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Satellite image processing is one of the important research areas in the field of digital image processing and is a challenging task for the researchers. It is often required to remove noise and smooth the image to highlight certain features of interest for image analysis and extracting significant information from satellite images often termed as image enhancement. It is an important step for overall image recognition and interpretation process and is a pre processing step that serves as an important step towards the solution for image analysis. Image enhancement can be performed in spatial or frequency domains. In this paper, we focus on spatial domain enhancement techniques with respect to satellite images. Some of the important image enhancement techniques such as contrast stretching, decorrelation stretch, histogram equalization and contrast limited adaptive histogram equalization are experimented and compared for visual interpretability. Two parameters, Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR) are used for performance evaluation. The techniques are tested using 20 LandSat satellite images with different illumination effects. The experimentation was carried out using soft computing tool Matlab. It was observed that for satellite images, contrast stretching gives better results as compared to other techniques.

Image Enhancement; Contrast Stretching; Mean Squared Error (MSE); Peak Signal to Noise ratio (PSNR); Landsat Imagery

[1] G. Singh and A. Mittal, �Various Image Enhancement Techniques-A Critical Review�, International Journal of Innovation and Scientific Research, Vol. 10, No. 2, pp. 267�274, 2014.
[2] Ashamdeep Singh and Navdeep Kanwal, �Qualitative Comparative Study of Various Contrast Enhancement Techniques�, International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR), vol. 3, no. 1, pp. 19�24, 2013.
[3] J. Lisani, J. Michel, J. Morel, A. B. Petro, and C. Sbert, �An Inquiry on Contrast Enhancement Methods for Satellite Images�, IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 12, pp. 7044�7054, Dec 2016.
[4] M.A.Shaikh and S.B.Sayyad, �Color Image Enhancement Filtering Techniques for Agricultural Domain Using Matlab�, ISPRS International Symposium on �Operational Remote Sensing Applications: Opportunities, Progress and Challenges�, no. 224, Dec 9-12, 2014.
[5] P. Kaushik and Y. Sharma, �Comparison of different image enhancement techniques based upon Psnr & Mse�, International Journal of Applied Engineering Research, vol. 7, no. 11, pp. 2010�2014, 2012.
[6] �Remote Sensing-Digital Image Processing-Image Enhancement Filtering and edge enhancement�, Http://nptel.ac.in/courses/105108077/module4/lecture16.pdf, , pp. 1�19, Dec 10, 2016
