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A method of extending the depth of focus of the high-resolution X-ray imaging system employing optical lens and scintillator: a phantom study

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BIOMEDICAL ENGINEERING ONLINE 2015年第Sup1期14卷 S15-S15页

作者： Li, Guang Luo, Shouhua Yan, Yuling Gu, Ning Southeast Univ Sch Biol Sci & Med Engn Nanjing 210009 Peoples R China Santa Clara Univ Dept Bioengn Sch Engn Santa Clara CA 95053 USA

Background: The high-resolution X-ray imaging system employing synchrotron radiation source, thin scintillator, optical lens and advanced CCD camera can achieve a resolution in the range of tens of nanometers to sub-micrometer. Based on this advantage, it can effectively image tissues, cells and many other small samples, especially the calcification in the vascular or in the glomerulus. In general, the thickness of the scintillator should be several micrometers or even within nanometers because it has a big relationship with the resolution. However, it is difficult to make the scintillator so thin, and additionally thin scintillator may greatly reduce the efficiency of collecting photons. Methods: In this paper, we propose an approach to extend the depth of focus (DOF) to solve these problems. We develop equation sets by deducing the relationship between the high-resolution image generated by the scintillator and the degraded blur image due to defect of focus first, and then we adopt projection onto convex sets (POCS) and total variation algorithm to get the solution of the equation sets and to recover the blur image. Results: By using a 20 mu m thick unmatching scintillator to replace the 1 mu m thick matching one, we simulated a high-resolution X-ray imaging system and got a degraded blur image. Based on the algorithm proposed, we recovered the blur image and the result in the experiment showed that the proposed algorithm has good performance on the recovery of image blur caused by unmatching thickness of scintillator. Conclusions: The method proposed is testified to be able to efficiently recover the degraded image due to defect of focus. But, the quality of the recovery image especially of the low contrast image depends on the noise level of the degraded blur image, so there is room for improving and the corresponding denoising algorithm is worthy for further study and discussion.

关键词： Modulation Transfer Function recovery Image Optical Lens recovery algorithm Poisson Noise

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DSESP: Dual sparsity estimation subspace pursuit for the compressive sensing based close-loop ecg monitoring structure

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2019年第5期12卷 1311-1322页

作者： Yu, Wenbin Chen, Cailian Liu, Zhe Yang, Bo Guan, Xinping Shanghai Jiao Tong Univ Dept Automat Minist Educ Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Key Lab Syst Control & Informat Proc Minist Educ Shanghai 200240 Peoples R China

Compressive Sensing based ECG collecting system is an effective way for long time heart disease monitoring because of its precision as well as energy efficiency. Traditional Compressive Sensing methods are open-loop and the coordinator cannot obtain the recovery quality, which will impact the diagnosis results based on the recovered data. In this paper, a novel close-loop structure is designed for wireless ECG monitoring, which can maintain the recovery quality and the energy efficiency at a high level. Moreover, an improved Subspace Pursuit recovery method is designed to optimize the recovery quality. Simulations are made to prove that the recovery method is more efficient than most state-of-art recovery algorithm. Finally, an experimental testbed is built for evaluate the whole monitoring system. The results show that the recovery error can be controlled within the medical request while the energy efficiency can be improved by 12%.

关键词： ECG monitoring Compressive sensing recovery algorithm recovery quality

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Performance analysis of compressed sensing with uniform scalar quantisation

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INTERNATIONAL JOURNAL OF ELECTRONICS 2014年第8期101卷 1042-1055页

作者： Lu, Wei Wang, Desheng Liu, Jian Liu, Yingzhuang Huazhong Univ Sci & Technol Dept Elect & Informat Engn Wuhan 430074 Peoples R China

In compressed sensing, the sampling rate can be greatly reduced based on the sparsity of the original signal;however, quantisation is one key aspect in signal acquirement. This article studies the scalar quantisation and its impacts on the compressed sensing. We consider the case in which both the original and the measurement signals are quantised or discrete, which widely exists in the practical applications such as the finite precision signals in the computer and the finite-set original signal in communication systems. We derive the recovery probability bound and the necessary measurement number bound for compressed sensing with scalar quantisation and simulate three modified compressed sensing recovery algorithms. Simulations show that coarse quantisation can be applied in compressed sensing for discrete or quantised sparse signal with sampling and storage efficiency.

