MIMO-OFDM系统中快衰落信道的估计

A channel estimation approach for orthogonal frequency division multiplexing with multiple-input and multipleoutput （MIMO-OFDM） in rapid fading channels is proposed. This approach combines the advantages of an optimal training sequence based least-square （OLS） algorithm and an expectation-maximization （EM） algorithm. The channels at the training blocks are estimated using an estimator based on the OLS algorithm. To compensate for the fast Rayleigh fading at the data blocks, a time domain based Gaussian interpolation filter is presented. Furthermore, an EM algorithm is introduced to improve the performance of channel estimation by a few iterations. Simulations show that this channel estimation approach can effectively track rapid channel variation.     

Brain MRI segmentation using KFCM and Chan-Vese model

To extract region of interests (ROI) in brain magnetic resonance imaging (MRI) with more than two objects and improve the segmentation accuracy, a hybrid model of a kernel-based fuzzy c-means(KFCM) clustering algorithm and Chan-Vese (CV) model for brain MRI segmentation is proposed. The approach consists of two successive stages. Firstly, the KFCM is used to make a coarse segmentation, which achieves the automatic selection of initial contour. Then an improved CV model is utilized to subdivide the image. Fuzzy membership degree from KFCM clustering is incorporated into the fidelity term of the 2-phase piecewise constant CV model to obtain accurate multi-object segmentation. Experimental results show that the proposed model has advantages both in accuracy and in robustness to noise in comparison with fuzzy c-means (FCM) clustering, KFCM, and the hybrid model of FCM and CV on brain MRI segmentation.     

Dominant edge direction based fast intra mode decision in the H.264/AVC encoder

The H.264/AVC video coding standard uses an intra prediction mode with 4×4 and 16×16 blocks for luma and 8×8 blocks for chroma. This standard uses the rate distortion optimization （RDO） method to determine the best coding mode based on the compression performance and video quality. This method offers a large improvement in coding efficiency compared to other compression standards, but the computational complexity is greater due to the various intra prediction modes. This paper proposes a fast intra mode decision algorithm for real-time encoding of H.264/AVC based on the dominant edge direction （DED）. The DED is extracted using pixel value summation and subtraction in the horizontal and vertical directions. By using the DED, three modes instead of nine are chosen for RDO calculation to decide on the best mode in the 4×4 luma block. For the 16×16 luma and the 8 × 8 chroma, only two modes are chosen instead of four. Experimental results show that the entire encoding time saving of the proposed algorithm is about 67% compared to the full intra search method with negligible loss of quality.     

Objective Performance Evaluation of Video Segmentation Algorithms with Ground-Truth

While the development of particular video segmentation algorithms has attracted considerable research interest, relatively little effort has been devoted to provide a methodology for evaluating their performance. In this paper, we propose a methodology to objectively evaluate video segmentation algorithm with ground-truth, which is based on computing the deviation of segmentation results from the reference segmentation. Four different metrics based on classification pixels, edges, relative foreground area and relative position respectively are combined to address the spatial accuracy. Temporal coherency is evaluated by utilizing the difference of spatial accuracy between successive frames. The experimental results show the feasibility of our approach. Moreover, it is computationally more efficient than previous methods. It can be applied to provide an offline ranking among different segmentation algorithms and to optimally set the parameters for a given algorithm.     

Automatic segmentation of bladder in CT images

Segmentation of the bladder in computerized tomography （CT） images is an important step in radiation therapy planning of prostate cancer. We present a new segmentation scheme to automatically delineate the bladder contour in CT images with three major steps. First, we use the mean shift algorithm to obtain a clustered image containing the rough contour of the bladder, which is then extracted in the second step by applying a region-growing algorithm with the initial seed point selected from a line-by-line scanning process. The third step is to refine the bladder contour more accurately using the rolling-ball algorithm. These steps are then extended to segment the bladder volume in a slice-by-slice manner. The obtained results were compared to manual segmentation by radiation oncologists. The average values of sensitivity, specificity, positive predictive value, negative predictive value, and Hausdorffdistance are 86.5%, 96.3%, 90.5%, 96.5%, and 2.8 pixels, respectively. The results show that the bladder can be accurately segmented.     

Optimal production lot sizing model in a supply chain with periodically fixed demand considering learning effect

This paper presents an optimal production model for manufacturer in a supply chain with a fixed demand at a fixed interval with respect to the learning effect on production capacity.An algorithm is employed to find theoptimal dela time for production and production time sequentially.It is found that the optimal delay time for production and the production time are not static,but dynamic and variant with time.It is important for a manufacturer to schedule the production so as to prevent facilities and workers from idling.     

