Abstract: A video processing pipeline receives data derived from a feed of images of a plurality of objects passing in front of an inspection camera module forming part of a quality assurance inspection system. Quality assurance metrics for the object are generated by one or more containerized image analysis inspection tools forming part of the video processing pipeline using the received data for each object. Overlay images are later generated that characterize the quality assurance metrics. These overlay images are combined with the corresponding image of the object to generate an enhanced image of each of the objects. These enhanced images are provided to a consuming application or process for quality assurance analysis.

Abstract: Data is received that includes a feed of images of a plurality of objects passing in front of an inspection camera module forming part of a quality assurance inspection system. Within each image, it is detected whether an object is present within the image. Instance identifiers are assigned to each object. A single image is identified in which the object is optimally represented for each object using the corresponding instance identifier. These identified images are provided to a consuming application or process for quality assurance analysis.

Abstract: Each of a plurality of co-located inspection camera modules captures raw images of objects passing in front of the co-located inspection camera modules which form part of a quality assurance inspection system. The inspection camera modules have either a different image sensor or lens focal properties and generate different feeds of raw images. The co-located inspection camera modules can be selectively switched amongst to activate the corresponding feed of raw images. The activated feed of raw images is provided to a consuming application or process for quality assurance analysis.

Abstract: The present invention provides a method of fraud detection in telecommunication including 4 steps: collecting user-profiling data, analyzing encrypted text messages, extracting features, building fraudulent subscribers classification model, developing keyword rule to detect fraudulent subscribers, proposing multiple options to prevent fraudulent subscribers in realtime.

Abstract: Data is received that includes a feed of images of a plurality of objects passing in front of an inspection camera module forming part of a quality assurance inspection system. Thereafter, a machine learning model is used to generate a representation of each image. These representations are analyzed to determine a type of object captured in the corresponding image. This analysis can be provided to a consuming application or process for quality assurance analysis.

Abstract: Data is received that includes a feed of images of a plurality of objects passing in front of an inspection camera module forming part of a quality assurance inspection system. A representation is generated for each image using a first machine learning model. One or more second machine learning models are then used to analyze each image using the corresponding representation. The analyses can be provided to a consuming application or process for quality assurance analysis.

Abstract: Data is received that includes a feed of images of a plurality of objects passing in front of an inspection camera module forming part of an inspection system. Thereafter, a representation for each image is generated using a first machine learning model and based on the received data. Later, one or more second machine learning models can cluster the images using the corresponding representations into groups that each correspond to one of a plurality of different object attributes. Thereafter, access to the groups can be provided to a consuming application or process for analysis and the like. In some variations, the representations are analyzed by at least one third machine learning model prior to the clustering. In other variations, the representations are analyzed by at least one third machine learning model after the clustering. Related apparatus, systems, and methods are also described.

Abstract: Each of a plurality of co-located inspection camera modules captures raw images of objects passing in front of the co-located inspection camera modules which form part of a quality assurance inspection system. The inspection camera modules have either a different image sensor or lens focal properties and generate different feeds of raw images. The co-located inspection camera modules can be selectively switched amongst to activate the corresponding feed of raw images. The activated feed of raw images is provided to a consuming application or process for quality assurance analysis.

Abstract: Data is received that includes a feed of images of a plurality of objects passing in front of an inspection camera module forming part of a quality assurance inspection system. Within each image, it is detected whether an object is present within the image. Instance identifiers are assigned to each object. A single image is identified in which the object is optimally represented for each object using the corresponding instance identifier. These identified images are provided to a consuming application or process for quality assurance analysis.

Abstract: Data is received that includes a feed of images of a plurality of objects passing in front of an inspection camera module forming part of a quality assurance inspection system. Thereafter, it is detected whether there is an object within each image. Based on this detection, images in which each object is detected that meet predefined object representation parameters are identified (on an object-by-object basis, etc.). The identified images are provided to a consuming application or process for quality assurance analysis. Related apparatus, systems, techniques and articles are also described.

