Adaptive fault-tolerant controller design for airbreathing hypersonic vehicle with input saturation

The problem of fault-tolerant control is discussed for the longitudinal model of an airbreathing hypersonic vehicle(AHV) with actuator faults and external disturbances.Firstly,a fault-tolerant control strategy is presented for the longitudinal model of an AHV,which guarantees that velocity and altitude track their reference trajectories at an exponential convergence rate.However,this method needs to know the minimum value of the actuator efficiency factor and the upper bound of the external disturbances,which makes it not easy to implement.Then an improved adaptive fault-tolerant control scheme is proposed,where two adaptive laws are employed to estimate the upper bound of the external disturbances and the minimum value of the actuator efficiency factor,respectively.Secondly,the problem of designing a control scheme with control constraints is further considered,and a new adaptive fault-tolerant control strategy with input saturation is designed to guarantee that velocity and altitude track their reference trajectories.Finally,simulation results are given to show the effectiveness of the proposed methods.     

Pre-processing filter design at transmitters for IBI mitigation in an OFDM system

In order to meet the demands for high transmission rates and high service quality in broadband wireless communication systems, orthogonal frequency division multiplexing （OFDM） has been adopted in some standards. However, the inter-block interference （IBI） and inter-carrier interference （ICI） in an OFDM system affect the performance. To mitigate IBI and ICI, some pre-processing approaches have been proposed based on full channel state information （CSI）, which improved the system performance. A pre-processing filter based on partial CSI at the transmitter is designed and investigated. The filter coefficient is given by the optimization processing, the symbol error rate （SER） is tested, and the computation complexity of the proposed scheme is analyzed. Computer simulation results show that the proposed pre-processing filter can effectively mitigate IBI and ICI and the performance can be improved. Compared with pre-processing approaches at the transmitter based on full CSI, the proposed scheme has high spectral efficiency, limited CSI feedback and low computation complexity.     

Study of adaptive modulation and LDPC coding in multicarrier systems

An adaptive modulation （AM） algorithm is proposed and the application of the adapting algorithm together with low-density parity-check （LDPC） codes in multicarrier systems is investigated. The AM algorithm is based on minimizing the average bit error rate （BER） of systems, the combination of AM algorithm and LDPC codes with different code rates （half and three-fourths） are studied. The proposed AM algorithm with that of Fischer et al is compared. Simulation results show that the performance of the proposed AM algorithm is better than that of the Fischer＇s algorithm. The results also show that application of the proposed AM algorithm together with LDPC codes can greatly improve the performance of multicarrier systems. Results also show that the performance of the proposed algorithm is degraded with an increase in code rate when code length is the same.     

Novel robust fault diagnosis method for flight control systems

A novel robust fault diagnosis scheme, which possesses fault estimate capability as well as fault diagnosis property, is proposed. The scheme is developed based on a suitable combination of the adaptive multiple model （AMM） and unknown input observer （UIO）. The main idea of the proposed scheme stems from the fact that the actuator Lock-in-Place fault is unknown （when and where the actuator gets locked are unknown）, and multiple models are used to describe different fault scenarios, then a bank of unknown input observers are designed to implement the disturbance de-coupling. According to Lyapunov theory, proof of the robustness of the newly developed scheme in the presence of faults and disturbances is derived. Numerical simulation results on an aircraft example show satisfactory performance of the proposed algorithm.     

A Novel Approach to Construct a Composite Indicator by Maximizing Its Sum of Squared Correlations with Sub-Indicators

In the paper,a novel practical approach to construct a composite indicator(CI)is proposed.The key idea is to decide the weights of sub-indicators in constructing a composite indicator by maximizing the sum of squared correlations between the CI and sub-indicators.The CI obtained in this fashion has the maximum sum of squared correlations among all linear estimators.In addition,the simple,exact and explicit solutions of weights are proposed under the condition of non-negative irreducible matrix.Moreover,under this particular condition,the proposed method will become the principal component analysis.For illustration purpose,the proposed novel approach is utilized to calculate Sustainable Energy Index and Human Development Index which are two often-used cases to compare models in the literatures.The results show that the power of the proposed method outweigh the methods of Hatefi,et al.(2010)and Zhou,et al.(2007)in terms of the sum of absolute values of all sub-indicators’correlations.     

A New Method for Searching Cubes and Its Application to 815-Round Trivium

The cube attack proposed by Dinur and Shamir is one of the most important key-recovery attacks against Trivium. Recently division property based cube attacks have been extensively studied and significantly improved. In particular, the MILP modeling technique for the three-subset division property without unknown subset proposed by Hao, et al. at EUROCRYPT 2020 and the new technique with nested monomial predictions proposed by Hu, et al. at ASIACRYPT 2021 are best techniques to recover exact supe...     