[7] R. Aedla, �Satellite Image Contrast Enhancement Algorithm based on Plateau Histogram Equalization�, IEEE Region 10 Symposium, pp. 213�218, 2014.
[8] S. S. Al-amri, N. V Kalyankar, and S. D. Khamitkar, �Contrast Stretching Enhancement in Remote Sensing Image,� BIOINFO Sensor Networks, vol. 1, no. 1, pp. 06-09, 2011.
[9] B. Attachoo and P. Pattanasethanon, �A new approach for colored satellite image enhancement�, 2008 IEEE International Conference on Robotics and Biomimetics, Feb 21-26, pp. 1365�1370, 2009.
[10] B. D. Jadhav, �An Effective method for Satellite Image Enhancement�, IEEE International Conference on Computing and Automation (ICCCA2015), pp. 1171�1175, 2015.
[11] Ahire Rina B, V. S. Patil �Overview of Satellite Image Resolution Enhancement Techniques�, IEEE, pp. 0�3, 2013.
[12] P. Bidwai and D. J. Tuptewar, �Resolution and contrast enhancement techniques for grey level, color image and satellite image�, Proceedings - IEEE International Conference on Information Processing,, pp. 511�515, Dec 16-19, 2016.
[13] R. R. Naaraayan and R. S. Prasad, �Contrast Enhancement Of Satellite Images Using Advanced Block Based Dwt Technique�, IEEE International Conference on Recent Trends in Information Technology, 2016.
[14] S. Nimkar, S. Shrivastava, and S. Varghese, �Contrast Enhancement and Brightness Preservation Using Multi-Decomposition Histogram Equalization� Signal & Image Processing : An International Journal, vol. 4, no. 3, pp. 83�93, 2013.
[15] X. Sun, L. Zhang, H. Yang, T. Wu, Y. Cen, and Y. Guo, �Enhancement of Spectral Resolution for Remotely Sensed Multispectral Image�, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 5, pp. 2198�2211, 2015.
[16] R. P. Singh and M. Dixit, �Histogram Equalization : A Strong Technique for Image Enhancement�, International Journal of Signal Processing, Image Processing and Pattern Recognition, vol. 8, no. 8, pp. 345�352, 2015.
[17] V. Syrris, S. Ferri, D. Ehrlich, and M. Pesaresi, �Image Enhancement and Feature Extraction Based on Low-Resolution Satellite Data�, 1986 IEEE Journal Of Selected Topics In Applied Earth Observations And Remote Sensing vol. 8, no. 5, pp. 1�10, 2015.
[18] A. Vashisth, R. Sharma, �A Survey of Various Image Enhancement Methods on Different Types of Images�, International Journal of Computer Sciences and Engineering, vol. 3, Issue 2, pp. 10�14, 2015.
[19] R. C. Gonzalez and R. E. Woods, �Digital Image Processing�, Prentice-Hall, 3rd edition,ISBN:978-81-317-2695-2 , 2008.
[20] Thomas M. Lillesand, Ralph W. Kiefer, Jonathan W. Chipman, �Remote Sensing and Image Interpretation�, Wiley, 6th edition, ISBN 978-81-265-3223-0 , 2007.
[21] John A. Richards, Xiuping Jia, �Remote Sensing Digital Image Analysis-An Introduction�, Springer, 4th edition, ISBN-13 978-3-540-25128-6 2006.

