Enhancement of Latent Fingerprint Recognition Using Global Transform

Latent Fingerprints plays a vital role in identifying thefts, crime etc. Latent fingerprints are of 3 types. Noise in the Latent Fingerprints is removed by smoothing. Manual marking in Latent Fingerprint is slow and also latent examiner may make mistake while marking. The minutiae in the same latent marked by different latent examiners or even by the same examiner (but at different times) may not be the same. To overcome this issue new Orientation field estimation algorithm is introduced. It based on latent fingerprint feature extraction and edge detection. Orientation field estimation algorithm has dictionary construction stage. Dictionary Construction has 2 Stages. i) Offline stage ii) online stage. Orientation field estimation algorithm is applied for Overlapped fingerprint. Hough transform is used for detecting edges. It is shown that this method is slower to recognize latent fingerprint feature extraction and edge linking. In order to further increase the speed and perfect edge linking Hough transform method can be modified for better performance. Global transform is used for perfect edge linking and get the full fingerprint structure and comparison is made between two transforms to show which transform is better. DOI: 10.17762/ijritcc2321-8169.15034

Effect of substrate surface topography on forensic development of latent fingerprints with iron oxide powder suspension

This is a pre-print version of the article. The official published version can be accessed from the link below - Copyright @ 2010 Wiley-BlackwellLatent fingerprint deposition and effectiveness of detection are strongly affected by the surface on which prints are deposited. Material properties, surface roughness, morphology, chemistry and hydrophobicity can affect the usefulness or efficacy of forensic print development techniques. Established protocols outline appropriate techniques and sequences of processes for broad categories of operational surfaces. This study uses atomic force microscopy and scanning electron microscopy to investigate a series of surfaces classified as smooth, non-porous plastic. Latent prints developed with iron oxide powder suspension are analysed on a range of scales from macro to nano to help elucidate the interaction mechanisms between the latent fingerprint, development agent and underlying surface. Differences between surfaces have a strong effect, even within this single category. We show that both average roughness and topographical feature shape, characterised by skew, kurtosis and lay, are important factors to consider for the processing of latent fingerprints. Copyright (C) 2010 John Wiley & Sons, Ltd.This work is part-funded by the UK Home Office project 7088762

Substrate-specific clades of active marine methylotrophs associated with a phytoplankton bloom in a temperate coastal environment

Marine microorganisms that consume one-carbon (C1) compounds are poorly described, despite their impact on global climate via an influence on aquatic and atmospheric chemistry. This study investigated marine bacterial communities involved in the metabolism of C1 compounds. These communities were of relevance to surface seawater and atmospheric chemistry in the context of a bloom that was dominated by phytoplankton known to produce dimethylsulfoniopropionate. In addition to using 16S rRNA gene fingerprinting and clone libraries to characterize samples taken from a bloom transect in July 2006, seawater samples from the phytoplankton bloom were incubated with 13C-labeled methanol, monomethylamine, dimethylamine, methyl bromide, and dimethyl sulfide to identify microbial populations involved in the turnover of C1 compounds, using DNA stable isotope probing. The [13C]DNA samples from a single time point were characterized and compared using denaturing gradient gel electrophoresis (DGGE), fingerprint cluster analysis, and 16S rRNA gene clone library analysis. Bacterial community DGGE fingerprints from 13C-labeled DNA were distinct from those obtained with the DNA of the nonlabeled community DNA and suggested some overlap in substrate utilization between active methylotroph populations growing on different C1 substrates. Active methylotrophs were affiliated with Methylophaga spp. and several clades of undescribed Gammaproteobacteria that utilized methanol, methylamines (both monomethylamine and dimethylamine), and dimethyl sulfide. rRNA gene sequences corresponding to populations assimilating 13C-labeled methyl bromide and other substrates were associated with members of the Alphaproteobacteria (e.g., the family Rhodobacteraceae), the Cytophaga-Flexibacter-Bacteroides group, and unknown taxa. This study expands the known diversity of marine methylotrophs in surface seawater and provides a comprehensive data set for focused cultivation and metagenomic analyses in the future

Genome-inspired molecular identification in organic matter via Raman spectroscopy

Rapid, non-destructive characterization of molecular level chemistry for organic matter (OM) is experimentally challenging. Raman spectroscopy is one of the most widely used techniques for non-destructive chemical characterization, although it currently does not provide detailed identification of molecular components in OM, due to the combination of diffraction-limited spatial resolution and poor applicability of peak-fitting algorithms. Here, we develop a genome-inspired collective molecular structure fingerprinting approach, which utilizes ab initio calculations and data mining techniques to extract molecular level chemistry from the Raman spectra of OM. We illustrate the power of such an approach by identifying representative molecular fingerprints in OM, for which the molecular chemistry is to date inaccessible using non-destructive characterization techniques. Chemical properties such as aromatic cluster size distribution and H/C ratio can now be quantified directly using the identified molecular fingerprints. Our approach will enable non-destructive identification of chemical signatures with their correlation to the preservation of biosignatures in OM, accurate detection and quantification of environmental contamination, as well as objective assessment of OM with respect to their chemical contents

Fast and simple connectivity in graph timelines

In this paper we study the problem of answering connectivity queries about a \emph{graph timeline}. A graph timeline is a sequence of undirected graphs $G_1,\ldots,G_t$ on a common set of vertices of size $n$ such that each graph is obtained from the previous one by an addition or a deletion of a single edge. We present data structures, which preprocess the timeline and can answer the following queries: - forall$(u,v,a,b)$ -- does the path $u\to v$ exist in each of $G_a,\ldots,G_b$? - exists$(u,v,a,b)$ -- does the path $u\to v$ exist in any of $G_a,\ldots,G_b$? - forall2$(u,v,a,b)$ -- do there exist two edge-disjoint paths connecting $u$ and $v$ in each of $G_a,\ldots,G_b$ We show data structures that can answer forall and forall2 queries in $O(\log n)$ time after preprocessing in $O(m+t\log n)$ time. Here by $m$ we denote the number of edges that remain unchanged in each graph of the timeline. For the case of exists queries, we show how to extend an existing data structure to obtain a preprocessing/query trade-off of $\langle O(m+\min(nt, t^{2-\alpha})), O(t^\alpha)\rangle$ and show a matching conditional lower bound.Comment: 21 pages, extended abstract to appear in WADS'1