Abstract: A method and an electronic device with image data generating are disclosed. The electronic device includes: one or more processors; and memory storing instructions configured to cause the one or more processors to: input an input image to a target model that performs segmenting on the input image to generate a segmented image whose pixels have respective class labels predicted by the target model, calculate an optimization value for the input image based on the segmented image and based on a class label of a first grid area among a plurality of grid areas of a guide image, and optimize the input image based on the optimization value.

Abstract: A method of crowdsourcing annotation for medical image data based on image comparison, includes developing an annotation task; acquiring and verifying medical images according to requirements of the annotation task, and performing statistics on the medical images that meet verification standards to obtain an amount of image data acquired; archiving the medical images that meet the verification standards to form a medical image database; setting up annotation rules, and generating annotation schemes and annotation samples according to the requirements of the annotation task and annotation rules; distributing the medical images in the medical image database to multiple crowdsourcing annotation sides, so that each medical image being annotated by the multiple crowdsourcing annotation sides; annotating, by the crowdsourcing annotation sides, the distributed medical images according to the annotation schemes and the annotation examples to form the annotated images; and merging multiple annotated images correspondi

Abstract: Embodiments of present disclosure relate to adjusting a robot motion path. In the method for adjusting a robot motion path, a first processing procedure may be performed on a first workpiece to obtain a first product. Then, first process data may be obtained, where the first process data describes an attribute of the first processing procedure for obtaining the first product from the first workpiece. Next, based on the obtained first process data, a robot motion path of a second processing procedure that is to be performed on the first product by a robot may be adjusted. Further, embodiments of present disclosure provide apparatuses, systems, and computer readable media for adjusting a robot motion path.

Abstract: Embodiments of present disclosure relate to adjusting a robot motion path. In the method for adjusting a robot motion path, a first processing procedure may be performed on a first workpiece to obtain a first product. Then, first process data may be obtained, where the first process data describes an attribute of the first processing procedure for obtaining the first product from the first workpiece. Next, based on the obtained first process data, a robot motion path of a second processing procedure that is to be performed on the first product by a robot may be adjusted. Further, embodiments of present disclosure provide apparatuses, systems, and computer readable media for adjusting a robot motion path.

Abstract: The present invention discloses a method for building an MOS transistor model enclosing statistical variation of noise, including: measuring noise in MOS transistors from different dies; creating a noise distribution diagram in accordance with the obtained noise data of the MOS transistors; adding a variation of noise parameter in at least one noise model file into a library file of MOS transistor to simulate noise in MOS transistors; if a simulation result does not cover the noise data in the noise distribution diagram, changing the variation of the noise parameter until the simulation result covers the noise data in the noise distribution diagram; if the simulation result covers the noise data in the noise distribution diagram, adding corresponding variation of the noise parameter into the library file of MOS transistor as the MOS transistor model enclosing statistical variation of noise. The model obtained by the present invention is more precise.

Abstract: The present invention discloses a method for building an MOS transistor model enclosing statistical variation of noise, including: measuring noise in MOS transistors from different dies; creating a noise distribution diagram in accordance with the obtained noise data of the MOS transistors; adding a variation of noise parameter in at least one noise model file into a library file of MOS transistor to simulate noise in MOS transistors; if a simulation result does not cover the noise data in the noise distribution diagram, changing the variation of the noise parameter until the simulation result covers the noise data in the noise distribution diagram; if the simulation result covers the noise data in the noise distribution diagram, adding corresponding variation of the noise parameter into the library file of MOS transistor as the MOS transistor model enclosing statistical variation of noise. The model obtained by the present invention is more precise.