Abstract: Traditional food quality monitoring systems fail to monitor the variation of food quality in real-time scenarios. Existing machine learning approaches require dedicated data models for different classes of food items due to differences in characteristics of different food items. Also, to generate such data models, a lot of annotated data is required per food item, which are expensive. The disclosure herein generally relates to monitoring and shelf-life prediction of food items, and, more particularly, to system and method for real-time monitoring and shelf-life prediction of food items. The system generates a data model using a knowledge graph indicative of a hierarchical taxonomy for a plurality of categories of the plurality of food items, which in turn contains metadata representing similarities in physio-chemical degradation pattern of different classes of the food items. This data model serves as a generic data model for real-time shelf-life prediction of different food items.

Abstract: A computer-implemented method for defect analysis is provided. The computer-implemented method includes obtaining a plurality of sets of defect point coordinates, a respective set of the plurality of sets of detect point coordinates including coordinates of defect points in a respective substrate of a plurality of substrates, the coordinates of defect points in the respective substrate being coordinates in an image coordinate system; combining the plurality of sets of defect point coordinates according to the image coordinate system into a composite set of coordinates to generate a composite image; and performing a clustering analysis to classify defect points in the composite set in the composite image into a plurality of clusters.

Abstract: This disclosure presents a process to determine an alignment parameter for geosteering a wellbore undergoing drilling operations. The process can receive one or more azimuthal image log data sets, one or more geology logs, and other input parameters. The image log data sets can be transformed to better approximate the geology logs, such as transforming a 3D representation to a 2D representation and flattening out curves represented in the original image log data. The geology logs or transformed image log data can then be moved to create an approximate alignment between the other log data. The movement, which can be a sliding movement, a linear movement, a tilting movement, an angling movement, or a rotating movement, can be used to determine the determined alignment parameter or final alignment parameter. The alignment parameter can be used as input into a geosteering system for the wellbore.

Abstract: A method for assessing cardiothoracic ratio (CTR) includes following steps. A testing X-ray image database of a subject is provided. A first image data classifying step is performed, wherein the testing X-ray image database is classified by a first deep learning neural network classifier to obtain a testing chest X-ray image data. A second image data classifying step is performed, wherein the testing chest X-ray image data is classified by a second deep learning neural network classifier to obtain a target chest X-ray image data. A feature extracting step is performed, wherein a diameter of thoracic cavity and a diameter of cardiac silhouette of the target chest X-ray image data are captured automatically and then trained to achieve a convergence by a third deep learning neural network classifier. An assessing step is performed, wherein an assessing result of CTR is obtained according to a feature of CTR.

Abstract: A method of generating a segmentation confidence map by processing classification values each indicating a respective classification of a respective voxel of a retinal C-scan into a respective retinal layer class of a predefined set of retinal layer classes, the method comprising: generating, for each voxel, a respective confidence value which indicates a level of confidence in the classification of the voxel; for a retinal layer class of the predefined set, identifying a subset of the voxels such that the classification value generated for each voxel indicates a classification of the voxel into the retinal layer class; calculating, for each A-scan having voxels in the identified subset, a respective average of the confidence indicator values generated for the voxels; and using the calculated averages to generate the map, which indicates a spatial distribution of a level of confidence in the classification of the voxels.

Abstract: Various embodiments of the present disclosure provide for accelerated segmentation for reverse engineering of integrated circuits. In one example, an embodiment provides for receiving an SEM image for an integrated circuit, performing filtering and binarization with respect to the SEM image, extracting information associated with filter sizes for the filtering, extracting signatures related to a distribution for background pixels and foreground pixels of the SEM image, extracting respective distance to mean signatures for the background pixels and the foreground pixels, and segmenting the SEM image based at least in part on the filter sizes and the respective distance to mean signatures to generate a segmented image for the integrated circuit.