The Experts below are selected from a list of 32088 Experts worldwide ranked by ideXlab platform
Anil K Jain - One of the best experts on this subject based on the ideXlab platform.
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automated latent fingerprint recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019Co-Authors: Kai Cao, Anil K JainAbstract:Latent fingerprints are one of the most important and widely used evidence in law enforcement and forensic agencies worldwide. Yet, NIST evaluations show that the performance of state-of-the-art latent recognition systems is far from satisfactory. An automated latent fingerprint recognition system with high accuracy is essential to compare latents found at crime scenes to a large collection of reference prints to generate a Candidate List of possible mates. In this paper, we propose an automated latent fingerprint recognition algorithm that utilizes Convolutional Neural Networks (ConvNets) for ridge flow estimation and minutiae descriptor extraction, and extract complementary templates (two minutiae templates and one texture template) to represent the latent. The comparison scores between the latent and a reference print based on the three templates are fused to retrieve a short Candidate List from the reference database. Experimental results show that the rank-1 identification accuracies (query latent is matched with its true mate in the reference database) are 64.7 percent for the NIST SD27 and 75.3 percent for the WVU latent databases, against a reference database of 100K rolled prints. These results are the best among published papers on latent recognition and competitive with the performance (66.7 and 70.8 percent rank-1 accuracies on NIST SD27 and WVU DB, respectively) of a leading COTS latent Automated Fingerprint Identification System (AFIS). By score-level (rank-level) fusion of our system with the commercial off-the-shelf (COTS) latent AFIS, the overall rank-1 identification performance can be improved from 64.7 and 75.3 to 73.3 percent (74.4 percent) and 76.6 percent (78.4 percent) on NIST SD27 and WVU latent databases, respectively.
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automated latent fingerprint recognition
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Kai Cao, Anil K JainAbstract:Latent fingerprints are one of the most important and widely used evidence in law enforcement and forensic agencies worldwide. Yet, NIST evaluations show that the performance of state-of-the-art latent recognition systems is far from satisfactory. An automated latent fingerprint recognition system with high accuracy is essential to compare latents found at crime scenes to a large collection of reference prints to generate a Candidate List of possible mates. In this paper, we propose an automated latent fingerprint recognition algorithm that utilizes Convolutional Neural Networks (ConvNets) for ridge flow estimation and minutiae descriptor extraction, and extract complementary templates (two minutiae templates and one texture template) to represent the latent. The comparison scores between the latent and a reference print based on the three templates are fused to retrieve a short Candidate List from the reference database. Experimental results show that the rank-1 identification accuracies (query latent is matched with its true mate in the reference database) are 64.7% for the NIST SD27 and 75.3% for the WVU latent databases, against a reference database of 100K rolled prints. These results are the best among published papers on latent recognition and competitive with the performance (66.7% and 70.8% rank-1 accuracies on NIST SD27 and WVU DB, respectively) of a leading COTS latent Automated Fingerprint Identification System (AFIS). By score-level (rank-level) fusion of our system with the commercial off-the-shelf (COTS) latent AFIS, the overall rank-1 identification performance can be improved from 64.7% and 75.3% to 73.3% (74.4%) and 76.6% (78.4%) on NIST SD27 and WVU latent databases, respectively.
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latent fingerprint matching performance gain via feedback from exemplar prints
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014Co-Authors: Sunpreet S Arora, Kai Cao, Eryun Liu, Anil K JainAbstract:Latent fingerprints serve as an important source of forensic evidence in a court of law. Automatic matching of latent fingerprints to rolled/plain (exemplar) fingerprints with high accuracy is quite vital for such applications. However, latent impressions are typically of poor quality with complex background noise which makes feature extraction and matching of latents a significantly challenging problem. We propose incorporating top-down information or feedback from an exemplar to refine the features extracted from a latent for improving latent matching accuracy. The refined latent features (e.g. ridge orientation and frequency), after feedback, are used to re-match the latent to the top $K$ Candidate exemplars returned by the baseline matcher and resort the Candidate List. The contributions of this research include: (i) devising systemic ways to use information in exemplars for latent feature refinement, (ii) developing a feedback paradigm which can be wrapped around any latent matcher for improving its matching performance, and (iii) determining when feedback is actually necessary to improve latent matching accuracy. Experimental results show that integrating the proposed feedback paradigm with a state-of-the-art latent matcher improves its identification accuracy by 0.5-3.5 percent for NIST SD27 and WVU latent databases against a background database of 100k exemplars.
