Search results

Reference source not found. decoder using verelog programming in JPEG software get the function module .These modules The input data stream buffer module, File Flag Section Analysis Module, The Entropy Decoding Module, Against Zig_zag transformation Module, IDCT Module, Color Space Conversion Module through the top-level module connected to form the whole decoder, thus compete the decoding JPEG image.
Analysis of the result of running the system At the completion of treatment system, the watermarked and reduction for uncompressed images will be stored in the SD card, this section of the watermark embedding effect on image analysis.
Table 1 NC value embedded watermark image Attack Type \ Picture a b c No attack 0.9985 0.9987 0.9991 Noise Attack 0.9942 0.9512 0.9294 Cut Attack 0.8915 0.9877 0.6311 Spin attack 0.9851 0.9408 0.8630 Looking at the above data, the mean that is watermark implanted has the better resistance compared with the ordinary attacking and the robustness is better.

Theoretical Introduction of Digital Watermarks Digital watermarking technology is an important branch of information hiding technology, the digital watermark is hidden in the original data refers to the secret information that is watermark, through a certain algorithm some iconic information directly embedded into the multimedia content which, without affecting the original content value and the case of using, and can’t meet the perceived or notice human perception system, only by the extraction and identification of the device to the detection of a special technology is embedded watermark can be a text, logos, serial numbers, etc., a digital watermarking system divided into two models: watermark embedding model, the watermark extraction model.
Among them, the discrete wavelet transform has an excellent multi-resolution analysis and noise reduction capabilities, able to multi-scale image analysis, with anti-noise attack, robustness, and other characteristics.
Watermarking two dimensional data object identifier for authenticated distribution of digital multimedia contents [J].

In order to actively respond to and strive to achieve China's carbon emissions reduction targets, exploring the trail for a low-carbon trade and the way to reduce carbon emissions have become the inevitable choice for us.
Method and data Input-output Model.
Data Sources and Processing.

Introduction The utility industry across the world is trying to address numerous challenges that occur in exsiting electrical grid, including generation diversification, optimal deployment of expensive assets, demand response, energy conservation, and reduction of the industry’s overall carbon footprint.
NANs connect smart meters to local Data Aggregation Nodes (DANs) for AMI applications.
Fig.4 illustrates the possible types of connectivity using EPON communication network within AMI, where the tree topology is adopted for the EPON As shown in the Figure, smart meters (SMs) and the local Data Aggregation Nodes (DANs) are constructed using wireless connection, while the backhaul communication network connecting DAN with the utility’s backbone network is constructed using EPON, where the star topology is adopted.

Meanwhile, leading countries using fuel cells are focusing on transportable fuel cells and fuel cells for buildings having a small space requirement and high utilization as a result of the greenhouse gas reduction plan.
Differentiated data will be collected for analysis according to the objects and levels of analysis [3].
Choi: Data mining using R (2011), p.3 [7] D.H.

Introduction In the production of ground meat, processing begins with a size reduction of raw material.
Analysis of the above experimental data showed that P vs r relationship was a quadratic function.
Using MATLAB 7.0 software multivariable linear regression analysis of the data was performed to estimate coefficients in the Equations.

The third one is utilizing mutil-scale approach to investigate the mechanical characteristics of nanocomposite by combining MD with continuum mechanics, in which the difficulty lies in the accuracy improvement of data transition among different scales.
The Bottom-up Simulation Method for Nanoparticle-reinforced Composites The bottom-up method is a kind of multi-scale method, in which the material data is transferred from the bottom (nano-scale) to the top (macro-scale).
Fig. 4 The crack propagation rate with different mesh size The impact strength under different failure criterion The impact strength of nanocomposite,, is calculated through divided the reduction of kinetic energy of the pendulum,, by the area of the crack surface,,

Some properties data of fan blades are shown in Table 1, which are obtained by the use of a material hydraulic testing machine.
Next, taking advantage of model transient response analysis, and reduction and decoupling of coupled motion equations with structure model, the problem can be solved by superposition of individual model response.
Summate model response to get physical response: [ ]{ }ξφ=u （ 11） Simulation Results According to the experimental data, and meshing of the blade with PATRAN, which is divided into 826 tetrahedral elements, 1834 nodes, shown in Fig.3, we can find the former 100th natural frequency, natural modes and stress fringe by the means of NASTRAN, when blades are affected by wind loads and centrifugal force at the speed 980 r/min.

Several fabrication methods have been developed for the synthesis of silicon nitride powders including direct nitridation of silicon powder, carbothermic reduction of silicon dioxide, silicon imide synthesis and decomposition as well as combustion synthesis (CS) with outstanding advantages such as low energy consumption, simplicity of equipment, high efficiency and purity of products.
Based on our experimental data, the minimum initiation pressure Pcri is generally lower than or equal to the minimum propagation pressure Pcrp.
It is worth noting that these three critical pressure values are only experimental data and they depend greatly on the experimental parameters such as the reactant properties, the inert and active diluents, the external ignition energy and the nitrogen purity, etc.

Nitrogen doping changes the surface energy and morphology by a reduction of the height and width of crystallization steps.
Nitrogen concentration taken from SIMS data was: 2 x 1018 cm-3 for undoped SiC, 1 x 1019 cm-3 for SiC:N (3 vol.%) and 2 x 1019 cm-3 for SiC:N (10 vol.%).
By comparison of our electron Hall mobilities with data reported by Iwata et al. [12] one can expect in our samples charge compensation effects with the compensation ratio above 0.6.