Monitoring correlative financial data streams by local pattern similarity

Developing tools for monitoring the correlations among thousands of financial data streams in an online fashion can be interesting and useful work. We aimed to find highly correlative financial data streams in local patterns. A novel distance metric function slope duration distance （SDD） is proposed, which is compatible with the characteristics of actual financial data streams. Moreover, a model monitoring correlations among local patterns （MCALP） is presented, which dramatically decreases the computational cost using an algorithm quickly online segmenting and pruning （QONSP） with O（1） time cost at each time tick t, and our proposed new grid structure. Experimental results showed that MCALP provides an improvement of several orders of magnitude in performance relative to traditional naive linear scan techniques and maintains high precision. Furthermore, the model is incremental, parallelizable, and has a quick response time.     

Fast Non-Local Means Algorithm Based on Krawtchouk Moments

Non-local means(NLM)method is a state-of-the-art denoising algorithm, which replaces each pixel with a weighted average of all the pixels in the image. However, the huge computational complexity makes it impractical for real applications. Thus, a fast non-local means algorithm based on Krawtchouk moments is proposed to improve the denoising performance and reduce the computing time. Krawtchouk moments of each image patch are calculated and used in the subsequent similarity measure in order to perform a weighted averaging. Instead of computing the Euclidean distance of two image patches, the similarity measure is obtained by low-order Krawtchouk moments, which can reduce a lot of computational complexity. Since Krawtchouk moments can extract local features and have a good antinoise ability, they can classify the useful information out of noise and provide an accurate similarity measure. Detailed experiments demonstrate that the proposed method outperforms the original NLM method and other moment-based methods according to a comprehensive consideration on subjective visual quality, method noise, peak signal to noise ratio(PSNR), structural similarity(SSIM) index and computing time. Most importantly, the proposed method is around 35 times faster than the original NLM method.     

A Fast Fractal Image Compression Coding Method

Fast algorithms for reducing encoding complexity of fractal image coding have recently been an important research topic.Serach of the bast matched domain block is the most computation intensive part of the fractal encoding process.In this paper,a fast fractal approximation coding scheme implemented on a personal computer based on matching in range block‘s neighbours is presented.Experimental results show that the proposed algorithm is very simple in implementation,fast in encoding time and high in compression ratio while PSNR is almost the same as compared with Barnsley‘s fractal block coding.     

Detection of Selfish Behavior in Wireless Ad Hoc Networks Based on CUSUM Algorithm

The cumulative sum (CUSUM) algorithm is proposed to detect the selfish behavior of a node in a wireless ad hoc network. By tracing the statistics characteristic of the backoff time between successful transmissions, a wireless node can distinguish if there is a selfish behavior in the wireless network. The detection efficiency is validated using a Qualnet simulator. An IEEE 802.11 wireless ad hoc network with 20 senders and 20 receivers spreading out randomly in a given area is evaluated. The well-behaved senders use minimum contention window size of 32 and maximum contention window size of 1 024, and the selfish nodes are assumed not to use the binary exponential strategy for which the contention window sizes are both fixed as 16. The transmission radius of all nodes is 250 m. Two scenarios are investigated: a single-hop network with nodes spreading out in 100 m×100 m, and all the nodes are in the range of each other; and a multi-hop network with nodes spreading out in 1 000 m×1 000 m . The node can monitor the backoff time from all the other nodes and run the detection algorithms over those samples. It is noted that the threshold can significantly affect the detection time and the detection accuracy. For a given threshold of 0.3 s, the false alarm rates and the missed alarm rates are less than 5%. The detection delay is less than 1.0 s. The simulation results show that the algorithm has short detection time and high detection accuracy.     

一种基于图像滤波的加权 FCM 图像分割算法

针对模糊C均值（ FCM）图像分割算法受初始值影响较大以及对噪声的抑制作用较差的问题,提出一种基于图像滤波的加权FCM图像分割算法。该算法采用快速FCM算法进行初分割,降低了初始值的影响,同时引入自适应中值滤波器,并与加权FCM 算法相结合进行迭代滤波分割,不仅能很好地抑制噪声的影响而且能使分割更精确。利用该算法分别对人工合成的和真实的含噪图像进行分割实验,实验结果表明：本文算法对含噪图像有很好的分割结果。     

基于高斯分布估计的快速水平集分割方法研究

传统的CV模型只能用于分割灰度分布比较均匀、目标与背景灰度均值差异较大的图像,且因需要求解偏微分方程导致分割速度很慢.文章在传统快速水平集分割模型的基础上,将高斯分布估计引入速度项,使得快速水平集可以分割复杂的目标,并将HIS空间中的色调分量与强度分量进行融合,得到了一种彩色高斯快速模型.新算法具有分割速度快、可分割复杂目标的优点.     

