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Compressed sensing

Chee-Loon Ng, Fuu-Ming Kai, Ming-Hui Tee, Nicholas Tan, Harold F Hemond
Air pollution exposure causes seven million deaths per year, according to the World Health Organization. Possessing knowledge of air quality and sources of air pollution is crucial for managing air pollution and providing early warning so that a swift counteractive response can be carried out. An optical prototype sensor (AtmOptic) capable of scattering and absorbance measurements has been developed to target in situ sensing of fine particulate matter (PM2.5) and volatile organic compounds (VOCs). For particulate matter testing, a test chamber was constructed and the emission of PM2...
January 18, 2018: Sensors
Van Ha Tang, Abdesselam Bouzerdoum, Son Lam Phung
Compressed sensing techniques have been applied to through-the-wall radar imaging (TWRI) and multipolarization TWRI for fast data acquisition and enhanced target localization. The studies so far in this area have either assumed effective wall clutter removal prior to image formation or performed signal estimation, wall clutter mitigation, and image formation independently. This paper proposes a low-rank and sparse imaging model for jointly addressing the problem of wall clutter mitigation and image formation in multichannel TWRI...
April 2018: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Xu Ma, Fang Yang, Sicong Liu, Jian Song
With the rapid development of light emitting diode (LED), visible light communication (VLC) becomes an important technique for information transmission including underwater applications. However, accurate channel estimation for underwater VLC is still challenging due to the complex environment of the underwater VLC channel. In this paper, by utilizing a proper approximation, where the channel attenuation is linear with the frequency, a new compressive sensing (CS) based channel estimation approach is proposed...
January 8, 2018: Optics Express
Zhijing Zhu, Hao Chi, Tao Jin, Shilie Zheng, Xianbin Yu, Xiaofeng Jin, Xianmin Zhang
In this Letter, we propose an approach to achieving photonics-enabled compressive sensing of sparse wideband radio frequency signals in which an incoherent broadband source is applied, and the mixing and integration functions are realized in the optical domain. A spectrum shaper is employed to slice and encode the spectrum of the broadband light according to a predetermined random sequence. Because of the dispersion-induced group delay, the mixing between the incoming signal and the random bit sequence is achieved...
January 15, 2018: Optics Letters
Liheng Bian, Jinli Suo, Qionghai Dai, Feng Chen
Single-pixel imaging (SPI) is a novel technique that captures 2D images using a photodiode, instead of conventional 2D array sensors. SPI has high signal-to-noise ratio, wide spectral range, low cost, and robustness to light scattering. Various algorithms have been proposed for SPI reconstruction, including linear correlation methods, the alternating projection (AP) method, and compressive sensing (CS) based methods. However, there has been no comprehensive review discussing respective advantages, which is important for SPI's further applications and development...
January 1, 2018: Journal of the Optical Society of America. A, Optics, Image Science, and Vision
Takayuki Okazawa, Ippei Akita
A time-domain analog spatial compressed sensing encoder for neural recording applications is proposed. Owing to the advantage of MEMS technologies, the number of channels on a silicon neural probe array has doubled in 7.4 years, and therefore, a greater number of recording channels and higher density of front-end circuitry is required. Since neural signals such as action potential (AP) have wider signal bandwidth than that of an image sensor, a data compression technique is essentially required for arrayed neural recording systems...
January 11, 2018: Sensors
Hossein Zamani, Hamid Reza Bahrami, Preeti Chalwadi, Paul A Garris, Pedram Mohseni
This paper presents a novel compressive sensing framework for recording brain dopamine levels with fast-scan cyclic voltammetry (FSCV) at a carbon-fiber microelectrode. Termed compressive FSCV (C-FSCV), this approach compressively samples the measured total current in each FSCV scan and performs basic FSCV processing steps, e.g., background current averaging and subtraction, directly with compressed measurements. The resulting background-subtracted faradaic currents, which are shown to have a block-sparse representation in the discrete cosine transform domain, are next reconstructed from their compressively sampled counterparts with the block sparse Bayesian learning algorithm...
