A simulation model of electric hybrid buses was proposed, which is downloaded into the DSAPCE/Simulator using the Cruise real-time simulation modules as the simulation platform of the underlying vehicle model. Energy management control strategy based on the stateflow multi-mode operation control algorithm was established. The optimized algorithm was used to acquire high efficiency working areas as the control rules among the different modes, which were downloaded into TTC200 controller through automatic code generation technique. Then real-time loop test was carried out between TTC200 controller and Simulator hardware platform. The results show that, compared with offline simulation, the vehicle control strategy has good performance in real-time control and CAN bus communication works well, which reduces the subsequent vehicle debug cycle.

To explore the impact factors of driver's braking moment in car following under emergency conditions, the virtual driving behavior tests of several drivers were conducted on a driving simulators. Based on the driver's braking onset data under different scenarios, the impact factors of driver's braking moment were analyzed, and a new threat assessment measure, time to maximum deceleration level braking, was proposed. This measure can directly quantify the danger or threat level of the current dynamic situation. Then two braking moment forecasting models were established by applying linear regression and BP neural network respectively. Results show that the driver's age and gender, the motion state between two cars are the important impact factors. Comparisons show that the BP neural network forecasting model predicts more accurately, that is in agreement with the human natural judgment on the urgency and severity of threat.

In order to modify the hydraulic brake pressure accurately for Traction Control System (TCS), a Hydraulic Control Unit (HCU) test system based on dSPACE was developed. Using this system, the characteristic parameters of HCU valves were identified, and the relationship between the desired pressure gradient and target duty cycle in Pulse Width Modulation (PWM) brake control was obtained. In addition, the experiment and analysis results were used in the sliding-mode-based traction control algorithm and the wheel brake control logic based on target pressure gradient was proposed. The algorithm was verified using Matlab/Smulink and AMESim co-simulation. The results show that the proposed active brake control algorithm can provide high accurate regulation of wheel slip ratio, thus, the vehicle driving performance can be improved effectively.

The control consistency of Drive-Neutral-Reverse (DNR) clutch in the software development of the electric-hydraulic control system of Continuous Variable Transmission (CVT) is analyzed. The parameter diversity, uncertainty, and time-varying introduce difficulties to the open-loop control of DNR clutch. Regarding the filling time and the slipping process, a self-learning method is proposed, which takes the smooth turbine acceleration as the target. The learning rule is derived from the Lyapunov argument. The asymptotic convergence of the self-learning process is theoretically proved. To verify the effectiveness of the learning control algorithm in practical application, the control algorithm modeling is set up in the ETAS software, and the control algorithm is tested using rapid prototype simulation on real vehicle. The test results indicate that application effect of the control method is good.

The energy-saving characteristics of eddy current brake are analyzed, and two energy-saving evaluation indexes are proposed. The mathematical model of the optimal energy-saving design of eddy current retarder is established, in which the energy-saving evaluation indexes are taken as the objective function. Genetic algorithm is used to seek the global optimal solution of the design variables. The optimization results based on the energy-saving indexes are better than the optimization results in which the maximum braking torque is taken as the objective function. The proposed method can provide certain guidance to the structural design of the eddy current brake.

Different installation methods and different moving states of automobile can bring randomness to the relative position of wire in automobile wiring harness and ground height of wiring hardness. A statistic model is proposed to calculate the dynamic crosstalk of automobile wiring harness under the low frequency. The image method is employed to acquire the unit length distribution parameters of the wire with insulation layer, and the principle of statistics is applied to infer the mean value and variance of the dynamic crosstalk of wiring harness. The reasonable worst-case crosstalk value calculated by the proposed model is compared with the 273 times simulation results of random displacement spline interpolation to verify the accuracy and reliability of the model. The proposed statistic model can also adjust the relevant parameters flexibly according to practical situation and realize rapid prediction, which provide the basis to the design of vehicle electromagnetic compatibility. The model has very important practical significance.