Karl G. Linden - One of the best experts on this subject based on the ideXlab platform.
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Efficacy of inactivation of human enteroviruses by dual-wavelength germicidal ultraviolet (UV-C) light emitting diodes (LEDs).
Water, 2019Co-Authors: Sara E. Beck, Kelsie M. Carlson, Nichole E. Brinkman, Oliver R. Lawal, Laura A Boczek, Karl G. Linden, Samuel L HayesAbstract:The efficacy of germicidal ultraviolet (UV-C) light emitting diodes (LEDs) was evaluated for inactivating human enteroviruses included on the United States Environmental Protection Agency (EPA)’s Contaminant Candidate List (CCL). A UV-C LED device, emitting at peaks of 260 nm and 280 nm and the combination of 260/280 nm together, was used to measure and compare potential synergistic effects of dual wavelengths for disinfecting viral organisms. The 260 nm LED proved to be the most effective at inactivating the CCL enteroviruses tested. To obtain 2-log10 inactivation credit for the 260 nm LED, the fluences (UV doses) required are approximately 8 mJ/cm2 for coxsackievirus A10 and poliovirus 1, 10 mJ/cm2 for enterovirus 70, and 13 mJ/cm2 for echovirus 30. No synergistic effect was detected when evaluating the log inactivation of enteroviruses irradiated by the dual-wavelength UV-C LEDs.
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Transformation of Contaminant Candidate List (CCL3) compounds during ozonation and advanced oxidation processes in drinking water: Assessment of biological effects.
Water Research, 2016Co-Authors: Hana Mestankova, Kristin Schirmer, Silvio Canonica, Austa M. Parker, Nadine Bramaz, Urs Von Gunten, Karl G. LindenAbstract:Abstract The removal of emerging contaminants during water treatment is a current issue and various technologies are being explored. These include UV- and ozone-based advanced oxidation processes (AOPs). In this study, AOPs were explored for their degradation capabilities of 25 chemical contaminants on the US Environmental Protection Agency's Contaminant Candidate List 3 (CCL3) in drinking water. Twenty-three of these were found to be amenable to hydroxyl radical-based treatment, with second-order rate constants for their reactions with hydroxyl radicals ( OH) in the range of 3–8 × 109 M−1 s−1. The development of biological activity of the contaminants, focusing on mutagenicity and estrogenicity, was followed in parallel with their degradation using the Ames and YES bioassays to detect potential changes in biological effects during oxidative treatment. The majority of treatment cases resulted in a loss of biological activity upon oxidation of the parent compounds without generation of any form of estrogenicity or mutagenicity. However, an increase in mutagenic activity was detected by oxidative transformation of the following CCL3 parent compounds: nitrobenzene ( OH, UV photolysis), quinoline ( OH, ozone), methamidophos ( OH), N-nitrosopyrolidine ( OH), N-nitrosodi-n-propylamine ( OH), aniline (UV photolysis), and N-nitrosodiphenylamine (UV photolysis). Only one case of formation of estrogenic activity was observed, namely, for the oxidation of quinoline by OH. Overall, this study provides fundamental and practical information on AOP-based treatment of specific compounds of concern and represents a framework for evaluating the performance of transformation-based treatment processes.
