Sideslip angle estimation is the key technique of vehicle stability control system. For electric vehicle, the information of driving torque and braking torque on wheels can be fully utilized to calculate the longitudinal force on the tire. If the longitudinal slip of the tire is large enough, the lateral force on the tire can be expressed with the longitudinal force on the tire and the tire slip angle. If the longitudinal slip of the tire is small, the nonlinear tire model is locally linearized. In such way, the two degrees of freedom vehicle model can be simplified to a linear model, whose state variable is the vehicle lateral speed and can be used to estimate the sideslip angle of the vehicle. Simulation results show that the estimation accuracy of the sideslip angle with the linear estimation model is high enough, and its dependence on the road surface friction coefficient is small. In addition, the calculation is simple due to the linearity of the model. The estimation method can be applied to the vehicle stability control system.

Solenoid valves are mainly used as inlet and outlet valves in vehicle's anti-block system (ABS). The inlet valve is used to control the pressure increase ramp in the pressure-increase phase of the ABS. When the valve is in linear control mode, the control accuracy and dynamic response are poor. So a new stepped control method based on switch control is proposed, and the conditions that are suitable for stepped control are given. The key matching parameters are determined based on the characteristics of the response of solenoid valve under switch control mode. A test bench is built with a real vehicle brake system. The values of the key parameters are adjusted to meet the requirement of the stepped control. Then these adjusted parameters are used in an ABS algorithm on road test. Results show that the proposed method can quickly determine the reasonable ranges of the key parameters and the control accuracy is high.

To solve the issue of getting vertical speed for semi-active suspension control, an integral filter circuit and simulation method for vertical speed calculation are put forward based on the analysis of real signal components. The electrical simulation model with SimScape for off-line simulation is constructed. The hardware of the filter circuit for Hardware-In-the-Loop (HIL) test is designed. The integral filter circuit system is exploited through real vehicle acceleration signal. The acceleration signal under the real random road condition is processed through off-line calculation. Experiment result show that the signal obtained from the integral filter circuit system simulation is in high consistent with the signal obtained by HIL test, justifying the integral filter circuit system.

A dynamic trajectory planning method of intelligent driving is proposed. Under the proposed hierarchical framework with three different layers in trajectory planning, the micro level is the focus to determine that how vehicle travels to ensure its safety and to achieve optimal driving performance and efficiency. The micro driving maneuvers that constitute any complex driving tasks were extracted. A cubic polynomial is used to represent the planned lane-change trajectory. A trajectory family is generated based on a vehicle steady-state kinetic model, which is used to ensure its safety against the traffic obstacles. Finally, a trajectory is selected based on optimization among multiple objectives on driving safety, efficiency and performance. The proposed method is valid and effective under Simulink environment.

The thermal physical characteristic parameters of the power battery for pure electric vehicle are obtained by experiment and simulation. Then, using finite element simulation software ANSYS, the heating and cooling temperature fields of the battery are simulated and analyzed. The temperature filed distribution is applied to guide the design of the cooling system of the battery pack. Finally, the simulation results of the heating and insulating processes of the battery are used to complete the design of the heating and heat insulation systems, so as to realize the thermal management functions of the battery box. The feasibility of the cooling, heating and heat insulating system designs are verified by experiment.

During the synchronizing process of clutch, the reason and the rule of development of the inhomogeneous temperature field were investigated using Thermalelastic Instability (TEI) theory. A three dimensional finite element model was developed and the growth rates of the perturbation corresponding to different frequencies were calculated. The results show that the thermalelastic stability can be improved by the decrease in the clutch radius and the increase in the thickness of the friction disk. When the ratio between the inner and outer radii of the clutch is less than 0.6, as the ratio decreases the stability can be enhanced. When the thickness of the mating disk is less than 2 mm, the decrease in this thickness can also improve the stability.

