Classification in Twitter via Compressive Sensing

Milioris, Dimitris

doi:10.1109/infcomw.2015.7179360

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Thus the requirement lead us to perform further improvement to the HBLAST technique. In this dissertation our work perform to create a new compressive sensing HBLAST [7] algorithm which make use of existing scenario and improve it with balanced distribution technique. The proposed technique is based on CS based and data process Sequence alignment based scenario which make use of all the resources properly and outperform the complete distribution using the provided Hadoop based algorithm [8].…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

A Hybrid Approach for Sequence Alignment over Genome Data using Compressive Sensing and HBLAST

Gupta¹,

Pandey²,

Deen³

et al. 2018

IJCA

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Medical data is an exponential growth in all the hospitality service area. Genome is an special type of data which deals with the small unit of medical cells. Various matching operation over the genome data is required because of some medical issues arise in various cases. DNA matching, sequence matching, pattern analysis and matching is so called requirement in this area. There are some techniques such as BLAST, HBLAST, RMAP is involved and performed by past researcher. The technique use pre-processing and other filteration , sequence finding is performed. Past approach finds limitation where the large data processing, sequence detection and combine score generation for overall data processing is not performed. In this paper proposed approach is given which work towards the enhancement of previous approach extended with compressive sensing usage for prefetching of data and its filteration. It make use of compressive sensing with which a noise removal, filtering process is executed and thus a refined data is observed for Hadoop processing Mapping approach. Our proposed technique executed with different data set of sequence, count of data present in millions and it gives an effective results while comparing with existing scenario. A further implementation on security usage can performed by us.

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Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

A Hybrid Approach for Sequence Alignment over Genome Data using Compressive Sensing and HBLAST

Gupta¹,

Pandey²,

Deen³

et al. 2018

IJCA

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“…ese properties make CS have a wide range of applications in many fields of signals processing [2,8]. Some typical CS-based applications include single-pixel imaging [9][10][11][12][13], recovery of images and video [14], wireless image sensor networks [15], applications in classification problem [16,17], and some biomedical signals processing fields [4,[18][19][20][21], such as nuclear magnetic resonance imaging (MRI) [22][23][24] and electrocardiographic (ECG) signals processing [25,26].…”

Section: Introductionmentioning

confidence: 99%

Measurement Matrix Optimization via Mutual Coherence Minimization for Compressively Sensed Signals Reconstruction

Wei

Zhang

et al. 2020

Mathematical Problems in Engineering

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For signals reconstruction based on compressive sensing, to reconstruct signals of higher accuracy with lower compression rates, it is required that there is a smaller mutual coherence between the measurement matrix and the sparsifying matrix. Mutual coherence between the measurement matrix and sparsifying matrix can be expressed indirectly by the property of the Gram matrix. On the basis of the Gram matrix, a new optimization algorithm of acquiring a measurement matrix has been proposed in this paper. Firstly, a new mathematical model is designed and a new method of initializing measurement matrix is adopted to optimize the measurement matrix. Then, the loss function of the new algorithm model is solved by the gradient projection-based method of Gram matrix approximating an identity matrix. Finally, the optimized measurement matrix is generated by minimizing mutual coherence between measurement matrix and sparsifying matrix. Compared with the conventional measurement matrices and the traditional optimization methods, the proposed new algorithm effectively improves the performance of optimized measurement matrices in reconstructing one-dimensional sparse signals and two-dimensional image signals that are not sparse. The superior performance of the proposed method in this paper has been fully tested and verified by a large number of experiments.

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“…Because of the excellent performance of CS in the field of signal processing and image processing, compressive sensing has been widely applied in many fields since it was first proposed [7]. Some typical application research areas include radar imaging [10][11][12], images' recovery and wireless image sensor networks [13,14], applications in classification problems [15,16], biomedical signal processing [8,[17][18][19], such as nuclear magnetic resonance imaging (MRI) [20,21], electrocardiographic (ECG) signal processing [22,23] and single-pixel imaging [24][25][26][27][28]. For the single-pixel imaging, in 2008, Rice University invented a single-pixel imaging system (single-pixel camera) based on CS theory [25], which pioneered the application field of CS-based single-pixel imaging.…”

Section: Introductionmentioning

confidence: 99%

Optimization Methods of Compressively Sensed Image Reconstruction Based on Single-Pixel Imaging

Wei

Zhang

et al. 2020

Applied Sciences

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According to the theory of compressive sensing, a single-pixel imaging system was built in our laboratory, and imaging scenes are successfully reconstructed by single-pixel imaging, but the quality of reconstructed images in traditional methods cannot meet the demands of further engineering applications. In order to improve the imaging accuracy of our single-pixel camera, some optimization methods of key technologies in compressive sensing are proposed in this paper. First, in terms of sparse signal decomposition, based on traditional discrete wavelet transform and the characteristics of coefficients distribution in wavelet domain, a constraint condition of the exponential decay is proposed and a corresponding constraint matrix is designed to optimize the original wavelet decomposition basis. Second, for the construction of deterministic binary sensing matrices in the single-pixel camera, on the basis of a Gram matrix, a new algorithm model and a new method of initializing a compressed sensing measurement matrix are proposed to optimize the traditional binary sensing matrices via mutual coherence minimization. The gradient projection-based algorithm is used to solve the new mathematical model and train deterministic binary sensing measurement matrices with better performance. Third, the proposed optimization methods are applied to our single-pixel imaging system for optimizing the existing imaging methods. Compared with the conventional methods of single-pixel imaging, the accuracy of image reconstruction and the quality of single-pixel imaging have been significantly improved by our methods. The superior performance of our proposed methods has been fully tested and the effectiveness has also been demonstrated by numerical simulation experiments and practical imaging experiments.

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Classification in Twitter via Compressive Sensing

Cited by 4 publications

References 7 publications

A Hybrid Approach for Sequence Alignment over Genome Data using Compressive Sensing and HBLAST

A Hybrid Approach for Sequence Alignment over Genome Data using Compressive Sensing and HBLAST

Measurement Matrix Optimization via Mutual Coherence Minimization for Compressively Sensed Signals Reconstruction

Optimization Methods of Compressively Sensed Image Reconstruction Based on Single-Pixel Imaging

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