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To further improve the security of SHA-256, a compression function construction scheme based on chaotic S-box is proposed. Through the reasonable design of the iteration mode and using the nonlinearity, confusion, and anti-difference of chaotic S-box, the relationship between the plaintext and the hash value becomes more complicated and unpredictable, by the diffusion of the linear transformation P-box, the dependence between the plaintext and the hash value is further improved. The compression function is applied to the SHA-256 Hash Algorithm to test and analyze the distribution, sensitivity, confusion, diffusion, and collision resistance, and compared with the original SHA-256 and the mainstream Hash Algorithms. The results show that the proposed scheme can effectively improve the collision resistance of the SHA-256 Hash Algorithm and enhance the stability of confusion and diffusion with fewer cryptographic components and lower operational consumption to provide more reliable security guarantee for its application.

Aiming at the fault classification problem of the rolling bearing under the uncertain structure parameters work condition, this paper proposes a fault diagnosis method based on the interval support vector domain description (ISVDD). Firstly, intrinsic time scale decomposition is performed for vibration signals of the rolling bearing to get the time-frequency spectrum samples. These samples are divided into a training set and a test set. Then, the training set is used to train the ISVDD. Meanwhile, the dynamic decreasing inertia weight particle swarm optimization is applied to improve the training accuracy of ISVDD model. Finally, the performance of the four interval classifiers is calculated in rolling bearing fault test set. The experimental results show the advantages of the ISVDD model: (1) ISVDD can extend the support vector domain description to solve the uncertain interval rolling bearing fault classification problem effectively; (2) The proposed ISVDD has the highest classification accuracy in four interval classification methods for the different rolling bearing fault types.

An interval support vector deterministic optimization model (ISVD) is proposed for the fault classification problem of uncertainty samples in this paper. Firstly, based on the order relation theory, support vector machines using interval samples is transformed into ISVD, which is a two-objective optimization problem, with the midpoint and the radius optimized at the same time. Then, ISVD is converted into a single objective optimization model by the linear combination. The single objective optimization model includes Lagrange multiplier vector and interval sample vectors, both of which are nested. Thus, the nested particle swarm optimization (PSO) based on dynamic decreasing inertia weight is applied to select the optimal Lagrange multiplier vector of this model. Lastly, the effectiveness of the proposed method is proved by the data set of University of California Irvine (UCI) and the roller bearing fault experiments. The experimental results show: ISVD owns outstanding generalization ability with the help of the structured risk minimization and global optimization. The accuracy of the proposed ISVD is better than that of the native Bayes uncertain classification method 1 (NBU1), native Bayes uncertain classification method 2 (NBU2) and the formula-based Bayes classifier (FBC).

Aiming at the problem that the traditional fault diagnosis methods cannot deal with the uncertain samples, this paper proposes an interval fault diagnosis method and its parameter optimization. First, the interval Gauss function is used to construct the kernel of the proposed interval fault diagnosis method. The weights and offsets of this interval method are calculated by solving the equality equation. Second, with the goal of maximizing the classification accuracy, the Glowworm swarm optimization algorithm is used to select the parameters of this method. Finally, the experimental results of the rotor crack interval fault samples and the University of California Irvine samples show that this method has higher classification accuracy than the traditional interval classification methods.

In this paper, the basic theory of Voltera series is briefly introduced. The kernel of Volterra is identified by the polyhedron search algorithm. Different from the traditional diagnostic method based on signal processing, this method applies the Volterra series based on the system model to the fault diagnosis of the roller bearing system. The change of the Volterra series kernel is used to distinguish the different fault state of the roller bearing. The experimental results verify the feasibility and effectiveness of the method.

To ensure reliable ambiguity resolution, ambiguity validation is an indispensable step. It has been a challenge for many years and is far from being resolved. Over the past years, various ambiguity validation methods have been proposed, such as F-ratio test, R-ratio test, difference test, projector test, ellipsoidal integer aperture (EIA) estimator and penalized integer aperture (PIA) estimator. In this paper, through analysis and testing, we find that, when the aperture region of EIA is not allowed to be overlapped, the efficiency of ambiguity resolution with EIA is low and it is not applicable to fast static positioning or real-time kinematic (RTK) applications. Then EIA with overlapped aperture regions is recommended and the resulted fail-rate becomes upper bound of the actual one. After that, it is suggested that combined use of overlapped EIA and R-ratio test can increase the reliability of ambiguity resolution. Finally, numerical tests are carried out based on practical buoy data and simulated Galileo data.

