Abstract: A method is disclosed for selecting an optimal value for an adjustable parameter of a structured light metrology (SLM) system, for scanning an object. The SLM system performs test scans of the object to acquire a plurality of sets of measurements of the object, wherein a different value is used for the parameter for each test scan. For each test scan, a value of a quality metric is calculated, based on the set of measurements of the object associated with the test scan and simulation data representing a simulated scan of the object by the SLM system. A test scan is then identified that has a quality metric value that satisfies a specified optimization criterion; and a value of the adjustable parameter that was used for the identified test scan is selected as the optimal value of the adjustable parameter, for scanning the object.

Abstract: A system for evaluating the effectiveness of an anti-counterfeiting measure employed for an item is provided. The system trains a classifier to indicate whether the anti-counterfeiting measure of an evaluation item is genuine or counterfeit. For evaluation items that have been classified as genuine or counterfeit, the system applies the classifier to determine whether the anti-counterfeiting measure of that evaluation item is genuine or counterfeit. The system then generates an effectiveness metric that indicates whether the anti-counterfeiting measure is effective based on evaluation items that are assessed as being genuine whose anti-counterfeiting measures are classified as being counterfeit.

Abstract: A method is disclosed for selecting an optimal value for an adjustable parameter of a structured light metrology (SLM) system, for scanning an object. The SLM system performs test scans of the object to acquire a plurality of sets of measurements of the object, wherein a different value is used for the parameter for each test scan. For each test scan, a value of a quality metric is calculated, based on the set of measurements of the object associated with the test scan and simulation data representing a simulated scan of the object by the SLM system. A test scan is then identified that has a quality metric value that satisfies a specified optimization criterion; and a value of the adjustable parameter that was used for the identified test scan is selected as the optimal value of the adjustable parameter, for scanning the object.

Abstract: Systems and methods for controlling a material extrusion device to extrude a filament of an ink are provided. An extrusion printing control system collects from one or more sensors measurements representing an internal state of material extrusion processing during extrusion of the filament. In addition, the system collects an image of the filament as the filament is extruded. The system applies a classifier to the collected image to generate an image-derived state characterizing the filament. Based on the internal state and the image-derived state, the system estimates a derived state using a model. The system determines control parameters using the model to achieve a desired quality of the filament by minimizing a cost function based on the internal state, the image-derived state, the derived state, and constraints of the material extrusion device. Finally, the system provides the control parameters to a controller of the material extrusion device.

Abstract: A method of manufacturing a physical object comprises, during a process of manufacturing the physical object by a machine, capturing image data of at least a portion of the physical object and other sensor data related to the machine or to the at least a portion of the physical object. The method further comprises, during the process of manufacturing the physical object, for each of the plurality of pixels of the image data, coregistering the image data with the other sensor data on a pixel-by-pixel basis, storing the coregistered image data and other sensor data in association with each other in a data structure, and using at least a portion of the coregistered image data and other sensor to detect an anomaly in the physical object or in the process of manufacturing the physical object.

Abstract: Systems and methods performed for generating authentication information for an image using optical computing are provided. When a user takes a photo of an object, an optical authentication system receives light reflected and/or emitted from the object. The system also receives a random key from an authentication server. The system converts the received light to plenoptic data and uploads it to the authentication server. In addition, the system generates an optical hash of the received light using the random key, converts the generated optical hash to a digital optical hash, and uploads the digital optical hash to the authentication server. When the authentication server receives the upload, it verifies whether the time of the upload is within a certain threshold time from the sending of the random key and whether the digital optical hash was generated from the same light as the plenoptic data.

Abstract: A system for evaluating the effectiveness of an anti-counterfeiting measure employed for an item is provided. The system trains a classifier to indicate whether the anti-counterfeiting measure of an evaluation item is genuine or counterfeit. For evaluation items that have been classified as genuine or counterfeit, the system applies the classifier to determine whether the anti-counterfeiting measure of that evaluation item is genuine or counterfeit. The system then generates an effectiveness metric that indicates whether the anti-counterfeiting measure is effective based on evaluation items that are assessed as being genuine whose anti-counterfeiting measures are classified as being counterfeit.

