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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="sparse algorithm"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3716</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: sparse algorithm</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3716</span> An Improved Method to Compute Sparse Graphs for Traveling Salesman Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Y.%20Wang">Y. Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Traveling salesman problem (TSP) is NP-hard in combinatorial optimization. The research shows the algorithms for TSP on the sparse graphs have the shorter computation time than those for TSP according to the complete graphs. We present an improved iterative algorithm to compute the sparse graphs for TSP by frequency graphs computed with frequency quadrilaterals. The iterative algorithm is enhanced by adjusting two parameters of the algorithm. The computation time of the algorithm is <em>O</em>(<em>CN</em><sub>max</sub><em>n</em><sup>2</sup>) where <em>C</em> is the iterations, <em>N</em><sub>max</sub> is the maximum number of frequency quadrilaterals containing each edge and <em>n</em> is the scale of TSP. The experimental results showed the computed sparse graphs generally have less than 5<em>n</em> edges for most of these Euclidean instances. Moreover, the maximum degree and minimum degree of the vertices in the sparse graphs do not have much difference. Thus, the computation time of the methods to resolve the TSP on these sparse graphs will be greatly reduced. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=frequency%20quadrilateral" title="frequency quadrilateral">frequency quadrilateral</a>, <a href="https://publications.waset.org/abstracts/search?q=iterative%20algorithm" title=" iterative algorithm"> iterative algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20graph" title=" sparse graph"> sparse graph</a>, <a href="https://publications.waset.org/abstracts/search?q=traveling%20salesman%20problem" title=" traveling salesman problem"> traveling salesman problem</a> </p> <a href="https://publications.waset.org/abstracts/82737/an-improved-method-to-compute-sparse-graphs-for-traveling-salesman-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/82737.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">233</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3715</span> A Transform Domain Function Controlled VSSLMS Algorithm for Sparse System Identification</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Cemil%20Turan">Cemil Turan</a>, <a href="https://publications.waset.org/abstracts/search?q=Mohammad%20Shukri%20Salman"> Mohammad Shukri Salman</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The convergence rate of the least-mean-square (LMS) algorithm deteriorates if the input signal to the filter is correlated. In a system identification problem, this convergence rate can be improved if the signal is white and/or if the system is sparse. We recently proposed a sparse transform domain LMS-type algorithm that uses a variable step-size for a sparse system identification. The proposed algorithm provided high performance even if the input signal is highly correlated. In this work, we investigate the performance of the proposed TD-LMS algorithm for a large number of filter tap which is also a critical issue for standard LMS algorithm. Additionally, the optimum value of the most important parameter is calculated for all experiments. Moreover, the convergence analysis of the proposed algorithm is provided. The performance of the proposed algorithm has been compared to different algorithms in a sparse system identification setting of different sparsity levels and different number of filter taps. Simulations have shown that the proposed algorithm has prominent performance compared to the other algorithms. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20filtering" title="adaptive filtering">adaptive filtering</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20system%20identification" title=" sparse system identification"> sparse system identification</a>, <a href="https://publications.waset.org/abstracts/search?q=TD-LMS%20algorithm" title=" TD-LMS algorithm"> TD-LMS algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=VSSLMS%20algorithm" title=" VSSLMS algorithm"> VSSLMS algorithm</a> </p> <a href="https://publications.waset.org/abstracts/72335/a-transform-domain-function-controlled-vsslms-algorithm-for-sparse-system-identification" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/72335.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">360</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3714</span> Sparse Principal Component Analysis: A Least Squares Approximation Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Giovanni%20Merola">Giovanni Merola</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sparse Principal Components Analysis aims to find principal components with few non-zero loadings. We derive such sparse solutions by adding a genuine sparsity requirement to the original Principal Components Analysis (PCA) objective function. This approach differs from others because it preserves PCA's original optimality: uncorrelatedness of the components and least squares approximation of the data. To identify the best subset of non-zero loadings we propose a branch-and-bound search and an iterative elimination algorithm. This last algorithm finds sparse solutions with large loadings and can be run without specifying the cardinality of the loadings and the number of components to compute in advance. We give thorough comparisons with the existing sparse PCA methods and several examples on real datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=SPCA" title="SPCA">SPCA</a>, <a href="https://publications.waset.org/abstracts/search?q=uncorrelated%20components" title=" uncorrelated components"> uncorrelated components</a>, <a href="https://publications.waset.org/abstracts/search?q=branch-and-bound" title=" branch-and-bound"> branch-and-bound</a>, <a href="https://publications.waset.org/abstracts/search?q=backward%20elimination" title=" backward elimination"> backward elimination</a> </p> <a href="https://publications.waset.org/abstracts/14630/sparse-principal-component-analysis-a-least-squares-approximation-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/14630.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">381</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3713</span> Development of a Few-View Computed Tomographic Reconstruction Algorithm Using Multi-Directional Total Variation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Chia%20Jui%20Hsieh">Chia Jui Hsieh</a>, <a href="https://publications.waset.org/abstracts/search?q=Jyh%20Cheng%20Chen"> Jyh Cheng Chen</a>, <a href="https://publications.waset.org/abstracts/search?q=Chih%20Wei%20Kuo"> Chih Wei Kuo</a>, <a href="https://publications.waset.org/abstracts/search?q=Ruei%20Teng%20Wang"> Ruei Teng Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Woei%20Chyn%20Chu"> Woei Chyn Chu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Compressed sensing (CS) based computed tomographic (CT) reconstruction algorithm utilizes total variation (TV) to transform CT image into sparse domain and minimizes L1-norm of sparse image for reconstruction. Different from the traditional CS based reconstruction which only calculates x-coordinate and y-coordinate TV to transform CT images into sparse domain, we propose a multi-directional TV to transform tomographic image into sparse domain for low-dose reconstruction. Our method considers all possible directions of TV calculations around a pixel, so the sparse transform for CS based reconstruction is more accurate. In 2D CT reconstruction, we use eight-directional TV to transform CT image into sparse domain. Furthermore, we also use 26-directional TV for 3D reconstruction. This multi-directional sparse transform method makes CS based reconstruction algorithm more powerful to reduce noise and increase image quality. To validate and evaluate the performance of this multi-directional sparse transform method, we use both Shepp-Logan phantom and a head phantom as the targets for reconstruction with the corresponding simulated sparse projection data (angular sampling interval is 5 deg and 6 deg, respectively). From the results, the multi-directional TV method can reconstruct images with relatively less artifacts compared with traditional CS based reconstruction algorithm which only calculates x-coordinate and y-coordinate TV. We also choose RMSE, PSNR, UQI to be the parameters for quantitative analysis. From the results of quantitative analysis, no matter which parameter is calculated, the multi-directional TV method, which we proposed, is better. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing%20%28CS%29" title="compressed sensing (CS)">compressed sensing (CS)</a>, <a href="https://publications.waset.org/abstracts/search?q=low-dose%20CT%20reconstruction" title=" low-dose CT reconstruction"> low-dose CT reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=total%20variation%20%28TV%29" title=" total variation (TV)"> total variation (TV)</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-directional%20gradient%20operator" title=" multi-directional gradient operator"> multi-directional gradient operator</a> </p> <a href="https://publications.waset.org/abstracts/77716/development-of-a-few-view-computed-tomographic-reconstruction-algorithm-using-multi-directional-total-variation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/77716.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">256</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3712</span> Non-Local Simultaneous Sparse Unmixing for Hyperspectral Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fanqiang%20Kong">Fanqiang Kong</a>, <a href="https://publications.waset.org/abstracts/search?q=Chending%20Bian"> Chending Bian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sparse unmixing is a promising approach in a semisupervised fashion by assuming that the observed pixels of a hyperspectral image can be expressed in the form of linear combination of only a few pure spectral signatures (end members) in an available spectral library. However, the sparse unmixing problem still remains a great challenge at finding the optimal subset of endmembers for the observed data from a large standard spectral library, without considering the spatial information. Under such circumstances, a sparse unmixing algorithm termed as non-local simultaneous sparse unmixing (NLSSU) is presented. In NLSSU, the non-local simultaneous sparse representation method for endmember selection of sparse unmixing, is used to finding the optimal subset of endmembers for the similar image patch set in the hyperspectral image. And then, the non-local means method, as a regularizer for abundance estimation of sparse unmixing, is used to exploit the abundance image non-local self-similarity. Experimental results on both simulated and real data demonstrate that NLSSU outperforms the other algorithms, with a better spectral unmixing accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hyperspectral%20unmixing" title="hyperspectral unmixing">hyperspectral unmixing</a>, <a href="https://publications.waset.org/abstracts/search?q=simultaneous%20sparse%20representation" title=" simultaneous sparse representation"> simultaneous sparse representation</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20regression" title=" sparse regression"> sparse regression</a>, <a href="https://publications.waset.org/abstracts/search?q=non-local%20means" title=" non-local means"> non-local means</a> </p> <a href="https://publications.waset.org/abstracts/71689/non-local-simultaneous-sparse-unmixing-for-hyperspectral-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71689.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">245</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3711</span> Off-Grid Sparse Inverse Synthetic Aperture Imaging by Basis Shift Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mengjun%20Yang">Mengjun Yang</a>, <a href="https://publications.waset.org/abstracts/search?q=Zhulin%20Zong"> Zhulin Zong</a>, <a href="https://publications.waset.org/abstracts/search?q=Jie%20Gao"> Jie Gao</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a new and robust algorithm is proposed to achieve high resolution for inverse synthetic aperture radar (ISAR) imaging in the compressive sensing (CS) framework. Traditional CS based methods have to assume that unknown scatters exactly lie on the pre-divided grids; otherwise, their reconstruction performance dropped significantly. In this processing algorithm, several basis shifts are utilized to achieve the same effect as grid refinement does. The detailed implementation of the basis shift algorithm is presented in this paper. From the simulation we can see that using the basis shift algorithm, imaging precision can be improved. The effectiveness and feasibility of the proposed method are investigated by the simulation results. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=ISAR%20imaging" title="ISAR imaging">ISAR imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20reconstruction" title=" sparse reconstruction"> sparse reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=off-grid" title=" off-grid"> off-grid</a>, <a href="https://publications.waset.org/abstracts/search?q=basis%20shift" title=" basis shift"> basis shift</a> </p> <a href="https://publications.waset.org/abstracts/90250/off-grid-sparse-inverse-synthetic-aperture-imaging-by-basis-shift-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/90250.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">265</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3710</span> A Generalized Sparse Bayesian Learning Algorithm for Near-Field Synthetic Aperture Radar Imaging: By Exploiting Impropriety and Noncircularity</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pan%20Long">Pan Long</a>, <a href="https://publications.waset.org/abstracts/search?q=Bi%20Dongjie"> Bi Dongjie</a>, <a href="https://publications.waset.org/abstracts/search?q=Li%20Xifeng"> Li Xifeng</a>, <a href="https://publications.waset.org/abstracts/search?q=Xie%20Yongle"> Xie Yongle</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The near-field synthetic aperture radar (SAR) imaging is an advanced nondestructive testing and evaluation (NDT&E) technique. This paper investigates the complex-valued signal processing related to the near-field SAR imaging system, where the measurement data turns out to be noncircular and improper, meaning that the complex-valued data is correlated to its complex conjugate. Furthermore, we discover that the degree of impropriety of the measurement data and that of the target image can be highly correlated in near-field SAR imaging. Based on these observations, A modified generalized sparse Bayesian learning algorithm is proposed, taking impropriety and noncircularity into account. Numerical results show that the proposed algorithm provides performance gain, with the help of noncircular assumption on the signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=complex-valued%20signal%20processing" title="complex-valued signal processing">complex-valued signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=synthetic%20aperture%20radar" title=" synthetic aperture radar"> synthetic aperture radar</a>, <a href="https://publications.waset.org/abstracts/search?q=2-D%20radar%20imaging" title=" 2-D radar imaging"> 2-D radar imaging</a>, <a href="https://publications.waset.org/abstracts/search?q=compressive%20sensing" title=" compressive sensing"> compressive sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20Bayesian%20learning" title=" sparse Bayesian learning"> sparse Bayesian learning</a> </p> <a href="https://publications.waset.org/abstracts/108404/a-generalized-sparse-bayesian-learning-algorithm-for-near-field-synthetic-aperture-radar-imaging-by-exploiting-impropriety-and-noncircularity" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/108404.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">131</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3709</span> A New Framework for ECG Signal Modeling and Compression Based on Compressed Sensing Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Siavash%20Eftekharifar">Siavash Eftekharifar</a>, <a href="https://publications.waset.org/abstracts/search?q=Tohid%20Yousefi%20Rezaii"> Tohid Yousefi Rezaii</a>, <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Shamsi"> Mahdi Shamsi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The purpose of this paper is to exploit compressed sensing (CS) method in order to model and compress the electrocardiogram (ECG) signals at a high compression ratio. In order to obtain a sparse representation of the ECG signals, first a suitable basis matrix with Gaussian kernels, which are shown to nicely fit the ECG signals, is constructed. Then the sparse model is extracted by applying some optimization technique. Finally, the CS theory is utilized to obtain a compressed version of the sparse signal. Reconstruction of the ECG signal from the compressed version is also done to prove the reliability of the algorithm. At this stage, a greedy optimization technique is used to reconstruct the ECG signal and the Mean Square Error (MSE) is calculated to evaluate the precision of the proposed compression method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title="compressed sensing">compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=ECG%20compression" title=" ECG compression"> ECG compression</a>, <a href="https://publications.waset.org/abstracts/search?q=Gaussian%20kernel" title=" Gaussian kernel"> Gaussian kernel</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20representation" title=" sparse representation"> sparse representation</a> </p> <a href="https://publications.waset.org/abstracts/31469/a-new-framework-for-ecg-signal-modeling-and-compression-based-on-compressed-sensing-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31469.