Robert Boschgmbh

Sousa, Joao P.

Das, Samarjit

Samarjit Das

Sirajum Munir

Joseph Christopher Szurley

Vivek Jain

Shabnam Ghaffarzadegan

Jonathan Jenner Macoskey

Luca Bondi

Joao Pedro De Sousa

Jorge Guajardo Merchan

Yunze Zeng

Joao P Sousa

Shalabh Jain

Pongtep Angkititrakul

Stefan Gehrer

Boqiang Fan

Benjamin Hettwer

Long Huang

Billy Li

Gonzalo Vaca

Ashwin Shenoi

A method for self-diagnosing a data acquisition system for acquiring calibrated images of an area by a controller includes requesting a signal, from a sensor associated with a mobile platform in the area, removing from the signal, background noise associated with the mobile platform, thereby focusing the measurement to a foreground signal, wherein the background noise is removed from the foreground signal via a subspace approximation using singular value, requesting a previous-in-time signal indicative of a previous-in-time measurement of the parameter, wherein previous-in-time background noise is removed from a previous-in-time foreground signal via subspace approximation using singular value decomposition and in response to a change detection indicating a difference between a spectrogram of the background noise and a previous-in-time spectrogram of the previous-in-time background noise exceeding a predetermined threshold at a predetermined frequency, outputting a status signal indicative of a change in operating characteristics of the mobile platform.

Dynamic spatiotemporal beamforming self-diagonostic system

Systems and methods for converting a primary one-dimensional signal into a secondary one-dimensional signal of another modality. The primary signal is spliced into a plurality of consecutive frames. A first linear transformation transforms the frames into corresponding vectors. Positional encodings are provided on the vectors to encode relative positional information associated with each sample within each frame. A multi-head self-attention machine-learning model compares relative importance of the samples within each vector to each other in that vector to yield high-level representation vectors. A second linear transformation transforms the high-level representation vectors into corresponding secondary signal frames. The secondary signal frames are concatenated into a reconstructed one-dimensional secondary signal having a different modality than the primary signal.

Systems and methods for cross-modal signal inference using audio signals

A method data augmentation includes receiving audio stream data associated with at least one impulse event, receiving a label associated with the audio stream data, and detecting, using an onset detector, at least one peak of the at least one impulse event. The method also includes extracting at least one positive sample of the audio stream data associated with the at least one impulse event. The method also includes applying, to the at least one positive sample, the label associated with the audio stream data and extracting at least one negative sample of the audio stream data associated with the at least one impulse event. The method also includes augmenting training data based on the at least one positive sample and the at least one negative sample and training at least one machine-learning model using the augmented training data.

Systems and methods for false positive mitigation in impulsive sound detectors

Systems and methods for detecting symptoms of occupant illness is disclosed herein. In embodiments, a storage is configured to maintain a visualization application and data from one or more sources, such as an image source. A processor is in communication with the storage and a user interface. The processor is programmed to receive data from the one or more sources, execute human-detection models based on the received data, execute activity-recognition models to recognize symptoms of illness based on the data from the one or more sources, determine a location of the recognized symptoms, and execute a visualization application to display information in the user interface. The visualization application can show a background image with an overlaid image that includes an indicator for each location of recognized symptom of illness. Additionally, data from the audio source, image source, and/or radar source can be fused.

Systems and methods for detecting symptoms of occupant illness

A vehicle system that includes a vehicle seat, wherein the vehicle seat includes a seat-back portion, a seat-bottom portion, a head rest portion, wherein the vehicle seat includes one or more acoustic sensors configured to retrieve an acoustic signal associated with a passenger seat, and a processor in communication with at least the one or more acoustic sensors, wherein the processor is programmed to identify an anomaly associated with the passenger utilizing the acoustic signal, wherein the anomaly is identified via pre-processing the acoustic signal and extracting one or more features associated with the acoustic signal in response to the pre-preprocessing and utilizing a classifier to classify one or more features associated with the acoustic signal as either a normal condition or the anomaly, and output a notification associated with the anomaly in response to identifying the anomaly.

System and method of capturing physiological anomalies utilizing a vehicle seat

A system that includes one or more sensors installed in proximity to a machine configured to collect raw signals associated with an environment of the machine, are multi-layer spatial data that include time-stamp data. The system may include a processor in communication with the sensors and programmed to receive one or more raw signals, denoise the one or more raw signals to obtain a pre-processed signal, extract one or more features from the pre-processed signals, classify the one or more features to an associated class, wherein the associated class includes one or more of a normal class, abnormal class, or a potential-abnormal class, create fusion data by fusing the one or more features utilizing the associated class and the time-stamp data, and output a heat map on an overlaid image of the environment.

System and methods for monitoring machine health

A method includes receiving audio stream data associated with a data capture environment, and receiving sensor data associated with the data capture environment. The method also includes identifying at least some events in the sensor data, and calculating at least one offset value for at least a portion of the audio stream data that corresponds to at least one event of the sensor data. The method also includes synchronizing at least a portion of the sensor data associated with the portion of the audio stream data that corresponds to the at least one event of the sensor data, and labeling at least the portion of the audio stream data that corresponds to the at least one event of the sensor data. The method also includes generating training data using at least some of the labeled portion of the audio stream data, and training a machine learning model using the training data.

