Genetic Identification of Lophius budegassa and L. piscatorius by PCR-RFLP Analysis of a Mitochondrial tRNAGlu/Cytochrome bSegment

Identification of Lophius budegassa(black‐bellied angler) and L. piscatorius(angler) (Lophiiformes) was carried out on the amplification of a 486 bp tRNA^Glu/cytochrome b segment using the polymerase chain reaction (PCR). Direct DNA sequencing of 6 PCR products was carried out. Six restriction endonucleases (AluI, CfoI, HaeIII, HinfI, Mae, and ScrFI) with different species‐specific restriction fragment length polymorphism (RFLP) were selected. Digestions of PCR products from 30 individuals showed no intraspecific polymorphism. Double digestions (CfoI and HinfI, and HaeIII and ScrFI) were simpler and more rapid than single digestions. This technique is suitable for distinguishing tails of both Lophius species.     

Accuracy of approximations in MOSFET charge models

This paper analyzes the results of common approximations made in MOSFET charge modeling. The basis for the comparison is a charge-sheet model that is valid in all regions of operation. We show that proper modeling of surface potential as a function of position along the channel is more important for capacitance coefficient modeling accuracy than partitioning of inversion charge between source and drain. In addition, we show that there is a numerical error in previous charge-sheet formulations, and provide a solution for this problem     

An easy-to-use mismatch model for the MOS transistor

In this paper, a physics-based mismatch model is presented. It is demonstrated on a 0.18-/spl mu/m technology that a simple mismatch model can still be used to characterize deep-submicron technologies. The accuracy of the model is examined and found to be within 20% in the strong inversion region. Bulk bias dependence is modeled in a physical way. To extract the mismatch parameters, a weighted fit is introduced. It is shown that the width and length dependence of the mismatch parameters is given by the Pelgrom model.     

A PAC-Bayesian margin bound for linear classifiers

We present a bound on the generalization error of linear classifiers in terms of a refined margin quantity on the training sample. The result is obtained in a probably approximately correct (PAC)-Bayesian framework and is based on geometrical arguments in the space of linear classifiers. The new bound constitutes an exponential improvement of the so far tightest margin bound, which was developed in the luckiness framework, and scales logarithmically in the inverse margin. Even in the case of less training examples than input dimensions sufficiently large margins lead to nontrivial bound values and-for maximum margins-to a vanishing complexity term. In contrast to previous results, however, the new bound does depend on the dimensionality of feature space. The analysis shows that the classical margin is too coarse a measure for the essential quantity that controls the generalization error: the fraction of hypothesis space consistent with the training sample. The practical relevance of the result lies in the fact that the well-known support vector machine is optimal with respect to the new bound only if the feature vectors in the training sample are all of the same length. As a consequence, we recommend to use support vector machines (SVMs) on normalized feature vectors only. Numerical simulations support this recommendation and demonstrate that the new error bound can be used for the purpose of model selection.     

50 years of the IEEE Microwave Theory and Techniques Society

The early history of the IEEE Microwave Theory and Techniques Society (IEEE MTT-S) is summarized since its founding in 1952, and all administrative committee members and presidents are listed. Some of the more recent changes resulting from growth and multinational participation are described. Publications are discussed with editors listed for this Transactions, the IEEE Microwave and Wireless Components Letters, the IEEE Microwave Newsletter, and IEEE Microwave Magazine. The chronological evolution of the IEEE MTT-S's awards is presented, including a listing of all award winners. Distinguished lecturers and microwave symposia sites and chairpersons are also discussed. Early technology trends are described     

A robust classification procedure based on mixture classifiers and nonparametric weighted feature extraction

There are many factors to consider in carrying out a hyperspectral data classification. Perhaps chief among them are class training sample size, dimensionality, and distribution separability. The intent of this study is to design a classification procedure that is robust and maximally effective, but which provides the analyst with significant assists, thus simplifying the analyst's task. The result is a quadratic mixture classifier based on Mixed-LOOC2 regularized discriminant analysis and nonparametric weighted feature extraction. This procedure has the advantage of providing improved classification accuracy compared to typical previous methods but requires minimal need to consider the factors mentioned above. Experimental results demonstrating these properties are presented.     

Characterization of residual information for SeaWinds quality control

Recent work has shown the important properties of the wind inversion residual or maximum-likelihood estimator (MLE) for quality Control (QC) of QuikSCAT Hierarchical Data Format (HDF) observations. Since March 2000, the QuikSCAT near-real-time (NRT) Binary Universal Format Representation (BUFR) product is available. As this product is used for numerical weather prediction (NWP) assimilation purposes, a QC procedure for the BUFR product is needed. We study the behavior of the MLE in order to determine whether the HDF QC procedure is appropriate for BUFR data. A comparison using real HDF and BUFR data reveals that the MLE distributions of HDF and BUFR differ and are actually poorly correlated. One important difference between BUFR and HDF is the amount of signal averaging prior to wind inversion. The averaging reduces the number of observations used in the wind retrieval for the BUFR product as compared to HDF. We show with a simple example that different MLE distributions are indeed expected due to this averaging. We also run a simulation in order to link theory and reality and better understand the behavior of the MLE. Despite the different MLE behavior in BUFR and HDF, the quality of the retrieved winds, as compared with the European Centre for Medium-Range Weather Forecasts winds, is very similar. We develop an MLE-based QC procedure for BUFR, similarly to the one in HDF, and we compare both. The skill of the QC in BUFR is again very similar to the one in HDF, showing that despite the different MLE behavior in both formats, the properties of the MLE as a QC indicator remain very similar.     

Cryptanalysis of Polly Cracker

An attack on the public key cryptosystem Polly Cracker is described, that reveals the complete secret key σ ∈ F_q ⁿ by means of n (nonadaptively) chosen "fake" ciphertexts