S. Chib, M.S. Devi, "Performance Analysis of Enhancement Techniques for Satellite Images," International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.113-119, 2016.

The mining high utility pattern is new development in area of data mining. Problem of mining utility pattern with itemset share framework is tricky one as no anti-monotonicity property with interesting measure. Former works on this problem employ a two-phase, candidate generation approach with one exception that is however inefficient and not scalable with large database. This paper reviews former implementation and strategies to mine out high utility pattern in details. We will look ahead some strategies of mining sequential pattern.

Data Mining, Pattern Mining, High Pattern, Frequent Pattern, Utility Mining

[1] L. Geng and H. J. Hamilton, �Interestingness measure for data mining: A survey,� ACM Comput. Survey, Volume-38, No. 3, Page No 9, 2006
[2] A. Silberschatz and A. Tuzhilin, �On subjective measures of interestingness in knowledge discovery,� in Proc. ACM 1st Int. Conf. Knowl. Discovery Data Mining, Page No (275-281), 1995
[3] H. Yao, H. J. Hamilton, and L. Geng, �A unified framework for utility-based measures for mining itemsets,� in Proc. ACM SIGKDD 2nd Workshop Utility-Based Data Mining, pp (28�37), 2006.
[4] R. Agrawal, T. Imielinski, and A. Swami, �Mining association rules between sets of items in large databases,� in Proc. ACM SIGMOD Int. Conf. Manage. Data, pp. (207�216), 1993.
[5] R. Agrawal and R. Srikant, �Fast algorithms for mining association rules,� in Proc. 20th Int. Conf. Very Large Databases, pp. (487�499), 1994.
[6] J. Han, J. Pei, and Y. Yin, �Mining frequent patterns without candidate generation,� in Proc. ACM SIGMOD Int. Conf. Manage. Data, pp. (1�12), 2000.
[7] M. J. Zaki, �Scalable algorithms for association mining,� IEEE Trans. Knowl. Data Eng., Volume. 12, no. 3, pp. (372�390), May/Jun. 2000.
[8] H. Yao, H. J. Hamilton, and L. Geng, �A unified framework for utility-based measures for mining itemsets,� in Proc. ACM SIGKDD 2nd Workshop Utility-Based Data Mining, pp. (28�37), 2006.
[9] R. J. Hilderman, C. L. Carter, H. J. Hamilton, and N. Cercone, �Mining market basket data using share measures and characterized itemsets,� in Proc. PAKDD, pp. (72�86), 1998.
[10] H. Yao and H. J. Hamilton, �Mining itemset utilities from transaction databases,� Data Knowl. Eng., Volume- 59, No- 3, Page No (603�626), 2006.
[11] H. Yao, H. J. Hamilton, and C. J. Butz, �A foundational approach to mining itemset utilities from databases,� in Proc. SIAM Int. Conf. Data Mining, pp. (482�486), 2004.
[12] C. F. Ahmed, S. K. Tanbeer, B.-S. Jeong, and Y.-K. Lee, �Efficient tree structures for high utility pattern mining in incremental databases,� IEEE Trans. Knowl. Data Eng., Volume-21, No-12, Page No � (1708� 1721), Dec. 2009.
[13] A. Erwin, R. P. Gopalan, and N. R. Achuthan, �Efficient mining of high utility itemsets from large datasets,� in Proc. 12th Pacific-Asia Conf. Adv. Knowl. Discovery Data Mining, Page No- (554�561), 2008.
[14] Y.-C. Li, J.-S. Yeh, and C.-C. Chang, �Isolated items discarding strategy for discovering high utility itemsets,� Data Knowl. Eng., Volume-64, No. 1, Page No. (198�217), 2008.
[15] Y. Liu, W. Liao, and A. Choudhary, �A fast high utility itemsets mining algorithm,� in Proc. Utility-Based Data Mining Workshop SIGKDD, Page No. (253�262), 2005.
[16] V. S. Tseng, B.-E. Shie, C.-W. Wu, and P. S. Yu, �Efficient algorithms for mining high utility itemsets from transactional databases,� IEEE Trans. Knowl. Data Eng., Volume- 25, No- 8, Page No � (1772�1786), Aug. 2013.
[17] J. Han, J. Pei, and Y. Yin, �Mining frequent patterns without candidate generation,� in Proc. ACM SIGMOD Int. Conf. Manage. Data, Page No- (1�12), 2000.
[18] M. J. Zaki, �Scalable algorithms for association mining,� IEEE Trans. Knowl. Data Eng., Volume - 12, No- 3, Page No � (372�390), May/Jun. 2000.
