Archive

Abstract:

This work studies the trajectory tracking of a non-holonomic WMR experimentally. Experimental work includes two parts where part one involves path tracking for some desired shapes, while the second part includes path planning and obstacle avoidance in the considered environment. Different cases of the trajectory were studied such as (straight line, circular, elliptical, squared, and triangular shape trajectory) utilizing Python programming. Also, the image processing technique and gird graph method had been used for the study two cases of path planning with different obstacles and position of obstacles, also with different start and goal points. On the other hand, the number of obstacles between the two cases is not the same and the shape of obstacles is uniform or non-uniform, also different size of obstacles were considered where the robot should avoid these obstacles and reach the goal point.The errors had been calculating adopting on the encoder. Results showed a very good match between the simulation and the desired trajectory. Also, the grid graph method was efficient in path planning and obstacle avoidance.

Keywords:

Mobile robot,Nonholonomic,DDWMR,Grid graph,Experimental,

Refference:

I Ali Alouache, and Qinghe Wu, 2018 China. “Genetic Algorithms for Trajectory Tracking of Mobile Robot Based on PID Controller”. PP237-241.

II Anish Pandey and Dayal R. Parhi, 2017 India. “Optimum Path Planning of Mobile Robot in Unknown Static and Dynamic Environments Using Fuzzy-Wind Driven Optimization Algorithm”, J. Defence Technology. V13. PP47-58.

III ImenHassani et. al, 2018 Tunisia. ” “Robot Path Planning with Avoiding Obstacles in Known Environment Using Free Segments and Turning Points Algorithm”, J. Mathematical Problems in Engineering. V2018. PP: 1-13

IV Mahmood Ali Moqbel et.al, 2016 Malaysia, Yemen. “Robust Backstepping Tracking Control of Mobile Robot Based on Nonlinear Disturbance Observer”, J. International Journal of Electrical and Computer Engineering (IJECE). V6. N2. PP901-908.

V Mohamed Maghenem. et. al, 2017 France. “Global Tracking-Stabilization Control of Mobile Robots with Parametric Uncertainty”. J. International Federation of Automatic Control. V50. PP4114–4119.

VI Mehr-e-Munir, ShahidLatif, Muhammad Aamir Aman,Waleed Jan, Jehanzeb Khan, Improved Distance Measuring Using Laser Light, J. Mech. Cont.& Math. Sci.Vol.-13, No.-3, July-August (2018), pp 192-198

VII Nardênio Almeida Martins et. al, 2011 Brasil, France. “An Adaptive Variable Structure Controller for the Trajectory Tracking of a Nonholonomic Mobile Robot with Uncertainties and Disturbances”, J. JCS&T. V11:No1. PP.

VIII Ollero, A., Sanfeliu, A., Montano, L., Lau, N., and Cardeira, C., 2017 Spain. B. ROBOT 2017: Third Iberian Robotics Conference.

IX Sourish Ghosh and Joydeep Biswas, 2017 Canada. “Joint Perception and Planning For Efficient Obstacle Avoidance Using Stereo Vision”, C. International Conference on Intelligent Robots and Systems (IROS). V2017. PP1026-1031.

X Yones k. k.and Hadi N. H. 2020 Iraq. “Path tracking and backstepping control for a wheeled mobile robot (WMR) in a slipping environment”, C. 3rd International Conference on Engineering Sciences.PP1-17.

XI Zheng Mingliang, Canonical Equations of Singular Mechanical Systems in Terms of Quasi-coordinates, J. Mech. Cont.& Math. Sci., Vol.-14, No.-4, July-August (2019), pp 1-7

View Download

Abstract:

The present investigation deals with the oscillatory flow of a  Cassonfluid subjected to heat and mass transfer along a porous oscillating channel in presence of an external magnetic field.Here we consider the flow through a channel in which the fluid is injected on one boundary of the channel with a constant velocity,while it is sucked off at the other boundary with the same velocity.Galerkins technique is used to find expressions for the velocity,  temperature, concentration of mass, volumetric flow rate, shear stress, rate of heat, and mass transfer andfound their numerical solutions.The effects of various parameters like Hartmann number,radiative parameter,Reynolds number, permeability parameter,Schimdth number on flow variables are discussed and shown graphically.

Keywords:

Oscillating channel,radiative heat transfer,mass transfer,volumetric flow rate,shear stress,Casson fluid,

Refference:

I. Adhikari,S.D and Mishra,J.C,”Unsteady two-dimensional hydromagneticflow and heat transfer of a fluid “,Int.J.Appl.Math and Mech,7(4),p.1-20,(2011)
II. Amjad Ali,Humayun Farooq, and Attia Fatima,Scientific Reports 10,Articlenumber 10629,(2020).
III. Bitta,P.,Kandala,T., and Iyengar,V., Nonlinear Analysis:Modelling and control,18(4), p.399-411,(2013).
IV. Casson N .,C.C Mill,Ed.pp 84-102,perganon press,London,UK(1959).
V. ChandraSekhar et.al,AIP Conference Proceedings 2112,020144,(2019).
VI. Dhal,R.K., Banamali Jena and Mariappan,M., International Research
Journal of Adv. Engineering and Science,vol 2,issue 3,p.220-223,(2017).
VII. Ganesh,Ismail and Anand,Int.J.A.M ,oct,(2018).
VIII. Goutam Chakraborty, SupriyaPanja, “STEADY FLOW OFMICROPOLAR FLUID UNDER UNIFORM SUCTION”, J. Mech. Cont. & Math. Sci., Vol.-4, No.-2, January (2010), pp 523-529
IX. Kiema,D.W.,Manyonge,W.A., and Bitok,J.K.,Int.J.Scientific Research
and innovative technology,vol 2,No 2, (2015).
X. Kirubha Shankar,Ganesh and Ismail,”Exact solution of unsteady MHD flow through parallel plates”, IJAMAE,vol 1,issue 1,
XI. Kalyan Kumar,Ch., and Suripeddi Srinivas,Engg.Transactions,65,3,p.461-481,(2017).
XII. Kumar,L.,Narayana,S,Chemical.Engg.Sci,65,p.5582-5587,(2010).
XIII. Lin,Y., Tan G., Phan-Thien,N., Khoo,B,J.non.newtonian.Fluid.Mech,12, p.13-17,(2014).
XIV. MakindeO.D and Aziz A,Int.J.Thermal Sci,vol 49,p.1813-1820,(2010).
XV. Malik,B.,Nanda,.,B.Das,D.Saha,Das,D.S., Paul,K., Sch.J.Eng.Tech,1(1),p.27-38,(2013).
XVI. Mandal,M.S.,Mukhopadhyay,S.,and Layek,G.C.,Theoret.Appl.Mech,39(3),p.209-231,2012.
XVII. Manyonge,W A,Bitok J.K and Dionysis,W.K.,American J. Computational and Appl. Mathematics,3,no 4,p.220-224,(2013).
XVIII. Mitra Asish, “NUMERICAL SIMULATION ON LAMINAR FREE
CONVECTION FLOW AND HEAT TRANSFER OVER A VERTICAL
PLATE WITH CONSTANT HEAT FLUX”,J. Mech. Cont. & Math.
Sci., Vol.-10, No.-2, January (2016), pp 1487-1499.
XIX.Mohammed.Y.Abou-Zeid, SehamS.El-Zahrain,Hesham MMansour,J.Nuclear and Particle Physics,4(3),p.100-115,(2014).
XX. Narender,G.,Sharma,G., and Goverdhan,K., CVR Jour.Sci andTech,vol 15,p.106-114,(2018).
XXI. NoushimaHumera .G.,Rafiuddin and S.Mustafa,European.Journal ofEngg and Technology,vol 8, No 1 p.,(2020).
XXII. NoushimaHumera,G and Rafiuddin,Int.J.Emerging.Tech.Adv.Engg.vol 10, issue 5,p.82,2020.
XXIII. Samuel O.Adesanya,J.A.Falade,Ukaegbu,J.C., and Adekeye,K.S., Journal of Mathematics,volume 2016,8 pages,(2016).
XXIV. Srinivas,S.,Vijayalakshmi,A., and Subramanyam Reddy,A.,Communications in Applied Analysis,21,No 3,p.431-448,(2017).
XXV. Venkateshwarlu M and Padma P, Procedia Engineering,vol 127,0.791-799,(2015).
XXVI. Wang CY,Journal of Applied Mechanics, vol 38,p.553-555,(1971).

