New design of finger-vein identification algorithm against image rotation

Individual identification process is a very significant process that resides a large portion of day by day usages. Identification process is appropriate in work place, private zones, banks …etc. Individuals are rich subject having many characteristics that can be used for recognition purpose such as finger vein, iris, face …etc. Finger vein and iris key-points are considered as one of the most talented biometric authentication techniques for its security and convenience. SIFT is new and talented technique for pattern recognition. However, some shortages exist in many related techniques, such as difficulty of feature loss, feature key extraction, and noise point introduction. In this manuscript a new technique named SIFT-based iris and SIFT-based finger vein identification with normalization and enhancement is proposed for achieving better performance. In evaluation with other SIFT-based iris or SIFT-based finger vein recognition algorithms, the suggested technique can overcome the difficulties of tremendous key-point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and improvement steps are critical for SIFT-based recognition for iris and finger vein , and the proposed technique can accomplish satisfactory recognition performance. Keywords: SIFT, Iris Recognition, Finger Vein identification and Biometric Systems. © 2018 JASET, International Scholars and Researchers Association

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Finger Vein Network Enhancement with SVM

International Journal of Innovative Research in Computer and Communication Engineering ◽

10.15680/ijircce.2015.0307017 ◽

2015 ◽

Vol 03 (07) ◽

pp. 6479-6482

Author(s):

Mudasir Ahmad, Sahil Dalwal

Keyword(s):

Finger Vein

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Tire Rolling Kinematics Model for an Intelligent Tire Based on an Accelerometer

Tire Science and Technology ◽

10.2346/tire.20.190211 ◽

2020 ◽

Vol 48 (4) ◽

pp. 287-314

Author(s):

Yan Wang ◽

Zhe Liu ◽

Michael Kaliske ◽

Yintao Wei

Keyword(s):

Elastic Deformation ◽

Estimation Algorithm ◽

Force Sensor ◽

Euler Method ◽

Rolling Contact ◽

Body Motion ◽

Identification Algorithm ◽

Active Perception ◽

Kinematics Model ◽

Ring Model

ABSTRACT The idea of intelligent tires is to develop a tire into an active perception component or a force sensor with an embedded microsensor, such as an accelerometer. A tire rolling kinematics model is necessary to link the acceleration measured with the tire body elastic deformation, based on which the tire forces can be identified. Although intelligent tires have attracted wide interest in recent years, a theoretical model for the rolling kinematics of acceleration fields is still lacking. Therefore, this paper focuses on an explicit formulation for the tire rolling kinematics of acceleration, thereby providing a foundation for the force identification algorithms for an accelerometer-based intelligent tire. The Lagrange–Euler method is used to describe the acceleration field and contact deformation of rolling contact structures. Then, the three-axis acceleration vectors can be expressed by coupling rigid body motion and elastic deformation. To obtain an analytical expression of the full tire deformation, a three-dimensional tire ring model is solved with the tire–road deformation as boundary conditions. After parameterizing the ring model for a radial tire, the developed method is applied and validated by comparing the calculated three-axis accelerations with those measured by the accelerometer. Based on the features of acceleration, especially the distinct peak values corresponding to the tire leading and trailing edges, an intelligent tire identification algorithm is established to predict the tire–road contact length and tire vertical load. A simulation and experiments are conducted to verify the accuracy of the estimation algorithm, the results of which demonstrate good agreement. The proposed model provides a solid theoretical foundation for an acceleration-based intelligent tire.

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Localization and Non-Localization Node Identification Using Dream in MANET

SMART MOVES JOURNAL IJOSCIENCE ◽

10.24113/ijoscience.v2i2.77 ◽

2016 ◽

Vol 2 (2) ◽

Author(s):

Amit Singh ◽

Nitin Mishra ◽

Angad Singh

Keyword(s):

Ad Hoc Network ◽

Ad Hoc ◽

Mobile Ad Hoc Network ◽

Fundamental Problem ◽

Identification Algorithm ◽

Mobile Nodes ◽

Position Information ◽

Location Services ◽

Wireless Router ◽

Mobile Ad Hoc

A Wireless Mobile Ad-hoc Network consists of variety of mobile nodes that temporally kind a dynamic infrastructure less network. To modify communication between nodes that don’t have direct radio contact, every node should operate as a wireless router and potential forward knowledge traffic of behalf of the opposite node. In MANET Localization is a fundamental problem. Current localization algorithm mainly focuses on checking the localizability of a network and/or how to localize as many nodes as possible. It could provide accurate position information foe kind of expanding application. Localization provide information about coverage, deployment, routing, location, services, target tracking and rescue If high mobility among the mobile nodes occurs path failure breaks. Hence the location information cannot be predicted. Here we have proposed a localization based algorithm which will help to provide information about the localized and non-localized nodes in a network. In the proposed approach DREAM protocol and AODV protocol are used to find the localizability of a node in a network. DREAM protocol is a location protocol which helps to find the location of a node in a network whereas AODV is a routing protocol it discover route as and when necessary it does not maintain route from every node to every other. To locate the mobile nodes in a n/w an node identification algorithm is used. With the help of this algorithm localized and non-localized node can be easily detected in respect of radio range. This method helps to improve the performance of a module and minimize the location error and achieves improved performance in the form of UDP packet loss, received packet and transmitted packets, throughput, routing overhead, packet delivery fraction. All the simulation done through the NS-2 module and tested the mobile ad-hoc network.

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