Journal Vol – 15 No -2, February 2020

Abstract:

It can be easily understood that every relay node in traditional multi-hop (TM) communication networks only attends the previous node that is near to it, which is the difficulty in routing. Using directed graphs, the modeling of these networks is performed well in order to achieve the routing. In the networks of accumulative multi-hop (AM) communication, the routing problem is far-off from understanding and yet rather interested in it. The received data energy from earlier relay transmissions can be acquired by numerous relay nodes that assist communication between a single source and a single destination in the accumulative multi-hop network which is a simple one. At this point, in single-source single-destination accumulative multi-hop networks, the difficulty in finding the optimum paths is studied. A method of Load Balanced Energy efficient cross layer based Routing protocol for accumulative networks are implemented in this paper. The end-to-end network connectivity is enhanced as well as the faults at link or/and node level is reduced in this method. Using an energy efficient neighbor node choosing method, the establishment of a set of various paths is done from the source to the destination. Efficient load balancing is offered at the node and a constant route is discovered between the source and destination that meets the delay requirement. With respect to end to end delay, throughput, and energy consumption, the proposed system is outperformed which is demonstrated in the results of simulation.

Keywords:

Accumulative,Multi-hop,Multi-path routing,Cross layer approach,Load balancing,Energy efficiency,

Refference:

I. A. Molisch, N. Mehta, J. Yedidia, and J. Zhang, “Cooperative relay networks using fountain codes,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Nov. 2006, pp. 1.

II. Agbaria, A.; Gershinsky, G.; Naaman N. &Shagin, K. Extrapolation-based and QoS-aware real-time communication in wireless mobile ad hoc networks. In the 8th IFIP Annual Mediterranean Adhoc Networking Workshop, Med-Hoc-Net 2009. pp.21-26. doi: 10.1109/MEDHOCNET.2009.5205201.

III. Ahmed, M.; Elmoniem, Abd; Ibrahim, Hosny M.; Mohamed, Marghny H. &Hedar, Abdel Rahman. Ant colony and load balancing optimizations for AODV routing protocol. Int. J. Sensor Networks Data Commun., 2012, 1. doi: doi:10.4303/ijsndc/X110203.

IV. Cai, X., Duan, Y., He, Y., Yang, J., Li, C.: Bee-Sensor-C: an energy-efficient and scalable multipath routing protocol for wireless sensor net-works. Int. J. Distrib. Sensor Netw. 26 (2015).

V. I. Maric and R. D. Yates, “Cooperative multihop broadcast for wireless networks,” IEEE J. Sel. Areas Commun., vol. 22, no. 6, pp. 1080–1088, Aug. 2004.

VI. J. Castura and Y. Mao, “Rateless coding over fading channels,” IEEE Commun. Lett., vol. 10, no. 1, pp. 46–48, Jan. 2006.

VII. J. Chen, L. Jia, X. Liu, G. Noubir, and R. Sundaram, “Minimum energy accumulative routing in wireless networks,” in Proc. IEEE INFOCOM, vol. 3. Mar. 2005, pp. 1875–1886.

VIII. J. Gómez-Vilardebó, “Routing in Accumulative Multi-Hop Networks,” in IEEE/ACM Transactions on Networking, vol. 25, no. 5, pp. 2815-2828,Oct. 2017. doi: 10.1109/TNET.2017.2703909.
IX. J. Gomez-Vilardebo, “Heuristic routing algorithms for minimum energy cooperative multi-hop wireless networks,” in Proc. 20th Eur. Wireless Conf., May 2014, pp. 1–5.-12

X. J. Gomez-Vilardebo, “Routing in accumulative multi-hop networks,” in Proc. IEEE Conf. Comput. Commun. (INFOCOM), Apr. 2015, pp. 1814–1821.

XI. L. Sobrinho, “An algebraic theory of dynamic network routing,” IEEE/ACM Trans. Netw., vol. 13, no. 5, pp. 1160–1173, Oct. 2005.

XII. Mohapatra, S., Siddappa, M.: Improvised routing using Border Cluster Node for Bee-AdHoc-C: an energy-efficient and systematic routing protocol for MANETs. In: International Conference On Advances in Computer Applications, IEEE ICACA-2016 (2016).

XIII. R. Yim, N. Mehta, A. F. Molisch, and J. Zhang, “Progressive accumulative routing in wireless networks,” in Proc. IEEE Global Commun. Conf. (GLOBECOM), Nov. 2006, pp. 1–6.

XIV. S. C. Draper, L. Liu, A. F. Molisch, and J. S. Yedidia, “Cooperative transmission for wireless networks using mutual-information accumulation,” IEEE Trans. Inf. Theory, vol. 57, no. 8, pp. 5151–5162, Aug. 2011.

XV. Saleem, M., Farooq, M.: Beesensor: a bee-inspired power aware routing protocol for wireless sensor networks. In: Workshops on Applications of Evolutionary Computation, pp. 81–90. Springer Berlin Heidelberg (2007).

XVI. Siva, K. & P. Duraiswamy, K. A QoS routing protocol for mobile ad hoc networks based on the load distribution. In the IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 2010, pp.1-6. doi: 10.1109/ICCIC.2010.5705724.

XVII. Srivastava, S.; Daniel, A.K.; Singh, R. &Saini, J.P. Energyefficient position based routing protocol for mobile ad hoc networks. In the IEEE International Conference on Radar Communication and Computing (ICRCC), 2012, pp.18- 23. doi: 10.1109/ICRCC.2012.6450540.

XVIII. T. Girici and A. C. Kazez, “Energy efficient routing with mutual information accumulation,” in Proc. 10th Int. Symp. Modeling Optim. Mobile, Ad Hoc Wireless Netw. (WiOpt), May 2012, pp. 425–430.

XIX. Y. Yang and J. Wang, “Design guidelines for routing metrics in Multi-hop wireless networks,” in Proc. IEEE INFOCOM, Apr. 2008, pp. 1615–1623.

XX. Yu, J.Y., Chong, P.H.J.: A survey of clustering schemes for mobile ad hoc networks. IEEE Commun. Surv. Tutorials 7(1), 32–48 (2005).

XXI. Z. Yang and A. Høst-Madsen, “Routing and power allocation in asynchronous Gaussian multiple-relay channels,” EURASIP J. Wireless Commun. Netw., vol. 2006, no. 2, p. 35, 2006.

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Abstract:

Soft-learning courses are sought-after as well as late. The need to examine understudy's presentation and anticipating their exhibition is expanding alongside it. With the developing notoriety of instructive innovation, different information digging calculations appropriate for anticipating understudy execution have been surveyed. The best calculation is based on the idea of the forecast that the staff needs to make. As the measurement of understudy information broadens the need to address and manage the complexities of the information connection, it is a test for the discovery of the understudy at risk of being short-lived.  In this paper covers the ID3 and C4.5 algorithms used for Predictive Analytics on understudy's presentation and Big Data with cloud.

Keywords:

Soft-Learning Techniques,Machine Learning Approach,Basics of Predictive Analytics,Decision Tree Techniques (C4.5 and ID3),Big Data,

Refference:

I. A. M.Shahiri, W. Hussain and N. A. Rashid. “A Review on Predicting Student’s Performance using Data Mining Techniques”, Procedia Computer Science, vol. 72, pp. 414-422, 2015.
II. B. Logica and R. Magdalena, “Using Big Data in the Academic Environment”, Procedia Economics and Finance, vol. 33, pp. 277-286, 2015.
III. C. T. Tsai, et. al., “Exchanging course content mechanism for Moodle LMS”,In: Proc. of International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery, Huangshan, China, IEEE, pp. 464-467, 2010.
IV. D. Clanfield and J.Sivell, “Cooperative learning & social change: selected writings of CélestinFreinet. Our Schools: Canada. Firdausiah Mansur, AndiBesse, Yusof, Norazah& Othman, Mohd. Shahizan. (2011). Analysis of social learning network for Wiki in Moodle e-learning.
V. E. A. Kareem and M. G. Duaimi, “Improved Accuracy for Decision Tree Algorithm Based on Unsupervised Discretization”, International Journal of Computer Science and Mobile Computing, vol. 3, no. 6, pp. 176-183, Jun. 2014.
VI. H. Chauhan and A. Chauhan, “Implementation of decision tree algorithm C4.5”, International Journal of Scientific and Research Publications, vol. 3, no. 10, pp. 1-3, Oct. 2013.
VII. H. Gulati, “Predictive Analytics Using Data Mining Technique”,In: Proc. of 2nd International Conference on Computing for Sustainable Global Development, New Delhi, India, IEEE, 2015.
VIII. J. Han and M.Kamber, “Data Mining Conceptsandits Techniques”, Morgan Kauffmann Publishers, 2011. DOI: https://doi.org/10.1016/C2009-0-61819-5
IX. K. Kinley, “Faculty and students’ awareness and challenges of e-learning in a college of education”, Journal of the International Society for Teacher Education, vol. 14, no. 1, pp. 27-33, 2010.
X. M. A. Al-Barrak and M. Al-Razgan, “Prediction of Student’s Final GPA implementing Decision Trees: A Case Study”, International Journal of Information and Education Technology, vol. 6, no. 7, July 2016.
XI. M. G. M. Mohan, S. K. Augustin and V. S. K. Roshni,“A Big Data Approach for Classification and Prediction of Student Result Using Map Reduce”,IEEE Recent Advances in Intelligent Computational Systems, Trivandrum, India, IEEE, 2015.
XII. W. Dai and W. Ji, “Implementing Map Reduce with C4.5 Decision Tree Algorithm”, Journal of Database Theory and Application, vo. 7, no. 1, pp. 49-60, 2014.

