Abstract: Copper-tin-nickel brazing material prepared by alloys recycled from E-waste, preparation method therefor, and system thereof are provided. A preparation method for the copper-tin-nickel brazing material includes the following steps: (a) spreading nano-SiO2 on the bottom of crucible and then adding a crude copper-tin-iron-nickel alloy recycled from E-waste; (b) heating the crucible to melt the crude alloy into a metal liquid so that Zn and Pb in the metal liquid react with the SiO2 to form a slag that floats out; (c) introducing a refining gas to the bottom of metal liquid in step (b), thereby removing the scums or gases formed by Pb, Fe, S, and O in the metal liquid; (d) performing heat-preserving directional solidification on the metal liquid, to bias-aggregate the Fe and Sb at one end and remove the same to obtain a copper-based intermediate alloy; and smelting and powdering the copper-based intermediate alloy.

Abstract: An objective of the present invention is to provide a self-assembled polymer micelle-type ornithine donor, which has high bioavailability, low toxicity, and high therapeutic effectiveness, even when provided by oral administration. Another objective of the present invention is to provide a self-assembled polymer micelle-type ornithine donor which is effective in preventing or treating hepatic disorders. In order to achieve the above objectives, an acyl group is introduced into the primary amino group in the side chain of an ornithine unit in the poly(ornithine) segment of poly(ethylene glycol)-b-poly(ornithine), which spontaneously forms polymer micelles without polyion complex formation.

Abstract: Disclosed is a preparation method for charge reversal and reversibly crosslinked redox-sensitive nanomicelles, falling within the technical field of biomedical materials. The method comprises: synthesizing thiocinamide from lipoic acid and ethylenediamine under an N,N?-carbonyl diimidazole catalyst; and polymerizing thiocinamide, polyethylene glycol diglycidyl ether and lysine through a nucleophilic addition mechanism to prepare a poly(lysine-co-polyethylene glycol diglycidyl ether-co-thiocinamide) terpolymer. The micelle is endowed with excellent anti-protein nonspecific adsorption and enhanced cell uptake property through a self-assembly and protonation/deprotonation action; and a disulfide bond in lipoyl may form a linear polydisulfide structure under the action of 1,4-dithiothreitol, so that a micelle core is crosslinked, and a crosslinked structure is destroyed in the cell under a redox condition, and controlled release of a drug can be achieved.

Abstract: Disclosed is a preparation method for charge reversal and reversibly crosslinked redox-sensitive nanomicelles, falling within the technical field of biomedical materials. The method comprises: synthesizing thiocinamide from lipoic acid and ethylenediamine under an N,N?-carbonyl diimidazole catalyst; and polymerizing thiocinamide, polyethylene glycol diglycidyl ether and lysine through a nucleophilic addition mechanism to prepare a poly(lysine-co-polyethylene glycol diglycidyl ether-co-thiocinamide) terpolymer. The micelle is endowed with excellent anti-protein nonspecific adsorption and enhanced cell uptake property through a self-assembly and protonation/deprotonation action; and a disulfide bond in lipoyl may form a linear polydisulfide structure under the action of 1,4-dithiothreitol, so that a micelle core is crosslinked, and a crosslinked structure is destroyed in the cell under a redox condition, and controlled release of a drug can be achieved.

Abstract: A robot is made to recognize and manipulate different types of cable harnesses in an assembly line. This is achieved by using a stereo camera system to define a 3D cloud of a given cable harness. Pose information of specific parts of the cable harness are determined from the 3D point cloud, and the cable harness is then re-presented as a collection of primitive geometric shapes of known dimensions, whose positions and orientations follow the spatial position of the represented cable harness. The robot can then manipulate the cable harness by using the simplified representation as a reference.

