scholarly journals A Novel Technique for Image Authentication in Frequency Domain Using Discrete Fourier Transformation Technique (IAFDDFTT)

2008 ◽  
Vol 21 (1) ◽  
pp. 24-32 ◽  
Author(s):  
Nabin Ghoshal ◽  
Jyotsna Kumar Mandal
Author(s):  
Debnath Bhattacharyya ◽  
Jhuma Dutta ◽  
Poulami Das ◽  
Rathit Bandyopadhyay ◽  
S.K. Bandyopadhyay ◽  
...  

A combined Discrete Fourier Transformation (DFT) and Successive Division based image watermarking scheme has been proposed in this article. Due to intrinsic parameters: Imperceptibility, Data Embedding Capacity and Time of Execution, many spatial domain approaches are less efficient. Many frequency domain approaches satisfies imperceptibility and data embedding capacity, but utilizes more execution time. The proposed algorithm is a hybrid technique under frequency domain, which embeds watermark into host using Discrete Fourier Transformation (DFT) and successive division. Through DFT better imperceptibility is noticed and the algorithm utilizes very less execution time compared to other existing approaches. Performance analysis is done based on similarity between original and retrieved watermark images. The experimental results are better compared to other existing techniques.


Author(s):  
Yousun Li

In the time domain simulation of the response of an offshore structure under random waves, the time histories of the wave field should be generated as the input to the dynamic equations. Herein the wave field is the wave surface elevation, the water particle velocities and accelerations at structural members. The generated time histories should be able to match the given wave-field spectral descriptions, to trace the structural member motions if it is a compliant offshore structure, and be numerically efficient. Most frequently used generation methods are the direct summation of a limited number of cosine functions, the Fast Fourier Transformation, and the digital filtering model. However, none of them can really satisfy all the above requirements. A novel technique, called the Modulated Discrete Fourier Transformation, has been developed. Under this method, the wave time histories at each time instant is a summation of a few time-varying complex functions. The simulated time histories have continuous spectral density functions, and the motions of the structural members are well included. This method seems to be superior to all the conventional methods in terms of the above mentioned three requirements.


2018 ◽  
Vol 41 (8) ◽  
pp. 2338-2351 ◽  
Author(s):  
Anna Swider ◽  
Eilif Pedersen

In the phase of industry digitalization, data are collected from many sensors and signal processing techniques play a crucial role. Data preprocessing is a fundamental step in the analysis of measurements, and a first step before applying machine learning. To reduce the influence of distortions from signals, selective digital filtering is applied to minimize or remove unwanted components. Standard software and hardware digital filtering algorithms introduce a delay, which has to be compensated for to avoid destroying signal associations. The delay from filtering becomes more crucial when the analysis involves measurements from multiple sensors, therefore in this paper we provide an overview and comparison of existing digital filtering methods with an application based on real-life marine examples. In addition, the design of special-purpose filters is a complex process and for preprocessing data from many sources, the application of digital filtering in the time domain can have a high numerical cost. For this reason we describe discrete Fourier transformation digital filtering as a tool for efficient sensor data preprocessing, which does not introduce a time delay and has low numerical cost. The discrete Fourier transformation digital filtering has a simpler implementation and does not require expert-level filter design knowledge, which is beneficial for practitioners from various disciplines. Finally, we exemplify and show the application of the methods on real signals from marine systems.


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