Search

Brain is the most complex organ of human, which serves as the center of controlling most activities. A novel methodology called complex network is capable of characterizing the functional connectivity of human brain by means of graph theoretical measures. We designed and conducted experiments to record electroencephalograph (EEG) signals during left and right hand movement imagery tasks and then probed into brain activities by analyzing multichannel motor imagery signals from the perspective of complex networks. More specifically, we first utilized wavelet time–frequency analysis to calculate energy sequence of each channel and then modeled human brain as a graph by treating the channels of scalp EEG as nodes and determining interconnections according to the distance between energy sequences of each channel. The functional connectivity of derived brain networks could be interpreted with characteristics of nodes and edges. Results demonstrated that when subjects imagined left hand movements, the node betweenness centrality (BC) of right sensorimotor area was greater than that of left sensorimotor area. The node BC distribution was roughly opposite when imagining right hand movements. It could be concluded that nodes of contralateral sensorimotor regions were more likely to be activated to control information flows during motor imagery tasks.

Aiming at the three-phase inverter, an improved model predictive control strategy based on mixed logical dynamical(MLD) model is proposed. Firstly, the state equation of the three-phase inverter with the switching function is established. By including the logic variable to judge the switching state of each subinterval and its constraints and the conditional changes of the circuit, a three-phase inverter MLD model is built. Then, an improved model predictive control strategy based on convex optimization algorithm is come up with according to the model. The control strategy can effectively reduce the harmonic content of the output current and the dynamic adjustment time of the system, and the algorithm computation time is greatly shortened.

In this paper, a nonlinear model for transport aircraft with continuous airdropping heavy cargo is presented and back-stepping controller is employed. For the attitude subsystem and the velocity subsystem, the dynamics are transformed into the strict-feedback form by using back-stepping scheme respectively. In the back-stepping controller, the virtual controllers are designed to compensate control system and ensure the stability and reliability. Moreover, to avoid "explosion of complexity", command filter design is used. With the proposed controller, the bounded stability analysis is presented. The simulations compare the accurate model with the model with uncertainties, and proof the effectiveness and robustness of back-stepping controller in continuous airdropping heavy cargos.

A point clouds denoising method based on moving least-squares is presented in this paper. The moving least-squares method has a good ability to denoise a point cloud in 2D. However, there are some serious disculties associated with the moving least-squares technique for some cases, such as varying thickness of the point set and the effects from unwanted neighboring points. Lee improves moving least-squares technique using Euclidean minimum spanning tree, region expansion and refining iteration. In this paper, we take some modification of Lee's method by using a simple method based on the correlation of the point set to find the adaptive weighting parameter, and extends it to 3D. Experimental results show that our new approach is robust and effective on denoising unorganized point clouds.

The study of the global stability is essential for designing and controlling genetic regulatory networks. Most existing results on this issue are based on linear matrix inequality (LMI) approach, which results in checking the existence of feasible solutions to high dimensional LMIs. In our previous study, we present several stability conditions for genetic regulatory networks with time-varying delays, based on M-matrix theory and the non-smooth Lyapunov function. In this paper, we design a smooth Lyapunov function and employ M-matrix theory to derive new stability conditions for genetic regulatory networks with time-varying delays. Theoretically, these conditions are less conservative than existing ones in some cases. For genetic regulatory networks with n genes and n proteins, these conditions become to check if an n×n matrix is an M-matrix, which is much easier than existing results. To illustrate the effectiveness of our theoretical results, two genetic regulatory networks are analyzed.

Frozen soil is a widespread kind of soil at high altitudes and high latitudes. It is divided into two parts, permafrost and seasonally frozen soil. It accounts for about 79% of northern hemisphere. As the largest country in the world, the area of Russia frozen soil occupies a large proportion. The use of satellite remote sensing technology has promoted the rapid development of permafrost research since 1970s. In this paper, NOAA remote sensing data preprocessing in Asian part of Russia was done. According to the band characteristics of AVHRR data, split window algorithm was used to calculate surface temperature. Thus, interpreted permafrost and seasonally frozen ground were interpreted, and added up the change of frozen soil area. The conclusion of this research is that permafrost is being degraded to seasonally frozen ground every year. In the end of this paper, the reasons and the hazards were analyzed.