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reactions of thiocarbamate triazine and urea herbicides rdx and benzenes on epa contaminant Candidate List with ozone and with hydroxyl radicals
Water Research, 2008Co-Authors: Wei R Chen, Karl G. Linden, Michael S Elovitz, I H SuffetAbstract:Second-order rate constants of the direct ozone reactions [formula: see text] and the indirect OH radical reactions [formula: see text] for nine chemicals on the US EPA's Drinking Water Contaminant Candidate List (CCL) were studied during the ozonation and ozone/hydrogen peroxide advanced oxidation process (O(3)/H(2)O(2) AOP) using batch reactors. Except for the thiocarbamate herbicides (molinate and EPTC), all other CCL chemicals (linuron, diuron, prometon, RDX, 2,4-dinitrotoluene, 2,6-dinitrotoluene and nitrobenzene) show low reactivity toward ozone. The general magnitude of ozone reactivity of the CCL chemicals can be explained by their structures and the electrophilic nature of ozone reactions. The CCL chemicals (except RDX) are highly reactive toward OH radicals as demonstrated by their high [formula: see text] values. Ozonation at low pH, which involves mainly the direct ozone reaction, is only efficient for the removal of the thiocarbamates. Ozonation at high pH and O(3)/H(2)O(2) AOP will be highly efficient for the treatment of all chemicals in this study except RDX, which shows the lowest OH radical reactivity. Removal of a contaminant does not mean complete mineralization and reaction byproducts may be a problem if they are recalcitrant and are likely to cause health concerns.
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treatment of volatile organic chemicals on the epa contaminant Candidate List using ozonation and the o3 h2o2 advanced oxidation process
Environmental Science & Technology, 2006Co-Authors: Wei R Chen, Karl G. Linden, Charles M Sharpless, I H SuffetAbstract:Seven volatile organic chemicals (VOCs) on the EPA Contaminant Candidate List together with 1,1-dichloropropane were studied for their reaction kinetics and mechanisms with ozone and OH radicals during ozonation and the ozone/hydrogen peroxide advanced oxidation process (O3/H2O2 AOP) using batch reactors. The three aromatic VOCs demonstrated high reactivity during ozonation and were eliminated within minutes after ozone addition. The high reactivity is attributed to their fast, indirect OH radical reactions with kOH,M of (5.3−6.6) × 109 M-1 s-1. Rates of aromatic VOC degradation are in the order 1,2,4-trimethylbenzene > p-cymene > bromobenzene. This order is caused by the selectivity of the direct ozone reactions (kO3,M ranges from 0.16 to 304 M-1 s-1) and appears to be related to the electron-donating or -withdrawing ability of the substituent groups on the aromatic ring. The removal rates for the five aliphatic VOCs are much lower and are in the order 1,1-dichloropropane > 1,3-dichloropropane > 1,1-dich...
Kai Cao - One of the best experts on this subject based on the ideXlab platform.
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automated latent fingerprint recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019Co-Authors: Kai Cao, Anil K JainAbstract:Latent fingerprints are one of the most important and widely used evidence in law enforcement and forensic agencies worldwide. Yet, NIST evaluations show that the performance of state-of-the-art latent recognition systems is far from satisfactory. An automated latent fingerprint recognition system with high accuracy is essential to compare latents found at crime scenes to a large collection of reference prints to generate a Candidate List of possible mates. In this paper, we propose an automated latent fingerprint recognition algorithm that utilizes Convolutional Neural Networks (ConvNets) for ridge flow estimation and minutiae descriptor extraction, and extract complementary templates (two minutiae templates and one texture template) to represent the latent. The comparison scores between the latent and a reference print based on the three templates are fused to retrieve a short Candidate List from the reference database. Experimental results show that the rank-1 identification accuracies (query latent is matched with its true mate in the reference database) are 64.7 percent for the NIST SD27 and 75.3 percent for the WVU latent databases, against a reference database of 100K rolled prints. These results are the best among published papers on latent recognition and competitive with the performance (66.7 and 70.8 percent rank-1 accuracies on NIST SD27 and WVU DB, respectively) of a leading COTS latent Automated Fingerprint Identification System (AFIS). By score-level (rank-level) fusion of our system with the commercial off-the-shelf (COTS) latent AFIS, the overall rank-1 identification performance can be improved from 64.7 and 75.3 to 73.3 percent (74.4 percent) and 76.6 percent (78.4 percent) on NIST SD27 and WVU latent databases, respectively.
