Abstract: A 3D layered fabrication process based onMultilevel imprint lithography (MIL) is proposed and thecritical issues involving template fabrication and overlayaccuracy were investigated. A fast and low-cost templatefabrication process based on glass wet etching is introduced, in which, the etching mask of Cr metal film is partially remained on top of the template pattern protrusionafter etching, which facilitates the residual resist removaland pattern quality improvement. An imprint tool withan alignment subsystem based on computer micro-visionis developed and the experimental results show an averageoverlay accuracy within 1.5μm with a standard deviationwithin 0.33μm. Finally, as an instance of the proposedmethod, the process based on MIL, electroplating and liftoff was investigated and tri-layer metallic structures with apattern feature size of 20μm were fabricated using the optimized process, which demonstrates the feasibility of thelayered fabrication method.
Abstract: Many works have examined the problemof receive buffer blocking which is brought by traditionalconcurrent multi-path transfer protocol. A new concurrentMulti-path transport mechanism named Wireless concurrent multi-path transfer SCTP protocol (WCMT-SCTP) isproposed in this paper. WCMT-SCTP is designed for solving the receive buffer blocking problem. It can bind thecertain data streams to certain paths and use multiple active paths to transfer data simultaneously. An analysis isgiven to demonstrate the performance of WCMT-SCTP,and simulations are conducted to show the performanceimprovement of WCMT-SCTP in terms of throughput andthe independence of data streams. It is also shown thatanalysis results are fitting well with the simulation results.
Abstract: The problem of SQO (Semi-join query optimization) in data-sharing environment is addressed anda Perimeter-search-based semi-join query optimization algorithm (PSSJA) is presented. The algorithm can determine optimal sequence of semi-join operations, minimizethe data transmission cost, saves storage space, and reducethe search complexity in a costeffective manner. Experiment shows the effciency of our algorithm.
Abstract: Data-driven function label assignment hasbeen studied for English using Penn Treebank data. In thispaper, we address the question of whether such methodscan be applied to other languages and Treebank resources.In addition to simple extension of previous method, wealso proposed an effective way for assigning function labels to languages that lack of suffcient parse resources.We investigated three machine-learning algorithms to testour method which achieved the best F-score of 93.76 ongold-standard PoS (Part-of-speech) tagged Chinese text a statistically significant improvement over existing Chinese function labeling systems.
Abstract: A FCC-compliant ultra low-power lowcost 5th-derivative Gaussian Pulse generator (PG) for full3.1-10.6GHz Impulse-radio (IR) carrier-free Ultra wideband (UWB) transceiver was designed and fabricated infoundry 0.131m CMOS. This PG integrates three cascadestages to generate square wave, Gaussian pulse and 5thorder Gaussian derivative waveform, respectively. Measurement shows the lowest reported power consumption of5.1pJ/pulse at 100MHz Pulse repeating frequency (PRF),the smallest die area of only 0.02mm2, short 5th-derivativeGaussian pulse width of 770ps and peak to peak amplitudeof 66mV. The FCC-compliant IR-UWB pulse generatorpotentially allows up to 1.3Gbps throughput for low-costlow-power hi-QoS wireless multimedia and video streaming.
Abstract: MB1 is a very promising steganographyand few steganalysis methods have been proposed to attackMB1 so far. In this paper, we propose a specific steganalysis method which can effectively break MB1 steganography. We draw some useful propositions based on featurewhich is most sensitive to MB1, and propose an effectiveMB1 steganalysis method. This method outperforms theexisting MB1 steganalysis method in detection reliability,especially in low embedding rate case. What's more, forthe first time, our method also can estimate the embeddingrate of MB1 accurately. Experimental results show thatthe MB1 steganography has been successfully attacked byour method.
Abstract: High quality GaN nanocrystalline has beenprepared by sol-gel method. The results of X-ray diffraction (XRD), Selected-area electron diffraction (SAED)and High-resolution transmission electron microscopy(HRTEM) measurement indicate that as-prepared sampleis single crystalline GaN with wurtzite structure. Transmission electron microscopy (TEM) displays that the diameters of the grains of GaN nanocrystalline change from30nm to 100nm. X-ray photoelectron spectroscopy (XPS)confirms the formation of the bond between Ga and Nin the sample, and IR spectrum measurement showedE1 (TO) vibrational modes at 570cm-1. Vibrational frequency of GaN small clusters has been calculated usingDensity functional theory (DFT). Using the result, IRspectrum of the sample has been analyzed further.