A simplified model of Hydraulic Electromagnetic energy-regenerative Shock Absorber (HESA) was proposed. The pressure drop of the hydraulic circuit was analyzed from the loss in the hydraulic rectifier, the hydraulic motor and pipeline. The theoretical damping force of HESA was deduced. A HESA prototype was manufactured, the corresponding test bench was built. The experiment of the prototype was carried out, and the experimental data was compared with the theoretical data. Results indicate that, under the condition of excitation frequency of 1.67 Hz and excitation amplitude of 12.5 mm, the experimental damping force of HESA is in good agreement with the theoretical value. The deviation of the experimental data from the theoretical value increases with the amplitude. The deviation of the experimental data from the theoretical value in the compression stroke is smaller than that in the extension stroke.

Based on the finite element theory for 3D temperature field, a temperature field analysis module, King Mesh Analysis System_Hot Forming (KMAS_HF), was developed. In this module the blank and tools were modeled by shell and 3D tetrahedral elements respectively. During the numerical simulation, the boundary conditions of the sheet metal and water-cooled tools in practical hot forming process were considered; the transformation latent heat release of the blank was introduced into the analysis of the temperature field. The hot forming process of a typical U-shaped part simulated using KMAS_HF was compared with experiment. The numerical simulation result is in good agreement with the experimental result, which confirms that this module can accurately predict the temperature distribution in hot forming process.

Regarding fully signalized four-leg roundabouts, a single-approach-entering operation can be safely and efficiently performed. By using a kind of non-optimization methodology, a phase scheme with four entering vehicle phases, four circulating vehicle phases, and four pedestrian phases is established. Four temporal characteristics of right-of-way assignment are extracted from the design of phase scheme, based on which optimal cycle length and green splits can be determined to conditionally satisfy the temporal demands of different road users on right-of-way. Simulation experimental results indicate that the proposed signal timing plans will substantially prevent the test-bed roundabout performance from seriously deteriorating during peak periods. Also, it is recommended to integrate yield rule and traffic signalization at larger roundabouts on a time-of-day basis.

A neural network-based calculation method of deflection increasing coefficient of steel-concrete composite beam is proposed. This method takes the complexity and limitation of existing methods into consideration. The simply supported composite beam under concentrated load and uniformly distributed load is taken as example. Considering the effect of interface slip, the concept of deflection increasing coefficient is put forward. Sample data of the finite element model of composite beam are generated from simulation on ANSYS. BP neural network is trained and texted first using the sample data; then, it is applied to obtain the analytical formula of the deflection increasing coefficient.

The shear behavior of concrete beams reinforced with U-shaped high strength steel wire mesh at different prestress levels with new tension and anchor system is investigated. Six beams reinforced with high strength steel wire mesh at different prestress levels and shear span to depth ratio of 2.2 and 1.65 (3 specimens for each ratio) were tested and compared with one reinforced concrete beam without high strength steel wire mesh. The results show that the load capacity and the stiffness of beams reinforced with prestressed steel wire mesh increase with the prestress level of the steel wire mesh. If the prestress level is greater than 0.3, the increase rate of the load capacity is reduced. A model to predict the shear capacity of the beams is proposed. The predicted results using this model are in good agreement with the experiment results. The proposed model can be used for theoretical analysis and design of concrete beams strengthened with prestressed steel wire mesh.

The reliability of steel columns exposed to fire is analyzed, in which the capacity reduction and fire load factors are considered. A case study for evaluating the fire and steel column temperatures and the load capacity of the steel column is carried out to analyze the structural reliability of the column. The parameters affecting the structural reliability are treated as random variables, such as the fire load, dead load, live load, thermal absorptivity of structural boundaries, thickness, density and thermal conductivity of the insulation, ratio of floor area to the total area of the fire compartment, opening factor, and so on. The mechanical and geometric properties of the steel column (e.g. yield strength, elastic modulus, cross-section area, etc.) are also taken as random variables. The fire protection systems are related to the probability of fire occurrence. The retailed analysis results show that the capacity reduction and fire load factors should not be treated as constants in all design situations; they should be varied with fire protection system in the building and the compartment size.

