176 results about "Convolution filter" patented technology

The invention relates to the technical field of RFID chip manufacturing, in particular to an RFID chip image anomaly detection method and system based on intelligent learning. The method comprises the following steps: acquiring a real-time RFID chip sampling image set, performing feature alignment based on a registration template, generating an average grey-scale map by using an alignment image set, and obtaining a chip edge mask image set by using an edge search algorithm; segmenting the edge mask image set of the RFID chip to obtain an effective edge mask variance image set of the RFID chip; integrating the variance image set to obtain a pre-generated variance image; acquiring field environment data of the RFID chip and performing sensitivity coefficient estimation to obtain an edge sensitivity coefficient; and performing convolution filtering smoothing processing on the pre-generated variance image to obtain a chip edge variance image, and constructing an RFID chip image anomaly detection model based on the edge variance image and the average grey-scale image. According to the invention, the detection accuracy and stability are improved.

The invention discloses an ultra-weak light detection system and method based on an SPAD array and time correlation photon counting, and belongs to the technical field of ultra-weak light detection. A multi-channel SPAD array detection unit; a time-to-digital conversion module; the photon arrival time analysis unit comprises a TCSPC model, a noise modeling and suppression module and a self-adaptive convolution filter; a data compression and fitting module; a spatial denoising filter; an output unit; and a master control processing unit. Based on a multi-channel SPAD array detection unit, a time-to-digital conversion module, a TCSPC model, a noise modeling and suppression module, a self-adaptive convolution filter and the like, high-sensitivity imaging and quantitative detection of materials (such as black silicon) with extremely low reflectivity are realized. The system has the advantages of being good in real-time performance, high in time resolution, high in signal-to-noise ratio, high in on-chip processing capacity, high in integration and the like, and meanwhile the bottleneck of a traditional TCSPC method for large-scale photon data processing is solved.

Techniques for speech recognition models using modulation spectrum are disclosed herein. A modulation spectrum is generated from time series data output of an encoder layer of a speech recognition model and used as input into a decoder layer of the speech recognition model to improve accuracy of the model such as for recognizing subword units. The modulation spectrum is determined by applying a convolution filter to the output of the encoder layer of the speech recognition model. The time series data and / or the modulation spectrum can be normalized. A rectified linear unit activation function can be applied to the output of the convolution filter. The output of the encoder layer may be residually connected to the output of the rectified linear unit activation function prior to being input into the decoder layer.

The invention provides a brain age estimation method based on a dynamic fuzzy learnable brain network, and belongs to the technical field of medical image processing and artificial intelligence. According to the technical scheme, the method comprises the following steps that S1, brain nuclear magnetic resonance imaging of a subject is collected, and preprocessing and data division are carried out; s2, constructing graph structure data, and performing feature extraction and position information embedding on the data; s3, constructing a dynamic fuzzy learnable brain network model comprising a main branch and a local branch, and respectively extracting global and local connection features; s4, introducing a dynamic fuzzy multi-head self-attention module into the main branch to realize effective modeling of global features; s5, a local branch dynamically models a dependency relationship between channels through a convolution filter and a learnable graph attention module; s6, after the features of the main branches and the local branches are fused, brain age prediction is carried out through a multi-layer perceptron. According to the method, the modeling capability of the brain function connection mode is improved, and the brain age prediction task can be more effectively completed.

The invention discloses a TTPA-LSTM soft measurement method based on multiple sampling rates, and belongs to the field of soft measurement modeling and application in the modern industrial process. The method comprises the step of accurately predicting the concentration of butane at the bottom of the debutanizer in the industrial process by constructing a TTPA-LSTM model. According to the invention, through a non-incremental heuristic function related to a time interval in the time sensing module, short-term memory is weighted, more information is reserved while a down-sampling rate method is simulated, and the problem of difficult feature extraction caused by high and low sampling rates is solved; on the other hand, a time mode attention module is introduced, cross-time-step integration is achieved on obtained features through a two-dimensional convolution filter, the problem of multivariate time sequence time delay is solved, meanwhile, the prediction capacity of the model is improved, and the problem existing in actual industrial production is solved.