Abstract: We presented a novel interactive geneticalgorithm to effectively characterize a user's cognition onthe evaluated objects and the stochastic phenomena in theevolutionary process. A fuzzy number is adopted to express an individual's fitness to reflect a user's cognition,and a stochastic variable with normal distribution to depict the stochastic behavior in the fitness based on the error theory. The fuzzy number and the stochastic variableare transformed into different intervals with ? cut set leveland confidence level, respectively, and different individuals in the same generation are compared based on intervaldominance. The values of ? and stochastic parameter aredetermined according to the uncertainty degree of a user'scognition which is quantitatively described with the fuzzyentropy of a fuzzy number. Finally, the algorithm wasapplied to a fashion evolutionary design system and theexperimental results show its rationality and effciency.
Abstract: In this paper, we show that the optimalmakespan of the flow shop problem grows as O(n) whenthe size of the problem is large enough. And two lowerbounds with performance guarantee are presented to dealwith the objectives of minimizing respectively makespanand total weighted completion time. At the end of the paper, the effectiveness of the two lower bounds is showed bycomputational results.
Abstract: A novel macro-model for ESD circuit simulation with only five fitting parameters is proposed. Inthis model a new topology and a new multiplication factorequation are proposed as well as the extracting method.This modeling approach greatly reduces time and effortrequired for circuit design while making use of GGNMOS(Gate-grounded NMOS) as ESD (Electrostatic discharge)protection, which is widely used for integrated circuitsto protect IOs and power rails. The DC characteristicsof GGNMOS and transient behavior of GGNMOS underHBM (Human body model) stress are simulated using bothour macro-model and two-dimensional device simulator,Taurus (Synopsys). Good agreement has been obtained.
Abstract: Delay tolerant networks may become unexpectedly partitioned due to node mobility or variationin signal strength. However, most widely used models insome relative works are generally very simplistic. In orderto exploit intelligent forwarding algorithms, a novel nodalmobile model of delay tolerant networks is presented tomap reality with more accuracy. And several approachesare introduced to analyze the network structure, such asn-cliques, n-clans, degree, closeness and betweenness. Ourresearch results showed that the centralizations becamesmaller when the wireless connections were concerned. Itmeant that quite a number of nodes became potential relays because of the new structure of networks.
Abstract: In order to control the complexity of H.264encoder, we first proposed two effcient Complexity allocation and control (CAC) algorithms for both intra andinter coding respectively, based on a new Rate-distortioncomplexity optimization (RDCO) framework and the rearrangement of candidate modes according to the local-edgesand texture analysis. The CAC algorithms can be performed both at the frame level and the Macroblock (MB)level; then, within the proposed CAC algorithms, a wholecomplexity scalable control algorithm was given. Experimental results show that our proposed algorithm can makean appropriate cut-off point for the candidate modes sequence adaptively according to current energy conditionof a mobile device, so as to adjust the complexity at anytarget levels with minimum degradation in video quality.This can prolong the operational lifetime of the battery forhand-held devices.
Abstract: Our study tried to deal with a gene expression problem from the view of factor analysis. In order toovercome the instability problem caused by using traditional Independent component analysis (ICA), an ensemble classifier of DNA microarray data based on a selectiveICA method was proposed. At first, we analyzed the reconstruction error of each gene and selected a subset of independent components, which contributed relatively smallreconstruction errors, to reconstruct new samples. Afterthat, several Support vector machine (SVM) sub-classifierswere trained simultaneously. Finally, the best SVM subclassifiers with high correct rates were selected to participate in the ensemble, using a majority voting method. Results on three publicly available DNA microarray datasetsshow the feasibility and validity of our proposed method.
Abstract: The methods of manifold dimensionalityreduction proposed recently are just for single dimensionaldata and signals. So this paper proposed the general framework of Clifford manifold learning, and solved the problem of relationship between different dimensional signaland data using eigenmapping in local coordinate. We alsomentioned the nonlinear dimensionality reduction analysesmethod based on Clifford algebra, and established the homogeneous analyses model for Clifford nonlinear manifoldwith hybrid dimensional signals. The experiment and comparison proved the effciency of our method for nonlineardimensionality reduction of hybrid dimensional signals.
