Performance evaluation of an efficient reduced-complexity time synchronization approach for OFDM systems

This paper presents a performance analysis of a recently proposed preamble-based reduced-complexity (RC) two-stage synchronization technique. The preamble, composed of two identical subsequences, is first used to determine an uncertainty interval based on Cox and Schmidl algorithm. Then, a differential correlation-based metric is carried using a new sequence obtained by element wise multiplication of the preamble subsequence and a shifted version of it. This second step is performed to fine tune the coarse time estimate, by carrying the differential correlation-based metric over the uncertainty interval of limited width around the coarse estimate, thus leading to low computational load. In this paper, we first discuss some complexity issues of the RC approach compared to previously proposed algorithms. Then, we study the effect of the training sequence class and length choices on the synchronization performance in the case of multipath channels. The impact of the uncertainty interval width on the trade-off between performance and complexity is also studied. The two-stage approach was found to provide almost equal performance to those obtained by the most efficient differential correlation-based benchmarks. However, it has a very reduced computational load, equivalent to that of sliding correlation-based approaches.     

A comparative study of hygroscopic moisture content,electrical conductivity and ion chromatography for salt assessment in plasters of historical buildings

It has long been known that highly soluble salts or hydrate-forming salts can damage porous building material. There is a real challenge to provide salt assessment in compliance with good monuments diagnosis and restoration practices. The salts present in the masonry can be analysed qualitatively and quantitatively by means of several techniques. Nowadays, the standard analytical equipment as ion chromatography (IC) found in several laboratories is a reliable method for analysing a large variety of anions and cations in solutions (or building materials water extracts), in order to meet international standards and regulations. However, preliminary study and diagnosis of soluble salt present in monuments do not necessarily require such a precise (SD < 5%) and sensitive (ppm or ppb range) analyses. Some simple-to-use techniques, such as hygroscopic moisture content (HMC) and electrical conductivity (EC) of extracted salt solutions, are widely in use to provide indications on the presence of salts.This paper presents a comparison of salt assessment obtained by IC, HMC and EC methods, and provides the domain of their application to specific questions linked to the damaged building diagnosis and preservation.The comparison of the results obtained by IC, EC and HMC analyses highlights the limits of the EC method in providing quantitative information on soluble salt content. The EC technique seems to require some cautions when used for materials containing other high soluble phases such as portlandite. On the other hand, the HMC technique seems to be very useful in the field of historical masonry investigation since it can provide reliable semi-quantitative distribution of hygroscopic salts.     

Fuzzy and possibilistic shell clustering algorithms and theirapplication to boundary detection and surface approximation. I

Traditionally, prototype-based fuzzy clustering algorithms such as the Fuzzy C Means (FCM) algorithm have been used to find “compact” or “filled” clusters. Recently, there have been attempts to generalize such algorithms to the case of hollow or “shell-like” clusters, i.e., clusters that lie in subspaces of feature space. The shell clustering approach provides a powerful means to solve the hitherto unsolved problem of simultaneously fitting multiple curves/surfaces to unsegmented, scattered and sparse data. In this paper, we present several fuzzy and possibilistic algorithms to detect linear and quadric shell clusters. We also introduce generalizations of these algorithms in which the prototypes represent sets of higher-order polynomial functions. The suggested algorithms provide a good trade-off between computational complexity and performance, since the objective function used in these algorithms is the sum of squared distances, and the clustering is sensitive to noise and outliers. We show that by using a possibilistic approach to clustering, one can make the proposed algorithms robust     

Low-complexity fuzzy relational clustering algorithms for Webmining

This paper presents new algorithms-fuzzy c-medoids (FCMdd) and robust fuzzy c-medoids (RFCMdd)-for fuzzy clustering of relational data. The objective functions are based on selecting c representative objects (medoids) from the data set in such a way that the total fuzzy dissimilarity within each cluster is minimized. A comparison of FCMdd with the well-known relational fuzzy c-means algorithm (RFCM) shows that FCMdd is more efficient. We present several applications of these algorithms to Web mining, including Web document clustering, snippet clustering, and Web access log analysis     

Guest editorial: special issue on a decade of mining the Web

As editors of the Special Issue on a Decade of Mining the Web, we provide a brief overview of how Web mining evolved from the first Web mining workshop (WEBKDD’99) till today. We then introduce the papers of the special issue. Each of them is in a domain of Web mining research; it contains a survey of the past and a vision for the future.     

The fuzzy c spherical shells algorithm: A new approach

The fuzzy c spherical shells (FCSS) algorithm is specially designed to search for clusters that can be described by circular arcs or, generally, by shells of hyperspheres. A new approach to the FCSS algorithm is presented. This algorithm is computationally and implementationally simpler than other clustering algorithms that have been suggested for this purpose. An unsupervised algorithm which automatically finds the optimum number of clusters is not known. It uses a cluster validity measure to identify good clusters, merges all compatible clusters, and eliminates spurious clusters to achieve the final results. Experimental results on several data sets are presented.     

Fuzzy additive hybrid operators for network-based decision making

Evidence Aggregation Networks based on multiplicative fuzzy hybrid operators were introduced by Krishnapuram and Lee. They have been used for image segmentation, pattern recognition, and general multicriteria decision making. One of the drawbacks to these networks is that the training is complex and quite time consuming. In this article, we modify these aggregation networks to implement additive fuzzy hybrid connectives. We study the theoretical properties of two classes of such aggregation operators, one where the union and intersection components are based on multiplication, and the other where these components are derived from Yager connectives. These new networks have similar excellent properties such as backpropagation training and node interpretability for decision making under uncertainty as do their multiplicative precursors. They also have the advantage that training is easier since the derivatives of the additive hybrid operators are not as complex in form. the appropriate training algorithms are derived, and several examples given to illustrate the properties of the networks. © 1994 John Wiley & Sons, Inc.     

Dietary intake in the urban and rural populations of the Cap-Bon

The correlation between cytogenetic and histopathological findings were analysed in 189 meningiomas. The tumors were classified according to increasing degrees of anaplasia. We observed normal karyotype or only monosomy 22 in grade 1 (benign) tumors, while in grade 3 (anaplastic) only 1.5% of karyotypes were normal. Grade 2 (atypical) and 3 (anaplastic) tumors showed complex structural abnormalities. Loss of chromosome 14 were only found in grade 3. In cases with complex structural rearrangements, fluorescence in situ hybridization technique (FISH) has been realized and permitted a best identification of abnormalities. In our series, five patients recurred. They presented chromosomal abnormalities. These complex karyotypes in recurrent meningiomas might indicate aggressive tumor characteristics. Our results indicate histolopathological and cytogenetics correlations might represent a prognostic factor in meningiomas.     

Unsupervised learning of prototypes and attribute weights

In this paper, we introduce new algorithms that perform clustering and feature weighting simultaneously and in an unsupervised manner. The proposed algorithms are computationally and implementationally simple, and learn a different set of feature weights for each identified cluster. The cluster dependent feature weights offer two advantages. First, they guide the clustering process to partition the data set into more meaningful clusters. Second, they can be used in the subsequent steps of a learning system to improve its learning behavior. An extension of the algorithm to deal with an unknown number of clusters is also proposed. The extension is based on competitive agglomeration, whereby the number of clusters is over-specified, and adjacent clusters are allowed to compete for data points in a manner that causes clusters which lose in the competition to gradually become depleted and vanish. We illustrate the performance of the proposed approach by using it to segment color images, and to build a nearest prototype classifier.