35results about How to "Thickness reduction" patented technology

The invention discloses a method for improving blind hole degassing of a multi-stage circuit board and a multi-stage circuit board. The method includes the following steps: drilling a plug hole on a core board and metallizing the plug hole, then filling the plug hole with resin, and using an abrasive belt Metallize the surface of the resin after grinding the board; make the inner layer circuit on the core board; press the core board and copper foil into the first sub-board; drill the first blind hole and the first-order blind hole on the first sub-board, The first blind hole and the first-order blind hole are filled and leveled; the first sub-board and copper foil are pressed into the second sub-board; the second blind hole and the second-order blind hole are drilled on the second sub-board with controlled depth, and the second Blind holes and second-order blind holes are filled and leveled; the second sub-board and copper foil are pressed into a production board; third-order blind holes and through-holes are drilled on the production board with controlled depth, so that the third-order blind holes and through-hole metal Chemical; sequentially carry out subsequent processes on the production board to make a multi-level circuit board. The method of the invention can reduce the interlayer thickness of the blind hole, reduce the thickness-to-diameter ratio of the blind hole, and solve the problem that the blind hole is poorly opened due to excessive interlayer thickness.

The invention discloses an adaptive dictionary construction method for the sparse representation of face recognition. The method comprises a step of obtaining a face image to be tested and a plurality of face image samples, storing the face image samples into a training sample set, and extracting the face image to be tested and the LCP characteristic of each sample in the training sample set, a step of obtaining the average LBP characteristic similarity and average MiC characteristic similarity of all samples in the face image to be tested and the training sample set, a step of judging whether the LBP characteristic and the MiC characteristic belong to valid characteristics, and refusing the recognition of the face image to be tested, otherwise going to a next step, a step of marking the corresponding sample in the training sample set as the neighbor sample of the face image to be tested when the LBP characteristic similarity is not smaller than an LBP characteristic neighbor threshold and the MiC characteristic similarity is not larger than an MiC characteristic neighbor threshold in the valid characteristics, and a step of taking the neighbor sample of the face image to be tested as an atom and constructing an adaptive face recognition redundancy dictionary.

The invention discloses an adaptive redundant dictionary construction method for face recognition, including acquiring a face image to be tested and several face image samples, storing several face image samples into a training sample set, and extracting a face image to be tested and the LCP feature of each sample in the training sample set; obtain the average LBP feature similarity and the average MiC feature similarity of all samples in the face image to be tested and the training sample set; judge whether the LBP feature and the MiC feature all belong to legal features, if so, Then enter the next step; otherwise, refuse to identify the face image to be tested; in the legal feature, when the LBP feature similarity is not less than the LBP feature neighbor threshold and the MiC feature similarity is not greater than the MiC feature neighbor threshold, the corresponding sample in the training sample set is marked as the neighbor sample of the face image to be tested; the marked neighbor samples of the face image to be tested are used as atoms to construct a redundant dictionary for adaptive face recognition.

The invention discloses a self-adaptive dictionary construction method for sparsely represented face recognition, including acquiring a face image to be tested and several face image samples, storing several face image samples into a training sample set, and extracting a face image to be tested and the LCP feature of each sample in the training sample set; obtain the average LBP feature similarity and the average MiC feature similarity between the face image to be tested and all samples in the training sample set; judge whether the LBP feature and the MiC feature are legal features, if LBP Neither the feature nor the MiC feature is a legal feature, then refuse to recognize the face image to be tested; otherwise, go to the next step; in the legal feature, when the LBP feature similarity is not less than the LBP feature neighbor threshold and the MiC feature similarity is not greater than the MiC feature When the neighbor threshold is set, the corresponding samples in the training sample set are marked as the neighbor samples of the face image to be tested; the marked neighbor samples of the face image to be tested are used as atoms to construct an adaptive face recognition redundant dictionary.