Abstract: The belief function theory can effectivelymodel the belief induced by uncertain evidence. Withinits framework, when new pieces of evidence are available,belief is revised by Dempster rule of combination. Sincethe Dempster rule cannot determine the weight of focusing downwards and would lead to a counterintuitive resultfor combining high conflicting beliefs, its applicability isrestricted. A large number of alternative rules have beenproposed to solve these problems. We suggest some properties that a good rule should possess, and then a newrule is presented in this paper. This rule can determinethe weight of focusing downwards according to focal elements' cardinalities, and redistribute incompatible beliefmass proportionally to the subsets involved in the combination. The properties of our rule are clarified and comparedwith those of some other rules through several numericalexamples, which shows its superiority.
Abstract: There are a lot of time series in many fields,especially, the long memory time series, and one of maintasks to research them is how to estimate corresponding series parameters. There exist some current methods, suchas the Traditional maximum likelihood estimation (TMLE)and Least square estimation (LSE) etc., but the huge computation burden is always a bottleneck to utilize thembroadly in many applications. To overcome this diffculty,two new parameter estimation method, named identicallyMultiscale maximum likelihood estimation (MMLE), areproposed by combining Discrete wavelet transform (DWT)and Discrete wavelet package transform (DWPT) with theTMLE in this paper, respectively. These primary ideasare all as follows, firstly, applying the DWT/DWPT tothese time series, which possess of some good properties,such as orthogonal decomposition and decorrelation; secondly, analyzing the time series in a multiscale domain, andstudying their statistical properties in different scale, suchas mean, variance and covariance. These new algorithmscan effectively decrease computation complexity and obtain satisfying estimation precision illustrated by the dataanalysis and computer simulation.
Abstract: We propose a watermarking scheme basedon the multi-channel watermarking framework, which ismainly used to resist geometric attacks. This watermarking scheme generates watermarking templates from oneimage transform domain which is obtained after DWT(Discrete wavelet transform) and DFT (Discrete Fouriertransform) transformation to one of image channels andthen embeds these watermarking templates into anotherimage transformation domain which is obtained after DWTand DFT transformation to another image channel. Whenthe watermarking image undergoes geometric attacks,self-synchronization can be obtained in the procedure ofdetecting watermarking, because the geometric attackswhich watermarking template and watermarking image undergo are similar. Experimental results demonstrate thatthis watermarking scheme produced by the multi-channelframework is high robust to attacks such as geometric distortion, rotation, affne, JPEG compress and adding noiseetc..
Abstract: A low-complexity algorithm for Directionof arrival (DOA) estimation based on Conjugate gradient(CG) method is proposed in this paper. The orthogonalresidual vectors in CG method span the signal subspaceemployed by the Multiple signal classification (MUISC)spectrum. The computational complexity of the proposedalgorithm is greatly reduced, since it does not involve theestimation of the covariance matrix of observation dataand Eigenvalue decomposition (EVD). Besides, a new criterion for the dimension estimation of the signal subspaceis also proposed, which can significantly overcome the leakbetween the signal subspace and noise subspace in theabsence of the knowledge of source signal enumerationand desired signals. Simulation results are presented todemonstrate the approximate performance of the proposedmethod comparable to the traditional EVD method, andthe significant reduction in computational complexity dueto the use of finite conjugate iterations.
Abstract: Liver segmentation from Multi-slice spiral computed tomography (MSCT) is the key techniquein liver volume estimation, which is important for livercancer treatment. This paper proposes a fully automaticmethod to segment liver regions from MSCT. To beginwith, we took advantage of the correlations between neighbor slice images to get the initial fronts. Then a modifiedfast marching algorithm is applied to propagate the frontsuntil the stop criterion was satisfied. The areas includedby the propagated fronts were the liver regions. Completeliver regions of one case could be fetched from each sliceusing the similar approach one by one. Our method wastested on 562 slice images from 8 cases collected from a hospital in China. With the comparison of the doctors' manualsegmentation results, the difference percentage was 4.7%averagely with the maximum value 9.1% and the minimumone 1.4% respectively.