Abstract: Systems and methods performed for generating authentication information for an image using optical computing are provided. When a user takes a photo of an object, an optical authentication system receives light reflected and/or emitted from the object. The system also receives a random key from an authentication server. The system converts the received light to plenoptic data and uploads it to the authentication server. In addition, the system generates an optical hash of the received light using the random key, converts the generated optical hash to a digital optical hash, and uploads the digital optical hash to the authentication server. When the authentication server receives the upload, it verifies whether the time of the upload is within a certain threshold time from the sending of the random key and whether the digital optical hash was generated from the same light as the plenoptic data.

Abstract: A system is provided to automatically monitor and control the operation of a microfluidic device using machine learning technology. The system receives images of a channel of a microfluidic device collected by a camera during operation of the microfluidic device. Upon receiving an image, the system applies a classifier to the image to classify the operation of the microfluidic device as normal, in which no adjustment to the operation is needed, or as abnormal, in which an adjustment to the operation is needed. When an image is classified as normal, the system may make no adjustment to the microfluidic device. If, however, an image is classified as abnormal, the system may output an indication that the operation is abnormal, output an indication of a needed adjustment, or control the microfluidic device to make the needed adjustment.

Abstract: Systems and methods performed for generating authentication information for an image using optical computing are provided. When a user takes a photo of an object, an optical authentication system receives light reflected and/or emitted from the object. The system also receives a random key from an authentication server. The system converts the received light to plenoptic data and uploads it to the authentication server. In addition, the system generates an optical hash of the received light using the random key, converts the generated optical hash to a digital optical hash, and uploads the digital optical hash to the authentication server. When the authentication server receives the upload, it verifies whether the time of the upload is within a certain threshold time from the sending of the random key and whether the digital optical hash was generated from the same light as the plenoptic data.

Abstract: A system for rapidly adapting production of a product based on classification of production data using a classifier trained on prior production data is provided. A production control system includes a learning system and an adaptive system. The learning system trains a production classifier to label or classify previously collected production data. The adaptive system receives production data in real time and classifies the production data in real time using the production classifier. If the classification indicates a problem with the manufacturing of the product, the adaptive system controls the manufacturing to rectify the problem by taking some corrective action. The production classifier is trained using bootstrap data and corresponding example data extracted from prior production data. Once the bootstrap data is labeled, the corresponding example data is automatically labeled for use as training data.

Abstract: A system is provided to automatically monitor and control the operation of a microfluidic device using machine learning technology. The system receives images of a channel of a microfluidic device collected by a camera during operation of the microfluidic device. Upon receiving an image, the system applies a classifier to the image to classify the operation of the microfluidic device as normal, in which no adjustment to the operation is needed, or as abnormal, in which an adjustment to the operation is needed. When an image is classified as normal, the system may make no adjustment to the microfluidic device. If, however, an image is classified as abnormal, the system may output an indication that the operation is abnormal, output an indication of a needed adjustment, or control the microfluidic device to make the needed adjustment.

Abstract: Systems and methods performed for generating authentication information for an image using optical computing are provided. When a user takes a photo of an object, an optical authentication system receives light reflected and/or emitted from the object. The system also receives a random key from an authentication server. The system converts the received light to plenoptic data and uploads it to the authentication server. In addition, the system generates an optical hash of the received light using the random key, converts the generated optical hash to a digital optical hash, and uploads the digital optical hash to the authentication server. When the authentication server receives the upload, it verifies whether the time of the upload is within a certain threshold time from the sending of the random key and whether the digital optical hash was generated from the same light as the plenoptic data.