关键词： compressed sensing scalar quantisation recovery algorithm measurement bound error probability

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Resource efficient stochastic predictive control under packet dropouts

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IET CONTROL THEORY AND APPLICATIONS 2017年第11期11卷 1666-1673页

作者： Mishra, Prabhat K. Chatterjee, Debasish Quevedo, Daniel E. Indian Inst Technol Syst & Control Engn Bombay Maharashtra India Paderborn Univ Dept Elect Engn EIM E Paderborn Germany

This study presents a resource efficient framework for a class of stochastic control systems that utilises state dependent control strategies in order to reduce the online computational load. When the states are in a given neighbourhood of the desired operating point, the controller is switched off, and data from the feedback channel is not transmitted. Outside this neighbourhood, the authors pay close attention to the performance of the controller by adopting a stochastic predictive algorithm when the states are in a predefined comfort zone, and activate a recovery algorithm beyond the comfort zone that secures at least good qualitative properties. The authors demonstrate that the proposed controller leads to mean square boundedness of the closed loop states in the presence of stochastic noise, bounded control authority, and control channel erasures, while entailing a dramatic reduction in network traffic and computational resources.

关键词： stochastic systems predictive control networked control systems resource efficient stochastic predictive control packet dropouts stochastic control systems state dependent control strategies online computational load reduction stochastic predictive algorithm recovery algorithm mean square boundedness stochastic noise control channel erasures network traffic reduction

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A survey on recent advances in speech compressive sensing

A survey on recent advances in speech compressive sensing

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IEEE International Multi Conference on Automation, Computing, Control, Communication and Compressed Sensing (iMac4s)

作者： Shukla, Urvashi P. Patel, Niteen B. Joshi, Amit M. SCET EC Surat India SVNIT EC Res Scholar Surat India

ISBN: (纸本)9781467350907;9781467350891

Compressive sensing (CS) is one of the upcoming fields which have paved its way for different approaches towards the signal acquisition and processing systems. In past years, many fields of application have emerged, where speech is one of the most popular one. Due to stochastic nature of speech signal, we are still in search for a proper sparse representation of the signal, so that we could easily incorporate CS and thereby reduce the increasing burden on the ADC. In this paper, we discuss some of the works which have been carried out towards the sparse representation of the speech signals. The idea is to concatenate them on a common platform by exploiting the signal and its basis matrices. Each of the approaches has its own pros and corns for the sparse representation.

关键词： Compressive sensing data acquisition recovery algorithm sparsity speech synthesis

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Opti2: A reconstruction approach for periodic signals using compressive sensing

Opti2: A reconstruction approach for periodic signals using ...

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IEEE International Instrumentation and Measurement Technology Conference (I2MTC)

作者： Perez, Dailys Arronde Schoeffmann, Christian Zangl, Hubert Univ Klagenfurt Inst Smart Syst Technol Sensors & Actuators Klagenfurt Austria Silicon Austria Labs AAU SAL USE Lab Graz Austria

ISBN: (纸本)9781665483605

The reconstruction of frequency-sparse signals from a low number of samples using a compressed sensing concept is a widely studied problem and solutions based on greedy and relaxation methods are available. However, their performance is degraded in presence of spectral leakage, which affects the sparsity of the signal representation and consequently, its estimation accuracy. In this paper a two-stage optimization approach, called Opti2, is proposed for the reconstruction of periodic signals that can be expressed in terms of fundamental frequency and harmonics. The estimation provided by basis pursuit denoising (BPDN) sparse optimization approach is computed in the first stage and used as initial guess for the second stage, where a constrained non-linear optimization problem is solved in an iterative fashion, aiming to improve the estimation of the signal parameters. The evaluation of the proposed method with simulated and experimental data demonstrates that it outperforms existing approaches in term of accuracy, showing its robustness to noise, compression rate and dictionary refinement factor.

关键词： periodic signals compressive sampling recovery algorithm frequency sparse spectral leakage

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New Orhagonal Matching Pursuit algorithm for 2D Different Dimenssion Sparse Signal Reconstruction 1

New Orhagonal Matching Pursuit Algorithm for 2D Different Di...

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International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE)

作者： Zobly, Sulieman Mohammed Salih Univ Gezira Natl Canc Inst Dept Med Phys & Instrumentat Wad Madani Sudan

ISBN: (纸本)9781467378697

The recovery algorithm is the backbone of compressed sensing (CS). Most of the algorithms were designed for ID signal reconstruction and a few were designed for 2D signals. To reconstruct 2D signals, in most cases signals were converted into ID signal. In some application signals and images with 2D different dimension (N-by-M) can't be recovered with existing ID or 2D recovery algorithms, thus in this work we proposed a new recovery algorithm known as 2D Different Dimension Orthogonal Matching Pursuit (OMP) algorithm, so as to solve the problem of 2D Different Dimension Sparse Signals and images, which is represented an accomplishment of 2D OMP. In the 2D different dimension OMP the observation / measurement is processed as a matrix instead of vectors in the other CS algorithms. Comparing the proposed algorithm with current algorithms, the proposed algorithm reconstructs the 2D different dimension sparse signal faster than the other algorithms, and can be implemented easier.

