40results about How to "Speed up refactoring" patented technology

An integrated, multi-sensor, Level 1 screening device is described, which system provides a next-generation Explosives Detection System (EDS) that enables high throughput, while drastically reducing false alarms. In exemplary embodiments, the present system comprises a non-rotational, Computed Tomography (CT) system and a non-translational, X-ray diffraction (XRD) system, both in an inline configuration.

The invention discloses an erasure code encoding and a data reconstruction method that reduces data update cost, adds an additional diagonal check digit to each coded strip, and designs encoding rules for each diagonal check digit , Eliminate the dependency between the check bits in the array erasure code, reduce the number of check bits associated with each original data bit, and reduce the related check bit update caused by the array erasure code when the original data is updated Cost: Reconstruct the data in the case of single disk failure or double disk failure, and complete the recovery of invalid data when there are less than two data or verification disk failures, so as to realize the function of data fault tolerance. By increasing the storage overhead of a small amount of parity bits, the present invention has higher data encoding and data reconstruction speeds, and reduces the additional update cost of the parity disk when the erasure code system is updating data, which is beneficial to slow down the speed of new NVM Wear and tear of high-speed storage devices such as / SSD.

The invention discloses a block-based observation-based compressed sensing network and a full-image reconstruction method, which mainly solves the problem of poor image quality restored by the existing network. Its network includes an observation subnetwork and a reconstruction subnetwork. The observation subnetwork consists of the first convolutional layer; the reconstruction subnetwork includes a deconvolutional layer, a second convolutional layer, and 12 residual blocks of the same size. , the fifth convolutional layer and the sixth convolutional layer; each residual block includes the third convolutional layer and the fourth convolutional layer; the output of the first convolutional layer is connected to the input of the deconvolution layer, and the reverse The output of the convolution is connected to the input of the second convolutional layer, and the output of the second convolution is connected to 12 residual blocks in turn, and the output of the 12th residual block is connected to the input of the fifth convolutional layer The terminals are connected, and the output terminal of the second convolution and the output terminal of the fifth convolution are commonly connected to the input terminal of the sixth convolution. The network of the invention avoids the block effect of the reconstructed image, improves the image restoration quality, and can be used for image processing.

The invention discloses a method and a device for achieving RAID reconstitution. The method comprises the following steps: identifying that a bad sector exists in a disk of a RAID group; locking sectors of other disks except the disk where the bad sector locates in the RAID group whose location is the same as that of the bad sector, and sectors of the fault disk which causes a RAID reconstitution; respectively obtaining a first data in the sectors of the locked other disks and a second data in the sectors of the locked fault disk; verifying the data in the bad sector based on the obtained first data and second data as well as the priority of the RAID group; storing the verified data into a preserved disk sector in the disk where the bad sector locates, verifying a proof test value based on the verified data and the obtained first data, and storing the proof test value into a sector of a backup disk, wherein the location of the sector of the backup disk is the same as that of the bad sector. The technical scheme increases the efficiency of RAID reconstitution.

The invention discloses a fast reconstruction method of a magnetic resonance image based on a tensor product complex wavelet tight frame, and provides a new research method for fast magnetic resonance imaging. The present invention uses the Cartesian sampling trajectory mode to under-sample the K-space data, which greatly improves the scanning speed of the equipment; based on the sparse transformation of the tensor product-complex wavelet tight frame, the image is decomposed from multiple directions, and the accuracy of magnetic resonance imaging is improved. ;Use the projection fast iterative soft threshold algorithm to solve the convex optimization problem in image reconstruction, which accelerates the speed of MRI image reconstruction; and in the projection fast iterative soft threshold algorithm, the regularization is adaptively calculated based on the bivariate shrinkage method Parameters, not only eliminates the process of blindly selecting parameters, but also improves the quality of image reconstruction.