View Download

Abstract:

A-pillar is a vertical section, preliminary from footing level also shifting weight to the base. Hanging or stub section similarly a precipitous element which closes (as of model design/site condition) on its base level, lies on level part i.e., beam (horizontal section). Such sections (hanging or stub) where the burden was mulled over using point load. Present examination on G+12 model with/without hanging section is studied by applying response spectrum & time history procedures beneath quake load at zone two also differentiates with storey shears, lateral burden, storey relocations, storey drifts by using Etabs. From the final output, it was clear that storey shears, lateral burden, storey displacements, storey drifts are increased for the model without hanging sections concerning model with hanging section. 

Keywords:

storey shear,lateral burden,storey displacements,response spectrum, ETABS,

Refference:

I. Ajim G. Mujawar and Mohasinkhan N. Bargir (2020) Earthquake Analysis of High-Rise structure with hanging Column, International conference of emerging trends in engineering. https://doi.org/10.1007/978-3-030-24314-2_23

II. Anil Chopra, structural dynamics, applications and theory of earthquake engineering (third edition).

III. E.M Lui and Awkar J. C (1997), Seismic analysis of multi-storey semirigid frames”, Page no: 425-442, Issue 5, volume 21, Journal of Engineering Structures.

IV. G. Iyappan, N. Elakkiyarajan, And A. Naveen (2018) Seismic Analysis of Multi-storey structure with hanging Column.). (IOSR Journal of Engineering (IOSRJEN) ISSN (e): 2250-3021, ISSN (p): 2278-8719 PP 42-48)

V. I.S 1893, Part 1(2002) codebook of design of seismic force resistant structures. (https://law.resource.org/pub/in/bis/S03/is.1893.1.2002.pdf).

VI. I.S.456, (2000) code of plain and R.C. structures. (https://elibrarywcl.files.wordpress.com/2015/02/plain-and-reinforced-concrete.pdf).

VII. Lale, Kadam, Waykule (2016), studied oat conduct on the hanging column for seismic analysis of the multi-storey structure. IJCIET Volume 7 Issue 6. http://www.iaeme.com/MasterAdmin/uploadfolder/IJCIET_07_06_075/IJCIET_07_06_075.pdf.

VIII. Martin Williams, structural dynamics (first edition).

IX. Shashikant. K. Duggal, Seismic force resistant structures design (second edition).

X. Syed tajodeen (2014), seismic analysis of multi-storey structure with hangingcolumns.https://www.researchgate.net/publication/282905209_SEISMIC_ANALYSIS_OF_MULTISTOREY_BUILDING_WITH_FLOATING_COLUMNS.
XI. Usama Ali*1, Naveed Ahmad2, Yaseen Mahmood3, Hamza Mustafa4, Mehre Munir5, A comparison of Seismic Behavior of Reinforced Concrete Special Moment Resisting Beam-Column Joints vs. Weak Beam Column Joints Using Seismostruct, J. Mech. Cont.& Math. Sci.,Vol.-14, No.-3, May-June (2019), pp 289-314.
XII. Yaseen Mahmood*1, Khan Shahzada2, Usama Ali3, Abdul Farhan4, Syed Shujaat Ali Shah5, Fawad Ahmad6, FRAMEWORK FOR ASSESSING SEISMIC RESILIENCE OF CITIES, J. Mech. Cont.& Math. Sci., Vol.-14, No.-5, September – October (2019), 42-49

View Download

Abstract:

Segmentation of an image is the first step to extract required details from an image. It is a process of separating an image into unique regions containing each pixel with identical attributes. In this paper, an automatic segmentation algorithm is implemented to detect cancer cells from an image and label them in the original image.

Keywords:

Image Segmentation, Thresholding,Edge detection,Computed Tomography,

Refference:

I. Detect Cell Using Edge Detection and Morphology [online]. https://www.mathworks.com/help/images/detecting-a-cell-using-image-segmentation.html – Accessed: 17 July 2020.

II. Lindeberg, T. and Li, M. X. “Segmentation and Classification of Edges Using Minimum Description Length Approximation and Complementary Junction Cues”, Computer Vision and Image Understanding, Vol. 67, No. 1, pp. 88 – 98, 1997.

III. MATLAB:R2019b, The MathWorks, Inc, 2019.

IV. Narayanamma Laxmi K., R. V. Krishnaiah, P. Sammulal, “An Efficient Statistical Feature Selection Based Classification”, J. Mech. Cont.& Math. Sci., Vol.-14, No.-4, July-August (2019) , pp 27-40

V. Otsu, N, “A threshold selection method from gray-level histograms”. IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, pp. 62–66, 1979.

VI. Pham, D. L., Xu, C., and Prince, J. L. “Current Methods in Medical Image Segmentation”. Annual Review of Biomedical Engineering, Vol. 2, No. 1, pp. 315–337, 2000.

VII. Poornima, B., Ramadevi, Y. and Sridevi, T., “Threshold Based Edge Detection Algorithm”, International Journal of Engineering and Technology, Vol 3, No. 4, pp. 400–403, 2011.

VIII. Senthilkumaran, N., and Vaithegi, S., “Image segmentation by using thresholding techniques for medical images“, Computer Science & Engineering: An International Journal (CSEIJ), Vol. 6, No. 1, 2016.

