Depending on depth, transparency, and wavelength, the composite upwelling signals from water bodies with vegetated and non-vegetated bottoms are attributable to the water column constituents, the submerged vegetation canopy and/or the substrate. Composite spectral signals form a surrogate sandy vegetated and non-vegetated sandy bottom sat varying depths in a tank of water were analyzed to assess the impact of the substrate in the presence of clear water. Submerged vegetation at a depth of 130 cm in a tank of clear water was detectable in both the visible and NIR regions. In the case of non-vegetated sandy bottom, the maximum separability of the composite upwelling signal from bottom at varying depths was recorded in wavelength region between 600 and 700 nm. Bottom at depth greater than 50 cm did not impact the upwelling signals in wavelength range beyond 744 nm.

Recent work performed in this laboratory has demonstrated the feasibility of using tunable filter technologies in place of dispersive spectrometers and fixed filtering devices for the purpose of creating field transportable standoff Raman imaging systems. Recently, a development in the area of polymer science has led to the production of polymer mirrors which are lightweight compared to glass mirrors of similar size. In addition, the techniques used to produce these polymer mirrors make it easy to design low f/pound optical devices, with much higher optical speeds than identically sized glass mirrors. The performance of a low f/pound polymer mirror system in combination with a liquid crystal tunable filter for standoff Raman chemical imaging is demonstrated and evaluated.

High spectral remote sensing is a hopeful technology in diagnosing crop nutrition background. With surface spectral measurement and laboratory biochemical analysis, the relationship between crop properties and spectral remote sensing data has been established. Seven chemical components - total chlorophyll, water crude protein, soluble sugar, N, P, K - were analyzed by laboratory chemical analyzing instrument. Foliar spectral property was detected outdoors by surface spectrometer. Chemical concentrations have been related to foliar spectral properties through stepwise multiple regression. The statistical equations between the chemical concentrations and reflectance as well as its several transformations were established. They underscored good estimation performance for chlorophyll, water crude protein, N and K with high squared multiple correlation coefficients (R2) values and high believable level. Especially R2 value of the equation between crude protein concentration and the first derivative of reflectance is 0.9564, which is the best result in the study of the fresh leave biochemistry up to now. On the basis of field experiment, an airborne remote sensing for crop nutrition monitoring was conducted in Shunyi County, Beijing, PR China. The sensor, made by Chinese Academy of Sciences, is in visible and near IR band. By image processing, the crop biochemistry map is obtained.

A combination of two pattern recognition methods has been developed that allows the generation of geographical emission maps form multivariate environmental data. In such a projection into a visually interpretable subspace by a Kohonen Self-Organizing Feature Map, the topology of the higher dimensional variables space can be preserved, but parts of the information about the correct neighborhood among the sample vectors will be lost. This can partly be compensated for by an additional projection of Prim's Minimal Spanning Tree into the trained neural network. This new environmental receptor modeling technique has been adapted for multiple sampling sites. The behavior of the method has been studied using simulated data. Subsequently, the method has been applied to mapping data sets from the Southern California Air Quality Study. The projection of a 17 chemical variables measured at up to 8 sampling sites provided a 2D, visually interpretable, geometrically reasonable arrangement of air pollution source sin the South Coast Air Basin.

Airborne particle composition data were obtained from week long samples collected at the northernmost manned site in the world, Alert, Northwest Territories, Canada, during the period of 1980 to 1991. The results of previous 2-way and 3- way analysis of these data suggested that a mixed 2-way and 3-way model might better represent the data. The methodology to calculate such a mixed model has just been developed and this method is the Multilinear Engine (ME). ME has been used to estimate a mixed 2-way/3-way model for the Alert aerosol data. Five 2-way and two 3-way factors have been found to provide the best fit and interpretation of the data. Each factor represents a probable source with a distinctive compositional profile and seasonal variations. The five 2- way factors are (i) winter Arctic haze (ii) soil (iii) sea salt, (iv) sulfate with high acidity and (v) iodine. The two 3-way factors are (i) bromine and (ii) biogenic sulfur. The results obtained are consistent with the current understanding of the Arctic aerosol.

By the method of the computer laser-optical photometry the investigation of the cereal stability for the various diseases taken into consideration the stability of tomato seeds to their interaction with the phytopathogenes and the phytotoxicity of microscopic fungi on the wheat seedlings was carried out. Original result for the investigation of optical-physiological characteristics of plants and seeds are shown.

