Archive

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.

XX Pirlo, G., Impedovo, D., Fairhurst, M., Pirlo, G., Impedovo, D., & Fairhurst, M. (2014). Advances in digital handwritten signature processing: a human artefact for e-society. World Scientific Publishing Co., Inc.

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:

I. A. Khazri, “Automatic License Plate Detection & Recognition using deep learning”, towards data science, 2019. web: https://towardsdatascience.com/automatic-license-plate-detection-recognition-using-deep-learning-624def07eaaf?gi=fc80f0526b7.

II. Saharkiz, “Nearest Neighbor Algorithm Implementation and Overview ” , Code project, 2009. Web: https://www.codeproject.com/Articles/32970/K-Nearest-Neighbor-Algorithm-Implementation-and-Ov.
III. D. Hoiem, Y. Chodpathumwan, Q. Dai, “Diagnosing Error in Object Detectors”, Computer Vision – ECCV, Springer Berlin Heidelberg, Berlin, Heidelberg, Vol. 1, No. 1, Pp. 340-353, 2012.
IV. H. Bay, T. Tuytelaars, L. Van Gool, “SURF: Speeded Up Robust Features”, Computer Vision – ECCV, Springer Berlin Heidelberg, Berlin, Heidelberg, Vol. 5, No. 1, Pp. 404-417, 2006.
V. H. A.-H. Kahdum, “Leukocytes Image Segmentation and Classification Based on Geometrical Features and Naïve Bayes Classifier,” Master Degree, College of Science, University of Baghdad, Baghdad – Iraq, 2019.
VI. Kusumadewi, C.A. Sari, E.H. Rachmawanto, “License Number Plate Recognition using Template Matching and Bounding Box Method”, Journal of Physics: Conference Series, IOP Publishing, Vol. 1, No. 1, Pp. 012067,2019.
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:

I. Acharya U Rajendra, Hagiwara Yuki, Deshpande Sunny Nitin, Suren S, Koh Joel En Wei, Oh Shu Lih, Arunkumar N Ciaccio Edward J, Lim Choo Min,” Characterization of focal EEG signals: a review”, Future Generation Computer Systems, vol 91, pp 290-299, 2019.
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.
XIX. Grm Klemen, truc, Vitomir, Artiges Anais, Caron Matthieu, Ekenel,: Strengths and weaknesses of deep learning models for face recognition against image degradations, Iet Biometrics, vol 7, no 1, , pp 81- 89, 2017.

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.
XXI. Gupta Puneet, Gupta Phalguni “Multibiometric authentication system using slap fingerprints, palm dorsal vein, and hand geometry”, vol 65, no 12, pp 9777-9784, 2018
XXII. Geetika, Manavjeet Kaur “Fuzzy Vault with Iris and Retina: A Review” International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 4, April 2013.
XXIII. Hafemann Luiz G, Sabourin Robert, Oliveira Luiz S, “Offline handwritten signature verification—literature review ” , Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA),pp 1- 8 , 2017.
XXIV. Harakannanavar Sunil S, Puranikmath Veena I “Comparative survey of iris recognition”, International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), pp 280-283, 2017.
XXV. Israa M. Alsaadi, “Physiological Biometric Authentication Systems, Advantages, Disadvantages and Future Development: A Review: In proc, international journal of scientific and technology research, vol 4 issue 12, December 2015.
XXVI. Koike-Akino, T.; Mahajan, R.; Marks, T.K.; Tuzel, C.O.; Wang, Y.; Watanabe, S.; Orlik, P.V ” High-Accuracy User Identification Using EEG Biometrics”, pp 854-858, 2016.
XXVII. Khalaf, B.A., Mostafa, S.A., Mustapha, A., Mohammed, M.A. and Abduallah, W.M.. “Comprehensive review of artificial intelligence and statistical approaches in distributed denial of service attack and defence methods”. IEEE Access, 7, pp.51691-51713, 2019.
XXVIII. Lumini Alessandra, Nanni Loris “Overview of the combination of biometric matchers”, Information Fusion,vol 33,pp 71-85, 2017.
XXIX. M. Abo-Zahhad, Sabah M. Ahmed, Sherif N. Abbas ” A New EEG Acquisition Protocol for Biometric Identification Using Eye Blinking Signals “, International Journal of Intelligent Systems and Applications vol 7,no 6, 2015.
XXX. Madane Manisha, Thepade Sudeep, “Score level fusion based bimodal biometric identification using thepade’s sorted n-ary block truncation coding with varied proportions of iris and palmprint traits”, Procedia Computer Science vol 79, pp 466-473, 2016
XXXI. Ma, Lan and Minett, James W and Blu, Thierry and Wang, William SY “Resting state EEG-based biometrics for individual identification using convolutional neural networks”, pp 2848-2851, 2015.
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.
XL. Nitin Kaushal and Purnima Kaushal “Human Identification and Fingerprints: A Review ” Journal of Biometrics & Biostatistics, vol 2,. ISSN:2155-6180 JBMBS, an open access journal,2011.
XLI. Ram K. Nawasalkar1, Harshal R. Lawange2, Surajkumar D. Gupta3, Pradeep K. Butey4 “Study of comparison of human bio-signals for emotion detection using HCI “, international journal of Emerging trends and technology in computer science (IJETTCS) April 2013.
XLII. Rajan Prasad Tripathi, Rhythem Goyal ” Finger Print recognition using biometric analysis and munutia features , In proc , Fourth International Conference on Image Information Processing (ICIIP), 2017.