Systems and methods for automatic alignment between audio recordings and labels extracted from a multitude of asynchronous sensors in urban settings

An intelligent audio analytic apparatus (IAAA) and method for space system. The IAAA comprises a processor, a computer readable medium, and a communication module. The instructions include audio data processing algorithms configured to identify and predict impending anomalies associated with the space system using at least one neural network.

Intelligent audio analytic apparatus (IAAA) and method for space system

An acoustic imaging system includes a controller. The controller may be configured to receive a signal from a microphone and reverberation channel data, update latent variables, latent labels, a source amplitude, and a phase estimation based on an optimization of the signal and reverberation channel data to obtain updated latent variables, updated latent labels, an updated source amplitude, and an updated phase estimation, generate, via a conditional generative adversarial network (cGAN) of the updated latent variables and the updated latent labels, an acoustic source map tuned via the updated source amplitude and the updated phase estimation, optimize the acoustic source map, and output the optimized acoustic source map.

Synthetic aperture acoustic imaging with deep generative model

Systems and methods for detecting symptoms of occupant illness is disclosed herein. In embodiments, a storage is configured to maintain a visualization application and data from one or more sources, such as an audio source, an image source, and/or a radar source. A processor is in communication with the storage and a user interface. The processor is programmed to receive data from the one or more sources, execute human-detection models based on the received data, execute activity-recognition models to recognize symptoms of illness based on the data from the one or more sources, determine a location of the recognized symptoms, and execute a visualization application to display information in the user interface. The visualization application can show a background image with an overlaid image that includes an indicator for each location of recognized symptom of illness. Additionally, data from the audio source, image source, and/or radar source can be fused.

A method for labeling audio data includes receiving video stream data and audio stream data that corresponds to at least a portion of the video stream data. The method also includes labeling, at least some objects of the video stream data. The method also includes calculating at least one offset value for at least a portion of the audio stream data that corresponds to at least one labeled object of the video stream data. The method also includes synchronizing at least a portion of the video stream data with the portion of the audio stream data. The method also includes labeling at least the portion of the audio stream data that corresponds to the at least one labeled object of the video stream data and generating training data using at least some of the labeled portion of the audio stream data.

Systems and methods for automatic extraction and alignment of labels derived from camera feed for moving sound sources recorded with a microphone array

An audio and vibration based power distribution equipment condition monitoring system and method is provided. The system includes an intelligent audio analytic (IAA) device and a sensing assembly. The IAA device has computer executable instructions such as an audio data processing algorithms configured to identify and predict impending anomalies associated with one or more power distribution equipment using one or more neural networks.

System and method for audio and vibration based power distribution equipment condition monitoring

A method for imaging a room by a controller includes requesting a signal, indicative of a measurement of a parameter, from a sensor associated with a position and direction of a mobile platform in the room, wherein the measurement from the sensor includes background noise, distortions, and a foreground signal of interest, in response to the mobile platform reaching a new position, requesting a second measurement of the parameter from the sensor associated with the new position of the mobile platform, spatially aggregating the signal from the sensor and associated position and direction to create an energy map via spatio-dynamic beamforming, wherein the background noise and distortions are reduced, by spatially averaging beamformed information acquired across multiple locations to synthesize signals indicative of the foreground signal of interest, analyzing the energy map to identify a state of an apparatus in the room, and outputting a foreground beamformed image.

Dynamic spatiotemporal beamforming

A system includes memory, a processor in communication with the memory. The processor is programmed to define a fingerprint that includes a baseline measurement of a physical attribute of at least a separate processor during an enrollment period of the system, wherein the enrollment period includes measuring the physical attribute of the processor prior to runtime operation, receiving a runtime measurement from a sensor regarding the physical attribute of at least the separate processor during runtime, comparing the runtime measurement of the physical attribute to the fingerprint, and outputting a multi-dimensional domain image in response to the runtime measurement.

System and method for improving measurements of an intrusion detection system by transforming one dimensional measurements into multi-dimensional images

A system for training a deep learning system to detect engine knock with accuracy associated with high fidelity knock detection sensors despite using data from a low fidelity knock detection sensor. The system includes an engine, a high fidelity knock detection sensor, a low fidelity knock detection sensor, and an electronic processor. The electronic processor is configured to receive first data from the high fidelity knock detection sensor. The electronic processor is also configured to receive second data from the low fidelity knock detection sensor. The electronic processor is further configured to map the first data to the second data, train the deep learning system, using training data including the mapped data, to determine a predicted peak pressure using data from the low fidelity knock detection sensor, receive third data from the low fidelity knock detection sensor, and using the third data, determine the predicted peak pressure.

Training a deep learning system to detect engine knock with accuracy associated with high fidelity knock detection sensors despite using data from a low fidelity knock detection sensor

Growing community of inventors

Sewickley, PA, United States of America

Samarjit Das