For the limitations of traditional counting and statistic methods in drawing up load spectrum and the particularity of high-speed train gear drive load measurement, an indirect measurement scheme of torque of high-speed train drive system is proposed based on motor active power. Statistic calculation of the torque-revolution history of the gear drive system is carried out. Synthesizing extreme value theory and non-parametric probability density estimation, a load data statistical method is introduced. The probability density function of the overall load samples is obtained, and load extreme extrapolation and load spectrum compression are realized. Accordingly, high-speed train gear transmission unit's load spectrum is established. A case study based on measured network end power of rolling stock is presented to verify the proposed scheme.

An improved drag model for wet clutch is proposed based on the surface tension of lubricant, surface waviness and nonparallelism of friction pairs. The radial pressure distribution equation is derived by considering the surface tension and surface wettability. The thickness of oil film is deduced under the influences of surface waviness and nonparallelism of the friction pairs. Results of numerical calculation indicate that the surface tension, surface waviness and nonparallelism of friction pairs all lead to increase in the drag torque of wet clutch. The drag torque increases rapidly with rise of the nonparallelism, while the influence of the surface waviness is not obvious. The effectiveness of proposed model is verified by comparison of this model with existing model and with test data.

In order to generate feasible dynamic trajectory adapting to different road environments, the traditional trajectory generation method is improved based on the optimal control theory. The dynamic cost function is created, which includes three cost terms, time, energy, and clearance from obstacles. The influence of different weights to the trajectory features is studied, and the heuristic weight regulation rules and regulation procedure are obtained. The simulation results show that the improved trajectory generation method is feasible. The weight regulation rules and procedure are right; the optimal driving trajectory can be dynamically generated. Several constraints are obtained and the driving trajectory can adapt to different road environments.

A fish uses its central fin to interfere with fluid to improve its stability. Inspired by such phenomenon, a car spoiler with similar function is installed on the car trunk lid, which is called the first scheme. Because of drag increasing of the first scheme, a riblet bionic functional surface is mounted on the rear window and trunk lid, which is called the second scheme. Under high speed, the above two schemes and the original car model are numerically analyzed. The results show that, compared with the original car model, the aerodynamic lift coefficient of the first scheme is reduced from -0.06455 to -0.1516, which means that the adhesion of the rear wheel is increased, thus, the car's handling stability is improved. Compared with the first scheme, the drag coefficient of the second scheme is reduced by 4.938% through combed fluid flow and delayed separation of boundary layer.

The mechanism of buses interference with trucks is studied under complicated conditions, including two-lane upslope one-way road and multiple types of vehicles. An interference model is proposed for the capacity reduction of freeway bottlenecks caused by trucks. The model is applied to estimate the impact of truck lane restriction on the passing capacity. It is found that the system capacity is maximized when all the trucks share one lane. Truck lane restriction can effectively enhance the passing capacity of freeway.

The dynamic theory of vehicle system is applied to develop the nonlinear dynamic model of underactuated vehicle in the vehicle and ground coordinate system. To simplify the process of checking and solving, the proposed model is linearized using accurate linearization method. On the basis of the linearized model, the dynamic model of tractor trailer system in polar coordinate system is established for vehicle trajectory in small turning curvature. In addition, the algorithm based on variable structure sliding model control is employed to make the state vectors of the vehicle system to converge to the hyperplane. Finally, the tracking simulations for smooth trajectory and smaller turning radius trajectory are analyzed in detail. Experiment results reveal that the designed feedback controller can improve the tracking performance of the tractor semi-trailer vehicle and drive the deviated vehicle back to the desired trajectory.

To realize the dynamic detection and early-warning of wear of brake shoe of truck, the wear rule of S-shaped cam drum brake was analyzed. Then a dynamic detection method of the wear of brake shoe was proposed based on resistance classification. A calculation model of the resistance classification was built to calculate the resistance values of the dynamic detection sensor for wear status of brake shoe with 12 classes. A testing platform was built with Jie-Fang Sailing heavy truck and Freescale microcontrollers and the detection test was carried out. The test results show that the thresholds of change of output voltage between two different early-warning classes was higher than 0.25 V. The wear status of a 12 mm brake shoe can be dynamically detected with 12 grades under the significant level of α = 0.01. The feasibility and accuracy of the dynamic detection method of wear status of brake shoe were verified.