A model for defect recognition on a surface under inspection comprises a first supervised machine learning classifier configured for classifying image units of an image into at least defective and defect-free image units; multiple sets of convolution filters perform feature extraction, wherein each such set comprises one or more convolution filters defined by a set of individual filter parameters; said convolution filters are applied to one or more labelled reference images of a surface to obtain multiple groups of feature maps; multiple trained first machine learning classifiers are obtained using said feature maps; each trained first machine learning classifier is provided with an input test set and a respective quality parameter is obtained; an optimized set of convolution filters is applied to one or more labelled training images in order to train a copy of said first machine learning classifier.

The present invention relates to a method for determining the concentration of a chemical compound of a fluid by means of an optical measurement system for measuring a light intensity spectrum, such that: (i) by means of a learning base comprising a plurality of learning light intensity spectra measured for a plurality of learning fluids each having a predetermined concentration, a model is constructed in order to determine the concentration of at least one chemical compound in a fluid by training a one-dimensional convolutional neural network on the learning base, the network being such that the number of convolutional filters per layer decreases with the position of the layer in the network; (ii): by means of the optical measurement system, at least one light intensity spectrum of the fluid is measured; (iii) the constructed model is applied to the measured light intensity spectrum, and the concentration of the chemical compound in the fluid is determined.

The invention discloses a rotating machine intelligent diagnosis method based on structured pruning and knowledge fusion distillation, and the method comprises the steps: training a teacher network through a training set, carrying out the structured pruning of a percentile threshold value on the teacher network based on the L2 norm calculation and normalization of a convolution filter, and generating a student network with a consistent structure. A KFD strategy including feature-level distillation and logit-level distillation is utilized to carry out deep supervision on a student network, and two types of distillation losses are weighted and fused, so that a student model still keeps relatively strong feature characterization capability and category discrimination capability under a high pruning rate. Asymmetric integer quantization is adopted for the trained student network, so that the reasoning overhead is reduced, and the embedded adaptability is improved. A general neural network operator IP core is arranged on an FPGA, efficient deployment of a quantitative student model is achieved, and low-power-consumption and low-delay real-time fault diagnosis is achieved. The method has the advantages of being high in precision, light in model weight, easy to deploy and the like, and is suitable for on-line monitoring of industrial field rotating machinery.

A rolling bearing fault feature extraction method based on variable scale multipoint kurtosis deconvolution comprises the following steps: a, sampling a rolling bearing vibration acceleration signal to obtain a rolling bearing fault vibration signal; b, constructing a toeplitz autocorrelation matrix; c, constructing a variable-scale multipoint kurtosis deconvolution filter; d, filtering the rolling bearing fault vibration signal by using an optimal deconvolution filter to obtain a fault impact signal; e, performing envelope demodulation processing on the fault impact signal, extracting an envelope of the fault impact signal, and obtaining an envelope spectrum through spectral analysis; and f, judging the fault type of the rolling bearing according to the envelope spectrum. According to the method, the optimal target vector of the deconvolution is searched by constructing the variable-scale multipoint kurtosis index, the optimal filter is constructed, the fault impact signal is extracted through the deconvolution, the fault impact signal is subjected to envelope analysis, the fault feature frequency of the rolling bearing is extracted, and the fault feature of the rolling bearing can be accurately extracted.

The invention discloses an image distortion correction method, device and system and a storage medium, which are used for improving the aliasing elimination effect of a display screen image. The method comprises the following steps: partitioning a display screen image to be corrected to generate a plurality of image sub-block sets; inputting the plurality of image sub-blocks into a scale-invariant distortion correction model for distortion correction, and generating a plurality of transformation matrixes which are transformation coefficient sets of pixel point coordinates before and after correction; constructing an adaptive convolution filter according to the plurality of transformation matrixes; inputting the plurality of image sub-blocks after distortion correction into the adaptive convolution filter for anti-aliasing convolution and interpolation calculation, and outputting the plurality of image sub-blocks after pixel point optimization correction; performing multi-scale anti-mixing correction on the edge areas of the plurality of corrected image sub-blocks; and splicing the plurality of image sub-blocks to generate a complete target correction image.

The invention relates to an FPGA (Field Programmable Gate Array)-based surfacing workpiece surface defect detection method and device. The method comprises the following steps: acquiring a magnetic induction intensity data matrix; performing phase-locked amplification on the magnetic induction intensity data matrix to obtain an original feature data matrix; performing convolution filtering processing on the original feature data matrix to obtain a smooth feature data matrix; performing binarization processing on the smooth feature data matrix to obtain a binarized data matrix; whether magnetic field distortion exists on the surface of the surfacing workpiece or not is determined according to the binary data matrix; and when the magnetic field distortion exists on the surface of the surfacing workpiece, judging whether the magnetic field distortion is caused by surface defects of the workpiece or not, and outputting a detection report according to a judgment result. Defect detection is realized by utilizing hardware, the advantage of high operational capability of the FPGA is fully utilized, and the efficiency of defect detection is remarkably improved.

