Abstract: Systems, methods, and media for machine vision-guided robotic loading are provided. Such systems and methods can allow for more accurate object detection so as to allow for robotic picking or loading of an object from a pile or group of objects. In some embodiments, a camera is controlled to capture a background image of the object or group of objects. One or more illumination sources (such as, e.g., laser illuminators) are controlled to project immunization toward the object or group of objects within the field-of-view of the camera. The camera may be controlled to capture a scan image comprising the projected illumination. Based on the scan image, a depth map can be determined, which is provided to a machine learning model along with the background image. The output of the machine learning model can then provide information associated with one or more objects, such as location information, orientation information, and the like.

Abstract: A non-contact system for the sensing the concentration of a compound includes a hyperspectral imaging device configured to capture a hyperspectral image of a fluid, a flow cell configured to enable the capturing of a hyperspectral image of a fluid, a process, and a memory. The memory includes instructions stored thereon which, when executed by the processor, cause the system to generate a hyperspectral image of the fluid in the flow cell, generate several spectral signals based on the hyperspectral image, provide the spectral signal as an input to a machine learning network, and predict by the machine learning network the concentration of a compound in a fluid.

Abstract: A non-contact system for the sensing the concentration of a compound includes a hyperspectral imaging device configured to capture a hyperspectral image of a fluid, a flow cell configured to enable the capturing of a hyperspectral image of a fluid, a process, and a memory. The memory includes instructions stored thereon which, when executed by the processor, cause the system to generate a hyperspectral image of the fluid in the flow cell, generate several spectral signals based on the hyperspectral image, provide the spectral signal as an input to a machine learning network, and predict by the machine learning network the concentration of a compound in a fluid.

Abstract: A non-contact system for the sensing of pH includes a hyperspectral imaging device configured to capture a hyperspectral image of a fluid, a flow cell configured to enable the capturing of a hyperspectral image of a fluid, a process, and a memory. The memory includes instructions stored thereon, which, when executed by the processor, cause the system to generate a hyperspectral image of the fluid in the flow cell, generate several spectral signals based on the hyperspectral image, provide the spectral signal as an input to a machine learning network, and predict by the machine learning network a pH of a fluid.

Abstract: A non-contact system for the sensing of pH includes a hyperspectral imaging device configured to capture a hyperspectral image of a fluid, a flow cell configured to enable the capturing of a hyperspectral image of a fluid, a process, and a memory. The memory includes instructions stored thereon, which, when executed by the processor, cause the system to generate a hyperspectral image of the fluid in the flow cell, generate several spectral signals based on the hyperspectral image, provide the spectral signal as an input to a machine learning network, and predict by the machine learning network a pH of a fluid.

Abstract: An automated method for crab meat picking which has an image processing system configured with an imaging sub-system and an image processing sub-system acting in cooperation with each other where the image processing system operates in accordance with a processing routine. An image of a crab to be processed is obtained by the imaging sub-system which is transmitted to the image processing system where the processing routine determines appropriate cut lines to be made on the crab being processed based on instructions from the image processing sub-system. The crab being processed is then cut in accordance with the appropriate cutting lines developed.

Abstract: Systems, methods, and media for machine vision-guided robotic loading are provided. Such systems and methods can allow for more accurate object detection so as to allow for robotic picking or loading of an object from a pile or group of objects. In some embodiments, a camera is controlled to capture a background image of the object or group of objects. One or more illumination sources (such as, e.g., laser illuminators) are controlled to project immunization toward the object or group of objects within the field-of-view of the camera. The camera may be controlled to capture a scan image comprising the projected illumination. Based on the scan image, a depth map can be determined, which is provided to a machine learning model along with the background image. The output of the machine learning model can then provide information associated with one or more objects, such as location information, orientation information, and the like.

Abstract: A non-contact system for the sensing the concentration of a compound includes a hyperspectral imaging device configured to capture a hyperspectral image of a fluid, a flow cell configured to enable the capturing of a hyperspectral image of a fluid, a process, and a memory. The memory includes instructions stored thereon which, when executed by the processor, cause the system to generate a hyperspectral image of the fluid in the flow cell, generate several spectral signals based on the hyperspectral image, provide the spectral signal as an input to a machine learning network, and predict by the machine learning network the concentration of a compound in a fluid.

Abstract: A non-contact system for the sensing of pH includes a hyperspectral imaging device configured to capture a hyperspectral image of a fluid, a flow cell configured to enable the capturing of a hyperspectral image of a fluid, a process, and a memory. The memory includes instructions stored thereon, which, when executed by the processor, cause the system to generate a hyperspectral image of the fluid in the flow cell, generate several spectral signals based on the hyperspectral image, provide the spectral signal as an input to a machine learning network, and predict by the machine learning network a pH of a fluid.

Abstract: Intelligence guided system and method for fruits and vegetables processing includes a conveyor for carrying produces, various image acquiring and processing hardware and software, water and air jets for cutting and controlling the position and orientation of the produces, and a networking hardware and software, operating in synchronism in an efficient manner to attain speed and accuracy of the produce cutting and high yield and low waste produces processing. The 2nd generation strawberry decalyxing system (AVID2) uniquely utilizes a convolutional neural network (AVIDnet) supporting a discrimination network decision, specifically, on whether a strawberry is to be cut or rejected, and computing a multi-point cutline curvature to be cut along by rapid robotic cutting tool.

Abstract: A vision guided intelligent system for automated crab meat picking operates in a fully automated or semi-automatic modes of operation using a crab meat picking routine based on (a) the CNN model localization of back-fin knuckles algorithm, and/or (b) the Deep Learning model which accurately locates not only knuckle positions, but also crab legs and crab cores, with a high pixel accuracy (up to 0.9843), and low computation time. The subject system uses a concept of analyzing crab morphologies obtained from digital crab images, and, using a Deep Learning architecture integrated in the system, segments crab images into five regions of interest in a single step with high accuracy and efficiency. The image segmentation results are used for generating crab cut lines in XYZ and angular directions, determining starting cutting points in Z plane, and guiding cutting tools and end effectors to automatically cut crabs and harvest crab meat.

Abstract: Intelligence guided system and method for fruits and vegetables processing includes a conveyor for carrying produces, various image acquiring and processing hardware and software, water and air jets for cutting and controlling the position and orientation of the produces, and a networking hardware and software, operating in synchronism in an efficient manner to attain speed and accuracy of the produce cutting and high yield and low waste produces processing. The 2nd generation strawberry decalyxing system (AVID2) uniquely utilizes a convolutional neural network (AVIDnet) supporting a discrimination network decision, specifically, on whether a strawberry is to be cut or rejected, and computing a multi-point cutline curvature to be cut along by rapid robotic cutting tool.

Abstract: A thermally conductive composition particularly well suited for use as an underfill composition including a curable resin and filler particles having an average diameter of less than 25 microns, wherein the filler particles are present in an amount sufficient to provide a thermal conductivity of greater than 0.5 W/mK and a viscosity of less than 0.600 Pa·s at 90° C. as measured with a 20 mm parallel plate at shear rate of 30 1/s.