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Abstract:

Since forty years of computing history, cloud computing has made revolutionary changes. The daily life of human beings is completely depended on this advancement. Data centres are the backbone for the cloud computing. During the time of peak hours, load will be heavy on data center. Load balancing is needed. It provides better services to the end-user. Existing load balancing algorithms have their drawbacks. Hybrid algorithm approach is also a way to balance the load in cloud computing. Many efforts are made by several researchers in this direction. Combination of Round robin, Throttled, Equally Spread Current Execution, and Artificial Bee Colony Optimization algorithms as a hybrid algorithm (RTEAH) has shown improved results, hence it can be considered. 

Keywords:

Cloud computing,Distributed Computing,Virtual Machine,Data Center,Downtime,

Refference:

I. A. Addison and C. Andrews, “Low-Latency Trading in Cloud Environment ”, Conf. Comp. Science and Eng. and Embed. & Ubiquitous Computing, NewYork, USA, pp.272 282, 2019.

II. A. Sharma and S. K. Peddoju, “Response time-based load balancing in Cloud Comp.”, Conf. on Contrl, Instrumt., Comm. and Comp. Tech., Kanyakumari District, India,pp.1287 1293,2014.

III. Chen, X., “Decentralized Computation Offloading Game For Mobile Cloud Comp. ”, Decentralized Comp. Offload. Game for Mob.Cloud Comp. IEEE Trans. on Parl. and Dist. Sys., Vol. 26, No.4, pp. 974 983. 2015.

IV. K. Ha, P. Pillai,“The Impact of Mobile Multimedia Appli. on Data Center Consolidation”, IEEE Intl. Conf. on Cloud Eng.,California, USA, pp.166 176 , 2013.

V. Linthicum, D. S., “ Understanding Complex Cloud Patterns ” , IEEE Cloud Comp., Vol. 3,No. 1, pp.8 11, 2016.

VI. Mavrogeorgi, N., Gogouvitis, S., “ Dynamic Rule Based SLA Management in Clouds”. IEEE Sixth Intl. Conf. on Cloud Comp., Santa Clara, CA, USA, pp. 964 965, 2013.

VII. Rani, E., &Kaur, H., “ Study on fundamental usage of Cloud Simsimu. And algo. of resource allocation in cloud comp. ”, 8th Intl. Conf. on Comp., Communic. and Network. Tech., IEEE Conference, Delhi, India, pp.2 7,2017.

VIII. Ritu, S. Jain, “ A Trust Model in Cloud Computing Based on Fuzzy Logic ”,IEEE Intl. Conf. On Recent Trends InEle. Info. Comm. Tech., Bangalore, India, 47 52, 2016

IX. S. A. Narale and P. K. Butey, “IEEE Intl. Conf. 2nd Intl. Conf. on Inventive Comm. and Comp. Tech.”, Coimbatore, India, pp.1464 1467,2018.

X. Shakeel, F., & Sharma, S. “ Green cloud computing: A review on efficiency of data centres and virtualization of servers”, Intl. Conf. on Comp., Comm. and Automation ,Greater Noida, India,pp.1264 1267,2017.

XI. Wang, Z., Zeng, J., “ Cloud Auditor: A Cloud Auditing Framework Based on Nested Virtualization”, IEEE 3rd Intl Conf. on Cyber Security and Cloud Comp., Beijing, China, pp.50 53, 2016.

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Abstract:

The estimation of the regular regression model requires several assumptions to be satisfied such as "linearity". One problem occurs by partitioning the regression curve into two (or more) parts and then joining them by threshold point(s). This situation is regarded as a linearity violation of regression. Therefore, the multiphase regression model is received increasing attention as an alternative approach which describes the changing of the behavior of the phenomenon through threshold point estimation. Maximum likelihood estimator "MLE" has been used in both model and threshold point estimations. However, MLE is not resistant against violations such as outliers' existence or in case of the heavy-tailed error distribution. The main goal of this paper is to suggest a new hybrid estimator obtained by an ad-hoc algorithm which relies on data driven strategy that overcomes outliers. While the minor goal is to introduce a new employment of an unweighted estimation method named "winsorization"  which is a good method to get robustness in regression estimation via special technique to reduce the effect of the outliers. Another specific contribution in this paper is to suggest employing "Kernel" function as a new weight (in the scope of the researcher's knowledge).Moreover, two weighted estimations are based on robust weight functions named "Cauchy" and "Talworth". Simulations have been constructed with contamination levels (0%, 5%, and 10%) which associated with sample sizes (n=40,100). Real data application showed the superior performance of the suggested method compared with other methods using RMSE and R² criteria.

Keywords:

Data-driven strategy,kernel,multiphase regression,robustness,threshold point,winsorization,

Refference:

I. Acitas, S. and Senoglu, B., (2020). “Robust change point estimation in two-phase linear regression models: An application to metabolic pathway data”. Journal of Computational and Applied Mathematics, Vol. 363, pp 337–349.

II. Chen, C.W.S., Chan, J. S.K., Gerlach, R., and Hsieh, W. Y.L., (2011). “A comparison of estimators for regression models with change points”. Stat Comput, Vol. 21, pp 395–414.

III. Dehnel, G., (2016). “M-Estimators in Business Statistics”.Statistics in Transition new series, Vol. 17, No. 4, pp 1–14.

IV. Fearnhead, P. and Rigaill, G., (2017). “Changepoint Detection in the Presence of Outliers”.Journal of the American Statistical Association, Vol. 114, No. 525, pp 169-183.

V. Ganocy, S. J. and Sun, J., (2015). “Heteroscedastic Change Point Analysis and Application to Footprint Data”.Journal of Data Science, Vol. 13, pp 157-186.

VI. Hernandez, E.L., (2010). ” Parameter Estimation in Linear-LinearSegmentedRegression. M.Sc. thesis, Department of Statistics, Brigham Young University,

VII. Julious, S.A., (2001). “Inference and Estimation in a Change point Regression Problem”. The Statistician, Vol. 50, Part 1, pp 51-61.

VIII. Klotsche, J. and Gloster, A. T., (2012). “Estimating a Meaningful Point of Change:A Comparison of Exploratory Techniques Based on Nonparametric Regression”. Journal of Educational and Behavioral Statistics Vol. 37, pp 579-600.

IX. Liu, Z., (2011). “Empirical Likelihood Method for Segmented Linear Regression”.Ph.D. Dissertation, Faculty of the Charles E. Schmidt, College of Science, Florida Atlantic University, USA.

X. Muggeo, V. M. R., (2003). “Estimating regression models with unknown break-points”, Statist.Med., Vol. 22, pp 3055–3071.

XI. Muggeo, V. M. R., (2017).”Interval estimation for the breakpoint in segmented regression: a smoothed score-based approach”. Aust. N. Z. J. Stat. Vol. 59, No.3, pp 311–322.

XII. Pusparum, M., (2017). “Winsor Approach in Regression Analysis with Outlier”.Applied Mathematical Sciences, Vol. 11, No. 41, pp 2031-2046.

XIII. Ryan, S.E. and Porth, L. S., (2007). “A Tutorial on the Piecewise Regression Approach Applied to Bedload Transport Data”. General Technical Report RMRS-GTR-189. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 41 p.

XIV. Whitehead, N., Hill, H.A., Brogan, D.J. and Blackmore-Prince, C., (2002). Exploration of threshold analysis in the relation between stressful life events and preterm delivery”. American Journal of Epidemiology Vol. 155, pp 117–124.

XV. Yale, C. and Forsythe, A.B., (1976). “Winsorized Regression”, Technometrics, Vol.18 No.3, pp 291-300.

XVI. Zhang, F., Li, Q.,(2017).”Robust bent line regression”. J. Statist. Plann. Inference, Vol.185,pp41-55.

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Abstract:

Development has been achieved to the road vehicle industry so as to manufacture types of automobiles with high ride passenger comfort. One requirement needed to obtain good quality of drive handling efficient operating characteristics of the model system which is equipped for road automobile. The suspension systems are mainly used to restrain externally disturbance from affecting ride tripper rest .Our research had been presented (Fuzzy PID) control for investigate active road vehicle suspension controller. The Fuzzy logic function is used to improve tuning and performance the gain of the road vehicle suspension with PID controller. Undesired displacements of the road vehicle body during dynamic process are presented and compared for two road vehicle models with PID controller and FUZZY PID controller. The final simulated results show the influence of the active road vehicle suspension controller on the efficiency of ride road vehicle handling however raising the strength and execute slick driving. Then, a robust control is executed to optimize these operating characteristics of the suspension systems to improve the road vehicle.