Abstract: According to one embodiment, a method includes receiving light on a photoconductive layer of an electrophoretic deposition (EPD) device, the EPD device having a chamber defined by a first sheet, a second sheet and a spacer between the first and second sheets, where the first sheet is nonopaque and includes the photoconductive layer, where the second sheet is nonopaque and spaced from the first sheet, where a fluidic solution having a plurality of particles is in the chamber. The particles in the solution are attracted from suspension to illuminated portions of the photoconductive layer in the absence of an external voltage applied to the first and second sheets. The particles become deposited on the illuminated portions of the photoconductive layer.

Abstract: Systems and methods for controlling a material extrusion device to extrude a filament of an ink are provided. An extrusion printing control system collects from one or more sensors measurements representing an internal state of material extrusion processing during extrusion of the filament. In addition, the system collects an image of the filament as the filament is extruded. The system applies a classifier to the collected image to generate an image-derived state characterizing the filament. Based on the internal state and the image-derived state, the system estimates a derived state using a model. The system determines control parameters using the model to achieve a desired quality of the filament by minimizing a cost function based on the internal state, the image-derived state, the derived state, and constraints of the material extrusion device. Finally, the system provides the control parameters to a controller of the material extrusion device.

Abstract: According to one embodiment, a method includes receiving light on a photoconductive layer of an electrophoretic deposition (EPD) device, the EPD device having a chamber defined by a first sheet, a second sheet and a spacer between the first and second sheets, where the first sheet is nonopaque and includes the photoconductive layer, where the second sheet is nonopaque and spaced from the first sheet, where a fluidic solution having a plurality of particles is in the chamber. The particles in the solution are attracted from suspension to illuminated portions of the photoconductive layer in the absence of an external voltage applied to the first and second sheets. The particles become deposited on the illuminated portions of the photoconductive layer.

Abstract: According to one embodiment, a system includes a first sheet being nonopaque, a second sheet being nonopaque spaced from the first sheet, a spacer positioned between the first sheet and the second sheet, a chamber defined between the first and second sheets and the spacer, and a fluidic solution in the chamber. In addition, the first sheet includes a photoconductive layer. Moreover, solution has a plurality of particles, where the particles in the solution are attracted to illuminated portions of the photoconductive layer upon illumination of said portions in the absence of an external voltage applied to the first and second sheets.

Abstract: A system is provided to automatically monitor and control the operation of a microfluidic device using machine learning technology. The system receives images of a channel of a microfluidic device collected by a camera during operation of the microfluidic device. Upon receiving an image, the system applies a classifier to the image to classify the operation of the microfluidic device as normal, in which no adjustment to the operation is needed, or as abnormal, in which an adjustment to the operation is needed. When an image is classified as normal, the system may make no adjustment to the microfluidic device. If, however, an image is classified as abnormal, the system may output an indication that the operation is abnormal, output an indication of a needed adjustment, or control the microfluidic device to make the needed adjustment.

Abstract: A system is provided to automatically monitor and control the operation of a microfluidic device using machine learning technology. The system receives images of a channel of a microfluidic device collected by a camera during operation of the microfluidic device. Upon receiving an image, the system applies a classifier to the image to classify the operation of the microfluidic device as normal, in which no adjustment to the operation is needed, or as abnormal, in which an adjustment to the operation is needed. When an image is classified as normal, the system may make no adjustment to the microfluidic device. If, however, an image is classified as abnormal, the system may output an indication that the operation is abnormal, output an indication of a needed adjustment, or control the microfluidic device to make the needed adjustment.

Abstract: A system is provided to automatically monitor and control the operation of a microfluidic device using machine learning technology. The system receives images of a channel of a microfluidic device collected by a camera during operation of the microfluidic device. Upon receiving an image, the system applies a classifier to the image to classify the operation of the microfluidic device as normal, in which no adjustment to the operation is needed, or as abnormal, in which an adjustment to the operation is needed. When an image is classified as normal, the system may make no adjustment to the microfluidic device. If, however, an image is classified as abnormal, the system may output an indication that the operation is abnormal, output an indication of a needed adjustment, or control the microfluidic device to make the needed adjustment.