[19] J. Pei, J. Han, and V. Lakshmanan, �Pushing convertible constraints in frequent itemset mining,� Data Mining Knowl. Discovery, Volume - 8, No - 3, Page No � (227�252), 2004.
[20] C. Bucila, J. Gehrke, D. Kifer, and W. M. White, �Dualminer: A dual-pruning algorithm for itemsets with constraints,� Data Mining Knowl. Discovery, Volume - 7, No - 3, Page No � (241�272), 2003.
[21] F. Bonchi, F. Giannotti, A. Mazzanti, and D. Pedreschi, �ExAnte: A preprocessing method for frequent-pattern mining,� IEEE Intell. Syst., Volume - 20, No - 3, Page No � (25�31), May/Jun. 2005.
[22] L. De Raedt, T. Guns, and S. Nijssen, �Constraint programming for itemset mining,� in Proc. ACM SIGKDD, Page No � (204�212), 2008.
[23] R. Bayardo and R. Agrawal, �Mining the most interesting rules,� in Proc. 5th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, Page No- (145�154), 1999.
[24] S. Morishita and J. Sese, �Traversing itemset lattice with statistical metric pruning,� in Proc. 19th ACM Symp. Principles Database Syst., Page No - (226�236), 2000.
[25] R. J. Hilderman and H. J. Hamilton, �Measuring the interestingness of discovered knowledge: A principled approach,� Intell. Data Anal., Volume -7, No- 4, Page No- (347�382), 2003.
[26] P. N. Tan, V. Kumar, and J. Srivastava,, �Selecting the right objective measure for association analysis,� Inf. Syst., Volume - 29, No - 4, Page No � (293�313), 2004.
[27] R. J. Hilderman, C. L. Carter, H. J. Hamilton, and N. Cercone, �Mining market basket data using share measures and characterized itemsets,� in Proc. PAKDD, Page No -(72�86), 1998.
[28] H. Yao, H. J. Hamilton, and C. J. Butz, �A foundational approach to mining itemset utilities from databases,� in Proc. SIAM Int. Conf. Data Mining, Page No � (482�486), 2004.
[29] C. H. Cai, A. W. C. Fu, C. H. Cheng, and W. W. Kwong, �Mining association rules with weighted items,� in Proc. Int. Database Eng. Appl. Symp., Page No � (68�77), 1998.
[30] T. Y. Lin, Y. Y. Yao, and E. Louie, �Value added association rules,� in Proc. 6th Pacific-Asia Conf. Adv. Knowl. Discovery Data Mining, Page No - (328�333), 2002.
[31] S. Lu, H. Hu, and F. Li, �Mining weighted association rules,� Intell. Data Anal., Volume- 5, No - 3, Page No � (211�225), 2001.
[32] Y. Shen, Q. Yang, and Z. Zhang, �Objective-oriented utility-based association mining,� in Proc. IEEE Int. Conf. Data Mining, Page No � (426�433), 2002.
[33] R. Chan, Q. Yang, and Y. Shen, �Mining high utility itemsets,� in Proc. Int. Conf. Data Mining, Page No �(19�26), 2003.
[34] G.-C. Lan, T.-P. Hong, and V. S. Tseng, �An efficient projectionbased indexing approach for mining high utility itemsets,� Knowl. Inf. Syst., Volume - 38, No - 1, Page No � (85�107), 2014.
[35] U. Yun, H. Ryang, and K. H. Ryu, �High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates,� Expert Syst. Appl., Volume - 41, No - 8, Page No � (3861�3878), 2014.
[36] S. Dawar and V. Goyal, �UP-Hist tree: An efficient data structure for mining high utility patterns from transaction databases,� in Proc. 19th Int. Database Eng. Appl. Symp, Page No � (56�61), 2015.
[37] M. Liu and J. Qu, �Mining high utility itemsets without candidate generation,� in Proc. ACM Conf. Inf. Knowl. Manage. ,Page No � (55�64) , 2012.
[38] J. Liu, Ke Wang, and C. M. Fung, �Mining High Utility Pattern in One Phase without Generating Candidate,� IEEE Transaction on Knowledge and Data Engineering, Volume - 28, No � 5, Page No � (1245�1257), May- 2016.
[39] Jun-Zhe. Wang and Juin-Long Huang, �Itemset mining of High Utility Sequential Pattern in Incremental Database,� ACM. CIKM�16, Indianapolis, IN, USA, pp � (1245�1257), October 24th- 28th, 2016.
[40] M. Zihayat, Z. Zhenhua Hu, A. An and Yonggang Hu �Distributedand Parallel High Utility Sequential Pattern Mining,� Technical Report EECS � 2016-03, April 12th, 2016
[41] Jiaqi Ge and Yuni Xia, �Distributed Sequential Pattern Mining in Large Scale Uncertain Databases,� PAKDD Springer International Publishing Switzerland, pp � (17 -29) , 2016