Keywords:

Fuzzy PID controller,active suspension,quarter vehicle model,MATLAB simulation,

Refference:

I. Alleyne, A., & Hedrick, J. K. (1995). Nonlinear adaptive control of active suspensions. IEEE transactions on control systems technology, 3(1), 94-101.
II. Agharkakli, A., Sabet, G. S., & Barouz, A. (2012). Simulation and analysis of passive and active suspension system using quarter car model for different road profile. International Journal of Engineering Trends and Technology, 3(5), 636-644.‏
III. Changizi, N., & Rouhani, M. (2011). Comparing PID and fuzzy logic control a quarter car suspension system. The journal of mathematics and computer science, 2(3), 559-564.‏
IV. Dukkipati, R. V. (2007). Solving vibration analysis problems using MATLAB. New Age International.‏
V. Ghasemalizadeh, O., Taheri, S., Singh, A., & Goryca, J. (2014). Semi-active Suspension Control using Modern Methodology: Comprehensive Comparison Study. arXiv preprint arXiv:1411.3305.‏
VI. Gordon, T. J., Marsh, C., & Milsted, M. G. (1991). A comparison of adaptive LQG and nonlinear controllers for vehicle suspension systems. Vehicle System Dynamics, 20(6), 321-340.‏
VII. Gysen, B. L., Paulides, J. J., Janssen, J. L., & Lomonova, E. A. (2009). Active electromagnetic suspension system for improved vehicle dynamics. IEEE Transactions on Vehicular Technology, 59(3), 1156-1163.‏
VIII. Hatch, M. R. (2000). Vibration simulation using MATLAB and ANSYS. CRC Press.‏
IX. Tiwari, P., & Mishra, G. (2014). Simulation of quarter-car model. JOSR Journal of Mechanical and Civil Engineering, 11(2), 85-88.‏
X. Yagiz, Nurkan, and Yuksel Hacioglu. “Backstepping control of a vehicle with active suspensions.” Control Engineering Practice16.12 (2008): 1457-1467.‏

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Abstract:

Tracking wheeled mobile robot control is a complicated problem encounter in robotic science. Many issues occurring that are affecting the control of nonlinear robot in actual application. The applications would include uncertainties parameter and internal disturbances. The factors restrict the study of mobile robot tracing control.  In this study we modified adaptive sliding mode controller for nonholonomic wheeled mobile robot. The kinematic controller used to produce the desired tracking velocities as input term after that used suggested of the dynamic controller to overcome the uncertainties, disturbance and chattering effect of the sliding controller. according to stability of Lyapunov, the final controlled system is proven to be globally asymptotically stable. Proposed control system is verified and validated using MATLAB\SIMULINK to track the required WMR trajectory. A comparison between PI adaptive sliding mode and PI sliding mode is done. Simulated result portrays that in the presence of continuous disturbances and uncertainties and presented work with very good accuracy and fast error convergence and robustness.

Keywords:

Wheeled mobile robot,kinematic control,dynamic control,sliding mode control,adaptive control,

Refference:

I. A .Bloch, & Drakunov, S. (1994, December). Stabilization of a nonholonomic system via sliding modes. In Proceedings of 1994 33rd IEEE Conference on Decision and Control (Vol. 3, pp. 2961-2963). IEEE.‏
II. B S .Park., Yoo, S. J., Park, B J.., & Choi, Y. H. (2008). Adaptive neural sliding mode control of nonholonomic wheeled mobile robots with model uncertainty. IEEE Transactions on Control Systems Technology, 17(1), 207-214.‏
III. B .d’Andréa-Novel., Campion, G., & Bastin, G. (1995). Control of nonholonomic wheeled mobile robots by state feedback linearization. The International journal of robotics
IV. BeloboMevo, B., Saad, M. R., & Fareh, R. (2018, May). Adaptive Sliding Mode Control of Wheeled Mobile Robot with Nonlinear Model and Uncertainties. In 2018 IEEE Canadian Conference on Electrical & Computer Engineering (CCECE) (pp. 1-5). IEEE.‏
V. D .Young, K., Utkin, V. I., & Ozguner, U. (1996, December). A control engineer’s guide to sliding mode control. In Proceedings. 1996 IEEE International Workshop on Variable Structure Systems.-VSS’96- (pp. 1-14). IEEE.‏
VI. 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.‏
VII. D. Chwa,Seo, J. H., Kim, P., & Choi, J. Y. (2002, May). Sliding mode tracking control of nonholonomic wheeled mobile robots. In Proceedings of the 2002 American Control Conference (IEEE Cat. No. CH37301) (Vol. 5, pp. 3991-3996). IEEE.‏
VIII. F. Hamerlain, K .Achour., T. Floquet., & Perruquetti, W. (2005, December). Higher order sliding mode control of wheeled mobile robots in the presence of sliding effects. In Proceedings of the 44th IEEE Conference on Decision and Control (pp. 1959-1963). IEEE.‏

IX. G. Klančar, Matko, D., & Blažič, S. (2009). Wheeled mobile robots control in a linear platoon. Journal of Intelligent and Robotic Systems, 54(5), 709-731.‏ research, 14(6), 543-559.‏
X. Gu, D., & Hu, H. (2002). Neural predictive control for a car-like mobile robot. Robotics and Autonomous Systems, 39(2), 73-86.‏
XI. H .Mehrjerdi, & M. Saad, (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.‏
XII. Ibrahim, A. E. S. B. (2016). Wheeled Mobile Robot Trajectory Tracking using Sliding Mode Control. JCS, 12(1), 48-55.‏
XIII. Ibari, Benaoumeur, et al. “Backstepping approach for autonomous mobile robot trajectory tracking.” Indonesian Journal of Electrical Engineering and Computer Science 2.3 (2016): 478-485.‏
XIV. J. Wu,Xu, G., & Yin, Z. (2009). Robust adaptive control for a nonholonomic mobile robot with unknown parameters. Journal of Control Theory and Applications, 7(2), 212-218.‏
XV. Kanayama, Y., Kimura, Y., Miyazaki, F., & Noguchi, T. (1990, May). A stable tracking control method for an autonomous mobile robot. In Proceedings., IEEE International Conference on Robotics and Automation (pp. 384-389). IEEE.‏
XVI. Liyong, Y., & Wei, X. (2007, July). An adaptive tracking method for non-holonomic wheeled mobile robots. In 2007 Chinese Control Conference (pp. 801-805). IEEE.‏
XVII. M .Yang, J., & H. Kim, J. (1999). Sliding mode control for trajectory tracking of nonholonomic wheeled mobile robots. IEEE Transactions on robotics and automation, 15(3), 578-587.‏
XVIII. Normey-Rico, Julio E., et al. “Mobile robot path tracking using a robust PIDcontroller.” Control Engineering Practice 9.11 (2001): 1209-1214.
XIX. R .Rashid., Elamvazuthi, I., Begam, M., & Arrofiq, M. (2010). Fuzzy-based navigation and control of a non-holonomic mobile robot. arXiv preprint arXiv:1003.4081.‏
XX. R. Dhaouadi., & Hatab, A. A. (2013). Dynamic modelling of differential-drive mobile robots using lagrange and newton-euler methodologies: A unified framework. Advances in Robotics & Automation, 2(2), 1-7.‏
XXI. R .Fierro., & Lewis, F. L. (1997). Control of a nonholomic mobile robot: Backstepping kinematics into dynamics. Journal of robotic systems, 14(3), 149-163.‏
XXII. T .Fukao., Nakagawa, H., & Adachi, N. (2000). Adaptive tracking control of a nonholonomic mobile robot. IEEE transactions on Robotics and Automation, 16(5), 609-615.‏

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Abstract:

The performance of the solar updraft tower system (SUTS) investigates numerically by comparing between two quarters circular thermal solar collectors (with and without porous absorber plate). The porous copper foam 10 PPI and porosity 0.9 is used as an absorber plate. The present work aims to study the effect of variation the heights of the air inlet (3, 5, and 8) cm respectively utilized conventional flat and porous metal foam absorber plate. The physical quantities inside flat and porous absorber plate are simulated. A set of assumptions are adopted such as a steady state condition, three dimensional, Darcy  and energy equations. The numerical simulation are approximated k- ϵ turbulent model by a Re-Normalization Group (RNG) and discrete ordinates (DO) radiation model equations. The numerical study is analyzed by using ANSYS FLUENT program (version 18.2) to solve the governing equations. The results showed that variation in the heights of the air inlet with  the presence of the porous absorber plate is more effective than the conventional flat plate on the performance of the SUTS. The maximum performance of the system is predicted with the height of the air inlet of 3 cm by using the porous metal foam absorber plate

Keywords:

Solar tower,porous metal foam,performance of the solar tower,ANSYS FLUENT,renewable energy,

Refference:

I. Abdulcelil BUĞUTEKİN. An experimental investigation of the effect of periphery height and ground temperature changes on the solar chimney system. J. of Thermal Science and Technology, Isı Bilimi ve Tekniği Dergisi. 2012; 32 (1): 51-58.

