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Serving as a basic and key research of Tibetan information processing, Tibetan syntax analysis plays an important role in promoting the research on Tibetan natural processing technology such as machine translation, Tibetan information retrieval and semantic analysis. The existing Tibetan syntactic analysis system shows poor performance in general fields. Based on unique characteristics of Tibetan, through analysis and study on Tibetan syntax, the efficiency of sentence processing system can be improved, thus speeding up the progress of Tibetan sentence processing. In this paper, we proposed a syntactic parsing method based on context free grammars using up-bottom parsing technology.

This article presents the topic of Three Dimensional facial reconstruction approaches and some used methods. In this paper, we implement three-dimensional facial reconstruction algorithms based on various face databases using single image as an input and analyzing their performance on several aspects. Researchers proposed many applications for this issue, but most have their drawbacks and limitations. Secondly, we discuss about three-dimensional shapes and models based on facial techniques in detail. It concludes with an analysis of several implementations and with some technical discussions about 3D facial reconstruction.

In coal mines the environment is complex, crowded with personnel and equipment, as well as lack of colors. The safety in production is influenced by visual spatial perception is whether or not accurate. The visual spatial perception precision plays a significant even crucial role in positioning. By simulating the environment in coal mines, the positioning experiment about participants’ visual spatial perception performance of different target objects in two models—from far to near and from near to far has been carried out. Experimental results showed that visual spatial perception distance was significantly shorter than target range, that visual spatial perception in deep pink was the poorest. In addition, the H has become appreciable and regular with visual spatial perception changing. The experimental results provided references for setting warning marks, safe distances and choosing colors for infrastructure in coal mines.

Automatic vectorization in the field of image processing and recognition is one of development direction. This paper does some relevant researches on satellite cloud image preprocessing, image segmentation, image vector quantization. To improve the effect of noise reduction and preserving image details, this paper puts forward an improved adaptive median filter algorithm; For increasing the speed of image segmentation, this paper puts forward automatic layering algorithm combined with color information. Finally, this paper puts forward automatic vector quantization algorithm based on satellite cloud images and we developed an automatic vector quantization prototype system of satellite cloud images. The research results suggest that our automatic vector quantization algorithm has satellite cloud information automatic extraction function, identification function and vector quantization function.

The quality of wireless media is described by three parameters. These parameters are its transmission range, transmission rate and reliability. In the conventional OFDM systems one parameter can be increased on the cost of decreasing other two parameters. However by combining MIMO with OFDM systems, all the three parameters can be improved simultaneously. Symbol detection and channel estimation are the two essential tasks of MIMO-OFDM system. These tasks can be excellently achieved by various other recently developed algorithms such as maximum likelihood (ML) detector, LMS, RLS etc. All these algorithms face a common problem of robustness. Also the complexity of these algorithms is very high in the system with large number of transmitters and receivers and having large constellation size. Therefore, we are using the NLMS estimator. But it doesn’t provide the optimal solution. Genetic algorithm has the advantages of significantly less computational complexity, greater robustness and is closer to the optimal solution. Hence in this paper we are using Genetic algorithm based NLMS estimator to accomplish these tasks and to achieve results near to optimal solution. Comparisons between the results obtain from GA optimized NLMS estimator and plane NLMS estimator has been shown for better understanding.

In this paper, delay-limited transmission of Quasi-stationary sources over block fading channels is considered. Considering distortion outage probability as the performance measure, two source and channel coding schemes with power adaptive transmission are presented. The delay limited transmission of a quasi-stationary source over a block fading channel is from the perspective of source and channel coding design and it utilizes performance scaling laws. Because In a Quasi -stationary sources time utilization takes a major role. For that the first one is optimized for fixed rate transmission, and hence enjoys simplicity of implementation. The second one is a high performance scheme, which also benefits from optimized rate adaptation with respect to source and channel states. In high SNR regime, the performance scaling laws in terms of outage distortion exponent and asymptotic outage distortion gain are derived, where two schemes with fixed transmission power and adaptive or optimized fixed rates are considered as benchmarks for comparisons. Various analytical and numerical results are provided which demonstrate a superior performance for source and channel optimized rate and power adaptive scheme.

Aiming at solving the fusion problem of multimodal medical images, a novel medical image fusion algorithm is present in this paper. The multiwavelet is combined with the NSDFB to construct the proposed Multi-NSDFB transform. The source images can be decomposed into several lowpass coefficient and highpass coefficient. And all coefficients can be decomposed into four direction subbands. The modified spatial frequency is adopted to motivate the pulse coupled neural network to select the every direction subbands coefficients. Experiment results demonstrate that the proposed algorithm can not only extract more important visual information from source images, but also effectively avoid the introduction of artificial information. The present scheme outperforms the redundant discrete wavelet transform-based, and the Ripplet transform-based in terms of both visual quality and objective evaluation.

