Abstract: A method of protecting data applied in a computer device is provided. A designated file is encrypted by using a symmetric key to obtain an encrypted file. The symmetric key is encrypted by using a first public key of a first pair of keys and a first ciphertext is obtained. Information related to the designated file is obtained, and the related information of the designated file is uploaded to a blockchain. When decrypting the encrypted file, the related information of the designated file is downloaded from the blockchain to obtain the related information of the designated file. The symmetric key is obtained by decrypting a first ciphertext of the related information by using a first private key of the first pair of keys, and a decrypted file is obtained by decrypting the encrypted file by using the symmetric key.

Abstract: A method for detecting defects in images, is employed in a computer device, and stored in a storage medium. The method trains an autoencoder model using unblemished images, inputting an image to be detected into the autoencoder model, and obtaining a reconstructed image. An image error is calculated between the image to be detected and the reconstructed image, and the image error is inputted into a student's t-distribution and a calculation result is obtained. In response that the calculation result falls within a preset defect determination criterion range, the image to be detected is determined to be an unblemished image. In response that the calculation result does not fall within the preset defect determination criterion range, the image to be detected is determined to be a defective image. The method improves the efficiency and accuracy of defect detection.

Abstract: A method for training a depth estimation model implemented in an electronic device includes obtaining a first image pair from a training data set; inputting the first left image into the depth estimation model, and obtaining a disparity map; adding the first left image and the disparity map, and obtaining a second right image; calculating a mean square error and cosine similarity of pixel values of all corresponding pixels in the first right image and the second right image; calculating mean values of the mean square error and the cosine similarity, and obtaining a first mean value of the mean square error and a second mean value of the cosine similarity; adding the first mean value and the second mean value, and obtaining a loss value of the depth estimation model; and iteratively training the depth estimation model according to the loss value.

Abstract: A method for detecting a product for defects implemented in an electronic device includes detecting images of a product for defects by a first defect detection model in a preset period, and obtaining a detection result; when a ratio of the number of negative sample images is greater than a preset threshold, training an autoencoder model; obtaining historical positive sample images of the product, inputting the history positive sample images into the trained autoencoder model, and calculating a latent feature; inputting the latent feature of each history positive sample image into a decoding layer of the trained autoencoder model, and calculating newly added positive sample images; training the first defect detection model and obtain a second defect detection model; and inputting images of a product to be detected to the second defect detection model, and obtaining a detection result of the product.

Abstract: A method for measuring a growth height of a plant, an electronic device, and a storage medium are provided. The method controls a camera device to obtain a color image and a depth image of a plant to be detected. The color image is detected by a detection model which is pre-trained, and a plurality of detection boxes which includes a plurality of plants to be detected is obtained. The color image and the depth image are aligned to create an alignment image. A plurality of target boxes is acquired from the alignment image, and depth values of the plurality of target boxes are determined. The quantity of the target boxes and a height of one or more plants to be detected are determined, no manual operations are required.

Abstract: A manufacturing method of a semiconductor structure includes the following operations. A stacked structure is formed on a substrate. The stacked structure includes semiconductor layers and sacrificial layers that are alternately stacked, in which the sacrificial layers include germanium, and germanium concentrations of the sacrificial layers decrease from bottom to top. A dummy gate structure is formed on the stacked structure. A spacer is formed on both sides of the dummy gate structure. The dummy gate structure is removed, thereby forming an opening. The sacrificial layers are removed from the opening. A gate structure is formed to cover the semiconductor layers. In another manufacturing method, the stacked structure includes semiconductor layers and sacrificial layers that are alternately stacked, in which thicknesses of the semiconductor layers increase from bottom to top, or thicknesses of the sacrificial layers increase from bottom to top.

Abstract: The present disclosure provides a microbolometer including a substrate, a readout circuit layer disposed above the substrate, a first vanadium oxide layer disposed above the readout circuit layer, a second vanadium oxide layer disposed on the first vanadium oxide layer, and an infrared absorbing layer disposed above the second vanadium oxide layer, in which an oxygen content of the second vanadium oxide is higher than that of the first vanadium oxide layer.

Abstract: A method of identifying characters in images extracts features of a detection image including characters. Enhancement processing is performed on the detection image according to the features to obtain an enhanced image. Closed edges of the characters are detected in the enhanced image. First rectangular outlines of the characters are determined according to the closed edges. The first rectangular outlines are corrected to obtain second rectangular outlines. The characters are cropped from the detection image according to the second rectangular outlines. The method identifies characters in images accurately and rapidly.

Abstract: A chip pin connection status display method applied to a computer device is provided. The method includes generating a two-dimensional matrix with n rows and m columns according to a total number n of solder balls and a total number m of pins of a chip. Once an input signal and an input position of the input signal from the two-dimensional matrix are detected, display is performed at the input position on the two-dimensional matrix according to a type of the input signal and the input position of the input signal.

Abstract: In an image processing method, a detection image and a marked image are obtained. An image segmentation model is applied to segment a first segmented image from the detection image. The first segmented image is corrected according to the marked image to obtain a second segmented image. A size of the second segmented image is adjusted to obtain an adjusted segmented image. The adjusted segmented image is used as a standard segmented image of the detection image. The method improves accuracy of image segmentation and recognition.