In order to determine reasonable lighting parameter of crosswalk of urban road and ensure driver's recognition of crossing pedestrian, experimental research on driver distance recognition at night was conducted. The impacts of the number, the state (static or moving) and the cloth color of crossing pedestrians on the driver recognizing distance were analyzed. The relationship between driver recognizing distance and average luminance at different driving speed was also studied. Then a model was established to relate the driver recognizing distance, the average luminance and the driving speed. A calculation method of the average luminance value of crosswalk of urban road at night was presented based on safe discriminating conditions between driver recognizing distance and response-braking distance. A case study using this method was carried out. Results show that it is more difficult for drivers to recognize crossing pedestrian at night when single pedestrian is static and the cloth color is dark. There exist a positive logarithm relation between driver recognizing distance and average luminance, and a negative linear relation between driver recognizing distance and driving speed.

According to 4881 crash scene investigation data of accident database of a middle-size city, a Support Vector Machine (SVM) model is established for accident severity recognition, which is classified into low risk (property loss only) and high risk (injury or death involved). Grid Search (GS) and Genetic Algorithm (GA) are applied to find the best combination of penalty parameter C and Radial Basis Function (RBF) kernel parameter g. Then, the sensitivity analysis method is employed to evaluate the potential impacts of variables on the accident severity. The results show that the accuracies of the SVM model on both training and testing datasets are around 80%, which means better generalization performance: 8 variables of accident and vehicle attributes significantly influence the accident severity classification, which can be inferred as key factors.

This article studies the lane-changing behavior of vehicles in urban traffic using cellular automata model. In the simulation of lane-changing behavior in mixed traffic flow under asymmetric lane-changing rules, the impact of buses on the lane-changing behavior of social vehicles is analyzed by adding transit influencing factors as term of motivation. Results show that the presence of buses increases the lane-changing probability of social vehicles. Under the condition of low traffic density, the lane-changing probability of social vehicles with the bus ratio, and at the same time the average speed drops in corresponding road sections. In addition, when considering the bus influencing factors, the lane-changing probability obviously decreases.

Base on the elastic foundation beam model in the foundation work, the differential relationship of the load-internal force-deformation of the piles is established. Each pile component of the multi rows of the piles is divided into a group of bars according to different differential equation. A differential equation is established for each bar, and all the equations are integrated into a differential equation set. The internal force and deformation of the multiple rows of piles are obtained according to the boundary condition, the deformation compatibility of the bar node and the balance conditions of the internal force. Case study is carried out and the results verify the feasibility of the proposed method for internal force analysis of multiple rows of piles.

In order to improve the utilization of energy and the variable torque transmission efficiency of loaders, a full series hybrid scheme is proposed based on the analysis of the working condition of a 5 t loader. A fuzzy logic control strategy regarding to this scheme is developed. By this control strategy the system required torque can be online estimated; then, taking the required torque and the SOC value of the super capacitor as the inputs, the output torques of the engine, the generator and the motor can be achieved. Results show that the SOC value of the super capacitor is stable, the engine output torque basically falls the high efficiency zone, which can save about 16.6% fuel than the conventional loaders, so this strategy can improve the fuel economy.

The structure of the asymmetric multi-speed hydraulic motor is analyzed. Then a differential connection method of this type of motor is proposed. The output rotational speed and output torque formulae of the hydraulic motor with different differential connections are obtained through differential connection of the 2 inner 3 outer motor. The general formulae of the output torque and rotational speed are derived about differential connection of this type of motor. At the same time, in order to avoid the failure point and the repeated phenomenon, the effect of the motor factor selection on the differential connection is analyzed. The results show that there more ways of differential connection of asymmetric multi-speed hydraulic motor than that of the ordinary hydraulic motor and it is limited by the motor factor. This study may establish the theoretical foundation for further research of differential connection of this new type of hydraulic motor.

