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Compressed sensing

Junxue An, Chunsheng Jin, Andra Dédinaité, Jan Holgersson, Niclas G Karlsson, Per Martin Claesson
Interfacial properties of two brush-with-anchor mucins, C-P55 and C-PSLex, have been investigated at the aqueous solution/poly(methylmethacrylate) (PMMA) interface. Both are recombinant mucin-type fusion proteins, produced by fusing the glycosylated mucin part of P-selectin glycoprotein ligand-1 (PSLG-1) to the Fc part of a mouse immunoglobulin in two different cells. They are mainly expressed as dimers upon production. Analysis of the O-glycans shows that the C-PSLex mucin has the longer and more branched side chains, but C-P55 has slightly higher sialic acid content...
April 21, 2017: Langmuir: the ACS Journal of Surfaces and Colloids
Jing Liu, Kaiyu Huang, Guoxian Zhang
We consider the joint sparsity Model 1 (JSM-1) in a decentralized scenario, where a number of sensors are connected through a network and there is no fusion center. A novel algorithm, named distributed compact sensing matrix pursuit (DCSMP), is proposed to exploit the computational and communication capabilities of the sensor nodes. In contrast to the conventional distributed compressed sensing algorithms adopting a random sensing matrix, the proposed algorithm focuses on the deterministic sensing matrices built directly on the real acquisition systems...
April 20, 2017: Sensors
Yu Zhou, Sam Kwong, Hainan Guo, Xiao Zhang, Qingfu Zhang
Sparse signal reconstruction can be regarded as a problem of locating the nonzero entries of the signal. In presence of measurement noise, conventional methods such as l₁ norm relaxation methods and greedy algorithms, have shown their weakness in finding the nonzero entries accurately. In order to reduce the impact of noise and better locate the nonzero entries, in this paper, we propose a two-phase algorithm which works in a coarse-to-fine manner. In phase 1, a decomposition-based multiobjective evolutionary algorithm is applied to generate a group of robust solutions by optimizing l₁ norm of the solutions...
April 14, 2017: IEEE Transactions on Cybernetics
Abbas Kazemipour, Ji Liu, Krystyna Solarana, Daniel Nagode, Patrick Kanold, Min Wu, Behtash Babadi
OBJECTIVE: Common biological measurements are in the form of noisy convolutions of signals of interest with possibly unknown and transient blurring kernels. Examples include EEG and calcium imaging data. Thus, signal deconvolution of these measurements is crucial in understanding the underlying biological processes. The objective of this paper is to develop fast and stable solutions for signal deconvolution from noisy, blurred and undersampled data, where the signals are in the form of discrete events distributed in time and space...
April 13, 2017: IEEE Transactions on Bio-medical Engineering
Mahmut Şamİl Sağiroğlu, M Oğuzhan Külekcİ
The DNA sequencing data analysis pipelines require significant computational resources. In that sense, cloud computing infrastructures appear as a natural choice for this processing. However, the first practical difficulty in reaching the cloud computing services is the transmission of the massive DNA sequencing data from where they are produced to where they will be processed. The daily practice here begins with compressing the data in FASTQ file format, and then sending these data via fast data transmission protocols...
April 17, 2017: Journal of Computational Biology: a Journal of Computational Molecular Cell Biology
Laurène Donati, Masih Nilchian, Sylvain Trépout, Cédric Messaoudi, Sergio Marco, Michael Unser
A central challenge in scanning transmission electron microscopy (STEM) is to reduce the electron radiation dosage required for accurate imaging of 3D biological nano-structures. Methods that permit tomographic reconstruction from a reduced number of STEM acquisitions without introducing significant degradation in the final volume are thus of particular importance. In random-beam STEM (RB-STEM), the projection measurements are acquired by randomly scanning a subset of pixels at every tilt view. In this work, we present a tailored RB-STEM acquisition-reconstruction framework that fully exploits the compressed sensing principles...