January 2018: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Akhilandeshwari Ravichandran, Feng Wen, Jing Lim, Mark Seow Khoon Chong, Jerry K Y Chan, Swee-Hin Teoh
Cells respond to physiological mechanical stresses especially during early fetal development. Adopting a biomimetic approach, it is necessary to develop bioreactor systems to explore the effects of physiologically relevant mechanical strains and shear stresses for functional tissue growth and development. This study introduces a multimodal bioreactor system that allows application of cyclic compressive strains on premature bone grafts that are cultured under biaxial rotation (chamber rotation about two axes) conditions for bone tissue engineering...
January 3, 2018: Journal of Tissue Engineering and Regenerative Medicine
You Zhao, Xing He, Tingwen Huang, Junjian Huang
In this paper, we investigate a more general sparse signal recovery minimization model and a smoothing neural network optimal method for compress sensing problem, where the objective function is a Lp-q minimization model which includes nonsmooth, nonconvex, and non-Lipschitz quasi-norm Lp norms 1≥p>0 and nonsmooth Lq norms 2≥p>1, and its feasible set is a closed convex subset of Rn. Firstly, under the restricted isometry property (RIP) condition, the uniqueness of solution for the minimization model with a given sparsity s is obtained through the theoretical analysis...
December 20, 2017: Neural Networks: the Official Journal of the International Neural Network Society
Edward W Li, Olivia C McKee-Muir, Penney M Gilbert
Satellite cells, adult stem cells in skeletal muscle tissue, reside within a mechanically dynamic three-dimensional microenvironment. With each contraction-relaxation cycle, a satellite cell is expected to experience tensile, shear, and compressive stresses, and through cell-extracellular matrix interactions, also gauge the stiffness of the niche. Via mechanoreceptors, cells can sense these biophysical parameters of the niche, which serve to physically induce conformational changes that impact biomolecule activity, and thereby alter downstream signal transduction pathways and ultimately cell fate...
2018: Current Topics in Developmental Biology
Heejin Kwon, Scott Reid, Dongeun Kim, Sangyun Lee, Jinhan Cho, Jongyeong Oh
PURPOSE: This study aimed to evaluate image quality and diagnostic performance of a recently developed navigated three-dimensional magnetic resonance cholangiopancreatography (3D-MRCP) with compressed sensing (CS) based on parallel imaging (PI) and conventional 3D-MRCP with PI only in patients with abnormal bile duct dilatation. METHODS: This institutional review board-approved study included 45 consecutive patients [non-malignant common bile duct lesions (n = 21) and malignant common bile duct lesions (n = 24)] who underwent MRCP of the abdomen to evaluate bile duct dilatation...
January 4, 2018: Abdominal Radiology
Yangde Gao, Mohammad Karimi, Aleksey A Kudreyko, Wanqing Song
In the marine systems, engines represent the most important part of ships, the probability of the bearings fault is the highest in the engines, so in the bearing vibration analysis, early weak fault detection is very important for long term monitoring. In this paper, we propose a novel method to solve the early weak fault diagnosis of bearing. Firstly, we should improve the alternating direction method of multipliers (ADMM), structure of the traditional ADMM is changed, and then the improved ADMM is applied to the compressed sensing (CS) theory, which realizes the sparse optimization of bearing signal for a mount of data...
December 30, 2017: ISA Transactions
(no author information available yet)
This month: compressed sensing (Cleary/Regev), metabolism (Rabinowitz, Gilmore, Chandrasekaran), microbiology (Coles, Cooper/Hasty), phosphosite stoichiometry by proteomics (Kirschner), interaction networks (Pappu, Tan, Babu/Emili), immunology (Łuksza/Greenbaum), mechanosensitive transcription (Elosegui-Artola/Roca-Cusachs).
December 27, 2017: Cell Systems
Takaho Tsuchiya, Masashi Fujii, Naoki Matsuda, Katsuyuki Kunida, Shinsuke Uda, Hiroyuki Kubota, Katsumi Konishi, Shinya Kuroda
Cells decode information of signaling activation at a scale of tens of minutes by downstream gene expression with a scale of hours to days, leading to cell fate decisions such as cell differentiation. However, no system identification method with such different time scales exists. Here we used compressed sensing technology and developed a system identification method using data of different time scales by recovering signals of missing time points. We measured phosphorylation of ERK and CREB, immediate early gene expression products, and mRNAs of decoder genes for neurite elongation in PC12 cell differentiation and performed system identification, revealing the input-output relationships between signaling and gene expression with sensitivity such as graded or switch-like response and with time delay and gain, representing signal transfer efficiency...
