Research and Realization of Ten-print Data Quality Control Techniques for Imperial Scale Automated Fingerprint Identification System

As the first individualization-information processing equipment put into practical service worldwide,Automated Fingerprint Identification System(AFIS)has always been regarded as the first choice in individualization of criminal suspects or those who died in mass disasters.By integrating data within the existing regional large-scale AFIS database,many countries are constructing an ultra large state-of-the-art AFIS(or Imperial Scale AFIS)system.Therefore,it is very important to develop a series of ten-print data quality controlling process for this system of this type,which would insure a substantial matching efficiency,as the pouring data come into this imperial scale being.As the image quality of ten-print data is closely relevant to AFIS matching proficiency,a lot of police departments have allocated huge amount of human and financial resources over this issue by carrying out manual verification works for years.Unfortunately,quality control method above is always proved to be inadequate because it is an astronomical task involved,in which it has always been problematic and less affiant for potential errors.Hence,we will implement quality control in the above procedure with supplementary-acquisition effect caused by the delay of feedback instructions sent from the human verification teams.In this article,a series of fingerprint image quality supervising techniques has been put forward,which makes it possible for computer programs to supervise the ten-print image quality in real-time and more accurate manner as substitute for traditional manual verifications.Besides its prominent advantages in the human and financial expenditures,it has also been proved to obviously improve the image quality of the AFIS ten-print database,which leads up to a dramatic improvement in the AFIS-matching accuracy as well.

Automated Detection of Contaminated Radar Image Pixels in Mountain Areas

In mountain areas, radar observations are often contaminated （1） by echoes from high-speed moving vehicles and （2） by point-wise ground clutter under either normal propagation （NP） or anomalous propagation （AP） conditions. Level Ⅱ data are collected from KMTX （Salt Lake City, Utah） radar to analyze these two types of contamination in the mountain area around the Great Salt Lake. Human experts provide the ＂ground truth＂ for possible contamination of either type on each individual pixel. Common features are then extracted for contaminated pixels of each type. For example, pixels contaminated by echoes from high-speed moving vehicles are characterized by large radial velocity and spectrum width. Echoes from a moving train tend to have larger velocity and reflectivity but smaller spectrum width than those from moving vehicles on highways. These contaminated pixels are only seen in areas of large terrain gradient （in the radial direction along the radar beam）. The same is true for the second type of contamination - pointwise ground clutters. Six quality control （QC） parameters are selected to quantify the extracted features. Histograms are computed for each QC parameter and grouped for contaminated pixels of each type and also for non-contaminated pixels. Based on the computed histograms, a fuzzy logical algorithm is developed for automated detection of contaminated pixels. The algorithm is tested with KMTX radar data under different （clear and rainy） weather conditions.

Derivation of Regression Coefficients for Sea Surface Temperature Retrieval over East Asia

Among the regression-based algorithms for deriving SST from satellite measurements, regionally optimized algorithms normally perform better than the corresponding global algorithm. In this paper, three algorithms are considered for SST retrieval over the East Asia region （15°-55°N, 105°-170°E）, including the multi-channel algorithm （MCSST）, the quadratic algorithm （QSST）, and the Pathfinder algorithm （PFSST）. All algorithms are derived and validated using collocated buoy and Geostationary Meteorological Satellite （GMS-5） observations from 1997 to 2001. An important part of the derivation and validation of the algorithms is the quality control procedure for the buoy SST data and an improved cloud screening method for the satellite brightness temperature measurements. The regionally optimized MCSST algorithm shows an overall improvement over the global algorithm, removing the bias of about -0.13℃ and reducing the root-mean-square difference （rmsd） from 1.36℃ to 1.26℃. The QSST is only slightly better than the MCSST. For both algorithms, a seasonal dependence of the remaining error statistics is still evident. The Pathfinder approach for deriving a season-specific set of coefficients, one for August to October and one for the rest of the year, provides the smallest rmsd overall that is also stable over time.

Development and case study of a science-based software platform to support policy making on air quality

This article describes the development and implementations of a novel software platform that supports real-time, science-based policy making on air quality through a user-friendly interface. The software, RSM-VAT, uses a response surface modeling（RSM） methodology and serves as a visualization and analysis tool（VAT） for three-dimensional air quality data obtained by atmospheric models. The software features a number of powerful and intuitive data visualization functions for illustrating the complex nonlinear relationship between emission reductions and air quality benefits. The case study of contiguous U.S.demonstrates that the enhanced RSM-VAT is capable of reproducing the air quality model results with Normalized Mean Bias 〈 2% and assisting in air quality policy making in near real time.