II. Ali Ghaffari and Ramin Mehdipour. Modeling and Improving the Performance of Cabinet Solar Dryer Using Computational Fluid Dynamics. Int. J. Food Eng. 2015; 11(2): 157–172.

III. Ali, S.A.G. Study the effect of upstream riblet on wing- wall junction. M.Sc, Thesis, University of Technology, Iraq. 2011.

IV. Calmidi V. V., and Mahajan R. L. Forced convection in high porosity metal foams. Journal of Heat Transfer, 2000; 122 (8): Õ 557.

V. Chang Xu, Zhe Song, Lea-der Chen, Yuan Zhen. Numerical investigation on porous media heat transfer in a solar tower receiver., Renewable Energy, 2011, Vol. 36, pp. 1138-1144.

VI. Dr.Hamza D and Salman Al.Obaidi. Expermental research to evaluate solar chimney power plant in Baghdad/Iraq and determine its performance. SYLWAN.English Edition, Printed in Poland, 2017.

VII. Elizabeth Marie Heisler. Exploring alternative designs for solar chimneys using computational fluid dynamics. Thesis, the Virginia Polytechnic Institute and State University, 2014.

VIII. Guo C.X., Zhang W.J., and Wang D.B. Numerical investigation of heat transfer enhancement in latent heat storage exchanger with paraffin/graphite foam. 10th Int. Conf. on Heat Transfer, Fluid Mechanics and Thermodynamics 2014; July: 14 – 26.

IX. Holman, J.P.,” Heat Transfer”, 10th Edition, McGraw-Hill Higher Education, 2010.

X. Ming T., Liu W., and Xu G. Analytical and numerical investigation of the solar chimney power plant systems. International Journal of Energy Research, 2006; 30 (9) : 861–873.

XI. M.M. Rahman, S. Mojumder, S. Saha, S.Mekhilef, R. Saidur. Effect of solid volume fraction and tilt angle in a quarter circular solar thermal collectors filled with CNT–water nanofluid. International Communications in Heat and Mass Transfer, 2014; 57: 79–90.

XII. Prof. Dr. Arkan khilkhal Husain, Asst.Prof.Dr Waheeds Shate Mohammad, and Lecturer. Abbas JassimJubear. Numerical simulation of the influence of geometric parameter on the flow behavior in a solar chimney power plant system. Journal of Engineering, 2014; 20 (8): 88-108.

XIII. Richard A. H. Performance evaluation of a solar chimney power plant. M.Sc. Thesis, Mechanical Engineering Department, University of Stellenboch, 2000.

XIV. Saraswat A., Verma A., Khandekar S., and Das M.K. Latent heat thermal energy storage in a heated semi-cylindrical cavity: experimental results and numerical validation. Proceedings of the 23rd National Heat and Mass Transfer Conference, India, IHMTC, 862, 2015.

XV. Sarmad A. Abdul Hussein and Dr. Mohammed A. Nima. Numerical analysis of solar tower system utilized with flat plate and porous absorber. Journal of Mechanical Engineering Research and Developments (JMERD), 2019; 42(5): 216-223.

XVI. Wei Chen and Man Qu. Analysis of the heat transfer and airflow in solar chimney drying system with porous absorber. Renewable Energy, 2014; 63: 511-518.

XVII. Wei Chen, Wei Liu. Numerical analysis of the heat transfer in solar composite wall collector system with porous absorber. Journal of Solar Energy, 2005; vol.26, Journal Issue 6, pp.882-6.

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Abstract:

The signals of the electroencephalogram (EEG) have been applied for detecting as well as registering the electrical efficiency in the human brain.  In this paper, EEG signals have been utilized for human identification. The reliability regarding a lot of biometric systems aren’t adequate due to the possibility of being copied or faked. Thus the brain signatures have been applied as potential biometric identifiers. The aim of this paper is to apply sample entropy and graph entropy as feature extraction. While in classification Support vector machine (SVM) and K-Nearest Neighbor (KNN) have achieved. Machine Learning Repository (UCI) used as dataset. Experimental consequences on this dataset demonstrate substantial enhancement in the classification accuracy as compared with other testified results in the literature. Results showed that the classification accuracy with SVM for biometric identification is 90.8% while with K-NN is 83.7% .Our study using13channels to feature extraction.

Keywords:

Electroencephalogram (EEG),Support vector machine,K-Nearest Neighbor,Machine learning,

Refference:

I. Blake, C., and C. J. Merz. “UCI repository of machine learning databases, Department of Information and Computer Science, University of California, Irvine, CA, 1998.” URL:< http://www. archive. ics. uci. edu/ml (2015).
II. Chong Yeh Sai, Norrima Mokhtar, Hamzah Arof, Paul Cumming, Masahiro Iwahashi,” Automated Classification and Removal of EEG Artifacts With SVM and Wavelet-ICA”, IEEE Journal of Biomedical and Health Informatics , Volume: 22 , Issue 3 , Page(s): 664 – 670 , MAY 2018.
III. Damastuti Natalia, Aisjah Aulia Siti , Masroeri Agoes A,Cassification of Ship-Based Automatic Identification Systems Using K- Nearest Neighbors”International Seminar on Application for Technology of Information and Communication, IEEE, pp 331-335, 2019.
IV. Deming Zhang, Guodong Yin, Weichao Zhuang, Xianjian Jin ” Recognition Method for Multi-Class Motor Imagery EEG Based on Channel Frequency Selection “IEEE, 2018 .
V. Dharani, M., and M. Sivachitra. “Motor imagery signal classification using semi supervised and unsupervised extreme learning machines.” 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS). IEEE, 2017.
VI. Duaa AIQattan, Francisco Sepulveda,” Towards Sign Language Recognition UsingEEG-Based Motor Imagery Brain Computer Interface “5th International Winter Conference on Brain-Computer Interface (BCI), I EEE 2017, DOI: 10.1109/IWW-BCI.2017.7858143
VII. Gilbert, S., Dumontheil, I., Simons, J., Frith, C., Burgess, P.: Wandering minds: the default network and stimulus-independent thought. Sci. Mag. 315(5810), 393–395 ,2007

VIII. G. Paul and J. Irvine, “Fingerprint authentication is here but are we ready for what it brings?,” IEEE Consumer Electronics Magazine, 2015.
IX. Helma, Christoph, Eva Gottmann, and Stefan Kramer. “Knowledge discovery and data mining in toxicology.” Statistical methods in medical research 9.4 (2000): 329-358.
X. Kucyi, Aaron, et al. “Spontaneous default network activity reflects behavioral variability independent of mind-wandering.” Proceedings of the National Academy of Sciences 113.48 (2016): 13899-13904..
XI. Middendorf, M., Mcmillan, G., Calhoun, G., Jones, K.S.: Brain computer interfaces based on steady state visual evoked response. IEEE Trans. Rehabil. Eng. 8(2), 211–214 ,2000.
XII. Minshew, N., Keller, T.: The nature of brain dysfunction in autism: functional brain imaging studies. Curr. Opin. Neurol. 23, 124–130 , 2010
XIII. Mustafa Turan Arslan, Server Göksel Eraldemir, Esen” Channel Selection from EEG Signals and Application of Support Vector Machine on EEG Data” IEEE, 2017.
XIV. Nivedha R, Brinda M, Devika Vasanth, Anvitha M and Suma K.EG based Emotion Recognition using SVM and PSO ” IEEE, 2017.
XV. Rabie A. Ramadan and Athanasios V. Vasilakos,” Brain Computer Interface: Control Signals Review “Neurocomputing, Volume 223, Pages 26–44. , 5 February 2017.
XVI. Raihan Khalil, Abdollah Arasteh, Ajay Krishno Sarkar “EEG BasedBiometrics Using Emotional Stimulation Data ” IEEE Region 10 Humanitarian Technology Conference (R10-HTC), Dhaka, Bangladesh , 21 – 23 Dec 2017.
XVII. Rihman, J S, and J R Moorman. “Physiological Time-Series Analysis UsingApproximate Entropy and Sample Entropy.” American journal of physiology. Heart and circulatory physiology 278(6): 2000,. http://www.ncbi.nlm.nih.gov/pubmed/10843903.,
XVIII. Syed Anwar, Tahira Batool, Muhammad Majid ,” Event Related Potential (ERP)based Lie Detection using a Wearable EEG headset ” IEEE 16th International Bhurban Conference on Applied Sciences & Technology (IBCAST), 2019.
XIX. Sivachitra, M., and S. Vijayachitra. “Planning and Relaxed State EEG Signal Classification Using Complex Valued Neural Classifier for Brain Computer Interface.” PP 8-11, 2015.
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XXI. S.Dhivya, A.Nithya, ” A REVIEW ON MACHINE LEARNING ALGORITHM FOR EEG SIGNAL ANALYSIS ” Proceedings of the 2nd International conference on Electronics, Communication and Aerospace Technology, 2018.
XXII. Scholkopf, B., Sung, K., Burges, C., Girosi, F., Niyogi, P., Poggio, T.,Vapnik, V.,“Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifers”, N142,1996.
XXIII. V. Sankara Narayanan, R. Elavarasan, C.N. Gnanaprakasam, N. Sri Madhava Raja*, R. Kiran Kumar ” Heuristic Algorithm based Approach to Classify EEG Signals into Normal and Focal” IEEE International Conference on System, Computation, Automation and Networking (ICSCA) page 1-5 , 2018 DOI: 10.1109/ICSCAN.2018.8541180.
XXIV. WAEL H. KHALIFA, MOHAMED I. ROUSHDY, ABDEL-BADEEH M. SALEM, ” User Identification System Based on EEG Signals” The Sixth International Conference on Intelligent Computing and Information Systems (ICICIS ).pp 14-16, 2013, Cairo, Egypt ,2013.
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XXVI. Zhu, Guohun, Yan Li, Peng (Paul) Wen, and Shuaifang Wang“Analysis of Alcoholic EEG Signals Based on Horizontal Visibility Graph Entropy.” (1–4): 19–25,2014.

