面向能力培养的嵌入式系统课程群建设研究

能力型、创新型人才是社会发展的需求,也是高等教育的培养目标。为了培养具备能力和创新素质的嵌入式系统方面的人才,本文进行了嵌入式系统课程群的建设,研究了相应的教学方法,从理论和实践上培养学生的能力,培养创新素质。     

基于模糊决策的风力发电产业评价研究

根据风力发电技术产业的特点,构建改进层次分析与模糊一致矩阵相结合的方案决策数学模型,该模型既可以减少确定判断矩阵的盲目性,又可以避开评价体系中各等级间的隶属度确定问题,使评价结果更加可靠,适合于信息尚不完整、受主观因素影响较强的评价。实例验证了这一模型的合理性、可行性和有效性,具有实用价值。     

Poisson image fusion based on Markov random field fusion model

In this paper, we present a gradient domain image fusion framework based on the Markov Random Field (MRF) fusion model. In this framework, the salient structures of the input images are fused in the gradient domain, then the final fused image is reconstructed by solving a Poisson equation which forces the gradients of the fused image to be close to the fused gradients. To fuse the structures in the gradient domain, an effective MRF-based fusion model is designed based on both the per-pixel fusion rule defined by the local saliency and also the smoothness constraints over the fusion weights, which is optimized by graph cut algorithm. This MRF-based fusion model enables the accurate estimation of region-based fusion weights for the salient objects or structures. We apply this method to the applications of multi-sensor image fusion, including infrared and visible image fusion, multi-focus image fusion and medical image fusion. Extensive experiments and comparisons show that the proposed fusion model is able to better fuse the multi-sensor images and produces high-quality fusion results compared with the other state-of-the-art methods.     

SSO功能实现中的数据整合方案初探

SSO(Single Sign On)是并购整合后期的技术整合中必须要实现的一个功能,而通常不能直接将并购者的用户数据与被并购者的用户数据直接合并,本文着重分析了在实现SSO数据整合中可能存在的问题,并针对各个问题给出了初步的解决方案.     

笨蛋

我喜欢挑战,总是不停地给自己寻找新目标。我不喜欢安逸的感觉,安逸会让我心里发慌。当我发现,现在做的事,十几二十年都可能没有大改变,看现在就可以预见未来,我就不再对它感兴趣了。所以,我总是不停地经历转折。     

Locally linear discriminant embedding: An efficient method for face recognition

In this paper an efficient feature extraction method named as locally linear discriminant embedding (LLDE) is proposed for face recognition. It is well known that a point can be linearly reconstructed by its neighbors and the reconstruction weights are under the sum-to-one constraint in the classical locally linear embedding (LLE). So the constrained weights obey an important symmetry: for any particular data point, they are invariant to rotations, rescalings and translations. The latter two are introduced to the proposed method to strengthen the classification ability of the original LLE. The data with different class labels are translated by the corresponding vectors and those belonging to the same class are translated by the same vector. In order to cluster the data with the same label closer, they are also rescaled to some extent. So after translation and rescaling, the discriminability of the data will be improved significantly. The proposed method is compared with some related feature extraction methods such as maximum margin criterion (MMC), as well as other supervised manifold learning-based approaches, for example ensemble unified LLE and linear discriminant analysis (En-ULLELDA), locally linear discriminant analysis (LLDA). Experimental results on Yale and CMU PIE face databases convince us that the proposed method provides a better representation of the class information and obtains much higher recognition accuracies.     

Web proxy cache replacement scheme based on back-propagation neural network

Web proxy caches are used to reduce the strain of contemporary web traffic on web servers and network bandwidth providers. In this research, a novel approach to web proxy cache replacement which utilizes neural networks for replacement decisions is developed and analyzed. Neural networks are trained to classify cacheable objects from real world data sets using information known to be important in web proxy caching, such as frequency and recency. Correct classification ratios between 0.85 and 0.88 are obtained both for data used for training and data not used for training. Our approach is compared with Least Recently Used (LRU), Least Frequently Used (LFU) and the optimal case which always rates an object with the number of future requests. Performance is evaluated in simulation for various neural network structures and cache conditions. The final neural networks achieve hit rates that are 86.60% of the optimal in the worst case and 100% of the optimal in the best case. Byte-hit rates are 93.36% of the optimal in the worst case and 99.92% of the optimal in the best case. We examine the input-to-output mappings of individual neural networks and analyze the resulting caching strategy with respect to specific cache conditions.     

显卡进化之旅（2）：竞争带来的图形芯片的迅速进步

上期中,我们为大家带来了早期显卡的发展情况——显卡从无到有,从2D到3D,从性能到性能变强,其中的故事也非常的精彩。在经历了早期群雄逐鹿的时代后,显卡市场逐渐呈NVIDIA和ATI两分天下的局面。     

Herfindahl index applied to Fourier analysis

Since the harmonic components in the signal to be analyzed are uncertain and diverse, it is difficult to make the sampling frequency and the signal length be harmonically related to the component frequencies included in the signal, and the leakage is produced in the discrete Fourier transform. Based on the definition of the Herfindahl index in economics, the spectrum concentration can be defined and computed. The appropriate number of points can be decided according to the peak location of the spectrum concentration. The method and the examples are specified in detail in the paper.