用APRGA求解二维导体柱电磁逆散射

从电磁散射的积分方程出发，利用点匹配法和脉冲基函数求解电磁散射问题，以测量的散射场和计算的散射场的偏差程度为目标函数，将待优化变量设置为描述导体柱轮廓的形状函数的傅立叶展开式系数，Ai0，Ai1…AiN/2,Bi1,Bi2…BiN/2,k=1,2…k，通过参数自适应实数遗传算法（APRGA）对待优化变量进行优化，使目标函数达到最小值来对自由空间中导体柱族进行电磁成像，证实了APRGA比BRGA有更好的收敛性能和成像精度，更强的抗随机噪声干扰能力。     

An extended inverse QR adaptive filtering algorithm

A so-called inverse QR algorithm was recently introduced for recursive adaptive filtering under the exponentially weighted least-squares criterion. It has some attractive features, including the absence of inversions. The extension to the multi-channel case does require inversion however. We present a new derivation of the inverse QR algorithm, based on the technique of Sayed and Kailath, for reformulating the above adaptive filtering problem as a state-space estimation problem. A well-known square-root covariance algorithm for the latter problem is shown to directly give (a multi-channel version of) the inverse QR algorithm. A new extended square-root covariance algorithm is then applied to get a new inversion-free ‘extended inverse QR’ algorithm, even in the multi-channel case.     

An adaptive approach to suppress powerful impulsive interference

In this paper the problem of impulsive noise suppression by using adaptive prediction technique is considered. The Time Domain Notch Filter structure and a procedure to estimate the filter coefficients are proposed. The considered algorithm is linear and does not require any auxiliary reference input. Performance evaluation indicates that the use of this algorithm does not change the signal-to-noise ratio (SNR) whose value remains almost independent of impulsive interference power. On the other hand, the signal-to-impulsive interference ratio (SIR) in the output increases with a decrease in the input SIR. This property of the proposed algorithm is the most essential difference between it and the commonly employed algorithms. The results of computer simulation of a noncoherent detection system applied to the on-off-keying modulation are given. The obtained results confirm that the proposed algorithm enables a transmission in the background of high power impulsive interference.     

An adaptive network traffic prediction approach for LDoS attacks detection

Low‐rate denial of service (LDoS) attacks reduce throughput and degrade quality of service (QoS) of network services by sending out attack packets with relatively low average rate. LDoS attack flows are difficult to detect from normal traffic since it has the property of low average rate. The research on network traffic analysis and modeling shows that network traffic measurement data are irregular nonlinear time series. To characterize and analyze network traffic between attack and non‐attack situations, the adaptive normal and abnormal ν‐support vector regression (ν‐SVR) prediction models are constructed on the basis of the reconstructed phase space. In this paper, the dimension of reconstructed phase space for ν‐SVR is optimized by Bayesian information criteria method, and the parameter in the radial basis function is adaptively adjusted by minimizing the within‐class distance and maximizing the between‐class distance in the feature space. The nonthreshold decision function is obtained through calculating the prediction error of adaptive normal and abnormal ν‐SVR prediction models, which is adopted to detect LDoS attacks. Experiments in NS‐2 environment show that the adaptive ν‐SVR prediction model can effectively predict the network traffic measurement time series, and the probability distribution of time series generated by the adaptive ν‐SVR prediction model is quite similar to that of the network traffic measurement data. Experiments also clearly demonstrate the superiority of the proposed approach in LDoS attacks detection.     

Construction of sparse signal representations with adaptive multiscale orthogonal bases

We propose a new approach to construct adaptive multiscale orthonormal (AMO) bases of R^N that provide highly sparse signal representations. Our new multilayer AMO basis design produces a high proportion of small scale vectors. The basis vectors are built from small scale to large scales, layer by layer. For each layer, the basis vector maximizes a p-norm measure of sparsity. We compare the sparsity ratios SR (i.e. the percentage of negligibly small coefficients) obtained with AMO and Daubechies wavelet bases for seven families of piecewise smooth signals with randomly located discontinuities. The signals are composed of polynomial, sinusoidal and exponential pieces. In all cases, AMO bases produce a SR increase ranging from 6% to 37%. AMO bases have three main advantages over wavelets. First, they are found automatically by solving a sequence of optimization problems, which eliminates the problem of selecting a wavelet for a given signal. Second, they can provide a significantly sparser representation. Finally, they have the ability to produce zero coefficients for a larger family of piecewise smooth signals. The drawbacks of AMO bases are computational: the basis computation is more expensive, the basis vectors require storage space and no fast transform is known.