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.
XLVI. Snyder David , Ghahremani Pegah , Povey Daniel , Garcia-Romero Daniel, Carmiel, Yishay, Khudanpur Sanjeev “Deep neural network-based speaker embeddings for end-to-end speaker verification” Spoken Language Technology Workshop (SLT) , pp 165-170 , 2016
XLVII. Tazwar Muttaqi, S. Hossein Mousavinezhad “User Identification System Using Biometrics Speaker Recognition by MFCC and DTW along with signal processing package” International Conference on Electro/Information Technology (EIT), 2018 .
XLVIII. Teddy Mantoro, Media A. Ayu, Suhendi.”Multi-Faces Recognition Process Using Haar Cascades and Eigenface Methods”, 6th International Conference on Multimedia Computing and Systems (ICMCS), 2018.
XLIX. Urmila Kalshetti, Akshay Goel, Prakhar Srivastava, Mayuri Ingole, Devika Bhide “Human Authentication from Brain EEG Signals using Machine Learning” International Journal of Pure and Applied Mathematics Vol 118, No. 24 , 2018.
L. Vikramaditya Agarwal, Akshay Sahai, Akshay Gupta, Nidhi Jain,: Human Identification and Verification based on Signature, Fingerprint and Iris Integration , In In Proc , 6th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO) , pp 46-461 , 2017.
LI. Wael Khalifa Kenneth Revett and Abdel Badeeh Salem “AIS Inspired Approach for User Identification Based on EEG Signals”, ISBN: 978-960-474-344-5, 2014.
LII. Wu, Changsheng and Ding, Wenbo and Liu, Ruiyuan and Wang, Jiyu and Wang, Aurelia C and Wang, Jie and Li, Shengming and Zi, Yunlong and Wang, Zhong Lin” Keystroke dynamics enabled authentication and identification using triboelectric nanogenerator array”, Materials Today, vol 21, no 3, pp 216-222, 2018.
LIII. Xiaoxiang Xu, Li Zhang, Fanzhang Li MSSVT “Multi-scale feature extraction for single face recognition”, 24th International Conference on Pattern Recognition (ICPR), August 2018 20-24.
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
LV. Yap Hui-Yen, Choo Yun-Huoy, Khoh Wee-How, “Overview of acquisition protocol in EEG based recognition system”, International Conference on Brain Informatics, pp 129-138 , 2017.
LVI. Zhendong Mu: EEG Feature Extraction Based on Rough Set,3rd International Conference on Management, Education, Information and Control (MEICI), 2015.

