An energy conduction model for cell image segmentation

Cell image segmentation is an essential step in cytopathological analysis.Although their execution speed is fast,the results of cell image segmentation by conventional pixel-based,edge-based and continuity-based methods are often coarse.Fine structures in a cell image can be obtained with a method that quickly adjusts the threshold levels.However,the processing time of such a method is usually long and the final results may be sensitive to intensity differences and other factors.In this article,a new energy model is proposed that synthesizes a differential equation from the conventional and level set methods,and utilizes the nonuniformity property of cell images (e.g.cytoplasms are more uneven than the background).The feasibility and robustness of the proposed model was demonstrated by processing relatively complicated background images of both simulated and real cell images.     

A new model for analyzing laminar forced convective enhanced heat transfer in latent functionally thermal fluid

In this paper, a new model to analyze laminar forced convective enhanced heat transfer in latent functionally thermal fluid is developed. The main characteristics of the model are- i) a new formula of the specific heat at constant pressure is used; ii ) a real heat transfer process is considered; that is, heat transfer processes occur not only between working fluid and microcapsules, but also between the mixture and tube wall; iii) the new method, which combines the newly developed axisymmetrical dual reciprocity boundary element method (DRBEM) with finite difference method (FDM), is used to solve the control equations of this problem. The new model is validated by experimental data. Some new physical results on the variational characteristics of the specific heat at constant pressure with space and time during phase-change process, the time-marching history of the phase-change interfaces and so on are obtained. Several main physical factors that affect enhanced heat transfer in latent functionally thermal fluid are numerically analyzed. Some new understandings for the mechanism of enhanced heat transfer in the functionally fluid are obtained.     

An eSnake model for medical image segmentation

A novel scheme of external force for detecting the object boundary of medical image based on Snakes (active contours) is introduced in the paper. In our new method, an electrostatic field on a template plane above the original image plane is designed to form the map of the external force. Compared with the method of Gradient Vector Flow (GVF), our approach has clear physical meanings. It has stronger ability to conform to boundary concavities, is simple to implement, and reliable for shape segmenting. Additionally, our method has larger capture range for the external force and is useful for medical image preprocessing in various applications. Finally, by adding the balloon force to the electrostatic field model, our Snake is able to represent long tube-like shapes or shapes with significant protrusions or bifurcations, and it has the specialty to prevent Snake leaking from large gaps on image edge by using a two-stage segmentation technique introduced in this paper. The test of our models proves that our methods are robust, precise in medical image segmentation.     

A New Mean Reversion Model of Close-End Fund

On the basis of fractal theory, one of the nonlinear theories, this paper studies the validity of Chinese fund market fractal time sequence through Hurst exponent, calculates the H value and proposes a new close-end fund mean reversion model. Meanwhile, this paper validates the mean reversion time sequence for consecutive 54 week data of fund market. The result indicates that this model can effectively prove that Chinese close-end fund market follows the biased random walk. The research also proves that the fund discount does have mean reversion tendency and averagely the fund with high discount has a higher excess yield than that of the fund with low discount. The mean excess yield and the ratio between discount rate deviation and standard deviation demonstrate a descending relationship. The optimum investment period based on ＂mean reversion＂ is one month. Consequently this model provides a new arbitrage method through the discount of close-end fund.     

Segmentation of tumor ultrasound image via region-based Ncut method

To segment the tumor region precisely is a prerequisite for ultrasound navigation and treatment. In this paper, a normalized cut method to segment tumor ultrasound image is proposed by means of simple linear iterative clustering for presegmentation procedure. The first step, we use simple linear iterative clustering algorithm to divide the image into a number of homogeneous over-segmented regions. Then, these regions are regarded as nodes, and a similarity matrix is constructed by comparing the histograms of each two regions. Finally, we apply the Ncut method to merging the over-segmented regions, then the image segmentation process is completed. The results show that the proposed segmentation scheme handles the strong speckle noise, low contrast, and weak edges well in ultrasound image. Our method has high segmentation precision and computation efficiency than the pixel-based Ncut method.     