December 2017: PLoS Computational Biology
Juan A Aguilar, Alan M Kenwright
Historically, the resolution of multidimensional NMR has been orders of magnitude lower than the intrinsic resolution that NMR spectrometers are capable of producing. The slowness of Nyquist sampling, as well as the existence of signals as multiplets instead of singlets have been two of the main reasons for this underperformance. Fortunately, two compressive techniques have appeared that can overcome these limitations. Compressive sensing, also known as compressed sampling, (CS), avoids the first limitation exploiting the compressibility of typical NMR spectra, thus allowing sampling at sub-Nyquist rates, while pure shift techniques eliminate the second issue "compressing" multiplets into singlets...
December 26, 2017: Magnetic Resonance in Chemistry: MRC
Yi Jiang, Elliot Padgett, Robert Hovden, David A Muller
Electron tomography (ET) has become a standard technique for 3D characterization of materials at the nano-scale. Traditional reconstruction algorithms such as weighted back projection suffer from disruptive artifacts with insufficient projections. Popularized by compressed sensing, sparsity-exploiting algorithms have been applied to experimental ET data and show promise for improving reconstruction quality or reducing the total beam dose applied to a specimen. Nevertheless, theoretical bounds for these methods have been less explored in the context of ET applications...
December 7, 2017: Ultramicroscopy
Qian Zhang, Tao Jiang, Dong Hae Ho, Shanshan Qin, Xixi Yang, Jeong Ho Cho, Qijun Sun, Zhong Lin Wang
Electronic skin based on multimodal sensing array is ready to detect various stimuli in different categories by utilizing highly sensitive materials, sophisticated geometry designs, and integration of multifunctional sensors. However, it is still difficult to distinguish multiple and complex mechanical stimuli in a local position by conventional multimodal E-skin, which is significantly important in the signals feedback of robotic fine motions and human-machine interactions. Here, we present a transparent, flexible and self-powered multi-stage sensation matrix based on piezoelectric nanogenerators (NGs) constructed in a crossbar design...
December 20, 2017: ACS Nano
Meng Lyu, Wei Wang, Hao Wang, Haichao Wang, Guowei Li, Ni Chen, Guohai Situ
In this manuscript, we propose a novel framework of computational ghost imaging, i.e., ghost imaging using deep learning (GIDL). With a set of images reconstructed using traditional GI and the corresponding ground-truth counterparts, a deep neural network was trained so that it can learn the sensing model and increase the quality image reconstruction. Moreover, detailed comparisons between the image reconstructed using deep learning and compressive sensing shows that the proposed GIDL has a much better performance in extremely low sampling rate...
December 19, 2017: Scientific Reports
Dennis J Lee, Charles F LaCasse, Julia M Craven
Channeled spectropolarimetry measures the spectrally resolved Stokes parameters. A key aspect of this technique is to accurately reconstruct the Stokes parameters from a modulated measurement of the channeled spectropolarimeter. The state-of-the-art reconstruction algorithm uses the Fourier transform to extract the Stokes parameters from channels in the Fourier domain. While this approach is straightforward, it can be sensitive to noise and channel cross-talk, and it imposes bandwidth limitations that cut off high frequency details...
December 11, 2017: Optics Express
Fengqiang Li, Huaijin Chen, Adithya Pediredla, Chiakai Yeh, Kuan He, Ashok Veeraraghavan, Oliver Cossairt
Three-dimensional imaging using Time-of-flight (ToF) sensors is rapidly gaining widespread adoption in many applications due to their cost effectiveness, simplicity, and compact size. However, the current generation of ToF cameras suffers from low spatial resolution due to physical fabrication limitations. In this paper, we propose CS-ToF, an imaging architecture to achieve high spatial resolution ToF imaging via optical multiplexing and compressive sensing. Our approach is based on the observation that, while depth is non-linearly related to ToF pixel measurements, a phasor representation of captured images results in a linear image formation model...
December 11, 2017: Optics Express
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