Methods and apparatus for improving a CCCM mode are provided. According to one method, a down-sampled luma block is generated by applying a target down-sampled kernel to a luma block, where the target down-sampled kernel is selected from a set of filters comprising a plurality of down-sampled kernels. A convolutional cross-component model predictor for a target chroma sample in a chroma block is determined, wherein the convolutional cross-component model predictor comprises terms generated by applying a convolution filter to the location of the target downsampled luma sample. A final predictor for the target chroma sample is generated from a set of prediction candidates including a convolutional cross-component model predictor. According to another method, a sample size condition for deciding whether to apply a CCCM mode is simplified by checking a block width, a block height, a block area, or any combination.

Systems, methods, and instrumentalities described herein may involve bi-prediction blending using non- scalar template-based blending parameters. In examples, a device (e.g., a video encoding and / or decoding device may) receive a block. The device may determine one or more reference blocks. The device may determine one or more linear spatial convolutional filters. The device may derive one or more prediction samples of the block by applying the one or more linear spatial convolutional filters to the one or more prediction samples or the one or more reference blocks.

An example device for filtering video data includes a memory configured to store video data; and a processing system comprising one or more processors implemented in circuitry, the processing system being configured to: apply one or more neural network processing blocks to intermediate filtered video data, each of the neural network processing blocks including a first 1×1 convolutional filter, a parametric rectified linear unit (PReLU) filter, a second 1×1 convolutional filter, and a 3×3 convolutional filter; apply additional neural network processing blocks to output of the one or more neural network processing blocks to form filtered video data; and output the filtered video data.

In one embodiment, a method includes providing sequence information of a DNA sequence as an input to a neural network model, generating activations by convolutional filters of the neural network model based on the sequence information, wherein each convolutional filter is associated with a weight, identifying motifs from the DNA sequence based on the convolutional filters and their weights by the neural network model based on a regularization function configured to enable each convolutional filter to learn a distinct motif, generating a linear vector of attention scores for the motifs and identifying interactions between the motifs by attention layers of the neural network model based on the activations, determining motif instances and an associated syntax by the neural network model based on the linear vector of attention scores and the interactions, and generating predictions associated with genomic regulatory functions based on the motif instances and the associated syntax.

Methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to implement dynamic triplet convolution for convolutional neural networks are disclosed. An example apparatus disclosed herein for a convolutional neural network is to calculate one or more scalar kernels based on an input feature map applied to a layer of the convolutional neural network, ones of the one or more scalar kernels corresponding to respective dimensions of a static multidimensional convolutional filter associated with the layer of the convolutional neural network. The disclosed example apparatus is also to scale elements of the static multidimensional convolutional filter along a first one of the dimensions based on a first one of the one or more scalar kernels corresponding to the first one of the dimensions to determine a dynamic multidimensional convolutional filter associated with the layer of the convolutional neural network.

Systems and methods for image generation are provided. An aspect of the systems and methods includes obtaining a text prompt, generating a style vector based on the text prompt, generating an adaptive convolution filter based on the style vector, and generating an image corresponding to the text prompt based on the adaptive convolution filter.

The technology disclosed relates to a system that comprises a spatial convolution network and a temporal convolution network. The spatial convolution network is configured to process a window of per-cycle sequencing image sets and generate respective per-cycle spatial feature map sets. Trained coefficients of spatial convolution filters in spatial convolution filter banks of respective sequences of spatial convolution filter banks vary between sequences of spatial convolution layers in respective sequences of spatial convolution layers. The temporal convolution network is configured to process the per-cycle spatial feature map sets on a groupwise basis and generate respective per-group temporal feature map sets. Trained coefficients of temporal convolution filters in respective temporal convolution filter banks vary between temporal convolution filter banks in respective temporal convolution filter banks.

