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 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 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 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.

Abstract: A method of decoding video data includes generating a fusion of predictors from two or more reference lines of samples relative to a block of video data based on an intra-prediction mode. The method further includes decoding the block of video data using the fusion of predictors and the intra-prediction mode.

Abstract: A method of coding video data comprises determining, based on a comparison of a threshold and a quantity of reference samples in a selected template, whether the selected template is allowed, wherein a mode index indicates which template from among a plurality of templates is the selected template, wherein each of the templates includes a different set of reconstructed samples that neighbor a current coding unit (CU) of a current picture of the video data; based on determining that the selected template is allowed and that a convolutional cross-component model (CCCM) mode is to be used, applying the CCCM mode to predict chroma samples of the current CU based on reconstructed luma samples of the current CU and the reference samples in the selected template; and; and encoding or decoding the current CU based on the predicted chroma samples of the current CU.

Abstract: A video coder may code a block of video data using an intra prediction mode determined from a most probable mode list. The video coder may construct a general most probable mode list containing N entries, wherein the N entries of the general most probable mode list are intra prediction modes, and wherein a planar mode is an ordinal first entry in the general most probable mode list, construct a primary most probable mode list from a first Np entries in the general most probable mode list, where Np is less than N, and construct a secondary most probable mode list from a remaining (N?Np) entries in the general most probable mode list. The video coder may then determine a current intra prediction mode for a current block of video data using the primary most probable mode list or the secondary most probable mode list.

Abstract: A method of characterizing a serum or plasma portion of a specimen in a specimen container provides an HILN (hemolysis, icterus, lipemia, normal) determination. Pixel data of an input image of the specimen container is processed by a classification network to identify whether the specimen contains plasma or serum. Pixel data representing a plasma sample are forwarded to a segmentation/classification/regression network trained with plasma samples for HILN determination. Pixel data representing a serum sample are forwarded to a transformation network, wherein the serum sample pixel data is transformed into pixel data that matches pixel data of a corresponding previously-collected plasma sample by changing sample color, contrast, intensity, and/or brightness. The transformed serum sample pixel data are forwarded to the segmentation/classification/regression network for HILN determination. Quality check modules and testing apparatus configured to carry out the method are also described, as are other aspects.

Abstract: An encoding, a decoding method, a system for encoding and decoding, an encoder, and a decoder are provided. The encoding method includes the following. In a palette mode, if colors of pixels of a coding unit block are all represented by one or more major colors of the coding unit block, a flag is set as a first state value, and if the color of at least one pixel of the coding unit block is not represented by the one or more major colors of the coding unit block, the flag is set as a second state value. The encoding method further includes establishing a palette table corresponding to the coding unit block according to a state value of the flag and the one or more major colors.

Abstract: An encoding, a decoding method, a system for encoding and decoding, an encoder, and a decoder are provided. The encoding method includes the following. In a palette mode, if colors of pixels of a coding unit block are all represented by one or more major colors of the coding unit block, a flag is set as a first state value, and if the color of at least one pixel of the coding unit block is not represented by the one or more major colors of the coding unit block, the flag is set as a second state value. The encoding method further includes establishing a palette table corresponding to the coding unit block according to a state value of the flag and the one or more major colors.