Abstract: Scalp Laplacian is the 2D Laplace operation on the scalp surface potential. In the last thirtyyears, three approaches were developed for the Laplacianestimate. Among them, the moderate and global methodswere both based on various potential interpolation functions for various head model, whereas the earliest and simplest local method, the difference approximation, was stillbased on a planar surface model. In this work, the local difference approximation approach was extended to aspherical surface model. The 2nd and 4th order approximation were derived and a proportional coeffcient difference was revealed between the planar and the sphericalsurface models. This work provided the theoretical basisof the local difference approach for Laplacian on a sphericalhead surface.
Abstract: The first aim of this paper is to discuss the complexity of a class of cryptographically goodBoolean function-plateaued functions. Based on properties of the Walsh transform of Boolean functions, we showthat plateaued functions still keep high nonlinear after being decomposed. We then prove that the normality ofany given plateaued function has strong relationship withthe normality of its component functions. At last, a secondary construction of m-variable plateaued functions fromm-variable plateaued functions was presented. We demonstrate that a class of functions with given cryptographicproperty can be constructed, and generally the constructedfunction does not belong to Maiorana-McFarland's class.
Abstract: We presented a novel interactive geneticalgorithm with surrogate models based on an individual'sinterval fitness in this paper. In this algorithm, the surrogate models are generated based on an individual's intervalfitness. The adopted surrogate models are two radial basis function networks to model the upper limit and thelower limit of an individual's fitness, respectively. Having been trained using the gradient descent approach withdata obtained in the process of the evolutions, these modelsare then applied to estimate all individuals' fitness in thesubsequent evolutions. The surrogate models continuouslyupdate during the evolutions in order to improve their precision. We quantitatively analyzed the performance of thealgorithm in alleviating user fatigue and increasing the opportunity to look for the optimal solutions. In addition, wealso applied the algorithm to a fashion evolutionary designsystem. The results show that the proposed algorithm isadvantageous.
Abstract: As an advanced video compression standard, H.264/AVC has been applied to various fields suchas video surveillance, video conference, and wireless videocommunication. This paper presents a novel scene cut detection method in H.264/AVC baseline profile compression domain, which takes advantage of the available features from H.264/AVC bitstreams, including chroma prediction modes, motion vectors, macroblock types, and soon. Moreover, in this method, four new criterions usedfor scene cut detection have been proposed, i.e. the distribution difference of chroma prediction modes, the distribution difference of macroblock types, the accumulativemotion amount, and the difference of motion vector angles.The thresholds of the criterions are mainly determined bythe minimum error Bayesian decision. Experimental results show that the proposed method can detect the scenecuts at I-frames and P-frames correctly without the information of bi-directional prediction which is not availablein H.264/AVC baseline profile.
Abstract: A particle estimation algorithm where theweight of the particle is related to angle between observation vectors is presented for nonliear system state. Whenthe likelihood has a bimodal nature, this algorithm leads tomore accurate state estimates than Sequential importanceresampling (SIR), Auxiliary particle filter (APF), Regularized particle filter (RPF), and Gaussian particle filter(GPF).
Abstract: Power-saving is a critical issue for Wirelesssensor networks (WSNs). A Learning-based power effcientrouting (LPER) algorithm is proposed. In the LPER, afitness function, which balances network lifetime, energyconsumption, and packet delay, is constructed and used inan ant colony system to establish the optimal route. Inaddition, reinforcement learning is applied in predictingthe energy consumption of neighboring nodes. The LPERis able to optimize network lifetime of WSNs, while keeping energy consumption and packet delay in a relative lowlevel. Numeric experiments show the LPER outperformsthe Minimal spanning tree (MST) and the Least energytree (LET) based routing algorithms in terms of networklifetime and packet delay.
Abstract: This paper proposes an energy-effcientand collision-free medium access control protocol namedTDMA-S (Time division multiple access MAC protocolfor Sensor networks). Nodes in TDMA-S schedule theirtransmissions based on neighborhood information to determine when they can transmit, listen or sleep. TDMA-Scollects the neighborhood information using a distributedalgorithm and avoids that two nodes within the interference range are assigned the same transmitting slot. Theprotocol is shown to be fair and reliable. Simulation results indicate that TDMA-S outperforms contention-basedprotocol (e:g., S-MAC) and schedule-based protocol (e:g.,NAMA) in terms of delay and energy consumption.