Based on rational fraction approximation, an implementation approach for Gaussian filter is presented. The coefficients in the rational fraction are determined by interpolation approximation. The deduced rational fraction is an optimization approximation function with high approximation accuracy. The maximum amplitude deviation from theoretical Gaussian filter is 0.88% in the whole frequency domain, and is less than 2‰ in cutoff frequency. The approximation filter is designed by the poles in the left half plane of S domain, and the corresponding digital filter in Z domain is obtained by backward difference transformation. The corresponding zero-phase-shift digital filtering algorithm is achieved by zero-phase-shift technique that meets the requirements of surface measurement. This method is applied to surface roughness measurement to determine the mean line of surface profile and separate surface roughness. The algorithm has high computation efficiency because it can be implemented by only one stage zero-phase-shift filtering, and provides a new method for surface measurement.

To overcome the shortcoming of the current ducted tail rotor that is unable to provide lift force, a ducted tail rotor with asymmetric layout was proposed. The aerodynamic performance of the topological structure of the ducted tail rotor was theoretically and numerically studied. The effects of the flight speed and opening angular displacement on the lift force, pull force and moment were obtained respectively. The aerodynamic characteristics of the symmetric and asymmetric ducted tail rotors were compared. It was shown that this ducted tail rotor with asymmetric layout can not only produce axial force to control aircraft attitude, but also produce radial force to provide additional lift force for the aircraft. Therefore, proposed ducted tail rotor with asymmetric layout has certain feasibility and application value.

The structure and performance of the transition piece of gas combustor were taken as the research objects, and a simplified double chamber model was designed with the distribution of holes in three rows. The response surface model was established by adopting Orthogonal Design of Experiment (ODOE) and approximate modeling method. The flow field and temperature field were solved using this response surface model. The polynomial regression equations were built about the weighted average temperature and the minimum pressure on the inner-wall of the double chamber model regarding the impinging hole angle and the injection orientation. The optimum impinging hole angle and the injection orientation were found using the interior point method to solve the response surface model. The simulation results show that the cooling efficiency the optimum design proposal was 7.53% higher than that of the initial design proposal. The simulation results and the comparative analysis may provide valuable reference for the impinging hole angle design in double chamber model.

To effectively estimate the test deviation of nano-indentation due to surface roughness, a finite element simulation of nano-indentation is carried on pure aluminium. A two dimension model is adopted and the maximum height R_y of the model contour is regarded as the assessment of the sample surface roughness parameters. Parameter D represents the horizontal distance between the peaks and valleys of the contour. The angle θ between the valley's midperpendicular and the tangent of adjacent convex is introduced. Compared with the smooth surface, for different initial contact, simulation results show obvious diversity: for initial contact at the peak of the rough surface, the maximum load is smaller; for initial contact at the valley of the rough surface, the maximum load is bigger; for initial contact at the edge of indenter and the bulge of the contour, the result is relatively close to the smooth one.

The high cycle fatigue performance of TRIP automobile steel is investigated. First, a series of high cycle fatigue experiments of TRIP steel were conducted. Then, the fatigue limit and fatigue life factor at 800 MPa tensile strength level were studied, and compared with that of DP steel plate at the same strength level. Finally, the microstructure of the fatigue fracture and influence of TRIP effect on the fatigue property of TRIP steel were analyzed. Results show that the TRIP effect improves the fatigue resistance of the steel to some extent. Under the 107 times cycle base condition, the fatigue limit of TRIP steel is obviously higher than that of DP steel, and its fatigue resistance performance is better. The crack source and fatigue area of TRIP steel show obvious ductile fracture characteristics.

Based on Action-Critic-Identifier (ACI) and Radial Basis Function (RBF) neural network, a novel decentralized reinforcement learning optimal control method for time varying constrained reconfigurable modular robot is presented. The continuous time nonlinear optimal control problem of strongly coupled uncertainty robotic system is solved. The dynamics of the robot is described as a synthesis of interconnected subsystems. As a precondition to the continuous-time MDPs performance indicators, the optimal value function, optimal control policy and global uncertainty of the subsystems are estimated combing with ACI and RBF network. The optimal conditions of HJB equation with regard to the subsystem are satisfied, so that the reconfigurable modular robot system can track the desired trajectory in a short time and the estimation error can converge to zero in finite time. The stability of the system is confirmed by Lyapunov theory. Simulations are performed to illustrate the effectiveness of the proposed decentralized control scheme.