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">462</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3708</span> Sparse-View CT Reconstruction Based on Nonconvex L1 − L2 Regularizations</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ali%20Pour%20Yazdanpanah">Ali Pour Yazdanpanah</a>, <a href="https://publications.waset.org/abstracts/search?q=Farideh%20Foroozandeh%20Shahraki"> Farideh Foroozandeh Shahraki</a>, <a href="https://publications.waset.org/abstracts/search?q=Emma%20Regentova"> Emma Regentova</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The reconstruction from sparse-view projections is one of important problems in computed tomography (CT) limited by the availability or feasibility of obtaining of a large number of projections. Traditionally, convex regularizers have been exploited to improve the reconstruction quality in sparse-view CT, and the convex constraint in those problems leads to an easy optimization process. However, convex regularizers often result in a biased approximation and inaccurate reconstruction in CT problems. Here, we present a nonconvex, Lipschitz continuous and non-smooth regularization model. The CT reconstruction is formulated as a nonconvex constrained L1 − L2 minimization problem and solved through a difference of convex algorithm and alternating direction of multiplier method which generates a better result than L0 or L1 regularizers in the CT reconstruction. We compare our method with previously reported high performance methods which use convex regularizers such as TV, wavelet, curvelet, and curvelet+TV (CTV) on the test phantom images. The results show that there are benefits in using the nonconvex regularizer in the sparse-view CT reconstruction. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=computed%20tomography" title="computed tomography">computed tomography</a>, <a href="https://publications.waset.org/abstracts/search?q=non-convex" title=" non-convex"> non-convex</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse-view%20reconstruction" title=" sparse-view reconstruction"> sparse-view reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=L1-L2%20minimization" title=" L1-L2 minimization"> L1-L2 minimization</a>, <a href="https://publications.waset.org/abstracts/search?q=difference%20of%20convex%20functions" title=" difference of convex functions"> difference of convex functions</a> </p> <a href="https://publications.waset.org/abstracts/70473/sparse-view-ct-reconstruction-based-on-nonconvex-l1-l2-regularizations" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/70473.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">316</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3707</span> Compressed Sensing of Fetal Electrocardiogram Signals Based on Joint Block Multi-Orthogonal Least Squares Algorithm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Xiang%20Jianhong">Xiang Jianhong</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Cong"> Wang Cong</a>, <a href="https://publications.waset.org/abstracts/search?q=Wang%20Linyu"> Wang Linyu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> With the rise of medical IoT technologies, Wireless body area networks (WBANs) can collect fetal electrocardiogram (FECG) signals to support telemedicine analysis. The compressed sensing (CS)-based WBANs system can avoid the sampling of a large amount of redundant information and reduce the complexity and computing time of data processing, but the existing algorithms have poor signal compression and reconstruction performance. In this paper, a Joint block multi-orthogonal least squares (JBMOLS) algorithm is proposed. We apply the FECG signal to the Joint block sparse model (JBSM), and a comparative study of sparse transformation and measurement matrices is carried out. A FECG signal compression transmission mode based on Rbio5.5 wavelet, Bernoulli measurement matrix, and JBMOLS algorithm is proposed to improve the compression and reconstruction performance of FECG signal by CS-based WBANs. Experimental results show that the compression ratio (CR) required for accurate reconstruction of this transmission mode is increased by nearly 10%, and the runtime is saved by about 30%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=telemedicine" title="telemedicine">telemedicine</a>, <a href="https://publications.waset.org/abstracts/search?q=fetal%20ECG" title=" fetal ECG"> fetal ECG</a>, <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title=" compressed sensing"> compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=joint%20sparse%20reconstruction" title=" joint sparse reconstruction"> joint sparse reconstruction</a>, <a href="https://publications.waset.org/abstracts/search?q=block%20sparse%20signal" title=" block sparse signal"> block sparse signal</a> </p> <a href="https://publications.waset.org/abstracts/154614/compressed-sensing-of-fetal-electrocardiogram-signals-based-on-joint-block-multi-orthogonal-least-squares-algorithm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/154614.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">127</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3706</span> Linear Frequency Modulation-Frequency Shift Keying Radar with Compressive Sensing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ho%20Jeong%20Jin">Ho Jeong Jin</a>, <a href="https://publications.waset.org/abstracts/search?q=Chang%20Won%20Seo"> Chang Won Seo</a>, <a href="https://publications.waset.org/abstracts/search?q=Choon%20Sik%20Cho"> Choon Sik Cho</a>, <a href="https://publications.waset.org/abstracts/search?q=Bong%20Yong%20Choi"> Bong Yong Choi</a>, <a href="https://publications.waset.org/abstracts/search?q=Kwang%20Kyun%20Na"> Kwang Kyun Na</a>, <a href="https://publications.waset.org/abstracts/search?q=Sang%20Rok%20Lee"> Sang Rok Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a radar signal processing technique using the LFM-FSK (Linear Frequency Modulation-Frequency Shift Keying) is proposed for reducing the false alarm rate based on the compressive sensing. The LFM-FSK method combines FMCW (Frequency Modulation Continuous Wave) signal with FSK (Frequency Shift Keying). This shows an advantage which can suppress the ghost phenomenon without the complicated CFAR (Constant False Alarm Rate) algorithm. Moreover, the parametric sparse algorithm applying the compressive sensing that restores signals efficiently with respect to the incomplete data samples is also integrated, leading to reducing the burden of ADC in the receiver of radars. 24 GHz FMCW signal is applied and tested in the real environment with FSK modulated data for verifying the proposed algorithm along with the compressive sensing. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressive%20sensing" title="compressive sensing">compressive sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=LFM-FSK%20radar" title=" LFM-FSK radar"> LFM-FSK radar</a>, <a href="https://publications.waset.org/abstracts/search?q=radar%20signal%20processing" title=" radar signal processing"> radar signal processing</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20algorithm" title=" sparse algorithm"> sparse algorithm</a> </p> <a href="https://publications.waset.org/abstracts/51309/linear-frequency-modulation-frequency-shift-keying-radar-with-compressive-sensing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51309.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">481</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3705</span> Image Reconstruction Method Based on L0 Norm</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jianhong%20Xiang">Jianhong Xiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hao%20Xiang"> Hao Xiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Linyu%20Wang"> Linyu Wang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Compressed sensing (CS) has a wide range of applications in sparse signal reconstruction. Aiming at the problems of low recovery accuracy and long reconstruction time of existing reconstruction algorithms in medical imaging, this paper proposes a corrected smoothing L0 algorithm based on compressed sensing (CSL0). First, an approximate hyperbolic tangent function (AHTF) that is more similar to the L0 norm is proposed to approximate the L0 norm. Secondly, in view of the "sawtooth phenomenon" in the steepest descent method and the problem of sensitivity to the initial value selection in the modified Newton method, the use of the steepest descent method and the modified Newton method are jointly optimized to improve the reconstruction accuracy. Finally, the CSL0 algorithm is simulated on various images. The results show that the algorithm proposed in this paper improves the reconstruction accuracy of the test image by 0-0. 