Abstract: The decentralized estimation problem ofdynamic stochastic process in a sensor network restrictedby communication bandwidth and energy is considered.Due to these constraints, only quantized messages of theoriginal information from local sensor are available. For thedynamic system composed by a state-vector model and aset of corresponding observation-vector models linked by anetwork, an adaptive quantization strategy and sequentialfiltering are used to design fusion algorithms. In terms ofdifferent forms of the transmitted information, two novelfusion filters based on the conventional Kalman filtering(KF) are presented by use of quantized measurements andinnovations respectively, abbreviated as KFQM and KFQI.In contrast, the latter has better estimation accuracy under the same bandwidth constraint condition. This is because that there is less information loss in the process ofquantizing innovations. Computer simulations show theeffectiveness of two novel filters.
Abstract: This paper presents a robust and effcient detection algorithm for MIMO wireless systems. Theproposed scheme employs a novel combination of bitlevel signal representation, MMSE-preprocessing and Malgorithm. The proposed bit-level algorithm exploits thestructure of the QAM constellation by separating eachbit of the signal to reduce the associated complexity ofthe listing and metric updating steps. In the joint detection/decoding systems, the proposed scheme visits lessnumber of nodes by exploiting the extrinsic informationobtained by the decoder. Our scheme may be employed inthe rank-deficient system. Simulation results demonstratethat the bit-level detector achieves almost the same performance as the existing M-algorithm and it achieves a moresignificant gain in terms of complexity reduction.
Abstract: Object tracking and the Quality of services(QoS) control is one of the key issues in wireless sensornetworks. In order to track objects and provide information about the location, trajectory and identity for eachobject, we propose a new object tracking and QoS Control scheme using both infrared sensors and video cameras.Object searching is done using a three level wavelet representation, which can significantly reduce the searchingspace. Hierarchical wavelet representation will also facilitate QoS control in the wireless sensor network environment. The experimental result shows that this new methodcan achieve better performance than existing algorithms,and can be applied to dynamically track the bandwidthvariation.
Abstract: High speed satellite communication networks are emerging as a part of the future global wirelesscommunication systems. However, existing transmissioncontrol protocols for satellite communication networks donot provide satisfactory performance over high speed satellite links due to their ineffcient congestion avoidance algorithms. This paper identifies the reason for low throughputof a widely used protocol - Space communications protocol specification (SCPS) - in such networks, and proposes anew congestion avoidance algorithm to overcome the drawback of the congestion avoidance algorithm used in SCPS.Simulation results show that, compared with its legacycounterpart, the proposed new algorithm can achieve significant improvement in throughput performance with avariety of link BER ranges in high speed satellite communication networks.
Abstract: In large-scale IP-based communication systems, faults are often caused by multiple factors of uncertainty, so how to locate the root causes of these faultsquickly and precisely plays a key role in network management. This paper presents a fault localization technique based on Bayesian probabilistic inference. Equippedwith enhanced ability to handle negative and positivesymptoms, our method can diagnose multiple simultaneous faults and be resilient to noise in the symptom information. Moreover, it achieves good accuracy and precisionwith low cost of time. In the simulation study, we compareour approach with a prior fault localization technique IHU.Results show that our method offers better accuracy andprecision compared to IHU in noisy environment at the expense of less time especially in large-scale communicationsystems.
Abstract: The security of elliptic curve cryptosystems is based on the intractability of the discrete logarithmproblem. The GHS attack provides a way of attacking elliptic curve discrete logarithm problem. Galbraith et al.extended the GHS attack to a much larger class of ellipticcurves.In this paper, we apply Galbraith et al.'s idea to theGHS attack of hyperelliptic curves over non-prime fieldsof characteristic not two. The idea is that we first construct an effciently-computable homomorphism and thenmap the hyperelliptic curve to a new hyperelliptic curve.Hence the discrete logarithm problem can be transformedinto a discrete logarithm problem on a new hyperellipticcurve for which the generalized GHS attack is potentialeffective.
Abstract: In conductive electromagnetic compatibility, the estimation of noise source impedance like switchedmode power supply's impedance is somewhat a troublesome problem since it is uneasy to detect the internal noisesource impedance and also this impedance may vary quitedifferently due to the various type of sources. In this paper an effcient estimation approach of conductive noisesource impedance is presented where single current probemethod in conjunction with fast Hilbert transform methodis employed for amplitude characterization and phase characterization of source impedance, respectively. The principle and application of this approach are described, andsome experiments are accomplished to verify the approach.
