NEW LIMITED MEMORY SYMMETRIC RANK ONE ALGORITHM FOR LARGE-SCALE UNCONSTRAINED OPTIMIZATION

A new limited memory symmetric rank one algorithm is proposed. It combines a modified self-scaled symmetric rank one （SSR1） update with the limited memory and nonmonotone line search technique. In this algorithm, the descent search direction is generated by inverse limited memory SSR1 update, thus simplifying the computation. Numerical comparison of the algorithm and the famous limited memory BFGS algorithm is given. Comparison results indicate that the new algorithm can process a kind of large-scale unconstrained optimization problems.

New scale factor correction scheme for CORDIC algorithm

To overcome the drawbacks such as irregular circuit construction and low system throughput that exist in conventional methods, a new factor correction scheme for coordinate rotation digital computer（ CORDIC） algorithm is proposed. Based on the relationship between the iteration formulae, a new iteration formula is introduced, which leads the correction operation to be several simple shifting and adding operations. As one key part, the effects caused by rounding error are analyzed mathematically and it is concluded that the effects can be degraded by an appropriate selection of coefficients in the iteration formula. The model is then set up in Matlab and coded in Verilog HDL language. The proposed algorithm is also synthesized and verified in field-programmable gate array （FPGA）. The results show that this new scheme requires only one additional clock cycle and there is no change in the elementary iteration for the same precision compared with the conventional algorithm. In addition, the circuit realization is regular and the change in system throughput is very minimal.

A new adaptive mutative scale chaos optimization algorithm and its application

Based on results of chaos characteristics comparing one-dimensional iterative chaotic self-map x = sin（2/x） with infinite collapses within the finite region[-1, 1] to some representative iterative chaotic maps with finite collapses （e.g., Logistic map, Tent map, and Chebyshev map）, a new adaptive mutative scale chaos optimization algorithm （AMSCOA） is proposed by using the chaos model x = sin（2/x）. In the optimization algorithm, in order to ensure its advantage of speed convergence and high precision in the seeking optimization process, some measures are taken： 1） the searching space of optimized variables is reduced continuously due to adaptive mutative scale method and the searching precision is enhanced accordingly; 2） the most circle time is regarded as its control guideline. The calculation examples about three testing functions reveal that the adaptive mutative scale chaos optimization algorithm has both high searching speed and precision.

Modified Frequency Scaling Algorithm for FMCW SAR Data Processing

This paper presents a modified frequency scaling algorithm for frequency modulated continuous wave synthetic aperture radar （FMCW SAR） data processing. The relative motion between radar and target in FMCW SAR during reception and between transmission and reception will introduce serious dilation in the received signal. The dilation can cause serious distortions in the reconstructed images using conventional signal processing methods. The received signal is derived and the received signal in range-Doppler domain is given. The relation between the phase resulting from antenna motion and the azimuth frequency is analyzed. The modified frequency scaling algorithm is proposed to process the received signal with serious dilation. The algorithm can effectively eliminate the impact of the dilation. The algorithm performances are shown by the simulation results.

A hybrid genetic algorithm based on mutative scale chaos optimization strategy

In order to avoid such problems as low convergent speed and local optimalsolution in simple genetic algorithms, a new hybrid genetic algorithm is proposed. In thisalgorithm, a mutative scale chaos optimization strategy is operated on the population after agenetic operation. And according to the searching process, the searching space of the optimalvariables is gradually diminished and the regulating coefficient of the secondary searching processis gradually changed which will lead to the quick evolution of the population. The algorithm hassuch advantages as fast search, precise results and convenient using etc. The simulation resultsshow that the performance of the method is better than that of simple genetic algorithms.

Spectral matching algorithm based on nonsubsampled contourlet transform and scale-invariant feature transform

A new spectral matching algorithm is proposed by us- ing nonsubsampled contourlet transform and scale-invariant fea- ture transform. The nonsubsampled contourlet transform is used to decompose an image into a low frequency image and several high frequency images, and the scale-invariant feature transform is employed to extract feature points from the low frequency im- age. A proximity matrix is constructed for the feature points of two related images. By singular value decomposition of the proximity matrix, a matching matrix （or matching result） reflecting the match- ing degree among feature points is obtained. Experimental results indicate that the proposed algorithm can reduce time complexity and possess a higher accuracy.

A CLASS OF COLLINEAR SCALING ALGORITHMS FOR UNCONSTRAINED OPTIMIZATON

A Class of Collinear Scaling Algorithms for Unconstrained Optimization. An appealing approach to the solution of nonlinear optimization problems based on conic models of the objective function has been in troduced by Davidon (1980). It leads to a broad class of algorithms which can be considered to generalize the existing quasi-Newton methods. One particular member of this class has been deeply discussed by Sorensen (1980), who has proved some interesting theoretical properties. In this paper, we generalize Sorensen’s technique to Spedicato three-parameter family of variable-metric updates. Furthermore, we point out that the collinear scaling three- parameter family is essentially equivalent to the Spedicato three-parameter family. In addition, numerical expriments have been carried out to compare some colliner scaling algorithms with a straightforward implementation of the BFGS quasi-Newton method.