To solve the problem of stability control of Oxidation-Reduction Potential (ORP), which is caused by time variation, nonlinearity and time-delay in the process of cobalt removal using arsenic salt, an improved operational pattern based PID parameter tuning method is put forward. In order to improve the reliability and speed of operational pattern matching, first, an improved fuzzy C-mean clustering algorithm for operational pattern clustering is presented. Then, a two-step search method is used to match the similar operational patterns. Finally, since the system parameters of the cobalt removal process vary with time, to improve the reliability of the reused operational pattern solutions, which are PID parameters, different weighting values are assigned to the operational solutions in different times when the operational pattern is reused. A real industrial cobalt removal process control results show that the proposed method can effectively reduce the fluctuation of ORP degree and deal with complicated changing conditions.

In order to solve the difficulties of predicting intrusion attempts and finding network vulnerability, an automatic identification method of intrusion attempts is proposed, which is based on three layers attack graph. This method builds the network's three layers attack graph based on the analysis of the underlying alarm data. Then it determines the quantitative attack graph from the analysis of the probability of the intrusion attempts. Finally, the critical host in the network is found by the generation algorithm of the minimum key point set. Thus, the manager can get the right network security policy. It is verified that the proposed intrusion identification method is feasible, effective and simple.

In the Fixed Priority Non-preemptive Scheduling (FPNS) research for Controller Area Network (CAN) message set, the existing Worst Case Response Time (WCRT) analysis may draw wrong conclusions to the schedulability of certain message sets. To overcome this problem, based on the message busy period concept of PFNS for CAN message set, this paper reanalyzes the scenarios that message enters the transmit queue of node for the first time and the subsequent times, and revises the worst case response time. The WCRT calculated from this worst case is employed to revise the optimistic result obtained by tradition WCRT analysis, and eliminate the pessimistic factor introduced in the modified WCRT analysis.

An energy-efficient data transmission schemed, named C2MIMO, was proposed for disaster monitoring sensor networks. Because the scheme is based on clustering and cooperative Multiple-Input Multiple-Output (MIMO), the monitor and cluster are constructed first. During this process, one primary header and some slave headers are selected as cooperative sending and receiving nodes. Then, the network delivers the gathered data to sink cluster-by-cluster. Through the cooperative MIMO energy consumption model and simulation results, the C²MIMO scheme shows its adaption to long distance transmission and bad channel status.

To solve the problem of face occlusion recognition, an algorithm, which combines Mind-Evolution-Based Machine Learning (MEBML) and local face feature, is proposed. First, a new method of LBP offset feature group extraction is presented to define a new feature contrast rule. By contrasting images between face occlusion and non-occlusion, the local area score of similarity is obtained. Second, similartaxis and dissimilation evolutionary operations are carried out for all local areas. Then, the areas of face occlusion and non-occlusion are obtained. Finally, if the non-occlusion area is large enough and centralized, the feature of this area can be used to recognize face even it is partially covered. Experiment results demonstrate that the new method can recognize 92% face occlusion images with low error ratio.

With unknown types of image noise, it is difficult to determine the Meanshift smooth window, which leads the details of image to be blurry. To overcome this problem, a multi-scale Meanshift algorithm of image denoising is proposed. This algorithm combines the advantages of 'digital microscope' of Wavelet and the characteristics of Meanshift of non-parametric probability density estimation and rapid template matching. So it is very efficient to remove the unknown noise of a group of actual distance image at night. In the implementation of the algorithm, first, the image is carried out two-dimensional discrete Wavelet transform, and the low frequency sub-image and the detailed high frequency sub-band are decomposed. Then, different from traditional process, high frequency sub-image is kept unchanged, and the smooth algorithm is implemented on the low frequency sub-image. Finally, the noise is removed based on the reconstruction of the decomposed sub-images. The algorithm not only makes up for the defect of the single Meanshift algorithm, which is difficult to determine the smooth window, leading to the image details be filtered, but also solves the denoising problem on a group of actual distance images at night, whose Signal-to-Noise Ratio (SNR) is 34.29. Experiment results show that the proposed algorithm has higher ability to remove noise, and gets a higher SNR.

