基于二次型约束的鲁棒最小二乘恒模算法

阵列指向性偏差会导致线性约束最小二乘恒模算法(LSCMA)的性能急剧下降,为此提出了一种基于二次型约束的鲁棒LSCMA算法。该算法通过对期望信号波达方向附近小区域内的方向向量的误差模值进行约束来构造一种新的代价函数,并在此函数下迭代更新权重向量,以提高算法的鲁棒性。该算法收敛速度快,稳态性能好,能够有效地解决干扰捕获问题,对阵列指向性偏差具有很强的鲁棒性,从而改善了阵列输出的信干噪比,使其更接近最优值。仿真结果表明:与线性约束最小二乘恒模算法相比,所提鲁棒算法提高了输出性能,降低了计算量,易于实时实现,且能适应实际复杂的通信环境。     

基于异频切换负载均衡算法研究与实现

在商用LTE基站系统中,小区之间存在业务量不均衡的情况,特别是热点区域,某些小区处于高负载状态,为了均衡小区的业务负载,引入了移动负载均衡(MLB)技术。选择合适用户(UE)切换到相邻低负载小区,从而有效地提高了整体网络资源效率和吞吐量,同时也能增强用户的性能体验。提出了一种以用户的RSRP和邻区PRB利用率为参数,基于UE业务量的异频切换负载均衡算法,选择出适量的UE切换到目标邻区中,从而达到网络负载均衡。根据实际基站调试结果表明,该算法能够快速精确地切换特定业务量的UE,明确了要卸载的业务量,并且提升了网络吞吐量和UE吞吐量。     

适用于UHF RFID认证协议的Hash函数构造算法

在标签硬件资源受限情况下,为提高UHF RFID系统认证的安全性能,提出一种Hash函数构造算法.算法选择6个混沌映射组合之一来构造Hash函数,通过切换每组中的2个混沌映射生成混沌序列数组,再经过全序列取数策略,将生成的两组数值映射为最终的Hash值.结果分析表明,该算法具有很好的单向性、初值敏感性,在硬件资源和安全性能两方面满足了UHF RFID系统的安全认证要求.     

一种粗粒度可重构体系结构多目标优化映射算法

针对多约束下的行流水粗粒度可重构体系结构的硬件任务划分映射问题,提出了一种多目标优化映射算法.该算法根据运算节点执行时延、依赖度等因素构造了累加概率权值函数,在满足可重构单元面积和互连等约束下,通过该函数值动态调整就绪节点的映射调度次序,当一块可重构单元阵列当前行映射完毕后,就自动换行,当一块阵列被填满,就切换到下一块,当一个数据流图映射完毕后,就自动计算划分块数等参数.实验结果表明,与层贪婪映射算法相比,文中算法平均执行总周期降低了8.4%(RCA_4×4)和5.3%(RCA_6×6),与分裂压缩内核映射算法相比,文中算法平均执行总周期降低了20.6%(RCA_4×4)和21.0%(RCA_6×6),从而验证了文中提出算法的有效性.     

基于瑞利多径衰落信道的信号包络频谱感知

为提高信号采样值之间的相关性和降低噪声对感知性能的影响,该文提出基于信号包络自相关矩阵的频谱感知算法。首先对采样信号等间隔时长截取,以相邻间隔的采样值计算信号自相关性,并构造出近似自相关矩阵。其次依据矩阵次对角线元素性质构造了统计量。分别计算了该统计量的检测概率分布函数与虚警概率分布函数,分析了频谱感知算法的检测性能,算法优化了信号相关性的计算,降低了噪声对感知性能的影响。最后通过仿真验证了不同参数对检测概率和虚警概率的影响,并提出了进一步提高检测性能的措施。     

基于用户偏好的异构网络选择算法研究

针对当前大多数网络选择算法没有充分考虑用户基于业务的网络偏好这一问题,提出了一种基于用户偏好的异构网络选择算法。该算法充分考虑业务需求、用户偏好及网络性能差异,通过层次分析法和距离分析法分别计算用户对各候选网络的主观和客观偏好权重,根据用户策略动态确定综合权重,并由此结合各属性权重计算网络性能,选出最优网络。仿真结果表明该算法可以准确选择接入网络,并当用户在网络边缘做兵乓运动时有效减少切换次数。     

一维鲁棒混沌映射及S盒的设计

本文利用Li-York混沌判别定理,构造了一类分段线性连续混沌映射.基于此类映射,建立了一个构造一类分段非线性鲁棒混沌映射的判别定理.作为应用,构造了一个由多项式函数和三角函数映射合成的分段非线性鲁棒混沌映射.通过计算该混沌映射的分岔图,验证了映射在参数范围内的混沌性.作为鲁棒混沌的应用,设计了三个基于分段非线性鲁棒混沌映射的伪随机数发生器.在此基础上,利用混沌映射对初始参数的敏感性,提出了批量生成S盒的算法.S盒密码性能的分析结果表明,生成的S盒具有良好的密码学性能,可以较好的抵抗线性与差分攻击,为密码算法的研究发展提供基础与条件.     