关键词： compressed sensing recovery algorithm sparse signal orthogonal matching prusuit

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GPU Implementation of Orthogonal Matching Pursuit for Compressive Sensing

GPU Implementation of Orthogonal Matching Pursuit for Compre...

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17th IEEE International Conference on Parallel and Distributed Systems (ICPADS)

作者： Fang, Yong Chen, Liang Wu, Jiaji Huang, Bormin NW A&F Univ Coll Informat Engn Yangling Shaanxi Peoples R China Xidian Univ Minist Educ Key Lab Intelligent Percept & Image Understanding Xian Shaanxi Peoples R China Univ Wisconsin Space Sci & Engn Ctr Madison WI USA

ISBN: (纸本)9780769545769

recovery algorithms play a key role in compressive sampling (CS). Currently, a popular recovery algorithm for CS is the orthogonal matching pursuit (OMP), which possesses the merits of low complexity and good recovery quality. Considering that the OMP involves massive matrix/vector operations, it is very suited to being implemented in parallel on graphics processing unit (GPU). In this paper, we first analyze the complexity of each module in the OMP and point out the bottlenecks of the OMP lie in the projection module and the least-squares module. To speedup the projection module, Fujimoto's matrix-vector multiplication algorithm is adopted. To speedup the least-squares module, the matrix-inverse-update algorithm is adopted. Experimental results show that +40x speedup is achieved by our implementation of OMP on GTX480 GPU over on Intel(R) Core(TM) i7 CPU. Since the projection module occupies more than 2/3 of the total run time, we are looking for a faster matrix-vector multiplication algorithm.

关键词： compressive sampling recovery algorithm orthogonal matching pursuit graphics processing unit

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Cyclic Spectrum Estimation Under Compressive Sensing by the Strip Spectral Correlation algorithm 14

Cyclic Spectrum Estimation Under Compressive Sensing by the ...

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14th IEEE International Wireless Communications and Mobile Computing Conference (IWCMC)

作者： Gao, Yulong Wang, Song Chen, Yanping Wei, Yuming Harbin Inst Technol Commun Res Ctr Harbin 150080 Heilongjiang Peoples R China Harbin Univ Commerce Sch Comp & Informat Engn Harbin 150028 Heilongjiang Peoples R China

ISBN: (纸本)9781538620700

Cyclic spectrum is a basic tool to study the cyclostationary of signal. It has the advantages of high resolution, anti-interference ability and low channel environment sensitivity. These properties are beneficial to process digital modulated signals. There are two time-smoothed algorithms: the FFT Accumulation Method (FAM) and the Strip Spectral Correlation algorithm (SSCA). SSCA is suitable for fast estimation and engineering realizable, while FAM has shortcoming of complex computation. But both methods need high sampling rate that requires high performance analog-to-digital converter. To improve the efficiency of cyclic spectrum estimation, we propose CS-SSCA algorithm in the framework of Compressive Sensing (CS). The cyclic spectrum can be recovered via compressed samples by SSCA. The theoretical analysis and simulation results prove that the improved algorithm displays better performance than CS-FAM under the same conditions.

关键词： compressive Sensing cyclic spectrum density recovery algorithm

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An Improved Compressive Sensing Image Fusion algorithm Based on NSCT Transform

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2nd International Conference on Mechatronics and Control Engineering (ICMCE 2013)

作者： Wu, Yanhai Yan, Di Ma, Mengxin Wu, Nan Xian Univ Sci & Technol Xian 710054 Shaanxi Peoples R China Datang Mobile Xian Commun Equipment Co Xian 710061 Peoples R China Shaanxi Inst Educ Xian 710061 Peoples R China

ISBN: (纸本)9783037858943

A modified compressive sensing image fusion algorithm is proposed in this paper that is based on the NSCT transform. The algorithm is improved by introducing the theory of compressive sensing into image fusion that uses the NSCT transform to make a specific image be sparse on which only the high frequency coefficient is specifically measured;The improved algorithm then process the image fusion by retrieving the maximal value of the gradient of the neighborhood average from the measured high frequency coefficient, and accordingly, maximizing the absolute value of the neighborhood variance to the low-frequency counterpart. Afterwards, the improved algorithm can reconfigure the fusion image by using the MSP reconfiguration algorithm with final deliverable of the fusion image by committing to the NSCT reverse transform. Simulation results show that the improved algorithm is superior to other hand-on algorithms both in visual effect and in objective evaluation. In the case that the storage and transmission data are limited, the algorithm comes forth better effect of image fusion that is verified to be possesses of high value in practice.

关键词： Compressive Sensing Image Fusion NSCT Fusion Rules recovery algorithm

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