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Intra-subject enveloped multilayer fuzzy sample compression for speech diagnosis of Parkinson's disease
Machine learning-based Parkinson’s disease (PD) speech diagnosis is a current research hotspot. However, existing methods use each corpus sample as...
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Microarray Image Lossless Compression Using General Entropy Coders and Image Compression Standards
Lossless compression is still a challenging task in the case of microarray images. This research proposes two algorithms that aim to improve the...
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Convolutional neural networks with attention module and compression strategy based on second-order information
In convolutional neural networks, the second-order representation can effectively enhance the nonlinear modeling capability of first-order models,...
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Compression we live by: cognitive dynamics and strategies of compression as a viable tool of composition in micronarrative
This paper argues that compression is a hallmark of creativity, demonstrating how textual compression is processed cognitively in the mind....
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A single-frame infrared small target detection method based on joint feature guidance
Single-frame infrared small target detection is affected by the low image resolution and small target size, and is prone to the problems of small...
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Block based learned image compression
Efficient image compression is very important for storage, retrieval, processing and transmission of image contents. The objective is to find a...
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A Complex-Valued Neural Network Based Robust Image Compression
Recent works on learned image compression (LIC) based on convolutional neural networks (CNNs) have achieved great improvement with superior...
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Modulation recognition network compression based on a randomly perturbation convolutional kernel activation map** method
Deep-learning-based automatic modulation recognition techniques have been extensively explored for wireless communication systems, because of their...
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2C-Net: integrate image compression and classification via deep neural network
Providing effective support for intelligent vision tasks without image reconstruction can save numerous computational costs in the era of big data....
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Data Compression Algorithm of Power System
Data compression refers to the use of software performance to improve system utilization without increasing hardware costs. Therefore, in the process...
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FE-YOLO: YOLO ship detection algorithm based on feature fusion and feature enhancement
The technology for detecting maritime targets is crucial for realizing ship intelligence. However, traditional detection algorithms are not ideal due...
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Video Compression Method Using Vector Quantization
As the number of users viewing video content on the Internet increases, video traffic is growing rapidly. When viewing video over the Internet, poor...
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Mixed Entropy Model Enhanced Residual Attention Network for Remote Sensing Image Compression
In recent years, deep learning has been widely employed in the field of image compression, the most significant of which is the lossy image...
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Digital Image Compression
To eliminate redundant data and highlight useful information, it is generally necessary to compress the image. The typical compression process can be...
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Adaptive temporal compression for reduction of computational complexity in human behavior recognition
The research on video analytics especially in the area of human behavior recognition has become increasingly popular recently. It is widely applied...
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Sparse feature selection via local feature and high-order label correlation
Recently, some existing feature selection approaches neglect the correlation among labels, and almost manifold-based multilabel learning models do...
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Seismic data compression: an overview
Seismic Data (SD) have been for several decades used as one of the main inspection and exploration tools in various fields, particularly petroleum...
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Real-time progressive compression method of massive data based on improved clustering algorithm
In order to realize the real-time progressive compression of massive data and ensure the quality of compressed data, a real-time progressive...
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Spectral–Spatial Feature Completely Separated Extraction with Tensor CNN for Multispectral Image Compression
Considering the rich spectral and spatial information of multispectral image, a learned multispectral image compression method named spectral-spatial...