Abstract: An image processing method includes analyzing multiple images data based on Illumination-invariant Feature Network (IF-NET) with an image processing device to generate corresponding sets of eigenvector, in which image data includes a first image data related to at least one first feature of the sets of eigenvector, and a second image data related to at least one second feature of the sets of eigenvector; choosing a corresponding first training set of tiles and second training set of tiles from the first image data and second image data with an image processing device based on IF-NET, and computing on both training set of tiles to generate a least one loss value; and adjusting IF-NET based on a least one loss value. An image processing system is also disclosed herein.

Abstract: Disclosed herein are electrospun fibrous matrix and its production method. The method mainly includes the steps of, mixing a first polymer and a drug to form a first mixture, and sonicating the first mixture until a plurality of microparticles are formed with the drug encapsulated therein; and mixing the plurality of microparticles with a second polymer to form a second mixture, subjecting the second mixture to a wet electrospinning process to form the electrospun fibrous matrix. The thus-produced electrospun fibrous matrix is characterized by having a plurality of first and second fibrils woven together, in which each second fibril has a plurality of drug-encapsulated microparticles independently integrated and disposed along the longitudinal direction of the second fibril. Also encompassed in the present disclosure is a method for treating a wound of a subject. The method includes applying the present electrospun fibrous matrix to the wound of the subject to accelerate wound healing.

Abstract: An analog circuit is calibrated to perform neural network computing. Calibration input is provided to a pre-trained neural network that includes at least a given layer having pre-trained weights stored in the analog circuit. The analog circuit performs tensor operations of the given layer using the pre-trained weights. Statistics of calibration output from the analog circuit is calculated. Normalization operations to be performed during neural network inference are determined. The normalization operations incorporate the statistics of the calibration output and are performed at a normalization layer that follows the given layer. A configuration of the normalization operations is written into memory while the pre-trained weights stay unchanged.

Abstract: An image processing method includes analyzing multiple images data based on Illumination-invariant Feature Network (IF-NET) with an image processing device to generate corresponding sets of eigenvector, in which image data includes a first image data related to at least one first feature of the sets of eigenvector, and a second image data related to at least one second feature of the sets of eigenvector; choosing a corresponding first training set of tiles and second training set of tiles from the first image data and second image data with an image processing device based on IF-NET, and computing on both training set of tiles to generate a least one loss value; and adjusting IF-NET based on a least one loss value. An image processing system is also disclosed herein.