A signal translating method may include, according to one aspect of the present application, receiving a source signal of a first domain; identifying erroneous features and effective features from the source signal; translating the source signal of the first domain into a first virtual signal of a second domain, the first virtual signal is that in which erroneous features included in the source signal has been removed; and outputting the first virtual signal. Therefore, the virtual signal of the second domain in which the erroneous features removed may be output.

A fairing skin repair method based on measured wing data includes fairing skin registration. Data set P1 through denoising and filtering wing point cloud data is reorganized to obtain a key point set P. A histogram feature descriptor in a normal direction of any key point in set P and a skin point cloud data Q is calculated. Euclidean distance between feature descriptors of two points is calculated through K-nearest neighbor algorithm, and points with high similarity are added into a set M. A clustering is performed on set M using a Hough voting algorithm to obtain a local point cloud set P′ in set P. The method includes fairing skin repair. The boundary line of the point frame is projected onto Q, and a distance between a projection line on the point cloud and the boundary line is calculated to obtain an amount of skin to be repaired.

A method for a neural network includes receiving an input from a vector of inputs, determining a table index based on the input, and retrieving a hash table from a plurality of hash tables, wherein the hash table corresponds to the table index. The method also includes determining an entry index of the hash table based on an index matrix, wherein the index matrix includes one or more index values, and each of the one or more index values corresponds to a vector in the hash table and determining an entry value in the hash table corresponding to the entry index. The method also includes determining a value index, wherein the vector in the hash table includes one or more entry values, and wherein the value index corresponds to one of the one or more entry values in the vector and determining a layer response.

A method of object authentication based on digital blueprints and physical fingerprints comprising the steps of acquiring a set of training blueprints and fingerprints, training, object enrollment and object authentication. The method uses a pair of a mapper realized as an encoder and a decoder and a set of multi-metric scores originating from the decomposition of mutual information and applied to both the output of the encoder and decoder and producing a feature vector for a one-class classifier. The method is trained only on the original physical objects and does not use any fakes for reliable authentication.

Systems and methods are disclosed for performing operations comprising: receiving a plurality of training images representing different phases of a periodic motion of a target region in a patient; applying a model to the plurality of training images to generate a lower-dimensional feature space representation of the plurality of training images; clustering the lower-dimensional feature space representation of the plurality of training images into a plurality of groups corresponding to the different phases of the periodic motion; and classifying a motion phase associated with a new image of the target region in the patient based on the plurality of groups of the clustered lower-dimensional feature space representation of the plurality of training images.

An improved modal decomposition method applicable to the analysis and reconstruction of the measured flow fields of internal solitary waves includes the following steps: S1. generating internal solitary waves and measuring a two-dimensional flow field sequence during the interaction of the internal solitary waves with the terrain; S2. extracting a target information from the two-dimensional flow field sequence and obtaining a flow field snapshot; S3. stitching the snapshots into a snapshot matrix in time order; S4. plotting the information density curve; S5. normalizing the information density curve and determining split points; S6. setting the snapshot matrix between two adjacent split points as a linear evolutionary characteristic stage; and S7. performing dynamic mode decomposition on the linear evolutionary characteristic stages.

An adaptive modeling method for generating misregistration data for a semiconductor device wafer (SDW) including calculating a fitting function for a group of SDWs (GSDW) having units, including measuring an SDW in said GSDW, thereby generating test data sets corresponding to the units, removing non-unit-specific values (NUSVs) from the test data sets, thereby generating cleaned test data sets, and analyzing the cleaned test data sets, thereby generating the fitting function, and generating misregistration data for at least one additional SDW (ASDW) in the GSDW, including measuring the ASDW, thereby generating run data sets, removing NUSVs from the run data sets, thereby generating cleaned run data sets, fitting each of the cleaned run data sets to the fitting function, thereby generating coefficient sets, and calculating misregistration data for the ASDW, at least partially based on the fitting function and the coefficient sets.

Methods and a device for imaging objects including unsupervised classifying and data analyzing of the object to detect and identify the structure of the object and further display the object's structure underlying structure, for example the arrangement of and relationships between the parts or elements of the object by using a location module configured to record the physical location of an antenna array.

A method is provided for hyperspectral inversion of a phosphorus content of rubber tree leaves. The method includes: acquiring hyperspectral data of to-be-detected rubber tree leaves; extracting key wavelengths of the rubber tree leaves according to the hyperspectral data and a pre-established wavelength extraction model, where the key wavelengths are related to the phosphorus content of the rubber tree leaves, and the pre-established wavelength extraction model is obtained by learning and training hyperspectral sample data and sample phosphorus content data pairs in a pre-established sample database by adopting a competitive adaptive reweighted sampling (CARS) algorithm and a successive projection algorithm (SPA); and inputting the key wavelengths into a pre-established phosphorus content prediction model to calculate the phosphorus content of the to-be-detected rubber tree leaves. Moreover, the CARS algorithm and the SPA are comprehensively applied to extract the key wavelengths closely related to the phosphorus content of the rubber tree leaves.

An indoor target positioning method based on an improved convolutional neural network (CNN) model includes acquiring and preprocessing target camera serial interface (CSI) data of a to-be-positioned target and matching the preprocessed target CSI data with fingerprints in a positioning fingerprint database to obtain coordinate information of the to-be-positioned target. The generation method of the positioning fingerprint database includes: collecting indoor WiFi signals by a software defined radio (SDR) platform to obtain indoor CSI data corresponding to the WiFi signals, and preprocessing the indoor CSI data; partitioning the preprocessed indoor CSI data into a plurality of data subsets through a clustering algorithm; training an improved CNN model by the data subsets to obtain a trained improved CNN model; and generating the positioning fingerprint database by the trained improved CNN model and the preprocessed indoor CSI data.