Long-range predictive adaptive fuzzy relational control

A class of long-range predictive adaptive fuzzy relational controllers is presented. The plant behavior is described over an extended time horizon by a fuzzy relational model which is identified based on input-output closed-loop observations of the plant variables. In this class of adaptive controllers the control law attempts to minimize a quadratic cost over an extended control horizon. When used with linear models, this approach has revealed a significant potential for overcoming the limitations of one-step ahead schemes, such as the stabilization of non-minimum phase plants. Here, a uniform framework is adopted for implementing both the fuzzy model and the fuzzy controller, namely distributed fuzzy relational structures gaining from their massive parallel processing features and from the learning capabilities typical of the connectivist approaches. Issues such as maintenance during the adaptation process of the meaning of linguistic terms used at both fuzzy systems interfaces are addressed, namely by introducing a new design methodology for on-line fuzzy systems interface adaptation. The examples presented reinforce the claim of the usefulness of this new approach.     

可调直方图均衡化的正则解释及其改进

将直方图均衡化看作典型的不适定问题,提出了可调直方图均衡化的正则化解释,以及为了获得较好的图像增强效果并给出其改进措施.首先,对可调直方图均衡化方法所对应的目标函数进行简化,得到了基于信息能量作为正则项的可调直方图均衡化崭新解释;其次,考虑到信息熵与信息能量之间呈对偶关系,于是获得了一种基于信息熵作为正则项的可调直方图...     

嵌套移动网络中基于代价函数的自适应路由优化机制

该文针对嵌套移动网络中的路由优化问题,依据子网的实时通信和运动模式,提出了一种自适应路由优化机制。该机制将嵌套子网划分绑定更新、均衡通信和数据路由3种通信模式,在不同模式下采取相对应的路由优化策略,合理均衡绑定更新和数据隧道封装传输之间的代价开销。仿真结果表明,该机制减少了数据传输的封装次数和隧道长度,具有较小的传输时延,实现了移动过程中数据传输代价最小化,对子网的动态变化具有良好的适应性。     

基于广义周期性的单通道多分量正弦调频信号分离和参数估计

针对噪声条件下的单通道多分量正弦调频(SFM)信号,该文提出一种信号分离和参数提取方法。利用正弦调频信号的广义周期性进行奇异值分解,以求出分量信号的调制频率;通过离散点搜索,估计出分量信号的调频(FM)初始相位、调制指数及载频,并对这些估计值利用信赖域算法进行优化,减小误差;利用内积计算,估计分量信号的幅度和初始相位。此外,还利用自相关矩阵特征值分解估计混合信号的信噪比(SNR),并根据信噪比确定停止分解的阈值。在仿真与分析中,针对具体的信号详细说明了该方法的各步骤,并在不同信噪比条件下分析了该方法的参数估计精确度。     

Edge preserving adaptive anisotropic diffusion filter approach for the suppression of impulse noise in images

This paper proposes a new anisotropic diffusion approach to remove the impulse noise and retain the fine details. The proposed approach contains two stages, the first stage detects the impulse noise, and the second stage removes the noisy pixel and retains the fine details of the original image. The Laplacian operator is used to fine-tune the image quality of the restored image in the anisotropic diffusion filter. The proposed approach is tested with PSNR, IEF, correlation factor, and NSER for different test images and the results are compared against existing algorithms. The simulation results show that the proposed approach gives better results than the existing denoising algorithms.     

一种适用于混响环境的双传声器自适应指向性算法

在许多语音信号处理系统(如助听器)中,都采用了指向性算法处理在空间上相互分离的信号,但在客厅、会议室等室内环境中混响的存在严重影响了指向性系统的降噪性能,现有的去混响算法不能有效地抑制干扰噪声。该文提出一种适用于混响环境的自适应双传声器指向性算法,采用两个间距很小的全向性传声器,将自适应零限波束形成(ANF)结构和利用概率模型抑制混响的方法相结合,实现了在混响环境中的自适应指向性。与现有指向性和抑制混响算法相比,该算法采用一个简单的结构同步实现了指向性和抑制混响,具有较低的复杂度和较强的实时性。仿真验证了算法在混响环境中的指向性降噪性能。     

一种小波变换的小波系数均衡图像增强算法

介绍了几种经典的图像增强算法,依据经典直方图均衡化算法思想,提出了在小波变换下的小波系数均衡化图像增强算法,并给出了计算步骤。与经典直方图均衡化算法、同态滤波实验结果相比较,表明提出的算法可有效增强图像、抑制噪声。     

A throughput-efficient adaptive transmit-receive scheme for indoor DS-UWB systems

In this paper, a novel adaptive transmit-receive scheme is presented for indoor Direct Sequence Ultra-WideBand （DS-UWB） systems. In the proposed scheme, a simple switch module is introduced to improve the system throughput. Furthermore, adaptive detection is implemented via an improved Least Mean Squares （LMS） algorithm. The convergence behavior and the Bit Error Rate （BER） performance of the proposed scheme are examined under the realistic channel models. Simulation results show that, nearly without loss of the BER performance, the proposed scheme can obtain at least 2/3 improvement in training overhead compared with the conventional approach.     

Adaptive multichannel decision feedback equalization for volterra type nonlinear communication channels

A model-based approach for the decision feedback equalization of Volterra type nonlinear communication channels is proposed such that the linear model-based decision feedback equalization can be considered as a special case of the proposed approach. In designing the decision feedback equalizer, the nonlinear decision feedback equalization problem is visualized as a linear, multichannel equalization problem. A complete modified Gram–Schmidt orthogonalization of the input vector is achieved by using modified sequential processing multichannel lattice stages. The elements of the multichannel desired signal vector are then estimated from the new orthogonal set by using only scalar operations. The probability of error performance of the proposed equalizer is improved by the estimation of the elements of the desired signal vector through a sigmoid activation function so that a polynomial perceptron equalizer is realized. The comparative computational complexity calculations and performance results of the proposed decision feedback equalizer are also provided.     

雷达高分辨距离像分类器的参数自适应学习算法

雷达高分辨率距离像具有目标姿态敏感性的特点,在识别时的一种解决方法是对目标不同角域建立不同的统计模型。在给定系统参数条件下,选择目标划分角域个数及每个角域覆盖范围是影响识别器运算量及识别性能的关键。该文给出了一种基于数据的自适应学习上述分类器参数的算法,基于联合高斯分布的数据模型通过迭代算法来确定数据划分边界,并自动确定目标角域个数。与等间隔数据划分方法相比,本文方法在降低识别运算量的同时,可以提高识别性能。基于实测数据的实验结果表明该方法是有效的。