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Abstract:

This study proposes an analytical solution method for investigating vibrational characteristics for a tubular cylindrical shell of a finite-length and bares a circumferential part-through fissure. The effect of different  parameters i,e, length, depth and the fissure's location, on the vibrational characteristics, were also investigated. The equations for motion, that are founded on the classical shell theory  for the fissured shell were  transformed into simpler equations via Donell–Mushtari–Vlasov (DMV) hypothesis. The equivalent bending stiffness of the shell (D) was calculated by an exponential function while taking into consideration the effect of the fissure. The analytical approach gave us results for a structure with simply supported (S-S) at both ends boundary conditions. The natural frequencies were obtained by solving the general equations on a program built for "MATLAB" SOFTWARE. The results that were obtained from the suggested modal were confirmed by the use of a modal created by ANSYS APDL ver.15 in addition to the results that were attained from literature. There was a passable agreement between the results of the analytical and FE model. The results set forth that as fissure's parameters, length & depth, Increasing them reduces the natural frequency, In addition to this, the natural frequency will also decrease if the fissure is located in the middle of the shell is larger than  if it were in other locations.

Keywords:

Cylindrical shell,vibration characteristics,Part-Through fissure,natural frequency,

Refference:

I. Aruna Rawat, Vasant Matsagar, and A K Nagpal, “Finite element analysis of thin circular cylindrical shells,” Proc Indian NatnSciAcad 82 June pp. 351, 2016.

II. A. Roytman, and O. Titova, “An analytical approch to determining the dynamic characteristics of a cylindrical shell with closing cracks,” J. Sound Vib., Vol. 254 pp. 379–386, 2002.

III. A. Vaziri, and H. E. Estekanchi, “Buckling of cracked cylindrical thin shells under combined internal pressure and axial compression,” Thin-Walled Structures, Vol.44, pp. 141–151, 2006.

IV. C. Wang, and J.C.S. Lai, “Prediction of natural frequencies of finite length circular cylindrical shells,” Applied Acoustics Vol. 59, pp. 385-400, 2000.

V. H. J. Abbas, M. J. Jweeg, Muhannad Al-Waily, Abbas Ali Diwan “Experimental Testing and Theoretical Prediction of Fiber Optical Cable for Fault Detection and Identification’ Journal of Engineering and Applied Sciences, Vol. 14, No. 02, pp. 430-438, 2019.

VI. J.L. Sewall, and E. C. Naumann, “An experimental and analytical vibration study of thin cylindrical shells with and without longitudinal stiffeners,” NASA TN D-4705, 1968.

VII. J. Xin, J. Wang, J. Yao, and Q. Han, “Vibration, buckling and dynamic stability of a cracked cylindrical shell with time varying rotating speed,” Mechanics based Design of Structures and Machines, Vol. 39, pp. 461–490, 2011.

VIII. K.H. Ip, and P.C. Tse, “locating damage in circular cylindrical composite shells based on frequency sensitivities and mode shapes,” Eur. J. Mech., Vol. 21, pp. 615–628, 2002.

IX. K. Moazzeza, H. SaeidiGoogarchin, and S.M.H. Sharifi, “Natural frequency analysis of a cylindrical shell containing a variably oriented surface crack utilizing Line-Spring model,” thin-walled structures, Vol. 125, pp. 63–75, 2018.

X. K. Nikpour, “Diagnose of axisymmetric cracks in orthotropic cylindrical shells by vibration measurement,” J. Compos. Sci. Technol. 39, pp. 45–61, 1990.

XI. L. Sarker, Y. Xiang, X.Q. Zhu, and Y.Y. Zhang, “Damage detection of circular cylindrical shells by Ritz method and wavelet analysis,” Electronic Journal of Structural Engineering, Vol. 14, pp. 62–74, 2015.

XII. Mohsin Abdullah Al-Shammari “Experimental and FEA of the Crack Effects in a Vibrated Sandwich Plate’ Journal of Engineering and Applied Sciences, Vol. 13, No. 17, pp. 7395-7400, 2018.

XIII. Muhannad Al-Waily, Maher A.R. Sadiq Al-Baghdadi, RashaHayder Al-Khayat “Flow Velocity and Crack Angle Effect on Vibration and Flow Characterization for Pipe Induce Vibration’ International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS, Vol. 17, No. 05, pp.19-27, 2017.

XIV. M. I. Friswell and J. E. T. Penny, “Crack Modeling for Structural Health Monitoring, Structural Health Monitoring,” Vol. 1(2), pp. 139-148, 2002.

XV. Muhsin J. Jweeg, Ali S. Hammood, Muhannad Al-Waily “A Suggested Analytical Solution of Isotropic Composite Plate with Crack Effect,” International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS, Vol. 12, No. 05, 2012.

XVI. Muhsin J. Jweeg, E. Q. Hussein, K. I. Mohammed “Effects of Cracks on the Frequency Response of a Simply Supported Pipe Conveying Fluid’ International Journal of Mechanical & Mechatronics Engineering IJMME-IJENS, Vol. 17, 05, 2017.

XVII. M. Javidruzi, A. Vafai, J.F. Chen, and J.C. Chilton, “Vibration, buckling and dynamic stability of cracked cylindrical shells,” Thin-Walled Struct., Vol. 42, pp. 79–99, 2004.

XVIII. R. D. Cook, ”Concepts and applications of finite element analysis,” John Wiley and Sons, 2nd edn. New York, 1981.

XIX. S. Moradi, and V. Tavaf, “Crack detection in circular cylindrical shells using differential quadrature method,” Int. J. of Pres. Vessels and Pip., pp. 209–216, 2013.

XX. Singiresu S. Rao, Vibration of Continuous Systems 15 John Wiley and Sons, pp. 541–587, 2007.

XXI. T. Yin, and H.F. Lam, “Dynamic analysis of finite-length circular cylindrical shells with a circumferential surface crack,” J. Eng. Mech., Vol.139 pp. 1419–1434, 2013.

XXII. V. V. Bolotin, “The dynamic stability of elastic systems,” Holden-Day, 1964.

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Abstract:

This paper presents the application of automation techniques in different areas of aerospace industry such as; 4D printing prospects, automated aircraft tracking, laser marking, automated fiber placement, acoustic emission detection, analysis of the aircraft carrier for landing task, flexible and automated production, function allocation between automation and human pilot, automated selection and assembly, autonomous control reconfiguration, sensor monitoring during the process. In this study, we have explored the existing automation techniques and also find out a better way to implement in the future to minimize the human efforts and time. These technologies based on automation and artificial intelligence that will help us to make the process more efficient, stable and flexible. Moreover, aspects of the changeability and adaptiveness of the automation system have to be considered. The aim of this study to identify the opportunities and scope for future research trends in the field of aerospace industries.

Keywords:

Automation,Robotics,Artificial Intelligence,4-D Printing,3-D printing,Aerospace,

Refference:

I. Beeco JA, Joyce D. Automated aircraft tracking for park and landscape planning. Landscape and Urban Planning. 2019;186:103–11.

II. Centobelli P, Teti R, Andersen LA. Sensor monitoring during tack welding of aerospace components. Procedia CIRP. 2015;33:327–32.

III. Denkena B, Schmidt C, Weber P. Automated fiber placement head for manufacturing of innovative aerospace stiffening structures. Procedia Manufacturing. 2016;6:96–104.

IV. Dammann M, Schüppstuhl T. Automated selection and assembly of sets of blades for jet engine compressors and turbines. Procedia Manufacturing. 2018;16:53–60.

V. Eschena H, Harnischa M, Schüppstuhla T. Flexible and automated production of sandwich panels for aircraft interior. Procedia Manufacturing. 2018;18:35–42.

VI. Gao Y, Liu Y, Wang C, Li X, Ou G. Design and evaluation of a high performance distributed expert system (HPDES) for aerospace ground verification system. Procedia Computer Science. 2012;9:1380–9.

