scholarly journals Fingerprint Enhancement, Minutiae Extraction and Matching Techniques

2020 ◽  
Vol 08 (05) ◽  
pp. 55-74
Author(s):  
Sek Socheat ◽  
Tianjiang Wang
Keyword(s):  
Minutiae Extraction ◽  
Fingerprint Enhancement ◽  
Matching Techniques

A COMBINATORIAL APPROACH TO FINGERPRINT BINARIZATION AND MINUTIAE EXTRACTION USING EUCLIDEAN DISTANCE TRANSFORM

2007 ◽  
Vol 21 (07) ◽  
pp. 1141-1158 ◽  
Author(s):  
XUEFENG LIANG ◽  
ARIJIT BISHNU ◽  
TETSUO ASANO
Keyword(s):  
Euclidean Distance ◽  
Linear Time ◽  
Time Algorithm ◽  
Distance Transform ◽  
Fingerprint Matching ◽  
Fingerprint Image ◽  
Binary Images ◽  
Minutiae Extraction ◽  
Euclidean Distance Transform ◽  
Matching Techniques

Most of the fingerprint matching techniques require extraction of minutiae that are ridge endings or bifurcations of ridge lines in a fingerprint image. Crucial to this step is either detecting ridges from the gray-level image or binarizing the image and then extracting the minutiae. In this work, we firstly exploit the property of almost equal width of ridges and valleys for binarization. Computing the width of arbitrary shapes is a nontrivial task. So, we estimate the width using Euclidean distance transform (EDT) and provide a near-linear time algorithm for binarization. Secondly, instead of using thinned binary images for minutiae extraction, we detect minutiae straightaway from the binarized fingerprint images using EDT. We also use EDT values to get rid of spurs and bridges in the fingerprint image. Unlike many other previous methods, our work depends minimally on arbitrary selection of parameters.

scholarly journals Accurate Fingerprint Enhancement and Identification Using Minutiae Extraction

2017 ◽  
Vol 05 (14) ◽  
pp. 28-38 ◽  
Author(s):  
K. A. Perichiappan Perichappan ◽  
Sreenivas Sasubilli
Keyword(s):  
Minutiae Extraction ◽  
Fingerprint Enhancement

scholarly journals Text Classification in Clinical Practice Guidelines Using Machine-Learning Assisted Pattern-Based Approach

2021 ◽  
Vol 11 (8) ◽  
pp. 3296
Author(s):  
Musarrat Hussain ◽  
Jamil Hussain ◽  
Taqdir Ali ◽  
Syed Imran Ali ◽  
Hafiz Syed Muhammad Bilal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Clinical Practice ◽  
Clinical Practice Guidelines ◽  
Practice Guidelines ◽  
Machine Learning Algorithms ◽  
Nominal Group ◽  
Specific Information ◽  
Matching Techniques ◽  
Disease Specific

Clinical Practice Guidelines (CPGs) aim to optimize patient care by assisting physicians during the decision-making process. However, guideline adherence is highly affected by its unstructured format and aggregation of background information with disease-specific information. The objective of our study is to extract disease-specific information from CPG for enhancing its adherence ratio. In this research, we propose a semi-automatic mechanism for extracting disease-specific information from CPGs using pattern-matching techniques. We apply supervised and unsupervised machine-learning algorithms on CPG to extract a list of salient terms contributing to distinguishing recommendation sentences (RS) from non-recommendation sentences (NRS). Simultaneously, a group of experts also analyzes the same CPG and extract the initial patterns “Heuristic Patterns” using a group decision-making method, nominal group technique (NGT). We provide the list of salient terms to the experts and ask them to refine their extracted patterns. The experts refine patterns considering the provided salient terms. The extracted heuristic patterns depend on specific terms and suffer from the specialization problem due to synonymy and polysemy. Therefore, we generalize the heuristic patterns to part-of-speech (POS) patterns and unified medical language system (UMLS) patterns, which make the proposed method generalize for all types of CPGs. We evaluated the initial extracted patterns on asthma, rhinosinusitis, and hypertension guidelines with the accuracy of 76.92%, 84.63%, and 89.16%, respectively. The accuracy increased to 78.89%, 85.32%, and 92.07% with refined machine-learning assistive patterns, respectively. Our system assists physicians by locating disease-specific information in the CPGs, which enhances the physicians’ performance and reduces CPG processing time. Additionally, it is beneficial in CPGs content annotation.

scholarly journals The fermionic universal one-loop effective action

2020 ◽  
Vol 2020 (11) ◽  
Author(s):  
Sebastian A. R. Ellis ◽  
Jérémie Quevillon ◽  
Pham Ngoc Hoa Vuong ◽  
Tevong You ◽  
Zhengkang Zhang
Keyword(s):  
Path Integral ◽  
Effective Action ◽  
Covariant Derivative ◽  
Heavy Fermion ◽  
Axial Vector ◽  
Derivative Expansion ◽  
Universal Structure ◽  
Pseudo Scalar ◽  
Matching Techniques ◽  
Analytical Expressions

Abstract Recent development of path integral matching techniques based on the covariant derivative expansion has made manifest a universal structure of one-loop effective Lagrangians. The universal terms can be computed once and for all to serve as a reference for one-loop matching calculations and to ease their automation. Here we present the fermionic universal one-loop effective action (UOLEA), resulting from integrating out heavy fermions (Dirac or Majorana) with scalar, pseudo-scalar, vector and axial-vector couplings. We also clarify the relation of the new terms computed here to terms previously computed in the literature and those that remain to complete the UOLEA. Our results can be readily used to efficiently obtain analytical expressions for effective operators arising from heavy fermion loops [13].

scholarly journals Load Resistance Optimization of Bi-Stable Electromagnetic Energy Harvester Based on Harmonic Balance

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1505
Author(s):  
Sungryong Bae ◽  
Pilkee Kim
Keyword(s):  
External Load ◽  
Harmonic Balance ◽  
Energy Harvester ◽  
Load Resistance ◽  
Electromagnetic Energy ◽  
Analytic Approach ◽  
Accurate Estimation ◽  
Vibration Source ◽  
Optimal Load ◽  
Matching Techniques

In this study, a semi-analytic approach to optimizing the external load resistance of a bi-stable electromagnetic energy harvester is presented based on the harmonic balance method. The harmonic balance analyses for the primary harmonic (period-1T) and two subharmonic (period-3T and 5T) interwell motions of the energy harvester are performed with the Fourier series solutions of the individual motions determined by spectral analyses. For each motion, an optimization problem for maximizing the output power of the energy harvester is formulated based on the harmonic balance solutions and then solved to estimate the optimal external load resistance. The results of a parametric study show that the optimal load resistance significantly depends on the inductive reactance and internal resistance of a solenoid coil––the higher the oscillation frequency of an interwell motion (or the larger the inductance of the coil) is, the larger the optimal load resistance. In particular, when the frequency of the ambient vibration source is relatively high, the non-linear dynamic characteristics of an interwell motion should be considered in the optimization process of the electromagnetic energy harvester. Compared with conventional resistance-matching techniques, the proposed semi-analytic approach could provide a more accurate estimation of the external load resistance.

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