Information Technology (IT) production environment is relatively mature enough to successfully execute the disaster recovery (DR). The RMI provides a quantitative analysis in terms of a set of categories for elements that characterize the environment and multiple elements for each category. At least some of the elements depend upon the extent to which automation components have been leveraged for disaster recovery. A summation of the scoring elements, which may be a weighted summation, results in an overall quantitative metric. The metric can used to determine whether or not disaster recovery can be expected to be successful.

Novel and useful system and methods of several functional safety mechanisms for use in an artificial neural network (ANN) processor. The mechanisms can be deployed individually or in combination to provide a desired level of safety in neural networks. Multiple strategies are applied involving redundancy by design, redundancy through spatial mapping as well as self-tuning procedures that modify static (weights) and monitor dynamic (activations) behavior. The various mechanisms of the present invention address ANN system level safety in situ, as a system level strategy that is tightly coupled with the processor architecture. The NN processor incorporates several functional safety concepts which reduce its risk of failure that occurs during operation from going unnoticed. The mechanisms function to detect and promptly flag and report the occurrence of an error with some mechanisms capable of correction as well. The safety mechanisms cover data stream fault detection, software defined redundant allocation, cluster interlayer safety, cluster intralayer safety, layer control unit (LCU) instruction addressing, weights storage safety, and neural network intermediate results storage safety.

Fuse logic is configured to selectively enable certain group of fuses of a fuse array to support one of column (or row) redundancy in one application or error correction code (ECC) operations in another application. For example, the fuse logic may decode the group of fuses to enable a replacement column (or row) of memory cells in one mode or application, and decodes a subset of the group of fuses to retrieve ECC data corresponding to a second group of fuses are encoded to enable a different replacement column or row of memory cells in a second mode or application. The fuse logic includes an ECC decode logic circuit that is selectively enabled to detect and correct errors in data encoded in the second group of fuses based on the ECC data encoded in the subset of fuses of the first group of fuses.

Error injection techniques involve, while a data storage system is in an error injection mode, injecting information representing an error of a storage device array into a first switch, such that the information representing the error is passed from a first downstream port of the first switch to a computing device through a second switch, the first and second switches being connected to the storage device array via downstream ports, and the first downstream port being connected to a second downstream port of the second switch; and determining error handling capability of the data storage system by obtaining a handling result of the information representing the error from the computing device. Accordingly, errors from storage devices can be simulated to facilitate detecting error handling in the entire I/O path comprehensively.