The characteristics of ion current signal were investigated by experiment on a Direct Injection Homogeneous Charge Compression Ignition (DI-HCCI) engine test bench fueled with ethanol. The Chemkin-Pro software was utilized to analyze the chemical reaction path of the ionization reactions in the ethanol combustion process, which could be employed to explain the phenomenon observed in experiment. Results show that, as the engine load increases, the intensity of the ion current signal is enhanced, the start phase and peak phase of the signal are advanced, the maximum rise rate of the ion current signal is raised, i.e. the ionization reaction is accelerated.

In order to improve the self-starting capacity of Vertical Axis Wind Turbine (VAWT), the variation rule of blade pitch angle, which is suitable for the cases of low Tip Speed Ratio (TSR), is proposed; an actuator for collective pitch control is designed, which can change the blade pitch angle of multiple blades. A prototype is presented from the aspects of actuator of collective pitch control, airfoil selection, structure of wind wheel and control system. The performance tests of the prototype were carried out. The results show that the best thickness of the symmetrical airfoil is 15%, while the maximum thickness of asymmetrical airfoil is less than 10.4%. The theoretical data show similar variation trend but is 20%-30% higher than experimental data. The results also show that, to improve the self-starting toque, it is effective to increase the frontal area of the blade at downwind region of upstream.

The geometrical model of typical plane bending state of heat exchange pipe and the constitutive model are established. Then the thermal deformation characteristics of pipe-soil structure in heat transfer at different temperatures are analyzed. It is focused on the influence of typical initial thermal deformation of bending pipe on the development of pipe-soil gap evolution. Results show that larger pipe-soil gap appears in the concave side of the bending heat exchange pip. The gap distribution around the buried pipe is about half of the pipe perimeter. The larger gap of smaller bending deflection or no bending heat exchange pipe mainly appears at the corresponding area between two pipes of U-type heat exchange pipe. The magnitude of pipe-soil gap increases with the bending degree of the heat exchange pipe.

It takes the loader of parallel hybrid power as the object to study the control strategy. Through analyzing the working character of a five-ton loader's load spectrum, the control method of fuzzy logic was put forward. Take the required torque of system which was estimated online and super capacitor's SOC as the input to get output torque of engine and electromotor. The result shows that the control strategy raises the fuel economy of the engine. Comparing with traditional loader, it can save about 7.8% fuel and achieve the goal of energy-saving.

Taking two-parameter Weibull distribution as the basis function, an optimization model is established based on the maximum likelihood method for parameter estimation, and the model is solved by improved Expectation-Maximization (EM) algorithm. Bayesian random classification algorithm, which is used for parameter initialization, is proposed in the improved EM algorithm. Radial basis function interpolation is used to solve the transcendental equation in the maximization step of the EM algorithm. The performance of the improved EM algorithm is compared with traditional algorithms by case study. Approximation performance of finite Weibull mixture distributions is analyzed with specific cases. The distribution law of the reliability data of ten punching machine tools, which come from testing field, is investigated by Weibull mixture distributions. The relationship between KS test statistic and the number of Weibull distributions is analyzed. The case study suggests that the fourfold Weibull mixture distributions could be better reflect the real law of the punching machine tools in early stage.

A new method using a double-sensor difference based algorithm for analyzing human segment rotational angles in two directions for segmental orientation analysis in three-dimensional (3D) space was presented. A wearable sensor system based only on triaxial accelerometers was developed to obtain the pitch and yaw angles of thigh segment with an accelerometer approximating translational acceleration of the hip joint and two accelerometers measuring the actual accelerations on the thigh. To evaluate the method, the system was tested on a mechanical arm of two degrees of freedom and on the thighs of eight volunteer subjects. The results show that, without integration and switching between different sensors, using only one kind of sensor, the wearable sensor system is suitable for ambulatory analysis of normal gait orientation of thigh and shank in two directions of the segment-fixed local coordinate system in 3D space. It can then be applied to assess spatio-temporal gait parameters and monitoring the gait function of patients in clinical settings.

