40,618 research outputs found
Single-amplifier integrator-based low power CMOS filter for video frequency applications
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An incremental approach to MSE-based feature selection
Feature selection plays an important role in classification systems. Using classifier error rate as the evaluation function, feature selection is integrated with incremental training. A neural network classifier is implemented with an incremental training approach to detect and discard irrelevant features. By learning attributes one after another, our classifier can find directly the attributes that make no contribution to classification. These attributes are marked and considered for removal. Incorporated with a Minimum Squared Error (MSE) based feature ranking scheme, four batch removal methods based on classifier error rate have been developed to discard irrelevant features. These feature selection methods reduce the computational complexity involved in searching among a large number of possible solutions significantly. Experimental results show that our feature selection methods work well on several benchmark problems compared with other feature selection methods. The selected subsets are further validated by a Constructive Backpropagation (CBP) classifier, which confirms increased classification accuracy and reduced training cost
Classification of finite dimensional modules of singly atypical type over the Lie superalgebras sl(m/n)
We classify the finite dimensional indecomposable sl(m/n)-modules with at
least a typical or singly atypical primitive weight. We do this classification
not only for weight modules, but also for generalized weight modules. We obtain
that such a generalized weight module is simply a module obtained by
``linking'' sub-quotient modules of generalized Kac-modules. By applying our
results to sl(m/1), we have in fact completely classified all finite
dimensional sl(m/1)-modules.Comment: 17 pages, Late
User manual of the CATSS system (version 1.0) communication analysis tool for space station
The Communication Analysis Tool for the Space Station (CATSS) is a FORTRAN language software package capable of predicting the communications links performance for the Space Station (SS) communication and tracking (C & T) system. An interactive software package was currently developed to run on the DEC/VAX computers. The CATSS models and evaluates the various C & T links of the SS, which includes the modulation schemes such as Binary-Phase-Shift-Keying (BPSK), BPSK with Direct Sequence Spread Spectrum (PN/BPSK), and M-ary Frequency-Shift-Keying with Frequency Hopping (FH/MFSK). Optical Space Communication link is also included. CATSS is a C & T system engineering tool used to predict and analyze the system performance for different link environment. Identification of system weaknesses is achieved through evaluation of performance with varying system parameters. System tradeoff for different values of system parameters are made based on the performance prediction
On indecomposable modules over the Virasoro algebra
It is proved that an indecomposable Harish-Chandra module over the Virasoro
algebra must be (i) a uniformly bounded module, or (ii) a module in Category
, or (iii) a module in Category , or (iv) a module which
contains the trivial module as one of its composition factors.Comment: 5 pages, Latex, to appear in Science in China
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