VII. Holford KM, Eaton MJ, Hensman JJ, Pullin R, Evans SL, Dervilis N, et al. A new methodology for automating acoustic emission detection of metallic fatigue fractures in highly demanding aerospace environments: An overview. Progress in Aerospace Sciences. 2017;90:1–11.

VIII. Hess RA. Analysis of the Aircraft Carrier Landing Task, Pilot+ Augmentation/Automation. IFAC-PapersOnLine. 2019;51(34):359–65.

IX. Han W, Bai X, Xie J. Assessment Model of the Architecture of the Aerospace Embedded Computer. Procedia Engineering. 2015;99:991–8.

X. Idris H, Enea G, Lewis TA. Function Allocation between Automation and Human Pilot for Airborne Separation Assurance. IFAC-PapersOnLine. 2016;49(19):25–30.

XI. Jun X, Junjia H, Chunyan Z. Dynamic analysis of contact bounce of aerospace relay based on finite difference method. Chinese Journal of Aeronautics. 2009;22(3):262–7.

XII. Möller C, Schmidt HC, Koch P, Böhlmann C, Kothe S-M, Wollnack J, et al. Machining of large scaled CFRP-Parts with mobile CNC-based robotic system in aerospace industry. Procedia manufacturing. 2017;14:17–29.

XIII. Ntouanoglou K, Stavropoulos P, Mourtzis D. 4D Printing Prospects for the Aerospace Industry: a critical review. Procedia Manufacturing. 2018;18:120–9.

XIV. Pei C, Zongji C, Rui Z, Chen W. Autonomous control reconfiguration of aerospace vehicle based on control effectiveness estimation. Chinese Journal of Aeronautics. 2007;20(5):443–51.

XV. Velotti C, Astarita A, Leone C, Genna S, Minutolo FMC, Squillace A. Laser marking of titanium coating for aerospace applications. Procedia CIRP. 2016;41:975–80.

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Abstract:

In this present study, we have discussed the different types of waste materials from different industries like mechanical, electrical, electronics, automobiles, medical and agriculture industries. Moreover, industrial waste is one of the biggest problems in INDIA. Thereafter, the waste management process is required to recycle waste materials. The main problem is in the recycling process is to higher cost and time-consuming. The main aim of this study to identify the biomass converted material like pyrolysis of biomass, biomass-waste, gasification of harmful gases, carbon-based supercapacitor, silica, macadamia shell waste, lithium Sulphur battery and rise husk etc. from the different industries and try to convert that materials into a useful materials by using different types of conversion approaches. There are different types of conversion techniques are available like CVD, hydrothermal process, Thermolysis, Pyrolysis, combustion, and chemical treatments. However, this process applied on the basis of types of waste material. Moreover, in this study, we have discussed the different types of biomass converted waste materials and their conversion approaches.

Keywords:

Graphene,Waste,Bio precursors,Biomass,Glucose,Rice husk,Hemp,

Refference:

I. B. Ruiz, R. P. Girón, I. Suárez-Ruiz, and E. Fuente, “From fly ash of forest biomass combustion (FBC) to micro-mesoporous silica adsorbent materials,” Process Safety and Environmental Protection, vol. 105, pp. 164–174, 2017.

II. G. V. Nivea Raghavan, Sakthivel Thangavel, “A short review on preparation of graphene from waste and bioprecursors,” Applied Energy, vol. 7, pp. 246–254, 2017.

III. J. A. S. Costa and C. M. Paranhos, “Systematic evaluation of amorphous silica production from rice husk ashes,” Journal of Cleaner Production, vol. 192, pp. 688–697, 2018.

IV. K. Yang et al., “Biomass‐Derived Porous Carbon with Micropores and Small Mesopores for High‐Performance Lithium–Sulfur Batteries,” Chemistry–A European Journal, vol. 22, no. 10, pp. 3239–3244, 2016.

V. L. H. Nguyen and V. G. Gomes, “High efficiency supercapacitor derived from biomass based carbon dots and reduced graphene oxide composite,” Journal of Electroanalytical Chemistry, vol. 832, pp. 87–96, 2019.

VI. M. Chen et al., “Honeycomb‐like Nitrogen and Sulfur Dual‐Doped Hierarchical Porous Biomass‐Derived Carbon for Lithium–Sulfur Batteries,” ChemSusChem, vol. 10, no. 8, pp. 1803–1812, 2017.

VII. N. Zhou et al., “Silicon carbide foam supported ZSM-5 composite catalyst for microwave-assisted pyrolysis of biomass,” Bioresource technology, vol. 267, pp. 257–264, 2018.

VIII. N. Raghavan, S. Thangavel, and G. Venugopal, “A short review on preparation of graphene from waste and bioprecursors,” Applied Materials Today, vol. 7, pp. 246–254, 2017.

IX. R. Zhong and B. F. Sels, “Sulfonated mesoporous carbon and silica-carbon nanocomposites for biomass conversion,” Applied Catalysis B: Environmental, vol. 236, pp. 518–545, 2018.

X. S. Maroufi, M. Mayyas, and V. Sahajwalla, “Waste materials conversion into mesoporous silicon carbide nanocermics: Nanofibre/particle mixture,” Journal of Cleaner Production, vol. 157, pp. 213–221, 2017.

XI. S. Imtiaz et al., “Biomass-derived nanostructured porous carbons for lithium-sulfur batteries,” Science China Materials, vol. 59, no. 5, pp. 389–407, 2016.

XII. S. S. Shams, L. S. Zhang, R. Hu, R. Zhang, and J. Zhu, “Synthesis of graphene from biomass: a green chemistry approach,” Materials Letters, vol. 161, pp. 476–479, 2015.

XIII. V. S. Samane Maroufi, Mohannad Mayyas, “Waste Materials Conversion into Mesoporous Silicon Carbide Nanoceramics: Nanofiber/Particle Mixture,” Journal of cleaner production, 2017.

XIV. W. Wang, L. Tian, W. Song, L. Lv, and Z. Tu, “Twisted angle effects in the absorption spectra of carbon nanotube,” Optik, vol. 171, pp. 845–849, 2018.

XV. W. Du, X. Wang, X. Sun, J. Zhan, H. Zhang, and X. Zhao, “Nitrogen-doped hierarchical porous carbon using biomass-derived activated carbon/carbonized polyaniline composites for supercapacitor electrodes,” Journal of Electroanalytical Chemistry, vol. 827, pp. 213–220, 2018.

XVI. W. Zhang et al., “Hierarchical porous carbon prepared from biomass through a facile method for supercapacitor applications,” Journal of colloid and interface science, vol. 530, pp. 338–344, 2018.

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Abstract:

A composite material is a combination of two or more materials arranged in the form of layer one on the other layer using binding material through some prescribed methods. The Bio-epoxy composite is prepared by hand layup method using Bio-epoxy, natural fiber, and particulate here we discuss the properties and characterization of the composite such as reaction with moisture content in soil, degradability and reaction with water. In this paper, the moisture absorption rate as a function of time is discussed with reference to the Bio-epoxy prepared. The matrix structure is the same as that of any composite but the reinforced material used is the hybrid being the natural epoxy. The bio epoxy hybrid composite is having high moisture absorbing capacity which leads to low flexibility in the specimen. The natural fiber used here as well as the particulate is readily degradable in soil when exposed to a specific time. The matrix reinforced hybrid composite used here is flexible in its very nature indicating its adaptability to various uses. The composite is eco-friendly. There is also a comparison between the traditional epoxy and the bio composites to check the time required for degradation. The usage of these bio composites makes the surrounding less harmful and it also cost-effective. There was also a test conducted for bio composites with water and soil for 72 hours. The natural fibers used havea high affinity for water therefore degradation easily takes place along with reinforced particulate material that is Tulsi seeds. In this article synthesis and characterization of hybrid bio-epoxy composite, and the reaction of these composites in wet environmental conditions are discussed.

Keywords:

Green Composites,Areca fiber,Ocimumtenuiflorum (Tulsi) seeds,bio epoxy,degradation,

Refference:

I. A. D. La Rosa, G. Recca, J. Summerscales, A. Latteri, G. Cozzo, and G. Cicala, “Bio-based versus traditional polymer composites. A life cycle assessment perspective,” Journal of cleaner production, vol. 74, pp. 135–144, 2014.

II. A. Shakeri and M. Raghimi, “Studies on mechanical performance and water absorption of recycled newspaper/glass fiber-reinforced polypropylene hybrid composites,” Journal of Reinforced Plastics and Composites, vol. 29, no. 7, pp. 994–1005, 2010.

III. A. Ashori and S. Sheshmani, “Hybrid composites made from recycled materials: Moisture absorption and thickness swelling behavior,” Bioresource technology, vol. 101, no. 12, pp. 4717–4720, 2010.

IV. B. Szolnoki et al., “Development of natural fibre reinforced flame retarded epoxy resin composites,” Polymer Degradation and Stability, vol. 119, pp. 68–76, 2015.

V. E. Munoz and J. A. García-Manrique, “Water absorption behaviour and its effect on the mechanical properties of flax fibre reinforced bioepoxy composites,” International Journal of Polymer Science, vol. 2015, 2015.