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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:

I. Abbasi, A., & Sardroodi, J. J. (2019). TiO2/graphene oxide heterostructures for gas-sensing: Interaction of nitrogen dioxide with the pristine and nitrogen modified nanostructures investigated by DFT. Surface Review and Letters, 26(04), 1850170.
II. Ahmad, K., Riegler, M., Pogorelov, K., Conci, N., Halvorsen, P., & De Natale, F. (2017). Jord: a system for collecting information and monitoring natural disasters by linking social media with satellite imagery. Paper presented at the Proceedings of the 15th International Workshop on Content-Based Multimedia Indexing.
III. Ashraf, N., Mushtaq, M., Sultana, B., Iqbal, M., Ullah, I., & Shahid, S. A. (2013). Preliminary monitoring of tropospheric air quality of Lahore City in Pakistan. Sustainable Development, 3(1), 19-28.
IV. Beirle, S., Platt, U., Wenig, M., & Wagner, T. (2003). Weekly cycle of NO 2 by GOME measurements: A signature of anthropogenic sources. Atmospheric Chemistry and Physics, 3(6), 2225-2232.
V. Biresselioglu, M. E., Demir, M. H., Rashid, A., Solak, B., & Ozyorulmaz, E. (2019). What are the Preferences of Household Energy Use in Pakistan?: Findings from a National Survey. Energy and Buildings, 109538.
VI. Boersma, K. F., Jacob, D. J., Eskes, H. J., Pinder, R. W., Wang, J., & Van Der A, R. J. (2008). Intercomparison of SCIAMACHY and OMI tropospheric NO2 columns: Observing the diurnal evolution of chemistry and emissions from space. Journal of Geophysical Research: Atmospheres, 113(D16).
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VIII. Dong, Y., & Xu, L. (2019). Aggregate risk of reactive nitrogen under anthropogenic disturbance in the Pearl River Delta urban agglomeration. Journal of cleaner production, 211, 490-502.
IX. Ghude, S. D., Beig, G., Fadnavis, S., & Polade, S. (2009). Satellite derived trends in NO2 over the major global hotspot regions during the past decade and their inter-comparison. Environmental Pollution, 157(6), 1873-1878.
X. Ghude, S. D., Fadnavis, S., Beig, G., Polade, S., & Van Der A, R. (2008). Detection of surface emission hot spots, trends, and seasonal cycle from satellite‐retrieved NO2 over India. Journal of Geophysical Research: Atmospheres, 113(D20).
XI. Goldberg, D. L., Lu, Z., Streets, D. G., de Foy, B., Griffin, D., McLinden, C. A., . . . Eskes, H. (2019). Enhanced capabilities of TROPOMI NO2: Estimating NOx from North American cities and power plants. Environmental science & technology.
XII. Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18-27.
XIII. Hvidtfeldt, U. A., Sørensen, M., Geels, C., Ketzel, M., Khan, J., Tjønneland, A., . . . Raaschou-Nielsen, O. (2019). Long-term residential exposure to PM2. 5, PM10, black carbon, NO2, and ozone and mortality in a Danish cohort. Environment international, 123, 265-272.
XIV. Ingmann, P., Veihelmann, B., Langen, J., Lamarre, D., Stark, H., & Courrèges-Lacoste, G. B. (2012). Requirements for the GMES Atmosphere Service and ESA’s implementation concept: Sentinels-4/-5 and-5p. Remote Sensing of Environment, 120, 58-69.
