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Abstract: This paper presents an application of a fuzzy goal programming approach to mobile application marketing.A special type of membership function (generalized bell membership function) is used to solve the system.Firstly the problem has been formulated with equal priorities and then with unequal priorities. The main goal of this problem is to maximize the profit and sales of the mobile application marketing. Finally, the optimal result has been obtained.
Keywords: Fuzzy goal programming, Bell membership function, Mobile application, Equal and unequal Priority.
References:
1. Belmokaddem, M; Mekidiche, M and Sahed, A (2009): Application of a Fuzzy Goal Programming Approach with Different Importance and Priorities to Aggregate Production Planning, Journal of Applied Quantitative Method, 4(, 3), 317-330.
2. Biswas, A and Pal, B.B (2005): Application of Fuzzy Goal Programming Technique.
3. Chang, C.T (2007): Binary Fuzzy Goal Programming, European Journal of Operational Research 180, 27-37.
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6. Chen, H.K (1994): A Note on a Fuzzy Goal Programming Algorithm by Tiwari, Dharmar and Rao. Fuzzy sets and system 62,287-290.
7. Chen, L.H and Tsai, F.C(2001), Fuzzy Goal Programming with Different Importance and Priorities, European Journal of Operational Research society 133,548-556.
8. Gupta and Bhattacharjee (2012): Two Weighted Fuzzy Goal Programming Methods to Solve Multi objective Goal Programming Problem, Journal of Applied Mathematics,2012,1-20.
9. Hannan, E.L (1981): On Fuzzy Goal Programming, Decision sciences 12(, 3), 522-248.
10. Hannan, E.L (1981a): Linear Programming with Multiple Fuzzy Goals, Fuzzy sets and systems 6, 235-248
11. Hannan, E.L (1982): Contrasting Fuzzy Goal Programming and Fuzzy Multicriteria Programming, Decision sciences 13, 337-339.
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13. Ignizio, J.P (1976): Goal Programming & Extensions, Lexington Books, Lexington, M.A
14. Ignizio, J.P (1982): Note and Communications on the (Re) Discovery of Fuzzy Goal Programming, Decision sciences 13(2), 331-336.
15. Ijiri Y (1965): Management Goals & Accounting for Control, North Holland, Amsterdam.
16. Jinturkar, A.M and Deshmukh, S.S (2011): A Fuzzy Mixed Integer Goal Programming Approach for Cooling and Heating Energy Planning in Rural India, Expert systems with Application, 38, 11377-11381.
17. Lin, C.C (2004): A Weighted Max-Min Model for Fuzzy Goal Programming, Fuzzy sets and systems, 142.
18. Maged G.Iskander: Exponential Membership Functions in Fuzzy Goal Programming: A Computational Application to a Production Problem in the Textile Industry.
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Abstract: Low density parity check (LDPC) codes are error-correcting codes that offer huge advantages in terms of coding gain, throughput and power dissipation in digital communication systems. Error correction algorithms are often implemented in hardware for fast processing to meet the real-time needs of communication systems. However, traditional hardware implementation of LDPC decoders require large amount of resources, rendering them unsuitable for use in energy constrained sensor nodes of wireless sensor networks (WSN). This paper investigates the use of short-length LDPC codes for error correction in WSN. It presents the LDPC decoder designs, implementation, resource requirement and power consumption to judge their suitability for use in the sensor nodes of WSN. Due to the complex interconnections among the variable and check nodes of LDPC decoders, it is very time consuming to use traditional hardware description language (HDL) based approach to design these decoders. This paper presents an efficient automated high-level approach to designing LDPC decoders using a collection of high-level modeling tools. The automated high-level design methodology provides a complete design flow to quickly and automatically generate, test and investigate the optimum (length) LDPC codes for wireless sensor networks to satisfy the energy constraints while providing acceptable bit-error-rate performance.
Keywords: Error Correction Coding; Wireless Communication; Wireless Sensor Networks; Digital System.
References:
1. R. Gallager, "Low-density parity-check codes," IRE Transactions on Information Theory, vol. 8, pp. 21-28, 1962.
2. S. J. Johnson, "Introducing low-density parity-check codes," University of Newcastle, Australia, 2006.
3. M. Eroz, F. W. Sun, and L. N. Lee, "DVB-S2 low density parity check codes with near Shannon limit performance," International Journal of Satellite Communications and Networking, vol. 22, pp. 269-279, 2004.
4. T. Mohsenin and B. M. Baas, "Split-Row: A reduced complexity, high throughput LDPC decoder architecture," in International Conference on Computer Design, ICCD, San Jose, 2007, pp. 320-325.
5. Darabiha, A. C. Carusone, and F. R. Kschischang, "A bit-serial approximate min-sum LDPC decoder and FPGA implementation," in IEEE International Symposium onCircuits and Systems, ISCAS, Island of Kos, 2006.
6. D. Culler, D. Estrin, and M. Srivastava, "Overview of wireless sensor networks," IEEE Computer, Special Issue in Sensor Networks, vol. 37, pp. 41–49, 2004.
7. C. Buratti, A. Conti, D. Dardari, and R. Verdone, "An Overview on Wireless Sensor Networks Technology and Evolution," Sensors, vol. 9, p. 6869, 2009.
8. M. Tubaishat and S. Madria, "Sensor networks: an overview," IEEE potentials, vol. 22, pp. 20-23, 2003.
9. Akyildiz, T. Melodia, and K. Chowdhury, "A survey on wireless multimedia sensor networks," Computer Networks, vol. 51, pp. 921-960, 2007.
10. S. M. Aziz and D. M. Pham, "Energy Efficient Image Transmission in Wireless Multimedia Sensor Networks," IEEE Communications Letters, vol. 17, pp. 1084 -1087, June 2013.
11. D. M. Pham and S. M. Aziz, "Object extraction scheme and protocol for energy efficient image communication over Wireless Sensor Networks," Computer Networks, Elsevier, Online, July 2013.
12. E. Sanchez, F. Gandino, B. Montrucchio, and M. Rebaudengo, "Increasing effective radiated power in wireless sensor networks with channel coding techniques," in International Conference on Electromagnetic in Advanced Applications, ICEAA, 2007, pp. 403-406.
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