Relevance feedback for CBIR: a new approach based on probabilistic feature weighting with positive and negative examples.

In content-based image retrieval, understanding the user's needs is a challenging task that requires integrating him in the process of retrieval. Relevance feedback (RF) has proven to be an effective tool for taking the user's judgement into account. In this paper, we present a new RF framework based on a feature selection algorithm that nicely combines the advantages of a probabilistic formulation with those of using both the positive example (PE) and the negative example (NE). Through interaction with the user, our algorithm learns the importance he assigns to image features, and then applies the results obtained to define similarity measures that correspond better to his judgement. The use of the NE allows images undesired by the user to be discarded, thereby improving retrieval accuracy. As for the probabilistic formulation of the problem, it presents a multitude of advantages and opens the door to more modeling possibilities that achieve a good feature selection. It makes it possible to cluster the query data into classes, choose the probability law that best models each class, model missing data, and support queries with multiple PE and/or NE classes. The basic principle of our algorithm is to assign more importance to features with a high likelihood and those which distinguish well between PE classes and NE classes. The proposed algorithm was validated separately and in image retrieval context, and the experiments show that it performs a good feature selection and contributes to improving retrieval effectiveness.     

一种用于PIR控制芯片的级联积分梳状滤波器

从级联积分梳状滤波器(CIC)的原理出发,考虑到红外热释电传感器(PIR)采集信号的频率变化范围,设计了用于PIR控制芯片并对PIR传感器采集信号进行滤波的CIC滤波器。在MODSIM中对滤波器电路进行仿真,结果表明,低通和高通达到设计指标。对流片后的样片进行测试,在0.5~7 Hz频率范围内有比较明显的带通效果,带通外的频率需要增大输入信号幅值才能触发。与国外同类PIR控制芯片PS206作对比,基本达到了PS206芯片的性能。     

高耐压低负阻大功率晶体管理论分析

从理论上分析了影响晶体管发射极一集电极击穿后负阻摆幅的因素，通过SUPREM-Ⅲ分别模拟镓管和镓硼管的杂质浓度分布，得到了镓硼管低负阻摆幅和镓管低的电流增益原因。     

Unsupervised learning of a finite mixture model based on the Dirichlet distribution and its application

This paper presents an unsupervised algorithm for learning a finite mixture model from multivariate data. This mixture model is based on the Dirichlet distribution, which offers high flexibility for modeling data. The proposed approach for estimating the parameters of a Dirichlet mixture is based on the maximum likelihood (ML) and Fisher scoring methods. Experimental results are presented for the following applications: estimation of artificial histograms, summarization of image databases for efficient retrieval, and human skin color modeling and its application to skin detection in multimedia databases.     

A hybrid SEM algorithm for high-dimensional unsupervised learning using a finite generalized Dirichlet mixture.

This paper applies a robust statistical scheme to the problem of unsupervised learning of high-dimensional data. We develop, analyze, and apply a new finite mixture model based on a generalization of the Dirichlet distribution. The generalized Dirichlet distribution has a more general covariance structure than the Dirichlet distribution and offers high flexibility and ease of use for the approximation of both symmetric and asymmetric distributions. We show that the mathematical properties of this distribution allow high-dimensional modeling without requiring dimensionality reduction and, thus, without a loss of information. This makes the generalized Dirichlet distribution more practical and useful. We propose a hybrid stochastic expectation maximization algorithm (HSEM) to estimate the parameters of the generalized Dirichlet mixture. The algorithm is called stochastic because it contains a step in which the data elements are assigned randomly to components in order to avoid convergence to a saddle point. The adjective "hybrid" is justified by the introduction of a Newton-Raphson step. Moreover, the HSEM algorithm autonomously selects the number of components by the introduction of an agglomerative term. The performance of our method is tested by the classification of several pattern-recognition data sets. The generalized Dirichlet mixture is also applied to the problems of image restoration, image object recognition and texture image database summarization for efficient retrieval. For the texture image summarization problem, results are reported for the Vistex texture image database from the MIT Media Lab.     

α粒子注入对SRAM存储单元的影响研究

针对目前SRAM存储单元所面临的α粒子注入引起的软错误问题,首先采用一个简化的反相器模型,模拟其在α粒子注入时的输出变化;然后将该输出用作SRAM存储单元电路仿真的输入信号,研究α粒子注入对存储单元双稳电路稳定性的影响,其中,α粒子的注入通过一个电流源来模拟;最后,比较两种电流源模型下存储单元的存储情况。可以看出,pMOS等效电阻越大或节点电容越小,α粒子的注入越容易导致存储单元软错误的发生。也就是说,临界电荷越小,发生软错误的可能性越大。     

Road vectors update using SAR imagery: a snake-based method

The paper presents an approach for roads detection based on synthetic aperture radar (SAR) images and road databases. The vectors provided by the database are refined using active contours (snakes). In this framework, we firstly develop a restoration filter based on the frost filter achieving an acceptable compromise between speckle elimination and lines preserving. This is followed by a line plausibility calculation step which is used to deform the snake from its initial location toward the final solution. The snake is reformulated using finite elements method. The setting of the snake parameters is not an obvious problem especially when they are tuned by trial-and-error process. We propose a new automatic computational rule for the snake parameters. Our approach is validated by a series of tests on synthetic and SAR images.     

Characterization of hydrotreated Cu-ZSM-5 catalyst for the selective catalytic reduction of NO by n-decane

The steam treatment of Cu-ZSM-5 prepared by solid ion exchange involves an important decrease of the NO conversion. Characterization of the solids by NO-TPD, H₂-TPR, ²⁷Al MAS NMR shows that the catalyst deactivation is mainly due to change in copper species rather than the collapse of the zeolite framework.This revised version was published online in December 2005 with corrections to the Cover Date.     

A global approach for solving evolutive heat transfer for image denoising and inpainting.

This paper proposes an alternative to partial differential equations (PDEs) for solving problems in computer vision based on evolutive heat transfer. Traditionally, the method for solving such physics-based problems is to discretize and solve a PDE by a purely mathematical process. Instead of using the PDE, we propose to use the global heat principle and to decompose it into basic laws. We show that some of these laws admit an exact global version since they arise from conservative principles.We also show that the assumptions made about the other basic Iaws can be made wisely, taking into account knowledge about the problem and the domain. The numerical scheme is derived in a straightforward way from the modeled problem, thus providing a physical explanation for each step in the solution. The advantage of such an approach is that it minimizes the approximations made during the whole process and it modularizes it, allowing changing the application to a great number of problems. We apply the scheme to two applications: image denoising and inpainting which are modeled with heat transfer. For denoising, we propose a new approximation for the conductivity coefficient and we add thin lines to the features in order to block diffusion.     

超薄栅MOS器件热载流子应力下SILC的产生机制

通过测量界面陷阱的产生,研究了超薄栅nMOS和pMOS器件在热载流子应力下的应力感应漏电流(SILC).在实验结果的基础上,发现对于不同器件类型(n沟和p沟)、不同沟道长度(1、0.5、0.275和0.135μm)、不同栅氧化层厚度(4和2.5nm),热载流子应力后的SILC产生和界面陷阱产生之间均存在线性关系.这些实验证据表明MOS器件减薄后,SILC的产生与界面陷阱关系非常密切.