This paper investigates single epoch ambiguity resolution performance using Galileo four frequency data. Two commonly used ambiguity resolution methods are used in the tests, including the Cascade Ambiguity Resolution (CAR) and the Least-Squares Ambiguity Decorrelation Adjustment (LAMBDA) methods. For CAR method, four optimal combinations are carefully selected according to their wavelength to noise ratios and success rate for ambiguity fixing. The test results show, in general, the LAMBDA method performs better than the CAR method. The speed of ambiguity resolution is closely related to the carrier phase measurement precision. With carrier phase measurement precision of 3 mm, single epoch ambiguity resolution can be achieved at every epoch with simulated 1-s interval 24-h Galileo data (total epochs 86,400). With the increase of carrier phase noise, ambiguity resolution performances become worse. When the noise level is increased to 12 mm, single epoch ambiguity resolution can only be achieved about 50% of epochs.

GPS has been widely used for land, sea and air navigation. However, due to signal blockage and severe multipath environments in urban areas, GPS alone cannot satisfy most land vehicle navigation requirements. Dead Reckoning (DR) systems have been widely used to bridge the gaps of GPS and to smooth GPS position errors. However, the DR drift errors increase with time rapidly and frequent calibrations are required. As land vehicles have to be on roads, digital map can be used to constrain the locations of vehicles, known as map-matching, which is an efficient way to improve the performance of positioning systems while working under urban environment. In this paper, a simplified map-matching algorithm is proposed for an in-vehicle navigation unit, as the processing power of in-vehicle processor is limited. Extensive field tests have been conducted in Hong Kong and Macau. The results reveal this map-matching algorithm can improve the performance of the vehicle navigation unit significantly.

For both static and kinematic relative positioning with GPS, the success of baseline resolution depends closely on the reliable fixing of integer ambiguities of carrier phase measurements. Low noise code correlated dual-frequency GPS receivers offer accurate pseudo-range measurements on both carrier frequencies. With the help of a combination of code and carrier phase measurements, wide-lane ambiguities can be easily determined and used for the improvement of ambiguity resolutions on both L1 and L2 frequencies. A carrier smoothing technique is utilized to eliminate the noise of code measurements. Baselines ranging from 1.5 km to 48 km are used to test the proposed method.

A new mathematical approach, wavelet transformation, is introduced for GPS data analysis. Based on its good features on both time-domain and frequency-domain, one can obtain a true time-frequency representation of a signal. The so-called mutiresolution analysis (MRA) will be used for data analysis, such as for the removal of noises, and the detection and rejection of gross errors from signal. In this paper the authors present a wavelet-based algorithm for the modeling and prediction of SA effect by combining time series analysis methods. A new self-adaptive Kalman filtering algorithm is presented for anti-SA. Some preliminary test results from experimental data are also summarized.

A suitable method of combined data processing is needed to analyse the verticalcrustal deformation of a monitored area, where a large number of levelling and gravityobservations have been obtained. The paper is intended to put forward a practical method toprocess dynamically both levelling and gravity measurements. There are two main problems inthe combined adjustment. One is that the levelling network does not coincide with thegravimetric network, and the other is that the transformation parameters between gravity andheight are uncertain. The method presented in the paper may be used to evaluate thetransformation parameters and the rates of vertical deformation simultaneously. It has beenapplied to process the levelling and gravimetric observations, which were completed in theBeijing area from 1983 to 1991. It turns out that the method of the adjustment is feasible, theformula for weighting the two kinds of observations is appropriate, and the distribution of thegravity-altitude factor is in agreement with the general law.

Lagrangian support vector machine(LSVM) is a kind of method with good generalization ability. But, LSVM is not suitable for classification online because the computation complexity. So in this paper, a fast LSVM is proposed. This method can deduce running time because it fully utilizes the historical training results and reduces memory and calculates time. Finally, an example is accomplished to demonstrate the effect of fast LSVM.

Based on the Bayesian principle and the fact that GPS carrier-phase ambiguities are integers, the posterior distribution of the ambiguities and the position parameters is derived. This is then used to derive the maximum posterior likelihood solution of the ambiguities. The accuracy of the integer ambiguity solution and the position parameters is also studied according to the posterior distribution. It is found that the accuracy of the integer solution depends not only on the variance of the corresponding float ambiguity solution but also on its values.