Archive

Abstract:

Now days, Security plays an important role in day-to-day life.  The use of the internet in human life has become the day to day activity and with the internet the use of automation devices has increased. All transaction needs to secure authentication to complete. Hence, we have introduced a Face Recognition method. It can apply in many fields such as to authenticate users, security issues etc., It mainly plays a significant role in real time surveillance systems. We implemented the Convolution neuron network to automatically create dataset and recognition with the graphical user interface. Before creating a dataset the system takes permission from the user then it creates the dataset and trains the model for farther authentication.

Keywords:

Security,deep learning,neural network,authentication,

Refference:

I E. I. Abbas, M. E. Safi And K. S. Rijab, “Face Recognition Rate Using Different Classifier Methods Based On Pca,” 2017 International Conference On Current Research In Computer Science And Information Technology (Iccit), Slemani, 2017, Pp. 37-40, Doi: 10.1109/Crcsit.2017.7965559.

II H.-W. Ng, S. Winkler. A Data-Driven Approach to Cleaning Large Face Datasets. Proc. Ieee International Conference on Image Processing (Icip), Paris, France, Oct. 27-30, 2014.

III M. R. Reshma and B. Kannan, “Approaches On Partial Face Recognition: A Literature Review,” 2019 3rd International Conference on Trends In Electronics And Informatics (Icoei), Tirunelveli, India, 2019, Pp. 538-544, Doi: 10.1109/Icoei.2019.8862783.

IV O. M. Parkhi, A.Vedaldi, A. Zisserman Deep Face Recognition British Machine Vision Conference, 2015.

V Praveen P., Rama B(2020). “An Optimized Clustering Method To Create Clusters Efficiently” Journal Of Mechanics Of Continua And Mathematical Sciences, ISSN (Online): 2454 -7190 Vol.-15, No.-1, January (2020) pp 339-348 ISSN (Print) 0973-8975,https://doi.org/10.26782/jmcms.2020.01.00027 .

VI P.Kumara Swamy, Dr.C.V.Guru Rao, Dr.V.Janaki, “Functioning Of Secure Key Authentication Scheme In” In International Journal Of Pure And Applied Mathemat, Volume 118, Issue 14, Page No(S) 27 – 32, MAR. 2018, [ISSN(Print):1314-3395].

VII R. Prema and P. Shanmugapriya, “A Review: Face Recognition Techniques For Differentiate Similar Faces and Twin Faces,” 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (Icecds), Chennai, 2017, Pp. 2899-2902, Doi: 10.1109/Icecds.2017.8389985.

VIII Sharmila, Raman Sharma, Dhanajay Kumar, Vaishali Puranik, Kritika Gautham,”Performance Analysis Of Human Face Recognition Techniques” In 2019 Ieee

IX Sharma, Sudha and Soni, Alpesh and Malviya, Vijay, Face Recognition Based On Convolution Neural Network (Cnn) Applications in Image Processing: A Survey (April 15, 2019). Proceedings of Recent Advances in Interdisciplinary Trends in Engineering & Applications (Raitea) 2019.

X Surface[3D] Measurement Through Easy-Snap Phase Shift Fringe Projection.” Springerprofessional.De,Https://Www.Springerprofessional.De/En/3d-Surface-Measurement-Through-Easy-Snap-Phase-Shift-Fringe-Proj/15447362. Accessed 26 Mar. 2020.

XI Sallauddin Md Et. “A Comprehensive Study on Traditional Ai and Ann Architecture.” International Journal of Advanced Science and Technology, Vol. 28, No. 17, Dec. 2019, Pp. 479–87.

XII Yerrolla Chanti, Kothanda Raman, K. Seenanaik, Dandugudum Mahesh, B.Bhaskar” An Enhanced On Bidirectional LI-FI Attocell Access Point Slicing and Virtualization Using Das2 Conspire” International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-8, Issue-2S3, July 2019.

XIII Yerrolla Chanti, Dr. K. Seena Naik2, Rajesh Mothe3, Nagendar Yamsani4, Swathi Balija5” A Modified Elliptic Curve Cryptography Technique For Securing Wireless Sensor Networks” International Journal Of Engineering &Technology 2018.

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Abstract:

IoT devices, or any of the various problems inside the net update, are nonstandardregisteringdevicesthatbepartofremotelyuptodate a network and highlight theopportunityupdatetransmitstatistics[III].IoTconsistsof growing internet community past gadgets,whichcontainpcsupdated,workstations,cellphonesandmedicines, uptodateanyassortmentofactuallymoronicornonnetempowered bodily gadgets andpopularupdate.Implantedwithage, those devices can talk andfunction connection over the net, and they might be remotely located and overseen [X].To updatedis coupononline communication, being Growingage,hasupdate an appealing area of research in audiosystemhoweverPromising packages like quick assortmentcontactless discussion for mobilephone and different superior devices the same. Rigt now, valid facts and direction of NFC is up-to-date be beautifully save updated up for the headway of capacity and up to date reduce the scaffold hollow between its critical Online and alertness exercise. Proper now, proposed up-to-date NFC might be applied for sharing little evaluations along with contacts, and bootstrapping rapid institutions with percentage larger media and various records and boat Wi-Fi wireless, software content material fabric, contactless installments, examine NFC labels amongst advanced gadgets [II][I]. We more over have investing the NFC corporation business enterprise natural system and present day destiny market propensities. In diverse terms this compressive in NFC wireless duration manages advancement of statistics.

Keywords:

NFC,IOT,RIFD,BLUETOOTH.,

Refference:

I APC, Inside NFC: how near field communication works. August 17, 2011. http://apcmag.com/insidenfc-how-near-field-communication-works.htm.

II Bura Vijay Kumar1, Yerrolla Chanti2, D. Kothandaraman3, A. Harshavardhan4, Sangameshwar Kanugula5 S” INTERNET OF THINGS MIDDLEWARE ARCHITECTURE FOR COMMUNICATION” Studia Rosenthaliana (Journal for the Study of Research) ISSN NO: 0039-3347.dec 2019.

