1,721,266 research outputs found
A recursive formula for even order harmonic series
No full textAbstractA useful recursive formula for obtaining the infinite sums of even order harmonic series Σ∞n=1 (1/n2k), k = 1, 2, …, is derived by an application of Fourier series expansion of some periodic functions. Since the formula does not contain the Bernoulli numbers, infinite sums of even order harmonic series may be calculated by the formula without the Bernoulli numbers. Infinite sums of a few even order harmonic series, which are calculated using the recursive formula, are tabulated for easy reference
DETECTION OF COMPOSITE SIGNALS .2. EXAMPLES AND PERFORMANCE COMPARISON
No full textExamples of the locally optimum detection scheme for weak composite signals are shown for some well-known probability density functions. Asymptotic performance and finite sample-size performance characteristics of the locally optimum detectors are compared to those of other common detectors.Korea Science and Engineering Foundation (KOSEF) under Grant 893-0801-011-
Rank-based detection of weak random signals in a multiplicative noise model
No full textMultiplicative noise is known to be useful in modeling some environment, which is difficult to describe by additive noise model. In this paper, nonparametric detection of weak random signals in multiplicative noise is considered. The locally optimum detector based on signs and ranks of observations is derived for good weak-signal detection performance under any noise probability density function. The detector has similarities to the locally optimum detector for random signals in multiplicative noise. It is shown that the nonparametric detector asymptotically has almost the same performance as the locally optimum detector. (C) 1997 Published by Elsevier Science B.V.Korea Research Foundation (KRF), Korea, under a grant for Faculty Research Abroad and the Natural Sciences and Engineering Research Council (NSERC), Canad
APPROXIMATION OF OPTIMUM QUANTIZER DETECTOR USING A UNIFORM QUANTIZER AND A CODER
No full textA suboptimum quantization-detection system in which the locally optimum nonlinearity is replaced by a uniform quantizer and a coder for the uniform quantizer output levels is proposed. The proposed detection system does not require iterations to obtain the parameters of the quantizer and is easily implementable in practice. We develop a design procedure of the suboptimum quantizer and obtain the parameters of the suboptimum quantizer for the generalized Gaussian noise density. We also compare the performance of the suboptimum quantization detector with that of other detectors.Korea Advanced Institute of Science and Technology (KAIST) under an Internal Gran
A NEW NOISELESS CODING TECHNIQUE FOR BINARY IMAGES
No full textAir Force Office of Scientific Research under Grant 82-002
ASYMPTOTIC-DISTRIBUTION OF THE MUSIC NULL SPECTRUM
No full textIn this correspondence we derive the asymptotic distribution of the MUSIC null spectrum, from which an exact expression of the asymptotic variance of the MUSIC null spectrum can be obtained. The result presented in this correspondence is a simpler alternative and a special case of a more general result recently obtained by Lee and Wengrovitz
Asymptotic performance of a user detection scheme in Nakagami interference
No full textDetection of the existence of a desired user is considered in this paper. We assume that the signal to noise ratio is high enough to ignore the effects of noise compared with those of the interference by other users. The inter-user interference and user signals are modeled by the Nakagami model. We observation model for this situation is proposed, the locally optimum test statistic is derived under the model, and the asymptotic performance of the test statistic is compared with that of the envelope detector. We show that the locally optimum detector has performance better than the conventional envelope detector. (C) 1997 Published by Elsevier Science B.V.Ministry of Information and Communication (MIC) under grant from the University Basic Research Fund
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