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automated latent fingerprint recognition
arXiv: Computer Vision and Pattern Recognition, 2017Co-Authors: Kai Cao, Anil K JainAbstract:Latent fingerprints are one of the most important and widely used evidence in law enforcement and forensic agencies worldwide. Yet, NIST evaluations show that the performance of state-of-the-art latent recognition systems is far from satisfactory. An automated latent fingerprint recognition system with high accuracy is essential to compare latents found at crime scenes to a large collection of reference prints to generate a Candidate List of possible mates. In this paper, we propose an automated latent fingerprint recognition algorithm that utilizes Convolutional Neural Networks (ConvNets) for ridge flow estimation and minutiae descriptor extraction, and extract complementary templates (two minutiae templates and one texture template) to represent the latent. The comparison scores between the latent and a reference print based on the three templates are fused to retrieve a short Candidate List from the reference database. Experimental results show that the rank-1 identification accuracies (query latent is matched with its true mate in the reference database) are 64.7% for the NIST SD27 and 75.3% for the WVU latent databases, against a reference database of 100K rolled prints. These results are the best among published papers on latent recognition and competitive with the performance (66.7% and 70.8% rank-1 accuracies on NIST SD27 and WVU DB, respectively) of a leading COTS latent Automated Fingerprint Identification System (AFIS). By score-level (rank-level) fusion of our system with the commercial off-the-shelf (COTS) latent AFIS, the overall rank-1 identification performance can be improved from 64.7% and 75.3% to 73.3% (74.4%) and 76.6% (78.4%) on NIST SD27 and WVU latent databases, respectively.
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latent fingerprint matching performance gain via feedback from exemplar prints
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014Co-Authors: Sunpreet S Arora, Kai Cao, Eryun Liu, Anil K JainAbstract:Latent fingerprints serve as an important source of forensic evidence in a court of law. Automatic matching of latent fingerprints to rolled/plain (exemplar) fingerprints with high accuracy is quite vital for such applications. However, latent impressions are typically of poor quality with complex background noise which makes feature extraction and matching of latents a significantly challenging problem. We propose incorporating top-down information or feedback from an exemplar to refine the features extracted from a latent for improving latent matching accuracy. The refined latent features (e.g. ridge orientation and frequency), after feedback, are used to re-match the latent to the top $K$ Candidate exemplars returned by the baseline matcher and resort the Candidate List. The contributions of this research include: (i) devising systemic ways to use information in exemplars for latent feature refinement, (ii) developing a feedback paradigm which can be wrapped around any latent matcher for improving its matching performance, and (iii) determining when feedback is actually necessary to improve latent matching accuracy. Experimental results show that integrating the proposed feedback paradigm with a state-of-the-art latent matcher improves its identification accuracy by 0.5-3.5 percent for NIST SD27 and WVU latent databases against a background database of 100k exemplars.
Huaping Chen - One of the best experts on this subject based on the ideXlab platform.
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ant colony optimization algorithm for scheduling jobs with fuzzy processing time on parallel batch machines with different capacities
Applied Soft Computing, 2019Co-Authors: Zhaohong Jia, Jianhai Yan, Joseph Y T Leung, Huaping ChenAbstract:Abstract We study the problem of scheduling on parallel batch processing machines with different capacities under a fuzzy environment to minimize the makespan. The jobs have non-identical sizes and fuzzy processing times. After constructing a mathematical model of the problem, we propose a fuzzy ant colony optimization (FACO) algorithm. Based on the machine capacity constraint, two Candidate job Lists are adopted to select the jobs for building the batches. Moreover, based on the unoccupied space of the solution, heuristic information is designed for each Candidate List to guide the ants. In addition, a fuzzy local optimization algorithm is incorporated to improve the solution quality. Finally, the proposed algorithm is compared with several state-of-the-art algorithms through extensive simulated experiments and statistical tests. The comparative results indicate that the proposed algorithm can find better solutions within reasonable time than all the other compared algorithms.