III D. Kothandaraman1, Y. Chanti2, B. Vijaykumar3, A. Harshavardhan4, K. Seena Naik5” Indoor Users Motion Direction Detection Using Orientation Sensor with BLE in Internet of Things” Studia Rosenthaliana (Journal for the Study of Research) ISSN NO: 0039-3347.dec 2019.

IV D.M. Monteiro, J.J.P.C. Rodrigues, J. Lloret, “A Secure NFC Application for Credit Transfer among Mobile Phones”, International Conference on Computer, Information and Telecommunication Systems (CITS), 2012, pp. 1- 5.

V E. Desai, M.G. Shajan, “A Review on the Operating Modes of Near Field Communication”, International Journal of Engineering and Advanced Technology (IJEAT), Volume-2, Issue-2, 2012. Ber Security (CIACS), 2014, pp. 35- 38.

VI E. Macias, J. Wyatt, “NFC Active and Passive Peer-toPeer Communication Using the TRF7970A”, April 2014, http://www.ti.com/lit/an/sloa192/sloa192.pdf.

VII Internet source ofWikipedia .com

VIII K. Seena Naik and E. Sudarshan ”Smart Healthcare Monitoring System using Raspberry Pi on IoT Platform” ARPN Journal of Engineering and Applied Sciences ©2006-2019 Asian Research Publishing Network (ARPN). All rights reserved. VOL. 14, NO. 4, FEBRUARY 2019. ISSN 1819-66.

IX [N.A. Chattha. “NFC – Vulnerabilities and Defense” Conference on Information Assurance and Cyber Security (CIACS), 2014, pp. 35- 38.

X P. V. Nikitin. “An Overview of Near Field UHF RFID,” in Proc. IEEE Int. Conf. RFID, Mar. 2007, pp. 167-174.

XI Shirsha Ghosh, Joyeeta Goswami, Abhishek Kumar and Alak Majumder” Department of Electronics & Communication Engineering, National Institute of Technology, Arunachal Pradesh, Yupia, India” Issues in NFC as a Form of Contactless Communication: A Comprehensive Survey” 2015 International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, T.N., India. 6 – 8 May 2015. pp.245-252.

XII V. Coskun, K. Ok, B. Ozdenizci “Near Field Communication, from theory to practice”, Wiley Publication.

XIII Yerrolla Chanti1, Seena Naik Korra2, Bura Vijay Kumar3, A. Harshavardhan4, D. Kothandaraman5 “New Technique using an IoT Robot to Oversight the Smart Domestic Surroundings” Studia Rosenthaliana (Journal for the Study of Research) ISSN NO: 0039-3347.dec 2019.

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Abstract:

The objective of this paper is to solve pure integer programming problems without using Gomorian constraints. In this, CMI method is used for solving linear programming problems instead of simplex method. In CMI method, there is no need to calculate net evaluations, which is essential and mandatory in pre-existing methods. By discarding the calculation of net evaluations, the iterations in the procedure gets reduced or remains atmost equal in number. After getting a non-integer value in final CMI table, here we use a reduction technique instead of adding Gomorian constraint to get the integer solution directly.The main advantage of using this reduction technique is to avoid using, any additional constraints and the Dual simplex method for getting an integer solution. With the elimination of the above processes, the integer solutions are arrived very easily. Hence this new approachof pure integer programming problemensures time conservation at various levels in deriving the optimal solutions.  This proposed method is illustrated withexamples.

Keywords:

CMI Method,LPP,IPP,Optimal Solution,Reduction technique,

Refference:

I G.B. Dantzig, Maximization of linear function of variables subject to linear inequalities Koop man cowls commission Monograph, 1951).

II Handy A.Taha: ‘Operations Research An Introduction’ 8th edition by Pearson Publication.

III Kalpana Lokhande; Pranay.Khobragade and .W. Khobragade: Alternative approach to simplex method, International journal of engineering and innovative Technology, volume 4, Issue 6, pg: 123-127.

IV P.Pandian and M.Jayalakshmi: A new approach for solving a class of pure integer linear programming problems, International journal of advanced engineering technology.

V S.Cynthiya Margaret Indrani and Dr.N.Srinivasan: ‘CMI –M Technique for the solution of linear Programming problem,’ International Journal of Research and Analytical Reviews, October 2018, Volume-5, Issue-4.Pg-76-82ded.

VI S.Cynthiya Margaret Indrani and Dr.N.Srinivasan: ‘CMI Method for the solution of linear formulating problem’, Journal of Emerging Technologies and Innovative Research, September 2018, Volume 5, Issue 9, Pg. 248-253.

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Abstract:

There are high necessities to create counterfeit arms for some brutal circumstances where human communications are displaying difficulties or unrealistic (for example outlandish circumstances). This paper presents data, strategies and methods which are fundamental for building a mechanical arm constrained by the developments of ordinary human arm (Gesture Robotic Arm) whose information is gaining by utilizing the Accelerometer. The improvement of this arm depends on the ARM stage in which all are interfaced with one another by utilizing lpc2148 smaller scale controller. The model of automated arm of this paper has been actualized practically.Thedeveloped mechanical arm of this paper is followed the development of human arm with a decent exactness. Usage of this arm could be normal for beating the issues, for example, picking or setting object that are away from the users.