血管分割可视化中的快速交互型渲染方法

Medical diagnosis software and computer-assisted surgical systems often use segmented image data to help clinicians make decisions. The segmentation extracts the region of interest from the background, which makes the visualization clearer. However, no segmentation method can guarantee accurate results under all circumstances. As a result, the clinicians need a solution that enables them to check and validate the segmentation accuracy as well as displaying the segmented area without ambiguities.With the method presented in this paper, the real CT or MR image is displayed within the segmented region and the segmented boundaries can be expanded or contracted interactively. By this way, the clinicians are able to check and validate the segmentation visually and make more reliable decisions. After experiments with real data from a hospital, the presented method is proved to be suitable for efficiently detecting segmentation errors. The new algorithm uses new graphic processing uint (GPU) shading functions recently introduced in graphic cards and is fast enough to interact on the segmented area, which was not possible with previous methods.     

基于蚁群模糊聚类算法的图像分割研究

论文提出了一种基于蚁群动态模糊聚类算法的计算机图像分割方法,有效地利用蚁群算法的聚类分析能力,克服了FCM算法对初始化的敏感,动态地确定了聚类数目和中心。然后利用蚁群聚类算法得到的模型进行修改,再进行模糊聚类弥补蚁群算法的不足。最后将该算法应用到计算机图像分割技术。对比实验表明,该算法实验表明该算法速度快、划分特性好,可以准确地分割出目标。     

基于形态学重建的分水岭图像分割实验教学研究

现有分水岭算法对噪声敏感,易出现过度分割现象,导致图像分割边缘不明显.在传统分水岭算法基础上,通过形态学开闭重建来清除图像中的噪声点,并采用最小覆盖运算修改梯度图像,使得局部最小区域仅出现在标记位置,从而消除过分割现象.实验结果表明,与传统标记分水岭算法相比,用改进的算法对硬币图像和火焰图像进行处理,硬币轮廓识别率提高...     

一种客观的视频对象分割算法性能评价方法

While the development of particular video segmentation algorithms has attracted considerable research interest, relatively little effort has been devoted to provide a methodology for evaluating their performance.In this paper, we propose a methodology to objectively evaluate video segmentation algorithm with ground-truth, which is based on computing the deviation of segmentation results from the reference segmentation.Four different metrics based on classification pixels, edges, relative foreground area and relative position respectively are combined to address the spatial accuracy.Temporal coherency is evaluated by utilizing the difference of spatial accuracy between successive frames.The experimental results show the feasibility of our approach.Moreover, it is computationally more efficient than previous methods.It can be applied to provide an offline ranking among different segmentation algorithms and to optimally set the parameters for a given algorithm.     

基于正交矩的纹理分割

在识别一幅图像中的界面或物体时，一般先要进行纹理分割．本提出了基于勒让得矩的纹理分割方法．首先在图像的小窗口中计算矩值，然后用一个非线性转换器把它转化成纹理特征．再用这些特征组成特征向量作为输入数据．接着采用RBF人工神经网络对提取的特征进行分割．用k均值算法训练RBF人工神经网络的隐层．输出层的训练是采用基于LMS的监督式数学模型．该算法成功地分割了许多灰度级图像．和基于几何矩的纹理分割相比，用正交矩可以降低分割错误率．     

Hankel矩阵离散Sine变换的快速算法

利用Hankel矩阵的结构特点导出一递推关系式,给出了Hankel矩阵离散Sine变换（DST）的一个快速算法．该算法所需要的存贮空间为D（N）,计算变换矩阵的肼个元素所需的计算量为O（NlogN）＋O（M）．     

光伏阵列故障状态红外图像的分割研究与实现

依据光伏阵列故障状态红外图像的特点,提出了一种高效基于迭代的两级阈值分割算法。实验表明:该算法能较好地分割出光伏阵列中存在的正常、故障、老化三种区域,同时算法以乘法和加法运算为主,便于DSP的高速运算。最后,采用TMS320 DSP算法标准(XDAIS)对分割算法进行创建与封装,极大地方便了系统集成。