A switching-based SVPWM control method for three-phase active power filter is proposed in this paper. First, error model of the switched system is established. And then, according to the space voltage vector diagram and three-phase supply voltage waveforms, the supply voltage is divided into six sectors. In each sector, a combination of switching subsystem is set up according to the requirement of quadratic stability condition. Finally, the common Lyapunov function is selected and therefore, the subsystem is determined, which has minimum derivative of Lyapunov function and meets the conditions above. The validity of the proposed control method is proved with Matlab/Simulink and experimental results.

In order to minimize the system impairments produced as a result of repeater circuits, optical amplifiers are utilized in wave division multiplexed (WDM) systems these days, which are capable of amplifying complete WDM signal in optical domain directly. Although WDM systems are capable of transmitting data at high data rates still, it faces many serious problems which degrade the performance of the optical transmission system such as four-wave mixing (FWM). In this paper, the effect of channel spacing in WDM systems and chromatic dispersion coefficient value on optical transmission system’s performance in terms of eye diagrams and input-output spectrums is discussed. The outcomes of the simulations show that by using unequal channel spacing FWM is reduced and it can be further reduced by increasing the value of dispersion coefficient.

A blind separation algorithm was proposed for the type of permuted alias images with morphological diversity in this paper. Firstly, permuted alias image mode in sparse domain was presented and separation scheme based on sparse domain was proposed. Non-subsampled Contourlet transform and Waveatom transform are respectively used as two type of dictionaries for piecewise cartoon and texture image. Then the permuting image can be separated from permuted alias image by morphological component analysis algorithm. The results show that our algorithm can separate effectively texture image from the permuted alias image not being affected by size, location number and types of texture image for a permuted alias image composed by piecewise smooth part and texture part.

In this paper 40 Gbps DWDM system is implemented in presence of FWM and chromatic dispersion under the impact of equal and unequal channel spacing. Dispersion is mitigated by using dispersion compensation fibre. An analysis based on comparison of DWDM system is carried out with equal channel spacing of 0.2nm, 0.3nm, 0.4nm, 0.8nm and 1.6nm with a distance of 680km. System is also analyzed for unequal channel spacing of 0.27nm, 0.275nm, 0.28nm and 0.4nm, 0.45nm and 0.5nm among four channels.

Because the traditional partial differential exist image blur, information read errors and large error defects, this thesis proposes the image registration of differential equations. This theory is based on differential equations to classify different classification, to build the model for different types for differential equations based on the application of image registration, and finally to take simulation experiments for different application of differential equations according to different images. Simulation results show that: the model of partial differential equations in this paper is easier to extract and recognize image information extraction, so it is good for reading, but also more clearly and accurately to achieve an effective registration.

As one of the most important video compression technologies, inter prediction coding is highly efficient in reducing the temporal redundancy of video sequence. However, complicated inter prediction for the latest High Efficiency Video Coding standard (HEVC) brings high computational complexity and seriously restricts the encoding speed. In this paper, a fast inter prediction algorithm based on Rate-Distortion (RD) cost is proposed to improve inter prediction of HEVC. First, the splitting of Largest Coding Unit (LCU) is determined according to the RD costs with best Coding Unit (CU) size being 64x64 in the reference picture. Then, for other CUs in lower depths, the comparable RD costs are selected from encoded CUs in the same depth at the same Coding Tree Unit (CTU) based on the local homogeneity in spatial domain. By comparing the RD cost of current CU with its corresponding RD threshold, the splitting is terminated in advance. In this way, the proposed fast inter prediction algorithm can avoid the traversal of all CUs in the coding tree structure and improve the encoding speed. Experimental results show that the algorithm can save about 30% encoding time on the basis of ensuring visual quality and compression ratio of videos. Therefore, the computational complexity can be reduced greatly.

In this paper, the performance analysis of 8X10 Gbps WDM system for different data formats has been done. The performance is analyzed for Non return-to-zero (NRZ) and Return-to-Zero (RZ) modulation formats to find maximum transmission distance for 80Gbps WDM system. It has been found that for NRZ the system travel transmission distance of 962 km and for RZ the system travel transmission distance of 1203 km. The total of eight channels with data rate of 10Gbps and equal channel spacing of 300 GHZ has been taken.

