Abstract: An apparatus comprises at least one processing device configured to receive a request, from an application running on a virtual computing instance, to consume data from an external device coupled to a physical hardware port of a host device, the external device having a first interface with a physical driver running on a host operating system of the host device, the first interface having a first interface type. The processing device is also configured to instantiate a second interface on the host operating system of the host device, the second interface having a second interface type different than the first interface type. The processing device is further configured to establish a service mesh for the application by linking the instantiated second interface with (i) a virtual driver running on the virtual computing instance and (ii) the physical driver running on the host operating system of the host device.

Abstract: An apparatus comprises at least one processing device configured to maintain a register of interface endpoints for external devices coupled to physical hardware ports of host devices and virtual ports associated with virtual devices running on virtual computing instances. The processing device is also configured to generate, in response to establishment of a given service mesh enabling a given application running on a given virtual computing instance to consume data from a given external device coupled to a given physical hardware port of a given host device, a logical-to-physical mapping between (i) a given virtual identifier of a first interface endpoint in the register of interface endpoints and (ii) a given physical identifier of a second interface endpoint in the register of interface endpoints. The processing device is further configured to control exchange of data for the given service mesh based on the logical-to-physical mapping and one or more policies.

Abstract: A system for processing data in an industrial environment includes a sensor that communicates sensor data acquired from a factory floor as a multicast data stream where each frame is tagged with a sequence identifier indicative of a position of that frame in the data stream. A network switch distributes the data stream to a group of multicast endpoints. The system includes a controller with a plurality of modularly connected processing units. Each processing unit is further connected to the network switch and configured as a respective endpoint of the group of multicast endpoints. Each processing unit selectively processes frames of the data stream in dependence of the sequence identifier of individual frames by executing a data processing module, to produce an output result associated with each processed frame. The controller assembles the output result and the sequence identifier of each processed frame from the plurality of processing units.

Abstract: A real-time computer system for automated acoustic anomaly detection in a programmable logic controller (PLC) is disclosed, the computer system being deployed onto a backplane of the PLC. The computer system includes at least one processor with a real-time operating system and a memory having algorithmic modules stored thereon executable by the processor. The modules include a digital signal processing module configured to apply a windowing function to the sound signal data captured by a sensor, the sound signal data representative of sound emitted by an energized work product under quality inspection. The modules further include a feature extraction component configured to extract acoustic features from each sound window, and an anomaly detector module configured to operate a machine learning-based model to execute acoustic anomaly detection according to results of a classification operation on the acoustic features.

Abstract: Described embodiments provide a technique for commissioning an AI-based inspection system. In particular, the described embodiments provide a tool for field engineers to perform tasks including data collection, and training, testing and deployment of an AI algorithm, onsite, with instant feedback. The described tasks are carried out in the same physical environment to arrive at the same sensor setting that is then used by the field device on which the AI algorithm is deployed. Deployment cycle is significantly reduced. The technique leverages the recognition that inspection tasks in industrial settings are repetitive in nature and are carried out in a controlled environment, whereby the AI algorithm need not be generalized beyond the specific use case.