CS Algorithm Research Articles

Compressed Sensing-Based Data Gathering in WSN

This paper proposes an improved CS algorithm for WSN environmental monitoring. First, the algorithm applys approximate joint sparsity model (JSM) to analyze data. Further, with the improved clustering routing data gathering, that is, within each cluster applying CS-based data gathering methods, and cluster headers using the shortest path routing to sink node. Extensive simulation results show that this approach not only shrinks the distribution range of data processing to reduce recovery error, but greatly lessens numbers of data transmission times to maintain energy balance of the entire network.

Compressed Sensing-Based Data Gathering in WSN

This paper proposes an improved CS algorithm for WSN environmental monitoring. First, the algorithm applys approximate joint sparsity model (JSM) to analyze data. Further, with the improved clustering routing data gathering, that is, within each cluster applying CS-based data gathering methods, and cluster headers using the shortest path routing to sink node. Extensive simulation results show that this approach not only shrinks the distribution range of data processing to reduce recovery error, but greatly lessens numbers of data transmission times to maintain energy balance of the entire network.

Multivariate pursuit image reconstruction using prior information beyond sparsity

The prior information of images plays an important role in reducing the computational complexity of CS inversion and improving the reconstruction quality. A wavelet-based multivariate pursuit algorithm, which exploits the prior information of images that goes beyond simple sparsity, is developed in this paper. The proposed method reconstructs the image wavelet coefficients from the multiple measurements in a multivariate manner, and uses the extracted image edge as the prior information to guide the pursuit process of algorithm in CS recovery. By means of the interaction of edge information and multivariate joint recovery, the proposed algorithm significantly improves the reconstruction quality of those images with the obvious edges and high sparsity, such as CT, MRI images. Numerical experiments demonstrate that the proposed algorithm returns superior reconstructed quality and remains higher computational efficiency than other state-of-the-art CS algorithms.

Structural Design Optimization of Vehicle Components Using Cuckoo Search Algorithm

Abstract In order to meet today's vehicle design requirements and to improve the cost and fuel efficiency, there is an increasing interest to design light-weight and cost-effective vehicle components. In this research, a new optimization algorithm, called the Cuckoo Search Algorithm (CS) algorithm, is introduced for solving structural design optimization problems. This research is the first application of the CS to the shape design optimization problems in the literature. The CS algorithm is applied to the structural design optimization of a vehicle component to illustrate how the present approach can be applied for solving structural design problems. Results show the ability of the CS to find better optimal structural design.

The Evaluation and Contrast of SAR Noise Jamming Effect Based on the RD and CS Algorithm

该文针对RD与CS两种成像算法的差异，首先分析了两种算法在压制性噪声干扰环境下信号与噪声干扰的成像处理增益，推导出两种算法的信号-干扰功率比(信干比)处理增益公式并进行了分析。理论分析结果表明，在采样率仅略大于信号带宽条件下，RD和CS成像算法的信干比处理增益基本一致；在过采样条件下，RD算法由于两维匹配处理从而获得比CS算法大的信干比处理增益，但如果对过采样信号进行滤波处理后，CS算法的信干比处理增益与RD算法一致。最后，采用对计算机仿真数据和真实数据进行处理的方法验证了该文的理论分析和结论。

Stemming Indonesian

Stemming words to (usually) remove suffixes has applications in text search, machine translation, document summarization, and text classification. For example, English stemming reduces the words "computer," "computing," "computation," and "computability" to their common morphological root, "comput-." In text search, this permits a search for "computers" to find documents containing all words with the stem "comput-." In the Indonesian language, stemming is of crucial importance: words have prefixes, suffixes, infixes, and confixes that make matching related words difficult. This work surveys existing techniques for stemming Indonesian words to their morphological roots, presents our novel and highly accurate CS algorithm, and explores the effectiveness of stemming in the context of general-purpose text information retrieval through ad hoc queries.

Left ventricular ejection fraction and volumes from gated blood pool tomography: Comparison between two automatic algorithms that work in three-dimensional space

Background. Two different algorithms, which are fast and automatic and which operate in 3-dimensional space, were compared in the same group of patients to compute left ventricular ejection fraction (LVEF) and volumes from gated blood pool tomography. One method, developed at Cedars-Sinai Medical Center (CS), was dependent on surface detection, whereas the other method, developed at the Free University of Brussels (UB), used image segmentation. Methods and Results. Gated blood pool tomograms were acquired in 92 consecutive patients after injection of 740 MBq of technetium 99m-labeled human serum albumin. After reconstruction and reorientation according to the left ventricular long axis, LVEF and left ventricular volumes were measured with the CS and UB algorithms. Measurements of LVEF were validated against planar radionuclide angiocardiography (PRNA) results. The success rates of the algorithms were 87% for CS and 97% for UB. Agreement between LVEF measured with CS and UB (LVEFCS = 0.91 · LVEFUB − 0.85; r = 0.87) and between LVEF measured with CS and PRNA (LVEFCS = 1.04 · LVEFPRNA − 4.75; r = 0.80) and UB and PRNA (LVEFUB = 0.98 · LVEFPRNA + 4.42; r = 0.82) was good. For left ventricular volumes, linear regression analysis showed good correlation between both methods with regard to end-diastolic volumes (r = 0.81) and end-systolic volumes (r = 0.91). On average, end-diastolic volumes were similar and end-systolic volumes were slightly higher with CS than with UB. Consequently, significantly lower LVEFs were observed with CS than with UB. Conclusions. Good correlation was observed between CS and UB for both left ventricular volumes and ejection fraction. In addition, measurements of LVEF obtained with both algorithms correlated fairly well with those obtained from conventional PRNA over a wide range of values. (J Nucl Cardiol 2001;8:466-71.)