DCT系数的分块奇异值非对称水印算法

提出了基于DCT中频系数的分块奇异值非对称数字水印算法。该算法利用Logistic混沌映射和Walsh序列构造嵌入水印,将水印嵌入到由最大奇异值构成的特征空间,并采用相关值计算方法对水印进行检测。理论分析和实验结果表明,该非对称水印算法检测性能良好,具有很强的鲁棒性。     

异构无线网络中边缘用户小区选择算法

在异构网络中,部署多种小功率基站,会出现大量的“小区边缘区域”,边缘用户的性能会受到影响。为此,提出了一种边缘用户小区选择算法,通过发送端进行预编码抑制干扰,提高用户接收到的信干噪比。根据小区当前用户数,动态地调整给微小区边缘用户参考接收信号添加的偏置值,避免边缘用户接入用户数过多的拥堵小区。仿真结果表明,该算法与参考算法相比,能够有效降低宏小区负载,平衡不同微小区间负载,提高轻载微小区的网络吞吐量和资源利用率。     

基于改进离散粒子群算法的认知无线电频谱分配

原始的二进制粒子算法在认知无线电频谱分配过程中存在收敛速度慢、最优值不理想的问题,并且对二进制粒子群算法在频谱分配过程中的惯性权重参数设置缺乏理论研究。针对这一问题,在分析二进制粒子群算法的基本原理和计算步骤的基础上,探讨了二进制粒子群算法ω最适宜的取值。对原始二进制粒子群算法进行了改进,提出了采用混沌Logistic映射的方式进行初始种群的构造,保证了初始种群在解空间的均匀分布。引入了基于种群聚集度的粒子变异思想,使得新算法与原算法相比,不易陷入局部最优解。仿真结果表明,利用该算法进行认知无线电频谱分配时,收敛速度较快,得到的系统总效益值也较好。     

Learning Behaviors of Stochastic Gradient Radial Basis Function Network Algorithms for Odor Sensing Systems

Learning behaviors of a radial basis function network (RBFN) using a singular value decomposition (SVD) and stochastic gradient (SG) algorithm, together named RBF‐SVD‐SG, for odor sensing systems are analyzed, and a fast training method is proposed. RBF input data is from a conducting polymer sensor array. It is revealed in this paper that the SG algorithm for the fine‐tuning of centers and widths still shows ill‐behaving learning results when a sufficiently small convergence coefficient is not used. Since the tuning of centers in RBFN plays a dominant role in the performance of RBFN odor sensing systems, our analysis is focused on the center‐gradient variance of the RBFN‐SVD‐SG algorithm. We found analytically that the steady‐state weight fluctuation and large values of a convergence coefficient can lead to an increase in variance of the center‐gradient estimate. Based on this analysis, we propose to use the least mean square algorithm instead of SVD in adjusting the weight for stable steady‐state weight behavior. Experimental results of the proposed algorithm have shown faster learning speed and better classification performance.     

一种非均匀环境中双端口干涉SAR/GMTI杂波抑制算法

 本文针对非均匀环境对杂波抑制干涉SAR/GMTI系统的影响,提出了一种新的自适应杂波抑制算法,利用系统参数、成像几何关系以及接收通道误差推算空域对消权系数的先验值,并将其作为约束条件来控制自适应算法的求解,从而得到对消权系数较为准确的估计值.实测数据处理结果表明,此算法能够在非均匀环境中有效地降低权系数的估计误差,从而达到满意的杂波抑制效果.是一种实际可行的双(多)端口杂波抑制方案.     

Multi-Channel Subspace Mapping Using an Information Maximization Criterion

A new hybrid information maximization (HIM) algorithm is derived. This algorithm is able to perform subspace mapping of multi-channel signals, where the input (feature) vector for each of the channels is linearly transformed to an output vector. The algorithm is based on maximizing the mutual information (MI) between input and output sets for each of the channels, and between output sets across channels. Such formulation leads to a substantial redundancy reduction in the output sets, and the extraction of higher order features that exhibit coherence across time and/or space. In this paper, we develop the proposed algorithm and show that it combines efficiently the strengths of two well-known subspace mapping techniques, namely the principal component analysis (PCA) and the canonical correlation analysis (CCA). Unlike CCA, which is limited to two channels, the HIM algorithm can easily be extended to multiple channels. A number of simulations and real experiments are conducted to compare the performance of HIM to that of PCA and CCA.     

Multi-cell uplink power allocation game for user minimum performance guarantee in OFDMA systems

The multi-cell uplink power allocation problem for orthogonal frequency division multiplexing access (OFDMA) cellular networks is investigated with the uplink transmission power allocation on each co-frequency subchannel being defined as a multi-cell non-cooperative power allocation game (MNPG). The principle of the design oftbe utility function is given and a novel utility function is proposed for MNPG. By using this utility function, the minimum signal to interference plus noise ratio (SINR) requirement of a user can be guaranteed. It can be shown that MNPG will converge to the Nash equilibrium and that this Nash equilibrium is unique. In considering the simulation results, the effect of the algorithm parameters on the system performance is discussed, and the convergence of the MNPG is verified. The performance of MNPG is compared with that of traditional power allocation schemes, the simulation results showing that the proposed algorithm increases the cell-edge user throughput greatly with only a small decrease in cell total throughput; this gives a good tradeoff between the throughput of cell-edge users and the system spectrum efficiency.     

