Design and Analysis of Linear Feedback Shift Register Based on Various Tap Connections

Saravanan, S.; Lavanya, M.; Sai, R. Vijay; Kumar, Ravi

doi:10.1016/j.proeng.2012.06.079

Cited by 10 publications

(1 citation statement)

References 3 publications

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“…PRNG commonly used to implement measurement matrices into CS-CIS are Linear Feedback Shift Registers (LFSRs) [12][13] LFSRs consist of a series of sequentially connected flip-flops. Some of the outputs of these flip-flops, besides being connected to the input of the flip-flop that they precede, are also connected, by means of XOR logic gates, to the input of the very first one.…”

Section: Rip Of Binary Matrices Produced By Pseudo Random Binary mentioning

confidence: 99%

Compressive Imaging Using RIP-Compliant CMOS Imager Architecture and Landweber Reconstruction

Trevisi¹,

Akbari²,

Trocan³

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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In this paper we present a new image sensor architecture for fast and accurate compressive sensing (CS) of natural images. Measurement matrices usually employed in compressive sensing CMOS image sensors (CS-CIS) are recursive pseudo-random binary matrices. We have proved that the restricted isometry property (RIP) of these matrices is limited by a low sparsity constant. The quality of these matrices is also affected by the non-idealities of pseudo-random numbers generators (PRNG). To overcome these limitations, we propose a hardware-friendly pseudo-random ternary measurement matrix generated on-chip by means of class III elementary cellular automata (ECA). These ECA present a chaotic behaviour that emulates random CS measurement matrices better than other PRNG. We have combined this new architecture with a blockbased CS smoothed-projected Landweber (BCS-SPL) reconstruction algorithm. By means of single value decomposition (SVD) we have adapted this algorithm to perform fast and precise reconstruction while operating with binary and ternary matrices. Simulations are provided to qualify the approach.Index Terms-CMOS image sensor architecture, compressive sensing, Landweber reconstruction, power spectral density, random binary matrix RIP proof, single value decomposition, ternary measurement matrix.

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Section: Rip Of Binary Matrices Produced By Pseudo Random Binary mentioning

confidence: 99%