98dB. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=smoothed%20L0" title="smoothed L0">smoothed L0</a>, <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title=" compressed sensing"> compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20processing" title=" image processing"> image processing</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20reconstruction" title=" sparse reconstruction"> sparse reconstruction</a> </p> <a href="https://publications.waset.org/abstracts/155598/image-reconstruction-method-based-on-l0-norm" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/155598.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">115</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3704</span> HR MRI CS Based Image Reconstruction</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Krzysztof%20Malczewski">Krzysztof Malczewski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Magnetic Resonance Imaging (MRI) reconstruction algorithm using compressed sensing is presented in this paper. It is exhibited that the offered approach improves MR images spatial resolution in circumstances when highly undersampled k-space trajectories are applied. Compressed Sensing (CS) aims at signal and images reconstructing from significantly fewer measurements than were conventionally assumed necessary. Magnetic Resonance Imaging (MRI) is a fundamental medical imaging method struggles with an inherently slow data acquisition process. The use of CS to MRI has the potential for significant scan time reductions, with visible benefits for patients and health care economics. In this study the objective is to combine super-resolution image enhancement algorithm with CS framework benefits to achieve high resolution MR output image. Both methods emphasize on maximizing image sparsity on known sparse transform domain and minimizing fidelity. The presented algorithm considers the cardiac and respiratory movements. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=super-resolution" title="super-resolution">super-resolution</a>, <a href="https://publications.waset.org/abstracts/search?q=MRI" title=" MRI"> MRI</a>, <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title=" compressed sensing"> compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse-sense" title=" sparse-sense"> sparse-sense</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20enhancement" title=" image enhancement"> image enhancement</a> </p> <a href="https://publications.waset.org/abstracts/6021/hr-mri-cs-based-image-reconstruction" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6021.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">430</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3703</span> A Sparse Representation Speech Denoising Method Based on Adapted Stopping Residue Error</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qianhua%20He">Qianhua He</a>, <a href="https://publications.waset.org/abstracts/search?q=Weili%20Zhou"> Weili Zhou</a>, <a href="https://publications.waset.org/abstracts/search?q=Aiwu%20Chen"> Aiwu Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A sparse representation speech denoising method based on adapted stopping residue error was presented in this paper. Firstly, the cross-correlation between the clean speech spectrum and the noise spectrum was analyzed, and an estimation method was proposed. In the denoising method, an over-complete dictionary of the clean speech power spectrum was learned with the K-singular value decomposition (K-SVD) algorithm. In the sparse representation stage, the stopping residue error was adaptively achieved according to the estimated cross-correlation and the adjusted noise spectrum, and the orthogonal matching pursuit (OMP) approach was applied to reconstruct the clean speech spectrum from the noisy speech. Finally, the clean speech was re-synthesised via the inverse Fourier transform with the reconstructed speech spectrum and the noisy speech phase. The experiment results show that the proposed method outperforms the conventional methods in terms of subjective and objective measure. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=speech%20denoising" title="speech denoising">speech denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20representation" title=" sparse representation"> sparse representation</a>, <a href="https://publications.waset.org/abstracts/search?q=k-singular%20value%20decomposition" title=" k-singular value decomposition"> k-singular value decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=orthogonal%20matching%20pursuit" title=" orthogonal matching pursuit"> orthogonal matching pursuit</a> </p> <a href="https://publications.waset.org/abstracts/66670/a-sparse-representation-speech-denoising-method-based-on-adapted-stopping-residue-error" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/66670.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">499</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3702</span> Sparsity Order Selection and Denoising in Compressed Sensing Framework</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahdi%20Shamsi">Mahdi Shamsi</a>, <a href="https://publications.waset.org/abstracts/search?q=Tohid%20Yousefi%20Rezaii"> Tohid Yousefi Rezaii</a>, <a href="https://publications.waset.org/abstracts/search?q=Siavash%20Eftekharifar"> Siavash Eftekharifar</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Compressed sensing (CS) is a new powerful mathematical theory concentrating on sparse signals which is widely used in signal processing. The main idea is to sense sparse signals by far fewer measurements than the Nyquist sampling rate, but the reconstruction process becomes nonlinear and more complicated. Common dilemma in sparse signal recovery in CS is the lack of knowledge about sparsity order of the signal, which can be viewed as model order selection procedure. In this paper, we address the problem of sparsity order estimation in sparse signal recovery. This is of main interest in situations where the signal sparsity is unknown or the signal to be recovered is approximately sparse. It is shown that the proposed method also leads to some kind of signal denoising, where the observations are contaminated with noise. Finally, the performance of the proposed approach is evaluated in different scenarios and compared to an existing method, which shows the effectiveness of the proposed method in terms of order selection as well as denoising. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title="compressed sensing">compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=data%20denoising" title=" data denoising"> data denoising</a>, <a href="https://publications.waset.org/abstracts/search?q=model%20order%20selection" title=" model order selection"> model order selection</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20representation" title=" sparse representation"> sparse representation</a> </p> <a href="https://publications.waset.org/abstracts/31470/sparsity-order-selection-and-denoising-in-compressed-sensing-framework" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31470.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">483</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3701</span> Performance Analysis and Optimization for Diagonal Sparse Matrix-Vector Multiplication on Machine Learning Unit</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Qiuyu%20Dai">Qiuyu Dai</a>, <a href="https://publications.waset.org/abstracts/search?q=Haochong%20Zhang"> Haochong Zhang</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiangrong%20Liu"> Xiangrong Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Diagonal sparse matrix-vector multiplication is a well-studied topic in the fields of scientific computing and big data processing. However, when diagonal sparse matrices are stored in DIA format, there can be a significant number of padded zero elements and scattered points, which can lead to a degradation in the performance of the current DIA kernel. This can also lead to excessive consumption of computational and memory resources. In order to address these issues, the authors propose the DIA-Adaptive scheme and its kernel, which leverages the parallel instruction sets on MLU. The researchers analyze the effect of allocating a varying number of threads, clusters, and hardware architectures on the performance of SpMV using different formats. The experimental results indicate that the proposed DIA-Adaptive scheme performs well and offers excellent parallelism. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=adaptive%20method" title="adaptive method">adaptive method</a>, <a href="https://publications.waset.org/abstracts/search?q=DIA" title=" DIA"> DIA</a>, <a href="https://publications.waset.org/abstracts/search?q=diagonal%20sparse%20matrices" title=" diagonal sparse matrices"> diagonal sparse matrices</a>, <a href="https://publications.waset.org/abstracts/search?q=MLU" title=" MLU"> MLU</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20matrix-vector%20multiplication" title=" sparse matrix-vector multiplication"> sparse matrix-vector multiplication</a> </p> <a href="https://publications.waset.org/abstracts/161003/performance-analysis-and-optimization-for-diagonal-sparse-matrix-vector-multiplication-on-machine-learning-unit" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161003.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">134</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3700</span> Scalable Learning of Tree-Based Models on Sparsely Representable Data</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fares%20Hedayatit">Fares Hedayatit</a>, <a href="https://publications.waset.org/abstracts/search?q=Arnauld%20Joly"> Arnauld Joly</a>, <a href="https://publications.waset.org/abstracts/search?q=Panagiotis%20Papadimitriou"> Panagiotis Papadimitriou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Many machine learning tasks such as text annotation usually require training over very big datasets, e.g., millions of web documents, that can be represented in a sparse input space. State-of the-art tree-based ensemble algorithms cannot scale to such datasets, since they include operations whose running time is a function of the input space size rather than a function of the non-zero input elements. In this paper, we propose an efficient splitting algorithm to leverage input sparsity within decision tree methods. Our algorithm improves training time over sparse datasets by more than two orders of magnitude and it has been incorporated in the current version of scikit-learn.org, the most popular open source Python machine learning library. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=big%20data" title="big data">big data</a>, <a href="https://publications.waset.org/abstracts/search?q=sparsely%20representable%20data" title=" sparsely representable data"> sparsely representable data</a>, <a href="https://publications.waset.org/abstracts/search?q=tree-based%20models" title=" tree-based models"> tree-based models</a>, <a href="https://publications.waset.org/abstracts/search?q=scalable%20learning" title=" scalable learning"> scalable learning</a> </p> <a href="https://publications.waset.org/abstracts/52853/scalable-learning-of-tree-based-models-on-sparsely-representable-data" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52853.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">263</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3699</span> KSVD-SVM Approach for Spontaneous Facial Expression Recognition</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dawood%20Al%20Chanti">Dawood Al Chanti</a>, <a href="https://publications.waset.org/abstracts/search?q=Alice%20Caplier"> Alice Caplier</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Sparse representations of signals have received a great deal of attention in recent years. In this paper, the interest of using sparse representation as a mean for performing sparse discriminative analysis between spontaneous facial expressions is demonstrated. An automatic facial expressions recognition system is presented. It uses a KSVD-SVM approach which is made of three main stages: A pre-processing and feature extraction stage, which solves the problem of shared subspace distribution based on the random projection theory, to obtain low dimensional discriminative and reconstructive features; A dictionary learning and sparse coding stage, which uses the KSVD model to learn discriminative under or over dictionaries for sparse coding; Finally a classification stage, which uses a SVM classifier for facial expressions recognition. Our main concern is to be able to recognize non-basic affective states and non-acted expressions. Extensive experiments on the JAFFE static acted facial expressions database but also on the DynEmo dynamic spontaneous facial expressions database exhibit very good recognition rates. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=dictionary%20learning" title="dictionary learning">dictionary learning</a>, <a href="https://publications.waset.org/abstracts/search?q=random%20projection" title=" random projection"> random projection</a>, <a href="https://publications.waset.org/abstracts/search?q=pose%20and%20spontaneous%20facial%20expression" title=" pose and spontaneous facial expression"> pose and spontaneous facial expression</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20representation" title=" sparse representation"> sparse representation</a> </p> <a href="https://publications.waset.org/abstracts/51683/ksvd-svm-approach-for-spontaneous-facial-expression-recognition" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/51683.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">305</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3698</span> Partially Knowing of Least Support Orthogonal Matching Pursuit (PKLS-OMP) for Recovering Signal</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Israa%20Sh.%20Tawfic">Israa Sh. Tawfic</a>, <a href="https://publications.waset.org/abstracts/search?q=Sema%20Koc%20Kayhan"> Sema Koc Kayhan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Given a large sparse signal, great wishes are to reconstruct the signal precisely and accurately from lease number of measurements as possible as it could. Although this seems possible by theory, the difficulty is in built an algorithm to perform the accuracy and efficiency of reconstructing. This paper proposes a new proved method to reconstruct sparse signal depend on using new method called Least Support Matching Pursuit (LS-OMP) merge it with the theory of Partial Knowing Support (PSK) given new method called Partially Knowing of Least Support Orthogonal Matching Pursuit (PKLS-OMP). The new methods depend on the greedy algorithm to compute the support which depends on the number of iterations. So to make it faster, the PKLS-OMP adds the idea of partial knowing support of its algorithm. It shows the efficiency, simplicity, and accuracy to get back the original signal if the sampling matrix satisfies the Restricted Isometry Property (RIP). Simulation results also show that it outperforms many algorithms especially for compressible signals. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title="compressed sensing">compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=lest%20support%20orthogonal%20matching%20pursuit" title=" lest support orthogonal matching pursuit"> lest support orthogonal matching pursuit</a>, <a href="https://publications.waset.org/abstracts/search?q=partial%20knowing%20support" title=" partial knowing support"> partial knowing support</a>, <a href="https://publications.waset.org/abstracts/search?q=restricted%20isometry%20property" title=" restricted isometry property"> restricted isometry property</a>, <a href="https://publications.waset.org/abstracts/search?q=signal%20reconstruction" title=" signal reconstruction"> signal reconstruction</a> </p> <a href="https://publications.waset.org/abstracts/16008/partially-knowing-of-least-support-orthogonal-matching-pursuit-pkls-omp-for-recovering-signal" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/16008.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">240</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3697</span> Sparse Unmixing of Hyperspectral Data by Exploiting Joint-Sparsity and Rank-Deficiency</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fanqiang%20Kong">Fanqiang Kong</a>, <a href="https://publications.waset.org/abstracts/search?q=Chending%20Bian"> Chending Bian</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this work, we exploit two assumed properties of the abundances of the observed signatures (endmembers) in order to reconstruct the abundances from hyperspectral data. Joint-sparsity is the first property of the abundances, which assumes the adjacent pixels can be expressed as different linear combinations of same materials. The second property is rank-deficiency where the number of endmembers participating in hyperspectral data is very small compared with the dimensionality of spectral library, which means that the abundances matrix of the endmembers is a low-rank matrix. These assumptions lead to an optimization problem for the sparse unmixing model that requires minimizing a combined <em>l<sub>2,p</sub>-</em>norm and nuclear norm. We propose a variable splitting and augmented Lagrangian algorithm to solve the optimization problem. Experimental evaluation carried out on synthetic and real hyperspectral data shows that the proposed method outperforms the state-of-the-art algorithms with a better spectral unmixing accuracy. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=hyperspectral%20unmixing" title="hyperspectral unmixing">hyperspectral unmixing</a>, <a href="https://publications.waset.org/abstracts/search?q=joint-sparse" title=" joint-sparse"> joint-sparse</a>, <a href="https://publications.waset.org/abstracts/search?q=low-rank%20representation" title=" low-rank representation"> low-rank representation</a>, <a href="https://publications.waset.org/abstracts/search?q=abundance%20estimation" title=" abundance estimation"> abundance estimation</a> </p> <a href="https://publications.waset.org/abstracts/71439/sparse-unmixing-of-hyperspectral-data-by-exploiting-joint-sparsity-and-rank-deficiency" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71439.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">261</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3696</span> A Hybrid Classical-Quantum Algorithm for Boundary Integral Equations of Scattering Theory</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Damir%20Latypov">Damir Latypov</a> </p> <p class="card-text"><strong>Abstract:</strong></p> A hybrid classical-quantum algorithm to solve boundary integral equations (BIE) arising in problems of electromagnetic and acoustic scattering is proposed. The quantum speed-up is due to a Quantum Linear System Algorithm (QLSA). The original QLSA of Harrow et al. provides an exponential speed-up over the best-known classical algorithms but only in the case of sparse systems. Due to the non-local nature of integral operators, matrices arising from discretization of BIEs, are, however, dense. A QLSA for dense matrices was introduced in 2017. Its runtime as function of the system's size N is bounded by O(√Npolylog(N)). The run time of the best-known classical algorithm for an arbitrary dense matrix scales as O(N².³⁷³). Instead of exponential as in case of sparse matrices, here we have only a polynomial speed-up. Nevertheless, sufficiently high power of this polynomial, ~4.7, should make QLSA an appealing alternative. Unfortunately for the QLSA, the asymptotic separability of the Green's function leads to high compressibility of the BIEs matrices. Classical fast algorithms such as Multilevel Fast Multipole Method (MLFMM) take advantage of this fact and reduce the runtime to O(Nlog(N)), i.e., the QLSA is only quadratically faster than the MLFMM. To be truly impactful for computational electromagnetics and acoustics engineers, QLSA must provide more substantial advantage than that. We propose a computational scheme which combines elements of the classical fast algorithms with the QLSA to achieve the required performance. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=quantum%20linear%20system%20algorithm" title="quantum linear system algorithm">quantum linear system algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=boundary%20integral%20equations" title=" boundary integral equations"> boundary integral equations</a>, <a href="https://publications.waset.org/abstracts/search?q=dense%20matrices" title=" dense matrices"> dense matrices</a>, <a href="https://publications.waset.org/abstracts/search?q=electromagnetic%20scattering%20theory" title=" electromagnetic scattering theory"> electromagnetic scattering theory</a> </p> <a href="https://publications.waset.org/abstracts/130056/a-hybrid-classical-quantum-algorithm-for-boundary-integral-equations-of-scattering-theory" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/130056.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">154</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3695</span> Sparse Signal Restoration Algorithm Based on Piecewise Adaptive Backtracking Orthogonal Least Squares</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Linyu%20Wang">Linyu Wang</a>, <a href="https://publications.waset.org/abstracts/search?q=Jiahui%20Ma"> Jiahui Ma</a>, <a href="https://publications.waset.org/abstracts/search?q=Jianhong%20Xiang"> Jianhong Xiang</a>, <a href="https://publications.waset.org/abstracts/search?q=Hanyu%20Jiang"> Hanyu Jiang</a> </p> <p class="card-text"><strong>Abstract:</strong></p> the traditional greedy compressed sensing algorithm needs to know the signal sparsity when recovering the signal, but the signal sparsity in the practical application can not be obtained as a priori information, and the recovery accuracy is low, which does not meet the needs of practical application. To solve this problem, this paper puts forward Piecewise adaptive backtracking orthogonal least squares algorithm. The algorithm is divided into two stages. In the first stage, the sparsity pre-estimation strategy is adopted, which can quickly approach the real sparsity and reduce time consumption. In the second stage iteration, the correction strategy and adaptive step size are used to accurately estimate the sparsity, and the backtracking idea is introduced to improve the accuracy of signal recovery. Through experimental simulation, the algorithm can accurately recover the estimated signal with fewer iterations when the sparsity is unknown. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=compressed%20sensing" title="compressed sensing">compressed sensing</a>, <a href="https://publications.waset.org/abstracts/search?q=greedy%20algorithm" title=" greedy algorithm"> greedy algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=least%20square%20method" title=" least square method"> least square method</a>, <a href="https://publications.waset.org/abstracts/search?q=adaptive%20reconstruction" title=" adaptive reconstruction"> adaptive reconstruction</a> </p> <a href="https://publications.waset.org/abstracts/161616/sparse-signal-restoration-algorithm-based-on-piecewise-adaptive-backtracking-orthogonal-least-squares" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161616.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">148</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3694</span> Sparse Representation Based Spatiotemporal Fusion Employing Additional Image Pairs to Improve Dictionary Training</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Dacheng%20Li">Dacheng Li</a>, <a href="https://publications.waset.org/abstracts/search?q=Bo%20Huang"> Bo Huang</a>, <a href="https://publications.waset.org/abstracts/search?q=Qinjin%20Han"> Qinjin Han</a>, <a href="https://publications.waset.org/abstracts/search?q=Ming%20Li"> Ming Li</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Remotely sensed imagery with the high spatial and temporal characteristics, which it is hard to acquire under the current land observation satellites, has been considered as a key factor for monitoring environmental changes over both global and local scales. On a basis of the limited high spatial-resolution observations, challenged studies called spatiotemporal fusion have been developed for generating high spatiotemporal images through employing other auxiliary low spatial-resolution data while with high-frequency observations. However, a majority of spatiotemporal fusion approaches yield to satisfactory assumption, empirical but unstable parameters, low accuracy or inefficient performance. Although the spatiotemporal fusion methodology via sparse representation theory has advantage in capturing reflectance changes, stability and execution efficiency (even more efficient when overcomplete dictionaries have been pre-trained), the retrieval of high-accuracy dictionary and its response to fusion results are still pending issues. In this paper, we employ additional image pairs (here each image-pair includes a Landsat Operational Land Imager and a Moderate Resolution Imaging Spectroradiometer acquisitions covering the partial area of Baotou, China) only into the coupled dictionary training process based on K-SVD (K-means Singular Value Decomposition) algorithm, and attempt to improve the fusion results of two existing sparse representation based fusion models (respectively utilizing one and two available image-pair). The results show that more eligible image pairs are probably related to a more accurate overcomplete dictionary, which generally indicates a better image representation, and is then contribute to an effective fusion performance in case that the added image-pair has similar seasonal aspects and image spatial structure features to the original image-pair. It is, therefore, reasonable to construct multi-dictionary training pattern for generating a series of high spatial resolution images based on limited acquisitions. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=spatiotemporal%20fusion" title="spatiotemporal fusion">spatiotemporal fusion</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20representation" title=" sparse representation"> sparse representation</a>, <a href="https://publications.waset.org/abstracts/search?q=K-SVD%20algorithm" title=" K-SVD algorithm"> K-SVD algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=dictionary%20learning" title=" dictionary learning"> dictionary learning</a> </p> <a href="https://publications.waset.org/abstracts/74785/sparse-representation-based-spatiotemporal-fusion-employing-additional-image-pairs-to-improve-dictionary-training" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/74785.