Image registration is an essential step before image reconstruction especially in medical image processing. With the way of microscopic imaging to rebuild the periodontal tissue spatial structure, the slice used for the microscopic observation will lead to error. In the paper, the error will be reflected in the images of rabbit teeth slice. In order to reduce the error, the paper respectively applied the rigid and non-rigid registration method in the image processing. Because of the specialty of the slice of the teeth and the complication of the soft tissue around the teeth, the rigid registration and the non-rigid registration were both used in the proposed application. The rigid registration was used for reducing the difference between the registration image and the target image which we called the whole registration. The local registration was solved by the nonrigid registration, the B-splines registration, which further decrease the images' diversity. The images of the rabbit's teeth were used to verify the effectiveness of this approach.

This paper deals with the problem of the delay-dependent guaranteed cost H ∞ control for an improvement interval system with time-varying delay. The time-varying delay is assumed to be in an interval and the derivative of the interval time-varying delay is not restricted, and where only the upper and lower bounds are known, this allows a fast time-varying function. Based on the Lyapunov-Ktasovskii functional, which fractionizes the average delay not the lower bound and deal with easily lower bound to be 0, and free weighting matrices approach without using the general model transformation, and bounding technique to handle the uncertain nonlinear matrices between the B and K. A delay-dependent bounded real lemma (BRL) and then a criterion for existence a state feedback controller is established. This guarantees the cost function upper bound and disturbance attenuation lever for all admissible uncertainties. A comparison Numerical example is given to illustrate the effectiveness of the proposed design method.

Because the traditional cooling methods such as ice cooling and natural convection cooling can not meet the special requirements of ornamental fish breeding, the way based on mechanical refrigeration, heat exchange system, water supply system and automatic control system is designed to meet the cooling rate of 1~1.5 ?/h. It includes calculating cooling capacity, temperature difference of circulating water, flow rate of cooling medium, controlling flow rate of circulating water and cooling medium by valve, designing the water supply system. Practice proves the way feasible. Survival rate of ornamental fish under the cooling system is beyond 98 percent.

Now the traditional manual mode has already cannot satisfy modern management requirements for basic farmland and reserve cultivated land, Using computer technology, GIS technology can offer more convenient management tools and methods for land resources professionals. This paper, according to the principles of data standardization, technology modernization, scientific management, application of integrated technology, using the software design method, through in-depth analysis of basic farmland and reserve cultivated land management functional requirements, the basic farmland and reserve cultivated land management information system's overall architecture, database structure and functional structure were designed. It provided a platform design methods and ideas of inquiry, statistics and analysis system, in order to establish a platform with GIS and Web technology.

Taking advantage of the characteristic of curvelet—multi-scale, locality, and directivity, a curvelet-based method for the suppression of linear interference in prestack seismic data is proposed. In this method, the seismic data is represented with curvelet by different frequencies and directors. By analyzing the curvelet coefficients, four different combinations between effective signal and linear interference are found. For the sake of reducing the damage of effective signal, a special solution is designed for each situation, according to the characteristic of the situation. It is shown from the simulation that with this method, the linear interference can be suppressed simply, rapidly and effectively, while minimizing the damage on effective signal.