XV. Irizar, J., Melf, M., Bartsch, P., Koehler, J., Weiss, S., Greinacher, R., . . . Martin, D. (2019). Sentinel-5/UVNS. Paper presented at the International Conference on Space Optics—ICSO 2018.
XVI. Kaltenbaugh, A. D. (2019). Comparison of Satellite and Ground-Based NO2 Measurements in the Mid-Atlantic Region during the 2018 OWLETS-2 Campaign. Department of Atmospheric and Oceanic Science, University of Maryland
XVII. Kaplan, G., Avdan, Z. Y., & Avdan, U. (2019). Spaceborne Nitrogen Dioxide Observations from the Sentinel-5P TROPOMI over Turkey. Paper presented at the Multidisciplinary Digital Publishing Institute Proceedings.
XVIII. Lamsal, L., Martin, R., Van Donkelaar, A., Steinbacher, M., Celarier, E., Bucsela, E., . . . Pinto, J. (2008). Ground‐level nitrogen dioxide concentrations inferred from the satellite‐borne Ozone Monitoring Instrument. Journal of Geophysical Research: Atmospheres, 113(D16).
XIX. Liu, L., Zhang, X., Xu, W., Liu, X., Li, Y., Lu, X., . . . Zhang, W. (2017). Temporal characteristics of atmospheric ammonia and nitrogen dioxide over China based on emission data, satellite observations and atmospheric transport modeling since 1980. Atmospheric Chemistry and Physics, 17(15), 9365-9378.
XX. Mapes, B. E. (2001). Water’s two height scales: The moist adiabat and the radiative troposphere. Quarterly Journal of the Royal Meteorological Society, 127(577), 2353-2366.
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XXII. Richter, A., & Burrows, J. (2002). Tropospheric NO2 from GOME measurements. Advances in Space Research, 29(11), 1673-1683.
XXIII. Richter, A., Wittrock, F., Ladstätter-Weißenmayer, A., & Burrows, J. (2002). GOME measurements of stratospheric and tropospheric BrO. Advances in Space Research, 29(11), 1667-1672.
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XXV. Shen, L., Jacob, D. J., Liu, X., Huang, G., Li, K., Liao, H., & Wang, T. (2019). An evaluation of the ability of the Ozone Monitoring Instrument (OMI) to observe boundary layer ozone pollution across China: application to 2005–2017 ozone trends. Atmospheric Chemistry and Physics, 19(9), 6551-6560.
XXVI. Shon, Z.-H., Kim, K.-H., & Song, S.-K. (2011). Long-term trend in NO2 and NOx levels and their emission ratio in relation to road traffic activities in East Asia. Atmospheric environment, 45(18), 3120-3131.
XXVII. Tariq, S., & Ali, M. (2015). Tropospheric NO2 trends over South Asia during the last decade (2004–2014) using OMI data. Advances in Meteorology, 2015.
XXVIII. Tariq, S., Ali, M., Mahmood, K., Batool, S. A., & Rana, A. D. (2014). A study of tropospheric NO2 variability over Pakistan using OMI data. Atmospheric Pollution Research, 5(4), 709-720.
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XXXI. Zheng, Z., Yang, Z., Wu, Z., & Marinello, F. (2019). Spatial Variation of NO2 and Its Impact Factors in China: An Application of Sentinel-5P Products. Remote Sensing, 11(16), 1939.