A.A. Tale, N.R. Wankhade, "High Utility Pattern Mining � A Deep Review," International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.120-124, 2016.

Presently all programmers want to perform their tasks much faster than before. So, Parallel Processing comes into the picture to satisfy the increasing demands. Till a long time, parallel programs were only written either for multiprocessing environment or multi-computing environment. However, both of these parallel processing systems have some relative advantages and disadvantages. Distributed Shared Memory (DSM) system is a new and attractive area of research which combines the advantages of both shared-memory parallel processors (multiprocessors) and distributed systems (multi-computers). However, in DSM environment there are some critical issues like memory consistency that should be handled carefully. In this paper, an overview of DSM is given after a brief description of Distributed Computing Systems. Later various implementation issues and consistency models related to DSM are shown. Then an example of a simple program is given that can be implemented in DSM environment using Open SHMEM.

Parallel Programming; Multiprocessing; Multicomputing; Distributed Shared Memory (DSM); Consistency Models

[1] Pradip K.Sinha, �Distributed Operating Systems: Concepts and Designs� published by IEEE Computer Society Press,2004.
[2] Andrew S. Tanenbaum, �Distributed Operating Systems� published by PEARSON Education, Fifth Impression,2008.
[3] Ryan Saptarshi Ray, Utpal Kumar Ray, Ashish Anand,Parama Bhaumik, �Distributed Shared Memory � A Survey and Implementation Using Openshmem� published in Int. Journal of Engineering Research and Applications, ISSN: 2248-9622, Vol. 6, Issue 2, (Part - 1) February 2016, pp.49-52.
[4] David Mosberger, �Memory Consistency Models�, Paper submitted to Department of Computer Science, The University of Arizona, Tucson, AZ 85721.
[5] Radhika Gogia, Preeti Chhabra, Rupa Kumari, �CONSISTENCY MODELS IN DISTRIBUTED SHARED MEMORY SYSTEMS�, Research Article published in IJCSMC, Vol. 3, Issue. 9, September 2014, pg.196 � 201.
[6] Kai Li, �Shared Virtual Memory on Loosely Coupled Microprocessors�, PhD Thesis submitted to Yale University, September 1986.
[7] M. J. Flynn, �Computer Architecture: Pipelined and Parallel Processor Design� published by Jones and Barlett, Boston, 1995.
[8] Jelica Protic, Milo Tomasevic, Veljko Milutinovic, �A Survey of Distributed Shared Memory Systems� published in Proceedings of the 28th Annual Hawaii International Conference on System Sciences,1995.
[9] PGAS Forum,http://www.pgas.org/
[10] B. Chapman, T. Curtis, S. Pophale, S. Poole, J. Kuehn, C. Koelbel, L. Smith �Introducing OpenSHMEM, SHMEM for the PGASCommunity�, published in Proceedings of Partitioned Global AddressSpace Conference, 2010.
[11] Message Passing Interface (MPI) standard, http://www-unix.mcs.anl.gov/mpi/
[12] http://openshmem.org/site/Documentation/Manpages/Browse
[13] Faizul Navi Khan, Kapil Govil, �Reliability Based Task Allocation Scheme to Enhance the Performance of Distributed Environment�, Research Article published in IJCSE Vol. 2, Issue. 8, September 2014, pg.99.

D. Das, R.S. Ray, U.K. Ray , "Implementation and Consistency Issues in Distributed Shared Memory," International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.125-131, 2016.

Thermal issue is a critical challenge in 3D IC design. To eliminate hotspots, physical layouts are always adjusted by shifting or duplicating hot blocks. However, these modifications may degrade the packing area as well as interconnect distribution greatly. In this paper, we propose some novel thermal-aware incremental changes to optimize these multiple objectives including thermal issue in 3D ICs. Furthermore, to avoid random incremental modification, which may be inefficient and need long runtime to converge, here potential gain is modeled for each candidate incremental change. Based on the potential gain, a novel thermal optimization flow to intelligently choose the best incremental operation is presented. We distinguish the thermal-aware incremental changes in three different categories: migrating computation, growing unit and moving hotspot. Mixed integer linear programming (MILP) models are devised according to these different incremental changes. Experimental results show that migrating computation, growing unit and moving hotspot can reduce max on-chip temperature by 7%, 13% and 15% respectively on MCNC/GSRC benchmarks. Still, experimental results also show that the thermal optimization flow can reduce max on-chip temperature by 14% compared to an existing 3D floorplan tool CBA, and achieve better area and total wirelength improvement than individual operations do.

