A LiDAR-based object detection method includes clustering a point cloud acquired from LiDAR, selecting a to-be-divided cluster among clusters generated in the clustering, and selecting division points according to a geometrical feature formed with adjacent points from among points belonging to the to-be-divided cluster, and dividing the to-be-divided cluster based on a representative point determined by at least some of the division points.

A method of clustering patterns of an integrated circuit includes; providing a pattern image and numeric data, as input data corresponding to a first pattern to a first model, wherein the first model is trained by a plurality of sample images and a plurality of sample values, obtaining a content latent variable using the first model, and grouping a plurality of content latent variables corresponding to a plurality of patterns into a plurality of clusters based on a Euclidean distance, wherein the numeric data represents at least one attribute of the first pattern.

A method of clustering patterns of an integrated circuit includes; providing a pattern image and numeric data, as input data corresponding to a first pattern to a first model, wherein the first model is trained by a plurality of sample images and a plurality of sample values, obtaining a content latent variable using the first model, and grouping a plurality of content latent variables corresponding to a plurality of patterns into a plurality of clusters based on a Euclidean distance, wherein the numeric data represents at least one attribute of the first pattern.

A method for semi-supervised learning via different modalities, the method may include obtaining a training sensed information units of a first modality that are associated with a certain pattern; obtaining multimodality information units that are untagged; wherein a multimodality information unit comprises a first modality portion and a second modality portion; searching for certain pattern related multimodality information units, wherein a certain pattern related multimodality information unit comprises a first modality portion that is related to the certain pattern; clustering the second portions of the certain pattern related multimodality information units to provide second portion clusters; generating certain pattern identifiers based on the second portion clusters; and responding to the generating of the certain pattern identifiers; wherein the responding comprises at least one out of storing the certain pattern identifiers, transmitting the certain pattern identifiers, and generating notifications to be sent once a signature of a query sensed information unit of the second modality comprises the certain pattern identifier.

A method for semi-supervised learning via different modalities, the method may include obtaining a training sensed information units of a first modality that are associated with a certain pattern; obtaining multimodality information units that are untagged; wherein a multimodality information unit comprises a first modality portion and a second modality portion; searching for certain pattern related multimodality information units, wherein a certain pattern related multimodality information unit comprises a first modality portion that is related to the certain pattern; clustering the second portions of the certain pattern related multimodality information units to provide second portion clusters; generating certain pattern identifiers based on the second portion clusters; and responding to the generating of the certain pattern identifiers; wherein the responding comprises at least one out of storing the certain pattern identifiers, transmitting the certain pattern identifiers, and generating notifications to be sent once a signature of a query sensed information unit of the second modality comprises the certain pattern identifier.

Systems and methods that address an optimized method to handle portfolio constraints such as integer budget constraints and solve portfolio optimization problems that map both to mixed binary and quadratic binary optimization problems. A digital processor is used to create a hierarchical clustering; this clustering is leveraged to allocate capital to sub-clusters of the hierarchy. Once the sub-clusters are sufficiently small, a quantum processor is used to solve the portfolio optimization problem. Thus, the innovation employs clustering to reduce an optimization problem to sub-problems that are sufficiently small enough to be solved using a quantum computer given available qubits.

There is provided a method and a system configured obtain an image of a semiconductor specimen including one or more arrays, each including repetitive structural elements, and one or more regions, each region at least partially surrounding a corresponding array and including features different from the repetitive structural elements, wherein the PMC is configured to, during run-time scanning of the semiconductor specimen, perform a correlation analysis between pixel intensity of the image and pixel intensity of a reference image informative of at least one of the repetitive structural elements, to obtain a correlation matrix, use the correlation matrix to distinguish between one or more first areas of the image corresponding to the one or more arrays and one or more second areas of the image corresponding the one or more regions, and output data informative of the one or more first areas of the image.

There is provided a method and a system configured obtain an image of a semiconductor specimen including one or more arrays, each including repetitive structural elements, and one or more regions, each region at least partially surrounding a corresponding array and including features different from the repetitive structural elements, wherein the PMC is configured to, during run-time scanning of the semiconductor specimen, perform a correlation analysis between pixel intensity of the image and pixel intensity of a reference image informative of at least one of the repetitive structural elements, to obtain a correlation matrix, use the correlation matrix to distinguish between one or more first areas of the image corresponding to the one or more arrays and one or more second areas of the image corresponding the one or more regions, and output data informative of the one or more first areas of the image.

The present disclosure provides a system for monitoring unstructured environments. A predetermined path can be determined according to an assignment of geolocations to one or more agronomically anomalous target areas, where the one or more agronomically anomalous target areas are determined according to an analysis of a plurality of first images that automatically identifies a target area that deviates from a determination of an average of the plurality of first images that represents an anomalous place within a predetermined area, where the plurality of first images of the predetermined area are captured by a camera during a flight over the predetermined area. A camera of an unmanned vehicle can capture at least one second image of the one or more agronomically anomalous target areas as the unmanned vehicle travels along the predetermined path.

An angled optical pattern is decoded. To decode an optical pattern imaged at an angle, an area of interest of an image is received. A start line and an end line of the optical pattern are estimated. Corners of the optical pattern are localized. A homography is calculated based on the corners. And a scanline of the optical pattern is rectified based on the homography.