April 6, 2017: Ultramicroscopy
Thomas Benkert, Kai Tobias Block, Samantha Heller, Melanie Moccaldi, Daniel K Sodickson, Sungheon Gene Kim, Linda Moy
OBJECTIVES: The aim of this study was to assess the applicability of Dixon radial volumetric encoding (Dixon-RAVE) for comprehensive dynamic contrast-enhanced 3D magnetic resonance imaging (MRI) of the breast using a combination of radial sampling, model-based fat/water separation, compressed sensing, and parallel imaging. MATERIALS AND METHODS: In this Health Insurance Portability and Accountability Act-compliant prospective study, 24 consecutive patients underwent bilateral breast MRI, including both conventional fat-suppressed and non-fat-suppressed precontrast T1-weighted volumetric interpolated breath-hold examination (VIBE)...
April 11, 2017: Investigative Radiology
Yishan Su, Xiaomei Fu, Guangyao Han, Naishen Xu, Zhigang Jin
In this paper, compressed sensing (CS) theory is utilized in a medium-access control (MAC) scheme for wireless sensor networks (WSNs). We propose a new, cross-layer compressed sensing medium-access control (CL CS-MAC) scheme, combining the physical layer and data link layer, where the wireless transmission in physical layer is considered as a compress process of requested packets in a data link layer according to compressed sensing (CS) theory. We first introduced using compressive complex requests to identify the exact active sensor nodes, which makes the scheme more efficient...
April 10, 2017: Sensors
Xu Ma, Dongxiang Shi, Zhiqiang Wang, Yanqiu Li, Gonzalo R Arce
This paper proposes to use the a-priori knowledge of the target layout patterns to design data-adaptive compressive sensing (CS) methods for efficient source optimization (SO) in lithography systems. A set of monitoring pixels are selected from the target layout based on blue noise random patterns. The SO is then formulated as an under-determined linear problem to improve image fidelity according to the monitoring pixels. Adaptive projections are then designed, based on the a-priori knowledge of the target layout, in order to further reduce the dimension of the optimization problem, while trying to retain the SO performance...
March 20, 2017: Optics Express
Eliel Hojman, Thomas Chaigne, Oren Solomon, Sylvain Gigan, Emmanuel Bossy, Yonina C Eldar, Ori Katz
In deep tissue photoacoustic imaging the spatial resolution is inherently limited by the acoustic wavelength. Recently, it was demonstrated that it is possible to surpass the acoustic diffraction limit by analyzing fluctuations in a set of photoacoustic images obtained under unknown speckle illumination patterns. Here, we purpose an approach to boost reconstruction fidelity and resolution, while reducing the number of acquired images by utilizing a compressed sensing computational reconstruction framework. The approach takes into account prior knowledge of the system response and sparsity of the target structure...
March 6, 2017: Optics Express
Tao Yu, Weiwei Cai
Tomographic absorption spectroscopy (TAS) is experiencing a surge of interest due to recent progress in laser technology and advanced imaging concepts such as nonlinear tomography and compressed sensing. Nevertheless, even though numerous algorithms have been adapted from other tomographic areas such as medical imaging and engineering process control, and applied in TAS applications, systematic comparison between those methods has not been investigated. In this work, we aim to test major inversion algorithms on both the so-called rank-deficient (RD) and discrete ill-posed (DIP) problems...
March 10, 2017: Applied Optics
Xin Yuan, Yangyang Sun, Shuo Pang
Our temporally compressive imaging system reconstructs a high-speed image sequence from a single, coded snapshot. The reconstruction quality, similar to that of other compressive sensing systems, often depends on the structure of the measurement, as well as the choice of regularization. In this paper, we report a compressive video system that also captures the side information to aid in the reconstruction of high-speed scenes. The integration of the side information not only improves the quality of reconstruction, but also reduces the dependence of the reconstruction on regularization...
April 1, 2017: Applied Optics
Michael Bianco, Peter Gerstoft
To provide constraints on the inversion of ocean sound speed profiles (SSPs), SSPs are often modeled using empirical orthogonal functions (EOFs). However, this regularization, which uses the leading order EOFs with a minimum-energy constraint on the coefficients, often yields low resolution SSP estimates. In this paper, it is shown that dictionary learning, a form of unsupervised machine learning, can improve SSP resolution by generating a dictionary of shape functions for sparse processing (e.g., compressive sensing) that optimally compress SSPs; both minimizing the reconstruction error and the number of coefficients...
