Symbolic Macromodeling for Statistical Simulation of Operational Amplifiers

Symbolic circuit simulator is traditionally applied to the small-signal analysis of analog circuits. This paper establishes a symbolic behavioral macromodeling method applicable to both small-signal and large-signal analysis of general two-stage operational amplifiers （op-amps）. The proposed method creates a two-pole parametric macromodel whose parameters are analytical functions of the circuit element parameters generated by a symbolic circuit simulator. A moment matching technique is used in deriving the analytical model parameter. The created parametric behavioral model can be used for op-amps performance simulation in both frequency and time domains. In particular, the parametric models are highly suited for fast statistical simulation of op-amps in the time-domain. Experiment results show that the statistical distributions of the op-amp slew and settling time characterized by the proposed model agree well with the transistor-level results in addition to achieving significant speedup.

Symbol Analysis of "The Love Song of J.Alfred Prufrock"

While the character,theme and modernism of T.S. Eliot's poem "The Love Song of J. Alfred Prufrock" have been explored variously,the language techniques in this poem have been rarely investigated. This thesis tries to analyze these symbols to find the theme of the poem. By using symbol analysis, the values and the spiritual world of the early 20th century generation can be clearly known. There are four sections in this thesis. The first section makes a brief introduction of T. S. Eliot. The second section gives the basic theory about symbol and symbolism. In the third section, different symbol analyses with different details are presented by five subsections, that is, symbols of streets, symbols of body parts, symbols of ocean, symbols about time and other symbols. In the fourth section, the theme of the poem —the disillusionment of the post-war generation is analyzed.The conclusion summarizes the importance of symbolic language techniques used in the poem.

通用可组合符号化分析群组密钥协商协议(英文)

Canetti and Herzog have already proposed universally composable symbolic analysis(UCSA) to analyze mutual authentication and key exchange protocols. However,they do not analyze group key exchange protocol. Therefore,this paper explores an approach to analyze group key exchange protocols,which realize automation and guarantee the soundness of cryptography. Considered that there exist many kinds of group key exchange protocols and the participants’ number of each protocol is arbitrary. So this paper takes the case of Burmester-Desmedt(BD) protocol with three participants against passive adversary(3-BD-Passive) . In a nutshell,our works lay the root for analyzing group key exchange protocols automatically without sacrificing soundness of cryptography.

Hierarchical Clustering of Complex Symbolic Data and Application for Emitter Identification

It is well-known that the values of symbolic variables may take various forms such as an interval, a set of stochastic measurements of some underlying patterns or qualitative multi-values and so on. However, the majority of existing work in symbolic data analysis still focuses on interval values. Although some pioneering work in stochastic pattern based symbolic data and mixture of symbolic variables has been explored, it still lacks flexibility and computation efficiency to make full use of the distinctive individual symbolic variables. Therefore, we bring forward a novel hierarchical clustering method with weighted general Jaccard distance and effective global pruning strategy for complex symbolic data and apply it to emitter identification. Extensive experiments indicate that our method has outperformed its peers in both computational efficiency and emitter identification accuracy.

Feature selection on probabilistic symbolic objects

In data analysis tasks, we are often confronted to very high dimensional data. Based on the purpose of a data analysis study, feature selection will find and select the relevant subset of features from the original features. Many feature selection algorithms have been proposed in classical data analysis, but very few in symbolic data analysis （SDA） which is an extension of the classical data analysis, since it uses rich objects instead to simple matrices. A symbolic object, compared to the data used in classical data analysis can describe not only individuals, but also most of the time a cluster of individuals. In this paper we present an unsupervised feature selection algorithm on probabilistic symbolic objects （PSOs）, with the purpose of discrimination. A PSO is a symbolic object that describes a cluster of individuals by modal variables using relative frequency distribution associated with each value. This paper presents new dissimilarity measures between PSOs, which are used as feature selection criteria, and explains how to reduce the complexity of the algorithm by using the discrimination matrix.