Abstract: The present disclosure provides a data search system and a data search method for determining whether there is stored information data matched with query information data in a storage circuit. The data search system comprises a storage circuit, a control circuit, and a feature extraction circuit, wherein the storage circuit comprises at least one storage unit which comprises a memristor crossbar array, a read/write unit, a decoder and a multiplexer, and the feature extraction circuit is configured to extract feature values of the query data. In the data search method, both the data matching process and the data storage process are performed in the memristor crossbar array under the control of the control circuit, which thus largely reduces the amount of data transmission while greatly improving the speed of data search using the characteristics of parallel calculation of the memristor crossbar array.

Abstract: The present disclosure relates to methods and systems for managing and controlling resources. A system for managing and controlling resources may comprise a storage device configured to store a set of instructions, one or more processors configured to communicate with the storage device. When executing the set of instructions, the one or more processors may be configured to cause the system to collect resource usage data through one or more meters, process the resource usage data based on a preset algorithm to determine resource usage state, and control at least the one or more meters and/or one or more smart devices to perform specified operations based on the resource usage state. The present disclosure provides methods and systems for managing and controlling resources based on meters or smart devices controlled by the network.

Abstract: A system and method for robustly calibrating a vision system and a robot is provided. The system and method enables a plurality of cameras to be calibrated into a robot base coordinate system to enable a machine vision/robot control system to accurately identify the location of objects of interest within robot base coordinates.

Abstract: The present disclosure provides a data search system and a data search method for determining whether there is stored information data matched with query information data in a storage circuit. The data search system comprises a storage circuit, a control circuit, and a feature extraction circuit, wherein the storage circuit comprises at least one storage unit which comprises a memristor crossbar array, a read/write unit, a decoder and a multiplexer, and the feature extraction circuit is configured to extract feature values of the query data. In the data search method, both the data matching process and the data storage process are performed in the memristor crossbar array under the control of the control circuit, which thus largely reduces the amount of data transmission while greatly improving the speed of data search using the characteristics of parallel calculation of the memristor crossbar array.

Abstract: Disclosed herein are systems and methods for machine vision. A machine vision system includes a motion rendering device, a first image sensor, and a second image sensor. The machine visions system includes a processor configured to run a computer program stored in memory that is configured to determine a first transformation that allows mapping between the first coordinate system associated with the motion rendering device and the second coordinate system associated with the first image sensor, and to determine a second transformation that allows mapping between the first coordinate system associated with the motion rendering device and the third coordinate system associated with the second image sensor.

Abstract: This invention provides a system and method that ties the coordinate spaces at the two locations together during calibration time using features on a runtime workpiece instead of a calibration target. Three possible scenarios are contemplated: wherein the same workpiece features are imaged and identified at both locations; wherein the imaged features of the runtime workpiece differ at each location (with a CAD or measured workpiece rendition available); and wherein the first location containing a motion stage has been calibrated to the motion stage using hand-eye calibration and the second location is hand-eye calibrated to the same motion stage by transferring the runtime part back and forth between locations. Illustratively, the quality of the first two techniques can be improved by running multiple runtime workpieces each with a different pose, extracting and accumulating such features at each location; and then using the accumulated features to tie the two coordinate spaces.

Abstract: The present disclosure relates to a method and system for controlling a smart device. The method may include obtaining correlating information of the smart device. The correlating information may include at least one of smart device information or a communication status of the smart device. The method may include determining indication information of the smart device by processing the correlating information of the smart device according to a preset algorithm. The method may include performing designated operations related to the smart device based at least in part on the indication information.

Abstract: The present disclosure provides methods and systems for information processing. The method may include obtaining object information of at least one object and user information of at least one authorized user; establishing a corresponding relationship between the object information of the at least one object and the user information of the at least one authorized user; determining, based to the corresponding relationship, a first object corresponding to user information of a first authorized user among the at least one authorized user; and sending the user information of the first authorized user to the first object, enabling the first object to verify information of an operator of the first object based on the user information of the first authorized user and processing an operation request of the operator based on a verification result.

Abstract: Described are machine vision systems and methods for simultaneous kinematic and hand-eye calibration. A machine vision system includes a robot and a 3D sensor in communication with a control system. The control system is configured to move the robot to poses, and for each pose: capture a 3D image of calibration target features and robot joint angles. The control system is configured to obtain initial values for robot calibration parameters, and determine initial values for hand-eye calibration parameters based on the initial values for the robot calibration parameters, the 3D image, and joint angles. The control system is configured to determine final values for the hand-eye calibration parameters and robot calibration parameters by refining the hand-eye calibration parameters and robot calibration parameters to minimize a cost function.

Abstract: A method for preparing a doped optical fibre preform includes formulating, a rare earth material or a functional metal material and a co-doping agent into a doping solution, mixing a high-purity quartz powder with the doping solution, drying same at a temperature of 100° C.-150° C. for 12-48 hours, crushing and screening the same to obtain a doped quartz powder; depositing the doped quartz powder onto the surface of a target rod to form a doped core layer; replacing the doped quartz powder with the high-purity quartz powder, and depositing the high-purity quartz powder onto the surface of the doped core layer to form a quartz outer cladding; and removing the target rod, and gradually collapsing the entirety formed from the doped core layer and the quartz outer cladding at a high temperature to obtain the doped optical fibre preform.

