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Traditional synthesis/analysis sparse representation models signals in a one dimensional (1D) way, in which a multidimensional (MD) signal is converted into a 1D vector. 1D modeling cannot sufficiently handle MD signals of high dimensionality in limited computational resources and memory usage, as breaking the data structure and inherently ignores the diversity of MD signals (tensors). We utilize...
An analysis sparse model represents an image signal by multiplying it using an analysis dictionary, leading to a sparse outcome. It transforms an image (two dimensional signal) into a one-dimensional (1D) vector. However, this 1D model ignores the two dimensional property and breaks the local spatial correlation inside images. In this paper, we propose a two dimensional (2D) analysis sparse model...
Inter-vehicle communication (IVC) network has become an important topic in research for its potential advantages in enhancing traffic security and efficiency. This paper investigates the communication performance of IVC networks in two lane bidirectional traffic streams. By changing the transmission range of communication units and penetration rate of equipped vehicles, we studied several performance...
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