CAPITAL DECISIONS WITH TEMPORAL RESOLUTION OF UNCERTAINTY: A MEAN-VARIANCE APPROACH

Financial Economics,

Breeding Tobacco Varieties

The principal objectives in breeding tobacco are yield, field and handling characteristics, disease resistance, and quality. Of these objectives, major emphasis has been placed on breeding disease-resistant varieties. Resistant varieties have provided one of the most effective means of combating many of the pathogens that attack the tobacco plant. However, the transfer of genes for disease resistance into susceptible varieties has been accompanied in many cases by other characteristics which are undesirable. It is often a difficult task to combine acceptable type, yield, and quality with desired factors for disease resistance into a single variety

Performance bounds for polynomial phase parameter estimation with nonuniform and random sampling schemes

Copyright © 2000 IEEEEstimating the parameters of a cisoid with an unknown amplitude and polynomial phase using uniformly spaced samples can result in ambiguous estimates due to Nyquist sampling limitations. It has been shown previously that nonuniform sampling has the advantage of unambiguous estimates beyond the Nyquist frequency; however, the effect of sampling on the Cramer-Rao bounds is not well known. This paper first derives the maximum likelihood estimators and Cramer-Rao bounds for the parameters with known, arbitrary sampling times. It then outlines two methods for incorporating random sampling times into the lower variance bounds, describing one in detail. It is then shown that for a signal with additive white Gaussian noise the bounds for the estimation with nonuniform sampling tend toward those of uniform sampling. Thus, nonuniform sampling overcomes the ambiguity problems of uniform sampling without incurring the penalty of an increased variance in parameter estimation.Jonathan A. Legg and Douglas A. Gra

NITROGEN MANAGEMENT IN SOUTHEASTERN MINNESOTA

Crop Production/Industries,

Can Machines Think in Radio Language?

People can think in auditory, visual and tactile forms of language, so can machines principally. But is it possible for them to think in radio language? According to a first principle presented for general intelligence, i.e. the principle of language's relativity, the answer may give an exceptional solution for robot astronauts to talk with each other in space exploration.Comment: 4 pages, 1 figur

Improving accuracy and efficiency of mutual information for multi-modal retinal image registration using adaptive probability density estimation

Mutual information (MI) is a popular similarity measure for performing image registration between different modalities. MI makes a statistical comparison between two images by computing the entropy from the probability distribution of the data. Therefore, to obtain an accurate registration it is important to have an accurate estimation of the true underlying probability distribution. Within the statistics literature, many methods have been proposed for finding the 'optimal' probability density, with the aim of improving the estimation by means of optimal histogram bin size selection. This provokes the common question of how many bins should actually be used when constructing a histogram. There is no definitive answer to this. This question itself has received little attention in the MI literature, and yet this issue is critical to the effectiveness of the algorithm. The purpose of this paper is to highlight this fundamental element of the MI algorithm. We present a comprehensive study that introduces methods from statistics literature and incorporates these for image registration. We demonstrate this work for registration of multi-modal retinal images: colour fundus photographs and scanning laser ophthalmoscope images. The registration of these modalities offers significant enhancement to early glaucoma detection, however traditional registration techniques fail to perform sufficiently well. We find that adaptive probability density estimation heavily impacts on registration accuracy and runtime, improving over traditional binning techniques. © 2013 Elsevier Ltd

Feature Neighbourhood Mutual Information for multi-modal image registration: An application to eye fundus imaging

© 2014 Elsevier Ltd. All rights reserved. Multi-modal image registration is becoming an increasingly powerful tool for medical diagnosis and treatment. The combination of different image modalities facilitates much greater understanding of the underlying condition, resulting in improved patient care. Mutual Information is a popular image similarity measure for performing multi-modal image registration. However, it is recognised that there are limitations with the technique that can compromise the accuracy of the registration, such as the lack of spatial information that is accounted for by the similarity measure. In this paper, we present a two-stage non-rigid registration process using a novel similarity measure, Feature Neighbourhood Mutual Information. The similarity measure efficiently incorporates both spatial and structural image properties that are not traditionally considered by MI. By incorporating such features, we find that this method is capable of achieving much greater registration accuracy when compared to existing methods, whilst also achieving efficient computational runtime. To demonstrate our method, we use a challenging medical image data set consisting of paired retinal fundus photographs and confocal scanning laser ophthalmoscope images. Accurate registration of these image pairs facilitates improved clinical diagnosis, and can be used for the early detection and prevention of glaucoma disease