Abstract: Generally described, one or more aspects of the present application relate to improving the process of generating and deploying software applications in a network environment, particularly software applications that incorporate or rely upon machine learning models. More specifically, the present disclosure provides specific user interface features and associated computer-implemented features that may effectively, from a user's perspective, remove most of the complexities associated with writing and deploying code and developing and improving machine learning models. For example, the present disclosure may provide user-friendly visual building blocks that allow users to build and customize machine learning workflows that can then be turned into a full software application and optimized and deployed at target destinations of the users' choice.

Abstract: Presented herein are systems, methods and apparatuses for increasing reliability of face recognition in analysis of images captured by drone mounted imaging sensors, comprising: recognizing a target person in one or more iterations, each iteration comprising: identifying one or more positioning properties of the target person based on analysis of image(s) captured by imaging sensor(s) mounted on a drone operated to approach the target person, instructing the drone to adjust its position to an optimal facial image capturing position selected based on the positioning property(s), receiving facial image(s) of the target person captured by the imaging sensor(s), receiving a face classification associated with a probability score from machine learning model(s) trained to recognize the target person, and initiating another iteration in case the probability score does not exceed a certain threshold. Finally, the face classification may be outputted for use by one or more face recognition based systems.

Abstract: Generally described, one or more aspects of the present application relate to improving the process of generating and deploying software applications in a network environment, particularly software applications that incorporate or rely upon machine learning models. More specifically, the present disclosure provides specific user interface features and associated computer-implemented features that may effectively, from a user's perspective, remove most of the complexities associated with writing and deploying code and developing and improving machine learning models. For example, the present disclosure may provide user-friendly visual building blocks that allow users to build and customize machine learning workflows that can then be turned into a full software application and optimized and deployed at target destinations of the users' choice.

Abstract: Generally described, one or more aspects of the present application relate to improving the process of generating and deploying software applications in a network environment, particularly software applications that incorporate or rely upon machine learning models. More specifically, the present disclosure provides specific user interface features and associated computer-implemented features that may effectively, from a user's perspective, remove most of the complexities associated with writing and deploying code and developing and improving machine learning models. For example, the present disclosure may provide user-friendly visual building blocks that allow users to build and customize machine learning workflows that can then be turned into a full software application and optimized and deployed at target destinations of the users' choice.

Abstract: Provided herein are systems and methods for applying adaptive classes thresholds to enhance object detection Machine Learning (ML) models by receiving a plurality of labeled feature vectors extracted from a plurality of images associated with a plurality of objects, one or more subsets of the plurality of feature vectors are associated with respective object(s) and labeled accordingly, computing an adaptive threshold for each object in a plurality of iterations, each iteration comprising: (1) computing deviation of a respective feature vector of the subset from an aggregated feature vector, (2) computing, in case the deviation is within a predefined value, a threshold enclosing the respective feature vector, and (3) adjusting the adaptive threshold to enclose the threshold of the respective feature vector and outputting the adaptive threshold(s) for classifying unlabeled feature vectors to class(s) of respective object(s) associated with the adaptive threshold(s) in which the unlabeled feature vectors fall.

Abstract: Presented herein are systems and methods for increasing reliability of object detection, comprising, receiving a plurality of images of one or more objects captured by imaging sensor(s), receiving an object classification coupled with a first probability score from machine learning model(s) trained to detect the object(s) and applied to the image(s), computing a second probability score for classification of the object(s) according to physical attribute(s) of the object(s) estimated by analyzing the image(s), computing a third probability score for classification of the object(s) according to a movement pattern of the object(s) estimated by analyzing at least some consecutive images, computing an aggregated probability score aggregating the first, second and third probability scores, and outputting, in case the aggregated probability score exceeds a certain threshold, the classification of each object coupled with the aggregated probability score for use by object detection based system(s).

Abstract: Presented herein are systems and methods for increasing reliability of object detection, comprising, receiving a plurality of images of one or more objects captured by imaging sensor(s), receiving an object classification coupled with a first probability score from machine learning model(s) trained to detect the object(s) and applied to the image(s), computing a second probability score for classification of the object(s) according to physical attribute(s) of the object(s) estimated by analyzing the image(s), computing a third probability score for classification of the object(s) according to a movement pattern of the object(s) estimated by analyzing at least some consecutive images, computing an aggregated probability score aggregating the first, second and third probability scores, and outputting, in case the aggregated probability score exceeds a certain threshold, the classification of each object coupled with the aggregated probability score for use by object detection based system(s).

Abstract: Provided herein are systems and methods for applying adaptive classes thresholds to enhance object detection Machine Learning (ML) models by receiving a plurality of labeled feature vectors extracted from a plurality of images associated with a plurality of objects, one or more subsets of the plurality of feature vectors are associated with respective object(s) and labeled accordingly, computing an adaptive threshold for each object in a plurality of iterations, each iteration comprising: (1) computing deviation of a respective feature vector of the subset from an aggregated feature vector, (2) computing, in case the deviation is within a predefined value, a threshold enclosing the respective feature vector, and (3) adjusting the adaptive threshold to enclose the threshold of the respective feature vector and outputting the adaptive threshold(s) for classifying unlabeled feature vectors to class(s) of respective object(s) associated with the adaptive threshold(s) in which the unlabeled feature vectors fall.

Abstract: Presented herein are systems, methods and apparatuses for increasing reliability of face recognition in analysis of images captured by drone mounted imaging sensors, comprising: recognizing a target person in one or more iterations, each iteration comprising: identifying one or more positioning properties of the target person based on analysis of image(s) captured by imaging sensor(s) mounted on a drone operated to approach the target person, instructing the drone to adjust its position to an optimal facial image capturing position selected based on the positioning property(s), receiving facial image(s) of the target person captured by the imaging sensor(s), receiving a face classification associated with a probability score from machine learning model(s) trained to recognize the target person, and initiating another iteration in case the probability score does not exceed a certain threshold. Finally, the face classification may be outputted for use by one or more face recognition based systems.

Abstract: Systems and methods for spinwave-based metrology in accordance with embodiments of the disclosure involve generating and detecting spinwaves in a sample having a ferromagnetic material; and determining a material thickness, a material integrity measure, a presence of a manufacturing defect, a categorical type of manufacturing defect, and/or a manufacturing process statistic corresponding to spinwave behavior in the sample. In an embodiment, spinwaves are generated by way of concurrent exposure of a target measurement site of the sample to each of a bias magnetic field and radiation (e.g., microwave or radio frequency radiation) produced by a first set of integrated waveguides. A response signal corresponding to a behavior of spinwaves within the target measurement site can be generated by way of a second set of integrated waveguides.

Abstract: Systems and methods for spinwave-based metrology in accordance with embodiments of the disclosure involve generating and detecting spinwaves in a sample having a ferromagnetic material; and determining a material thickness, a material integrity measure, a presence of a manufacturing defect, a categorical type of manufacturing defect, and/or a manufacturing process statistic corresponding to spinwave behavior in the sample. In an embodiment, spinwaves are generated by way of concurrent exposure of a target measurement site of the sample to each of a bias magnetic field and radiation (e.g., microwave or radio frequency radiation) produced by a first set of integrated waveguides. A response signal corresponding to a behavior of spinwaves within the target measurement site can be generated by way of a second set of integrated waveguides.