Keywords:

Gesture Robotic Arm,Motion Perception, Accelerometer,lpc2148 smaller scale controller,

Refference:

I. Aggarwal, L., Gaur, V., & Verma, P., (2013) “Design and Implementation of a Wireless Gesture Controlled Robotic Arm with Vision”, International Journal of ComputerApplications (0975 – 8887), 79 (13), pp. 39–43.
II. Brahmani, K., Roy, K. S., & Ali, M., (2013) “Arm 7 Based Robotic Arm Control by Electronic Gesture Recognition Unit Using MEMS”, International Journal ofEngineering Trends and Technology, 4 (4), pp. 1245–1248.
III. Dadi, R., Sallauddin, Pasha, S.N., Harshavardhan, A. &Kumarawamy, P. 2019, “Adapting best path for mobile robot by predicting obstacle size”, International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 9 Special Issue 2, pp. 200-202.
IV. Deshpande, Vivek, and P. M. George. “Kinematic Modelling and Analysis of 5 DOF Robotic Arm.” International Journal of Robotics Research and Development (IJRRD) 4.2 (2014): 17-24.
V. Dharaskar, R. V., Chhabria, S. A., &Ganorkar, S., (2009) “Robotic Arm Control Using Gesture and Voice”, International Journal of Computer, InformationTechnology&Bioinformatics (IJCITB), 1 (1), pp. 41–46.
VI. Gandhi, K. R. U. T. A. R. T. H., et al. “Motion controlled robotic arm.” International Journal of Electronics and Communication Engineering (IJECE) 2.5 (2013): 81-86.
VII. Humbe, A. B., et al. “Review of laser plastic welding process.” Int. J. Res. Eng. Technol 2 (2014): 191-206.
VIII. Humbe, A. B., P. A. Deshmukh, and M. S. Kadam. “The Review Of Articulated R12 Robot And Its Industrial Applications.” International Journal of Research in Engineering & Technology 2.2 (2014): 113-118.
IX. J.Tarunkumar., P. Ramchander Rao. & M. Sampath Reddy. 2019, “IOT based Email Enabled Smart Home Automation System”, International Journal of Recent Technology and Engineering, vol.8, no.1C2. 80-82.
X. Khajone, S. A., Mohod, S. W., &Harne, V. M., (2015) “Implementation of Wireless Gesture Controlled Robotic Arm”, International Journal of Innovative research in Computer and Communication Engineering, 3 (1), pp. 375–379.
XI. Mohapatro, Gourishankar, Ruby Mishra, and Shah Shubham Kamlesh. “Preliminary Testing And Analysis Of An Optimized Robotic Arm, For Ct Image Guided Medical Procedures.” International Journal of Mechanical and Production Engineering Research and Development (IJMPERD) 7.6, (2017) 239-246
XII. Neto, P., Pires, N. J., & Moreira, P. A., (2009) “Accelerometer-Based Control of anIndustrial Robotic Arm”, International Journal of Electronics, 6, pp. 167 – 173.
XIII. Ramesh, D., Pasha, S.N. &Sallauddin, M. 2019, “Cognitive-based adaptive path planning for mobile robot in dynamic environment”. First International Conference on Artificial Intelligence and Cognitive Computing. Advances in Intelligent Systems and Computing, vol 815. Springer, Singapore.
XIV. Shilpa, N., Sridevi, C. & Anand, M. 2019, “Object tracking robot by using raspberry pi with open computer vision (CV)”, Journal of Advanced Research in Dynamical and Control Systems, vol. 11, no. 7, pp. 762-766.
XV. Waldherr, S., Romero, R., &Thrun, S., (2000) “A Gesture Based Interface for Human-Robot Interaction”, Autonomous Robots in Springer, 9 (2), pp. 151 – 173.
XVI. Zabbar, Md Ajijul Bin, and ChistyNafiz Ahmed. “Design & Implementation of an Unmanned Ground Vehicle (UGV) Surveillance Robot.” International Journal of Electrical and Electronics Engineering (IJEEE) 5.6 (2016): 2278-9944.

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Abstract:

This paper suggests a scheme for trajectory tracking on a two wheeled mobile robot using integral sliding mode control method in the presence of external disturbances and inertia uncertainties. In this study the modified adaptive sliding mode controller for nonholonomic wheeled mobile robot is developed. Nonlinear control used to combine the kinematic and dynamic controller to follow the desired path. Firstly, the desired path is created. Secondly, the kinematic tracking controller used linear and angular velocities form reference model and feeds posture and velocities errors as input term in the sliding controller. Finally, the dynamic control was used to follow the path. Proposed control system is verified and validated using MATLAB\SIMULINK to track the required WMR trajectory and it is shown that the suggested system has better transient efficiency on different trajectories with acceptable steady stateerror.

Keywords:

Wheeled mobile robot,dynamic uncertainty,Kinematic and dynamic controller,Dynamic control,Transient efficiency,

Refference:

I Al-Araji, Ahmed S., & Ibraheem, B. A. (2019). A Comparative Study of Various Intelligent Optimization Algorithms Based on Path Planning and Neural Controller for Mobile Robot. Journal of Engineering, 25(8), 80–99. https://doi.org/10.31026/j.eng.2019.08.06

II Al-Araji, Ahmed Sabah. (2014). Development of kinematic path-tracking controller design for real mobile robot via back-stepping slice genetic robust algorithm technique. Arabian Journal for Science and Engineering, 39(12), 8825–8835.

III Antonelli, G., Chiaverini, S., & Fusco, G. (2007). A fuzzy-logic-based approach for mobile robot path tracking. IEEE Transactions on Fuzzy Systems, 15(2), 211–221.

IV Bessas, A., Benalia, A., & Boudjema, F. (2016). Integral sliding mode control for trajectory tracking of wheeled mobile robot in presence of uncertainties. Journal of Control Science and Engineering, 2016.

V Binh, N. T., Tung, N. A., Nam, D. P., & Quang, N. H. (2019). An adaptive backstepping trajectory tracking control of a tractor trailer wheeled mobile robot. International Journal of Control, Automation and Systems, 17(2), 465–473.

VI Chwa, D. (2004). Sliding-mode tracking control of nonholonomic wheeled mobile robots in polar coordinates. IEEE Transactions on Control Systems Technology, 12(4), 637–644.

VII Das, T., & Kar, I. N. (2006). Design and implementation of an adaptive fuzzy logic-based controller for wheeled mobile robots. IEEE Transactions on Control Systems Technology, 14(3), 501–510.