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Abstract:

The modern digital systems especially those dealing with enormous data consumption application are facing a very complicated problem of high latency in these memory access application. Latency seems to be a major hurdle in the performance of modern memory dependent systems as it experiences delay in the processing. This high latency depends upon too many factors especially applications involving memory access operation. Out of these major factors one is of the binding and allocation application. Number of different approaches in the recent past has adopted to optimize the high latency in memory access application. Yet the modern embedded system faces high latency still due to enormous data transfer. In our approach we focus to optimize the latency of modern digital system by dividing the memory into groups. Following by activating, the fourth coming commands in advance in idle slots of different memory modules. The approach is called slag time management. In our algorithm effective distribution of memory into modules activating the later command in advance is followed by the advance dynamic buffers for saving the most frequently access arrays in it.The proposed technique of dividing the memory into modules utilizing the memory management idle slot management in use of advance of dynamic buffers has significantly approved the overall of latency of

Keywords:

Array binding and allocation,Dynmic random-access memory (DRAM),effective sheduling,empty slots management,memory latency,multi-core processors on chip (MPSoC).,

Refference:

I. David Tawei Wang, “Modern DRAM Memory Systems: Performance Analysis and Scheduling Algorithm”, University of Maryland libraries,2005.
II. Fraboulet, G. Huard, A. Mignotte, “Loop Alignment for Memory Access Optimization”, 12th International Symposium on System Synthesis, 1999.
III. H. Shin, C. Kim, “A Simple Yet Effective Technique for Partitioning”, IEEE Transaction on Very Large Integration (VLSI) System, pp. 380- 386, 1993.
IV. J. I. Gomez, P. Marchal, S. Verdoorlaege, L. Pinuel, F. Catthoor, “Optimizing the Memory Bandwidth with Loop Morphing”, 15th IEEE International Conference on Application-Specific Systems, Architectures and Processors (ASAP’04), 2004.
V. N. Kim, R. Peng, “A memory Allocation and Assignment Method Using Multi-Way Partitioning”, IEEE International SoC Conference, 2004.
VI. Prince, Betty, “High Performance Memories: New Architecture DRAMs and SRAMs — Evolution and Function”, 1st edition, 1996.
VII. P. R. Panda, N. D. Dutt and A. Nicolau, “Incorporating DRAM Access Modes into High-Level Synthesis”, IEEE Transaction on Computer-Aided Design, Vol. 17, pp. 96-106,1998.
VIII. P. R. Panda, N. D. Dutt, A. Nicolau, “Exploiting Off-Chip Memory Access Modes in High-Level Synthesis”, International Conference on Computer-Aided Design (ICCAD ’97), 1997.
IX. P. R. Panda, F. Catthoor, N. D. Dutt, K. Danckaert, E. Brockmeyer, C. Kulkarni, A. Vandercappelle, and P. G. Kjeldsberg, “Data and Memory Optimization Techniques for Embedded Systems”, ACM Transaction Design Automation Electron System, Vol. 6, no. 2, pp. 149-206, 2001.
X. P. R. Panda, “Memory Bank Customization and Assignment in Behavioral Synthesis”, ICCAD, 1999.
XI. P. Marchal, J. I. Gomez, F. Catthoor, “Optimizing the Memory Bandwidth with Loop Fusion”, IEEE International Conference on Hardware/Software Codesign and System Synthesis (CODES + ISSS’04), 2004.
XII. S. Daud, H. shin, “Cycle Accurate Memory Delay Modeling for Off-Chip DRAMs”, System on Chip Conference, 2009.
XIII. S. J. E. Wilton and N. P. Jouppi, “CACTI: An Enhanced Cache Access and Cycle Time Model,” IEEE Journal of Solid state circuits, Vol. 31, pp. 667-688, 1996.
XIV. T. Kim, J. Kim, “Integration of Code Scheduling, Memory Allocation, and Array Binding for Memory Access Optimization”, IEEE Transaction on Computer Aided Design of Integrated circuits and systems, vol. 26, no. 1, pp. 142-151, 2007.
XV. T. Wada, S. Rajan, and S. A. Przbylski, “An Analytical Access Time Model for On-Chip Cache Memories”, IEEE Journal of Solid State circuits, Vol. 27, pp. 1147-1156, 1992.

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Abstract:

The Median Filter (MF) is one of the problems that need massive computational resources to perform its operation in a moderate time.  The MF can be implemented on traditional CPUs and GPUs.  Investigating the performance in terms of processing time of the MF on different architectures can provide the researchers with wider vision to optimally select the computational resources that best fit the required time needed to remove salt and pepper noise.  This paper shows the impact of different parameters affecting the MF processing time.  Resolution of the frame, frame rate per second, and the MF r value are investigated in order to decide both the preferred architecture and algorithm.  OpenMP has been deployed on CPUs and CUDA has been deployed on Nvidia GPGPU K20.  Experimental results show that histogram approach and K20 using CUDA are the best choice for processing 4K resolution with r > 2 and HD resolution with r > 4. For VGA resolution and r > 6, histogram approach and CPU using OpenMP are the best choice.  The paper provides a way to select the architecture-algorithm pair suitable for implementing the MF

Keywords:

CUDA,GPU,Histogram Approach,Median Filter,OpenMP ,

Refference:

I. C. M. Wu and Y. C. Chiang, “Insertion Sort Circuit Design Applied on the Median Filter,” in 2018 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2018, 2018.

II. D. S. Richards, “VLSI Median Filters,” IEEE Trans. Acoust., 1990.

III. G. Gupta, “Algorithm for Image Processing Using Improved Median Filter and Comparison of Mean, Median and Improved Median Filter,” Int. J. Soft Comput., 2011.

IV. H. M.Faheem and B. König-Ries, “A New Scheduling Strategy for Solving the Motif Finding Problem on Heterogeneous Architectures,” Int. J. Comput. Appl., 2014.

V. K. Verma, B. Kumar Singh, and A. S. Thokec, “An enhancement in adaptive median filter for edge preservation,” in Procedia Computer Science, 2015.

VI. L. Hayat, M. Fleury, and A. F. Clark, “Two-dimensional median filter algorithm for parallel reconfigurable computers,” IEE Proc. Vision, Image Signal Process., 1995.

VII. M. Fayez, H. M. Faheem, I. Katib, and N. R. Aljohani, “Real-time image scanning framework using GPGPU – Face detection case study,” in Proceedings of the 2016 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2016, 2016.

VIII. M. Vega-Rodríguez and J. Sánchez-Pérez, “An FPGA-based implementation for median filter meeting the real-time requirements of automated visual inspection systems,” Proc. 10th IEEE Mediterr. Conf. Control Autom. (MED ’02), 2002.