Abstract: Methods, apparatus, and systems are provided for integrated driver expression recognition and vehicle interior environment classification to detect driver condition for safety. A method includes obtaining an image of a driver of a vehicle and an image of an interior environment of the vehicle. Using a machine learning method, the images are processed to classify a condition of the driver and of the interior environment of the vehicle. The machine learning method includes general convolutional neural network (CNN) and CNN with adaptive filters. The adaptive filters are determined based on influence of filters. The classification results are combined and compared with predetermined thresholds to determine if a decision can be made based on existing information. Additional information is requested by self-motivated learning if a decision cannot be made, and safety is determined based on the combined classification results. A warning is provided to the driver based on the safety determination.

Abstract: This invention provides a system and method that ties the coordinate spaces at the two locations together during calibration time using features on a runtime workpiece instead of a calibration target. Three possible scenarios are contemplated: wherein the same workpiece features are imaged and identified at both locations; wherein the imaged features of the runtime workpiece differ at each location (with a CAD or measured workpiece rendition available); and wherein the first location containing a motion stage has been calibrated to the motion stage using hand-eye calibration and the second location is hand-eye calibrated to the same motion stage by transferring the runtime part back and forth between locations. Illustratively, the quality of the first two techniques can be improved by running multiple runtime workpieces each with a different pose, extracting and accumulating such features at each location; and then using the accumulated features to tie the two coordinate spaces.

Abstract: Various embodiments include systems and methods structured to provide recognition of an object in an image using a learning module trained using decomposition of the object into components in a number of training images. The training can be based on an overall objectness score of the object, an objectness score of each component of the object, a pose of the object, and a pose of each component of the object for each training image input. Additional systems and methods can be implemented in a variety of applications.

Abstract: Described are machine vision systems and methods for simultaneous kinematic and hand-eye calibration. A machine vision system includes a robot and a 3D sensor in communication with a control system. The control system is configured to move the robot to poses, and for each pose: capture a 3D image of calibration target features and robot joint angles. The control system is configured to obtain initial values for robot calibration parameters, and determine initial values for hand-eye calibration parameters based on the initial values for the robot calibration parameters, the 3D image, and joint angles. The control system is configured to determine final values for the hand-eye calibration parameters and robot calibration parameters by refining the hand-eye calibration parameters and robot calibration parameters to minimize a cost function.

Abstract: A system and method for robustly calibrating a vision system and a robot is provided. The system and method enables a plurality of cameras to be calibrated into a robot base coordinate system to enable a machine vision/robot control system to accurately identify the location of objects of interest within robot base coordinates.

Abstract: This invention provides a system and method that ties the coordinate spaces at the two locations together during calibration time using features on a runtime workpiece instead of a calibration target. Three possible scenarios are contemplated: wherein the same workpiece features are imaged and identified at both locations; wherein the imaged features of the runtime workpiece differ at each location (with a CAD or measured workpiece rendition available); and wherein the first location containing a motion stage has been calibrated to the motion stage using hand-eye calibration and the second location is hand-eye calibrated to the same motion stage by transferring the runtime part back and forth between locations. Illustratively, the quality of the first two techniques can be improved by running multiple runtime workpieces each with a different pose, extracting and accumulating such features at each location; and then using the accumulated features to tie the two coordinate spaces.

Abstract: A system and method for robustly calibrating a vision system and a robot is provided. The system and method enables a plurality of cameras to be calibrated into a robot base coordinate system to enable a machine vision/robot control system to accurately identify the location of objects of interest within robot base coordinates.

Abstract: Described are machine vision systems and methods for simultaneous kinematic and hand-eye calibration. A machine vision system includes a robot or motion stage and a camera in communication with a control system. The control system is configured to move the robot or motion stage to poses, and for each pose: capture an image of calibration target features and robot joint angles or motion stage encoder counts. The control system is configured to obtain initial values for robot or motion stage calibration parameters, and determine initial values for hand-eye calibration parameters based on the initial values for the robot or motion stage calibration parameters, the image, and joint angles or encoder counts. The control system is configured to determine final values for the hand-eye calibration parameters and robot or motion stage calibration parameters by refining the hand-eye calibration parameters and robot or motion stage calibration parameters to minimize a cost function.

Abstract: Methods, apparatus, and systems are provided for integrated driver expression recognition and vehicle interior environment classification to detect driver condition for safety. A method includes obtaining an image of a driver of a vehicle and an image of an interior environment of the vehicle. Using a machine learning method, the images are processed to classify a condition of the driver and of the interior environment of the vehicle. The machine learning method includes general convolutional neural network (CNN) and CNN with adaptive filters. The adaptive filters are determined based on influence of filters. The classification results are combined and compared with predetermined thresholds to determine if a decision can be made based on existing information. Additional information is requested by self-motivated learning if a decision cannot be made, and safety is determined based on the combined classification results. A warning is provided to the driver based on the safety determination.

Abstract: Various embodiments include systems and methods structured to provide recognition of an object in an image using a learning module trained using decomposition of the object into components in a number of training images. The training can be based on an overall objectness score of the object, an objectness score of each component of the object, a pose of the object, and a pose of each component of the object for each training image input. Additional systems and methods can be implemented in a variety of applications.