VIII Ding, Y., Liu, C., Lu, S., & Zhu, Z. (2018). Hyperbolic Sliding Mode Trajectory Tracking Control of Mobile Robot. 2018 3rd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2018).

IX Esmaeili, N., Alfi, A., & Khosravi, H. (2017). Balancing and trajectory tracking of two-wheeled mobile robot using backstepping sliding mode control: design and experiments. Journal of Intelligent & Robotic Systems, 87(3–4), 601–613.

X Fierro, R., & Lewis, F. L. (1998). Control of a nonholonomic mobile robot using neural networks. IEEE Transactions on Neural Networks, 9(4), 589–600. https://doi.org/10.1109/72.701173

XI Fukao, T., Nakagawa, H., & Adachi, N. (2000). Adaptive tracking control of a nonholonomic mobile robot. IEEE Transactions on Robotics and Automation, 16(5), 609–615.

XII Hadi, N. H. (2005). Fuzzy control of mobile robot in slippery environment. 1(2), 41–51.

XIII Hamoudi, A. K. (2016). Design and Simulation of Sliding Mode Fuzzy Controller for Nonlinear System. Journal of Engineering, 22(3), 66–76.

XIV Kanayama, Y., Kimura, Y., Miyazaki, F., & Noguchi, T. (1990). A stable tracking control method for an autonomous mobile robot. Proceedings., IEEE International Conference on Robotics and Automation, 384–389.

XV Li, Y., Wang, Z., & Zhu, L. (2010). Adaptive neural network PID sliding mode dynamic control of nonholonomic mobile robot. The 2010 IEEE International Conference on Information and Automation, 753–757.

XVI Liu, Y., Zhu, J. J., Williams II, R. L., & Wu, J. (2008). Omni-directional mobile robot controller based on trajectory linearization. Robotics and Autonomous Systems, 56(5), 461–479.

XVII Martins, F. N., Celeste, W. C., Carelli, R., Sarcinelli-Filho, M., & Bastos-Filho, T. F. (2008). An adaptive dynamic controller for autonomous mobile robot trajectory tracking. Control Engineering Practice, 16(11), 1354–1363. https://doi.org/10.1016/j.conengprac.2008.03.004

XVIII Mehrjerdi, H., & Saad, M. (2011). Chattering reduction on the dynamic tracking control of a nonholonomic mobile robot using exponential sliding mode. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 225(7), 875–886.

XIX Raja, P., & Pugazhenthi, S. (2012). Optimal path planning of mobile robots: A review. International Journal of Physical Sciences, 7(9), 1314–1320.

XX Rao, A. M., Ramji, K., Rao, B. S. K. S. S., Vasu, V., & Puneeth, C. (2017). Navigation of non-holonomic mobile robot using neuro-fuzzy logic with integrated safe boundary algorithm. International Journal of Automation and Computing, 14(3), 285–294.

XXI Samson, C., & Ait-Abderrahim, K. (1991). Feedback control of a nonholonomic wheeled cart in cartesian space. Proceedings. 1991 IEEE International Conference on Robotics and Automation, 1136–1137.

XXII Saud, L. J., & Hasan, A. F. (2018). Design of an Optimal Integral Backstepping Controller for a Quadcopter. Journal of Engineering, 24(5), 46–65.

XXIII Umar, S. N. H., Bakar, E. A., Soaid, M. S., & Samad, Z. (2014). Study on multi tasks of line following differential wheeled mobile robot for in-class project. International Journal of Modelling, Identification and Control, 21(1), 47–53.

XXIV Wu, X., Jin, P., Zou, T., Qi, Z., Xiao, H., & Lou, P. (2019). Backstepping trajectory tracking based on fuzzy sliding mode control for differential mobile robots. Journal of Intelligent & Robotic Systems, 96(1), 109–121.

XXV Xu, Y. (2008). Chattering free robust control for nonlinear systems. IEEE Transactions on Control Systems Technology, 16(6), 1352–1359.

XXVI Yang, J.-M., & Kim, J.-H. (1999). Sliding mode control for trajectory tracking of nonholonomic wheeled mobile robots. IEEE Transactions on Robotics and Automation, 15(3), 578–587.

XXVII Young, K. D., Utkin, V. I., & Ozguner, U. (1999). A control engineer’s guide to sliding mode control. IEEE Transactions on Control Systems Technology, 7(3), 328–342.

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Abstract:

This paper presents the influence of cycling impact loading and temperature on dynamic crack propagation in thin plates for two types of aluminum plates (7075, 6061) with aspect ratio (1.5,2) and plate boundary conditions (CSCS& SFSF). Using analytical solution and numerical analysis, crack lengths have (3, 5) mm and crack angle (45^o). Analytical solution using program (MATLAB-16), the purpose of analytical solution to get the mechanical and thermal stress with time at crack tip in thin aluminum plate, then calculate the dynamic crack propagation under the effect of these stresses. Numerical analysis using program (ANSYS-18 APDL) based on finite element method, the purpose of numerical analysis to obtain mechanical and thermal stress respect with time at the tip of the crack in thin aluminum plate, then calculate the dynamic crack propagation under the mechanical and thermal stresses effect. The results showed that the dynamic crack propagation increased as the crack length increased, and also found that the dynamic crack propagation decreased as the aspect ratio of the plate increased.

