Abstract: An example computing platform is configured to (i) receive a two-dimensional (2D) image file comprising a construction drawing, (ii) generate, via semantic segmentation, a first set of polygons corresponding to respective areas of the 2D image file, (iii) generate, via instance segmentation, a second set of polygons corresponding to respective areas of the 2D image file, (iv) generate, via unsupervised image processing, a third set of polygons corresponding to respective areas of the 2D image file, (v) based on (a) overlap between polygons in the first, second, and third sets of polygons and (b) respective confidence scores for each of the overlapping polygons, determine a set of merged polygons corresponding to respective areas of the 2D image file, and (vi) cause a client station to display a visual representation of the 2D image file where each merged polygon is overlaid as a respective selectable region of the 2D image file.

Abstract: An example computing platform is configured to (i) receive a two-dimensional (2D) image file comprising a construction drawing, (ii) generate, via semantic segmentation, a first set of polygons corresponding to respective areas of the 2D image file, (iii) generate, via instance segmentation, a second set of polygons corresponding to respective areas of the 2D image file, (iv) generate, via unsupervised image processing, a third set of polygons corresponding to respective areas of the 2D image file, (v) based on (a) overlap between polygons in the first, second, and third sets of polygons and (b) respective confidence scores for each of the overlapping polygons, determine a set of merged polygons corresponding to respective areas of the 2D image file, and (vi) cause a client station to display a visual representation of the 2D image file where each merged polygon is overlaid as a respective selectable region of the 2D image file.

Abstract: An example computing platform comprising is configured to (i) receive, via one or more cameras positioned on a construction site, a plurality of images, (ii) detect, within the plurality of images, a plurality of objects being worn by respective workers on the construction site, (iii) select, from the plurality of images, a set of images depicting a particular worker, and (iv) based on the selected set of images depicting the particular worker, determine a plurality of trade probabilities for the particular worker, each trade probability in the plurality of trade probabilities indicating a likelihood that the particular worker belongs to a particular trade from among a plurality of trades.

Abstract: A computer-implemented method for monitoring productivity, health and safety risks posed by activities and objects, and other signals present at industrial sites comprises: receiving data inputs from input devices at an industrial site; selecting a data model that is programmed to detect activities or objects associated with workers or equipment present at the industrial sites; applying the data inputs to the data model to receive output data specifying whether the activities or objects associated with workers or equipment are present at the industrial site; and if they are present: based the output data, determining characteristics of the activities or objects; based on the characteristics, determining whether that the activities or objects cause any productivity, health or safety risks at the industrial site; and if so, generating notifications indicating the health or safety risks at the industrial site.

Abstract: An example computing platform comprising is configured to (i) receive, via one or more cameras positioned on a construction site, a plurality of images, (ii) detect, within the plurality of images, a plurality of objects being worn by respective workers on the construction site, (iii) select, from the plurality of images, a set of images depicting a particular worker, and (iv) based on the selected set of images depicting the particular worker, determine a plurality of trade probabilities for the particular worker, each trade probability in the plurality of trade probabilities indicating a likelihood that the particular worker belongs to a particular trade from among a plurality of trades.

Abstract: An example computing platform is configured to (i) receive a two-dimensional (2D) image file comprising a construction drawing, (ii) generate, via semantic segmentation, a first set of polygons corresponding to respective areas of the 2D image file, (iii) generate, via instance segmentation, a second set of polygons corresponding to respective areas of the 2D image file, (iv) generate, via unsupervised image processing, a third set of polygons corresponding to respective areas of the 2D image file, (v) based on (a) overlap between polygons in the first, second, and third sets of polygons and (b) respective confidence scores for each of the overlapping polygons, determine a set of merged polygons corresponding to respective areas of the 2D image file, and (vi) cause a client station to display a visual representation of the 2D image file where each merged polygon is overlaid as a respective selectable region of the 2D image file.

Abstract: A computer-implemented method for monitoring productivity, health and safety risks posed by activities and objects, and other signals present at industrial sites comprises: receiving data inputs from input devices at an industrial site; selecting a data model that is programmed to detect activities or objects associated with workers or equipment present at the industrial sites; applying the data inputs to the data model to receive output data specifying whether the activities or objects associated with workers or equipment are present at the industrial site; and if they are present: based the output data, determining characteristics of the activities or objects; based on the characteristics, determining whether that the activities or objects cause any productivity, health or safety risks at the industrial site; and if so, generating notifications indicating the health or safety risks at the industrial site.

Abstract: An example computing platform comprising is configured to (i) receive, via one or more cameras positioned on a construction site, a plurality of images, (ii) detect, within the plurality of images, a plurality of objects being worn by respective workers on the construction site, (iii) select, from the plurality of images, a set of images depicting a particular worker, and (iv) based on the selected set of images depicting the particular worker, determine a plurality of trade probabilities for the particular worker, each trade probability in the plurality of trade probabilities indicating a likelihood that the particular worker belongs to a particular trade from among a plurality of trades.

