Wavelet Analysis

Wavelet Analysis

Wavelet Analysis

Digital hiding is the technology that uses encryption algorithm to make some iconic copyright information embedded into the content of the mutimedia product. It could prove the copyright of the mutimedia product and the content integrity of the digital product. But it could not affect the value and application of the mutimedia product’s content and could not be observed and recognized in the perception system. It could only use the professional device or professional software to refine the copyright information.In order to improve the security of digital hidden information, this paper improves some kinds of the chaos algorithm was and apply the improved algorithm in the digital information hiding technology. By a large number of experiments and tests, it has been proved that the improved chaos algorithm has a good security and robustness for the protection of the digital information hiding.In the concern of the algorithm of the embedded process in multimedia information, this paper summarizes the recent wavelet transform algorithms and thereby improves the algorithm. In order to make the hidden information embedded into the multimedia information, research on the embedded algorithm of the binary image and the gray image, and improve the digital information hiding algorithm of wavelet transform. The new embedded algorithm make the digital image into N-level wavelet decomposition. Using the different algorithm embedded into the hidden information of the low frequency and high frequency of the decompose image.the decompose image at high frequency and low frequency bands have used different algorithms embedded in the hidden information. Before the hidden information was embedded, use the chaos algorithm to do some encrypted work and data splitter work of the hidden image,which could greatly improve the privacy and security of multimedia information.The study were also hidden on the algorithm of binary image and gray image of the hidden algorithm MATLAB simulation and robustness against experiments to recent research results were compared and analyzed by comparing the obtained results, The proposed algorithm shows improvement in terms of hidden information on the safety as well as the robustness of the improvement.

Vehicle navigation systems often employ an inertial measuring unit IMU) to complement the Global Positioning System GPS) in the event of satellite signal loss due to blockage or jamming. The added redundancy of the inertial navigation system INS) can be invaluable to the end user； and as such, the integration of GPS with inertial sensors has become a standard practice. The relatively high cost of INS has been preventing their use for land vehicle applications. Recently, MEMS-based INS has become commercially available at low cost. These relatively low cost inertial sensors have the potential to allow an affordable vehicle navigation system to be developed. Compared to tactical and navigational grade INS, MEMS-based sensors are less expensive but are more susceptible to the short and long term low and high frequency) errors that present themselves as correlated noise. Signal processing techniques such as, optimal low-pass filtering and wavelet de-noising have been successful at minimizing the short-term errors, but they have not been able to effectively eliminate the long-term errors mixed with the dynamic motion of the vehicle. Newer techniques such as the Fast Orthogonal Search FOS) has shown some improvement over other techniques at removing long and short term inertial sensor errors. The primary objective of this research is to use advanced signal processing techniques such as Wavelet Multi-Resolution Analysis WMRA), Wavelet Packet Transform WPT) and high-resolution spectral analysis using Fast Orthogonal Search FOS) to attempt to remove the short and long-term errors of inertial sensors prior to processing them with GPS. In addition, a variation of FOS known as FOS-Wavelet Transform FOS-WT) was developed to provide high resolution wavelet analysis. FOS vi WT makes use of exponentially decaying sinusoids as candidate functions to further improve the accuracy of the FOS model. This research will focus on examining the merits and the limitations of the above de-noising techniques when applied to a MEMS-based INS. The removal of the correlated sensor errors should result in a significant increase in accuracy of the overall vehicle position. Four road test experiments in a land vehicle were conducted. During these tests, real GPS data and MEMS-inertial sensor measurements were collected. Analysis of the data with WMRA and WPT has shown successful removal of the short-term sensor errors but not the long term errors, and that FOS and FOS-WT have successfully removed the short term errors as well as some of the long term errors, resulting in a significant increase in the overall positioning accuracy of the land vehicle.