Keywords:

Stress,dynamic crack propagation,crack tip,analysis,plate,

Refference:

I E.E. Gdoutos, “Fracture Mechanics an Introduction”, 2005.
II James M. Gere, “Mechanics of Materials”, 2004.
III Hoai Nam Le, and Catherine Gardin, “Analytical calculation of the stress intensity factor in a surface cracked plate submitted to thermal fatigue loading”, Engineering Fracture Mechanics 77, PP.2354–2369, 2010.
IV Mahmut Uslu, Og˘uzhan Demir, and Ali O. Ayhan, “Surface Cracks in Finite Thickness Plates under Thermal and Displacement-Controlled loads – Part 1: Stress Intensity Factors”, Engineering Fracture Mechanics, Vol. 115, PP. 284–295, 2014.
V Katarina Maksimović, Dragi Stamenković, Mirko Maksimović, and Ivana Vasović, “Determination of Fracture Mechanics Parameters Structural Components with Surface Crack under Thermo mechanical Loads”,Scientific Technical Review, Vol.66, PP.27-33, No.3, 2016.
VI Shiwei Ge, Yafei Xu, Xiao Zhou, and Shangyu Peng, “Thermal Stress Analysis of a Continuous Rigid Frame Bridge”, Annals of Civil and Environmental Engineering, 2017.
VII T. K. Varadan and K. Bhaskar, “Analysis of Plates Theory and Problems”, Department of Aerospace Engineering, India Institution of Technology, Madras, India, 1999.
VIII F. Arace, “Simplified Models for the Analysis of Wave-Controlled Impacts”, 2005.
IX Loke Sworappa and R. Dharni, “Laminated Architectural Glass Subjected to Blast, Impact Loading”, 2005.
X L.S. Srinath, “Advanced Mechanics of Solid”,3rd Edition, McGraw-Hill, 2009.
XI M. Gosz, and B. Moram, “Stress Intensity Factors along Three Dimensional Elliptical Crack Fronts”, U. S. Department of Transportation, 1998.
XII L.L. Faulkner, “Practical Fracture Mechanics in Design”, Marcel Dekker, 2005.
XIII [59] M. Mir Zaei, “Fracture Mechanical Engineering”, TMU, 2000.
XIV Madenci, Erdogan, and Ibrahim Guven, “The finite element method and applications in engineering using ANSYS”. Springer, 2015.
XV Stolarski, Tadeusz, Yuji Nakasone, and Shigeka Yoshimoto, “Engineering analysis with ANSYS software”. Butterworth Heinemann, 2006.
XVI ANSYS Release 18.0 Documentation.
XVII ASM International Handbook, “Properties and Selection”, Vol.2, 1992.

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Abstract:

There has been challenging the pattern recognition that more attention needs to be paid to this area Offline Signature Verification (OSV), particularly when it is relied upon to popularize fully on the skillful frauds that are not accessible during the preparation. Its difficulties additionally incorporate little training tests and great intra-class divergence. At times the crude signature can incorporate additional pixel known as noises or may not be in the legitimate structure where preprocessing is obligatory. Insomuch as a signature is preprocessed accurately, it leads to a superior outcome for both signature matching and fraud disclosure.For example; an  appropriate estimation of gamma value improves the contrast of the signature image, on another hand, Pre-preparing likewise comprises binarization, noise elimination, so forth...The proposed method is for extraction features (such as ;Energy, Contrast, Entropy,and Correlation) from Offline Signature Verification System. In this paper, the data processing deals with twain parallel styles viz signature training and signature testing analysis. Insomuch as that the extracted features from a signature picture doesn't powerful, this will cause higher verification error rates particularly for skillful fabrications in hacking the system.The results show that’s the (UTSig) and the combination of (NISDCC, CEDAR, SigComp2012).Comparing with the other researches, the results in this Paper is the best and the system is more efficientwith (UTSig) signature which were 97%.

Keywords:

Offline Signature Verification,Insomuch,estimation of gamma value,twain parallel styles,UTSig,NISDCC,CEDAR,SigComp2012,

Refference:

I Ahmed, Z. J. (2018). Fingerprints Matching Using the Energy and Low Order Moment of Haar Wavelet Subbands. Journal of Theoretical and Applied Information Technology, 96(18), 6191–6202.

II AL-OBIADIE, S. N. M. (2016). Emotion Detection Using Facial Image Based on Geometric Attributes. University of Baghdad.

III Aldhaher, E., & George, L. (2014). Detection of Diabetic Maculopathy Using Image Analysis Techniques -Introduction and Implementation.

IV Eds, A. D. H. (2018). New Trends in Information and Communications Technology Applications (Vol. 938). https://doi.org/10.1007/978-3-030-01653-1

V Ellen, D., Day, S., & Davies, C. (2018). Scientific examination of documents: methods and techniques. CRC Press.

VI Fadhil, R., & George, L. E. (2017). Finger Vein Identification and Authentication System. LAP Lambert Academic Publishing.

VII Ferrer, M. A., Alonso, J. B., & Travieso, C. M. (2005). Offline geometric parameters for automatic signature verification using fixed-point arithmetic. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(6), 993–997.

VIII George, L. E., Al-Daamy, N., Al-Daamy, S. A., & Ahmed, R. K. (2016). The using of graylevel co-occurrence matrix for features extruction of the breast cancer biopcy image (glcm). Int. J. Engg. Res. and Sci. & Tech, 5(1).

IX Gunjal, S. N., Dange, B. J., & Brahmane, A. V. (2016). Offline Signature Verification using Feature Point Extraction. International Journal of Computer Applications, 975, 8887.

X Hafemann, L. G. (2019). Learning features for Offline Handwritten Signature Verification by MANUSCRIPT-BASED THESIS PRESENTED TO ÉCOLE DE IN PARTIAL FULFILLMENT FOR THE DEGREE OF.

XI Hamza, R. M., & Al-Assadi, T. A. (2012). Genetic algorithm to find optimalGLCM features. Department of Computer Science College of Information Technology.

XII HASSAN, E. K. H., GEORGE, L. E., & MOHAMMED, F. G. (2018). Color image compression based on DCT, differential pulse coding modulation, and adaptive shift coding. Journal of Theoretical and Applied Information Technology, 96(11), 3160–3171.

XIII Inamdar, V. S., Rege, P. P., & Arya, M. S. (2010). Offline Handwritten Signature based Blind Biometric Watermarking and Authetication Technique using Biorthogonal Wavelet Transform. International Journal of Computer Applications, 11(1), 19–27. https://doi.org/10.5120/1547-1970

XIV Jabur, Z. F., & Ali, S. K. (2014). Off line Handwritten Signature Recognition based on Fusion of Global and GLCM Features Using Fuzzy Logic. JOURNAL OF THI-QAR SCIENCE, 4(3), 151–158.