IX. Smistad, E., Falch, T. L., Bozorgi, M., Elster, A. C. and Lindseth, F. “Medical image segmentation on GPUs—a comprehensive review,” Medical Image Analysis, Vol. 20, No. 1, pp. 1–18, 2015.

X. Sobel, I., “An Isotropic 3×3 Image Gradient Operator”. Presentation at Stanford A.I. Project 1968, 2014.

XI. Vasanthselvakumar R, Balasubramanian M, Palanivel S, “Detection and Classification of Kidney Disorders using Deep Learning Method”, J. Mech. Cont.& Math. Sci., Vol.-14, No.2, March-April (2019), pp 258-270.

XII. Wang, Z., “Image segmentation by combining the global and local properties”, Expert Systems with Applications, Vol. 87, pp. 30-40, 2017.

View Download

Abstract:

A smart and efficient irrigation system is being proposed which minimizes water consumption for commonly cultivated plants. The irrigation system has a wireless sensor network consisting of soilmoisture and temperature sensors, placed in the irrigated land. The system also has a wireless control unitthat will receive the sensor information from the wireless sensor network,send control signals to the relays on the water taps, and also wirelessly transmitssensor data to a web server. An algorithm is proposed to compute the exact amount of water needed for irrigation which uses the sensor data received from the wireless sensor network. The wireless control unit controls the water tap to release the amount of water needed for irrigation. The control unit also sends the sensor data to a web server using Wi-Fi and the Internet.A web application is used to read and inspect the sensor data from the server and for scheduling the irrigation through control commands. The system will be used for testing some commonly cultivated plants in a particular geographical location and is also intended to be used for other geographical locations. The software developed takes into account the plant and soil type, plant growth stages, plant evaporation data, soil conditions, and effective rainfall. This software will also determine the most suitable irrigation schedule for a particular crop.The system will be more useful in locations where water is scarce.

Keywords:

Mha,IoT,WSU,WCU,WSN,RCU,Wi-Fi,

Refference:

I Ashok Kumar Jain, “Water: A Manual for Engineers, Architects, Planners and Managers”, 2007, Daya Publishing House, Delhi – 110035.
II D. K. Fisher and H. A. Kebede, “A low-cost microcontroller-based system to monitor crop temperature and water status,” Comput. Electron. Agricult., vol. 74, no. 1, pp. 168–173, Oct. 2010.
III G. Yuan, Y. Luo, X. Sun, and D. Tang, “Evaluation of a crop water stress index for detecting water stress in winter wheat in the North China Plain,” Agricult. Water Manag., vol. 64, no. 1, pp. 29–40, Jan. 2004.
IV H. D. Kumar, “WATER WOES Conserving and Managing our Future Lifeline”, Daya Publishing House, Delhi 110035, 2006.
V https://www.dpi.nsw.gov.au/__data/assets/pdf_file/0004/127282/Irrigation-scheduling.pdf
VI https://agriinfo.in/criteria-for-scheduling-irrigation-or-approaches-for-irrigation-scheduling-20/
VII I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “A survey on sensornetworks,” IEEE Commun. Mag., vol. 40, no. 8, pp. 104–112, Aug. 2002.
VIII J. M. Blonquist, Jr., S. B. Jones, and D. A. Robinson, “Precise irrigation scheduling for turfgrass using a subsurface electromagnetic soil moisture sensor,” Agricult. Water Manag., vol. 84, nos. 1–2, pp. 153–165, Jul. 2006.
IX J. Yick, B. Mukherjee, and D. Ghosal, “Wireless sensor network survey,” Comput. Netw., vol. 52, no. 12, pp. 2292–2330, Aug. 2008.
X Jagdish Reddy, “Drip Irrigation versus Sprinkler Irrigation Farming”, http://www.agrifarming.in/drip-irrigation-vs-sprinkler/
XI Joaquín Gutiérrez, Juan Francisco Villa-Medina, Alejandra Nieto-Garibay, and Miguel Ángel Porta-Gándara, “Automated Irrigation System Using a Wireless Sensor Network and GPRS module”, IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, VOL. 63, NO. 1, JANUARY 2014
XII K.S. Bhaskar M.R.K. Rao P.N. Mendhe M.R. Suryavanshi, “Micro Irrigation Management in Cotton”, http://www.cicr.org.in/pdf/micro_irrigation.pdf
XIII K. S. Nemali and M. W. Van Iersel, “An automated system for controlling drought stress and irrigation in potted plants,” Sci. Horticult., vol. 110, no. 3, pp. 292–297, Nov. 2006.
XIV K. W. Migliaccio, B. Schaffer, J. H. Crane, and F. S. Davies, “Plant response to evapotranspiration and soil water sensor irrigation scheduling methods for papaya production in south Florida,” Agricult. Water Manag., vol. 97, no. 10, pp. 1452–1460, Oct. 2010.
XV O. M. Grant, M. J. Davies, H. Longbottom, and C. J. Atkinson, “Irrigation scheduling and irrigation systems: Optimising irrigation efficiency for container ornamental shrubs,” Irrigation Sci., vol. 27, no. 2, pp. 139–153, Jan. 2009.
XVI O. Mirabella and M. Brischetto, “A hybrid wired/wireless networking infrastructure for greenhouse management,” IEEE Trans. Instrum. Meas., vol. 60, no. 2, pp. 398–407, Feb. 2011.
XVII R. G. Allen, L. S. Pereira, D. Raes, and M. Smith, Crop Evapotranspiration-Guidelines for Computing Crop WaterRequirements—FAO Irrigation and Drainage Paper 56. Rome, Italy:FAO, 1998.
XVIII S. A. O’Shaughnessy and S. R. Evett, “Canopy temperature-based system effectively schedules and controls center pivot irrigation of cotton,”Agricult. Water Manag., vol. 97, no. 9, pp. 1310–1316, Apr. 2010.
XIX Sandeep CH., S. Naresh Kumar, P. Pramod Kumar, “SIGNIFICANT ROLE OF SECURITY IN IOT DEVELOPMENT AND IOT ARCHITECTURE”, J. Mech. Cont.& Math. Sci.,Vol.-15, No.-6, June (2020), pp 168-178.

