Build material handling unit (2) for a powder module (3) for an apparatus for additively manufacturing three-dimensional objects, which apparatus is adapted to successively layerwise selectively irradiate and consolidate layers of a build material (4) which can be consolidated by means of an energy source, wherein the build material handling unit (2) is coupled or can be coupled with a powder module (3), wherein the build material handling unit (2) is adapted to level and/or compact a volume of build material (4) arranged inside a powder chamber (5) of the powder module (3) by controlling the gas pressure inside the powder chamber (5).

The purpose of the present invention is to provide an image evaluation device and method which can detect unknown defects and which can prevent misrecognition by a machine learning model. This image evaluation device, which uses a machine learning classifier to classify defect information in a defect image of an electronic device, is characterized by being provided with: an image storage unit which stores a defect image of an electronic device; a defect region storage unit which stores defect region information that is in the defect image; a classifier which classifies the defect information with machine learning; an image extraction unit which, in the course of the defect image classification processing, extracts image-of-interest information which the classifier will focus on; and an evaluation unit which compares the image-of-interest information and the defect region information to evaluate the classifiability of the defect image.

A computer-implemented method for analyzing digital holographic microscopy (DHM) data for hematology applications includes receiving a DHM image acquired using a digital holographic microscopy system. The DHM image comprises depictions of one or more cell objects and background. A reference image is generated based on the DHM image. This reference image may then be used to reconstruct a fringe pattern in the DHM image into an optical depth map.

A multidimensional printer makes a multidimensional structure from a liquid composition and includes: an energetic crosslinking particle source; a vacuum chamber that receives energetic crosslinking particles from the energetic crosslinking particle source; a membrane that transmits the energetic crosslinking particles; and a sample chamber that: receives a liquid composition that includes a solvent and polymers, the polymers including a cross-linkable moiety subjected to the energetic crosslinking particles such that portions of the polymers proximate to the cross-linkable moieties subjected to the energetic crosslinking particles crosslink to form a solid crosslinked polymer structure, wherein the membrane isolates a vacuum of the vacuum chamber from vapor of the liquid composition in the sample chamber.

A method for automatically searching for at least one given textile pattern in a composite material reinforcement including a plurality of textile patterns, each textile pattern including a plurality of reinforcing yarns arranged according to a textile topology, the method including acquiring a three-dimensional image of the composite material reinforcement, and searching for the given textile pattern in the acquired three-dimensional image, using an artificial neural network trained on a training database to detect the given textile pattern in a three-dimensional image of a composite material reinforcement.

Innovations in the identification or classification of features in a data set are described, such as a data set representing measurements taken by a scientific instrument. For example, a task-specific processing component, such as a video encoder, is used to generate task-specific metadata. When the data set includes video frames, metadata can include information regarding motion of image elements between frames, or other differences between frames. A feature of the data set, such as an event, can be identified or classified based on the metadata. For example, an event can be identified when metadata for one or more elements of the data set exceed one or more threshold values. When the feature is identified or classified, an output, such as a display or notification, can be generated. Although the metadata may be useable to generate a task-specific output, such as compressed video data, the identifying or classifying is not used solely in production of, or the creation of an association with, the task-specific output.

A light field microscope may record a raw light field video of a sample. The raw video recording may be decomposed into a non-negative low-rank component and a non-negative sparse component. The low-rank component may correspond to a static portion of the sample, and the sparse component may correspond to a dynamically changing portion of the sample. Volume reconstruction may be performed on the sparse component to generate a three-dimensional video of the sample, with improved spatial resolution. In some cases, the decomposition is calculated by an alternating direction method of multipliers algorithm, with the non-negativity of the sparse component and low-rank component enforced after each iteration. In some cases, the volume reconstruction is calculated by Richardson-Lucy iteration with regularization. The sample may be fluorescent. The fluorescence may be indicative of neural activity in the sample.

Build material handling unit (2) for a powder module (3) for an apparatus for additively manufacturing three-dimensional objects, which apparatus is adapted to successively layerwise selectively irradiate and consolidate layers of a build material (4) which can be consolidated by means of an energy source, wherein the build material handling unit (2) is coupled or can be coupled with a powder module (3), wherein the build material handling unit (2) is adapted to level and/or compact a volume of build material (4) arranged inside a powder chamber (5) of the powder module (3) by controlling the gas pressure inside the powder chamber (5).

Visual observation of morphological features of a cell group or individual cells acquired in 3D image data is facilitated, thus improving observation accuracy. Provided is an image processing device that generates, on the basis of a plurality of 2D images acquired by a microscope at different focus positions on a cell clump, 3D images of respective cells constituting the cell clump, that processes the generated 3D images and analyzes feature amounts on the basis of at least one measurement parameter, that displays analysis results in a graph, that allows a user to select a region of interest on the displayed graph, and that generates, from the 3D images that correspond to the plurality of cells that are included in the selected region of interest, 2D display images each in a plane with reference to an axis that is determined on the basis of a shape feature of the corresponding cell and displays the 2D display images in a list.

Build material handling unit (2) for a powder module (3) for an apparatus for additively manufacturing three-dimensional objects, which apparatus is adapted to successively layerwise selectively irradiate and consolidate layers of a build material (4) which can be consolidated by means of an energy source, wherein the build material handling unit (2) is coupled or can be coupled with a powder module (3), wherein the build material handling unit (2) is adapted to level and/or compact a volume of build material (4) arranged inside a powder chamber (5) of the powder module (3) by controlling the gas pressure inside the powder chamber (5).