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Leading compressed sensing (CS) methods require m = O (k log(n)) compressive samples to perfectly reconstruct a k-sparse signal x of size n using random projection matrices (e.g., Gaussian or random Fourier matrices). For a given m, perfect reconstruction usually requires high complexity methods, such as Basis Pursuit (BP), which has complexity O(n3). Meanwhile, low-complexity greedy algorithms do...
In this paper we describe a practical implementation of a distributed video codec deployed on a real visual sensor platform, viz. the MicaZ/Cyclops platform. The codec supports two encoding schemes, one employs Discrete Cosine Transform (DCT) and the other operates on raw pixels. DCT scheme is more costly in terms of computational power consumption, on the other hand it leads to more compression and...
In this work, we propose an optimal joint bit allocation framework in order to provide unequal error protection (UEP) for scalable video coding. In the proposed scheme, video sequence is coded based on scalable video coding (SVC) extension of the H.264/AVC standard. We generate different streams according to changing quantization parameters. Each stream divides into different sub-streams in terms...
In this paper, we propose a new approach for compressed sensing of integer-valued signals using prime numbers. In particular, we utilize the logarithmic values of prime numbers to construct projection matrices that are capable of significant reductions in the number of observations (m) needed for the recovery of integer-valued signals when compared to leading compressed-sensing methods. At one extreme,...
Many wireless sensor network applications will impose a many-to-one traffic flow pattern. This phenomena in communication networks is referred to as reachback. The higher volume of traffic in traffic hotspots, which usually form around the base station, limits the number of sensors that can participate in the formation of a wireless sensor network with a fixed per sensor data rate. To alleviate the...
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