The invention discloses a radio frequency fingerprint identification method, device and equipment based on an enhanced complex convolutional neural network, and relates to the technical field of Internet of Things security, and the method comprises the steps: converting an I / Q signal transmitted by a to-be-identified radio frequency transmitter into a complex value radio frequency signal; and inputting the complex value radio frequency signal and the conjugate form thereof into the enhanced complex convolutional neural network to obtain an identification result of the radio frequency transmitter to be identified. A convolutional layer of a complex-valued convolutional block in the enhanced complex convolutional neural network is a wide linear complex-valued convolutional layer; and the wide linear complex value convolution layer performs convolution operation on the complex value radio frequency signal and the conjugate form thereof through two convolution filters with consistent parameters, and the processing results of the two convolution filters are added as the output of the wide linear complex value convolution layer. According to the method, the characteristics of the complex-valued radio-frequency signal are fully extracted through the wide linear complex-valued convolutional layer; and an SE attention mechanism module is introduced, so that the identification performance of the network model on the radio frequency fingerprint signal is effectively improved.

The invention relates to the technical field of construction site management, in particular to a highway construction site Internet of Things positioning and tracking system which comprises a fundamental frequency calculation module, a parameter generation module, a central frequency generation module, a central frequency calculation module and a central frequency calculation module. The fundamental frequency calculation module collects engine rotating speed and angular speed signals and calculates engine vibration fundamental frequency by combining ignition factors. The dynamic filtering module is used for configuring the weighting coefficient as a difference equation parameter, blocking bandwidth energy through convolution filtering and generating a purification angular velocity; the trajectory tracking module is used for calculating the purification angular velocity and a zero deviation value, calling a Runge-Kutta algorithm for integral generation of a course angle, and generating a position coordinate in combination with a driving speed. According to the method, the vibration fundamental frequency is inversely calculated by collecting the rotating speed of the engine, the blocking bandwidth is dynamically constructed according to the interference frequency, the frequency characteristic is mapped to be a difference equation coefficient, vibration noise is accurately stripped, course drift is eliminated, and it is ensured that the positioning coordinates matched with the actual track are output.

The invention provides an efficient image denoising method based on a hybrid filter technology, and belongs to the field of digital image processing. Traditional bilateral filtering and self-defined convolution filtering are fused to denoise an image; the method comprises the following steps: performing bilateral filtering on a noisy image for preprocessing, and performing self-defined convolution filtering on the preprocessed image to obtain a denoised image; the denoising precision can be improved while the real-time performance is ensured, and key pain points in the fields of industry, medical treatment, consumer electronics and the like are solved.

A method for feature extraction of detected objects, comprising the steps of: receiving a plurality of images, each depicting an object detected by the object detecting application; concatenating the plurality of images into a composite image according to a grid pattern; feeding the composite image through a convolutional neural network (CNN) trained for feature extraction, wherein each convolutional layer of the CNN is configured to, while convolving input data to the convolutional layer using a convolutional filter: determine a currently convolved image of the plurality of images by determining a centre coordinate of a subset of the input data currently covered by the convolutional filter, and mapping the centre coordinate to the grid pattern; and selectively nullifying all weights of the convolutional filter that cover input data derived from any of the plurality of images not being the currently convolved image.

Disclosed are an FPGA-based method and device for surface defect detection of a surfaced workpiece. The method comprises: acquiring a magnetic induction intensity data matrix (S10); performing lock-in amplification on the magnetic induction intensity data matrix to obtain an original feature data matrix (S20); performing convolution filtering processing on the original feature data matrix to obtain a smoothed feature data matrix (S30); performing binarization processing on the smoothed feature data matrix to obtain a binarized data matrix (S40); on the basis of the binarized data matrix, determining whether magnetic field distortion is present on the surface of a surfaced workpiece (S50); and when magnetic field distortion is present on the surface of the surfaced workpiece, determining whether the magnetic field distortion is caused by a surface defect of the workpiece, and outputting a detection report on the basis of the determination result (S60). Defect detection is realized by means of hardware, fully utilizing the strong computational capability of the FPGA, and significantly improving the defect detection efficiency.