XX Sowmya Gali, Venkatram N., “Multi-Context Cluster Based Trust Aware Routing For Internet of Things”, J. Mech. Cont.& Math. Sci.,Vol.-14, No.-5, September – October (2019), pp 396-418
XXI S. B. Idso, R. D. Jackson, P. J. Pinter, Jr., R. J. Reginato, and J. L. Hatfield, “Normalizing the stress-degree-day parameter for environmental variability,” Agricult. Meteorol., vol. 24, pp. 45–55, Jan. 1981.
XXII S. L. Davis and M. D. Dukes, “Irrigation scheduling performance by evapotranspiration-based controllers,” Agricult. Water Manag., vol. 98, no. 1, pp. 19–28, Dec. 2010.
XXIII X. Wang, W. Yang, A. Wheaton, N. Cooley, and B. Moran, “Efficient registration of optical and IR images for automatic plant water stress assessment,” Comput. Electron. Agricult., vol. 74, no. 2, pp. 230–237, Nov. 2010.
XXIV Y. Erdem, L. Arin, T. Erdem, S. Polat, M. Deveci, H. Okursoy, and H. T. Gültas, “Crop water stress index for assessing irrigation scheduling of drip-irrigated broccoli (Brassica oleracea L. var. Italica),” Agricult.WaterManag., vol. 98, no. 1, pp. 148–156, Dec. 2010.
XXV Y. Kim, J. D. Jabro, and R. G. Evans, “Wireless lysimeters for realtime online soil water monitoring,” Irrigation Sci., vol. 29, no. 5, pp. 423–430, Sep. 2011.
XXVI Y. Kim and R. G. Evans, “Software design for wireless sensor-based site-specific irrigation,” Comput. Electron. Agricult., vol. 66, no. 2, pp. 159–165, May 2009.
XXVII Y. Kim, R. G. Evans, and W. M. Iversen, “Remote sensing and control of an irrigation system using a distributed wireless sensor network,” IEEE Trans. Instrum. Meas., vol. 57, no. 7, pp. 1379–1387, Jul. 2008.

View Download

Abstract:

Localization is done with many different sensors in many different applications. Outdoor localization in an extremely static WSN typically uses several static anchor nodes with well-known positions to assist the localization of the blind nodes. These static anchor nodes that self-using GPS usually are more expensive and this contributes to a higher system cost. Differences between localization from static anchors and mobile anchors are Path designing it should be pre-planned, or it may react to data from blind-nodes. Localization of nodes with range-based techniques involves estimating the distance between a transmitter and receiver by using features of the transmitted signal like a radio signal Strength Indicator (RSSI) as delineated in this paper. This paper explores the use of mobile anchor nodes moving through a sensor field to localize the nodes in an outdoor setting using multilateration technique.

Keywords:

Localization,Sensor Networks,Mobile anchor nodes,Airborne anchors,Anchor node,

Refference:

I. Abdi, Fereydoon, and Abolfazl Toroghi Haghighat. “A Hybrid RSSI Based Localization Algorithm for WSN Using a Mobile Anchor Node.” In Fifth International Conference on Computing, Communications, and Networking Technologies (ICCCNT), 1–6. Hefei, China: IEEE, 2014. http://ieeexplore.ieee.org/document/6963058/.
II. Anup Kumar Paul, Takuro Sato, “Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications, and Challenges”. Journal of Sensor and Actuator Network, 2017, 6, 24.
III. A. Wichmann, T. Korkmaz, and A. S. Tosun, “Robot control strategies for task allocation with connectivity constraints in wireless sensor and robot networks,” IEEE Transactions on Mobile Computing, vol. 17, no. 6, pp. 1429–1441, 2017.
IV. Chanak, Prasenjit, Indrajit Banerjee, and R. Simon Sherratt. “Energy-Aware Distributed Routing Algorithm to Tolerate Network Failure in Wireless Sensor Networks.” Ad Hoc Networks 56 (March 2017): 158–172.
V. Y. Chanti, Dr.Seena Naik, M. Rajesh, Y.Nagender “A modified Elliptic Curve Cryptography Technique for Secur-ing Wireless Sensor Networks”, International Journal of Engineering & Technology, vol.7, no.1.8, pp. 230-232, 2018. DOI: 10.14419/ijet.v7i1.8.22959
VI. Chia-Ho Ou, and Kuo-Feng Ssu. “Sensor Position Determination with Flying Anchors in Three-Dimensional Wireless Sensor Networks.” IEEE Transactions on Mobile Computing, vol. 7, no. 9 (September 2008): 1084–1097.
VII. Deepak, N., Rajendra Prasad, C., & Sanjay Kumar, S. (2018). “Patient Health Monitoring using IOT”, International Journal of Innovative Technology and Exploring Engineering, 8(2), 454–457. https://doi.org/10.4018/978-1-5225-8021-8.ch002.
VIII. D Kothandaraman, Chanti Yerrolla, B Vijaykumar, A Harshavardhan, “Indoor Users Motion Direction Detection Using Orientation Sensor with BLE in the Internet of Things”. Studia Rosenthaliana, 2020.
IX. Faheem Zafari ; Athanasios Gkelias ; Kin K. Leung, “A Survey of Indoor Localization Systems and Technologies”, IEEE Communications Surveys & Tutorials, vol.21, no.3, pp. 2568-2599, 2019.
X. Fanourakis, Marios, and Katarzyna Wac. “ReNLoc: An Anchor-Free Localization Algorithm for Indirect Ranging.” In 2015 IEEE 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), 1–9. Boston, MA, USA: IEEE, 2015.
XI. Guerrero, E., H. G. Xiong, Q. Gao, G. Cova, R. Ricardo, and J. Estevez. “ADAL: A Distributed Range-Free Localization Algorithm Based on a Mobile Beacon for Wireless Sensor Networks.” In 2009 International Conference on Ultra Modern Telecommunications & Workshops, 1–7. St. Petersburg: IEEE, 2009. http://ieeexplore.ieee.org/document/5345556/.
XII. Hiral Patel, Hiral Patel. “3D Localization Algorithms for Wireless Sensor Networks.” IOSR Journal of Computer Engineering 12, no. 1 (2013): 61–66.
XIII. Kapoor, Rohan, Subramanian Ramasamy, Alessandro Gardi, Chad Bieber, Larry Silverberg, and Roberto Sabatini. “A Novel 3D Multilateration Sensor Using Distributed Ultrasonic Beacons for Indoor Navigation.” Sensors 16, no. 10 (October 8, 2016): 1637.
XIV. Kumar, Rajesh, Sushil Kumar, Diksha Shukla, Ram Shringar Raw, and Omprakash Kaiwartya. “Geometrical Localization Algorithm for Three Dimensional Wireless Sensor Networks.” Wireless Personal Communications 79, no. 1 (November 2014): 249–264.
XV. Prakash, Ved and Pandey, Suman and Singh, Ashish Kumar, “Basic Introduction of Wireless Sensor Network” (March 12, 2019). Proceedings of 2nd International Conference on Advanced Computing and Software Engineering (ICACSE) 2019. http://dx.doi.org/10.2139/ssrn.3351024.

