Phase space tomography methods and systems to facilitate the analysis and evaluation of complex, quasi-periodic system by generating computed phase-space tomographic images and mathematical features as a representation of the dynamics of the quasi-periodic cardiac systems. The computed phase-space tomographic images can be presented to a physician to assist in the assessment of presence or non-presence of disease. In some implementations, the phase space tomographic images are used as input to a trained neural network classifier configured to assess for presence or non-presence of pulmonary hypertension, including pulmonary arterial hypertension.

The present disclosure is related to systems and methods for orthosis design. The method includes obtaining a three-dimensional (3D) model associated with a subject. The method includes obtaining one or more reference images associated with the subject. The method includes determining, based on the 3D model and the one or more reference images, orthosis design data for the subject. The orthosis design data may be used to determine an orthosis for the subject.

A display apparatus and a display method for a multi-layer superimposition are provided. When a display presents a user interface, a touch trajectory from a user can be detected via a touch component, and a touch trajectory pattern is present in a first layer; upon obtaining the touch trajectory pattern, an interpolation operation can also be performed on the touch trajectory pattern in the first layer according to a background pattern in a second layer; finally, a converted pattern obtained after the interpolation operation and the background pattern are superimposed and presented on the display.

The present invention provides an image processing method, wherein the image processing method includes the steps of: receiving an image signal, wherein the image signal comprises a first frame and a second frame; performing motion estimation on the first frame and the second frame to generate an interpolated frame; for each of a plurality of areas of the interpolated frame, determining whether there is a block in the first frame that moves to the area, and determine whether there is a block in the second frame that moves to the area, to determine whether the area belongs to a cover area or an uncover area; and in response to a determination result indicating that the area belongs to the cover area or the uncover area, adjusting image contents of the interpolated frame.

The disclosed computer-implemented method may include (1) accessing a video portraying an object within a set of frames, (2) defining a subset of key frames within the video based on movement of the object across the set of frames, (3) generating, for each key frame within the subset of key frames, a spline outlining the object within the key frame, (4) receiving input to adjust, for a selected key frame within the subset of key frames, a corresponding spline, and (5) interpolating the adjusted spline with a spline in a sequentially proximate key frame to define the object in frames between the selected key frame and the sequentially proximate key frame. Various other methods, systems, and computer-readable media are also disclosed.

A system and method for generating soil moisture data from satellite images of a geographical area using a deep learning model 108 is provided. The system includes one or more satellites 102A-C, a soil moisture data generator server 106. The method includes, (i) receiving, by a soil moisture data generator server, satellite images of the geographical area, (ii) pre-processing first set of satellite images, second set of satellite images, and third set of satellite images, (iii) interpolating, using spline interpolation, pre-processed first set of images, pre-processed second set of images, and pre-processed third set of images to generate high-resolution set of images, (iv) generating hydrological parameters from the high-resolution set of images, (v) training, a deep learning model, by providing historical hydrological parameters and historical soil moisture data associated with historical satellite images as training data to generate trained deep learning model, (v) generating soil moisture data on daily basis.

Systems, methods, and devices are provided for automatically adjusting the scale or magnification of an intraluminal image on a display of an intraluminal imaging system based on a measured or computed size of the vessel. For example, a system may include a processor configured to receive, from an intraluminal imaging catheter or guidewire, a first intraluminal image of a body lumen, and compute a dimension of an anatomical feature of the body lumen based on the first intraluminal image. The processor computes a scaling factor for the first intraluminal image based on the dimension of the body lumen, scales the first intraluminal image by the scaling factor, and outputs the scaled first intraluminal image to a display in communication with the processor circuit.

Systems, methods, and computer-readable media for context-aware synthesis for video frame interpolation are provided. A convolutional neural network (ConvNet) may, given two input video or image frames, interpolate a frame temporarily in the middle of the two input frames by combining motion estimation and pixel synthesis into a single step and formulating pixel interpolation as a local convolution over patches in the input images. The ConvNet may estimate a convolution kernel based on a first receptive field patch of a first input image frame and a second receptive field patch of a second input image frame. The ConvNet may then convolve the convolutional kernel over a first pixel patch of the first input image frame and a second pixel patch of the second input image frame to obtain color data of an output pixel of the interpolation frame. Other embodiments may be described and/or claimed.

A display device for performing image processing by using a neural network including a plurality of layers, may obtain a plurality of pieces of model information respectively corresponding to pixels included in a first image based on object features respectively corresponding to the pixels; identify the plurality of pieces of model information respectively corresponding to the plurality of layers and the pixels input to the neural network based on information about a time point at which each of the pixels is processed in the neural network; update parameters of the plurality of layers, based on the plurality of pieces of model information; and obtain a second image by processing the first image via the plurality of layers to which the updated parameters are applied; and display the second image.

Disclosed is droplet ejecting apparatus that ejects droplets of a functional liquid onto a workpiece to draw a pattern. The droplet ejecting apparatus includes: a workpiece table; a droplet ejecting head configured to eject the droplets onto the workpiece placed on the workpiece table; a movement mechanism configured to relatively move the workpiece table and the droplet ejecting head in a main scanning direction and a sub-scanning direction; and a control unit configured to: detect a position of the workpiece or a position of the workpiece table while relatively moving the workpiece table and the droplet ejecting head along a plurality of scanning lines extending in the main scanning direction and set side by side in the sub-scanning direction; and create, based on a detection result, a correction table that indicates a correlation between a position of the movement mechanism and a positional correction amount of the workpiece table.