Abstract: System and method for measuring dyslipidemia condition of a subject using thermal imaging is disclosed. The disclosed system and method includes thermal sensors for capturing thermal images and/or videos of a body part; and a processing engine to detect a predefined region of the body part in each frame of the captured images and/or videos. The processing engine segments one or more portions from the predefined region in each frame of the captured images and/or videos to identify a ROI comprising arteries in the segmented portions. Based on the identified region of interest, the engine extracts pixel values, representing biosignals, from each frame of the captured images and/or videos to determine parameters associated with a rate of atherosclerotic, levels of lipids and lipoproteins, and hemodynamic factors of the subject. Further a risk score for the dyslipidemia condition based on the determined parameters using computational models is measured.

Abstract: An artificial intelligence system for analyzing imagery, the system comprising a computing device, the computing device designed and configured to receive a plurality of photographs related to a human subject; analyze the plurality of photographs to identify a conditional indicator contained within the plurality of photographs; generate a classification algorithm utilizing the conditional indicator, wherein the classification algorithm utilizes the conditional indicator as an input and outputs a conditional profile; and determine a conditional status of the human subject utilizing the conditional profile.

Abstract: An optical communication system includes an optical transmitter and one or more processors. The optical transmitter is configured to output an optical signal, and includes an average-power-limited optical amplifier, such as an erbium-doped fiber amplifier (EDFA). The one or more processors are configured to receive optical signal data related to a received power for a communication link from a remote communication system and determine that the optical signal data is likely to fall below a minimum received power within a time interval. In response to the determination, the one or more processors are configured to determine a duty cycle of the optical transmitter based on a minimum on-cycle length and a predicted EDFA output power and operate the optical transmitter using the determined duty cycle to transmit an on-cycle power that is no less than the minimum required receiver power for error-free operation of the communication link.

Abstract: Techniques for tracking eye movement in an augmented reality system identify a plurality of base images of an object or a portion thereof. A search image may be generated based at least in part upon at least some of the plurality of base images. A deep learning result may be generated at least by performing a deep learning process on a base image using a neural network in a deep learning mode. A captured image may be localized at least by performing an image registration process on the captured image and the search image using a Kalman filter model and the deep learning result.

Abstract: Embodiments disclosed herein involve a wearable-based system that can help determine in a privacy-preserving manner whether a user is healthy (and thus poses low risk of contagiousness), and use determinations of this nature to grant passage through a doorway. In one embodiment, a wearable device takes measurements of a user comprising a photoplethysmogram signal (PPG signal) and a temperature measurement. A computer calculates a health score based on a difference between baseline measurements of the user, measured one or more days earlier, and more current measurements of the user. Additionally, the computer calculates an extent of similarity between characteristics of the PPG signal in the current measurements and characteristics of the PPG signal in the baseline measurements. The computer authorizes the user's passage through the doorway responsive to the health score reaching a first threshold and the extent of the similarity reaching a second threshold.

Abstract: An image including a first object can be input to a deep neural network trained to detect objects. The deep neural network can output a first feature vector corresponding to the first object. A first distance can be measured from the first feature vector to a feature vector subspace determined using a k-means single value decomposition algorithm on an overcomplete dictionary of feature vectors. The first object can be determined to correspond to an anomaly based on the first distance.

Abstract: A system includes a housing and a first circuit board positioned inside the housing. The housing has top, bottom, left side, right side, front, and rear panels. The first circuit board has a length, a width, and a thickness, and the first circuit board has a first surface defined by the length and the width. The first surface of the first circuit board is substantially parallel to the front panel or at a second angle relative to the front panel in which the second angle is less than 60°. The system includes a first data processing module and a first optical interconnect module both electrically coupled to the first circuit board. The optical interconnect module is configured to receive first optical signals from a first optical link, convert the first optical signals to first electrical signals, and transmit the first electrical signals to the first data processing module.

Abstract: A method of predicting lane line types utilizing a heterogeneous convolutional neural network (HCNN) includes capturing an input image with one or more optical sensors disposed on a host member, passing the input image through the HCNN, the HCNN having at least three distinct sub-networks, the three distinct sub-networks: predicting object locations in the input image with a first sub-network; predicting lane line locations in the input image with a second sub-network; and predicting lane line types for each predicted lane line in the input image with a third sub-network.

Abstract: A system and a method for processing an image so as to perform instance segmentation. The system/method includes: a—inputting (S1) the image (IMG) to a first neural network configured to output an affinity graph (AF), and b—inputting (S2), to a second neural network, the affinity graph and a predefined seed-map (SM), so as to determine whether other pixels belong to a same instance, and set at a first value the value of the other pixels determined as belonging to the same instance.

Abstract: A device may receive images identifying interiors of buildings and movable objects and unmovable objects located in the interiors, and may train a machine learning model with the images to generate a trained machine learning model. The device may receive an image identifying an interior portion of a building and objects located in the interior portion, and may process the image, with the trained machine learning model, to identify a movable object and an unmovable object. The device may disregard the movable object to generate an image in which data identifying the movable object has been disregarded, and may process the image in which the data identifying the movable object has been disregarded, with a visual positioning system, to determine a location of the user device in the interior portion of the building. The device may perform one or more actions based on the location of the user device.