IX. N. A. Sabour, H. M. Faheem, and M. E. Khalifa, “Multi-agent based framework for target tracking using a real time vision system,” in 2008 International Conference on Computer Engineering and Systems, ICCES 2008, 2008.

X. O. Green, “Efficient scalable median filtering using histogram-based operations,” IEEE Trans. Image Process., 2018.

XI. R. Medhat, H. M. Faheem, and M. E. Khaleefa, “Efficient parallel architecture of median filter,” in Proceedings of the 9th IASTED International Conference on Parallel and Distributed Computing and Networks, PDCN 2010, 2010.

XII. S. Perreault and P. Hébert, “Median filtering in constant time,” IEEE Trans. Image Process., 2007.

XIII. Y. He, P. Liu, Z. Wang, Z. Hu, and Y. Yang, “Filter pruning via geometric median for deep convolutional neural networks acceleration,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019.

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Abstract:

In the research article an energy optimization method for electrical hardware's utilizing IoTs and wireless sensor is introduced as the Vitality utilization has become one the serious issue in the advanced electrical gear's because of this framework execution is influenced and happens shifts misfortunes. The proposed design improves energy optimization, and decreases the energy utilization. The significant target is to gauge the temperature and lessen vitality utilization utilizing remotely organized IoT and Simulink ideal. The proposed algorithm find the primary destinations of the machine taskand to improve its execution time, and also figure out the temperature of gadget and balance out the temperature, by observing progressively, decreasing vitality utilization and make a vitality productive framework. The equipment is designed with MCU (controlling), single-channel transfer (for exchanging), DHT 11(humidity and temperature sensor),Ac to Dc conversion(adaptor). For the reproduction of the task, Arduino IDE programming is utilized forevery electricalequipment. We can control and schedule the energy utilization capacity through the cayenne web interface using wireless module (undefended source web space for interfacing of the microcontroller), we can switch the states if electrical gear concluded this mesh and fire acquire its outcome and work as indicated by the booking of the hardware. For air temperature sensor Matlab Simulink is used for displaying for gear's energy enhancement the technique decreases the energy consumption of individual equipment’s by 4% as compared to the previously used techniques.

Keywords:

Dynamic Power Management,Real-time systems,Multicore Architecture,IOTs,Wireless sensor network,

Refference:

I. C.-h. Hsu and W.-c. Feng, “A power-aware run-time system for high-performance computing,” in Proceedings of the 2005 ACM/IEEE conference e on Supercomputing. IEEE Computer Society,2005.

II. D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche,J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al., Masteringthe game of Go with deep neural networks and tree search, Nature 529 (7587)(2016) 484–489.

III. D. Konar, K. Sharma, V. Sarogi and S. Bhattacharyya, “A Multi- Objective Quantum-Inspired Genetic Algorithm (Mo-QIGA) for Real-Time Tasks Scheduling in Multiprocessor Environment”, Procedia Computer Science, vol. 131, pp. 591-599, 2018.

IV. H. Khan, M. U. Hashmi, Z. Khan, R. Ahmad, and A. Saleem, Performance Evaluation for Secure DES-Algorithm Based Authentication & Counter Measures for Internet Mobile Host Protocol,” IJCSNS Int. J. Comput. Sci. Netw. Secur. VOL.18 No.12, December 2018, vol. 18, no. 12, pp. 181–185, 2018.

V. H. Khan, Q. Bashir, and M. U. Hashmi, “Scheduling based Energy Optimization Technique in multiprocessor Embedded Systems,” in 2018 International Conference on Engineering and Emerging Technologies (ICEET).doi:10.1109/iceet1.2018.8338643, 2018.

VI. H. Khan, S. Ahmad, N. Saleem, M. U. Hashmi, and Q. Bashir, “Scheduling Based Dynamic Power Management Technique for offline Optimization of Energy in Multi Core Processors,” Int. J. Sci. Eng. Res. Vol. 9, Issue 12, December-2018, vol. 9, no. 12, pp. 6–10, 2018.