XV Karouni, A., Daya, B., & Bahlak, S. (2011). Offline signature recognition using neural networks approach. Procedia Computer Science, 3, 155–161.

XVI Kaur, H., & Kaur, S. (2014). Offline Hindi Signature Recognition Using Surf Feature Extraction and Neural Networks Approach. Ijsr. Net, 3(8), 1141–1146.

XVII Mahanta, L. B., & Deka, A. (2013). A study on handwritten signature. International Journal of Computer Applications, 79(2).

XVIII Mohammed, S. N., & George, L. E. (2016). Illumination-Invariant Facial Components Extraction Using Adaptive Contrast Enhancement Methods. Current Journal of Applied Science and Technology, 1–13.

XIX Narwade, P. N., Sawant, R. R., & Bonde, S. V. (2018). Offline handwritten signature verification using cylindrical shape context. 3D Research, 9(4), 48.

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XXI Pratt, W. K. (1994). Digital Image Processing. In European Journal of Engineering Education (Vol. 19). https://doi.org/10.1080/03043799408928319

XXII Radhika, K. S., & Gopika, S. (2015). Online and offline signature verification: A combined approach. Procedia Computer Science, 46, 1593–1600. https://doi.org/10.1016/j.procs.2015.02.089

XXIII Rashidi, S., Fallah, A., & Towhidkhah, F. (2012). Feature extraction based DCT on dynamic signature verification. Scientia Iranica, 19(6), 1810–1819. https://doi.org/10.1016/j.scient.2012.05.007

XXIV Shakour, A. A. (2018). Biometric Authentication and Recognition System Using Hand Palm Images. Baghdad University.

XXV Sigari, M. H., Pourshahabi, M. R., & Pourreza, H. R. (2012). An ensemble classifier approach for static signature verification based on multi-resolution extracted features. International Journal of Signal Processing, Image Processing and Pattern Recognition, 5(1), 21–36.

XXVI Sindhu, B., & Jeeva, J. B. (2013). Automated Retinal Vessel Segmentation Using Morphological Operation And Threshold. International Journal of Scientific & Engineering Research, 4(5), 1614–1617. Retrieved from http://www.ijser.org

XXVII Soleimani, A., Araabi, B. N., & Fouladi, K. (2016). Deep Multitask Metric Learning for Offline Signature Verification. Pattern Recognition Letters, 80, 84–90. https://doi.org/10.1016/j.patrec.2016.05.023

XXVIII Soleimani, A., Fouladi, K., & Araabi, B. N. (2016a). Persian offline signature verification based on curvature and gradient histograms. 2016 6th International Conference on Computer and Knowledge Engineering (ICCKE), 147–152. IEEE.

XXIX Soleimani, A., Fouladi, K., & Araabi, B. N. (2016b). UTSig: A Persian offline signature dataset. IET Biometrics, 6(1), 1–8.

XXX Soleimani, A., Fouladi, K., & Araabi, B. N. (2017). UTSig: A Persian offline signature dataset. IET Biometrics, 6(1), 1–8. https://doi.org/10.1049/iet-bmt.2015.0058

XXXI Taylor, J. K., & Cihon, C. (2004). Statistical Techniques for Data Analysis. Retrieved from https://books.google.iq/books?id=yw6JwuAclCUC

XXXII Tuama, S. A., George, L. E., Okelola, M. O., Olabode, E. O., Mbah, E. N., Attah, A. J., … Sudharmaidevi, C. R. (2019). Current Research in Science and Technology Vol. 1. Current Research in Science and Technology Vol. 1, 1–17. https://doi.org/10.9734/bpi/crst/v1

XXXIII Tuama, S., & George, L. (2016). Retina Recognition Based on Texture Analysis: Building a system for individual recognition based on vasicular retina pattern.

XXXIV V.G., Y., & Patil, A. (2014). Offline and Online Signature Verification Systems: a Survey. International Journal of Research in Engineering and Technology, 3(3), 328–332.

XXXV Widiarti, A. R. (2011). Comparing Hilditch, Rosenfeld, Zhang-Suen, and Nagendraprasad-Wang-Gupta Thinning. International Journal of Computer and Information Engineering, 5(6), 563–567.

XXXVI Younesian, T., Masoudnia, S., Hosseini, R., & Araabi, B. N. (2019). Active Transfer Learning for Persian Offline Signature Verification. (February), 234–239. https://doi.org/10.1109/pria.2019.8786013

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Abstract:

Automatic License Recognition (ALPR) has been considered significant in many applications in intelligent transport and monitoring systems. As in other tasks of the computer vision, deep learning methods (DL) were implemented recently in the ALPR context, with a focus on country-specific Iraqi councils, like German or Old and Northern.  In this work, we proposed the DL-ALPR system from the beginning in the license plate detection phase of Iraqi plates according to the latest (YOLO) convolutional layers to detect single class. Utilizing a data set of Iraqi paintings collected by the researcher, and in the second stage, the detection plates are Recognition by extracting a set of license plate features using the SIFT and SURF algorithm, then using KNN to match the plates stored in the database to match them, the data is divided into two parts, part photos: 1300 pictures, And the second part, videos of the Iraqi vehicles in different environmental conditions, and the number is 35 videos. 1300 photos were divided 70% in the training phase and 30% in the testing phase and the results obtained in the testing phase were 99.2% for LP detection and 97.14% for recognition and the total accuracy of the system was 98.17%.

Keywords:

Automatic License Recognition,deep learning methods,Iraqi plates,SIFT and SURF algorithm,training phase,testing phase,

Refference:

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VII. J. Kim, “Automatic Vehicle License Plate Extraction Using Region-Based Convolutional Neural Networks and Morphological Operations”, Symmetry, Vol. 11, No. 7, Pp. 882,2019.