XVI. Rajesh Mothe, Swathi Balija, Yerrolla Chanti, Bura Vijay Kumar “A modified Fault Diagnosis Scheme in Wireless Sensor Networks”, International Journal of Engineering & Technology, vol.7, no.1.8, pp. 226-229, 2018. DOI: 10.14419/ijet.v7i1.8.22956.
XVII. R. N. Jadoon, W. Zhou, I. A. Khan, M. A. Khan, and W. Jadoon, “EEHRT: energy efficient technique for handling redundant traffic in zone-based routing for wireless sensor networks,” Wireless Communications and Mobile Computing, vol. 2019, Article ID 7502140, 12 pages, 2019.
XVIII. Seo, Hwa-jeong, and Kim, Ho-Won. “Four Anchor Sensor Nodes Based Localization Algorithm over Three-Dimensional Space.” Journal of Information and Communication Convergence Engineering 10, no. 4 (December 31, 2012): 349–358.
XIX. Sngh A., V. Yadav, M. K. Mishra, and M. Gore, “Localization scheme for three-dimensional wireless sensor networks using GPS enabled mobile sensor nodes”. International Journal of Next-Generation Networks (IJNGN), vol. 1, no. 1, pp. 60-72, 2009.
XX. Ssu, K.-F., C.-H. Ou, and H.C. Jiau. “Localization With Mobile Anchor Points in Wireless Sensor Networks.” IEEE Transactions on Vehicular Technology 54, no. 3 (May 2005): 1187–1197.
XXI. Singh Neetu, 2V.K Jain, “An Improvised Recommendation System For Mobile Plans Using Similarity Fusion”, J. Mech. Cont.& Math. Sci., Vol.-13, No.-4, September-October (2018), pp 189-197.
XXII. Sri Bindu. Sattu, “Digital Beam forming Algorithms for Radar Applications”, J. Mech. Cont.& Math. Sci., Vol.-14, No.-5, September – October (2019) , pp 527-542

View Download

Abstract:

Channel bonding is an authenticated approach used in wireless networks that improve data rate as well as reduces latency. Traditional networks like mobile networks, non-cellular networks, and wireless LAN and wireless sensor networks use traditionally the Channel bonding technique. To support channel bonding, effective frequencies assignment techniques are significant to use, and thus improving frequencies use. In multi-hop topologies, WSN usually generates a bunch of packets like scattered and event-driven transmission methods’ where data transmitted over many transitional hops. In this paper, we have thoroughly analyzed the various parameters affecting channel bonding as well as its application in wireless sensor networks.  Finally, various challenges for channel bonding implication in futuristic cognitive radio networks are also presented.

Keywords:

Channel bonding,mobile networks,non-cellular networks,wireless LAN,Channel aggregation,

Refference:

I A. A. Khan, M. H. Rehmani, and M. Reisslein, “Cognitive radio for smart grids: Survey of architectures, spectrum sensing mechanisms, and networking protocols,” IEEE Communications Surveys & Tutorials, to be published, 2015.
II A. Fragkiadakis, E. Tragos, and I. Askoxylakis, “A survey on security threats and detection techniques in cognitive radio networks,” IEEE Commun. Surveys & Tutorials, vol. 15, no. 1, pp. 428–445, February 2013
III A. M. Kavi K. Khedo, Rajiv Perseedoss, “A wireless sensor network air pollution monitoring system,” International Journal of Wireless & Mobile Networks, vol. Vol. 2, No. 2, pp. 31–45, 2010.
IV A. Popescu, “Cognitive radio networks,” in 9th International Conference on Communications, 2012, pp. pp. 11–15.
V Khan, Haroon, Mian Ahmad Jan, Muhammad Alam, and Wael Dghais. “A channel borrowing approach for cluster-based hierarchical wireless sensor networks.” Mobile Networks and Applications 24, no. 4 (2019): 1306-1316.
VI A. Sharma and E. M. Belding, “A case for application-aware channel access in wireless networks,” in 10th workshop on Mobile Computing Systems and Applications, 2009, p. 10.
VII C. -M. Cheng, P.-H. Hsiao, H. T. Kung, and D. Vlah, “Adjacent channel interference in dual-radio 802.11a nodes and its impact on multi-hop networking,” in Global Telecommunications Conference, 2006, pp. 1– 6.
VIII E. T. Yazdi, A. Willig, and K. Pawlikowski, “Frequency adaptation for interference mitigation in IEEE 802.15.4-based mobile body sensor networks,” Computer Communications, vol. Vol. 53, pp. 102–119, 2014.
IX H. B. Salameh, M. Krunz, and D. Manzi, “Spectrum bonding and aggregation with guard-band awareness in cognitive radio networks,” IEEE Transactions on Mobile Computing, vol. Vol. 13, No. 3, pp. 569 – 581, 2013.
X H. Lee, S. Vahid, and K. Moessner, “A survey of radio resource management for spectrum aggregation in lte-advanced,” IEEE Communications Surveys & Tutorials, vol. Vol. 16, No. 2, pp. 745–760, 2014.
XI H. Rahul, N. Kushman, D. Katabi, C. Sodini, and F. Edalat, “Learning to share: Narrowband-friendly wideband networks,” Computer Communication Review, vol. Vol. 38, No. 4, pp. 147–158, 2008.
XII H. S. Chiu, K. L. Yeung, and K.-S. Lui, “J-car: An efficient joint channel assignment and routing protocol for ieee 802.11-based multichannel multi-interface mobile ad hoc networks,” IEEE Transaction on Wireless Communications, vol. Vol. 8, No. 4, pp. 1706–1715, 2009.
XIII I. F. Akyildiz and E. Stuntebeck, “Wireless underground sensor networks: Research Challenges,” Ad Hoc Networks, vol. 4, no. 6, pp. 669– 686, 2006.
XIV K. Pelechrinis, T. Salonidis, H. Lundrgen, and N. Vaidya, “Experimental characterization of 802.11n link quality at high rates,” in 5th international workshop on Wireless network testbeds, 2010, pp. 39 – 46.
XV K. Shenai and S. Mukhopadhyay, “Cognitive sensor networks,” in PROC. 26th International Conference on Microelectronics, 2008, pp. 1 – 6.
XVI K.-H. Nguyen and W.-J. Hwang, “An efficient power control scheme for spectrum mobility management in cognitive radio sensor networks,” Embedded and Multimedia Computing Technology and Service, pp. 667–676, 2012.
XVII L. Deek, E. Garcia-Villegas, E. Belding, S.-J. Lee, and K. Almeroth, “Joint rate and channel width adaptation for 802.11 Mimo wireless networks,” in Sensor, Mesh and Ad Hoc Communications and Networks, 2013, pp. 167 – 175.
XVIII L. Li, C. Zhang, and Y. Li, “Qos-aware on-demand channel width adaptation protocols for multi-radio ad-hoc networks,” in Wireless Communications and Networking Conference, 2009, pp. 1 – 6.
XIX L. Shaowei, “Markov decision processes with applications in wireless sensor networks: A survey,” IEEE Communications Surveys & Tutorials, vol. 17, no. 3, pp. 1239–1267, 2015.
XX L. Xu, K. Yamamoto, and S. Yoshida, “Performance comparison between channel-bonding and multi-channel CSMA,” in Wireless Communication and Networking Conference, 2007, pp. 406 – 410.
XXI M. A. Rahman and M. Krunz, “Stochastic guard-band-aware channel assignment with bonding and aggregation for dsa networks,” IEEE Transactions on Wireless Communications, pp. 1–11, 2015.
XXII M. H. Rehmani and Y. Faheem, Eds., Cognitive Radio Sensor Networks: Applications, Architectures, and Challenges. IGI-Global, 2014.
XXIII M. H. Rehmani, M. Shadaram, S. Zeadally, and P. Bellavista, “Special issue on recent developments in cognitive radio sensor networks,” Elsevier Pervasive and Mobile Computing, vol. 22, pp. 1–2, 2015.
XXIV M. Y. Kemal Akkaya, “A survey on routing protocols for wireless sensor networks,” Ad Hoc Networks, vol. Vol. 3, No. 3, pp. 325–349, 2005.
XXV N. Hasan, W. Ejaz, S. Lee, and H. Kim, “Knapsack-based energy-efficient node selection scheme for cooperative spectrum sensing in cognitive radio sensor networks,” IET Communications, vol. Vol. 6, No. 17, pp. 2998–3005, 2012.
XXVI O. B. Akan, O. B. Karli, and O. Ergul, “Cognitive radio sensor networks,” IEEE Network, vol. Vol. 23, No. 4, pp. 34–40, 2009.
XXVII P. Huang, X. Yang, and L. Xiao, “Dynamic channel bonding in multicarrier wireless networks,” in 21st International Conference on Network Protocols, 2013, pp. 1 – 10.
XXVIII Bukhari, Syed Hashim Raza, Mubashir Husain Rehmani, and Sajid Siraj. “Remaining idle time aware intelligent channel bonding schemes for cognitive radio sensor networks.” Wireless Networks 25, no. 8 (2019): 4523-4539.
XXIX P. Steenkiste, D. Sicker, G. Minden, and D. Raychaudri, “Future directions in cognitive radio network research,” National Science Foundation Workshop Report, Tech. Rep. Vol. 4, No. 1, 2009.
XXX R. Carrano, D. Passos, L. Magalhaes, and C. Albuquerque, “Survey and taxonomy of duty cycling mechanisms in wireless sensor networks,” IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 181– 194, 2014.
XXXI R. Chandra, R. Mahajan, J. Moscibroda, R. Raghavendra, and P. Bahl, “A case for adapting channel width in wireless networks,” ACM SIGCOMM Computer Communication Review, vol. Vol. 38, No. 4, pp. 135–146, 2008.
XXXII R. Gummadi, R. Patra, H. Balakrishnan, and E. Brewer, “Interference avoidance and control,” in Hot Topics in Networks HotNets, 2008, pp. 13 – 18.
XXXIII S. Anand, K. Hong, R. Chandramouli, S. Sengupta, and K. P. Subbalakshmi, “Security vulnerability due to channel aggregation/bonding in lte and hspa+ networks,” in Global Telecommunications Conference, 2011, pp. 1 – 5.
XXXIV Niaz, Fahim, Muhammad Khalid, Zahid Ullah, Nauman Aslam, Mohsin Raza, and M. K. Priyan. “A bonded channel in cognitive wireless body area network based on IEEE 802.15. 6 and the internet of things.” Computer Communications 150 (2020): 131-143.
XXXV S. Bayhan and F. Alagoz, “A markovian approach for best-fit channel selection in cognitive radio networks,” Ad Hoc Networks, vol. 12, pp. 165–177, 2014.
XXXVI S. Joshi, P. Pawelczak, D. Cabric, and J. Villasenor, “When channel bonding is beneficial for opportunistic spectrum access networks,” IEEE Transactions on Wireless Communications, vol. Vol. 11, No. 11, pp. 3942–3956, 2012.
XXXVII Peng, Min, Caihong Kai, and Lusheng Wang. “Constrained channel bonding based on maximum achievable throughput in WLANs.” WIRELESS NETWORKS (2020).
XXXVIII S. Movassaghi, M. Abolhasan, J. Lipman, D. Smith, and A. Jamalipour, “Wireless body area networks: A survey,” IEEE Communications Surveys & Tutorials, vol. 16, no. 3, pp. 1658–1686, Third Quarter, 2014.
XXXIX J S Banerjee, A Chakraborty, Chattopadhyay, Reliable Best-Relay Selection for Secondary Transmission in Co-operation Based Cognitive Radio Systems: A Multi-Criteria Approach, J. Mech. Cont.& Math. Sci., Vol.-13, No.-2, May-June, pp 24-42
XL Saad Hassan Kiani, Sohail Imran, Mehr-e- Munir, MujeebAbdullah, A High Miniaturaized Antenna for Wi-Max and Small Wireless Technologies, J. Mech. Cont.& Math. Sci., Vol.-14, No.-1, January-February (2019), pp 250-257

View Download

Abstract:

A numerical algorithm is presented for the laminar natural convection flow of a nanofluid along with an isothermal vertical plate. Nanofluid is treated as a two-component mixture as per Boungiorno model, and as such, the effects of Brownian motion and thermophoresis is incorporated. The equations governing the flow are higher-order nonlinear partial differential equations, and subsequently, they are transformed into a set of nonlinear ordinary differential equations using similarity transformation. Finally, they are reduced to a first-order system and we integrate them using Newton Raphson and adaptive Runge-Kutta methods. For the whole numerical procedure, computer codes are developed in the Matlab environment. We compute dimensionless stream function (s), longitudinal velocity (s′), temperature (θ), and nanoparticle volume fraction (f) and illustrate them graphically for various values of five pertinent dimensionless parameters, namely, Prandtl number (Pr), Lewis number (Le), buoyancy-ratio parameter (Nr), Brownian motion Parameter (Nb), and thermophoresis parameter (Nt). The reduced Nusselt number (Nur) is found to be a decreasing function of each of Nr (buoyancy-ratio parameter), Nb (Brownian motion parameter), and Nt (Thermophoresis parameter). The results of the present simulation agree

Keywords:

Brownian Motion,Isothermal Vertical Plate,Nano Fluid,Natural Convection,Thermophoresis,Two-Phase Model,

Refference:

I. Asish Mitra, NUMERICAL SIMULATION OF LAMINAR CONVECTION FLOW AND HEAT TRANSFER AT THE LOWER STAGNATION POINT OF A SOLID SPHERE., J. Mech. Cont.& Math. Sci.,Vol.-10, No.-1, October (2015), pp 1469-1480
II. Asish Mitra, NUMERICAL SIMULATION ON LAMINAR CONVECTION FLOW AND HEAT TRANSFER OVER AN ISOTHERMAL HORIZONTAL PLATE, J. Mech. Cont.& Math. Sci.,Vol.-10, No.-2, January (2016), pp 1521-1534