Using separated Tx/Rx and two-way diodes, the full-wave recording system for Magnetic Resonance Spectroscopy (MRS) groundwater exploration can increase the amplitude of the received MRS signal, and decrease the dead time of MRS exploration from 40 ms to 18 ms. Considering the characteristics that this full-wave recording system requires high-precision synchronization, large amount of data acquisition and fast data transmission, a full-wave recording system with the core of CompactRIO is developed, which can provide a practical platform for processing algorithm such as the reference noise cancellation algorithm. Experiment results show that the developed system, which is based on the state machine and multi-threading, is reliable and has the ability of providing effective full-wave data for the MSR inversion.

Multi-conductor transmission line equations belong to the first order hyperbolic partial differential equations, which are converted into first-order linear constant coefficient equations by means of the first-order partial differential equations. It applies the characteristic line of the partial differential equations to construct its first-order upwind scheme, conducts a flux splitting of the coefficient matrix of the linear equations and combines the boundary conditions of resistive load to calculate the transient response of the transmission line. Finally, by simulation, the calculation results of this work are compared with that of the traditional PDTD format and Lax-Wendroff format with second order accuracy. It is demonstrated that the proposed calculation method overcomes the non-physical oscillation of traditional PDTD format and Lax-Wndroff format at the discontinuous solutions. At the same time the new method has the characteristics of simple and stable, wide range of application and similar efficiency to those two calculation methods.

The interface of welding joint of aluminum and steel was analyzed using XRD, EDS and the related phase diagram. It was observed that a large number of Al-Fe Intermetallic Compounds (IMC), e. g. Fe₂Al₅ and FeAl₃, was formed in the joint interface. In order to study the relationship between Al-Fe IMC and the welding temperature, a model for Gibbs free energy calculation in the formation of Al-Fe IMC was established based on the basic law of thermodynamics. The Gibbs free energy of Al-Fe IMC with the variation of the temperature was simulated using this model. The results show that the primary Al-Fe IMCs in the interface are Fe₂Al₅ and FeAl₃. The Gibbs free energy for the generation of Fe₂Al₅ is lower than that of FeAl₃, so Fe₂Al₅ is generated prior to FeAl₃ in the welding process of joining of aluminum and galvanized steel.

Miniaturization and high computational performance are demanded when Model Predictive Control (MPC) is applied to fast dynamic system. In order to meet these requirements, a novel hardware implementation method for MPC on a Field Programmable Gate Array (FPGA) chip is proposed. The Particle Swarm Optimization (PSO) algorithm is employed to solve the Quadratic Programming (QP) problem formed in MPC. By analyzing the characteristics, the MPC algorithm is optimized by parallelism-loop unrolling and pipelining to obtain an optimal MPC controller. Real-time simulation tests of electronic throttle control are performed to verify the MPC controller. The results show that the proposed scheme can improve the computational performance of MPC.

A nonlinear mathematic model for multi-cylinder forging hydraulic press system is established, in which the uncertainty of large load and the coupling of multi-cylinder is taken into consideration. The over actuation of the hydraulic cylinder is solved using control allocation method based on generalized inverse. Then a two-level nonlinear controller is proposed to solve the above problems. Employing Lyapunov Direct Method, the inner-loop controller controls the four SISO hydraulic cylinder pressure system to achieve accurate and fast tracking for the desired pressure. To control the uncertain large load MIMO system, the out-loop controller is designed using adaptive control strategy, with reference to the control allocation theory, to obtain the desired pressure of each cylinder. Simulation and experiment demonstrate that the strategy proposed is better than the sliding mode variable structure control strategy with single controller, and high-accuracy of the leveling and tracking control under slow running can be achieved.

An iterative robust filtering algorithm was proposed for a class of dynamic systems with input uncertainties, model parameter uncertainties and missing measurements. A norm-bounded variable was used to describe the system input uncertainties and model parameter uncertainties. The phenomenon that the measured data is missing during the transmission obeys a stochastic binary switch sequence with a known conditional probability distribution. A least-square robust filter was obtained via min-max game theory. Then, on this basis, the desired iterative robust filter was designed via an iterative tactic. Simulation results demonstrate the effectiveness of the proposed approaches.