VIII. J. Redmon, A. Farhadi, “YOLO9000: better, faster, stronger”, Proceedings of the IEEE conference on computer vision and pattern recognition, Vol. 1, No. 1, Pp. 7263-7271, 2017.

IX. J.T. Pedersen, “Study group SURF: Feature detection & description”, Department of Computer Science, Aarhus University, Pp. 1-12,2011

X. Mathew, Sheena S, “A Comparison of Sift And Surf Algorithm For The Recognition of An Efficient Iris Biometric System”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 5, No. 1, Pp. 37–42, 2016.

XI. P. Marzuki, F. Radzi, Y.C. Wong, N. Abdul Hamid, N. Ali, M. Mat ibrahim, “A design of license plate recognition system using convolutional neural network”, International Journal of Electrical and Computer Engineering (IJECE), Vol. 9, No. 2196, Pp.2, 2019.

XII. R. Girshick, “Fast r-cnn”, Proceedings of the IEEE international conference on computer vision, Vol. 1, No. 1, Pp. 1440-1448, 2015.
XIII. S. Geethapriya, N. Duraimurugan, S. Chokkalingam, “Real-Time Object Detection with Yolo”, International Journal of Engineering and Advanced Technology (IJEAT), Vol. 8, No. 1, Pp. 1440-1448, 2019.

XIV. S. Geethapriya, N. Duraimurugan, S. Chokkalingam, “Real-Time Object Detection with Yolo”, International Journal of Engineering and Advanced Technology (IJEAT), Vol. 8, No. 1, Pp. 1440-1448, 2019.

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Abstract:

Intelligent access control is one of the challenging tasksin the human identification, image analysis, and diagnoses disease and computer vision. The focus towards the intelligent access control has been increased in the last years due to its various, applications in different   domains. For this reason, it was used intelligent access control to facilitate the task of identifying the human.The objective of this paper is to analyse and evaluate the seven techniques for the intelligent access control and advantage and disadvantage of each type. In addition, represents biometrics characteristics in general. The Biometric feature is used to determine human identity including the brain signals. Through this study, brain signals are the best among the techniques. In this study, we first presented a survey of the Computational intelligence techniques in biometrics. All previous studies used brain EEG signals. Where different algorithms were used to extract, the features. These feature applied for human identification. The Accuracy achieved was up to 97% according to the studies found in this research

Keywords:

Computational intelligence,human identification,Biometrics,Finger print,EEG signals,

Refference:

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II. Ain Anil K, Nandakumar Karthik, Ross Arun, “50 years of biometric research: Accomplishments, challenges, and opportunities”, Pattern Recognition Letters, vol 79, pp 80-105, 2016.
III. Abhilash Kumar Sharma, Ashish Raghuwanshi, Vijay Kumar Sharma “Biometric System- A Review “, International Journal of Computer Science and Information Technologies, vol 6, no 5,pp 4616-4619, 2015.
IV. Arti B. Waghode, C A Manjare “Biometric Authentication of Person using finger knuckle” , International Conference on Computing, Communication, Control and Automation (ICCUBEA), pp 1-7, 2017.
V. Akshay Bapat and Vivek Kanhangad “Segmentation of hand from cluttered backgrounds for hand geometry biometrics, IEEE Region 10 Symposium (TENSYMP),pp 1- 4, 2017.
VI. Ajay Kumar, David CM Wong, Helen C Shen, and Anil K Jain “Personal verification using palm print and hand geometry biometric”, International Conference on Audio-and Video-Based Biometric Person Authentication, pp 668-678, 2003.
VII. Andrew Boles, Paul Rad,” Voice Biometrics: Deep Learning-based Voiceprint Authentication System ” 12th System of Systems Engineering Conference (SoSE), 2017.
VIII. Angadi, Shanmukhappa and Hatture, Sanjeevakumar “Hand geometry based user identification using minimal edge connected hand image graph”, IET Computer Vision, vol 12, no 5, pp 744-752, 2018.
IX. Angadi, S.A., Hatture, S.M.” Biometric person identification system: a multimodal approach employing spectral graph characteristics of hand geometry and palm print “, International Journal of Intelligent Systems and Applications, vol 8, no 3, 2016.
X. Abdulkareem, K.H., Mohammed, M.A., Gunasekaran, S.S., Al-Mhiqani, M.N., Mutlag, A.A., Mostafa, S.A., Ali, N.S. and Ibrahim, D.A “A Review of Fog Computing and Machine Learning” Concepts, Applications, Challenges, and Open Issues. IEEE Access, 7, pp.153123-153140, 2019.
XI. Abd Ghani, M.K., Mohammed, M.A., Arunkumar, N. et al. Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques. Neural Comput & Applic 32, 625–638 (2020). https://doi.org/10.1007/s00521-018-3882-6.
XII. Carmen Camara, Pedro Peris-Lopez, and Juan E. Tapiador “Human Identification Using Compressed ECG Signals” Avda. de la Universidad, 2015.