Maximum Intensity Projection (MIP) is one of the most common methods for the visualization of volumetric data. MIP works by traversing all viewing rays and finding the maximum data value along each of them. The main limitation of MIP is that it cannot adequately depict the spatial relationships of overlapping tissues. Context-preserving illustrative volume rendering model is an extension to direct volume rendering (DVR). In volume rendering this model can simultaneously visualize interior and exterior structures while preserving clear shape cues using a function of shading intensity, gradient magnitude, distance to the eye point, and previously accumulated opacity to selectively reduce the opacity in less important data regions. It is controlled by two user-specified parameters. In this paper, we advance MIP based on this model. On one hand, we improve the opacity computation method correspond with the Context-preserving illustrative volume rendering model. On the other hand, for lighting of the objects the Phong-shading is employed in the new algorithm. Through shading and combining MIP with the Context-preserving illustrative volume rendering, the new MIP images greatly communicate 3D shape, depth information. Furthermore, we show that the new algorithm achieves real-time performance on current graphics hard-ware.

To orient at special environment (such as weak satellite signals or no signals areas of high-rise apartment buildings in the city areas tunnels underground space indoor and under canopy, and the disaster environment and the region that satellite navigation cannot reach) precision navigation, positioning and monitoring, to aim at breaking through technical problems of high-precision angular measurement and rapid long-range precision measurement, and developing the high-end Smart Station positioning system products of physical structure and data-processing integration, to realize high-precision, low-cost and real time localization and precise monitoring with engineering deformation in special environment. This paper adds the location baseline position monitoring and three-dimensional location and other special functions based on the increase of accuracy of photoelectric angle measuring distance; solution positioning in the special environment; to solve bottleneck technology issue of low efficiency low accuracy poor reliability and multi-factor interference in the special environment and realize high-precision, low-cost and real-time location and precise monitoring.

Speeding is recognized as a major contributing factor in traffic accidents on freeway. In order to reduce speed-related accidents, accurate real-time speed limit enforcement system is very critical to freeway. This paper presents an automatic speed limit enforcement system on freeway based on cameras and the Field Programmable Gate Array (FPGA) implementation to achieve this objective. Taking the advantage of the powerful parallel processing architecture of FPGA, this FPGA-based system can provide a real-time, low-cost, high-performance hardware platform for multi-channel video system to achieve real-time image processing. Once the speeding vehicle is detected, the system will immediately record the panoramic view of the vehicle passing and basic characteristics of the vehicle, shoot of the vehicle number plate, the vehicle speed, passing time, etc., and send this information to the monitoring center by the wireless modem through the serial interface and the GPRS network. The experiments indicated that the speed limit enforcement system can detect the vehicle speed accurately and provide the necessary proof for the police to enact punishment.

To get accurate estimation of flight state, a new fusion estimation method was presented. In the scheme, equipments of INS, GPS, wireless radio, and infrared equipments with different wavelength were considered. Firstly, three dimensional motion model of an aircraft was described, and then model simplification was discussed. By analysis of deferent equipment measurement errors, a new method for equivalent computational measurements and equivalent error were proposed. The new method was used for flight data processing. The experimental results show that fusion estimation outperforms any single measurement.

The aim of this study is 1)to estimate the land use /cover changes from 1987 to 2005 based on three LANDSAT TM images;2) to calculate the Ecosystem services value of Pingshuo Mine with the method of published by Costanza et al.. The results show that: During the year of 1987 to 2005, the arable land decrease is from 20816 hm2 to 18774.97 hm2, which declined more sharply than other land use types; the forest land and water declined 2124.19 hm2 and 72.83 hm2 respectively. Widespread build-up expansion took place in the study area, it increased by 4031.66hm2. According to our estimates, the total annual ecosystem service values in Pingshuo mine have declined by about RMB 29.38 million between 1987 and 2005. Comparing other land classification, there is a significant drop in ecosystem service value of forest land, which decreased from RMB 52.71 million in 1987 to RMB 39.36 million in 2005.The value of arable land declined by RMB 5.74 million, less than that of forest land. The ecosystem of water was severely disturbed which value drop from RMB 5.54 million in 1987 to RMB −9.57 million in 2005

Performance evaluation of e-government is system engineering, the paper use the econometrics based on systems engineering and how econometrics is used to solve economic problems in e-government of China. This paper adopt the input-output system research method from engineering system technology, work together with econometrics to solve the headache problems which e-government faced today. The content of this paper includes three parts: Research review and hypothesis, data and the quantitative model, the mainly problems of e-government economics. In order to research a number of evaluation modeling factors, the paper tackle the original data of related investigation and study, summarizes the facts from a questionnaire, compares the actual situation of different e-government departments, and finally makes quantitative analysis.

