Abstract: A video encoder and video decoder are configured to code video data using multiple reference line processing. The video encoder and video decoder may determine a number of reference lines to use for multiple reference line processing based on an intra prediction mode, and decode a block of video data using the multiple reference line processing based on the number of reference lines.

Abstract: A method of characterizing a specimen to be analyzed in an automated diagnostic analysis system provides a segmentation determination and/or an HILN (hemolysis, icterus, lipemia, normal) determination of the specimen while providing characterization training updates based on the accuracy and/or confidence in the determinations. The method includes identifying an incorrect or low confidence segmentation or HILN determination, forwarding the incorrect or low confidence determination from the HILN network to a database, and providing one or more training images to the HILN network based on the incorrect or low confidence determination. Quality check modules and systems configured to carry out the method are also described, as are other aspects.

Abstract: A method of characterizing a specimen to be analyzed in an automated diagnostic analysis system provides an HILN classification (hemolysis, icterus, lipemia, normal) of the specimen along with a basis for that determination. The method includes assigning a hash code to each training image of a sample specimen used in the characterization training process. In response to an HILN determination for a test specimen, the method can retrieve via the hash code one or more of the closest matching training images upon which the HILN classification is based. The one or more of the closest matching training images can be displayed alongside of the one or more images of the test specimen. Quality check modules and systems configured to carry out the method are also described, as are other aspects.

Abstract: A method of characterizing a specimen and specimen container to be analyzed in an automated diagnostic analysis system. The method can provide a segmentation determination and/or an HILN determination (hemolysis, icterus, lipemia, or normal) of the specimen while protecting patient information. The method includes capturing an image of a specimen container via an image capture device, identifying a label affixed to the specimen container in the captured image via an anonymization network, and editing the captured image via the anonymization network to mask some or all information present in the label so that it is removed from the captured image. Quality check modules and systems configured to carry out the method are also described, as are other aspects.

Abstract: A calibration method is provided including identifying the imaging area on each light panel with respect to each imaging device. A center position of the imaging area of each light panel for each imaging device is determined. An optimal optical center of the imaging apparatus using the center position of the imaging area of each imaging device is determined. A tube calibration tool is installed in a carrier on a track, and the carrier is moved on the track so that a center of the tube calibration tool is located at a closest location to the optimal optical center of the imaging apparatus. The center of the tube calibration tool is used to determine a center of a region of interest (ROI) for backlight calibration. Methods for heath checking the calibration and apparatus used to carry out the calibration are provided as well as other aspects.

Abstract: An apparatus and a method for detecting heartbeat include a sensor configured to detect displacements of an object without contacting the object wherein the object displaces corresponding the a human's heartbeat, a data processing unit configured to extract a feature dataset from the detected displacement data, and a neural network configured to inference inter beat intervals from the extracted feature dataset using a pre-trained model.

Abstract: Machine learning is used to train a network to estimate a three-dimensional (3D) body surface and body regions of a patient from surface images of the patient. The estimated 3D body surface of the patient is used to determine an isocenter of the patient. The estimated body regions are used to generate heatmaps representing visible body region boundaries and unseen body region boundaries of the patient. The estimation of 3D body surfaces, the determined patient isocenter, and the estimated body region boundaries may assist in planning a medical scan, including automatic patient positioning.

Abstract: A method of aligning a component to a structure in a diagnostic laboratory system. The method includes aligning a position sensor to the structure; sensing a position of the component using the position sensor; and calculating the position of the component relative to the structure based at least in part on the sensing. Other methods, apparatus, and systems are disclosed.

Abstract: A method of determining a 3D center location of a specimen container on a track. The method includes providing a calibration object on the track; providing an initially calibrated image capture device adjacent to the track; moving the calibration object to at least two different longitudinal positions along the track; capturing a first image with the calibration object located at the first longitudinal position; capturing a second image with the calibration object located at the second longitudinal position; and determining a three-dimensional path trajectory of a center location along the track based at least upon the first image and the second image. The method can be used to determine a 3D center location of a specimen container imaged anywhere within a viewing area. Characterization apparatus and specimen testing apparatus adapted to carry out the methods are described, as are other aspects.

Abstract: A method coding video data includes receiving a block of video data, wherein chroma samples of the block of video data are subsampled relative to luma samples of the block of video data (e.g., 4:2:0 or 4:2:2 video content). A video coder may determine a subsampling technique, from a plurality of subsampling techniques, for the luma samples of the block of video data for a cross-component prediction mode, and may code the block of video data using the subsampling technique and the cross-component prediction mode. A first subsampling technique of the plurality of subsampling techniques includes not applying subsampling to the luma samples of the block of video data, and a second subsampling technique of the plurality of subsampling techniques includes a combination of downsampling filters to be applied to the luma samples of the block.

Abstract: Apparatus for robotic arm alignment in an automated sample analysis system includes a robotic arm, a sample tube carrier, a plurality of optical components (including, e.g., one or more cameras), and a controller. The controller is operative to process images received from the optical components to determine a first set of coordinates of a first marker relative to the sample tube carrier and determine a second set of coordinates of a second marker relative to the robotic arm. The controller is further operative to adjust the position of the robotic arm and/or the sample tube carrier in response to an excessive offset between the first and second sets of coordinates. In some embodiments, a positioning tool includes the first and second markers thereon. Methods of robotic arm alignment with a sample tube carrier in an automated sample analysis system are also provided, as are other aspects.