VII. H. Khan, M. U. Hashmi, Z. Khan, and R. Ahmad, “Offline Earliest Deadline first Scheduling based Technique for Optimization of Energy using STORM in Homogeneous Multi- core Systems,” IJCSNS Int. J. Comput. Sci. Netw. Secur. VOL.18 No.12, December 2018, vol. 18, no. 12, pp. 125–130, 2018.

VIII. M. Bohr, R. Chau, T. Ghani, and K. Mistry, “The High- k Solution,” IEEE Spectrum, vol. 44, no. 10, pp. 29-35, Oct. 2007.

IX. N. Fathima, “Website: www.ijirset.com Energy Aware Dynam Slack Allocation for Multiprocessor System,” pp. 7476–7483, 2017.

X. P. Nayak and B. Vathasavai, “Energy Efficient Clustering Algorithm for Multi-Hop Wireless Sensor Network Using Type-2 Fuzzy Logic,” in IEEE Sensors Journal, vol. 17, no. 14, pp. 4492-4499, 15 July15, 2017, doi: 10.1109/JSEN.2017.2711432.

XI. Q. Bashir, H. Khan, M. U. Hashmi, and S. Ali zamin, “A Survey on Scheduling Based Optimization Techniques in Multi-Processor Systems,” in Proceedings of the 3rd International Conference on Engineering & Emerging Technologies (ICEET), Superior University, Lahore, PK, 7-8 April, 2016., 2016.

XII. R. Ayoub, S. Sharifi and T. Rosing, “GentleCool: Cooling Aware -+ pages 295 – 298, 2010.

XIII. R. La Rosa, P. Livreri, C. Trigona, L. Di Donato, and G. Sorbello, “Strategies and techniques for powering wireless sensor nodes through energy harvesting and wireless power transfer,” Sensors (Switzerland), vol. 19, no. 12, 2019.

XIV. S. Shi, Q. Wang, P. Xu, X. Chu, Benchmarking state-of-the-art deep learning software tools, in: Proceedings of the 7th IEEE International Conference on Cloud Computing and Big Data, Macau, China, 2016.

XV. S. Kaxiras, Z. Hu, and M. Martonosi, “Cache Decay: Exploiting Generational Behavior to Reduce Cache Leakage Power,” Proc. Int’l Symp. Computer Architecture (ISCA ’01), pp. 240-251, 2001.

XVI. S. Yang, M. Powell, B. Falsafi, K. Roy, and T. Vijay kumar, “An Integrated Circuit/Architecture Approach to Reducing Leakage in Submicron High-PerformanceI-caches,” Proc. Seventh Int’l Symp. High- Performance Computer Architecture (HPCA’01), pp. 147-157, 2001.

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Abstract:

Artificial intelligence applications, including electronic games, have spread widely in our time among children and young people, and parents have suffered from the disruption of their children from them and the surrounding community due to sitting a lot with these applications and electronic games, especially in light of the pandemic of the Covid-19 virus, and children look at their interests, which leads to their addiction With the aim of developing their mental abilities while parents consider their interests to spend times and at the same time have many implications for achieving the goal of keeping the mind, and therefore the importance of research lies in clarifying how to preserve the mind through applications of artificial intelligence, interests and spoilers from electronic games and how to achieve them for the objective of keeping the mind, and research aims To define the objective of keeping the mind, artificial intelligence and electronic games, and to clarify its interests and spoils and how to bring interests and ward off evil through legitimate controls in order to achieve the objective of keeping the mind, the researcher used the analytical and critical inductive approach by collecting what related to the interests and spoils arising from the applications of artificial intelligence in electronic games on Achieve the intention of keeping the mind and its criticism and how to reduce spoilers by evil controls Consciousness, and the research consisted of preface and two researches, introducing the definition and legitimacy of the goal of mind keeping and artificial intelligence and electronic games, the first topic: the interests and spoils of artificial intelligence applications in electronic games to achieve the goal of keeping the mind, the second topic: legal controls for applications of artificial intelligence in electronic games to achieve a destination Preserving the mind, and a conclusion in it the most important results and recommendations, and the most important results are the importance of knowing the interests and spoils of the applications of artificial intelligence in electronic games and benefiting from these games in a way that achieves the objective of keeping the mind while working to increase its interests and ward off its corruption through the application of legal controls.

Keywords:

Objective for Keeping the Mind,Artificial Intelligence,Electronic Games,Objectives of Shariah,Covid-19,Electronic Education,

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