Weighted Variational Minimization Model for Wavelet Domain Inpainting with Primal-Dual Method

To preserve the edges and details of the image,a new variational model for wavelet domain inpainting was proposed which contained a non-convex regularizer. The non-convex regularizer can utilize the local information of image and perform better than those usual convex ones. In addition, to solve the non-convex minimization problem,an iterative reweighted method and a primaldual method were designed. The numerical experiments show that the new model not only gets better visual effects but also obtains higher signal to noise ratio than the recent method.     

Parallel scheduling strategy of web-based spatial computing tasks in multi-core environment

In order to improve the concurrent access performance of the web-based spatial computing system in cluster,a parallel scheduling strategy based on the multi-core environment is proposed,which includes two levels of parallel processing mechanisms.One is that it can evenly allocate tasks to each server node in the cluster and the other is that it can implement the load balancing inside a server node.Based on the strategy,a new web-based spatial computing model is designed in this paper,in which,a task response ratio calculation method,a request queue buffer mechanism and a thread scheduling strategy are focused on.Experimental results show that the new model can fully use the multi-core computing advantage of each server node in the concurrent access environment and improve the average hits per second,average I/O Hits,CPU utilization and throughput.Using speed-up ratio to analyze the traditional model and the new one,the result shows that the new model has the best performance.The performance of the multi-core server nodes in the cluster is optimized; the resource utilization and the parallel processing capabilities are enhanced.The more CPU cores you have,the higher parallel processing capabilities will be obtained.     

Image segmentation based on Mumford-Shah functional

In this paper, the authors propose a new model for active contours segmentation in a given image, based on Mumford-Shah functional (Mumford and Shah, 1989). The model is composed of a system of differential and integral equations. By the experimental results we can keep the advantages of Chan and Vese‘s model (Chan and Vese, 2001 ) and avoid the regularization for Dirac function. More importantly, in theory we prove that the system has a unique viscosity solution.     

Multipole BEM for 3-D Elasto-Plastic Contact with Friction

The analysis of 3-D elasto-plastic contact with friction is a highly nonlinear problem. The elements in the contact and plastic zones should be refined to obtain accurate information about the real size,displacement, and traction in the contact zone. However, the increase in the number of degrees of freedom is limited when traditional boundary element method (BEM) is used with the larger memory size and long CPU time required for the solution procedure. This paper describes the additional mathematical friction model to the 3-D elastic multipole BEM to develop a 3-D elasto-plastic contact multipole BEM. Numerical tests show that with this new method, the needed computer memory size is only 2% of the traditional BEM model with friction, which erases large-scale computing with refined meshes and improves the computational accuracy.     

A Fast Algorithm for GVF Snake Model

A research on difference scheme of image gravitational field in the GVF snake model is performed depending on the uniform stability and convergence conditions of the difference scheme.It is found that the original explicit forward difference scheme puts a strict restriction on the diffusion coefficient in the partial differential equation which decelerates the convergence speed of difference equation iteration.A new difference scheme is put forward,which has the advantage of unconditional uniform stability and convergence,and the restriction on the coefficient of partial differential equation is removed.Through increasing the value of the coefficient appropriately,the image of boundary information transmission becomes faster.Hence,iteration calculations are decreased rapidly for a given transmission range.The simulation experiments indicate that the new difference scheme be higher efficiency than the traditional one.     

