Abstract: A method of decoding video data and an electronic apparatus for performing the method are provided. The method includes: receiving, from the video signal, a picture frame that includes a first component, and a second component; determining a classifier for the first component based on a first set of one or more samples of the second component associated with a respective sample of the first component; determining a sample offset for the respective sample of the first component according to the classifier; and modifying a value of the respective sample of the first component based on the determined sample offset, the first component being a luma component and the second component being a first chroma component.

Abstract: A method for implementing folding M×N wavelength selective switch is provided. A one-dimensional single-mode fiber optic collimator array, a short-focus cylindrical mirror, a first long-focus cylindrical mirror, a retroreflector, a transmission phase diffraction grating, a second long-focus cylindrical mirror, a liquid crystal spatial light modulator, and a liquid crystal graphic loading control system are provided along beam transmission direction. The same set of optical elements is used for incident light and outgoing light by ingenious folding structure. The input port and output port of optical signal are consistent in spatial arrangement, thereby reducing space and improving port utilization. Based on composite liquid crystal chips, a working area of the liquid crystal spatial light modulator is doubled, and a quantity of accommodating ports is greatly increased. A quantity of M×N ports of the WSS can be increased greatly by the above structure and design.

Abstract: A method and an apparatus for image filtering in video coding using a neural network are provided. The method includes: loading, a plurality of quantization parameter (QP) map (QpMap) values at a plurality of QpMap channels into the neural network; and adjusting, according to a QP scaling factor, the plurality of QpMap values for the neural network to learn and filter a current input frame to the neural network.

Abstract: An electronic apparatus performs a method of decoding video data. The method comprises: receiving the video signal that includes a first component and a second component; receiving a plurality of offsets associated with the second component; utilizing a sample value of the first component to obtain a classifier associated with the second component; dividing a range of the sample value of the first component into several bands based on the classifier; selecting a band based on an intensity value of the sample value of the first component; selecting an offset from the plurality of offsets for the second component according to the selected band; and modifying the second component based on the selected offset.

Abstract: An electronic apparatus performs a method of decoding video data. The method includes receiving, from a video bitstream having a hierarchical structure, a first syntax element associated with a first level of the hierarchical structure, in accordance with a determination that the first syntax element indicates that a Cross-Component Sample Adaptive Offset (CCSAO) filter information is present in the first level, reconstructing, from the video bitstream, one or more regions under the first level jointly according to the CCSAO filter information, and in accordance with a determination that the first syntax element indicates that the CCSAO filter information is not present in the first level, reconstructing, from the video bitstream, the one or more regions separately according to the CCSAO filter information present in a second level of the hierarchical structure.

Abstract: A method of forming an acoustic transducer comprises providing a substrate and depositing a first structural layer on the substrate. The first structural layer is selectively etched to form at least one of an enclosed trench or an enclosed pillar thereon. A second structural layer is deposited on the first structural layer and includes a depression or a bump corresponding to the enclosed trench or pillar, respectively. At least the second structural layer is heated to a temperature above a glass transition temperature of the second structural layer causing the second structural layer to reflow. A diaphragm layer is deposited on the second structural layer such that the diaphragm layer includes at least one of a downward facing corrugation corresponding to the depression or an upward facing corrugation corresponding to the bump. The diaphragm layer is released, thereby forming a diaphragm suspended over the substrate.

Abstract: An image processing device includes a data transmitter/receiver and at least one processor. The processor obtains first data of a continuous first image, obtains performance information of a display of a first external device that displays a second image based on the first image, generates, from the first data, second data of the second image in accordance with the performance information, and outputs the second data to the first external device through the data transmitter/receiver.

Abstract: A method and an apparatus for image filtering in video coding using a neural network are provided. The method includes: loading a plurality of input patches associated with a current image to be coded, where the plurality of input patches include a first input patch with a first resolution, a second input patch with a second resolution, and a third input patch with a third resolution; and in response to determining that one resolution in the first resolution, the second resolution, and the third resolution is different from the other two resolutions, adjusting the first resolution, the second resolution, and the third resolution at one region of a plurality of regions before the neural network or in the neural network.

Abstract: An electronic apparatus performs a method of decoding video data, including: reconstructing, from a video bitstream, a picture frame that includes a luma component, a first and a second chroma components, and applying a trained neural network based in-loop filter to the reconstructed picture frame by: converting a first resolution of the samples of the at least one of the first and the second chroma components to a second resolution of the samples of the luma component when the first resolution is different from the second resolution; concatenating samples of at least one of the first and the second chroma components with the luma component; processing the concatenated samples using a convolutional neural network; and reconverting the samples of the at least one of the first and the second chroma components processed by the convolutional neural network from the second resolution back to the first resolution.

Abstract: An electronic apparatus performs a method of decoding video data. The method includes: receiving, from the video signal, a picture frame that includes a first component and a second component; reconstructing samples of the first component through a first in-loop filter; reconstructing samples of the second component through a second in-loop filter; determining a classifier for the first component from one or more reconstructed samples of the second component relative to a respective reconstructed sample of the first component; selecting a first sample offset for the respective reconstructed sample of the first component according to the classifier; applying a plurality of filters in parallel with the selection of the first sample offset to obtain a plurality of parallel offsets; and clipping an output of a combination of the respective reconstructed sample of the first component, the first sample offset, and the plurality of parallel offsets from the plurality of filters.