We examine the use of artificial neural networks to classify IR spectra of organophosphorus pesticides and chemically related compounds. The spectra used were contributed from commercial libraries, government agencies, and government contractors and include spectra of pesticides, industrial precursors, hydrolysis products and other organophosphorus compounds. The data were pretreated to reduce artifacts arising from the variety of collection sources. The treated spectra were divided into spectral 'bins' of equal frequency width and transduced into data vectors whose elements consisted of the average absorbance value of the corresponding spectral bin. The spectral data vectors served as inputs to neural networks examined as spectral classifiers.

Our project is to design and construct a 2nd-generation 2D fluorimeter for rapid detection and speciation of organic compounds in seawater. The purpose of this detection is to recognize the presence of anthropogenic hydrocarbons associated with submarine activity, and to use this hydrocarbon signature for tracking purposes.

A field portable, single measurement, excitation-emission matrix fluorometer is applied to five environmental analyses. All applications require the uses of multi-way spectral deconvolution and calibration methods in order to uniquely resolve the spectral profiles of the targets analytes from interfering fluorophores. A modified multi-way calibration methods are applied to extend the dynamic range of the fluorometer by permitting robust calibration in the presence of intense Rayleigh and Raman backgrounds and localized detector saturation. The multi-way resolution methods are applied to calibrate chemically induced fluorescence changes induced by a target analyte from that of a fluorescent probe designed for molecular recognition. The multi-way resolution and calibration methods are also applied to potential sensor design relying of photoinduced degradation to and form fluorescent products. The EEM and multi-way methods are finally demonstrated in determining binding constants and capacity factors for fluorescent metal-ligand interactions.

Spectral pattern recognition (SPR) methods are among the most powerful tools currently available for noninvasively examining the spectroscopic and other chemical data for environmental monitoring. Using spectral data, these systems have found a variety of applications in chemometric systems such as gas chromatography, fluorescence spectroscopy, etc. An advantage of SPR approaches is that they made no a priori assumption regarding the structure of the spectra. However, a majority of these systems rely on human judgement for parameter selection and classification.

Predictive spectroscopy for complex systems can be performed with pattern-recognition-based mathematical approaches. Among the pattern recognition techniques we have employed are principal components analysis and principal components regression, iterative target transformation factor analysis.

A genetic algorithm (GA) for pattern recognition analysis of optical sensor data has been developed. The GA selects feature sets based on their principal component (PC) plots. A good PC plot can only be generated using features whose variance or information is primarily about class differences. Hence, the principal component analysis routine in the fitness function of the GA acts as an information filter, significantly reducing the size of the search space since it limits its search to features whose PC plots show clustering on the basis of the class. In addition, the GA focuses on those classes and/or samples that are difficult to classify as it trains using a form of boosting. Samples that consistently classify correctly are not as heavily weighted as samples that are difficult to classify. Over time, the algorithm learns its optimal parameters in a manner similar to a perceptron. The pattern recognition GA integrates aspects of strong and weak learning to yield a 'smart' one-pass procedure for feature selection and pattern classification.

This paper develops a distributed knowledge-based spectral processing and classification system which functions in one of two modes, executive and assistant. In the executive mode the system functions as a stand-alone system, automatically performing all the tasks from spectral enhancement, feature extraction and selection, to spectral classification and interpretation using the optimally feasible algorithms. In the assistant mode the system leads the user through the entire spectral processing and classification process, allowing a user to select appropriate parameters, their weights, knowledge organization method and a classification algorithm. Thus, the latter mode can also be used for teaching and instruction. It is shown how novice users can select a set of parameters, adjust their weights, and examine the classification process. Since different classifiers have various underlying assumptions, provisions have been made to control these assumptions, allowing users to select the parameters individually and combined, and providing facilities to visualize the interrelationships among the parameters.

Microwave energy has been shown to be able to induce drastic rate increase for certain reactions. These rate enhancements have been proposed to originate from non-thermal dynamics. However, these model system have often been studied under high pressure or temperature with little thermal control, and the model system studied have not been of biological significance. Considering the prevalence of microwave energy in the current environment, it would be of interest to study the ability of microwave energy to influence biologically relevant reaction rates. We have developed a microwave reactor system that allows comparison of thermal- and microwave-catalyzed reactions under conditions amenable to biological systems. To exemplify the usefulness of this methodology, we have studied both the enzymatic hydrolysis of cellobiose and the copper-bipyridyl mediated hydrolysis of phosphodiesters. We have shown that in these system and under these biologically significant conditions, the rate enhancements provided by microwave irradiation are thermal in origin.

In this paper we describe a simple method of small-size images recognition. The method is based on optical expanded encoding of small-size images by using a random phase-only Fourier filter.