Abstract: Methods of predicting a fault in a diagnostic laboratory system include providing one or more sensors; generating data using the one or more sensors; inputting the data into an artificial intelligence algorithm, the artificial intelligence algorithm configured to predict at least one fault in the diagnostic laboratory system in response to the data; and predicting at least one fault in the diagnostic laboratory system using the artificial intelligence algorithm. Other methods, systems, and apparatus are also disclosed.

Abstract: An IC structure includes a first active area including a first plurality of fin structures extending in a first direction, a second active area including a second plurality of fin structures extending in the first direction, an electrical fuse (eFuse) extending in the first direction between the first and second active areas and electrically connected to each of the first and second pluralities of fin structures, a first plurality of gate structures extending over the first active area perpendicular to the first direction, a second plurality of gate structures extending over the second active area in the second direction, a first signal line extending in the first direction adjacent to the first active area and electrically connected to the first plurality of gate structures, and a second signal line extending in the first direction adjacent to the second active area and electrically connected to the second plurality of gate structures.

Abstract: An antifuse structure and IC devices incorporating such antifuse structures in which the antifuse structure includes an dielectric antifuse structure formed on an active area having a first dielectric antifuse electrode, a second dielectric antifuse electrode extending parallel to the first dielectric antifuse electrode, a first dielectric composition between the first dielectric antifuse electrode and the second dielectric antifuse electrode, and a first programming transistor electrically connected to a first voltage supply wherein, during a programming operation a programming voltage is selectively applied to certain of the dielectric antifuse structures to form a resistive direct electrical connection between the first dielectric antifuse electrode and the second dielectric antifuse electrode.

Abstract: A video decoder can be configured to determine that a current block in a current picture of the video data is coded in an affine prediction mode; determine one or more control-point motion vectors (CPMVs) for the current block; identify an initial prediction block for the current block in a reference picture using the one or more CPMVs; determine a current template for the current block in the current picture; and determine an initial reference template for the initial prediction block in the reference picture; and perform a motion vector refinement process to determine a modified prediction block based on a comparison of the current template to the initial reference template.

Abstract: A method of characterizing a serum or plasma portion of a specimen in a specimen container provides a fine-grained HILN index (hemolysis, icterus, lipemia, normal) of the serum or plasma portion of the specimen, wherein the H, I, and L classes may each have five to seven sub-classes. The HILN index may also have one un-centrifuged class. Pixel data of an input image of the specimen container may be processed by a deep semantic segmentation network having, in some embodiments, more than 100 layers. A small front-end container segmentation network may be used to determine a container type and boundary, which may additionally be input to the deep semantic segmentation network. A discriminative network may be used to train the deep semantic segmentation network to generate a homogeneously structured output. Quality check modules and testing apparatus configured to carry out the method are also described, as are other aspects.

Abstract: An example device for decoding video data includes one or more processors implemented in circuitry and configured to: generate an inter-prediction block for a current block of video data; generate an intra-prediction block for the current block of video data; generate a final prediction block for the current block of video data from the inter-prediction block and the intra-prediction block, including performing each of combined inter/intra prediction (CIIP) mode, overlapped block motion compensation (OBMC), and luma mapping with chroma scaling (LMCS) while generating the final prediction block; and decode the current block of video data using the final prediction block. To generate the final prediction block, the processors may perform LMCS on a first inter-prediction sub-block, combine the LMCS-mapped first inter-prediction sub-block with the intra-prediction block using CIIP, and perform OBMC between the first CIIP prediction block and a second inter-prediction sub-block.

Abstract: A memory bit cell includes a first memory cell including a first antifuse transistor and a first selection transistor, the first antifuse transistor being selectable between a first state or a second state in response to a word line program signal, the first selection transistor being configured to provide access to the first antifuse transistor in response to a word line read signal; a second memory cell including a second antifuse transistor and a second selection transistor, the second antifuse transistor being selectable between the first state or the second state in response to the word line program signal, the second selection transistor being configured to provide access to the second antifuse transistor in response to the word line read signal; a first word line to selectively provide the word line program signal; a second word line to selectively provide the word line read signal; and a bit line.

Abstract: Semiconductor structures are provided. A semiconductor structure includes a plurality of product regions over a semiconductor substrate, a plurality of alignment regions over the semiconductor substrate, and a plurality of first features formed in a material layer over the semiconductor substrate. Each of the alignment regions is surrounded by four of the product regions of a group, and each of the first features extends across two adjacent product regions in the group. The product regions are disposed in rows and columns of a first array, and the alignment regions are disposed in rows and columns of a second array, and the first and second arrays have a same center point.

Abstract: Methods of identifying a defect in a machine vision system. Embodiments of the method include providing a first imaging device having a first field of view; moving a reflective tool through the first field of view; capturing a plurality of images of the reflective tool at different locations in the first field of view using the first imaging device; and analyzing at least one of the plurality of images to identify one or more defects in the machine vision system. Systems and apparatus configured to carry out the methods are provided, as are other aspects.