Gender Recognition with Face Images Based on Partially Connected Neural Evolutionary

In this paper,a new type of neural network model - Partially Connected Neural Evolutionary (PARCONE) was introduced to recognize a face gender. The neural network has a mesh structure in which each neuron didn't connect to all other neurons but maintain a fixed number of connections with other neurons. In training,the evolutionary computation method was used to improve the neural network performance by change the connection neurons and its connection weights. With this new model,no feature extraction is needed and all of the pixels of a sample image can be used as the inputs of the neural network. The gender recognition experiment was made on 490 face images (245 females and 245 males from Color FERET database),which include not only frontal faces but also the faces rotated from-40°-40° in the direction of horizontal. After 300-600 generations' evolution,the gender recognition rate,rejection rate and error rate of the positive examples respectively are 96.2%,1.1%,and 2.7%. Furthermore,a large-scale GPU parallel computing method was used to accelerate neural network training. The experimental results show that the new neural model has a better pattern recognition ability and may be applied to many other pattern recognitions which need a large amount of input information.     

Blurred Defocused Image Restoration Based on FRFT

A technique for restoring the blurred image resulted from defocusing of the lens is proposed in this paper, which is based on fractional Fourier transform （FRFT）.The FRFT, as a powerful tool for the analysis of time-varying signals, is closely connected with the optical imaging system. FRFT also can describe optical imaging process just like Fresnel diffractions, so a defocused imaging model based on FRFT is established to explain the blur phenomena of defocusing image. The defocused imaging model is greatly different from the traditional point spread function （PSF） model, and enables to uncover the blur nature of non-focus image. Then, an image restoration method using the novel model is proposed to handle the blurred defocused image. The method adopted a new iterative phase retrieval approach which can approximately estimate phase signals from intensity signals of a single defocused image by means of FRFT. Restoring image may acquire sharp image by implementing inverse FRFT on complex image signal made from the estimated phase signals and intensity signals. Experimental results demonstrate that the method is effective in restoring blurred defocused image.     

A Novel Personalized Web Search Model

A novel personalized Web search model is proposed. The new system, as a middleware between a user and a Web search engine, is set up on the client machine. It can learn a user＇s preference implicitly and then generate the user profile automatically. When the user inputs query keywords, the system can automatically generate a few personalized expansion words by computing the term-term associations according to the current user profile, and then these words together with the query keywords are submitted to a popular search engine such as Yahoo or Google. These expansion words help to express accurately the user＇s search intention. The new Web search model can make a common search engine personalized, that is, the search engine can return different search results to different users who input the same keywords. The experimental results show the feasibility and applicability of the presented work.     

Fast Visual Modeling for Plant Based on Real Images

Structures of natural plants are complex and difficult to model. This paper proposes a fast visual modeling for plants based on a small set of images, and establishes a reasonable plant model.Based on knowledge about growth patterns of the plant, image segmentation and 3D reconstruction are first performed to construct the plant skeleton (trunk and major branches), from which the remainder of the plant grows. Then the system produces the realistic plant model images based on image synthesis and validation. It is unnecessary to acquire the complex structure (such as the complex production rules of L-systems). The method provides a high degree of control over the final shape by image validation,resulting in realistic reconstruction.     

Fuzzy entropy image segmentation based on particle swarm optimization

Particle swarm optimization is a stochastic global optimization algorithm that is based on swarm intelligence. Because of its excellent performance, particle swarm optimization is introduced into fuzzy entropy image segmentation to select the optimal fuzzy parameter combination and fuzzy threshold adaptively. In this study, the particles in the swarm are constructed and the swarm search strategy is proposed to meet the needs of the segmentation application. Then fuzzy entropy image segmentation based on particle swarm optimization is implemented and the proposed method obtains satisfactory results in the segmentation experiments. Compared with the exhaustive search method, particle swarm optimization can give the same optimal fuzzy parameter combination and fuzzy threshold while needing less search time in the segmentation experiments and also has good search stability in the repeated experiments. Therefore, fuzzy entropy image segmentation based on particle swarm optimization is an efficient and promising segmentation method.     