Abstract: A stereo camera system produces a stereo image pair of a cable harness, which is used to define a 3D point cloud of the cable harness at its current pose position. Pose information of specific parts of the cable harness are determined from the 3D point cloud, and the cable harness is then re-presented as a collection of primitive geometric shapes of known dimensions, whose positions and orientations follow the spatial position and orientation of the imaged cable harness. The length, position and number of geometric shapes are atomically determined from a 2D image segmentation of one of the images in the stereo image pair.

Abstract: A robot is made to recognize and manipulate different types of cable harnesses in an assembly line. This is achieved by using a stereo camera system to define a 3D cloud of a given cable harness. Pose information of specific parts of the cable harness are determined from the 3D point cloud, and the cable harness is then re-presented as a collection of primitive geometric shapes of known dimensions, whose positions and orientations follow the spatial position of the represented cable harness. The robot can then manipulate the cable harness by using the simplified representation as a reference.

Abstract: A method and system for estimating motion blur of an image associated with a moving object. The direction of one-dimensional motion blur may be estimated by inspecting a power spectrum associated with the image. A radon transform with respect to the image power spectrum is computed in the direction of the motion blur. A family of kernels with respect to the one-dimensional motion blur may then be defined utilizing a shutter triggering sequence associated with an image capturing device. The family of kernels may be modeled utilizing a modulation transfer function (MTF). Each modulation transfer function may be compared with the radon transform of the power spectrum associated with the image via a correlation function. The kernel with highest correlation with respect to the radon transform of the image power spectrum may be employed for de-blurring the image.

Abstract: A stereo camera system produces a stereo image pair of a cable harness, which is used to define a 3D point cloud of the cable harness at its current pose position. Pose information of specific parts of the cable harness are determined from the 3D point cloud, and the cable harness is then re-presented as a collection of primitive geometric shapes of known dimensions, whose positions and orientations follow the spatial position and orientation of the imaged cable harness. The length, position and number of geometric shapes are atomically determined from a 2D image segmentation of one of the images in the stereo image pair.

Abstract: A robot is made to recognize and manipulate different types of cable harnesses in an assembly line. This is achieved by using a stereo camera system to define a 3D cloud of a given cable harness. Pose information of specific parts of the cable harness are determined from the 3D point cloud, and the cable harness is then re-presented as a collection of primitive geometric shapes of known dimensions, whose positions and orientations follow the spatial position of the represented cable harness. The robot can then manipulate the cable harness by using the simplified representation as a reference.

Abstract: Aspects of the present invention comprise generating and using Multi-Order Contextual co-Occurrence (MOCO) descriptors to implicitly model the high level context using detection responses from a baseline object detector. In embodiments, a 1st-order context feature is computed as a set of randomized binary comparisons on a response map of the baseline object detector. The statistics of the 1st-order binary context features are further calculated to construct a higher-order co-occurrence descriptor, which, in embodiments, may be combined with other features such as the 0th-order context features and/or the 1st-order features to form the MOCO. In embodiments, combining the MOCO feature with the original image feature, the baseline object detector may be evolved to a stronger context aware detector.

Abstract: Aspects of the present invention include feature point matching systems and methods. In embodiments, a tree model is used to find candidate matching features for query feature points. In embodiments, the tree model may be pre-learned using a set of sample images, or alternatively, the tree model may be constructed using one or more of the input images. In embodiments, features in one of the stereo images are registered with the tree model, and then features from the other stereo image are queried through the tree model to identify their correspondences in the registered stereo image. As compared to prior brute force matching methodologies, embodiments disclosed herein reduce the complexity and calculation time for determining matching feature points in stereo images.

Abstract: Systems and methods for developing and using adaptive threshold values for different input images for object detection are disclosed. In embodiments, detector response histogram-based systems and methods train models for predicting optimal threshold values for different images. In embodiments, when training the model, an optimal threshold value for an image is defined as the value that maximizes the reduction of false positive image patches while preserving as many true positive image patches as possible. Once trained, the model may be used to set different threshold values for different images by inputting a detector response histogram for the image patches of an image into the model to determine a threshold value for detection.