A modern cross spectrum estimation method is proposed, which is the SVD algorithm based on cross-spectrum of the Auto-Regressive (AR) model for parameter estimation. Compared to previously proposed Levinson algorithm based on cross-spectrum of the AR model, the new method of this study can effectively overcome the influence of the estimation error of the cross correlation function r_xy(m). Simulation results show that the spectrum estimation accuracy of the proposed method is much better. A simulation platform in the measurement noise background is established. The anti-jamming performance of the Transform Domain Communication System (TDCS) based on the proposed method is studied. The results show that the TDCS can effectively suppress the measurement noise, greatly reduce the error rate under different disturbances, and improve the capacity of antidisturbance.

A two stage parallel gene selection method (TPM) for obtaining the optimal feature subset is proposed. A fuzzy multi-swarm particle optimization (FMP) is also proposed to extend the searching spaces, to overcome the problem of traditional algorithm to be locked to local optimum. The performance of the TMP is evaluated on five microarray datasets (leukemia dataset, colon dataset, breast cancer dataset, lung carcinoma dataset and brain cancer dataset). The comparison results show that the proposed method not only gets better quality of feature subset and higher classification accuracy, but also generates smaller feature subsets. The results of this study could provide a new idea to the field of gene expression.

An improved Unscented Particle Filter (UPF) algorithm is proposed to overcome the problem of particles degradation in the standard particle filter. The algorithm uses adaptive unscented Kalman filter based on Gaussian Sigma point selection to generate the proposal distribution function. Then it uses the Metropolis-Hastings (MH) algorithm to optimize particles, so that the approximation of the posterior probability density of the system is improved. Simulation results show that the improved UPF algorithm reduces particle degradation which exists in the particle filter algorithm, and improves tracking accuracy.

The content adaptation based on context in UPnP AV is investigated. A Context-based Content Adaptation Algorithm (CBCAA) is proposed. First, the algorithm obtains and classifies the context information. Then it builds the constraint model according to the different types of information, and the constraint satisfaction method is used to acquire Media Description Decision Set (MDDS). Finally, a bit-stream adaptation engine transcodes the media from source media to object media based on MDDS. CBCAA enables UPnP AV adapt the content to environment and provides intelligent services. Simulation results demonstrate the effectiveness of proposed algorithm.

The idea of embedding watermark at the client side is proposed base on the separable encrypted watermarking algorithm. Three sub-protocols are developed, which are watermarking embedding sub-protocol, watermarking disputing sub-protocol, and unbinding problem disputing sub-protocol. At the same time, some theoretical vulnerabilities are compensated further by combining partial encryption and permutation function about cryptography. Theoretical analysis and simulation results show that the watermarking protocol developed in this paper can effectively resolve not only the unbinding problem, but also the thread caused by role integrity, and avoid the influence of full encryption on the protocol performance.

The similarity between the steering vector and quaternion of acoustic vector sensor is utilized to derive and establish the quaternion expressing form of the steering vector, and the quaternion model of the acoustic vector sensor array is proposed. Moreover, the orthogonality of the steering vector and noise subspace is theoretically analyzed. Based on the quaternion Multiple Signal Classification (MUSIC) spatial spectrum, a 2-D Direction of Arrival (DOA) estimation method is proposed. Experiment results show that the resolution of the quaternion MUSIC algorithm is better than that of the vector MUSIC algorithm. In low Signal-to-Noise Ratio (SNR), the quaternion MUSIC algorithm has higher accuracy of DOA estimation. At the same time, this method can be applied not only to the L-shape array and plane array, but also to other array formations.