This paper deals with a hybrid optimization method for solving the optimization problems with inequality constraints and equality constraints, in which an adaptive penalty strategy is firstly adopted to convert the optimization problem with both equality constraints and inequality constraints to one only with lower and upper bound constraint of decision variables, then an adaptive real-coded genetic algorithm combined with a local search algorithm is employed to optimize the problem. For improving the optimization effect of the real-coded genetic algorithm, the adaptive crossover probability and mutation probability are designed. Finally, to examine the validity of the hybrid optimization method, the method and other two algorithms are used to optimize the traffic signal timings optimization problem of an isolated intersection, and a large number of simulations show that the hybrid optimization method proposed in this paper can work well in the traffic signal timings optimization problem.

Direct Volume-Rendering (DVR) is usually used in medical volume visualization, DVR images tend to be over-loaded with the content (blurred images). Thus, in many practical applications simpler techniques which do not rely on complex transfer functions are employed. For example, Maximum Intensity Projection (MIP) which depicts the maximum value along each viewing ray. In this paper, we improve the Illustrative context-Preserving volume Rendering based on Visualization Toolkit (VTK). In our method, it can simultaneously visualize interior and exterior structures while preserving clear shape cues. The main limitation of MIP is that it cannot adequately depict the spatial relationships of overlapping tissues. An approach has been proposed to eliminate this drawback: Local Maximum Intensity Projection (LMIP). LMIP is an extended version of MIP. LMIP selects the first local maximum value encountered that is larger than a pre-selected threshold value along an optical ray from the viewpoint in the viewing direction. In this paper we propose an improved local maximum intensity projection. An appropriate threshold and shading are computed in this improved method. We show that the improved method is a useful technology in volumetric dataset visualization. Keywords-direct volume rendering; MIP; LMIP; Shading

Lane boundary detection is the most important component of Driving Assistance System which aims at keeping drivers safe. In this paper, a method combining the edge characteristics with brightness of lane is discussed for traffic scene at night. First images are preprocessed by dual thresholding algorithm in green channel. Then, the edge is detected by a fast method based on single-direction gradient operator. Finally, noises such as headlights of vehicles, reflected lights and street lamps are removed through filter template. Experiment results indicate that the proposed approach is adapted to night condition.

This paper describes a novel fast mean shift algorithm based on an accelerated iteration strategy. This new method focuses on solving the problem of high calculation complexity when high data dimension or large data sets are involved in mean shift. By predicting the mean shift vector, improved method reduces the number of iteration and speed up the calculation. The application of traffic image filtering is provided also. Experiment results of traffic image filtering demonstrate the efficiency of the fast mean shift algorithm.

Thresholding is the basic way for traffic image processing. Two-dimensional (2-D) thresholding methods can get better results. They used a threshold vector to divide a 2-D histogram into object, background, edge/noise three parts. Object and background parts were the common parts of grayscale information and spatial information. Mutual information focuses on studying the cross-section between entropies of two distributions, so it was considered to improve some disadvantages of the current 2-D thresholding methods. Experimental results showed that the proposed method could get better results.

This paper presented an intrusion detection model, which can analyze encrypted datagram of IPSEC. In this model, after “Key and SA” was successfully exchanged, key exchange program first encrypted it, and then stored it to directory server. At the same time, intrusion detection system got “Key and SA” of connection by analyzing directory operation messages, and used it to decrypt datagram. Finally, this paper has analyzed model security and implementation method.