A note on two classes of Boolean functions with optimal algebraic immunity

Tu and Deng proposed a class of bent functions which are of optimal algebraic immunity under the assumption of a combinatorial conjecture.In this paper,the authors compute the dual of the Tu-Deng functions and then show that they are still of optimal algebraic immunity under the assumption of the same conjecture.For another class of Boolean functions constructed by Tang,et al.which are of optimal algebraic immunity with similar forms to Tu-Deng functions,the authors show that they are not bent functions by using some basic properties of binary complete Kloosterman sums.     

Local spatial properties based image interpolation scheme using SVMs

Image interpolation plays an important role in image process applications. A novel support vector machines （SVMs） based interpolation scheme is proposed with increasing the local spatial properties in the source image as SVMs input patterns. After the proper neighbor pixels region is selected, trained support vectors are obtained by training SVMs with local spatial properties that include the average of the neighbor pixels gray values and the gray value variations between neighbor pixels in the selected region. The support vector regression machines are employed to estimate the gray values of unknown pixels with the neighbor pixels and local spatial properties information. Some interpolation experiments show that the proposed scheme is superior to the linear, cubic, neural network and other SVMs based interpolation approaches.     

Polynomial-Time Key-Recovery Attacks Against NTRURe Encrypt from ASIACCS'15

In ASIACCS 2015, Nu nez, et al. proposed a proxy re-encryption scheme, named NTRURe Encrypt, based on NTRU, which allows a proxy to translate ciphertext under the delegator’s public key into a re-encrypted ciphertext that can be decrypted correctly by delegatee’s private key. Because of the potential resistance to quantum algorithm, high efficiency and various applications in real life,NTRURe Encrypt has drawn lots of attention and its security has been widely discussed and analyzed.In PQCrypto2...     

Parametric approach to track following control of FFSM

A robust task space tracking scheme is proposed for the free-flying space manipulator system.The dynamic equations of the system are derived via the law of momentum conservation,and then a linear state space representation is formulated by local linearization.A parametric approach is applied by using the eigenstructure assignment theory and the model reference method.A feedback stabilizing controller and a feedforward compensation controller are built based on the approach.Then an optimization procedure is followed after that to obtain the desired requirement and characteristics.Simulation results are presented to show the effectiveness of the proposed method.     

Low complexity hybrid power distribution combined with subcarrier allocation algorithm for OFDMA

To improve the total throughput of the uplink orthogonal frequency division multiple access system,a low complexity hybrid power distribution(HPD) combined with subcarrier allocation scheme is proposed.For the fairness mechanism for the subcarrier,the inter-cell interference is first analyzed to calculate the capacity of the multi-cell.The user selects the subcarrier with the largest channel gain.Based on the above subcarrier allocation scheme,a new kind of HPD scheme is proposed,which adopts the waterfilling-power-distributed scheme and the equal-power-distributed scheme in the cell-boundary and the cellcenter,respectively.Simulation results show that compared with the waterfilling-power-distributed scheme in the whole cell,the proposed HPD scheme decreases the system complexity significantly,meanwhile its capacity is 2% higher than that of the equal-powerdistributed scheme over the same subcarrier allocation.     

Robust iterative learning control for nonlinear systems with measurement disturbances

The iterative learning control (ILC) has been demon-strated to be capable of considerably improving the tracking perfor-mance of systems which are affected by the iteration-independent disturbance. However, the achievable performance is greatly degraded when iteration-dependent, stochastic disturbances are pre-sented. This paper considers the robustness of the ILC algorithm for the nonlinear system in presence of stochastic measurement disturbances. The robust convergence of the P-type ILC algorithm is firstly addressed, and then an improved ILC algorithm with a decreasing gain is proposed. Theoretical analyses show that the proposed algorithm can guarantee that the tracking error of the nonlinear system tends to zero in presence of measurement dis-turbances. The analysis is also supported by a numerical example.     

Knowledge-based adaptive polarimetric detection in heterogeneous clutter

The detection performance and the constant false alarm rate behavior of the conventional adaptive detectors are severely degraded in heterogeneous clutter. This paper designs and analyses a knowledge-based （KB） adaptive polarimetric detector in het-erogeneous clutter. The proposed detection scheme is composed of a data selector using polarization knowledge and an adaptive polarization detector using training data. A polarization data selector based on the maximum likelihood estimation is proposed to remove outliers from the heterogeneous training data. This selector can remove outliers effectively, thus the training data is purified for estimating the clutter covariance matrix. Consequently, the performance of the adaptive detector is improved. We assess the performance of the KB adaptive polarimetric detector and the adaptive polarimetric detector without a data selector using simulated data and IPIX radar data. The results show that the KB adaptive polarization detector outperforms its non-KB counterparts.     