An improved ant colony algorithm for path planning of searching robot is proposed. In this algorithm, a grid environment model is established and a taboo strategy is adopted to avoid path deadlock. Meanwhile, the retracing ants, which adopt different searching strategies between the forward and backward directions, are used to increase the convergent speed of the algorithm. Moreover, an objective function of path evaluation is constructed to improve the ability of path searching. Extensive experiments demonstrate that the proposed algorithm can plan the optimal path even in a complicated environment.

In order to avoid the radiation injury to the drivers in the detection process of the free toll lane in highway, the vehicle cab should be stepped aside; such vehicle is called green-vehicle. A new avoidance control method of green-vehicle cab is proposed using the finite state machine. First, on the basis of the mechanism of vehicle cab avoidance, the finite state machine model of green-vehicle cab avoidance is established. The program is developed based on the model for simulation and evaluation. Simulations are carried out to access and validate the finite state machine model. A large amount of real data measured from experiment is analyzed. The results prove that the control method can accurately and effectively achieve cab avoidance, and eliminate the misoperation caused by non green-vehicles.

In traditional manual detection of fracture splitting notch of auto connecting rod, the workload is heavy, the efficiency is low and the detecting error is big. To overcome these shortcomings, an improved machine vision inspection method is proposed. This method uses a CCD camera to obtain detection image, and filters out the background noise by homomorphic filtering technique to improve the quality of the detected images. It uses the self-adaptive threshold Canny edge detection method to extract the edge information. The target feature is recognized and judged by measuring its circularity and oblateness. The proposed method is verified by auto connecting rod detection experiment. Results show that using the proposed method can realize quick and accurate detection of auto connecting rod.

A testing platform of single cell lifespan was set up, which is able to simulate the operating temperature and cycle life of battery. The lithium manganese LiMn₂O₄ 90 Ah battery used in pure electric bus was taken as the research objective to simulate the lifetime test under the operation condition at 10 ℃, 25 ℃ and 40 ℃, and to analyze the influences of temperature and operating range of State of Charge (SOC) on the battery cycle life. The results show that, Arrhenius equation can be used to analyze the effect of temperature on the battery impedance and capacity in different aging states. The third order RC equivalent circuit and electrochemical impedance spectra are helpful to get the polarization states under different SOC ranges and analyze the mechanism of battery capacity retention. By comparing the lifespans under different accelerating stresses, an optimized control method is proposed, which can provide the basis for battery type selection and performance estimation in large scale application and prolonging the battery cycle life.

An improved genetic algorithm is proposed, which can solve the partitioning problem and the hardware implementation exploration simultaneously. In accordance with the characteristic of hardware delay-area contradiction in implementation, the mutation operator is chosen adaptively based on the Q-learning algorithm and greedy algorithm. The objectives are to avoid the blindness in mutation and to enhance the capability of local search of the genetic algorithm. Experiment results show that the improved genetic algorithm is more efficient than the greedy algorithm and the standard genetic algorithm in terms of searching quality and convergence.

Most existing service adaptation methods only consider the simple scenarios where two services interact and do not support the automatic generation of BPEL codes. To overcome these shortcomings, an automatic service adaptation approach for multi-service interactions is proposed. The signatures and behaviors of services are modeled by extending the finite automata. Based on the extended model, the compatibility for multiple services is analyzed. For incompatible cases, the syntax and operational semantic of the adaptor model are defined and the automatic generation algorithm for the adaptor model is presented. The generated adaptor model is then reduced in order to be automatically mapped into the BPEL codes. The feasibility of the proposed approach is validated by a case analysis.

A testing method for aggregate bandwidth of cloud storage file system is proposed, and a parallel file system aggregate bandwidth test software, called FSPoly, is developed. The testing results are analyzed in depth to verify FSPoly. Then FSPoly is used for the evaluations of the aggregate bandwidth of memory file system, network file system and parallel file system.