EMG oscillator model-based energy kernel method for characterizing muscle intrinsic property under isometric contraction

This paper presents a new method for estimating the isometric contraction force and the characterization of muscle’s intrinsic property.The method,called the energy kernel method,starts with converting the electromyography(EMG)signal into planar phase portraits,on which the elliptic distribution of the state points is named as the energy kernel,while that formed by the noise signal is called the noise kernel.Based on such stochastic features of the phase portraits,we approximate the EMG signal within a rectangular window as a harmonic oscillator(EMG oscillator).The study establishes the relationship between the energy of control signal(EMG)and that of output signal(force/power),and a characteristic energy is proposed to estimate the muscle force.On the other hand,the natural frequencies of the noise and the EMG signal can be attained with the energy kernel and noise kernel.In this way,the direct signal–noise recognition and separation can be accomplished.The results show that the representativeness of the characteristic energy toward the force is satisfactory,and the method is very robust since it combines the advantages of both RMS and MPF.Moreover,the natural frequency of the EMG oscillator is not governed by the MU firing rate of a specific muscle,indicating that this frequency correlates with the intrinsic property of muscle.The physical meanings of the model provide new insights into the understanding of EMG.     

Robust Threshold Guillou-Quisquater Signature Scheme

Tie deficiencies of the first threshold Guillou-Quisquater signature scheme presented by Li San Liu, Cheng-Kang Chu and Wen-Guey Tzeng are analysised at first, and then a new threshold Guillou-Quisquater signature scheme is presented. The new scheme is unforgeable and robust against any adaptive adversary if the base Guillou-Quisquater signature scheme is unforgeable under the chosen message attack and computing the discrete logarithm modulo a prime is hard. This scheme can also achieve optimal resilience. However, the new scheme does not need the assumption that N is the product of two safe primes. The basic signature scheme underlying the new scheme is exactly Guillot-Quisquater signature scheme, and the additional strong computation assumption introduced by the first threshold Guillou-Quisquater scheme is weaken.     

MECHANICAL PROPERTIES OF CHLORINATED POLYPROPYLENE——POLYPYRROLE FILM

The chlorinated polypropylene (PPC1)-polypyrrole (PPy) conducting films were successfully synthesized by a novel method for the first time. The mechanical properties of PPC1-PPy conducting films were investigated by means of stress-strain test and DMA. It was shown that the rupture strength and initial modulus increase with the increase of polypyrrole content , but the elongation at rupture decrease. Besides, the glass transition temperature (Tg) is increasing to a plateau when polypyrrole content attains 6%. Moreover, a new mechanical model to describe the mechanical properties of PPC1-PPy blend system is established by changing the correction factor in Mooney model. It was found that the theoretic value of the model is well coincident with the experimental results.     

Adaptive Contour Model for Real-Time Foreground Detection

A multiscale foreground detection method was developed to segment moving objects from a stationary background. The algorithm is based on a fixed-mesh-based contour model, which starts at the bounding box of the difference map between an input image and its background and ends at a final contour.An adaptive algorithm was developed to calculate an appropriate energy threshold to control the contours to identify the foreground silhouettes. Experiments show that this method more successfully ignores the negative influence of image noise to obtain an accurate foreground map than other foreground detection algorithms. Most shadow pixels are also eliminated by this method.     

Prediction of Solid-Liquid Equilibrium for KCI in Mixed Water-Ethanol Solutions Using the LIQUAC Model

The LIQUAC model is often used to predict vapor-liquid equilibria, osmotic coefficients, and mean ion activity coefficients for electrolyte systems. This paper describes a thermodynamic method to analyze solid-liquid equilibrium for electrolytes in mixed solvents solutions using the LIQUAC model. The KCI solubilities in mixed water-ethanol solutions are predicted with the LIQUAC model and its original interaction parameters. This method is also used to obtain new K^＋-ethanol interaction parameters in the LIQUAC model from the solubility data. The new interaction parameters accurately predict the vapor-liquid equilibrium data of K^＋ salts （including KCI, KBr, and KCOOCH3） in mixed water-ethanol solutions. The results illustrate the flexibility of the LIQUAC model which can predict not only vapor-liquid equilibrium but also solid-liquid equilibrium in mixed solvent systems.