A minimum norm algorithm is proposed to implement the joint estimation of direction of departure and direction of arrive under the background of Gaussian color noise. The minimum norm algorithm is based on the higher order cumulant aimed at the Uniform Linear Array (ULA) double-based MIMO radar. By utilizing the orthogonality between joint steering vector matrix and the noise subspace, a minimum norm vector is obtained from the all noise singular vectors. So the calculation complexity is reduced and the parameter estimation accuracy is improved. The simulation results show that the performance of the proposed minimum norm algorithm is better when SNR is low.

To further enhance the contrast of infrared image, improve the definition and suppress the noise, an adaptive enhancement method in Contourlet domain based on chaotic Particle Swarm Optimization (PSO) is proposed. First, Contourlet transform of the infrared image is performed. The proportion of low-pass image and detail image in the original image is adjusted, and the contrast is enhanced by linear gray stretch. Then, the coefficients of noisy bandpass directional subbands are adjusted by nonlinear gain function. An integrated quantitative evaluation function is used as the fitness of the chaotic PSO. In this evaluation function three indexes are taken into account, i.e. contrast, definition and signal-to-noise ratio. The optimal parameters, involved in the enhancement method in spatial domain and the nonlinear gain function for adjustment of coefficients of bandpass directional subbands base on Contourlet, are obtained by chaotic PSO algorithm. Experimental results for a large number of infrared images show that, compared with four existing image enhancement methods, the proposed method improves the contrast of enhanced infrared image, increases the definition, reduces the noise, and has a better overall visual effect.

Based on the symmetric Uniform Linear Array (ULA) and the second-order cyclic statistic, an efficient algorithm is proposed to deal with the localization of the mixed far-field and near-field cyclostationary sources. First, by exploiting the symmetric property of the divided subarrays, a rotational matrix is constructed, in which only the Direction-of-Arrival (DOA) information is contained. Then, the DOA estimations for both far-field and near-field sources are performed from a spectral search procedure. Finally, by substituting the obtained azimuth back into the two-dimensional MUSIC spectral function, the range estimations for the near-field sources is performed. Compared with previous works, the proposed algorithm saves computation cost, provides high estimation accuracy, and requires neither two-dimensional search nor parameter-pairing procedure. Computer simulations confirm the feasibility of the proposed algorithm.

For resource allocation in multi-relay assisted OFDM radio systems, most current relay and power allocation algorithms use Lagrangian dual decomposition technique to find the optimal solutions. This approach has good performance but the computational complexity is higher. To solve this problem, a resource allocation algorithm with low computational complexity is proposed. The proposed algorithm relaxes the integrality constraint on the relay selection factor. It takes both subcarriers channel conditions and the interference to the primary users introduced by the secondary users into account in the relay selection. A sub-optimal power allocation scheme is given on the basis of the properties of the total system power and interference constraints, in which the power allocation is conducted step by step. The system capacity is guaranteed with lower computational complexity. Simulation results show that the proposed algorithm can ensure that the interference introduced to the primary users is within the threshold, and can also improve the system capacity of the secondary users at the same time. Thus, the feasibility of the proposed algorithm is verified.

To reduce the probability of access failure caused by channel error, an algorithm featured by no backoff and fast retransmission was proposed for the access procedure of IEEE 802.11. The probability distribution of Medium Access Control (MAC) layer service was derived, and IBP/G_w/1/K discrete-time queuing model was established to analyze the QoS performance of M2M under unsaturated traffic condition and imperfect channel in the IEEE 802.11. To improve the transmission efficiency of massive M2M services, a dynamic window adjustment algorithm was put forward. Based on the tandem queuing network theory, a tandem queuing model was established to depict the real communication scenario, which was made up of wireless access network and IP core network. System end-to-end performance indicators were derived from M2M services. Simulation results indicate that the proposed algorithm can effectively improve the service efficiency of IEEE 802.11. The M2M services with low degree of burst canget better QoS when heterogeneous M2M services coexist in the network.