Electrical capacitance tomography (ECT) is an effective technique for visualizing the distribution of dielectric materials inside a closed pipe or vessel. The design of an ECT system based on LabVIEW is described in this paper. The system can be divided into three parts: sensor system, data acquisition system, and imaging computer. The data acquisition system designed takes C8051F700 MCU as the core to acquire capacitance data. The computer implements the functions such as data processing, image reconstruction, image display and system control. Serial communication is used between the data acquisition system and the computer. LabVIEW, a graphical programming language, is chosen as the development environment. An optimal thresholding scheme is proposed which can reduce low grey-level artifacts effectively. Equipped with a classic ECT sensor or a direct 3D ECT sensor, the system designed can work in 2D reconstruction mode or direct 3D reconstruction mode. The software design in LabVIEW is focused on.

The main drawback of the phase congruency feature employed in the feature similarity index (FSIM) image quality assessment (IQA) algorithm is its low computational efficiency. In this paper, a novel fast feature similarity index (FFSIM) for image quality assessment is proposed. Based on the fact that human visual system (HVS) responds to the brightness stimulus mainly complying with Weber's law, the proposed FFSIM only performs spatial filtering to quickly calculate the contrast between the current pixel and its background, which is used to compute Weber visual salience similarity and a weighting coefficient in pooling stage after applied nonlinear mapping. Weber contrast and the gradient magnitude play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use Weber weighting coefficient again as a weighting coefficient to derive a single quality score. As such, the multi-scale version of the FFSIM algorithm, i.e., MS-FFSIM is also proposed, which complies with the spatial frequency response characteristics of the HVS system. Extensive experiments performed on six publicly available IQA databases demonstrate that the proposed FFSIM and MS-FFSIM can achieve higher consistency with the subjective evaluations than state-of-the-art IQA metrics and the computational efficiency is greatly improved as well.

A non-constant envelope with high peaks is a main disadvantage of Orthogonal Frequency Division Multiplexing (OFDM). These high peaks produce signal excursions into non-linear region of operation of the Power Amplifier (PA) at the transmitter, thereby leading to non-linear distortions and spectral spreading. Many Peak to Average Power Ration (PAPR) reductions methods have been proposed in the literature. The objective of this review is to give a clear understanding of different techniques to reduce PAPR of the signal.

Organic Light Emitting Diode (OLED) displays have matured into current smartphones. How to prolong the lifetime of displays while preserving the display quality becomes a primary issue. In this paper, we propose a low image distortion constrained power-saving approach for OLED displays based on gamma correction and saturation scaling. We first investigate the impact of gamma correction and saturation scaling on the power of emissive displays. The results show that changing the gamma and saturation value can obtain lower display power consumption when original image color maps to another one. Thus, we integrate the gamma correction and the saturation scaling into a new low-power approach for OLED displays. However, low gamma and high saturation lead to distortion on displaying. To guarantee user experience in this paper, the CIEDE2000 color difference formula and the Mean Structural Similarity Index (MSSIM) are used to evaluate the effectiveness of our approach. The results show that our approach saves up significant display power with high image quality.

Image quality assessment (IQA) is crucial in image processing algorithms. In the state-of-the-art IQA index, the structural similarity (SSIM) index has been proved to be better objective quality assessment metric. However, the accuracy of SSIM is relatively lacking when used to access blurred images. And the component weights of structural similarity (SSIM) index are fixed in some past environments. So an improved assessment algorithm incorporating multiple linear regressions and SSIM index was proposed in this paper. We use regression analysis to adjust the component weight of SSIM index. So the improved algorithm is more accuracy on different distortion types’ quality assessment. Experimental results show that the improved SSIM algorithm is better than traditional methods in nonlinear regression correlation coefficient, Spearman correlation coefficient and out ratio.

In Digital image processing; many researches have been done on image denoising so far. Nowadays, the noise detection from an image is the most challenging task. Though, the various algorithms introduced for the detection of noise type from a noisy image, but these algorithms work only for detection of single type of noise. To overcome the limitation of the previous built algorithms, we investigate the data mining technique called Support Vector Machine. The SVM is a powerful supervised learning method which is to be used for the detection of mixed noise models. Broadly, this technique detects the different types of noise from a mixed noise image; noise can be either single or mixed type of noise. The different parameters have combined to describe the properties of these different noise models so as to perform the detection. The detecting algorithm has been achieved by applying the SVM on the training dataset of different medical images and further extensive tests are performed on the test dataset for detection of each noise type model. This detection technique clearly outperforms various techniques with the high accuracy of results for different proposed noise models.

