It is shown that in detecting sequentially a deterministic signal 0(0 in white noise 72(0 a similar identity (iii) in theorem 2.1, to the Wald's holds concerning a stopping time r determined by making use of a likelihood ratio. It is also shown that r has finite moments of any order under quite weak conditions over the signal. The exact A. S. N. E{y} in a constant signal case has been obtained and given by (2, 8). It is also considered a detection problem of a constant signal OW a in a colour-ed noise based on a sub-optimal statistic which become optimal when the noise were white. Similar properties of a stopping time r to those in the white noise case have been obtained in theorem 3.1. § 2. Detection of a deterministic signal in a white noise. We consider the following detection problem of a signal OW in the white noise 72(0; H; x(t) = W(t) H; x (t) = m(0+W(t),(2.1) where m(t) = 0(s)ds is the integrated signal and { W(t), 0 t < oo} is the Wiener 0 process which is considered to be the integrated form of the white noise 17(t). By Ho we mean that there is no signal in the (integrated) observationx(t) whose distribution is induced from the Wiener measure P0 and by Hl the observation x(t) is the sum of the signal m(t) and the noise W(t) whose distribution is induced by P1, e. a shift of Po by m(•). In order for the detection problem (2.1) to be non-singular, we assume that 0(•) is square integrable on each finite interval [0, t], 0 t < oo. Let us put

Conventional wisdom and common practice in acquisition and reconstruction of images from frequency data follow the basic principle of the Nyquist density sampling theory. This principle states that to reconstruct an image, the number of Fourier samples we need to acquire must match the desired

Abstract—A large number of algorithms have been proposed for feature subset selection. Our experimental results show that the sequential forward floating selection (SFFS) algorithm, proposed by Pudil et al., dominates the other algorithms tested. We study the problem of choosing an optimal feature

efficiently. With these new rotated features our sample face detector shows off on average a 10 % lower false alarm rate at a given hit rate. We also present a novel post optimization procedure for a given boosted cascade improving on average the false alarm rate further by 12.5%. 1

subject the data are momc mterference. We dm call attention to a number of common- = in be rp~crh of windows den used with the fd F ~- sampling and windowing. These operations can be performed transform. This paper includes a comprehensive catdog of data win- in either order. Sampling is well understood

behavior relations Greenwald et al. Predictive validity of the IAT (Draft of 30 Dec 2008) 2 Abstract (131 words) This review of 122 research reports (184 independent samples, 14,900 subjects), found average r=.274 for prediction of behavioral, judgment, and physiological measures

, loopy belief propagation and sampling. We found that loopy belief propagation always con verged in this case with the average number of iter ations equal to 10.2. The experimental protocol for the toyQMR network was similar to that of the PYRAMID network except that we randomized over structure as well

portfolios of specialized firms. This negativerelation holds throughout the 1980s in our sample. Finally, it holds for firms that have kept their number of segments constant over a number of years as well as for firms that have not. In our sample, firms that increase their number of segments have lower q

This paper presents a simple unsupervised learning algorithm for classifying reviews as recommended (thumbs up) or not recommended (thumbs down). The classification of a review is predicted by the average semantic orientation of the phrases in the review that contain adjectives or adverbs

, MUSCLE achieves average accuracy statistically indistinguishable from T-Coffee and MAFFT, and is the fastest of the tested methods for large numbers of sequences, aligning 5000 sequences of average length 350 in 7 min on a current desktop computer. The MUSCLE program, source code and PREFAB test data