Abstract: A block packaging method based on blockchain transaction is used in an electronic device. The electronic device controls each node in the blockchain to receive and store a blockchain transaction, and calculate a hash value of the each node according to the blockchain transaction, generate a package voting information according to the hash value of the each node, and send the package voting information to a voted node. The electronic device further controls the each node to record the number of votes as the voted nodes according to the package voting information to obtain the number of votes of the each node, take the node with the largest number of votes as a target node according to the number of votes, and pack the blockchain transaction into blocks and broadcast the blocks to all nodes in the blockchain.

Abstract: A mask member for a deposition mask is provided. The deposition mask has a resin film in which a pattern of opening portions is formed. The mask member includes a resin film and a reflective film. The reflective film is on a surface of the resin film. The reflective film is adapted to reflect light having wavelengths of laser light.

Abstract: A method and a device for detecting a placement of wafers in a wafer cassette are provided. The wafer cassette includes a plurality of receiving grooves. The receiving grooves hold and store the wafers. Light is emitted through the wafer cassette. The light passes through the gaps and is imaged, and the image is captured. Characteristic information is extracted from the image. The extracted characteristic information is compared with standard characteristic information of a preset image, and whether a placement of all the wafers in the wafer cassette is qualified and satisfactory is determined according to a compared result.

Abstract: A display panel includes a substrate, a plurality of conductive components on a surface of the substrate, a plurality of light-emitting diodes. The conductive components are on a surface of the substrate and spaced apart from each other. Each conductive component includes a first conductive part and a second conductive part. The second conductive part is electrically connected to the first conductive part. A projection of the second conductive part on the surface at least partially overlaps a projection of the first conductive part on the surface. Each light-emitting diode includes a binding electrode, and the binding electrode is electrically connected to the second conductive part. The first conductive part is made of metal; the second conductive part is made of a transparent conductive oxide. The binding electrode is made of metal. A eutectic material is formed between the second conductive part and the binding electrode.

Abstract: An optical imaging lens proofed against field curvatures, in an imaging module, and the module being used in an electronic device, is, from an object side to an image side, composed of a first lens with a positive refractive power, a second lens, a third lens, a fourth lens with a negative refractive power, a fifth lens, and a sixth lens with a negative refractive power. The optical imaging lens satisfies the formula ?3 mm?1<FNO/f6<?0.1 mm?1, ?0.065 mm/°<f6/FOV<?0.03 mm/°, wherein FNO is a F-number of the optical imaging lens, f6 is a focal length of the sixth lens, and FOV is a maximum field of view of the optical imaging lens.

Abstract: A method for detecting image abnormities, applied in an electronic device, and stored in a storage medium are provided, obtains images for analysis. The images are divided into a plurality of first divided images and a plurality of second divided images by reference to image size. A first abnormity score is obtained by inputting the image into a first pre-trained abnormity detection model. A plurality of second abnormity scores are obtained by inputting the first divided images into a second pre-trained abnormity detection model. A plurality of third abnormity scores are obtained by inputting the second divided images into a third pre-trained abnormity detection model. An abnormal type of the image is determined according to a preset abnormity database in response to an abnormity detected in the image, the method improves accuracy of defect detection.

Abstract: A method for improving the reconstruction of images obtains an object image and a reference image and extracts a first edge of the object image and a second edge of the reference image based on a predetermined algorithm. A first vector of the plurality of first pixels to the first edge and a second vector of the plurality of second pixels to the second edge are obtained and a determination made as to whether the first vector and the second vector are consistent. A loss between the first vector and the second vector is calculated if the first and second vector are not consistent and a predetermined model is corrected based on the loss, the reference image being reconstructed into the object image based on corrected predetermined model. An image reconstruction device and a non-transitory storage medium are also disclosed.

Abstract: A method of recognizing target objects in images obtains a detection image of a target object. A template image is generated according to the target object. The detection image is compared with the template image to obtain a comparison result. Candidate regions of the target object are determined in the detection image according to the comparison result. At least one target region of the target object is obtained from the candidate regions. The method detects target objects in images very rapidly.

Abstract: In an image comparison method, an original reference image and an original test image are obtained. The original reference image and the original test image are binarized to obtain a reference binary image and a test binary image. The reference binary image and the test binary image are detected edges to obtain a reference edge image and a test edge image. A morphological expansion is performed on the reference edge image to obtain an expanded reference edge image. An OR operation is performed on the extended reference edge image and the test edge image to obtain an extended test edge image. An XOR operation is performed on the expanded reference edge image and the expanded test edge image. The method improves the accuracy of image comparison.

Abstract: A method for detecting from images correct placement and function, or incorrect placement and function, of a clip of a transportation box of wafers in sterile or similar conditions obtains an image template comprising features of clip and obtains a first detection image of a working clip. An object region focusing on the imaged clip in the first detection image is determined according to the image template. Part of the working image is selected as a first preset location. The part of the image is masked to obtain a second detection image, the masking obscures the background region of the part of the image but not the clip-object region, and displays the unobscured clip-object region. The second detection image is input into a trained neural network model to determine whether the clip is in sealed or unsealed state. An electronic device and a non-transitory storage medium are also disclosed.