Abstract: Detection of moving targets in singlechannel SAR images is discussed. By introducing thetwo and three channel SAR-GMTI techniques in common use, a new detection algorithm based on 3 subaperture Cancellation-after-interferometry (CAI) is proposed, in which multi-aperture images are pre-formed byoverlapped frequency division of clutter bandwidth andthen compensated of magnitude and phase errors betweenthem. Simulation results show the proposed algorithmhaving better robustness against clutter uncertainty especially in strong clutter environments. Performance analysis of multi-channel detection method also show that theproposed single-channel algorithm has no direct capabilityof parameter estimation brought by substantial problemof correlation lost caused by shortage of multi-channel deployment.
Abstract: This paper proposes a new 2D directionfinding algorithm using vector hydrophone array. A vector hydrophone comprises of two or three velocity hydrophones plus an optional pressure hydrophone, all spatially co-located in a point-like geometry. The velocity hydrophones are identical but orthogonally oriented, measuring one Cartesian component of the incident sonar wavefield's velocity-vector. The pressure hydrophone measuresthe acoustic pressure in the wavefield. The proposed algorithm realizes the 2D direction estimation via two steps.In the first step, a pressure-particle-velocity field vectorsmoothing root-MUSIC algorithm is presented to estimatesources' elevation angles. In the second step, the estimatedelevation angles are applied to extract the direction cosinesand then to estimate sources' azimuth angles. Comparedwith most of the previous algorithms, the proposed algorithm offers advantages as: (1) uses the pressure-particlevelocity field vector sensor smoothing, which enables to offer more accurate elevation angles' estimates for correlatedsources, without losing of array aperture; (2) requires nospectral searching and direction cosine pairing-match procedures. Monte-Carlo simulations are presented to verifythe proposed algorithm's effectiveness.
Abstract: Empirical mode decomposition (EMD) isan adaptive signal processing method. However, it stilllacks a rigorous mathematical foundation. This paper investigates the EMD method using the nonuniform sampling theory. The first step of the EMD algorithm, identifying all the local extrema, can be regarded as a process ofextrema resampling. The extrema resampling rate changeswith the extrema distribution, so the EMD method isadaptive. By comparing the average extrema resamplingrate of a composite two-tones signal with the EMD results, we conclude that EMD will work properly only whenextrema resampling satisfies the sampling theorem of thelow-frequency component. Therefore, extrema resamplingcan explain the nature of the resolution properties of theEMD. Consequently, we gain an in depth understanding ofthe EMD method.
Abstract: In the reconstruction of complex permittivity, a lot of data in scattering fields measured around thebody consumed enormous calculation. In fact, the contribution of data measured in different areas is quite dissimilar to the reconstruction. In this paper, we propose a novelmethod to analyze the influence of data measured in different areas on the image quality before the reconstruction.By calculating the evaluation factor based on informationentropy and Bayesian theory, only the data measured incrucial areas are used to reconstruct the permittivity canobviously reduce the computing time. In a 2-D microwavebiomedical imaging, we have verified the feasibility of thismethod.
Abstract: In this paper, an unsupervised method isproposed for target classification in a polarimetric SARimage, based on the Gamma correction and the Self organizing map (SOM). After the gamma correction of thefeatures including the elements of the coherency matrixand its eigenvalues, the coeffcients of Freeman's decomposition and the polarization entropy, the authors use a SOMbased neural network to classify a polarimetric SAR imageinto different clusters. Using the AirSAR data, the authorsdemonstrate the effectiveness of the proposed method.
Abstract: A novel evolutionary strategy for Particle swarm optimization (PSO) to enhance the convergencespeed and avoid the local optima is presented. The positive experience and negative lesson from the individualparticle's cognition and the swarm's social knowledge areused to accumulate the system's intelligence and guidethe swarm's evolution behaviors. The new generation ofswarms (named as Child Swarm) and the adjacent formerswarms (named as Parent Swarm) are mixed to select thesurvival of the fittest. The eliminated particles are replaced by the random particles from the outside surroundings. Darwinian evolution method contributes to the convergence and the durative interactions between the swarmsand the surroundings who contribute to the global search.This new method can converges faster, gives more robustand precise result and can prevent prematurity more effectively. The corresponding simulation results are presented.