A Fault-tolerant Energy-saving Scheduling Algorithm base on Checkpoint Scheme (CPFTES) is proposed. The algorithm considers fault tolerance and power management in real-time system that the discrete frequency and voltage are provided by processors. The fault tolerance is achieved via optimal checkpoint scheme and the power management is carried out using dynamic voltage scaling. The optimal checkpoints can help the task to guarantee the timing constraints and reliability. If a task fails, it can restart from a saved checkpoint and execute at the maximum speed to ensure the deadlines are met. Two speed selection strategies, named H_SPEED and A_SPEED, of the algorithm are proposed. Simulation results show that CPFTES_A_SPEED can save about 66.18% energy compared to CPFTES_NODVS and save about 0~19.41% energy compared to CPFTES_H_SPEED under PXA250 processors.

In order to effectively prolong the lifetime of cluster-based Wireless Sensor Networks (WSNs), an improved Energy-Efficient Separation Algorithm (EESA) is proposed to solve the uneven energy dissipation problem. On the basis of inter-cluster energy balance, the basic clustering algorithm is improved. EESA uses network topology and energy consumption to calculate q cluster radius. The selection strategy of cluster heads is modified from the perspective of task separation, in which the task of a single cluster head is assigned to two nodes to obtain the intra-cluster energy balance. Simulation results show that EESA can avoid the energy hole problem effectively and reduce energy consumption of the whole WSN, thus prolonging the network lifetime.

In order to narrow the semantic gap between low-level visual features and high-level semantic concepts in image categorization, the clustering information from a density clustering algorithm and the characteristics of multi-instance learning framework in distinguishing ambiguous object are exploited. An image categorization method is proposed using Density Clustering on Region Features and Multi-Instance Learning (DCRF-MIL). First, the DCRF-MIL divides each image into number of regions and relines up all regions into a collection; then it uses a density clustering algorithm to learn the potential distribution information of the region features in the collection. Second, it treats an image as a bag and the regions as instances in the bag. Based on the cluster distribution information of region features, the bag is mapped into a vector in the cluster distribution space. Finally, a support vector machine classifier is constructed to predict the class label of the unlabeled image. The experiments on the Corel image data set and MUSK molecular activity prediction data set show that the DCRF-MIL method has high classification accuracy and it is easy to select its parameters.

To overcome the time overhead shortcoming of Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, a modified DBSCAN algorithm based on grid cell is proposed. Using this algorithm the data space is divided into grid cells and large number of unnecessary operations is eliminated, thus the region query process, which is the most time-consuming in DBSCAN algorithm, is optimized. The effects of different grid division methods are analyzed to select the optimal division method, thus improving the total operation efficiency of the algorithm. Simulation results verify that the modified DBSCAN algorithm based on grid cell has higher accuracy and lower time complexity.

A salient object segmentation method based on low-level visual feature and middle-level visual cues was proposed. First, the low-level visual feature of the original image was extract via color, intensity, orientation and local energy feature channels to build the saliency map. The salient feature mask was acquired via the maximum information entropy principle. Then In middle-level, the visual cues were applied for over-segmentation of an image into superpixels. In clustering, the spatial information of the feature vector was taken into consideration according to the salient intensity, and the initial parameters were automatically set. Thus, the superpixels after segmentation accurately approach the object contour. Finally, for segmenting the irregular object from background, the superpixels were classified using the salient feature mask, and the low-level and middle-level features were fused. The experiment results demonstrate that the proposed method is less sensitive to complex illumination and background, and can be used to segment contour accurately. Moreover, it can be applied to segment irregular objects from complex background.

For the delay problem of Internet, a Round Trip Time (RTT) latency measurement of network was proposed to study the impact of the Internet delay on the remote real-time drive control system of electric vehicle. The factors that affect the RTT latency were analyzed, and the RTT delay was measured and analyzed using active measurement. An effective remote control model of electric vehicle was proposed and simulations were conducted. The simulation results accurately reflect the impact of Internet delay on the control system of electric vehicle, and provide an effective tool for further research.