March 2017: Journal of the Acoustical Society of America
Yan Wang, Kean Chen
A low frequency sound field within a cylindrical cavity can be well approximated by a sparse set of Fourier-Bessel series in a spherical coordinate system. The approximation accuracy can be guaranteed as long as the series coefficients are well estimated by use of spherical microphone arrays (SMA). Conventional methods like spherical Fourier transform and Helmholtz equation least square require a large number of sensors, and it is difficult to estimate the high order coefficients in the presence of sensor noise...
March 2017: Journal of the Acoustical Society of America
Kent B Pfeifer, Komandoor E Achyuthan, Matthew Allen, Michele L B Denton, Michael P Siegal, Ronald P Manginell
Neutron sensing is critical in civilian and military applications. Conventional neutron sensors are limited by size, weight, cost, portability and helium supply. Here the microfabrication of gadolinium (Gd) conversion material-based heterojunction diodes for detecting thermal neutrons using electrical signals produced by internal conversion electrons (ICEs) is described. Films with negligible stress were produced at the tensile-compressive crossover point, enabling Gd coatings of any desired thickness by controlling the radiofrequency sputtering power and using the zero-point near p(Ar) of 50 mTorr at 100 W...
March 25, 2017: Journal of Radiation Research
Hui Li, Qingfu Zhang, Jingda Deng, Zong-Ben Xu
Iterative thresholding is a dominating strategy for sparse optimization problems. The main goal of iterative thresholding methods is to find a so-called k-sparse solution. However, the setting of regularization parameters or the estimation of the true sparsity are nontrivial in iterative thresholding methods. To overcome this shortcoming, we propose a preference-based multiobjective evolutionary approach to solve sparse optimization problems in compressive sensing. Our basic strategy is to search the knee part of weakly Pareto front with preference on the true k-sparse solution...
March 29, 2017: IEEE Transactions on Neural Networks and Learning Systems
Canyi Lu, Jiashi Feng, Shuicheng Yan, Zhouchen Lin
Accompanied with the rising popularity of compressed sensing, the Alternating Direction Method of Multipliers (ADMM) has become the most widely used solver for linearly constrained convex problems with separable objectives. In this work, we observe that many existing ADMMs update the primal variable by minimizing different majorant functions with their convergence proofs given case by case. Inspired by the principle of majorization minimization, we respectively present the unified frameworks of Gauss-Seidel ADMMs and Jacobian ADMMs, which use different historical information for the current updating...
March 29, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
Hugh Podmore, Alan Scott, Pavel Cheben, Aitor V Velasco, Jens H Schmid, Martin Vachon, Regina Lee
We demonstrate compressive-sensing (CS) spectroscopy in a planar-waveguide Fourier-transform spectrometer (FTS) device. The spectrometer is implemented as an array of Mach-Zehnder interferometers (MZIs) integrated on a photonic chip. The signal from a set of MZIs is composed of an undersampled discrete Fourier interferogram, which we invert using l<sub>1</sub>-norm minimization to retrieve a sparse input spectrum. To implement this technique, we use a subwavelength-engineered spatial heterodyne FTS on a chip composed of 32 independent MZIs...
April 1, 2017: Optics Letters
Ningquan Wang, Ruxiu Liu, A Fatih Sarioglu
Microfluidic processing of biological samples typically involves differential manipulations of suspended particles under various force fields in order to spatially fractionate the sample based on a biological property of interest. For the resultant spatial distribution to be used as the assay readout, microfluidic devices are often subjected to microscopic analysis requiring complex instrumentation with higher cost and reduced portability. To address this limitation, we have developed an integrated electronic sensing technology for multiplexed detection of particles at different locations on a microfluidic chip...
March 13, 2017: Journal of Visualized Experiments: JoVE
Pia M Jungmann, Christoph A Agten, Christian W Pfirrmann, Reto Sutter
The prevalence of orthopedic metal implants is continuously rising in the aging society. Particularly the number of joint replacements is increasing. Although satisfying long-term results are encountered, patients may suffer from complaints or complications during follow-up, and often undergo magnetic resonance imaging (MRI). Yet metal implants cause severe artifacts on MRI, resulting in signal-loss, signal-pileup, geometric distortion, and failure of fat suppression. In order to allow for adequate treatment decisions, metal artifact reduction sequences (MARS) are essential for proper radiological evaluation of postoperative findings in these patients...
March 25, 2017: Journal of Magnetic Resonance Imaging: JMRI
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