A Chirp Scaling Algorithm for Spaceborne SAR System with Large Squint Angle

A new Chirp Scaling algorithm for spaceborne synthetic aperture radar(SAR) with large squint angle is presented and compared with the Range-Doppler algorithm and the algorithm in literatur [6] in the paper. The simulation results of processing point target echocs show that the algorithm developed in this paper can give more accurate image especially in the case of large squint angle.

An Innovative Genetic Algorithms-Based Inexact Non-Linear Programming Problem Solving Method

In this paper, an innovative Genetic Algorithms (GA)-based inexact non-linear programming (GAINLP) problem solving approach has been proposed for solving non-linear programming optimization problems with inexact information (inexact non-linear operation programming). GAINLP was developed based on a GA-based inexact quadratic solving method. The Genetic Algorithm Solver of the Global Optimization Toolbox (GASGOT) developed by MATLABTM was adopted as the implementation environment of this study. GAINLP was applied to a municipality solid waste management case. The results from different scenarios indicated that the proposed GA-based heuristic optimization approach was able to generate a solution for a complicated nonlinear problem, which also involved uncertainty.

Betweenness-based algorithm for a partition scale-free graph

Many real-world networks are found to be scale-free. However, graph partition technology, as a technology capable of parallel computing, performs poorly when scale-free graphs are provided. The reason for this is that traditional partitioning algorithms are designed for random networks and regular networks, rather than for scale-free networks. Multilevel graph-partitioning algorithms are currently considered to be the state of the art and are used extensively. In this paper, we analyse the reasons why traditional multilevel graph-partitioning algorithms perform poorly and present a new multilevel graph-partitioning paradigm, top down partitioning, which derives its name from the comparison with the traditional bottom-up partitioning. A new multilevel partitioning algorithm, named betweenness-based partitioning algorithm, is also presented as an implementation of top-down partitioning paradigm. An experimental evaluation of seven different real-world scale-free networks shows that the betweenness-based partitioning algorithm significantly outperforms the existing state-of-the-art approaches.

Improved chirp scaling algorithm integrated motion compensation for parallel-track bistatic synthetic aperture radar

To compensate motion errors of images from the parallel-track bistatic synthetic aperture radar（BiSAR）,an improved chirp scaling algorithm（CSA） is proposed.Since velocity vector of the moving aircrafts in the parallel-track BiSAR system can not remain invariant in an aperture,an actual aperture is divided into subapertures so that it is reasonable to assume that the aircrafts move with constant acceleration vector in a subaperture.Based on this model,an improved CSA is derived.The new phase factors incorporate three-dimensional acceleration and velocity.The motion compensation procedure is integrated into the CSA without additional operation required.The simulation results show that the presented algorithm can efficiently resolve motion compensation for parallel-track BiSAR.

Analytic Theory of Finite Asymptotic Expansions in the Real Domain. Part II-C: Constructive Algorithms for Canonical Factorizations and a Special Class of Asymptotic Scales

This part II-C of our work completes the factorizational theory of asymptotic expansions in the real domain. Here we present two algorithms for constructing canonical factorizations of a disconjugate operator starting from a basis of its kernel which forms a Chebyshev asymptotic scale at an endpoint. These algorithms arise quite naturally in our asymptotic context and prove very simple in special cases and/or for scales with a small numbers of terms. All the results in the three Parts of this work are well illustrated by a class of asymptotic scales featuring interesting properties. Examples and counterexamples complete the exposition.

NON-LINEAR DYNAMIC MODEL RETRIEVAL OF SUBTROPICAL HIGH BASED ON EMPIRICAL ORTHOGONAL FUNCTION AND GENETIC ALGORITHM

Aiming at the difficulty of accurately constructing the dynamic model of subtropical high, based on the potential height field time series over 500 hPa layer of T106 numerical forecast products, by using EOF（empirical orthogonal function） temporal-spatial separation technique, the disassembled EOF time coefficients series were regarded as dynamical model variables, and dynamic system retrieval idea as well as genetic algorithm were introduced to make dynamical model parameters optimization search, then, a reasonable non-linear dynamic model of EOF time-coefficients was established. By dynamic model integral and EOF temporal-spatial components assembly, a mid-/long-term forecast of subtropical high was carried out. The experimental results show that the forecast results of dynamic model are superior to that of general numerical model forecast results. A new modeling idea and forecast technique is presented for diagnosing and forecasting such complicated weathers as subtropical high.

Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Secured Free Scale Networks against Malicious Attacks

Due to the recent proliferation of cyber-attacks,highly robust wireless sensor networks(WSN)become a critical issue as they survive node failures.Scale-free WSN is essential because they endure random attacks effectively.But they are susceptible to malicious attacks,which mainly targets particular significant nodes.Therefore,the robustness of the network becomes important for ensuring the network security.This paper presents a Robust Hybrid Artificial Fish Swarm Simulated Annealing Optimization(RHAFS-SA)Algorithm.It is introduced for improving the robust nature of free scale networks over malicious attacks(MA)with no change in degree distribution.The proposed RHAFS-SA is an enhanced version of the Improved Artificial Fish Swarm algorithm(IAFSA)by the simulated annealing(SA)algorithm.The proposed RHAFS-SA algorithm eliminates the IAFSA from unforeseen vibration and speeds up the convergence rate.For experimentation,free scale networks are produced by the Barabási–Albert(BA)model,and real-world networks are employed for testing the outcome on both synthetic-free scale and real-world networks.The experimental results exhibited that the RHAFS-SA model is superior to other models interms of diverse aspects.

THE APPLICATION OF GENETIC ALGORITHM IN NON-LINEAR INVERSION OF ROCK MECHANICS PARAMETERS

The non-linear inversion of rock mechanics parameters based on genetic algorithm is presented. The principIe and step of genetic algorithm is also given. A brief discussion of this method and an application example is presented at the end of this paper. From the satisfied result, quick, convenient and practical new approach is developed to solve this kind of problems.

An FPGA-based LDPC decoder with optimized scale factor of NMS decoding algorithm

Considering that the hardware implementation of the normalized minimum sum(NMS)decoding algorithm for low-density parity-check(LDPC)code is difficult due to the uncertainty of scale factor,an NMS decoding algorithm with variable scale factor is proposed for the near-earth space LDPC codes(8177,7154)in the consultative committee for space data systems(CCSDS)standard.The shift characteristics of field programmable gate array(FPGA)is used to optimize the quantization data of check nodes,and finally the function of LDPC decoder is realized.The simulation and experimental results show that the designed FPGA-based LDPC decoder adopts the scaling factor in the NMS decoding algorithm to improve the decoding performance,simplify the hardware structure,accelerate the convergence speed and improve the error correction ability.

Dempster-Shafer （D-S） algorithm with credit scale in spectrum sensing

In cognitive radio, the detection probability of primary user affects the signal receiving performance for both primary and secondary users significantly. In this paper, a new Dempster-Shafer （D-S） algorithm with credit scale for decision fusion in spectrum sensing is proposed for the purpose to improve the performance of detection in cognitive radio. The validity of this method is established by simulation in the environment of multiple cognitive users who know their signal to noise ratios （SNR） and a central node. The channels between the cognitive users and the central node are considered to be additive white Ganssian noise （AWGN）. Compared with traditional data fusion rules, the proposed D-S algorithm with credit scale provides a better detection performance.

An Improved Affine-Scaling Interior Point Algorithm for Linear Programming

In this paper, an Improved Affine-Scaling Interior Point Algorithm for Linear Programming has been proposed. Computational results of selected practical problems affirming the proposed algorithm have been provided. The proposed algorithm is accurate, faster and therefore reduces the number of iterations required to obtain an optimal solution of a given Linear Programming problem as compared to the already existing Affine-Scaling Interior Point Algorithm. The algorithm can be very useful for development of faster software packages for solving linear programming problems using the interior-point methods.

Scale, Complexity, and Cybersecurity Risk Management

Elementary information theory is used to model cybersecurity complexity, where the model assumes that security risk management is a binomial stochastic process. Complexity is shown to increase exponentially with the number of vulnerabilities in combination with security risk management entropy. However, vulnerabilities can be either local or non-local, where the former is confined to networked elements and the latter results from interactions between elements. Furthermore, interactions involve multiple methods of communication, where each method can contain vulnerabilities specific to that method. Importantly, the number of possible interactions scales quadratically with the number of elements in standard network topologies. Minimizing these interactions can significantly reduce the number of vulnerabilities and the accompanying complexity. Two network configurations that yield sub-quadratic and linear scaling relations are presented.

基于分数阶傅里叶变换和图像加权熵的chirp scaling算法

针对传统的基于傅里叶变换和匹配滤波实现的chirp scaling(CS)成像算法中多普勒参数随斜距变化以及成像分辨率低的问题,提出利用分数阶傅里叶变换(FRFT)对CS成像算法进行优化。首先建立斜视合成孔径雷达(SAR)回波信号模型,理论推导利用FRFT代替匹配滤波进行信号压缩。针对方位向最优旋转角的搜索问题,对得到的图像基于加权最小熵建立代价函数,利用动量法的梯度下降优化算法进行迭代计算,最终得到分辨率更高的SAR图像。为验证算法的有效性,分别在点目标仿真数据和实测SAR数据集上进行实验。结果表明,与传统CS成像算法相比,该算法的成像结果成像主瓣宽度更窄、旁瓣更低、成像更加清晰。