To control the urban transportation development scale and optimize the structure of traffic modes, the traffic environmental carrying capacity (TECC) was taken as constraint condition in this paper. Through analysis of theories on the transportation planning and the environment capacity, the integrated definition of TECC was composed by four components which were Traffic Environmental Resource Carrying Capacity (TERCC), Traffic Environmental Pollution Carrying Capacity (TEPCC), Traffic Environmental Mentality Carrying Capacity (TEMCC) and Traffic Environmental Economy Carrying Capacity (TEECC).The models of TERCC and TEPCC were put forward and the proper value range of some relative parameters was assessed. If the results of TECC calculation exceed the limitation, we can adjust the proportion of all traffic modes, or introduced into new traffic mode, or make some measures to improve TECC value. By the application to compute TECC of Xipsilaan city, these models got verification. Furthermore, the theories and the models of TECC also can provide solid support for the proper transportation planning and management of network, and offer effective reference for the appropriate feasibility research of highway project.

The second-order oscillating particle swarm optimization(SO-PSO) algorithm, which introduced the second-order oscillating evolutionary equation to the evolutionary equation of PSO, could adjust the particlespsila global and local search capability and avoid the local optimization. It was proposed to solve a mathematical model which was built for aircraft departure sequencing problem in this paper. The correlative implementation techniques and detailed design process of the algorithm were presented Then the simulation was performed to solve this sequencing problem using the SO-PSO algorithm. The results showed that the global optimal solution was obtained, so the SO-PSO algorithm was rational and feasible and curtailed the consumption of aircraft departure effectively.

The stability problem of multiple additive time-delay system is considered using Lyapunov- Krasovskii stability theory. Based on the LMI (linear matrix inequality), the robust stability criterion and robust state-feedback controller design are derived for systems which may contain non-parameter uncertainties. Finally, a numerical example illustrates the validity and the lower conservation of the proposed method.

The performance of the nonoscillatory central difference scheme with different limiters is investigated for solving the two-dimensional shallow water equations in this paper. The methods are applied to simulating two typical problems of shallow water equations, i.e. circular dam-break problem and shock focusing problem. The results show that there are differences in numerical performance when applying different limiters. An optimum-selected limiter is determined through analysis. The method using the optimum-selected limiter can provide a good numerical method for approximating the two- dimensional shallow water equations.

A new algorithm for estimating parameters and angle-of-arrival (AOA) of chirps using matching pursuit (MP) method is presented in this paper. This algorithm is based on the concept of sparse representation of signal and applied into fractional Fourier transform (FRFT) domain and 2D AOAs estimation in the end of the paper. The problem of source localization of chirps has once been solved with time-frequency analysis which is called spatial time-frequency distribution (STDF) method. This method replaces the covariance matrix in subspace methods with the results after time-frequency transform, and then obtains AOAs as common subspace methods do. Wigner-Ville Distribution is the most popular method used for source localization, however, how to eliminate the effect of cross-item in results is still a problem which will make the estimations unacceptable sometimes. Algorithm in this paper fist constructs atom-dictionaries according to system models and then projects observations onto dictionaries, finally obtains parameters and AOAs by solving least squares problems. In order to be more resistant to noise, we preprocess observations and dictionaries with FRFT and only use values near the peak, then utilize MP method in transform domain to obtain AOAs. Numerical simulations have proved the effectivity of the algorithm.

A novel algorithm using Rotational Invariance technique for AOA estimation is presented in this paper, which is based on sparse decomposition of signal, and applied into beam-space against the deficiencies of traditional DF algorithms and sub-space algorithms. The former methods are widely used in actual applications. However, they have many drawbacks such as low-resolution, bad robustness and etc. The latter ones which base on subspace method have gained great interest in the past two decades. What we know about this kind of methods are their super-resolution quality and their weaknesses as well, e.g. constrained by the number of elements, behaving badly when correlativity of sources grows. Approach proposed in this paper fist constructs atom-dictionaries using outputs of second sub-array and then transforms observations of fist sub-array and dictionaries in array-space to beam-space, and finally obtains AOAs through thinning sectors by solving least squares problems. Numerical simulations have proved the effectivity of the algorithm and good performance in low signal-to-noise ratio (SNR) regime.

