Steganography Arabic Text Based on Natural Language Process Documents

: Obscurity is a main reason whereas computers can not know natural language. It have made great transaction steps trend developing instrument to morphological and syntactic analyzers for Arabic . One of the manners used in security areas is steganography. The rapid development of steganography scripts, it is a large security and confidentiality problem, it becomes necessary to find appropriate protection because of the significance, accuracy and sensitivity of the data during transmitted. In this research is offer in a new method and to use one level to hide, this level is hiding by embedding and addition. The one level is embed a secret message twice, one bit in the LSB in the FFT and the addition of one kashida and add Single-Double Quotation in the same secret message. Using Random Singular Value Decomposition (RSVD) is NRG to find positions that are hiding within the text. Linguistic steganography is covering all the techniques that deal with using written natural language to hide secret message. in this research presents a linguistic steganography for scripts written in Arabic language, using kashida, Single-Double Quotation and Fast Fourier Transform on the bases of using new technique entitled Random Singular Value Decomposition (RSVD) as allocation to hide secret message. The proposed approach is an attempt to present a transform linguistic steganography using one level for hiding to improve implementation of kashida and Single-Double Quotation , and improve the security of the secret message by using Random Singular Value Decomposition (RSVD). Are testing this method in terms of security and capacity, transparency, and robustness and this is way better than previous methods. The proposed algorithm ideal steganography properties.

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Usage of singular value decomposition matrix for search latent semantic structures in natural language texts

2015 54th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) ◽

10.1109/sice.2015.7285567 ◽

2015 ◽

Author(s):

A. A. Kuandykov ◽

S. B. Rakhmetulayeva ◽

Y. M. Baiburin ◽

A. B. Nugumanova

Keyword(s):

Singular Value Decomposition ◽

Natural Language ◽

Singular Value ◽

Decomposition Matrix ◽

Value Decomposition

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Doppler distortion elimination using short-time sparse singular value decomposition strategy for wayside acoustic source fault diagnosis

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406219841078 ◽

2019 ◽

Vol 233 (15) ◽

pp. 5499-5514

Author(s):

Kesai Ouyang ◽

Wei Xiong ◽

Guoqiang Liu ◽

Qingbo He

Keyword(s):

Fault Diagnosis ◽

Singular Value Decomposition ◽

Condition Monitoring ◽

High Efficiency ◽

Simulation Analysis ◽

Rapid Development ◽

Singular Value ◽

Time Varying ◽

Value Decomposition ◽

Short Time

With the rapid development of transportation, wayside condition monitoring and fault diagnosis of key acoustic sources has attracted considerable attentions because of low cost and high efficiency. However, serious Doppler distortion exists in the wayside acquired signals when the monitored moving source passes by the system at high velocity, which makes it difficult for condition monitoring and fault diagnosis. This paper presents a novel method involving short-time sparse singular value decomposition to eliminate Doppler distortion in the wayside acquired signals based on a microphone array. The procedure of the proposed short-time sparse singular value decomposition is performed as follows. First, the Doppler distorted array signals are decomposed into a series of array segments by a sliding window with a proper window length. Afterwards, the time-varying direction of arrival of the corresponding array segments is acquired by individual sparse singular value decomposition. Then the fitting time-varying directions of arrival and time-domain interpolation resampling are employed to correct the Doppler distorted signals for recovering the objective characteristic frequency. Simulation analysis has validated that under heavy background noise situation, better localization accuracy and effectiveness of the proposed short-time sparse singular value decomposition could be achieved in comparison with other strategies like short-time multiple signal classification. Besides, the real data cases have verified the effectiveness of the proposed method, which shows great potential applications in wayside condition monitoring and fault diagnosis system for moving vehicles, trains, planes, etc.

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Removing Direct Wave of Through-Wall Radar Based on Singular Value Decomposition

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.301-303.442 ◽

2011 ◽

Vol 301-303 ◽

pp. 442-446

Author(s):

Hui Xu ◽

Bang Yu Li ◽

Li Yao

Keyword(s):

Singular Value Decomposition ◽

Decomposition Method ◽

Simulation Software ◽

Singular Value ◽

Echo Signal ◽

Direct Wave ◽

Wave Signal ◽

Singular Value Decomposition Method ◽

The One ◽

Value Decomposition

The problem of target detection which is affected by the direct wave is discussed in this paper. Singular Value Decomposition method is proposed for removing the direct wave. The echo signal is decomposed by use of Singular Value Decomposition method and the one-order eigenvalue of the eigenvalue matrix is set to zero, then the related components of echo signal are removed. The direct wave signal can be removed effectively using this method, and most of the target components of signal are preserved in the process of removing direct wave signal. HFSS simulation software is used for modeling and simulation, and the results show that the resolution of false target using Singular Value Decomposition method increases 11% compared with the Interference Canceling method for removing direct wave.

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