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">260</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3693</span> Channel Estimation Using Deep Learning for Reconfigurable Intelligent Surfaces-Assisted Millimeter Wave Systems</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ting%20Gao">Ting Gao</a>, <a href="https://publications.waset.org/abstracts/search?q=Mingyue%20He"> Mingyue He</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Reconfigurable intelligent surfaces (RISs) are expected to be an important part of next-generation wireless communication networks due to their potential to reduce the hardware cost and energy consumption of millimeter Wave (mmWave) massive multiple-input multiple-output (MIMO) technology. However, owing to the lack of signal processing abilities of the RIS, the perfect channel state information (CSI) in RIS-assisted communication systems is difficult to acquire. In this paper, the uplink channel estimation for mmWave systems with a hybrid active/passive RIS architecture is studied. Specifically, a deep learning-based estimation scheme is proposed to estimate the channel between the RIS and the user. In particular, the sparse structure of the mmWave channel is exploited to formulate the channel estimation as a sparse reconstruction problem. To this end, the proposed approach is derived to obtain the distribution of non-zero entries in a sparse channel. After that, the channel is reconstructed by utilizing the least-squares (LS) algorithm and compressed sensing (CS) theory. The simulation results demonstrate that the proposed channel estimation scheme is superior to existing solutions even in low signal-to-noise ratio (SNR) environments. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=channel%20estimation" title="channel estimation">channel estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=reconfigurable%20intelligent%20surface" title=" reconfigurable intelligent surface"> reconfigurable intelligent surface</a>, <a href="https://publications.waset.org/abstracts/search?q=wireless%20communication" title=" wireless communication"> wireless communication</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a> </p> <a href="https://publications.waset.org/abstracts/148896/channel-estimation-using-deep-learning-for-reconfigurable-intelligent-surfaces-assisted-millimeter-wave-systems" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148896.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">150</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3692</span> Supervised/Unsupervised Mahalanobis Algorithm for Improving Performance for Cyberattack Detection over Communications Networks</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Radhika%20Ranjan%20Roy">Radhika Ranjan Roy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deployment of machine learning (ML)/deep learning (DL) algorithms for cyberattack detection in operational communications networks (wireless and/or wire-line) is being delayed because of low-performance parameters (e.g., recall, precision, and f₁-score). If datasets become imbalanced, which is the usual case for communications networks, the performance tends to become worse. Complexities in handling reducing dimensions of the feature sets for increasing performance are also a huge problem. Mahalanobis algorithms have been widely applied in scientific research because Mahalanobis distance metric learning is a successful framework. In this paper, we have investigated the Mahalanobis binary classifier algorithm for increasing cyberattack detection performance over communications networks as a proof of concept. We have also found that high-dimensional information in intermediate features that are not utilized as much for classiﬁcation tasks in ML/DL algorithms are the main contributor to the state-of-the-art of improved performance of the Mahalanobis method, even for imbalanced and sparse datasets. With no feature reduction, MD offers uniform results for precision, recall, and f₁-score for unbalanced and sparse NSL-KDD datasets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mahalanobis%20distance" title="Mahalanobis distance">Mahalanobis distance</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20learning" title=" deep learning"> deep learning</a>, <a href="https://publications.waset.org/abstracts/search?q=NS-KDD" title=" NS-KDD"> NS-KDD</a>, <a href="https://publications.waset.org/abstracts/search?q=local%20intrinsic%20dimensionality" title=" local intrinsic dimensionality"> local intrinsic dimensionality</a>, <a href="https://publications.waset.org/abstracts/search?q=chi-square" title=" chi-square"> chi-square</a>, <a href="https://publications.waset.org/abstracts/search?q=positive%20semi-definite" title=" positive semi-definite"> positive semi-definite</a>, <a href="https://publications.waset.org/abstracts/search?q=area%20under%20the%20curve" title=" area under the curve"> area under the curve</a> </p> <a href="https://publications.waset.org/abstracts/161865/supervisedunsupervised-mahalanobis-algorithm-for-improving-performance-for-cyberattack-detection-over-communications-networks" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/161865.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">78</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3691</span> Automatic Target Recognition in SAR Images Based on Sparse Representation Technique</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ahmet%20Karagoz">Ahmet Karagoz</a>, <a href="https://publications.waset.org/abstracts/search?q=Irfan%20Karagoz"> Irfan Karagoz</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Synthetic Aperture Radar (SAR) is a radar mechanism that can be integrated into manned and unmanned aerial vehicles to create high-resolution images in all weather conditions, regardless of day and night. In this study, SAR images of military vehicles with different azimuth and descent angles are pre-processed at the first stage. The main purpose here is to reduce the high speckle noise found in SAR images. For this, the Wiener adaptive filter, the mean filter, and the median filters are used to reduce the amount of speckle noise in the images without causing loss of data. During the image segmentation phase, pixel values are ordered so that the target vehicle region is separated from other regions containing unnecessary information. The target image is parsed with the brightest 20% pixel value of 255 and the other pixel values of 0. In addition, by using appropriate parameters of statistical region merging algorithm, segmentation comparison is performed. In the step of feature extraction, the feature vectors belonging to the vehicles are obtained by using Gabor filters with different orientation, frequency and angle values. A number of Gabor filters are created by changing the orientation, frequency and angle parameters of the Gabor filters to extract important features of the images that form the distinctive parts. Finally, images are classified by sparse representation method. In the study, l₁ norm analysis of sparse representation is used. A joint database of the feature vectors generated by the target images of military vehicle types is obtained side by side and this database is transformed into the matrix form. In order to classify the vehicles in a similar way, the test images of each vehicle is converted to the vector form and l₁ norm analysis of the sparse representation method is applied through the existing database matrix form. As a result, correct recognition has been performed by matching the target images of military vehicles with the test images by means of the sparse representation method. 97% classification success of SAR images of different military vehicle types is obtained. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=automatic%20target%20recognition" title="automatic target recognition">automatic target recognition</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20representation" title=" sparse representation"> sparse representation</a>, <a href="https://publications.waset.org/abstracts/search?q=image%20classification" title=" image classification"> image classification</a>, <a href="https://publications.waset.org/abstracts/search?q=SAR%20images" title=" SAR images"> SAR images</a> </p> <a href="https://publications.waset.org/abstracts/71185/automatic-target-recognition-in-sar-images-based-on-sparse-representation-technique" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/71185.