To enhance the algorithm's ability of anti-regional attacks of digital images, a secure and highly robust digital watermarking algorithm is proposed. The concept of Random Quaternary Group (RQG) is introduced, and the RQGs of the host image are used to hide the original redundant watermark bits and the mean value of the 8×8 blocks of the RQGs. Before the watermark embedding process, the original watermark is encrypted by chaotic sequence; the performance evaluation function of the proposed algorithm is applied to search the high and low limits of the quantization step of watermark embedding. In watermark embedding process, the algorithm is used to select the optimal quantization step adaptively according to the content of each block. Experiment results show that the RQG strategy is secure; the proposed method has good watermark transparency and high robustness resisting conventional regional attack and other attacks of digital images.

The adaptivity of the existing methods of elephant flow identification is poor and can not meet the needs of engineering application. In order to solve this problem, a novel algorithm based on Least Recently Used (LRU) replacement was developed. The algorithm has two caches, flow merge cache and LRU cache. First, the packet goes into flow merge cache and is matched to flow records according to flow keywords, and the length of flow is estimated. In the meantime, the threshold is changed with the previous length of the flow estimated by LRU cache in real time. Then the estimation of the flow is compared with the threshold; if the estimated length of the flow is longer than threshold, the packet belongs to the flow can go into LRU cache and be further filtered, otherwise the packet is abandoned. To guarantee the filtering effect and real-time performance, a method of setting up the threshold of time span was proposed. Theoretical analysis and experiment results illustrate that the algorithm can guarantee the accuracy, improve the adaptivity, and is more suitable for engineering application.

Using correlated channel estimation, compressive sampling theory, signal reconstruction based on the basis pursuit technique and the average-consensus algorithm, the proposed algorithm realizes effective and accurate signal detection. The proposed algorithm is compared with the traditional energy detection scheme in the condition of Rayleigh fading. Results show that the proposed algorithm can achieve better performance than the energy detection scheme in low SNR and less sampling rate scenario. Meanwhile, the detection performance can also be improved when the cognitive radio users increase.

The underwater acoustic channel is the most complex channel. In order to achieve significant diversity gain in the underwater acoustic channel, a BCH-LDPC coded Multi In Multi Out-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) underwater acoustic system is developed and an improved receiver is proposed for the system. In the system, the outer and inner encoders are separated by the V-BLAST encoder in the transmitter. The improved receiver is based on the joint iterative detection and decoding algorithm. A portion of information is feedback from the outer decoder to reduce the operation times of the MIMO detection and improve the reliability of the initial soft information. The performance of the system using three different receivers is analyzed by simulation. Results show that the improved receiver can obtain more performance gain and the computational complexity is significantly lower. Besides, when the system coded rates are the same, the performance of the BCH-LDPC coded system it better than that of the separate coding system at high Signal-to-Noise Ratio (SNR).

A quantum routing scheme is proposed to teleport a quantum state from one quantum device to another wirelessly, even though the two devices do not share EPR pairs mutually. The scheme is based on entanglement swapping and quantum teleportation. The model of quantum wireless communication network and correlative communication protocol is presented. In order to be compatible with the conventional wireless communication network, the network architecture of a two-tier database structure is proposed. In terms of qubit transmission time and security, performance analysis results show that the proposed scheme is superior to quantum relay routing scheme. The proposed scheme has high scalability and practicability.

