Abstract: A method using a partitioned deep neural network with a constrained data cap includes receiving, at a first machine learning model, raw data and generating compressed code using the raw data. The method also includes identifying values, of a plurality of values of the compressed code, that are outside of a value range, and generating, using the first machine learning model, a prediction value for each identified value, the prediction value for each respective value of the identified values predicting whether a respective value indicates an anomaly in the raw data. The method also includes further compressing, using the first machine learning model, portions of the compressed code associated with prediction values that are greater than a threshold. The method also includes communicating the portions of the compressed code to a second machine learning model, and receiving, from the second machine learning model, diagnostics information responsive to the portions of the compressed code.

Abstract: In some embodiments, techniques are provided for document identification using a multimodal model that has been trained using one-shot learning. In one example, a first method of document image processing includes generating, for each template document image of a plurality of template document images, a corresponding fingerprint of a plurality of fingerprints; and based on the plurality of fingerprints, training a multimodal model. For each template document image of the plurality of template document images, generating the corresponding fingerprint may include detecting a plurality of regions within the template document image, wherein the plurality of regions comprises a plurality of text regions; and filtering the plurality of regions to obtain a plurality of regions of interest, wherein the fingerprint is based on the plurality of regions of interest.

Abstract: A method for signal anomaly detection includes receiving at least one signal and determining, using at least one machine learning anomaly detection model, whether a value associated with the at least one signal is within a range of an expected value. The method also includes, in response to a determination that the value associated with the at least one signal is not within the range of the expected value, incrementing a counter, and, in response to a determination that a value of the counter is greater than or equal to a threshold value, identifying, based on the at least one signal, signal anomaly information. The method also includes communicating the signal anomaly information to a remote computing device, receiving, from the remote computing device, diagnostics information responsive to the signal anomaly information, and, in response to receiving the diagnostics information, initiating at least one corrective action procedure.

Abstract: A method for vehicle analytics includes receiving data from at least one condition indicator sensor of a vehicle and receiving data from at least one usage indicator sensor of the vehicle. The method also includes updating a vehicle specific model corresponding to a vehicle master model, based on the data from the at least one condition indicator sensor and the at least one usage indicator sensor. The method also includes identifying, using the vehicle specific model, at least one usage trend of the vehicle and determining an estimate of a remaining useful life of at least one aspect of the vehicle based on the at least one usage trend of the vehicle.

Abstract: Disclosed are emulator architectures for steer-by-wire (SBW) vehicle systems, methods for making and for using such systems, and programmable control logic for operating emulator test systems. A method is disclosed for operating an emulator test system for motor vehicle SBW systems. The method includes a Central Processing Unit Workstation Controller (CWC) initiating a selected test procedure for the SBW system, and transmitting, to a Motor Controller (MC) of the test system, a target input curve corresponding to the initiated test procedure. The MC responsively commands a Driver Simulator Motor (DSM) to operate at a power input corresponding to the target input curve. An emulator, which is mechanically coupled to the DSM, transmits emulator state data resulting from operating the DSM to a Real-time Operating System (RTOS). The RTOS determines a vehicle response based on the emulator state data using a vehicle dynamics math model corresponding to the SBW system.

Abstract: Disclosed are emulator architectures for steer-by-wire (SBW) vehicle systems, methods for making and for using such systems, and programmable control logic for operating emulator test systems. A method is disclosed for operating an emulator test system for motor vehicle SBW systems. The method includes a Central Processing Unit Workstation Controller (CWC) initiating a selected test procedure for the SBW system, and transmitting, to a Motor Controller (MC) of the test system, a target input curve corresponding to the initiated test procedure. The MC responsively commands a Driver Simulator Motor (DSM) to operate at a power input corresponding to the target input curve. An emulator, which is mechanically coupled to the DSM, transmits emulator state data resulting from operating the DSM to a Real-time Operating System (RTOS). The RTOS determines a vehicle response based on the emulator state data using a vehicle dynamics math model corresponding to the SBW system.