There is data that shows that economic lose of several ten millions to a few hundred millions US$ at many prosperous nations every year because of traffic jam. One aspect of ITS (intelligence transportation system) aims at the characteristics of the traffic image. We plan to combine image compression and traffic application together. The basic thought of our method is make the image data into one dimensional data row using some kind of image scanning method. The scan data is unsteady usually. We must monotonize it automatically through machine learning before the polynomial approach. We use the polynomial approach to these data row, the record coefficient attain the purpose of the compression image

An image classification method based on fractal dimension was presented Moreover, the fractal dimension of small complex traffic image was studied. The result of the experiment showfractal dimension can be a good parameter in image classified and analysis. The classified result based on fractal dimension is the same as the result of classified according to person's subjective recept. To organize in a sequence of texture-image, texture-verge-image, texture-verge-smoo th-image, verge-smooth-image and smooth-image, the corresponding fractal dimension respectively is 2-2.1,2.1-2.35,2.35-2.45,2.45-2.5,2.5-3.0 and 2-2.2,2.2-2.45,2.45-2.55,2.55-2.6,2.6-3.0, when the box's side is 2 and 4. As for traffic image, the fractal dimension respectively is 2.4-3.0 and 2.8-3.0, when box's side is 2 and 4.

A polynomial-approach method for image compression coding was presented for the first time to the small complex traffic image. We analyzed the image scanning method affect to the polynomial-approach method. Furthermore, we also researched the way of accumulation that can make the violent wobble of the curve became more gentle. Finally, We propose an approach, which has three steps: raster scanning of the image data, monotonizing data according to self-adapt parameter choice and polynomial-approach. This method achieved the image compression coding successfully. Experiments on traffic images showed our method is simple and high efficiency. It is high performance on the small complex traffic image, especially in middle and low signal-to-noise ratio.

Altitude location for UAV by using federated filter is discussed, the fourth structure is selected, because its two sub-filters involving altitude sensor and the difference Global Positioning System (d-GPS) respectively are fully isolated from each other and thus make the federated filter more fault-tolerant and better in real-time performance. Data fusion based on this federated filter was simulated. When the d-GPS is working normally and simulation results show that values estimated by data fusion base on federated filter are very close to real values and the variance of federated filter converges to 10 m. When the electromagnetic environment is very bad, as is usually the case in war zone, the d-GPS is silent for a long time, variance based on federated filter converges to 13 m, bigger than the 10 m. Compared with previous paper variance based on Kalman converges to 15 m, higher than the 13 m. Federated filter can give much more accurate estimation than Kalman filter.

The bandwidth of rate system of dynamical tuned gyroscope affects the dynamic performance of system. Second-order optimization model is used to compensate the rebalance loop of gyroscope, and uses dominant pole to determine the cut-off frequency of second-order system inertial element. Additional zero cancels the pole of gyroscope in low frequency, and use additional pole to cancel the zero of gyroscope in high frequency. The optimization of parameters for rebalance loop and redesigned to circuits make the bandwidth increase to 80 Hz from 35 Hz, and the max measuring rang is enlarged from 70deg/s to 170deg/s, and the output noise is reduced to 0.7 mV(RMS) from 5 mV(RMS). It not only improves bandwidth and measuring range, but also reduces output noise. Simulating validates the dynamic performance of system, and experimentation tests the dynamic and static performance of system. The results of experimentation and simulation verify the rationality and validity of design.

According to a problem in the real world, a mathematical model is established for the multi-depot vehicle routing problem with time windows (MDVRPTW). In order to improve the computational efficiency, first, based on decomposition and coordination technology (DCT), the problem is decomposed into several sub-problems and the customers are decomposed into coupling and non-coupling customers by a heuristic method. Second, due to the bad convergence when traditional DCT is used to solve the problem, the coordination values are designed elaborately by means of an adaptive genetic algorithm. Moreover, a tabu search method is developed to solve VRPTW for each sub-system. Finally, the validity of the proposed method is proved by the practical instance.