In order to diversity problem of two-hop complete Decode-and-Forward (DF) relay transmission in time-varying channels, a hierarchical optimization model is proposed. This model is based on Nash bargaining fairness criterion, and it combines transmission mode selection, subcarrier pairing, channel assignment and power allocation. Taking the advantage of Lagrange Duality, the proposed model reduces the algorithm complexity, improves the system utility and enhances the overall throughput. Simulation results show that, compared with complete relay transmission models, direct transmission models and non-carrier-pairing transmission models, the proposed algorithm achieves great improvement in system throughput and ensures the users' rate requirement and fairness at the same time.

Current spectrum sensing methods in cognitive radio possess several shortcomings, such as poor detection performance in low Signal-to-Noise Ratio (SNR) simulation and poor robustness to the uncertainty of noise power, etc. To overcome these shortcomings, an exponential entropy-based spectrum sensing algorithm is proposed. According to the difference of exponential entropy between the situation of H₀ and H₁, the value of the exponential entropy of the received signal is estimated, and then compared with the threshold to judge whether the primary user's signal exists or not. The algorithm has the advantages of robustness to uncertainty of noise power, no requirement of prior information of the primary user's signal, and high detection probability in low SNR, etc. Monte Carlo simulation results show that under the situation of noise power uncertainty of 3 dB, sampling number of 1024, the detection probability can reach 100% when SNR is higher than -8 dB.

Carrier Frequency Offset (CFO) has great influence on the performance of OFDM system. In order to improve the accuracy of the CFO estimation, a novel blind CFO estimation algorithm is proposed for OFDM systems with constant modulus signaling. It is assumed that the channel changes slowly in time domain. The cost function based on the difference between signal powers of the two consecutive OFDM symbols in the frequency domain is derived, and the blind CFO estimation is accomplished by minimizing the cost function. Closed form solution to the proposed cost function is given theoretically, which greatly reduces the complexity. Simulation results show the performance of proposed algorithm is better than that of the classical one in high SNR circumstances, and it is robust to the changing order of channel responses.

In order to study the breaking and chewing process of food in mouth, a chewing broken model was using discrete element method. Taking soybean as an example, the dynamic chewing process was simulated and the corresponding parameters were tested, including that between soybean and soybean, and between soybean and teeth. To verify the feasibility of the chewing model, three-dimensional models of the first upper and lower molars were established using reverse engineering. The dentiform indenter that works with the texture analyzer was developed. Test of the soybean crushing with the dentiform indenter was carried out and the forces in the crushing process were analyzed. The changes of forces in the test are in line with that in the simulation. The simulation values could be amended by the linear regression model, and the regression coefficient approached 0.956. The analyzed results show that the chewing broken model can be used to simulate the chewing process of human, providing a new method for food texture evaluation.

This paper investigates the influences of Moisture Content (MC), Peroxide Value (PV), P-Anisidine Value (PAV), Methylglyoxal (MG), frying temperatures (140, 160, and 180 ℃) and frying time (1~5min) on the formation of Acrylamide (AA) in frying potato slices. The results show that both frying time and temperature affect the formation of AA, the content of AA increases gradually with frying time and temperature. PVG and MG significantly influence the formation of AA (p<0.01). PAV and MC also influence the formation of AA, but their influence is not as strong as PVG and MG. A prediction model is established which can be used as a tool to predict the formation of AA in the process of frying potato slices.

The effects of sodium caseinate content and proportion of sodium caseinate and soy protein on the droplet size distribution, surface protein and rheological properties of whipping cream were studied, and the possible mechanisms were also analyzed. Results show that with the increase in sodium caseinate, the particle size at the top cream decreases first and then increases, and sample of 4% gets the minimum value. The apparent viscosity, yield stress and surface protein content increase, while surface protein concentration increases first and then decreases; the shear-thinning characteristic is also improved first and then weakened. As the total protein content is fixed at 3%, with the increase in soy protein isolate ratio, the droplet size and yield stress increase, the shear-thinning characteristic is improved and the surface protein content decreases from 2.96% to 2.56%, the apparent viscosity increases from 0.154 Pa·s to 0.297 Pa·s at the shear rate of 60 s^-1.