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">365</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3690</span> Analysis of the Significance of Multimedia Channels Using Sparse PCA and Regularized SVD</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kourosh%20Modarresi">Kourosh Modarresi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The abundance of media channels and devices has given users a variety of options to extract, discover, and explore information in the digital world. Since, often, there is a long and complicated path that a typical user may venture before taking any (significant) action (such as purchasing goods and services), it is critical to know how each node (media channel) in the path of user has contributed to the final action. In this work, the significance of each media channel is computed using statistical analysis and machine learning techniques. More specifically, “Regularized Singular Value Decomposition”, and “Sparse Principal Component” has been used to compute the significance of each channel toward the final action. The results of this work are a considerable improvement compared to the present approaches. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multimedia%20attribution" title="multimedia attribution">multimedia attribution</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20principal%20component" title=" sparse principal component"> sparse principal component</a>, <a href="https://publications.waset.org/abstracts/search?q=regularization" title=" regularization"> regularization</a>, <a href="https://publications.waset.org/abstracts/search?q=singular%20value%20decomposition" title=" singular value decomposition"> singular value decomposition</a>, <a href="https://publications.waset.org/abstracts/search?q=feature%20significance" title=" feature significance"> feature significance</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20systems" title=" linear systems"> linear systems</a>, <a href="https://publications.waset.org/abstracts/search?q=variable%20shrinkage" title=" variable shrinkage"> variable shrinkage</a> </p> <a href="https://publications.waset.org/abstracts/19533/analysis-of-the-significance-of-multimedia-channels-using-sparse-pca-and-regularized-svd" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19533.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">309</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3689</span> Sparse Coding Based Classification of Electrocardiography Signals Using Data-Driven Complete Dictionary Learning</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Fuad%20Noman">Fuad Noman</a>, <a href="https://publications.waset.org/abstracts/search?q=Sh-Hussain%20Salleh"> Sh-Hussain Salleh</a>, <a href="https://publications.waset.org/abstracts/search?q=Chee-Ming%20Ting"> Chee-Ming Ting</a>, <a href="https://publications.waset.org/abstracts/search?q=Hadri%20Hussain"> Hadri Hussain</a>, <a href="https://publications.waset.org/abstracts/search?q=Syed%20Rasul"> Syed Rasul</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electrocardiogram" title="electrocardiogram">electrocardiogram</a>, <a href="https://publications.waset.org/abstracts/search?q=dictionary%20learning" title=" dictionary learning"> dictionary learning</a>, <a href="https://publications.waset.org/abstracts/search?q=sparse%20coding" title=" sparse coding"> sparse coding</a>, <a href="https://publications.waset.org/abstracts/search?q=classification" title=" classification"> classification</a> </p> <a href="https://publications.waset.org/abstracts/52418/sparse-coding-based-classification-of-electrocardiography-signals-using-data-driven-complete-dictionary-learning" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52418.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">386</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3688</span> Measuring and Evaluating the Effectiveness of Mobile High Efficiency Particulate Air Filtering on Particulate Matter within the Road Traffic Network of a Sample of Non-Sparse and Sparse Urban Environments in the UK</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Richard%20Maguire">Richard Maguire </a> </p> <p class="card-text"><strong>Abstract:</strong></p> This research evaluates the efficiency of using mobile HEPA filters to reduce localized Particulate Matter (PM), Total Volatile Organic Chemical (TVOC) and Formaldehyde (HCHO) Air Pollution. The research is being performed using a standard HEPA filter that is tube fitted and attached to a motor vehicle. The velocity of the vehicle is used to generate the pressure difference that allows the filter to remove PM, VOC and HCOC pollution from the localized atmosphere of a road transport traffic route. The testing has been performed on a sample of traffic routes in Non-Sparse and Sparse urban environments within the UK. Pre and Post filter measuring of the PM2.5 Air Quality has been carried out along with demographics of the climate environment, including live filming of the traffic conditions. This provides a base line for future national and international research. The effectiveness measurement is generated through evaluating the difference in PM2.5 Air Quality measured pre- and post- the mobile filter test equipment. A series of further research opportunities and future exploitation options are made based on the results of the research. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=high%20efficiency%20particulate%20air" title="high efficiency particulate air">high efficiency particulate air</a>, <a href="https://publications.waset.org/abstracts/search?q=HEPA%20filter" title=" HEPA filter"> HEPA filter</a>, <a href="https://publications.waset.org/abstracts/search?q=particulate%20matter" title=" particulate matter"> particulate matter</a>, <a href="https://publications.waset.org/abstracts/search?q=traffic%20pollution" title=" traffic pollution"> traffic pollution</a> </p> <a href="https://publications.waset.org/abstracts/104024/measuring-and-evaluating-the-effectiveness-of-mobile-high-efficiency-particulate-air-filtering-on-particulate-matter-within-the-road-traffic-network-of-a-sample-of-non-sparse-and-sparse-urban-environments-in-the-uk" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/104024.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">123</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3687</span> Satellite LiDAR-Based Digital Terrain Model Correction using Gaussian Process Regression</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Keisuke%20Takahata">Keisuke Takahata</a>, <a href="https://publications.waset.org/abstracts/search?q=Hiroshi%20Suetsugu"> Hiroshi Suetsugu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Forest height is an important parameter for forest biomass estimation, and precise elevation data is essential for accurate forest height estimation. There are several globally or nationally available digital elevation models (DEMs) like SRTM and ASTER. However, its accuracy is reported to be low particularly in mountainous areas where there are closed canopy or steep slope. Recently, space-borne LiDAR, such as the Global Ecosystem Dynamics Investigation (GEDI), have started to provide sparse but accurate ground elevation and canopy height estimates. Several studies have reported the high degree of accuracy in their elevation products on their exact footprints, while it is not clear how this sparse information can be used for wider area. In this study, we developed a digital terrain model correction algorithm by spatially interpolating the difference between existing DEMs and GEDI elevation products by using Gaussian Process (GP) regression model. The result shows that our GP-based methodology can reduce the mean bias of the elevation data from 3.7m to 0.3m when we use airborne LiDAR-derived elevation information as ground truth. Our algorithm is also capable of quantifying the elevation data uncertainty, which is critical requirement for biomass inventory. Upcoming satellite-LiDAR missions, like MOLI (Multi-footprint Observation Lidar and Imager), are expected to contribute to the more accurate digital terrain model generation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=digital%20terrain%20model" title="digital terrain model">digital terrain model</a>, <a href="https://publications.waset.org/abstracts/search?q=satellite%20LiDAR" title=" satellite LiDAR"> satellite LiDAR</a>, <a href="https://publications.waset.org/abstracts/search?q=gaussian%20processes" title=" gaussian processes"> gaussian processes</a>, <a href="https://publications.waset.org/abstracts/search?q=uncertainty%20quantification" title=" uncertainty quantification"> uncertainty quantification</a> </p> <a href="https://publications.waset.org/abstracts/148360/satellite-lidar-based-digital-terrain-model-correction-using-gaussian-process-regression" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/148360.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">182</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=sparse%20algorithm&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=sparse%20algorithm&page=3">3</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=sparse%20algorithm&page=4">4</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=sparse%20algorithm&page=5">5</a></li> <li class="page-item"><a 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