Showing papers by "Andrew L. Rukhin published in 2006"

Excitation-emission matrix fluorescence spectroscopy for natural organic matter characterization: a quantitative evaluation of calibration and spectral correction procedures.

Abstract: The influence of different data collection procedures and of wavelength-dependent instrumental biases on fluorescence excitation-emission matrix (EEM) spectral analysis of aqueous organic matter samples was investigated. Particular attention was given to fluorescence contours (spectral shape) and peak fluorescence intensities. Instrumental bias was evaluated by independently applying excitation and emission correction factors to the raw excitation and emission data, respectively. The peak fluorescence intensities of representative natural organic matter and tryptophan were significantly influenced by the application of excitation and emission spectral correction factors and by the manner in which the raw data was collected. Humification and fluorescence indices were also influenced by emission correction factors but were independent of reference (excitation) intensity normalization or correction. EEM surface contours were dependent on normalization of the fluorescence intensity to the reference intensity but were not influenced by either excitation or emission spectral correction factors. Authors should be explicit in how excitation and emission spectral correction procedures are implemented in their investigations, which will help to facilitate intra-laboratory comparisons and data sharing.

Association Characteristics in Spatial Statistics

Abstract: A coefficient of association for two spatial sequences is suggested. Some properties of this characteristic are discussed. By using the Central Limit Theorem for stationary random fields its limiting asymptotic normality is established for any error distribution admitting finite fourth moment. The mean and the variance of this limiting law are found. Several examples of moving average and autoregression models are presented.

Recognition Problem of Biometrics: Nonparametric Dependence Measures and Aggregated Algorithms

Abstract: This chapter explores the possibility of using nonparametric dependence characteristics to evaluate biometric systems or algorithms that play an important role in homeland security for the purpose of law enforcement, sensitive areas access, borders and airport control, etc. These systems, which are designed to either detect or verify a person’s identity, are based on the fact that all members of the population possess unique characteristics (biometric signatures) such as facial features, eye irises, fingerprints, and gait, which cannot be easily stolen or forgotten. A variety of commercially available biometric systems are now in existence; however, in many instances there is no universally accepted optimal algorithm. For this reason it is of interest to investigate possible aggregations of two or several different algorithms. Kittler, Hatef, Duin, and Matas [220] and Jain, Duin, and Mao ([193], Sec. 6) review different schemes for combining multiple matchers. We discuss here the mathematical aspects of a fusion for algorithms in the recognition or identification problem, where a biometric signature of an unknown person, also known as probe, is presented to a system. This probe is compared with a database of, say, N signatures of known individuals called the gallery. On the basis of this comparison, an algorithm produces the similarity scores of the probe to the signatures in the gallery, whose elements are then ranked accordingly. The top matches with the highest similarity scores are expected to contain the true identity. A common feature of many recognition algorithms is representation of a biometric signature as a point in a multidimensional vector space. The similarity scores are based on the distance between the gallery and the query (probe) signatures in that space (or their projections onto a subspace of a smaller dimension). Because of inherent commonality of the systems, the similarity scores and their resulting

Stochastic Model For The Number Of Atoms In A Magneto-Optical Trap

Abstract: In this article a Markov chain for the distribution of single atoms is suggested and studied. We explore a recursive model for the number of atoms present in a magneto-optical trap under a feedback regime with a Poisson-distributed load. Formulas for the stationary distribution of this process are derived. They can be used to adjust the loading rate of atoms to maximize the proportion of time that a single atom spends in the trap. The (approximate) optimal regime for the Poisson loading and loss processes is found.