Abstract: The method of the present disclosure includes: for a feature matrix of each channel of an image output from an intermediate layer of a neural network: determining row(s) and column(s) having same feature values at edges of the feature matrix as row(s) to be compressed and column(s) to be compressed, deleting the feature values of the row(s) to be compressed and the column(s) to be compressed, and reserving remaining feature values as reserved values; compressing the feature values of the row(s) to be compressed and the column(s) to be compressed to obtain edge value(s); and encoding the reserved values and the edge value(s), as well as a number of the row(s) to be compressed and a number of the column(s) to be compressed, and sending a result of the encoding to a decoder for restoring the feature matrix of the each channel by the decoder.

Abstract: A production task scheduling method, system and device for a flexible assembly job shop is provided.

Abstract: The invention relates to an image feature transmission method, device and system, and relates to the technical field of communication. The transmission method the following steps: extracting a feature matrix of a to-be-processed image for each channel by using a machine learning model; determining one or more incidence matrix pairs according to a comparison result of the correlation degree between the feature matrixes and a first threshold value; according to the information amount, determining a representation matrix and a represented matrix in the two feature matrixes of each incidence matrix pair; determining a corresponding relation between each representation matrix and each represented matrix; and carrying out quantization processing and coding processing on each representation matrix, the corresponding relation and the maximum characteristic value and the minimum characteristic value in each represented matrix, and then transmitting to a decoding end.

Abstract: A microbubble counting method for patent foramen ovale (PFO) based on deep learning is provided. The method includes: segmenting a target area of a left heart in an ultrasonic image; and generating a corresponding density map for a segmented target image using a convolutional neural network (CNN), and calculating a total number of the microbubbles in the segmented area by integration and summation. The method has the following beneficial effects: target segmentation is performed on the left atrium and left ventricular area of the heart using the neural network, and effective segmentation of the target area of the left heart is the key of obtaining parameters such as a size and form of the target area. The target area is quantitatively analyzed according to a segmentation result, and the number of the microbubbles in the target area is counted.