The present invention provides an image compression sensing method and system based on wavelet transform and convolution filter flow. The image compression sensing method includes: obtaining multiple image block measurement values; performing initial reconstruction on the multiple image block measurement values ​​to obtain an initial reconstructed image; inputting the initial reconstructed image into a deep reconstruction network for processing to obtain a final reconstructed image, and the deep reconstruction network includes a discrete wavelet transform network, a pixel-level filter network based on a convolution filter flow, and an inverse discrete wavelet transform fusion network connected in sequence. Applying a discrete wavelet transform network to the initial reconstructed image can expand the receptive field and reduce blocking artifacts; the discrete wavelet transform network decomposes the initial reconstructed image, and uses a pixel-level filter network to perform convolution filter flows on different sub-band images, thereby realizing simultaneous analysis of high-frequency and low-frequency components, extracting image features from different frequency domain levels, and considering the local neighborhood information of image pixels, effectively improving the quality of compressed sensing image reconstruction.

Provided are a method and an electronic device for performing convolutional neural network operations on a batch of input images using homomorphic encryption. The method includes: for each channel of the batch of the input images, grouping pixel values at the same location in the images included in the batch into a single vector, and encrypting the grouped vectors using a homomorphic encryption method to obtain a first ciphertext; for each channel and each coefficient of the ciphertext, grouping coefficients of the first ciphertext corresponding to all pixel locations to generate polynomials, and configuring the polynomials into a single matrix; multiplying the matrix by a polynomial matrix representing a convolution filter to obtain a result matrix, and performing a modulo operation on each element of the result matrix to generate an encrypted convolution operation result, the operation being performed on unencrypted matrices; and rearranging polynomial coefficients of the result matrix to generate a second ciphertext for the batch of the images after the convolution layer is applied, in which the second ciphertext is encrypted by the same homomorphic encryption method as the first ciphertext.

The invention belongs to the technical field of computers, and provides a Kolmogorov-Arnold convolution filter-based transfer learning method for human behavior recognition, and the method comprises the steps: firstly collecting and preprocessing sensor data of a wearable device, dividing the sensor data into a training set, a verification set and a test set, constructing a recognition model, and carrying out the recognition of the sensor data through a Kolmogorov-Arnold convolution filter. The method comprises the following steps: accessing a prompt module based on Kolmogorov-Arnold convolution to an input end of a pre-trained time sequence basic model with frozen backbone network parameters, accessing a classifier to an output end of the pre-trained time sequence basic model, then finely adjusting the prompt module and the classifier only by using training data, determining an optimal model through a verification set, and finally obtaining a time sequence basic model; and finally, the trained model is deployed on wearable equipment, real-time and high-precision human body behavior recognition is realized, and the conversion cost between different recognition tasks is greatly reduced.

The present invention is entitled System and Method for Image Segmentation and Classification Using Deep Down-Convolutional Neural Networks. The present invention provides methods and systems for image segmentation and / or classification using convolutional neural networks (CNNs). In one embodiment, a method includes: receiving an image having a first size, downsampling the image to produce a downsampled image of a predetermined size, wherein the predetermined size is less than the first size, feeding the downsampled image to a CNN, wherein a first convolutional layer of the CNN includes a plurality of first convolutional filters, each of the plurality of first convolutional filters having a receptive field size greater than a threshold receptive field size, identifying one or more anatomical structures of the downsampled image using the plurality of first convolutional filters; and mapping the one or more anatomical structures into a segmentation map or image classification using one or more subsequent layers of the CNN. In this way, the number of encoding layers of a trained CNN can be significantly reduced.

The invention discloses an industrial defect detection method based on local attention enhancement and sparse query driving, and the method comprises the steps: improving an industrial image defect detection model of a DETR network, introducing a local attention mechanism for enhancement in a shallow feature extraction stage of a backbone network, fusing the enhanced shallow features with deep features, and carrying out the extraction of the deep features, and predicting a small target candidate frame based on the enhanced shallow features and initializing the small target candidate frame into a sparse query vector. In a Transform encoder and a Transform decoder, a dual-channel parallel module of a large kernel decomposition convolution combined convolution filter bank is adopted to replace a traditional self-attention mechanism. According to the invention, the problems of low precision and slow convergence rate of small-size defect detection are effectively solved, and high precision and automation of industrial defect detection are realized.

The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional neural network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.

The focusing distance in a visual analytics system (e.g., the distance between the focal plane of the camera used when the image was captured and the actual focal plane for the in-focus image) may be determined by subjecting one or more images captured by such a system to a multi-layer analysis. In such an analysis, the input may be subjected to one or more convolution filters, and the final result of such convolution may be fed to a dense layer that can yield the focusing distance. This focusing distance may then be used to (re)focus the camera or for other purposes (such as generating an alert).