Abstract: A microelectromechanical systems (MEMS) die includes a first diaphragm and a second diaphragm, wherein the first diaphragm and the second diaphragm bound a sealed chamber. A stationary electrode is disposed within the sealed chamber between the first diaphragm and the second diaphragm. A tunnel passes through the first diaphragm and the second diaphragm without passing through the stationary electrode, wherein the tunnel is sealed off from the chamber. The MEMS die further includes a substrate having an opening formed therethrough, wherein the tunnel provides fluid communication from the opening, through the second diaphragm, and through the first diaphragm.

Abstract: A fixing device includes a supporting platform configured to support a first workpiece, a receiving member, a plurality of pressing members, and at least one rotating member. The receiving member is positioned on the supporting platform and includes a rotating shaft. An axis of the rotating shaft is parallel to a supporting surface of the supporting platform. Each pressing member includes a first end and a second end. The first end is rotatably connected to the rotating shaft, and the second end can press a second workpiece to the first workpiece. The rotating member is positioned above the receiving member and detachably connected to each pressing member. The at least one rotating member can drive the pressing members to rotate to press or release the second workpiece. The present disclosure reduces the space occupied by the pressing members. The pressing of individual points can be realized.

Abstract: Valve plugs including hardened tips are disclosed herein. An example apparatus disclosed herein includes a plug holder, a sleeve coupled to the plug holder, the sleeve including a lip, a valve plug tip retained by the lip, and a spacer disposed between the plug holder and the tip, the sleeve surrounding the spacer.

Abstract: An electronic apparatus performs a method of decoding video data. The method includes: receiving, from the video signal, a picture frame that includes a first component and a second component; determining a classifier for the second component from a set of one or more samples of the first component associated with a respective sample of the second component; determining whether to modify a value of the respective sample of the second component of a current block of the picture frame within a virtual boundary according to the classifier; in response to the determination to modify the value of the respective sample of the second component of the current block according to the classifier, determining a sample offset for the respective sample of the second component according to the classifier; and modifying the value of the respective sample of the second component based on the determined sample offset.

Abstract: Methods, compositions, and kits for adhering polymers and other materials to another material, and in particular to bone or bone-like structures or surfaces. A composition of matter includes a urethane dimethacrylate-methyl methacrylate copolymer with a plurality of first polymer regions based on urethane dimethacrylate and a plurality of second polymer regions based on methyl methacrylate. The method includes placing an orthopedic joint implant having an attachment surface in a joint space, applying a first non-urethane-containing precursor, a second urethane-containing precursor, and a initiator to the attachment surface; contacting the first and second precursors and the initiator with the joint surface; and copolymerizing the first and second precursors and forming an adhesive copolymer and attaching the implant to the joint.

Abstract: Methods, apparatuses, and non-transitory computer-readable storage mediums are provided for video decoding. A decoder may receive a video stream. The decoder may receive a control flag in a slice header level. The decoder may receive at least one syntax element in the slice header level. The decoder may entropy decode the video bitstream based on the control flag and the at least one syntax element.

Abstract: Methods, apparatuses, and non-transitory computer-readable storage mediums are provided for video encoding. An encoder may receive a video input. The encoder may obtain a quantization parameter based on the video input. The encoder may derive a rice parameter based on at least one predefined threshold, a coding bit-depth, and the quantization parameter. The encoder may entropy encode a video bitstream based on the rice parameter.

Abstract: An electronic apparatus performs a method of decoding video data. The method comprises: receiving, from the video signal, a picture frame that includes a first component and a second component; determining a classifier for the second component from a set of samples of the first component associated with a respective sample of the second component; when the set of samples of the first component associated with the respective sample of the second component is divided by a virtual boundary, copying one or more central subsets of the set of samples of the first component to a first boundary position and a second boundary position of the set of samples of the first component; determining a sample offset for the respective sample of the second component according to the classifier; and modifying the value of the respective sample of the second component based on the determined sample offset.

Abstract: Methods, apparatuses, and non-transitory computer-readable storage mediums are provided for encoding video. An encoder may obtain a first motion vector (MV) associated with a video block obtained from the video. The encoder may obtain a first prediction signal of the video block using the first MV. The encoder may identify a target MV by applying a gradient-based motion refinement algorithm in a recursive manner using the first prediction signal and the first MV. The encoder may obtain a second prediction signal of the video block based on the target MV.

Abstract: A method and an apparatus for hybrid training of neural networks for video coding are provided. The method includes: obtaining, in an offline training stage, an offline trained network by training a first neural network offline using a plurality of first data sets; refining, in an online training stage, a plurality of neural network layers using a plurality of second data sets, wherein the plurality of neural network layers comprise at least one neural network layer in the offline trained network or in a second neural network connected to the offline trained network.