结合显著度图和自适应能量线的快速图像缩放*

针对Seam Carving图像缩放方法中能量函数的局限性和动态规划算法的复杂性,提出了一种基于图像内容的快速图像缩放方法。结合基于视觉注意力模型的显著度图,完善图像内容的能量描述;同时,提出一种基于图像内容的粗、细能量线自适应机制,与层次加速动态规划一起,加速缩放效率。仿真实验结果表明,该算法在有效保护图像内容的同时,降低了算法的计算复杂度,减少了图像缩放时间。     

自适应的三维分形地形重建算法研究*

针对常规三维分形地形重建算法具有的速度慢、插值点分布不均匀、参数设置复杂等缺点,提出自适应的三维分形地形重建算法。该算法根据地形的起伏特征自动确定垂直压缩因子,根据子域面积进行均匀插值。实验结果表明,该方法插值效率更高,所获得的插值曲面更加真实,与常规分形插值相比具有一定的优越性。     

小波域声呐图像自适应增强

声呐图像受噪声污染严重、对比度低,给后期的定位识别带来不便,而传统的处理方法容易造成边缘模糊.针对这一问题,提出了一种图像自适应增强算法.该算法利用形态小波对声呐图像进行自适应的多分辨率分析,分别增强不同尺度上的信号或细节,通过多通道重构图像的加权实现去噪和对比度提高.仿真结果表明该算法快速有效,对高斯噪声和冲击性噪声都具有较好的鲁棒性,处理后的声呐图像边缘细节信息保留完好,得到了理想的增强效果.     

基于改进PSO算法的自适应均衡算法

传统的自适应均衡算法存在收敛速度慢,稳定性差、均衡效果不理想等缺点,从而使自适应均衡器在高速光纤通信系统中的应用受到限制.具有梯度搜索因子的Grads-PSO算法,结合了传统数值优化方法在计算速度和计算精度上的优势,将梯度法引入粒子群算法中.在梯度搜索因子的指导下,PSO算法的运算过程显得更加有规则,从而提高了算法的收敛速度和运算精度.因此,本文提出将改进PSO算法用作自适应均衡器均衡算法.通过仿真实验表明,改进PSO算法具有收敛速度快,计算精度高的优点,将其作为自适应均衡器的控制算法可收到很好的均衡效果,优于传统的控制算法.     

指纹图像的自适应方差分割法

指纹图像分割的目的是从图像背景中分离出有用的指纹区域。灰度方差法是一种简单快速的指纹图像分割算法,本文给出一种自适应阈值的灰度方差分割法。实验表明,本文方法选取的分割阈值能够较好的分离出指纹图像的前景和背景,是一种有效的指纹图像分割算法。     

准正交空时分组码的自适应设计

为了能在满足空时分组码完全分集的同时尽可能降低译码复杂度,提出了准正交空时分组码的自适应设计方案. 在准正交空时分组码的编码结构上添加自适应系数,根据当前信道状态信息计算自适应系数的具体数值. 设计的目的是保证传输的数据符号之间相互正交,彼此没有干扰,从而实现完全分集,并能采用复杂度低的单符号译码方法. 仿真结果表明,准正交空时分组码自适应方案的误码率性能要明显好于采用星座旋转的准正交空时分组码方案,只需要采用有限反馈的方法就能实现近似理想反馈的误码率性能.      

Local adaptive segmentation algorithm for 3-D medical image based on robust feature statistics

Medical image segmentation is of pivotal importance in computer-aided clinical diagnosis. Many factors, including noises, bias field effect, local volume effect, as well as tissue movement may affect the med- ical image, thus causing blurring or uneven characteristics when forming a picture. Such quality defects will inevitably impair the gray-scale difference between adjacent tissues and lead to insufficient segmentation or even leakage during tissue or organ segmentation. In the present investigation, a local adaptive segmentation algorithm for 3-D medical image based on robust feature statistics （LARFS） was proposed. By combining segmentation algorithm principles for traditional region growing （RG） and robust feature statistics （RFS）, the location and neighborhood image information of input seed point can be comprehensively analyzed by LARFS. Results show that, for different segmentation objects, under controlling the input parameter of growing factor within certain range, LARFS segmentation algorithm can adapt well to the regional geometric shape. And be- cause the robust feature statistics is applied in the contour evolution process, LARFS algorithm is not sensitive to noises and not easily influenced by image contrast and object topology. Hence, the leakage and excessive segmentation effects are ameliorated with a smooth edge, and the accuracy can be controlled within the effective error range.     

Adaptive joint bandwidth and power allocation in heterogeneous wireless access environment

Joint bandwidth and power allocation for a multi-radio access(MRA)system in a heterogeneous wireless access environment is studied.Since both the number of users being served by the system and the wireless channel state are time-varying,the optimal resource allocation is no longer a static optimum and will change with the varying network state.Moreover,distributed resource allocation algorithms that require iterative updating and signaling interactions cannot converge in negligible time.Thus,it is unrealistic to assume that the active user number and the wireless channel state remain unchanged during the iterations.In this paper,we propose an adaptive joint bandwidth and power allocation algorithm based on a novel iteration stepsize selection method,which can adapt to the varying network state and accelerate the convergence rate.A distributed solution is also designed for the adaptive joint resource allocation implementation.Numerical results show that the proposed algorithm can not only track the varying optimal resource allocation result much more quickly than a traditional algorithm with fixed iteration stepsize,but can also reduce the data transmission time for users and increase the system throughput.     

Adaptive detectors in the Krylov subspace

The validity of the application of the Krylov subspace techniques in adaptive filtering and detection is investigated. A new verification of the equivalence of two well-known methods in the Krylov subspace, namely the multistage Wiener filters （MWF） and the auxiliary-vector filtering （AVF）, is given in this paper. The MWF and AVF are incorporated into two well-known detectors, namely, the adaptive matched filter （AMF） and Kelly＇s generalized likelihood ratio test （CLRT） including their diagonally loaded versions, which form new detectors. Compared to the conventional AMF, CLRT, and their diagonally loaded versions as well as the reduced-rank AMF and GLRT, the probabilities of detection （PDs） of the new detectors are improved especially when the sample support is low. More importantly, the new detectors are robust of the rank selection of the clutter subspace compared to the reduced-rank AMF and GLRT. These new detectors all possess asymptotic constant false alarm rate （CFAR） property.     

Adaptive neural network control for a class of continuous stirred tank reactor systems

In this paper, the control problem of continuous stirred tank reactors （CSTR） is studied. The considered CSTR are required to contain unknown functions and unknown dead zone input. An adaptive controller that uses the neural networks （NNs） is provided to solve the unknown terms. The proposed approach overcomes the effect of the dead zone input. The dead zone input in the systems is compensated for by introducing a new Lyapunov form and Young＇s inequality. The backstepping procedure is exploited to implement controller design with adaptation laws. The stability is analyzed using Lyapunov method. The performance is examined for CSTR to confirm the effectiveness of the proposed approach based on computer simulation.