基于Huber范数和概率运动场的鲁棒图像超分辨重建算法

传统超分辨(SR)算法对配准误差、模型误差以及噪声过于敏感,这限制了其在实际中的应用。为了提高算法的鲁棒性,该文从配准和重建两方面对传统算法进行了改进。在配准阶段,通过引入概率运动场避免了算法对配准精度的依赖,同时利用Heaviside函数实现权重映射,进一步提高了算法的自适应性;在重建阶段,采用基于Huber范数的正则化估计,在提高重建鲁棒性的同时也保证了算法数值解的稳定性。实验表明该算法具有很好的鲁棒性,其重构性能优于现有的一些超分辨重建方法。     

A new handoff management system for heterogeneous wireless access networks

In heterogeneous wireless access networks, each mobile terminal may frequently need to change its base station (BS); this change is called a ‘handoff’. BSs have static parameters, which are related to their radio access technologies (RATs); however, they also contain dynamic parameters such as load and signal quality. Therefore, the problem of handoff decision includes two subproblems of RAT selection and BS selection. In this paper, first a ‘heterogeneous handoff management system’ for gathering all different required parameters is proposed. Second, a RAT Selection algorithm based on analytic hierarchy process and a BS Selection algorithm based on data envelopment analysis are designed. Finally, by means of ‘weight restriction’ technique, we develop a method for studying the impact of RAT Selection parameters on the performance of the network. Simulation results indicate that RAT Selection parameters have significant impact on the bandwidth utilization, energy consumption and the whole satisfaction of the users in heterogeneous wireless access networks.Copyright © 2012 John Wiley & Sons, Ltd.     

A GAUSSIAN MIXTURE MODEL-BASED REGULARIZATION METHOD IN ADAPTIVE IMAGE RESTORATION

A GMM （Gaussian Mixture Model） based adaptive image restoration is proposed in this paper. The feature vectors of pixels are selected and extracted. Pixels are clustered into smooth, edge or detail texture region according to variance-sum criteria function of the feature vectors. Then parameters of GMM are calculated by using the statistical information of these feature vectors. GMM predicts the regularization parameter for each pixel adaptively. Hopfield Neural Network （Hopfield-NN） is used to optimize the objective function of image restoration, and network weight value matrix is updated by the output of GMM. Since GMM is used, the regularization parameters share properties of different kind of regions. In addition, the regularization parameters are different from pixel to pixel. GMM-based regularization method is consistent with human visual system, and it has strong generalization capability. Comparing with non-adaptive and some adaptive image restoration algorithms, experimental results show that the proposed algorithm obtains more preferable restored images.     

一种自适应PCNN多聚焦图像融合新方法

该文通过分析脉冲耦合神经网络(PCNN)参数模型,结合多聚焦图像的基本特点和人眼视觉特性,提出了一种自适应PCNN多聚焦图像融合的新方法。该方法使用图像逐像素的清晰度作为PCNN对应神经元的链接强度,经过PCNN点火获得每幅参与融合图像的点火映射图,再通过判决选择算子,判定并选择各参与融合图像中的清晰部分生成融合图像。该方法中,其它参数如阈值调整常量等对于融合结果影响很小,解决了PCNN方法的参数调整困难的问题。实验结果表明,该方法的融合效果优于小波变换方法和Laplace塔型方法。     

A novel algorithm for voice conversion using Canonical Correlation Analysis

A novel algorithm for voice conversion is proposed in this paper. The mapping function of spectral vectors of the source and target speakers is calculated by the Canonical Correlation Analysis （CCA） estimation based on Gaussian mixture models. Since the spectral envelope feature remains a majority of second order statistical information contained in speech after Linear Prediction Coding （LPC） analysis, the CCA method is more suitable for spectral conversion than Minimum Mean Square Error （MMSE） because CCA explicitly considers the variance of each component of the spectral vectors during conversion procedure. Both objective evaluations and subjective listening tests are conducted. The experimental results demonstrate that the proposed scheme can achieve better performance than the previous method which uses MMSE estimation criterion.     

基于模糊Fisher准则的自适应降维模糊聚类算法

该文指出曹苏群等人提出的基于模糊Fisher准则(FFC)的半模糊聚类算法(FFC-SFCA)中的一个推导错误,结合模糊紧性和分离性(FCS)聚类算法提出新的聚类算法:FFC-FCS。FFC-FCS充分利用FFC的特征提取和降维特性,交替运行原始数据空间中FFC和投影空间中的FCS,通过对降维数据的聚类实现对原始数据的聚类。FFC-FCS不仅对低维数据具有优异的分类性能而且对高维数据也表现出一定的分类优势。实验结果表明,FFC-FCS 的性能明显优于原有的FCS算法,FFC-SFCA算法以及经典的模糊C-均值(FCM )算法。