Abstract: Described are platforms, systems, media, and methods for evaluating, monitoring, and/or treating a subject for brain injury based on machine learning analysis of one or more of brain imaging features, clinical features, demographic features, or speech features.

Abstract: A system may be configured for implementing neural volumetric reconstruction for coherent synthetic aperture sonar. Exemplary systems include means for measuring underwater objects using high-resolution Synthetic aperture sonar (SAS) by coherently combining data from a moving array to form high-resolution imagery. Such a system may receive a waveform from the measurements of the underwater object and optimize the waveform for deconvolving via an iterative deconvolution optimization process applying an adaptable approach to waveform compression where performance is tuned via sparsity and smoothness parameters. Such a system may deconvolve the wave form using pulse deconvolution and use the deconvolved waveforms in an analysis-by-synthesis optimization operation with an implicit neural representation to yield higher resolution and superior volumetric reconstruction scene of the underwater object.

Abstract: Tracking-based motion deblurring via coded exposure is provided. Fast object tracking is useful for a variety of applications in surveillance, autonomous vehicles, and remote sensing. In particular, there is a need to have these algorithms embedded on specialized hardware, such as field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs), to ensure energy-efficient operation while saving on latency, bandwidth, and memory access/storage. In an exemplary aspect, an object tracker is used to track motion of one or more objects in a scene captured by an image sensor. The object tracker is coupled with coded exposure of the image sensor, which modulates photodiodes in the image sensor with a known exposure function (e.g., based on the object tracking). This allows for motion blur to be encoded in a characteristic manner in image data captured by the image sensor. Then, in post-processing, deblurring is performed using a computational algorithm.

Abstract: Systems, methods, and apparatuses to restore degraded speech via a modified diffusion model are described. An exemplary system is specially configured to train a diffusion-based vocoder containing an upsampler, based on pairing original speech x and degraded speech mel-spectrum mT samples; train a deep convoluted neural network (CNN) upsampler based on a mean absolute error loss to match the estimated original speech {circumflex over (x)}? outputted by the diffusion-based vocoder by extracting the upsampler, generating a reference conditioner, and generating a weighted altered conditioner cTn?. The system further optimizes speech quality to invert non-linear transformation and estimate lost data by feeding the degraded mel-spectrum mT through the CNN upsampler and feeding the degraded mel-spectrum mT through the diffusion-based vocoder. The system then generates estimated original speech {circumflex over (x)}? based on the corresponding degraded speech mel-spectrum mT. Other related embodiments are described.

Abstract: Tracking-based motion deblurring via coded exposure is provided. Fast object tracking is useful for a variety of applications in surveillance, autonomous vehicles, and remote sensing. In particular, there is a need to have these algorithms embedded on specialized hardware, such as field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs), to ensure energy-efficient operation while saving on latency, bandwidth, and memory access/storage. In an exemplary aspect, an object tracker is used to track motion of one or more objects in a scene captured by an image sensor. The object tracker is coupled with coded exposure of the image sensor, which modulates photodiodes in the image sensor with a known exposure function (e.g., based on the object tracking). This allows for motion blur to be encoded in a characteristic manner in image data captured by the image sensor. Then, in post-processing, deblurring is performed using a computational algorithm.

Abstract: A system for generating a 4D representation of a scene in motion given a sinogram collected from the scene while in motion. The system generates, based on scene parameters, an initial 3D representation of the scene indicating linear attenuation coefficients (LACs) of voxels of the scene. The system generates, based on motion parameters, a 4D motion field indicating motion of the scene. The system generates, based on the initial 3D representation and the 4D motion field, a 4D representation of the scene that is a sequence of 3D representations having LACs. The system generates a synthesized sinogram of the scene from the generated 4D representation. The system adjusts the scene parameters and the motion parameters based on differences between the collected sinogram and the synthesized sinogram. The processing is repeated until the differences satisfy a termination criterion.

Abstract: The disclosure concerns biometeorological sensing devices including a processor communicatively coupled to a memory, and a plurality of sensors communicatively coupled to the processor. The plurality of sensors includes a humidity sensor, a UV sensor, an anemometer, an atmospheric thermometer, a globe thermometer, and a camera. The device also includes a network interface communicatively coupled to the processor. The processor is configured to estimate a mean radiant temperature (MRT) using data received from the plurality of sensors, identify a person in an image received from the camera, determine a bounding box that encloses the person in the image, generate a shadow map from the image, calculate an intersection over union (IOU) of the bounding box with the shadow map to determine if the person is in the shade, and transmit observed space usage and estimated MRT to a server communicatively coupled to the network interface.