A wavelet neural network method for fault diagnosis of push-pull circuits is presented. Firstly, output voltage signals under faulty conditions are obtained with simulation. Then wavelet coefficients of output voltage signals are gained by Daubechies wavelet decomposition, and faulty feature vectors are extracted from coefficients. After training the networks by faulty feature vectors, the wavelet neural networks model of the circuit fault diagnosis system is built. The simulation result shows the fault diagnosis method of the push-pull circuits with wavelet neural network is effective.

A new mathematical model for large-scale and real-time vehicle dispatching problems (LRVDP) is established in this paper. Due to the high complexity of the problems, an effective tabu search algorithm for application to LRVDP is proposed. The new algorithm applies dynamic oscillation and candidate list strategies, which are controlled by the success of the search as the solution progresses, to make best use of infeasible verses feasible space and promising verses the most promising neighbors' moves. The results show that the new algorithm can determine very good solutions in a quite-short computing time, thereby, solve LRVDP effectively.

By analyzing the shortcomings in traditional vehicle communication system, this paper presents an intelligent vehicle monitoring system and describes the design procedure of hardware and software in details. By comparing the two data transmission protocols in GPRS network, this paper takes UDP/IP as the vehicle communication protocol. The data transmission delay is studied in this paper at last.

Outflow rate and link travel time have generally been surmised to be important attributes of intelligent transportation system (ITS). In this paper, two constant differential equation models are presented separately while inflow pattern is known. This is based on the velocity/density relation function given by Greenshields and the modified velocity/density relation function presented by Jakyakrishnan and Tsai. Considering the analytical solution of above constant differential equations cannot be acquired, its numerical solution is obtained by Runge-Kutta-Fehlberg algorithm. We also get the link travel time function basing upon the inflow and outflow patterns. The numerical simulation indicates that the outflow patterns, which are obtained respectively by the two outflow models, are almost approach the real traffic flow status according the traffic survey. However, the link travel time, which comes from the outflow model, which is based on modified velocity/density relation function, is more approach the real travel time.

Traffic signal controller is playing more and more important roles in modern management and control of urban traffic. This paper presents an intelligent traffic signal controller based on multi-microcomputer technology. The architecture and fundamental functions of the intelligent traffic signal controller is firstly introduced in detail, then the human-computer interface based on visual technology designed for the controller is formulated, and finally an application case in practice is discussed.

To accurately estimate multi-section traffic states of freeway, a new multi-section error state equations are built up. In the new model, error propagations of both the traffic density and the average velocity are considered and the extended Kalman filter is used to estimate all traffic states. To avoid a series of high-dimension problems, a modified weighted Gram-Schmidt orthogonal U-D factorization method is used for the time update and measurement update of the extended Kalman filter to get high numerical stability and computational efficiency. Considered the structure of system matrix, block matrix is used in U-D factorization algorithm. Results of simulation to 100 section states of freeway and actual application show that the new method can be efficiently used to estimation and prediction freeway traffic flows.

The compression coding, generated by suitable compressing method, is applied in a genetic algorithm. Its length, together with its corresponding length of the non-compressed coding, can be altered. As one compression-code bit is ordinarily on behalf of several non-compressed bits, changes in any compression-code bit will create changes in several non-compressed ones. Therefore, the compression-coded genetic algorithm is helpful to improve the diversity of the model and the parallelism of the algorithms. In practice, according to the constrict condition and a self-adaptive decoding function, the compressed code genetic algorithm can avoid a premature convergence of the evaluation of multi-extremum in genetic algorithms in some degrees, therefore improve the search capacity of the global optimal solution. Through the multiobjective optimization by applying the compression coding genetic algorithms on the basis of the dynamic multi-driveway four-phase network model of urban traffic, the simulation result is confirmed to be feasible.