III. A. Bejan, Convection Heat Transfer, Wiley, New York, NY, 1984.
IV, A.V. Kuznetsov and D.A. Nield, Natural convective boundary-layer flow of a nanofluid past a vertical plate, Int. J. Thermal Sciences, 49, (2010) 243–247.
V. D. A. Nield, A.V. Kuznetsov, The onset of convection in a nanofluid layer, ASME J. Heat Transf, submitted for publication.
VI. D. A. Nield, A.V. Kuznetsov, Thermal instability in a porous medium layer saturated by a nanofluid, Int. J. Heat Mass Transf, 52 (2009) 5796–5801.
VII. J. Buongiorno, Convective transport in nanofluids, ASME J. Heat Transf. 128 (2006) 240–250.
VIII. Naphon, P., Nakharintr, L., 2015. Turbulent two-phase approach model for the nanofluids heat transfer analysis flowing through the mini channel heat sinks. Int. J. Heat Mass Transf. 82, 388–395.
IX. W. A. Khan and A. Aziz Natural convection flow of a nanofluid over a vertical plate with uniform surface heat flux, International Journal of Thermal Sciences, 50 (2011) 1207-1214 Transf, submitted for publication.

View Download

Abstract:

Rolled Homogenous Armour steel is an attractive candidate for use as armour because of its high hardness compared to other steel grades. The ballistic behavior of RHA is appraised using depth of penetration (DOP)and ballistic limit velocity V₅₀against 7.62mm armour piercing (AP) Projectile with the hardened steel core. The ballistic potency of the material is calculated by studying the influence of variation in thickness of plates and the projectile velocity. Velocity in the range of 400 to 854m/s is considered in the present analysis, based on the plate thickness. In the present study, a modified simple projectile simulation model which represents a 7.62 mm AP projectile is developed. Based on this, the DOP studies are done for three different plate thicknesses. Further, the ballistic limit velocity V₅₀ is obtained for various plate thicknessesby conducting a series of simulations using finite element based explicit dynamic solver.The obtained numerical results are compared with available benchmark experiments

Keywords:

Ballistic impact, 7.62mm armour piercing projectile,high hardness steel,armour plate,finite element modelling,Johnson-Cook strength, failure,

Refference:

I. Banerjee A, Dhar S, Acharyya S, Datta D, Nayak N, Numerical simulation of the ballistic impact of armour steel plate by typical armour piercing projectile, Procedia Engineering 173 (2017), p. 347 – 354.

II. Binar, T., Švarc, J., Vyroubal, P., Kazda, T., Rolc, S., &Dvořák, A. (2018). The comparison of numerical simulation of projectile interaction with transparent armour glass for buildings and vehicles. Engineering Failure Analysis, 92, 121-139.

III. Buchar J, Voldrich J, Rolc S, Lisy J. Ballistic performance of dual hardness armor. Proceedings of 20th international symposium on ballistics, Orlando; 2002, p. 23–27.

IV. Chocron S., Anderson Jr. C.E., Grosch D.J., and Popular C.H. Impact of the 7.62 mm APM2 projectile against the edge of a metallic target, International Journal of Impact Engineering, 25:423–437, 2001.

V. Demir T, Ubeyli M, YıldırımRO,Investigation on the ballistic impact behavior of various alloys against 7.62 mm armor-piercing projectile, Mater Des 2008; 29, pp. 2009–16.

VI. Doig, A., 1998a, Military Metallurgy, IOM CommunicationsLtd., London, Great Britain, pp. 1123.

VII. Doig, A., 1998b, Military Metallurgy, IOM CommunicationsLtd., London, Great Britain, pp. 6166.

VIII. FarrokhniaNavid, SeyedMojtabaMovahedifar, Investigating the behavior of steel structures with honeycomb damper ‎against blast and earthquake loads, J. Mech. Cont.& Math. Sci., Vol.-14, No.-4, July-August (2019), pp 74-92.

IX. Hiba Mudhafar Hashim, Ihsan Y. Hussain, “Natural Convection Cooling of PCB Equipped with Perforated Fins Heat Sink including Inclination and Vibration Effects”, J. Mech. Cont.& Math. Sci., Vol.-14, No.-5, September – October (2019). pp 62-77.

X. Kilic N, EkiciB.,Ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition, Mater Des 2013, 44, pp. 35–48.

XI. Li, C., Liu, K., Guo, X. J., & Yuan, L. X. (2018, July). Experimental Study on Ballistic Performance for Multi-hole Armor Steel Plates Against the 7.62 mm Armor Piercing Projectile. In IOP Conference Series: Materials Science and Engineering (Vol. 382, No. 2, p. 022060). IOP Publishing.

XII. NamıkKılıc,SaidBedir, AtılErdik, BülentEkici, AlperTasdemirci, Mustafa Güden, Ballistic behavior of high hardness perforated armor plates against 7.62 mm armor piercing projectile, Materials and Design 63 (2014) pp. 427–438.

XIII. Niezgoda T, Morka A. On the numerical methods and physics of perforation inthe high-velocity impact mechanics. World J Eng. p. 414.

XIV. Raghaven, K. S., Sastri, A. S., and Marcinkowski,M. J., 1969, “Nature of the Work-hardening BehaviorinHadfields Manganese steel,” Transactionof American Institute of MetallurgicalEngineering,Vol. 245, pp. 1569-1575.

XV. S. Dey, T. Borvik, O.S. Hopperstad, J.R. Leinum, and M. Langseth, The effect of target strength on the perforation of steel plates using three different projectile nose shapes,Int. J. Impact Eng., 2004, 30, pp. 1005–1038.

XVI. Senthil K, Tiwari G, Iqbal M A, et al. (2013) Impact response of single and layered thin plates, Proceedings of the Indian National Science Academy 79 (4), pp. 705–716.

XVII. Serjouei, A., Chi, R., Sridhar, I., & Tan, G. E. (2015). Empirical ballistic limit velocity model for bi-layer ceramic–metal armor. International Journal of Protective Structures, 6(3), 509-527.

XVIII. Stewart, M. G., &Netherton, M. D. (2019). Statistical variability and fragility assessment of ballistic perforation of steel plates for 7.62 mm AP ammunition. Defence Technology.

XIX. T. Borvik, M. Langseth, O.S. Hopperstad, and K.A. Malo, Ballistic penetration of Steel Plates,Int. J. Impact Eng., 1999, 22, p 855–886.

XX. U. S. Army Materials Technology Laboratory,1987, “Military Standard, V50 Ballistic Test forArmor, MIL-STD-662E,” Department of theNavy, Defense Printing Service, Philadelphia,PA.

XXI. U. S. Department of Defense, 1984, “MilitarySpecification: Armor Plate, Steel, Wrought, Homogeneous(for use in Combat-vehicles and forAmmunitionTesting),” MIL-A-12560G(MR),U.S. Army Materials Technology Laboratory,Watertown,MA.

