Enhanced audio data hiding synchronization using non linear filters

Spatio-temporal digital data from fMRI (functional Magnetic Resonance Imaging) are used to analyse and to model brain activation. To map brain functions, a well-defined sensory activation is offered to a test person and the hemodynamic response to neuronal activity is studied. This so-called BOLD effect in fMRI is typically small and characterised by a very low signal to noise ratio. Hence the activation is repeated and the three dimensional signal (multi-slice 2D) is gathered during relatively long time ranges (3-5 min). From the noisy and distorted spatio-temporal signal the expected response has to be filtered out. Presented methods of spatio-temporal signal processing base on non-linear concepts of data reconstruction and filters of mathematical morphology (e.g. alternating sequential morphological filters). Filters applied are compared by classifications of activations.

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Non-Linear Filters and Estimation of Diffusion Coefficients in the Heath-Jarrow-Morton Framework

IFAC Proceedings Volumes ◽

10.1016/s1474-6670(17)47129-1 ◽

1995 ◽

Vol 28 (7) ◽

pp. 321-325

Author(s):

Ramaprasad Bhar ◽

Carl Chiarella

Keyword(s):

Diffusion Coefficients ◽

Linear Filters ◽

Non Linear

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Non-linear Filters with Gain-type Additive Updates

Stochastic Dynamics, Filtering and Optimization ◽

10.1017/9781316863107.008 ◽

2018 ◽

pp. 432-466

Keyword(s):

Linear Filters ◽

Non Linear

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Reversible Audio Data Hiding in Spectral and Time Domains

Advances in Multimedia and Interactive Technologies - Multimedia Information Hiding Technologies and Methodologies for Controlling Data ◽

10.4018/978-1-4666-2217-3.ch002 ◽

2013 ◽

pp. 19-41 ◽

Cited By ~ 1

Author(s):

Akira Nishimura

Keyword(s):

Data Hiding ◽

Time Domain ◽

Reversible Data Hiding ◽

Linear Prediction ◽

Data Consistency ◽

Time Domains ◽

Audio Data ◽

Hidden Data ◽

The Time Domain ◽

Location Map

Reversible data hiding is a technique whereby hidden data are embedded in host data in such a way that the host data consistency is perfectly preserved and the host data are restored when extracting the hidden data. This chapter introduces basic algorithms for reversible data hiding, histogram shifting, histogram expansion, and compression. This chapter also proposes and evaluates two reversible data hiding methods, i.e., hiding data in the frequency-domain using integer Discrete Cosine Transform (DCT) and modified DCT and hiding in the time domain using linear prediction and error expansion. As no location map is required to prevent amplitude overflow, the proposed method in the time domain achieves a storage capacity of nearly 1 bit per sample of payload data. The proposed methods are evaluated by the payload amount, objective quality degradation of stego signal, and payload concealment.

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