Rummler, Felix

A computer-implemented method of determining a position, in particular a pose, of a platform, preferably an aircraft, the method comprising: receiving an input image of a portion of a surface, preferably the surface of the earth, the input image captured from a position of the platform; receiving a georeferenced reference representation of the portion; based on the reference representation, generating a synthetic image of the portion; determining a homography matrix between the synthetic image and the input image; and determining the position, in particular the pose of the platform based on the homography matrix.

According to the present disclosure, there is provided a computer-implemented method for annotating an image for object detection, the method comprising: obtaining information indicative of a geographic, in particular geodetic position of at least one object; obtaining, from a camera, at least one image, wherein the image represents the object; determining a first position of the object in the image, based on the geographic position of the object; outputting, based on the determined first position, an image portion or a reference to an image portion representing the object; obtaining an input to generate, in the image portion, an annotation of the image, in particular a bounding box, wherein the annotation of the image is indicative of a second position of the object in the image; and matching the annotation to the geographic position of the object in the image.

A microphone hub and a method of operating one or more microphone hubs, The microphone hub including an input operable to receive one or more analog audio signals from one or more microphones, an audio network interface (ANIF), at least one analogue to digital converter (ADC) coupled to the input and to the ANIF, and a power system operable to provide power to the microphone hub. The ADC is operable to convert each of the one or more analog audio signals to one or more digital audio signals. The ADC is further operable to send the one or more digital audio signals to the ANIF.

Systems, apparatus, articles of manufacture, and methods are disclosed. An example memory module includes: a memory die a first pseudo-split die having first interface circuitry; and a second pseudo-split die having second interface circuitry; and a multiplexer external to the memory die, the multiplexer having a first port connected to the first interface circuitry, a second port connected to the second interface circuitry, and a third port connected to memory controller circuitry.

In various embodiments, a method includes receiving an audio signal from a microphone system associated with a vehicle, receiving a video signal from a camera system associated with the vehicle, identify a facial classification model for classifying one or more facial makers in the video signal, determining that an occupant of the vehicle is speaking using the facial classification model, and generating an isolated audio signal corresponding to a speech signal in the audio signal using the facial classification model.

In at least one embodiment, an audio system is provided. The audio system includes one or more microphones and at least one controller. The one or more microphones are positioned within a listening space to provide a captured audio input signal. The at least one controller is programmed to receive the captured audio input signal and to receive a first signal indicative of an audio level in the listening space. The at least one controller determines a gain amount based on the audio level and to adjusts the captured audio input signal.

In at least one embodiment, a feedback amplifier is provided. The amplifier includes an input stage circuit (206) and a collector element (208). The input stage circuit (206) is configured to receive at least one input feedback signal having wide band frequency components that include high frequency components and low frequency components. The input stage circuit (206) includes a first gain stage (220), a filter, and a second gain stage (222). The first gain stage (220) amplifies the at least one input feedback signal to generate at least one amplified output signal. The filter filters the wide band frequency components on the at least one input feedback signal to generate at least one filtered feedback signal having a first low frequency component. The second gain stage (222) amplifies the at least one filtered feedback signal having the first low frequency component and to provide at least one gain output signal.

In at least one embodiment, an emulator system including at least one first audio controller is provided. The at least one first audio controller includes a primary field programmable gate array (FPGA), a universal audio network interface sub-board and a secondary FPGA. The primary FPGA is programmed to one of transmit and receive at least one audio signal in a vehicle. The universal audio network interface sub-board is operably coupled to the primary FPGA and is programmed to one or more of transmit or receive the at least one audio signal in accordance with one or more audio network interfaces. The secondary FPGA includes a first communication block that is programmed to enable the primary FPGA to one of transmit or receive the at least one audio signal in accordance with the one or more audio network interfaces to or from the universal audio network interface.

A method and system for analyzing over-the-air (OTA) client logs from vehicles includes receiving OTA client log files comprising error logs generated during OTA software updates, preprocessing the log files to generate cleaned log files by removing duplicated log lines and standardizing formats, segmenting the cleaned log files into log chunks, and analyzing the chunks using both rule-based and artificial intelligence (AI) approaches. The AI analysis includes generating vector embeddings for error-related log lines, comparing embeddings with an insignificant error database to filter out insignificant errors, retrieving error context information from a vector database, and generating error summaries using a large language model. The system displays analysis results via a failure analysis engineer (FAE) review interface and updates databases based on FAE feedback. This hybrid approach enables efficient processing of OTA client logs while maintaining accuracy through rule-based filtering and contextual AI analysis.

A legacy virtual machine (a virtual machine not operating in a secure environment) can be converted to a confidential virtual machine (a virtual that operates in a secure environment) on the fly, with little downtime experienced by the legacy virtual machine (VM) owner. A legacy VM operating either on a legacy platform (a platform not having confidential computing capabilities) or a confidential computing-capable platform can be converted to a confidential VM (CVM). The legacy VM can be migrated to another computing device as part of the conversion or be converted into a CVM that executes on the same computing device on which the legacy VM was running. A trusted security module can be responsible for starting a VM-to-CVM conversion session, validating the state of legacy virtual machine to be converted, provision a CVM with the state of the legacy virtual machine, and end a VM-to-CVM conversion session.