Abstract-In Compressed Sensing the matrices that satisfy the Restricted Isometry Property (RIP) play an important role. But to date, very few results for designing such matrices are available. For applications such as multiplier-less data compression, binary sensing matrices are of interest. The present work constructs deterministic and binary sensing matrices using Euler Squares. In particular, given a positive integer m different from p, p 2 for a prime p, we show that it is possible to construct a binary sensing matrix of size m × c(mµ) 2 , where µ is the coherence parameter of the matrix and c ∈ [1, 2). The matrices that we construct have small density (that is, percentage of nonzero entries in the matrix is small) with no function evaluation in their construction, which support algorithms with low computational complexity. Through experimental work, we show that our binary sensing matrices can be used for such applications as content based image retrieval. Our simulation results demonstrate that the Euler Square based CS matrices give better performance than their Gaussian counterparts.

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a b s t r a c tThe major impediment in controlling the interface between Bitumen-froth and Middlings in separation cells is the lack of safe and reliable sensors for interface level detection. This work describes a novel sensor for this problem using computer vision techniques on video frames captured from a sight glass camera. A simple edge detection method combined with state-space model based particle filtering is used to estimate the interface level and its quality. Industrial results show that the algorithm is robust to lighting changes and process abnormalities. Highly improved control performance results when the sensor estimates are used for feedback control.

Computed Tomography (CT) is one of the signicant research areas in the eld of medical image analysis. As X-rays used in CT image reconstruction are harmful to the human body, it is necessary to reduce the X-ray dosage while also maintaining good quality of CT images. Since medical images have a natural sparsity, one can directly employ compressive sensing (CS) techniques to reconstruct the CT images. In CS, sensing matrices having low coherence (a measure providing correlation among columns) provide better image reconstruction. However, the sensing matrix constructed through the incomplete angular set of Radon projections typically possesses large coherence. In this paper, we attempt to reduce the coherence of the sensing matrix via a square and invertible preconditioner possessing a small condition number, which is obtained through a convex optimization technique. The stated properties of our preconditioner imply that it can be used eectively even in noisy cases. We demonstrate empirically that the preconditioned sensing matrix yields better signal recovery than the original sensing matrix.