We focus on the issue of learning the topology of the non-overlapping multi-camera network, which includes recovering the nodes (entry and exit zones), transition time distribution and links. Firstly, the nodes associated with each camera view are identified using clustering method. Then, transition time distribution is modeled as a Gaussian distribution and is computed by accumulated cross correlation and Gaussian fitting. Finally, the mutual information is used to refine the possible links and the topology is recovered. Experimental results on simulated data and real scene demonstrate the effectiveness of the proposed method.

Aiming at the problem of the dim target real-time detection in Infrared search and track (IRST) systems, a fusion detection algorithm for infrared dim target based on temporal-spatial domain accumulation and difference is proposed. The proposed algorithm suppresses background and enhances targets by template filtering and difference processing in space domain, and the image sequences are processed by multi-frame energy accumulation and frame difference methods according to the target’s moving characteristics in time domain. After the fusion segmentation of the temporal and spatial processing results with definite rule, the target can be detected by the relation of the possible target’s position in adjoining frames according to target’s moving continuity and regularity. Experimental results show that the proposed algorithm enormously increases the target's SNR after the temporal-spatial fusion enhancement, and it has high detection probability and high detecting speed. In the meantime, the algorithm is easy to realize in hardware and can be applied effectively in the real-time target detection of the IRST systems.

Steganography is the science of hidden writing schemes in which the presence of concealed information is not noticeable. It is a gifted approach for secure electronic communication and transmission of secret data safely over the internet. This paper is the detailed experimental analysis and review of “Grey Level Modification Steganography for Secret Communication”. Analysis is based on popular image quality measures, security analysis, visual analysis, bit plane analysis and worst case situation. Experimental result of selected method of steganogrphgy is compared with the basic Least Significant Bit substitution and Least Significant Bit matching method of steganography. All these results are checked for both the color and grayscale images. Moreover this review and analysis could be a deep understanding of most popular and simple steganography method such as Grey Level Modification, Least Significant Bit substitution, Least Significant Bit matching etc and will be a helpful analysis for presenting new approaches in this field.

Features for representing the target are the fundamental ingredient when constructing the appearance model in the tracking problem. Only one type of features is utilized to represent the target in most current algorithms. However, the limited representation of a single feature might not resist all appearance changes of the target during the tracking process. To cope with this problem, we propose a novel tracking algorithm - Mean Kernel Tracker (MKT) - to robustly locate the object. The MKT combines three complementary features - Color, HOG (Histogram of Oriented Gradient) and LBP (Local Binary Pattern) - to represent the target. And Extensive experiments on public benchmark sequences show MKT performs favorably against several state-of-the-art algorithms.

Feature dimension reduction is important for speech emotion recognition. The classical linear discriminant analysis has been used widely in this field, but the best projection separating class from others can’t be obtained with the linear discriminant analysis method due to outlier class. To approach this problem, a novel distance weighted function based on the linear discriminant analysis is introduced, which can improve the separability of sample data and has low computational complexity. In order to evaluate the proposed algorithm’s performance, some experiments are performed on two speech databases: UCI and CASIA. Experimental results on the UCI database demonstrate that the presented algorithm has a better performance. Experimental results on CASIA show that the proposed algorithm yields an average accuracy of 88.78% in the classification of four emotions, revealing that it is a better choice as feature dimension reduction for emotion classification than the traditional algorithms.

Transmissibility function (TF) was used in structural state detection which examined the change of the response-only vibration characteristics. The technology of signal correlation was utilized to obtain the TF between two testing points. Multiple TFs under different states were calculated as basic TFs and formed a state matrix. Subsequently, non-negative matrix factorization (NMF) was performed for the state matrix. Then both the basic and testing TFs were projected to the feature subspace derived from NMF so as to obtain the state feature index vectors, respectively. Finally, the Euclidean distance between state feature index vectors was defined as the state indicator. The experimental results indicated that this method can achieve better detection accuracy than that using magnitude indicator. Actually, a result with 100 percent correct detection was achieved when proper rule of dimensionality reduction was selected. This method is essentially a multivariate statistical process monitoring (MSPM) method. It is feasible in vibration-based structural state detection in the situation where the excitation signals are unavailable or inaccessible.

The electrocardiogram (ECG) signal processing plays an important role in diagnosing cardiopathy. Sparse decomposition provides a new tool for ECG signal processing. Matching pursuit (MP) algorithm uses overcomplete dictionary to decompose the signal, so it can reflect the properties of the signal. In decomposition process, searching a best atom of the dictionary is an optimal problem; Genetic Algorithm (GA) is used to solve this problem. This paper proposed an improved MP algorithm based on GA to denoise the ECG signal, whose dictionary is composed of Gabor atoms. The experiment simulation results show that the proposed method can get a good denosing effect in a shorter time than not use GA algorithm.