VI. H.-S. Yang, H.-J. Kim, H.-J. Park, B.-J. Lee, and T.-S. Hwang, “Water absorption behavior and mechanical properties of lignocellulosic filler–polyolefin bio-composites,” Composite Structures, vol. 72, no. 4, pp. 429–437, 2006.

VII. H.-S. Yang, H.-J. Kim, H.-J. Park, B.-J. Lee, and T.-S. Hwang, “Water absorption behavior and mechanical properties of lignocellulosic filler–polyolefin bio-composites,” Composite Structures, vol. 72, no. 4, pp. 429–437, 2006.

VIII. K. G. Satyanarayana, G. G. C. Arizaga, and F. Wypych, “Biodegradable composites based on lignocellulosic fibers—An overview,” Progress in polymer science, vol. 34, no. 9, pp. 982–1021, 2009.

IX. K. L. Pickering, M. G. A. Efendy, and T. M. Le, “A review of recent developments in natural fibre composites and their mechanical performance,” Composites Part A: Applied Science and Manufacturing, vol. 83, pp. 98–112, 2016.

X. K. P. Ashik and R. S. Sharma, “A review on mechanical properties of natural fiber reinforced hybrid polymer composites,” Journal of Minerals and Materials Characterization and Engineering, vol. 3, no. 05, p. 420, 2015.

XI. M. Jawaid, H. P. S. A. Khalil, P. N. Khanam, and A. A. Bakar, “Hybrid composites made from oil palm empty fruit bunches/jute fibres: Water absorption, thickness swelling and density behaviours,” Journal of Polymers and the Environment, vol. 19, no. 1, pp. 106–109, 2011.

XII. N. Saba, M. Jawaid, O. Y. Alothman, and M. T. Paridah, “A review on dynamic mechanical properties of natural fibre reinforced polymer composites,” Construction and Building Materials, vol. 106, pp. 149–159, 2016.

XIII. P. B. Van Putten, P. J. Coenraads, and J. P. Nater, “Hand dermatoses and contact allergic reactions in construction workers exposed to epoxy resins,” Contact Dermatitis, vol. 10, no. 3, pp. 146–150, 1984.

XIV. S. Ma et al., “Synthesis and properties of a bio‐based epoxy resin with high epoxy value and low viscosity,” ChemSusChem, vol. 7, no. 2, pp. 555–562, 2014.

XV. S. M. Sapuan, M. Harimiand, and M. A. Maleque, “Mechanical properties of epoxy/coconut shell filler particle composites,” Arabian Journal for Science and Engineering, vol. 28, no. 2, pp. 171–182, 2003.

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Abstract:

A review on the challenges in underwater wireless network systems is discussed in this paper. In underwater network systems different methodologies have to be adopted in comparison to the overland network systems. Acoustic signals are used instead of electromagnetic signals. One of the issue is the propagation of electromagnetic signals through water. The marine environment also poses serious challenges in deploying the underwater wireless systems. The architecture and the applications of underwater network systems is also discussed.

Keywords:

Underwater wireless sensor networks,Acoustic sensor networks,oceanographic data collection,

Refference:

I. Barbosa, P.; White, N.M.; Harris, N.R., “Wireless Sensor Network for Localized Maritime Monitoring”, Proceedings of the 22nd International Conference on Advanced Information Networking and Applications, Okinawa, Japan, pp. 681–686, 2008.
II. Cayirci, E, Tezcan Hakan, DoganYasar, Coskun Vedat, “Wireless sensor networks for underwater survelliance systems”, Ad Hoc Networks., Vol. 4, pp 431-446, 2006.
III. Felemban, E., Shaikh, F. K., Qureshi, U. M., Sheikh, A. A., and Qaisar, S. B., “Underwater Sensor Network Applications: A Comprehensive Survey”. International Journal of Distributed Sensor Networks, 2015.
IV. G. A. Hollinger, S. Choudhary, P. Qarabaqi, Christopher Murphy, UrbashiMitra, Gaurav s. Sukhatme, Milica Stojanovic, Hanumant Singh and Franz Hover, “Underwater data collection using robotic sensor networks,” IEEE Journal on Selected Areas in Communications, Vol. 30, no. 5, pp. 899–911, 2012.
V. J.-H. Cui, J. Kong, M. Gerla, and S. Zhou, “The challenges of building mobile underwater wireless networks for aquatic applications,” IEEE Network, Vol. 20, no. 3, pp. 12–18, 2006.
VI. John Heidemann, Yuan Li, Affan Syed, Jack Wills and Wei Ye, “Underwater Sensor Networking: Research Challenges and Potential Applications”. USC/ISI Technical Report ISI-TR-2005-603.
VII. Liu, K.; Yang, Z.; Li, M.; Guo, Z.; Guo, Y.; Hong, F.; Yang, X.; He, Y.; Feng, Y.; Liu, Y. “Oceansense: Monitoring the sea with wireless sensor networks”. Mob. Comput. Commun. Rev., Vol. 14, pp 7–9, 2010.
VIII. Lu, K.; Qian, Y.; Rodriguez, D.; Rivera, W.; Rodriguez, M., “Wireless Sensor Networks for Environmental Monitoring Applications: A Design Framework”, Proceedings of the Global Communications Conference, Washington, DC, USA, pp. 1108–1112, 2007.
IX. M. C. Domingo and R. Prior, “Energy analysis of routing protocols for underwater wireless sensor networks,” Computer Communications, Vol. 31, no. 6, pp. 1227–1238, 2008.
X. Perez, C.A. Jimenez, M. Soto, F. Torres, R. López, J.A. Iborra, A., “A system for monitoring marine environments based on Wireless Sensor Networks”, In Proceedings of the IEEE Conference on OCEANS, Santander, Spain, pp. 1–6, 2011.
XI. Saha, S.; Matsumoto, M., “A Framework for Disaster Management System and WSN Protocol for Rescue Operation”, Proceedings of the IEEE Region 10 Conference on TENCON 2007, Taipei, Taiwan, pp. 1–4, 2007.
XII. Sharif-Yazd M., Khosravi M. R. and Moghimi M. K., “A Survey on Underwater Acoustic Sensor Networks: Perspectives on Protocol Design for Signaling, MAC and Routing”, Journal of Computer and Communications, Vol. 5, pp 12-23, 2017.
XIII. S. Premkumar Deepak and M. B. M. Krishnan, “Intelligent sensor based monitoring system for underwater pollution,” 2017 International Conference on IoT and Application (ICIOT), Nagapattinam, pp. 1-4, 2017.
XIV. S. Yoon, A. K. Azad, H. Oh, and S. Kim, “AURP: an AUV-aided underwater routing protocol for underwater acoustic sensor networks,” Sensors, Vol. 12, no. 2, pp. 1827–1845, 2012.
XV. U. Devee Prasan and S. Murugappan, “Underwater Sensor Networks: Architecture, Research Challenges and Potential Applications”. International Journal of Engineering Research and Applications, Vol. 2, Issue 2, pp.251-256, 2012.

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Abstract:

Tamil is one of the world's ancient languages. This paper focuses mainly in particular on OCR for the digitalization and conservation of texts and inscriptions in the Tamil language. A system that does not include obtaining either Standard size and shape or the color difference between background and foreground to recognize Palm Leaf Manuscript and stone inscriptions and obtaining information. A variety of algorithms have been analyzed for OCR texts for Tamils, and ancient letter conversion still has a big challenge to convert ancient Tamils into today's digital text format for Tamils.

Keywords:

Tamil,OCR,Manuscript,Script,Optical Character Recognition,Tamil Language,Tamil Script,

Refference:

I. A. V. S. Rao, N. V. Rao, L. P. Reddy, G. Sunil,T.S.K.Prabhu, and A. S. C. S. Sastry, “Adaptive binarization of ancient documents,” in Proceedings of the 2nd International Conference on Machine Vision (ICMV ’09), pp. 22–26, December 2009.
II. B. Gangamma, K. Srikanta Murthy, and A. V. Singh, “Restoration of degraded historical document image,” Journal of Emerging Trends in Computing and Information Sciences, vol. 3, no. 5,pp. 36–39, 2012.
III. C. L. Tan, R. Cao, and P. Shen, “Restoration of archival documents using a wavelet technique,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 10, pp. 1399–1404, 2002.
IV. E. Nadernejad, S. Sharifzadeh, and H. Hassanpour, “Edge detection techniques:evaluations and comparisons,” Applied Mathematical Sciences, vol. 2, no. 31, pp. 1507–1520, 2008.
V. G. R. M. Babu, P. Srimayee, and A. Srikrishna, “Heterogenous images using mathematical morphology,” Journal ofTheoretical and Applied Information Technology, vol. 15, no. 5, pp. 795–825, 2008.
VI. G. Raju and K. Revathy, “Wavepackets in the Recognition of Isolated Handwritten Characters”, Proceedings of the World Congress on Engineering 2007 Vol I WCE 2007, July 2 – 4, 2007, London, U.K.
VII. G. Rama mohanbabu,, P. Srimaiyee, A. Srikrishna , “Text extraction from hetrogenous images using mathematical morphology”, Journal of Theoretical and Applied Information Technology, vol. 16, no. 1, pp. 39 – 47 2010.
VIII. G.Y.Chen, T.D.Bui, A.Krzyzak. “Contour based handwritten numeral recognition using multiwavelets and neural networks”,Pattern Recognition, vol. 36, no. 7, pp. 1597 – 1604, 2003.
IX. H.K.Chethan, G.Hemantha Kumar, “ A Comparative Analysis of Different Edge based Algorithms for Mobile/Camera Captured Images”, International Journal of Computer Applications, vol. 7, no. 3, pp. 36-41, 2010.
X. H.K.Chethana, G.HemanthaKumar , “Image Dewarping and Text Extraction from Mobile Captured Distinct Documents”, Procedia Computer Science, vol. 2, pp. 330 – 337, 2010.
XI. Jian Yuan, Yi Zhang, KokKiong Tan, Tong Heng Lee, “Text Extraction from Images Captured via Mobile and Digital Devices”, 2009 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, pp. 566 – 571,2009.
XII. K. C. Kim, H. R. Byun, Y. J. Song et al., “Scene text extraction in natural scene images using hierarchical feature combining and verification,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR ’04), vol. 2, pp. 679–682, gbr, August 2004.
XIII. L. Agnihotri and N. Dimitrova, “Text detection for video analysis,” in Proceedings of IEEE International Workshop onContent-Based Access of Image and Video Libraries, pp. 109–113, June 1999.
XIV. M. Seeger and C. Dance, Binarising Camera Images for OCR, Xerox Research Centre, Meylan, France, 2000.
XV. P Wunsch, A F Laine. “Wavelet descriptors for multiresolution recognition of handprinted characters”. Pattern Recognition, vol. 28, no. 8, pp. 1237–1249, 1995.
XVI. P. Chevalier, L. Albera, P. Comon, and Ferreol, “Comparative performance analysis of eight blind source separation methods on radio communications signals,” in Proceedings of the International Joint Conference on Neural Networks, vol. 8, pp. 251–276, July 2004.
XVII. P. Tichavsk´y, Z. Koldovsk´y, and E. Oja, “Performance analysis of the FastICA algorithm and Cram´er-Rao bounds for linear independent component analysis,” IEEE Transactions on Signal Processing, vol. 54, no. 4, pp. 1189–1203, 2006.
XVIII. S P Chowdhury S DharA K Das B Chanda K McMenemy, “Robust Extraction of Text from Camera Images “, 2009 10th International Conference on Document Analysis and Recognition, pp. 1280 – 1284, 2009.
XIX. S W Lee, C H Kim, H Ma, Y Y Tang. “Multiresolution recognition of unconstrained handwritten numerals with wavelet transform and multilayer cluster neural network”. Pattern Recognition, vol. 29, pp. 1953–1956, 1996.
XX. S. Buzykanov, “Enhancement of poor resolution text images in the weighted sobolev space,” in Proceedings of the 19th International Conference on Systems, Signals and Image Processing (IWSSIP ’12), pp. 536–539, Vienna, Austria, April 2012.
XXI. S. Cherala and P. Rege, “Palm leaf manuscript/color document image enhancement by using improved adaptive binarization method,” in Proceedings of the 6th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP ’08), pp. 687–692, December 2008.
XXII. S. Choi, “Independent component analysis,” in Proceedings of the 12th WSEAS International Conference on Communications, pp. 159–162, July 2008.
XXIII. S. Choi, A. Cichocki, and S. I. Amari, “Flexible independent component analysis,” Journal of VLSI Signal Processing Systems for Signal, Image, and Video Technology, vol. 26, no. 1, pp. 25–38, 2000.
XXIV. Sachin Grover, KushalArora,Suman K. Mitra , “Text Extraction from Document Images using Edge Information”, 2009 Annual IEEE India Conference, 2009.
XXV. Seethalakshmi R, Sreeranjani TR., Balachandar T, “Optical Character Recognition for printed Tamil text using Unicode”, Journal of Zhejiang University Science, vol. 6A, no. 11, pp. 1297-1305, 2005
XXVI. U. Garain, A. Jain, A. Maity, and B. Chanda, “Machine reading of camera-held low quality text images: an ICA-based image enhancement approach for improving OCR accuracy,” in Proceedings of the 19th International Conference on Pattern Recognition (ICPR ’08), pp. 1–4, December 2008.
XXVII. Vinoth R, Rajesh R, Yoganandhan P, “Intelligence System for Tamil Vattezhuttu Optical Character Recognition”, International Journal of Computer Science & Engineering Technology, vol. 8, no. 04, pp. 22 -27 Apr 2017
XXVIII. X. S. Hua, P. Yin, and H. J. Zhang, “Efficient video text recognition using multiple frame integration,” in Proceedings of the International Conference on Image Processing, vol. 2, pp. 22–25, September 2004.

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Abstract:

In nature many social animals follow a cooperative behaviour for the common good of their colony. Swarm robotics is a method in which a collection of similar or dissimilar robots follow an organized behaviour pattern to perform some specific tasks. The robots interact and follow simple rules to coordinate a large number of robots. Here we focus on the recent developments in swarm robotics as applied to real world problems. Swarm robotics deals with the defining the rules for the cooperative behaviour and designing, modelling, validating, operating and maintaining the robotics system. Swarm robotics can be classified as per the design and analysis or as per the collective behaviour. The limitations and the future research directions for swarm robotics is also discussed.

Keywords:

Swarm Robotics,Social behaviour,Collective behaviour,Robots,

Refference:

I. Ampatzis, C., Tuci, E., Trianni, V., &Dorigo, M.,“Evolution of signaling in a multi-robot system: categorization and communication”, Adaptive Behavior, Vol. 16(1), pp. 5–26, 2008.
II. Bachrach, J., Beal, J., &McLurkin, J.,“Composable continuous-space programs for robotic swarms”, Neural Computing & Applications, Vol. 19(6), pp. 825–847, 2010.
III. Beni, G.,“From swarm intelligence to swarm robotics”, In Lecture notes in computer science:Swarm robotics, Berlin: Springer, Vol. 3342,pp. 1–9, 2005.
IV. Bonabeau, E., Dorigo, M., &Theraulaz, G.,“Swarm intelligence: from natural to artificial systems”, New York: Oxford University Press, 1999.
V. Brambilla, M., Pinciroli, C., Birattari, M., &Dorigo, M.,“Property-driven design for swarm robotics”, In Proceedings of 11th international conference on autonomous agents and multiagent systems (AAMAS 2012), Richland: IFAAMAS, pp. 139–146,2012.
VI. Dorigo, M., & ¸ Sahin, E., Guest editorial. “Autonomous Robots”, Vol. 17, pp. 111–113, 2004.
VII. Dorigo, M., &Birattari, M.,“Swarm intelligence”,Scholarpedia, Vol. 2(9), pp. 1462, 2007.
VIII. Ferrante, E., Brambilla, M., Birattari, M., &Dorigo, M.,“Socially-mediated negotiation for obstacle avoidance in collective transport”, In Springer tracts in advanced robotics: Vol. 83. Proceedings of the international symposium on distributed autonomous robotics systems (DARS 2010), Berlin: Springer, pp. 571–583,2013.
IX. Gazi, V., &Passino, K. M.,“Stability analysis of social foraging swarms: combined effects of attractant/repellent profiles”, In Proceedings of the 41st IEEE conference on decision and control, Piscataway: IEEE Press, Vol. 3, pp. 2848–2853,2002.
X. Pugh, J., &Martinoli, A.,“Parallel learning in heterogeneous multi-robot swarms”, In Proceedings of the IEEE congress on evolutionary computation, Piscataway: IEEE Press, pp. 3839–3846,2007.
XI. Riedmiller, M., Gabel, T., Hafner, R., & Lange, S.,“Reinforcement learning for robot soccer”, Autonomous Robots, Vol. 27(1), pp. 55–73, 2009.
XII. Sahin, E.,“Swarm robotics: from sources of inspiration to domains of application”, In Lecture notes in computer science: Berlin: Swarm robotics, Springer, Vol. 3342. pp. 10–20,2005.
XIII. Seeja G, ArockiaSelvakumar A, Berlin Hency V, “A Survey on Swarm Robotic Modeling, Analysis and Hardware Architecture”, Procedia Computer Science,Vol. 133, pp. 478–485, 2018.
XIV. Soysal, O., & ¸ Sahin, E.,“Probabilistic aggregation strategies in swarm robotic systems”, In Proceedings of the IEEE swarm intelligence symposium, Piscataway: IEEE Press, pp. 325–332,2005.
XV. Spears, W. M., & Spears, D. F.,“Physics-based swarm intelligence”, Berlin: Springer, 2012.
XVI. Waibel, M., Keller, L., &Floreano, D.,“Genetic team composition and level of selection in the evolution of cooperation”, IEEE Transactions on Evolutionary Computation, Vol. 13(3), pp. 648–660, 2009.

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