Abstract: An example computing platform is configured to (i) receive a two-dimensional (2D) image file comprising a construction drawing, (ii) generate, via semantic segmentation, a first set of polygons corresponding to respective areas of the 2D image file, (iii) generate, via instance segmentation, a second set of polygons corresponding to respective areas of the 2D image file, (iv) generate, via unsupervised image processing, a third set of polygons corresponding to respective areas of the 2D image file, (v) based on (a) overlap between polygons in the first, second, and third sets of polygons and (b) respective confidence scores for each of the overlapping polygons, determine a set of merged polygons corresponding to respective areas of the 2D image file, and (vi) cause a client station to display a visual representation of the 2D image file where each merged polygon is overlaid as a respective selectable region of the 2D image file.

Abstract: Photonic devices including a distributed Bragg reflector (DBR) having a stack of Group III-Nitride layers and Aluminum Scandium Nitride layers.

Abstract: Photonic devices having a photonic waveguiding layer, and a cladding layer, disposed on the photonic waveguiding layer, and where the cladding section is a material comprising Scandium. The cladding layer may include a material comprising Al1-xScxN material where 0<x?0.45.

Abstract: Photonic devices having a quantum well structure that includes a Group III-N material, and a Al1-xScxN cladding layer disposed on the quantum well structure, where 0<x?0.45, the Al1-xScxN cladding layer having a lower refractive index than the index of refraction of the quantum well structure.

Abstract: A Group III-Nitride quantum well laser including a distributed Bragg reflector (DBR). In some embodiments, the DBR includes Scandium. In some embodiments, the DBR includes Al1-xScxN, which may have 0<x?0.45.

Abstract: An example computing platform is configured to (i) receive a two-dimensional (2D) image file comprising a construction drawing, (ii) generate, via semantic segmentation, a first set of polygons corresponding to respective areas of the 2D image file, (iii) generate, via instance segmentation, a second set of polygons corresponding to respective areas of the 2D image file, (iv) generate, via unsupervised image processing, a third set of polygons corresponding to respective areas of the 2D image file, (v) based on (a) overlap between polygons in the first, second, and third sets of polygons and (b) respective confidence scores for each of the overlapping polygons, determine a set of merged polygons corresponding to respective areas of the 2D image file, and (vi) cause a client station to display a visual representation of the 2D image file where each merged polygon is overlaid as a respective selectable region of the 2D image file.

Abstract: An example computing platform comprising is configured to (i) receive, via one or more cameras positioned on a construction site, a plurality of images, (ii) detect, within the plurality of images, a plurality of objects being worn by respective workers on the construction site, (iii) select, from the plurality of images, a set of images depicting a particular worker, and (iv) based on the selected set of images depicting the particular worker, determine a plurality of trade probabilities for the particular worker, each trade probability in the plurality of trade probabilities indicating a likelihood that the particular worker belongs to a particular trade from among a plurality of trades.

Abstract: A computer-implemented method and system for neural network-based recognition of trade workers present on industrial sites is presented.

Abstract: An example computing platform is configured to (i) receive a two-dimensional (2D) image file comprising a construction drawing, (ii) generate, via semantic segmentation, a first set of polygons corresponding to respective areas of the 2D image file, (iii) generate, via instance segmentation, a second set of polygons corresponding to respective areas of the 2D image file, (iv) generate, via unsupervised image processing, a third set of polygons corresponding to respective areas of the 2D image file, (v) based on (a) overlap between polygons in the first, second, and third sets of polygons and (b) respective confidence scores for each of the overlapping polygons, determine a set of merged polygons corresponding to respective areas of the 2D image file, and (vi) cause a client station to display a visual representation of the 2D image file where each merged polygon is overlaid as a respective selectable region of the 2D image file.

Abstract: Photonic devices having a photonic waveguiding layer, and a cladding layer, disposed on the photonic waveguiding layer, and where the cladding section is a material comprising Scandium. The cladding layer may include a material comprising Al1-xScxN material where 0<x?0.45.

Abstract: Photonic devices having a quantum well structure that includes a Group III-N material, and a Al1-xScxN cladding layer disposed on the quantum well structure, where 0<x?0.45, the Al1-xScxN cladding layer having a lower refractive index than the index of refraction of the quantum well structure.

Abstract: A Group III-Nitride quantum well laser including a distributed Bragg reflector (DBR). In some embodiments, the DBR includes Scandium. In some embodiments, the DBR includes Al1-xScxN, which may have 0<x?0.45.