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For the compressed sensing of multiband signals, modulated wideband converter (MWC) is used as the sampling system, and the signal is reconstructed by the simultaneous orthogonal matching pursuit algorithm (SOMP) and its derivative algorithms. In order to find matching atoms, we need to obtain the inner product between atoms in sensing matrix and columns in residual matrix. Next, several inner products...
Within the trend of smart grid, distributed and intelligent metering has gained its popularity among large scale residential power network. Supervising and assessing power quality in time is the method to guarantee secure and trustworthy energy for the demand side, and harmonics measurement and analysis are of necessity. Harmonics metering under the large-scale distributed metering architecture faces...
Due to huge amount of visual surveillance data, network congestion and latency is serious problem for surveillance applications. To reduce the data amount, data compression must be done before transmission. Compressive sensing is a new theory that sample and compress signals at the same time with little cost of bandwidth and energy for transmission. While conventional compressive sensing requires...
Compared with terrestrial wireless sensor networks, underwater sensor network has a longer propagation delay. So many mature MAC protocols can't be directly applied in underwater environment. In this paper, the reservation multiple access method based on compressed sensing in underwater sensor network makes full use of the long propagation delay in the underwater environment and each node in the network...
A background modeling scheme in compressed sensing (CS) imaging system is proposed in this paper, in which background evaluation is performed on the measurement vectors directly before reconstruction. The estimated background measurement vector is constructed through average, running average, median and block-based selective method respectively. Then the estimated background image is reconstructed...
In this paper, a new algorithm for texture recognition and segmentation via sparse representation is presented. Firstly, we cast the texture recognition problem as one of classifying among multiple linear regression models, and solve the problem from a new perspective, sparse representation. This new theory decomposes the signal as a linear combination of the overcomplete dictionary by l1-minimization...
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