Image decomposition and staircase effect reduction based on total generalized variation

Total variation （TV） is widely applied in image process-ing. The assumption of TV is that an image consists of piecewise constants, however, it suffers from the so-cal ed staircase effect. In order to reduce the staircase effect and preserve the edges when textures of image are extracted, a new image decomposition model is proposed in this paper. The proposed model is based on the to-tal generalized variation method which involves and balances the higher order of the structure. We also derive a numerical algorithm based on a primal-dual formulation that can be effectively imple-mented. Numerical experiments show that the proposed method can achieve a better trade-off between noise removal and texture extraction, while avoiding the staircase effect efficiently.     

Polarimetric whitening filter for POLSAR image based on subspace decomposition

Speckle filtering is an indispensable pre-processing step for applications of polarimetric synthetic aperture radar （POLSAR）, such as terrain classification, target detection, etc. As one of the most typical methods, the polarimetric whitening filter （PWF） can be used to produce a minimum-speckle image by combining the complex elements of the scattering matrix, but polarimetric information is lost after the filtering process. A polarimetric filter based on subspaze decomposition which was proposed by Cu et al specializes in retrieving principle scattering characteristics, but the corresponding mean value of an image after filtering is not kept well. A new filter is proposed for improving the disadvantage based on subspace decomposition. Under the constraint that a weighted combination of the polarimetric SAR images equals to the output of the PWF, the Euclidean distance between an unfiltered parameter vector and a signal space vector is minimized so that noises can be reduced. It is also shown that the proposed method is equivalent to the subspace filter in the case of no constraint. Experimental results with the NASA/JPL airborne polarimetric SAR data demonstrate the effectiveness of the proposed method.     

Bayesian network learning algorithm based on unconstrained optimization and ant colony optimization

Structure learning of Bayesian networks is a wellresearched but computationally hard task.For learning Bayesian networks,this paper proposes an improved algorithm based on unconstrained optimization and ant colony optimization(U-ACO-B) to solve the drawbacks of the ant colony optimization(ACO-B).In this algorithm,firstly,an unconstrained optimization problem is solved to obtain an undirected skeleton,and then the ACO algorithm is used to orientate the edges,thus returning the final structure.In the experimental part of the paper,we compare the performance of the proposed algorithm with ACO-B algorithm.The experimental results show that our method is effective and greatly enhance convergence speed than ACO-B algorithm.     

A second order Mehrotra-type predictor-corrector algorithm for semidefinite optimization

Mehrotra-type predictor-corrector algorithm is one of the most effective primal-dual interior-point methods.This paper presents an extension of the recent variant of second order Mehrotra-type predictor-corrector algorithm that was proposed by Salahi,et al.（2006） for linear optimization.Based on the NT direction as Newton search direction,it is shown that the iteration-complexity bound of the algorithm for semidefinite optimization is O(n^3/2 log X⁰·S⁰/ε),which is similar to that of the corresponding algorithm for linear optimization.     

Turbo decoding using two soft output values

It is well known that turbo decoding always begins from the first component decoder and supposes that the apriori information is "0" at the first iterative decoding. To alternatively start decoding at two component decoders, we can gain two soft output values for the received observation of an input bit. It is obvious that two soft output values comprise more sufficient extrinsic information than only one output value obtained in the conventional scheme since different start points of decoding result in different combinations of the a priori information and the input codewords with different symbol orders due to the permutation of an interleaver. Summarizing two soft output values for erery bit before making hard decisions, we can correct more errors due to their complement. Consequently, turbo codes can achieve better error correcting performance than before in this way. Simulation results show that the performance of turbo codes using the novel proposed decoding scheme can get a growing improvement wi     

Compressive sensing for small moving space object detection in astronomical images

It is known that detecting small moving objects in astronomical image sequences is a significant research problem in space surveillance.The new theory,compressive sensing,provides a very easy and computationally cheap coding scheme for onboard astronomical remote sensing.An algorithm for small moving space object detection and localization is proposed.The algorithm determines the measurements of objects by comparing the difference between the measurements of the current image and the measurements of the background scene.In contrast to reconstruct the whole image,only a foreground image is reconstructed,which will lead to an effective computational performance,and a high level of localization accuracy is achieved.Experiments and analysis are provided to show the performance of the proposed approach on detection and localization.     

Improvement and performance of parallel multilevel fast multipole algorithm

The method of establishing data structures plays an important role in the efficiency of parallel multilevel fast multipole algorithm(PMLFMA).Considering the main complements of multilevel fast multipole algorithm(MLFMA) memory,a new parallelization strategy and a modified data octree construction scheme are proposed to further reduce communication in order to improve parallel efficiency.For far interaction,a new scheme called dynamic memory allocation is developed.To analyze the workload balancing performance of a parallel implementation,the original concept of workload balancing factor is introduced and verified by numerical examples.Numerical results show that the above measures improve the parallel efficiency and are suitable for the analysis of electrical large-scale scattering objects.