Abstract: In a stereoscopic pair of images, global homography at the image level is applied to feature points extracted from connected components (CC) to identify corresponding CC's and feature points, and to discard any CC's that do not have a corresponding pair in the stereoscopic pair of images. Local homography at the CC level is then applied to individual footprint areas of the previously identified paired CC to further clean feature point correspondence. Any CC or feature point or pixel within a paired CC footprint not satisfying local homography constraint is discarded. A correspondence is also extrapolated between unknown pixels within a paired CC footprint using a weighing mechanism and the unknown pixel's surrounding pixels that do have a known correspondence. This provides a dense correspondence of pixels, or feature points, which is then used to create a dense 3D point cloud of identified objects within a 3D space.

Abstract: Aspects of the present invention include feature point matching systems and methods. In embodiments, a tree model is used to find candidate matching features for query feature points. In embodiments, the tree model may be pre-learned using a set of sample images, or alternatively, the tree model may be constructed using one or more of the input images. In embodiments, features in one of the stereo images are registered with the tree model, and then features from the other stereo image are queried through the tree model to identify their correspondences in the registered stereo image. As compared to prior brute force matching methodologies, embodiments disclosed herein reduce the complexity and calculation time for determining matching feature points in stereo images.

Abstract: Systems and methods for object detection are presented herein. Embodiments of the present invention utilizing a cascade feature, one or more features at different scales, one or more multi-scale features in combination with a perspective feature, or combinations thereof to detect an object of interest in an input image. In embodiments, the various features are used to train classifiers. In embodiments, the trained classifiers are used in detecting an object of interest in one or more input images.

Abstract: Embodiments of the present invention include systems and methods for identifying and locating an object in an image. In embodiments, an object in an image may be identified by segmenting a first image of an object into one or more superpixels; extracting local descriptors from the first image, each of the descriptors having an interest point with a location; correlating the local descriptors to the superpixels based on locations of the local descriptors and superpixels; determining a probability for an object label for each of a set of the superpixels; and assigning an object label to each of the set of the superpixels based on the probability and a smoothness factor that includes weighting in terms of one or more of spatial, colors, angular distances between superpixels. The superpixels of an image may be concatenated to predict an object label for the image and to determine the location of the image.

Abstract: Aspects of the present invention include object detection training systems and methods and using object detection systems and methods that have been trained. Embodiments presented herein include hybrid learning approaches that combine global classification and local adaptations, which automatically adjust model complexity according to data distribution. Embodiments of the present invention automatically determine model complexity of the local learning algorithm according to the distribution of ambiguous samples. And, embodiments of the local adaptation from global classifier avoid the common under-training problem for local classifier.

Abstract: A robot is made to recognize and manipulate different types of cable harnesses in an assembly line. This is achieved by using a stereo camera system to define a 3D cloud of a given cable harness. Pose information of specific parts of the cable harness are determined from the 3D point cloud, and the cable harness is then re-presented as a collection of primitive geometric shapes of known dimensions, whose positions and orientations follow the spatial position of the represented cable harness. The robot can then manipulate the cable harness by using the simplified representation as a reference.

Abstract: In a stereoscopic pair of images, global homography at the image level is applied to feature points extracted from connected components (CC) to identify corresponding CC's and feature points, and to discard any CC's that do not have a corresponding pair in the stereoscopic pair of images. Local homography at the CC level is then applied to individual footprint areas of the previously identified paired CC to further clean feature point correspondence. Any CC or feature point or pixel within a paired CC footprint not satisfying local homography constraint is discarded. A correspondence is also extrapolated between unknown pixels within a paired CC footprint using a weighing mechanism and the unknown pixel's surrounding pixels that do have a known correspondence. This provides a dense correspondence of pixels, or feature points, which is then used to create a dense 3D point cloud of identified objects within a 3D space.