Abstract: Systems, methods, and apparatuses to restore degraded speech via a modified diffusion model are described. An exemplary system is specially configured to train a diffusion-based vocoder containing an upsampler, based on pairing original speech x and degraded speech mel-spectrum mT samples; train a deep convoluted neural network (CNN) upsampler based on a mean absolute error loss to match the estimated original speech {circumflex over (x)}? outputted by the diffusion-based vocoder by extracting the upsampler, generating a reference conditioner, and generating a weighted altered conditioner ??Tn. The system further optimizes speech quality to invert non-linear transformation and estimate lost data by feeding the degraded mel-spectrum mT through the CNN upsampler and feeding the degraded mel-spectrum mT through the diffusion-based vocoder. The system then generates estimated original speech {circumflex over (x)}? based on the corresponding degraded speech mel-spectrum mT. Other related embodiments are described.

Abstract: Various embodiments of systems and methods for adaptive video subsampling for energy-efficient object detection are disclosed herein.

Abstract: A system for generating a 4D representation of a scene in motion given a sinogram collected from the scene while in motion. The system generates, based on scene parameters, an initial 3D representation of the scene indicating linear attenuation coefficients (LACs) of voxels of the scene. The system generates, based on motion parameters, a 4D motion field indicating motion of the scene. The system generates, based on the initial 3D representation and the 4D motion field, a 4D representation of the scene that is a sequence of 3D representations having LACs. The system generates a synthesized sinogram of the scene from the generated 4D representation. The system adjusts the scene parameters and the motion parameters based on differences between the collected sinogram and the synthesized sinogram. The processing is repeated until the differences satisfy a termination criterion.

Abstract: Various embodiments of a system for linear unmixing of spectral images using a dispersion model are disclosed herein.

Abstract: Tracking-based motion deblurring via coded exposure is provided. Fast object tracking is useful for a variety of applications in surveillance, autonomous vehicles, and remote sensing. In particular, there is a need to have these algorithms embedded on specialized hardware, such as field-programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs), to ensure energy-efficient operation while saving on latency, bandwidth, and memory access/storage. In an exemplary aspect, an object tracker is used to track motion of one or more objects in a scene captured by an image sensor. The object tracker is coupled with coded exposure of the image sensor, which modulates photodiodes in the image sensor with a known exposure function (e.g., based on the object tracking). This allows for motion blur to be encoded in a characteristic manner in image data captured by the image sensor. Then, in post-processing, deblurring is performed using a computational algorithm.

Abstract: Various embodiments for system and method of adaptive lighting for data-driven non-line-of-sight imaging are disclosed.

Abstract: A depth of field imaging apparatus includes a light field imager and a time of flight imager combined in a single on-chip architecture. This hybrid device enables simultaneous capture of a light field image and a time of flight image of an object scene. Algorithms are described, which enable the simultaneous acquisition of light field images and a time of flight images. Associated hybrid pixel structures, device arrays (hybrid imaging systems), and device applications are disclosed.

Abstract: Various embodiments of systems and methods for adaptive video subsampling for energy-efficient object detection are disclosed herein.

Abstract: A configurable image processing system can process image data for multiple applications by including an image sensor capable of operating in a machine vision mode and a photography mode in response to an operating system command. When operating in machine vision mode, the image sensor may send image data to first processor for machine vision processing. When operating in photography mode, the image sensor may send image data to an image coprocessor for photography processing.

Abstract: Various embodiments for system and method of adaptive lighting for data-driven non-line-of-sight imaging are disclosed.

Abstract: A depth of field imaging apparatus includes a light field imager and a time of flight imager combined in a single on-chip architecture. This hybrid device enables simultaneous capture of a light field image and a time of flight image of an object scene. Algorithms are described, which enable the simultaneous acquisition of light field images and a time of flight images. Associated hybrid pixel structures, device arrays (hybrid imaging systems), and device applications are disclosed.

Abstract: A depth of field imaging apparatus includes a light field imager and a time of flight imager combined in a single on-chip architecture. This hybrid device enables simultaneous capture of a light field image and a time of flight image of an object scene. Algorithms are described, which enable the simultaneous acquisition of light field images and a time of flight images. Associated hybrid pixel structures, device arrays (hybrid imaging systems), and device applications are disclosed.

Abstract: A configurable image processing system can process image data for multiple applications by including an image sensor capable of operating in a machine vision mode and a photography mode in response to an operating system command. When operating in machine vision mode, the image sensor may send image data to first processor for machine vision processing. When operating in photography mode, the image sensor may send image data to an image coprocessor for photography processing.