Abstract: Each of a plurality of co-located inspection camera modules captures raw images of objects passing in front of the co-located inspection camera modules which form part of a quality assurance inspection system. The inspection camera modules have either a different image sensor or lens focal properties and generate different feeds of raw images. The co-located inspection camera modules can reside within a single standalone module and be selectively switched amongst to activate the corresponding feed of raw images. The activated feed of raw images is provided to a consuming application or process for quality assurance analysis.

Abstract: Disclosed herein are embodiments related to robot-aided product inspection. For example, an inspection apparatus may include a processing device to communicatively couple to a robotic apparatus, to a display device, and to a user input device. The processing device may: receive an image of an item, wherein the image was captured by the robotic apparatus; generate a proposed classification for the image, wherein the classification indicates an attribute of the item based on the image; cause the image to be displayed on the display device along with the proposed classification; and receive an indication from the user input device of a final classification of the image.

Abstract: Nucleic acid oligomers specific for human parvovirus genomic DNA. An assay for amplifying and detecting human parvovirus genotypes 1, 2 and 3 nucleic acid in biological specimens. Compositions for amplifying and detecting the presence of human parvovirus genotypes 1, 2 and 3 genomic DNA in human biological specimens.

Abstract: Nucleic acid oligomers specific for human parvovirus genomic DNA are disclosed. An assay for amplifying and detecting human parvovirus genotypes 1, 2 and 3 nucleic acid in biological specimens is disclosed. Compositions for amplifying and detecting the presence of human parvovirus genotypes 1, 2 and 3 genomic DNA in human biological specimens are disclosed.

Abstract: Data is received that includes a feed of images of a plurality of objects passing in front of an inspection camera module forming part of a quality assurance inspection system. Within each image, it is detected whether an object is present within the image. Instance identifiers are assigned to each object. A single image is identified in which the object is optimally represented for each object using the corresponding instance identifier. These identified images are provided to a consuming application or process for quality assurance analysis.

Abstract: A video processing pipeline receives data derived from a feed of images of a plurality of objects passing in front of an inspection camera module forming part of a quality assurance inspection system. Quality assurance metrics for the object are generated by one or more containerized image analysis inspection tools forming part of the video processing pipeline using the received data for each object. Overlay images are later generated that characterize the quality assurance metrics. These overlay images are combined with the corresponding image of the object to generate an enhanced image of each of the objects. These enhanced images are provided to a consuming application or process for quality assurance analysis.

Abstract: Disclosed herein are embodiments related to calibration and zeroing in robotic systems. For example, an apparatus for robotic zeroing may include a processing device to perform a joint zeroing operation on a first joint assembly of a robotic apparatus. The joint zeroing operation may include: determining a reference vector for zeroing the first joint assembly, wherein the first joint assembly is mechanically coupled to a base of the robotic apparatus; identifying an acceleration vector representative of an orientation of a second joint assembly of the robotic apparatus, wherein the first joint assembly is mechanically between the base and the second joint assembly; determining whether the acceleration vector is aligned with the reference vector; and, in response to a determination that the acceleration vector is not aligned with the reference vector, causing the first joint assembly to move.

Abstract: Disclosed herein are embodiments related to safety in robotic systems. For example, an apparatus for collaborative robotics may include a first segment, a second segment, and a joint assembly. The joint assembly may include a processing device and a stepper motor, the stepper motor may control a relative position of the first and second segments, the processing device may perform closed-loop control of the stepper motor and monitor one or more performance metrics, and the processing device may cause braking of the stepper motor when a value of at least one of the performance metrics is outside an allowable range.

Abstract: Disclosed herein are embodiments related to end effectors in robotic systems. For example, an end effector for a robotic apparatus may be couplable to the robotic apparatus, and the end effector may include a communication port for coupling to the robotic apparatus, a camera, and a lighting device, wherein a lighting color of the lighting device is controllable via the communication port.

Abstract: Disclosed herein are embodiments related to robot-aided product inspection. For example, an inspection apparatus may include a processing device to communicatively couple to a robotic apparatus, to a display device, and to a user input device. The processing device may: receive an image of an item, wherein the image was captured by the robotic apparatus; generate a proposed classification for the image, wherein the classification indicates an attribute of the item based on the image; cause the image to be displayed on the display device along with the proposed classification; and receive an indication from the user input device of a final classification of the image.