View Download

Abstract:

The peristaltic transport of MHD Newtonian fluid under the effect of Porous Medium in an inclined tapered asymmetric channel is analyzed mathematically. Convective Thermal and concentration is discussed. The governing equations, i.e. (continuity, motion, energy, and concentration) are simplified by using a long wavelength and small Reynolds number approximations into a system of non-linear differential equations which solved approximately with the help of Homotopy perturbation method for velocity, streamlines, temperature, and concentration. The impact of important, relevant parameters on the flow is discussed graphically. We noticed that the velocity curve and trapping phenomenon reduced by increasing the Hartman number the magnetic field parameter because of the existence of Lorentz force and increasing in ascending value of permeability parameter. Further, A reduction behavior of temperature and concentration profile is depicted with the higher value of the Biot number of heat and mass transfer.

Keywords:

Newtonian fluid,Homotopy perturbation,Porous medium,Convective thermal,

Refference:

I. Abbasi, F.M ; Hayat, T. and ALsaedi, A. “Effects of inclined magnetic field and Joule heating in mixed convective peristaltic transport of non-Newtonian fluids”.Bulletin of the Polish Academy of Sciences, Technical Sciences, 63(2), 501-514 (2015).
II. Abdul Gaffar. S; Ramachandra Prasad. V and Keshava Reddy. E. “Non-Newtonian thermal convection of eyring-powell fluid from an isothermal sphere with biot number effects”.Int. J. Industrial Mathematics, 8(2), Article ID IJIM-00694, 16 pages (2016) .
III. Ahmad, S.; Farooq, M.; Javed. M. and Anjum. A. “Double stratification effect in chemically reactive squeezed Stuterby fluid flow with thermal radiation and mixed convection”. J. Result in Physics, 8,1250-1259 (2018).
IV. Amina Panezai1; Abdul Rehman1; Naveed Sheikh; Saleem Iqbal ; Israr Ahmed ; Manzoor Iqbal and Muhammad Zulfiqar. “Mixed Convective Magnetohydrodynamic Heat Transfer Flow of Williamson Fluid Over a Porous Wedge”. American Journal of Mathematical and Computer Modelling, 4(3), 66-73 (2019).
V. Asha, S. K and Sunitha, G. “Mixed Convection Peristaltic Flow of a Eyring-Powell Nanofluid with Magnetic Field in a Non-Uniform Channe”l, Journal of Applied Mathematics and Computation (JAMC), 2(8), 332-344 (2018).
VI. Hayat.T; Naseema Aslam; Ijaz. M; Imran Khan. M. and Alsaedi. A.”MHD peristaltic motion of Johnson – Segalman fluid in an inclined channel subject to radiative flux and convective boundary conditions”, Computer Method and Program in Biomedicine, 180, (2019).
VII. Mekheimer. Kh. S; Abd elmaboud. Y. “The influence of heat transfer and magnetic field on peristaltic transport of a Newtonian fluid in a vertical annuus” Application of an endoscope, Physics Letters A, 372, 1657-1665 (2008).
VIII. Mohammed R. Salman and Ahmed M. Abdulhadi “Soret and Dufour effects in MHD peristalsis of pseudoplastic nano fluid with porous medium in tapered channel” International Journal of Science and Research, Volume 6 Issue 12,1939-1951(2017).
IX. Mohammed R. Salman and Ahmed M. Abdulhadi “Effects of MHD on peristalsistransport and heat transfer with variables viscosity in porous medium” InternationalJournal of Science and Research, Volume 7 Issue 2,612-623(2018).
X. Mohammed R. Salman and Ahmed M. Abdulhadi “Influence of heat and masstransfer on inclined (MHD) peristaltic of pseudoplastic nanofluid through the porous medium with couple stress in an inclined asymmetric channel” The Sixth ScientificConference “Renewable Energy and its Applications” IOP Publishing IOP Conf. Series: Journal of Physics: Conf. Series 1032 (2018) 012043 doi :10.1088/1742-6596/1032/1/012043(2018)
XI. Mohammed R. Salman and Ahmed M. Abdulhadi “Analysis of heat and mass transfer in a tapered asymmetric channel during peristaltic transport of (pseudoplastic nanofluid) with variable viscosity under the effect of (MHD)” Journal of AL-Qadisiyah for computer science and mathematics, Vol.10 No.3,80-96 (2018)
XII. Nabil T. Eldabe and| Galal M. Moatimid. “Mixed convective peristaltic flow of Eyring‐Prandtl fluid with chemical reaction and variable electrical conductivity in a tapered asymmetric channel”. Heat Transfer- Asian Res.;48,1946-1962,DOI: 10.1002/htj.21466, (2019).
XIII. Nadeem, S; Noreen Sher Akbar and Hameed, M. “Peristaltic transport and heat transfer of a MHD Newtonian fluid with variable viscosity”.Int. J. Numer. Meth. Fluids, 63,1375–1393 (2010).
XIV. Nuhad S.A. and Ali A. Z.”Semi-primary RΓ-submodule of multiplication RΓ-submodules”, Journal of mechanics of continua and mathematical sciences,vol.-15, No.-2, pp 1-9 ISSN (Print) 0973- 8975(2020).
XV. Obaid Ullah Mehmood; Ayesha Aleem Qureshi; Humaira Yasmin; Salah Uddin.”Thermo-mechanical analysis of non Newtonian peristaltic mechanism: Modified heat flux model”. https://doi.org/10.1016/j.physa.2019.124014.
XVI. Ramesh, K; and Devakar, M “Effects of Heat and Mass Transfer on the Peristaltic Transport of MHD Couple Stress Fluid through Porous Medium in a Vertical Asymmetric Channel”. Hindawi Publishing Corporation,Journal of Fluids, V. 2015, Article ID 163832, 19 pages,(2015).
XVII. SafiaAkram: Emad H. Aly; Farkhanada Afzal and Sohail Nadeem. “Effect of the Variable Viscosity on the Peristaltic Flow of Newtonian Fluid Coated with Magnetic Field: Application of Adomian Decomposition Method for Endoscope” Coatings, 9(8), 524(2019); https://doi.org/10.3390/coatings9080524.
XVIII. Sadia Ayub; Hayat. H; Asghar. S and Ahmad. B. “Thermal radiation impact in mixed convective peristaltic flow of third grade nanofluid”. Journal of Results in Physics,7,3687-3695,(2017).
XIX. Tasawar. Hayat, Javaria. Akrama, Ahmed Alsaedib, Hina Zahira, Endoscopy effect in mixed convective peristalsis of Powell-Eyring nanofluid, Journal of Molecular Liquids, 254,47-54 (2018).
XX. Zaheer Asghar and Nasir Ali.”Mixed convective heat transfer analysis for the peristaltic transport of viscoplastic fluid: Perturbation and numerical study” AIP Advances 9, 095001 (2019); https://doi.org/10.1063/1.5118846.

View Download