XIII. Connor, Patrick and Ross, Arun “Biometric recognition by gait: A survey of modalities and features, Computer Vision and Image Understanding”,vol 167, pp 1-27, 2018
XIV. Wang, Lei “Discovering phase transitions with unsupervised learning “, vol 94,pp 195-105, 2016.
XV. Champod Christophe, Lennard Chris J, Margot Pierre, Stoilovic Milutin, “Fingerprints and other ridge skin impressions”, 2017.
XVI. Dhillon, Parwinder Kaur and Kalra, Sheetal “A lightweight biometrics based remote user authentication scheme for IoT services “, Journal of Information Security and Applications,vol 34,pp 255-270,2017.
XVII. Emanuele Maiorana, Senior Member “Longitudinal Evaluation of EEG-based Biometric Recognition”, IEEE Transactions on Information Forensics and Security, vol. 13, no. 5, , pp 1123 – 1138, May 2018.
XVIII. Feng Lin, Kun Woo Cho, Chen Song, Wenyao Xu, Zhanpeng Jin : Brain Password: A Secure and Truly Cancelable Brain Biometrics for Smart Headwear, In Proc. Proceedings of the 16th Annual International Conference on Mobile Systems, Applications, and Services, pp 296-309, 2018.
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XX. Guodong Guo,and Na Zhang” What is the Challenge for Deep Learning in Unconstrained Face Recognition? “, 13th IEEE International Conference on Automatic Face & Gesture Recognition, 2018.
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XXXII. Mostafa, S.A., Mustapha, A., Hazeem, A.A., Khaleefah, S.H. and Mohammed, M.A.”. An agent-based inference engine for efficient and reliable automated car failure diagnosis assistance”. IEEE Access, 6, pp.8322-8331, 2018.
XXXIII. Mostafa, S.A., Mustapha, A., Mohammed, M.A., Hamed, R.I., Arunkumar, N., Ghani, M.K.A., Jaber, M.M. and Khaleefah, S.H.. “Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson’s disease”. Cognitive Systems Research, 54, pp.90-99, 2019.
XXXIV. M. Del Pozo-Banos, J. B. Alonso, J. R. Ticay-Rivas, and C. M. Travieso, “Electroencephalogram subject identification A review” Expert Systems With Applications, vol. 41, no. 15 Nov , pp 6537–6554, 2014.
XXXV. Mohammed, M.A., Ghani, M.K.A., Hamed, R.I. and Ibrahim, D.A”. Analysis of electronic methods for nasopharyngeal carcinoma: Prevalence, diagnosis,challenges and technologies. Journal of Computational Science, 21, pp.241-254, 2017.
XXXVI. Mehwish Leghari, Shahzad Memon, Asghar Ali Chandio,”Feature-Level Fusion of Fingerprint and Online Signature for Multimodal Biometrics”, International Conference on Computing, Mathematics and Engineering Technologies, 2018.
XXXVII. Mohammed, M.A., Al-Khateeb, B. and Ibrahim, D.A., 2016. Case based reasoning shell frameworkas decision support tool. Indian Journal of Science and Technology, 9(42), pp.1-8.
XXXVIII. Nguyen Kien, Fookes Clinton , Jillela Raghavender , Sridharan Sridha, Ross Arun,” Long range iris recognition A survey” , Pattern Recognition, vol 72 ,pp 123-143 , 2017.
XXXIX. Nanni, Loris Lumini Alessandra , Ferrara Matteo , Cappelli Raffaele ” Combining biometric matchers by means of machine learning and statistical approaches ” , Neurocomputing ,vol 149 , pp 526-535 , 2015.
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XLIII. Shaymaa adnan Abdulrahman, Mohamed Roushdy, Abdel-Badeeh M. Salem,: “Support vector machine approach for human identification based on EEG signals, journal of mechanics of continua and mathematical sciences ,vol 15 , number 2, 2020.
XLIV. Sun Lichao, Wang, Yuqi, Cao Bokai, Philip S Yu, Srisa-An Witawas, Leow Alex D, ” Sequential keystroke behavioural biometrics for mobile user identification via multi-view deep learning” , Joint European Conference on Machine Learning and Knowledge Discovery in Databases , pp 228-240,2017.
XLV. Shaymaa adnan Abdulrahman, Mohamed Roushdy, Abdel-Badeeh M. Salem, A “Survey of biometrics using electroencephalogram EEG” International Journal “Information Content and Processing”, Volume 6, Number 1, pp 18-32, 2019.
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LIV. Yasemin Bay, Meryem Erbilek, Ama Fosuah, Erbug Celebi “The Impact of Visual and Blind Signing on Signature Biometrics”, 9th International Conference on Computational Intelligence and Communication Networks, pp 161-164 , 2017
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Abstract:

Life on the Earth exists because of atmosphere that surrounds it. As with the passage of time population increases and with this increases anthropogenic activities increases which is adversely affecting our atmosphere. That is why temperature of cities is soaring up. As our atmosphere is occupied by different gases, whose increase or decrease can substantially affects our environment. The major air pollutants, due to human activities, are carbon monoxide ), carbon dioxide ( ), nitrogen dioxide ( ), ozone ( ), sulfur dioxide ( ) and particulate matter ( ).Among these pollutants,  plays a big role as it can be produced due to road traffic and combustion of fossil fuels. In this paper, we investigated  in Pakistan troposphere through Sentinel-5 Precursor (S5-P) satellite. Data from the S5-P, with TROPO phosphoric Monitoring Instrument (TROPOMI) as payload, became available in July 2018, having spatial resolution nine times higher than that of OMI. S5-P launched by European Space Agency(ESA) with one-day revisit cycle, has the capability to sense all atmospheric gases. Our area of study is Pakistan. We processed S5-P datasets in Google Earth Engine(GEE) and produced results of four seasons, during 2018-2019, of . Different regions of Pakistan, which have excess in its troposphere, are also shown. This increase is supported by the fact that with time the increase in urban population causes dramatic negative effects on the atmosphere. Compared to traditional methods, this study will substantially increase the capability of the government and policy makers to take timely action on anthropogenic activities in mentioned cities, in order to mitigate emission of . Our findings illustrate the decrease of in summer, and surges in autumn and vice versa. In autumn Karachi, Sheikhupura, Raiwind, Lahore, Jamber, Faisalabad and Rawalpindi have highest concentration of  . In winter excess  spots over Karachi, Sheikhupura, Lahore, Raiwind, Jamber and Rawalpindi are detected. After winter, spring season shows further decrease in  concentration in which Karachi, Dera Ghazi Khan Sheikhupura, Rawalpindi and Lahore have highest  concentration and in summer in Pakistan troposphere is further reduced to Sheikhupura, Raiwind and Jamber cities.

Keywords:

Earth,Atmosphere,Urban Pollution,NO4,Google Earth Engine,Sentinel 5P,Omi,

Refference:

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