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makespan minimization on single batch processing machine via ant colony optimization
Computers & Operations Research, 2012Co-Authors: Huaping ChenAbstract:This paper investigates the problem of minimizing makespan on a single batch-processing machine, and the machine can process multiple jobs simultaneously. Each job is characterized by release time, processing time, and job size. We established a mixed integer programming model and proposed a valid lower bound for this problem. By introducing a definition of waste and idle space ( WIS ), this problem is proven to be equivalent to minimizing the WIS for the schedule. Since the problem is NP-hard, we proposed a heuristic and an ant colony optimization (ACO) algorithm based on the theorems presented. A Candidate List strategy and a new method to construct heuristic information were introduced for the ACO approach to achieve a satisfactory solution in a reasonable computational time. Through extensive computational experiments, appropriate ACO parameter values were chosen and the effectiveness of the proposed algorithms was evaluated by solution quality and run time. The results showed that the ACO algorithm combined with the Candidate List was more robust and consistently outperformed genetic algorithm (GA), CPLEX, and the other two heuristics, especially for large job instances.
Nena Nwachuku - One of the best experts on this subject based on the ideXlab platform.
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Chlorine and ozone disinfection of Encephalitozoon intestinalis spores.
Water research, 2005Co-Authors: David E. John, Charles N. Haas, Nena NwachukuAbstract:Microsporidia are intracellular eukaryotic parasites which have the potential for zoonotic and environmental, including waterborne, transmission. Encephalitozoon intestinalis is a microsporidian pathogen of humans and animals and has been detected in surface water. It is also on the Contaminant Candidate List of potential emerging waterborne pathogens for the US EPA. We performed disinfection studies using chlorine and ozone on E. intestinalis spores with a cell-culture most-probable-number assay to determine infectivity. Chlorine experiments were performed at 5 degrees C at pH of 6, 7, and 8 with 1mg/L initial chlorine concentrations, while ozone experiments were performed at 5 degrees C and pH 7 with initial ozone doses of 1 and 0.5mg/L, both in buffered water. A derivation of Hom's model for disinfection kinetics under dynamic disinfectant concentrations was used to fit observed data and calculate concentration-time product (C*t) values. Chlorine C*t values varied with pH such that 99% (2-log(10)) C*t ranged from 12.8 at pH 6 to 68.8 at pH 8 (mg min/L). Ozone C*t values were approximately an order of magnitude less at 0.59--0.84 mg min/L, depending on initial concentration.
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disinfection resistance of waterborne pathogens on the united states environmental protection agency s contaminant Candidate List ccl
Journal of Water Supply Research and Technology-aqua, 2003Co-Authors: Charles P Gerba, Nena Nwachuku, Kelley RileyAbstract:In 1999, the United States Environmental Protection Agency developed a List of emerging waterborne microbial pathogens that may pose a risk in drinking water. This review deals with the disinfection resistance of microorganisms on the Contaminate Candidate List or CCL. Current disinfection practices in the United States appear to be capable of dealing with most of the microorganisms on the CCL, with the exception of Mycobacterium avium and adenoviruses. Mycobacterium avium is more resistant to most disinfectants than other waterborne bacteria and adenoviruses are the most resistant waterborne microorganisms to inactivation by ultraviolet disinfection. The microsporidium, Encephalitozoon intestinalis , shows significant resistance to inactivation by chemical disinfectants and further research on additional species of microsporidia appears to be warranted.
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comparative inactivation of enteroviruses and adenovirus 2 by uv light
Applied and Environmental Microbiology, 2002Co-Authors: Charles P Gerba, Dawn M Gramos, Nena NwachukuAbstract:The doses of UV irradiation necessary to inactivate selected enteric viruses on the U.S. Environmental Protection Agency Contaminant Candidate List were determined. Three-log reductions of echovirus 1, echovirus 11, coxsackievirus B3, coxsackievirus B5, poliovirus 1, and human adenovirus type 2 were effected by doses of 25, 20.5, 24.5, 27, 23, and 119 mW/cm2, respectively. Human adenovirus type 2 is the most UV light-resistant enteric virus reported to date.
