Improving Hydrostatic Driveline NVH Using a Pseudo Random Binary Sequence

Steganography is the special art of hidding important and confidential information in appropriate multimedia carrier. It also restrict the detection of hidden messages. In this paper we proposes steganographic method based on dct and entropy thresholding technique. The steganographic algorithm uses random function in order to select block of the image where the elements of the binary sequence of a secret message will be inserted. Insertion takes place at the lower frequency AC coefficients of the block. Before we insert the secret message. Image under goes dc transformations after insertion of the secret message we apply inverse dc transformations. Secret message will only be inserted into a particular block if entropy value of that particular block is greater then threshold value of the entropy and if block is selected by the random function. In Experimental work we calculated the peak signal to noise ratio(PSNR), Absolute difference , Relative entropy. Proposed algorithm give high value of PSNR and low value of Absolute difference which clearly indicate level of distortion in image due to insertion of secret message is reduced. Also value of relative entropy is close to zero which clearly indicate proposed algorithm is sufficiently secure.

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SCALING ANALYSIS OF THE A-PHASE DYNAMICS DURING SLEEP

Fractals ◽

10.1142/s0218348x20500504 ◽

2020 ◽

Vol 28 (02) ◽

pp. 2050050

Author(s):

V. E. ARCE-GUEVARA ◽

M. O. MENDEZ ◽

J. S. MURGUÍA ◽

A. ALBA ◽

H. GONZÁLEZ-AGUILAR ◽

...

Keyword(s):

Binary Sequence ◽

Fluctuation Analysis ◽

Scaling Analysis ◽

Scaling Exponent ◽

Sleep State ◽

Phase Dynamics ◽

A Value ◽

Sleep Condition ◽

Detrended Fluctuation

In this work, the scaling behavior of the sleep process is evaluated by using detrended fluctuation analysis based on wavelets. The analysis is carried out from arrivals of short and recurrent cortical events called A-phases, which in turn build up the Cyclic Alternating Pattern phenomenon, and are classified in three types: A1, A2 and A3. In this study, 61 sleep recordings corresponding to healthy, nocturnal frontal lobe epilepsy patients and sleep-state misperception subjects, were analyzed. From the A-phase annotations, the onsets were extracted and a binary sequence with one second resolution was generated. An item in the sequence has a value of one if an A-phase onset occurs in the corresponding window, and a value of zero otherwise. In addition, we consider other different temporal resolutions from 2[Formula: see text]s to 256[Formula: see text]s. Furthermore, the same analysis was carried out for sequences obtained from the different types of A-phases and their combinations. The results of the numerical analysis showed a relationship between the time resolutions and the scaling exponents; specifically, for higher time resolutions a white noise behavior is observed, whereas for lower time resolutions a behavior towards to [Formula: see text]-noise is exhibited. Statistical differences among groups were observed by applying various wavelet functions from the Daubechies family and choosing the appropriate sequence of A-phase onsets. This scaling analysis allows the characterization of the free-scale dynamic of the sleep process that is specific for each sleep condition. The scaling exponent could be useful as a diagnosis parameter in clinics when sleep macrostructure does not offer enough information.

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