### Petropulu A: Step-frequency radar with compressive sampling (SFR-CS

- In Proc. IEEE Int Acoustics Speech and Signal Processing (ICASSP) Conf 2010:1686–1689

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### total variation minimization based compressive wideband spectrum sensing for cognitive radios

, 2011

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### Dynamic Compressive Sensing: SPARSE RECOVERY ALGORITHMS FOR STREAMING SIGNALS AND VIDEO

, 2013

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### TITLE: CMOS IMAGE SENSORS WITH COMPRESSIVE SENSING ACQUISITION

"... ii This thesis is dedicated to my parents for supporting me with affection and love. The compressive sensing (CS) paradigm provides an efficient image acquisition technique through simultaneous sensing and compression. Since the imaging philosophy in CS im-agers is different from conventional imagin ..."

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ii This thesis is dedicated to my parents for supporting me with affection and love. The compressive sensing (CS) paradigm provides an efficient image acquisition technique through simultaneous sensing and compression. Since the imaging philosophy in CS im-agers is different from conventional imaging systems, new physical structures are required to design cameras suitable for CS imaging. While this work is focused on the hardware implementation of CS encoding for CMOS sensors, the image reconstruction problem of CS is also studied. The energy compaction properties of the image in different domains are exploited to modify conventional recon-struction problems. Experimental results show that the modified methods outperform the 1-norm and TV (total variation) reconstruction algorithms by up to 2.5dB in PSNR. Also, we have designed, fabricated and measured the performance of two real-time and area-efficient implementations of the CS encoding for CMOS imagers. In the first imple-mentation, the idea of active pixel sensor (APS) with an integrator and in-pixel current

### Noise Folding in Completely Perturbed Compressed Sensing

"... This paper first presents a new generally perturbed compressed sensing (CS) model = ( + )( + ) + , which incorporated a general nonzero perturbation into sensing matrix and a noise into signal simultaneously based on the standard CS model = + and is called noise folding in completely perturbed CS m ..."

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This paper first presents a new generally perturbed compressed sensing (CS) model = ( + )( + ) + , which incorporated a general nonzero perturbation into sensing matrix and a noise into signal simultaneously based on the standard CS model = + and is called noise folding in completely perturbed CS model. Our construction mainly will whiten the new proposed CS model and explore in restricted isometry property (RIP) and coherence of the new CS model under some conditions. Finally, we use OMP to give a numerical simulation which shows that our model is feasible although the recovered value of signal is not exact compared with original signal because of measurement noise , signal noise , and perturbation involved.

### Wavelet Based Compressive Sensing Techniques for Image Compression

"... Compressive sensing (CS) exploits the sparsity of the commonly encountered signals and provides the data compression at the first step of the image acquisition. In this paper, performance of various wavelet based CS techniques has been analysed. It is based on the concept that small collections of n ..."

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Compressive sensing (CS) exploits the sparsity of the commonly encountered signals and provides the data compression at the first step of the image acquisition. In this paper, performance of various wavelet based CS techniques has been analysed. It is based on the concept that small collections of non-adaptive linear projections of a sparse signal contain enough information for its effective reconstruction using some optimization procedure. Wavelet Transform is widely applied to the domain of CS to obtain the sparse representation of the signals to be compressed. The results of CS techniques prove that the image reconstruction quality obtained by wavelet based CS techniques is better than the practical image compression standards like JPEG.

### unknown title

"... 1 Schematic diagram of the receiver.Φl denotes the measurement matrix for the lth receive ..."

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1 Schematic diagram of the receiver.Φl denotes the measurement matrix for the lth receive

### Compressed sensing techniques for hyperspectral image recovery

"... Compressed Sensing (CS) theory is progressively gaining more interest over scientists of different fields. The reason is its potential to provide high resolution capture of physical signals from relatively few measurements, tipycally well below respect to the limit given by the Shannon/Nyquist sampl ..."

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Compressed Sensing (CS) theory is progressively gaining more interest over scientists of different fields. The reason is its potential to provide high resolution capture of physical signals from relatively few measurements, tipycally well below respect to the limit given by the Shannon/Nyquist sampling theorem. Sampling a signal with few measurements gives the big advantage of sampling and compressing simultaneously that signal. One of the fields which could gain more benefits from CS theory is image compression: in the normal compression process, we have to turn a large digital data set into a smaller one, but in many applications could be useful to avoid the initial large data set to begin with, and to acquire and sampling at the same time. We apply the CS theory to optimize the capturing process of Hyperspectral Images, which are characterized by an huge amount of data with high spatial and spectral correlation and, hence, allows a compact (i.e., quasi-sparse) representation in a 3D domain. The aim of the paper is twofold: (i) to investigate to sparseness degree S, i.e., the number of nonzero samples in the transform domain which are necessary to reconstruct the signal with satisfactory quality, i.e., with quality comparable to typical lossy compression schemes; (ii) to investigate the number of measurements M which are necessary to recon-struct the signal with satisfactory quality, whereas reconstruction is performed by means of l1-norm minimization and acquisition is performed by means of random matrices.

### and Robert W Boyd. Compressive object tracking using entangled photons. Applied Physics Letters, 102(23):231104, 2013.

, 2014

"... Dedicated with love to my wife Jennifer and my daughters Ada and Wren. iii ..."

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Dedicated with love to my wife Jennifer and my daughters Ada and Wren. iii

### unknown title

, 2014

"... Low-light-level imaging techniques have application in many diverse fields, ranging from biological sciences to security. We demonstrate a single-photon imaging system based on a time-gated inten-sified CCD (ICCD) camera in which the image of an object can be inferred from very few detected photons. ..."

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Low-light-level imaging techniques have application in many diverse fields, ranging from biological sciences to security. We demonstrate a single-photon imaging system based on a time-gated inten-sified CCD (ICCD) camera in which the image of an object can be inferred from very few detected photons. We show that a ghost-imaging configuration, where the image is obtained from photons that have never interacted with the object, is a useful approach for obtaining images with high signal-to-noise ratios. The use of heralded single-photons ensures that the background counts can be virtually eliminated from the recorded images. By applying techniques of compressed sensing and associated image reconstruction, we obtain high-quality images of the object from raw data comprised of fewer than one detected photon per image pixel. ar X iv