Based on the chaos forecasting model, a dissipative coupling detection method is proposed for detecting weak signal in chaotic background. The Radial Basis Function Neural Network (RBFNN) is applied to fit the chaos forecasting model. The synchronization between chaotic time series and chaos system is realized by combination of RBFNN and dissipative coupling. Then the synchronization error is used to detect the weak signal. The Rossler chaos system is taken as the object to test the feasibility of the proposed method and analyze its performance with weak signals of different frequencies. In order to avoid the mixed impact of noise and chaos time series to the performance of the method, the interference of noise intensity on weak signal detection is investigated in depth. Simulation results show that the proposed method can detect weak signal with different frequencies; under certain conditions, the method can detect weak periodic signal if the Signal-to-Clutter Ratio is bigger than -110 dB. The noise influence on the detection performance can be ignored if the Signal-to-Noise Ratio (SNR) ≥ 0 dB. On the basis of theoretical study, a new type chaotic evolution control experiment instrument MKS-CEC-Ⅲ is applied to produce practical Coullet chaotic time series and add weak signals with different frequencies into time series respectively. This method is used to detect the weak signal from the mixed signal. It is demonstrated that the method can detect added frequency signal from chaotic background and it can also be applied to other chaotic systems.

To overcome the disadvantages of low localization accuracy and poor efficiency of Monte Carlo Localization (MCL) algorithm in harsh Wireless Sensor Network (WSN), a self-adaptive localization algorithm based on MCL is proposed. The sample particles in different regions have different effects on unknown node localization accuracy. The proposed algorithm assigns self-adaptive weights to sample particles in different regions to position the unknown nodes. At the same time, the algorithm adds the constraint condition using the last-time sample particles. Simulation results show that the average localization error of the proposed self-adaptive MCL algorithm descends 13% at different degrees of irregularity. The average localization error descends about 10% at different node speeds. The network coverage rate reaches 99.19%.

The key problems in current methods of automatic extracting of marking cross points are analyzed. Then, a quick detection of the cross points based on the intersection of the barycenters and the accumulation volume is proposed, and a accurate locating algorithm by single-point-mode matching is designed to ensure the accuracy. Experimental results show that the algorithm is robust to noise, rotation, resampling error, illumination variations, a certain degree of view variation and perspective distortion. The locating accuracy of 0.01 to 0.05 pixels is obtained.

Visual saliency maps of the source images are extracted with various image features. A novel fusion rule for the low-frequency subbands of multi-scale image fusion algorithms is proposed, and a new multi-scale image fusion method is developed based on the visual saliency maps. Átrous wavelet is integrated with Nonsubsampled Contourlet Transform, and is applied to the experiment of multi-sensor and multi-focus image fusion. It is demonstrated that the proposed approach yields better results both in visual inspection and objective evaluation than the methods choosing low frequency coefficients based on average or neural network.

The existing Analog to Information Conversation (AIC) is based on uniform-low-rate information measurement, and the importance prior information contained in the signal is underused. Under the energy criterion, a Non-Uniform Information Acquisition (NUIA) method is proposed. After random modulation, the signal is sampled at non-uniform-rate that the bigger the energy is the higher the sampling rate is and vice versa. Combing the idea of support merger with pruning, a Variable Rate Matching Pursuit (VRMP) algorithm is proposed. The prior support set, which is united with the set of signal appropriation support, is able to promote the recovery accuracy. Compared with the Subspace Pursuit (SP) reconstruction of conventional AIC, the combination of NUIA-VRMP can obtain 50dB reconstruction gain at ultra-low-rate, (e. g. 20% Nyquist Rate).

A constrained mean shift method for extracting urban tree canopy of high-resolution images is presented. First, a wavelet is decomposed and a layered pyramid structure is established. Using a specific window, the mean of the low-frequency coefficient and the standard deviation of the high-frequency coefficient of each wavelet layer are computed. The computed mean and standard deviation are used to constitute a feature space in each layer; a multi-scale pyramid image feature space is constituted. Second, from the top of the pyramid, the mean shift of each layer is computed from the top layer of the pyramid, and the scale transfer between layers is carried out. The scale transfer may cause the feature space even more unsmooth, so a constrained mean shift method is adopted to realize preliminary urban tree canopy clustering segmentation. Finally, as the distinction of features in a feature space is difficult to guarantee the clustering accuracy at the edge, a further supervised segmentation method based on clustering features is taken to extract the tree canopy. Experiment results demonstrate that compared with traditional supervised methods and unsupervised methods, the proposed method can eliminate the effects of over-detailed images and other factors caused by high-resolution on extracting urban tree canopy.