Abstract: Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems comprise an integrated detection unit configured to record media content, identify preferred content, and communicate the identifications of preferred content for storage in a computationally efficient manner.

Abstract: A method and instructions for operating an image integrity and repeatability system can comprise: acquiring an original image; extracting a protocol from the original image; recording the original image to an image history; determining an area within an image frame of the original image as a skin area; creating a background as the inverse of the skin area within the image frame of the original image; and acquiring a subsequent image including: extracting an image parameter from the subsequent image, providing feedback based on the image parameter being outside a threshold, prohibiting capture of the subsequent image based on the image parameter being outside the threshold, displaying the background overlaid on the subsequent image, and storing the subsequent image to the image history based on the image parameter of the subsequent image being within the threshold.

Abstract: Disclosed systems and methods can include capturing the sequence of images of a monitored region that includes a sub-region of interest, processing the sequence of images using heuristics and rules of an artificial intelligence model to identify the plurality of discrete parts that are associated with a type of a unified entity, and processing the sequence of images using the heuristics and the rules of the artificial intelligence model to virtually link together a group of the plurality of discrete parts that correspond to a specific embodiment of the unified entity that is present in the sub-region of interest, wherein the heuristics and the rules of the artificial intelligence model can be developed from a training process that includes the artificial intelligence model receiving sample images delineating exemplary discrete parts on exemplary embodiments of the unified entity.

Abstract: An optical network optimization method is disclosed. The optimization method includes training a neural network, adjusting at least one of a plurality of auxiliary output values of a plurality of auxiliary neurons of the neural network, and performing inference with the neural network. A neural network and an attention mechanism are utilized to predict network performance key performance indicator(s) so as to achieve efficient routing optimization, network planning and fast failure recovery.

Abstract: A baseband processing unit includes a baseband processor configured to receive a plurality of component carriers of a radio access technology wireless service, and a delta-sigma digitization interface configured to digitize at least one carrier signal of the plurality of component carriers into a digitized bit stream, for transport over a transport medium, by (i) oversampling the at least one carrier signal, (ii) quantizing the oversampled carrier signal into the digitized bit stream using two or fewer quantization bits.

Abstract: A refrigerator system capable of reducing decrease in the accuracy of detecting change in a storage state and reducing a communication load for transmitting images of the inside of a storeroom, which are taken at a plurality of viewpoints to the outside of a refrigerator is provided. To achieve this, the refrigerator system includes: a camera configured to take the images of the inside of the storeroom; a camera moving device configured to move the camera at least in the vertical direction; a communication device configured to transmit the images taken by the camera to the outside of a main body; and a change detecting device configured to detect change in a food-item storage state of each shelf based on the images taken by the camera. The camera takes images of each shelf at a first viewpoint and a second viewpoint. The change detecting device detects change in the food-item storage state of the shelf based on the image taken at the first viewpoint.

Abstract: A system for using free-space optics to interconnect a plurality of computing nodes can include a plurality of optical transceivers that facilitate free-space optical communications among the plurality of computing nodes. The system may ensure a line of sight between the plurality of computing nodes and the optical transceivers to facilitate the free-space optical communications. The line of sight may be preserved by the position or placement of the computing nodes in the system. The position or placement of the computing nodes may be achieved by using different shaped enclosures for holding the computing nodes.

Abstract: Embodiments are disclosed for automatic object re-colorization in images. In some embodiments, a method of automatic object re-colorization includes receiving a request to recolor an object in an image, the request including an object identifier and a color identifier, identifying an object in the image associated with the object identifier, generating a mask corresponding to the object in the image, providing the image, the mask, and the color identifier to a color transformer network, the color transformer network trained to recolor objects in input images, and generating, by the color transformer network, a recolored image, wherein the object in the recolored image has been recolored to a color corresponding to the color identifier.

Abstract: A Video Semantic Segmentation System (VSSS) is disclosed that performs accurate and fast semantic segmentation of videos using a set of temporally distributed neural networks. The VSSS receives as input a video signal comprising a contiguous sequence of temporally-related video frames. The VSSS extracts features from the video frames in the contiguous sequence and based upon the extracted features, selects, from a set of labels, a label to be associated with each pixel of each video frame in the video signal. In certain embodiments, a set of multiple neural networks are used to extract the features to be used for video segmentation and the extraction of features is distributed among the multiple neural networks in the set. A strong feature representation representing the entirety of the features is produced for each video frame in the sequence of video frames by aggregating the output features extracted by the multiple neural networks.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a machine learning model. One of the methods includes obtaining a training data set for training a machine learning model, the training data set comprising a plurality of training inputs; determining a plurality of data augmentation policies, wherein each data augmentation policy defines a procedure for processing a training input to generate a transformed training input; for each data augmentation policy, training the machine learning model using the data augmentation policy; determining, for each data augmentation policy, a quality measure of the machine learning model that has been trained using the data augmentation policy; and selecting a final data augmentation policy based using the quality measures of the machine learning models.