3D IC technology, Temperature, Floor planning Problem

[1] J.Cong, J. Wei and Y. Zhang, “A Thermal-Driven Floorplanning Algorithm for 3D ICs”, in Procceedings of ICCAD, 2004
[2] W. L. Huang, G.M. Link, Y. Xie, N. Vijaykrishnan and M.J Irwin, “Interconnect and Thermal-Driven floorplanning for 3D microprocessors”, in Procceedings of ISQED, Mar. 2006
[3] Z.P. Gu, Y. Yang, J. Wang, R.P. Dick and L. Shang, “TAPHS: Thermal aware unified physical-level and high-level synthesis”, in Procceedings of ASP-DAC, 2006
[4] P. Zhou, Y. Ma, Z. Li, R.P. Dick, L. Shang, H. zhou, X.L. Hong and Q. Zhou, “3D-STAF: Scalable Temperature and Leakage Aware Floorplanning for Three Dimensional Integrated Circuits”, In procceedings of ICCAD, 2007
[5] C.H. Tsai and S.M.S Kang, “Standard cell placement for even on-chip thermal distribution”, in Procceedings of ISPD, 1999
[6] K. Skadron, M.R Stan, W. Huang, S. Velusamy, K. Sankaranarayanan D. Tarjan, “Temperature-aware Microarchitecture”, in Procceedings of ISCA, 2003.
[7] S. Heo, K. Barr and K. Asanovic, “Reducing power density through activity migration”, in Procceedings of ISLPED, Aug., 2003.
[8] T.D. Richardson and Y. Xie, “Evaluation of Thermal-aware design Techniques for Microprocessors”, in Proceedings of ASICON, 2005.
[9] J. Cong and M. Sarrafzadeh, “Incremental Physical Design”, in Procceedings of ISPD, 2000.
[10] J. Creshaw, M. Sarrafzadeh, P. Banerjee, P. Prabhakaran, “An incremental floorplanner”, in Proceedings of GLSVLSI,1999.
[11] S. Liao, M.A. Lopez and D. Mehta, “Constrained Polygon Transformations for Incremental Floorplanning”, ACM Trans. On DAES, Vol.6, No.3, July 2001.
[12] X. Tang, R. Tian and M.D.F Wong, “Optimal Redistribution of White Space for Wire length Minimization”, In procceedings of ASP-DAC, 2005
[13] X. Li, Y. Ma, X.L. Hong, S. Dong and J. Cong, “LP Based White Space Redistribution for Thermal Via Planning and Performance Optimization in 3D ICs”, in procceedings of ASP-DAC, 2008
[14] J. Cong and M. Sarrafzadeh, “Incremental Physical Design”, in Procceedings of ISPD, pp.84-92, may, 2000.
[15] H.Y. Jill, E.F.Y Young and R.L.S. Ching, “Block alignment in 3D floorplan using layered TCG”, in Procceedings of GLSVLSI, 2006.
[16] www.gnu.org/software/glpk/
[17] S. Sutanthavibul, E. Shragowitz and J.B. Rosen, “An Analytical Approach to Floorplan Design and Optimization”, in Procceedings of DAC, 1990.
[18] S.N. Adya, I.L. Markov, “Fixed-outline Floorplanning: Enabling Hierarchical Design”, IEEE Trans. On VLSI systems, Vol.11,No.1, pp.1120-1135, Dec.2003.
[19] P. Chen and E.S. Kuh, “Floorplan Sizing By linear Programming Approximation”, in Proceedings of DAC, 2000
[20] B. Lall, A. Ortega and H. Kabir, “Thermal Design Rules for Electronic Components on Conducting Boards in Passively Cooled Enclosures”, in Proceedings of inter-society Conference on Thermal Phenomerna, 1994

Simi P. Thomas, Reshma Chandran, Neethan Elizabeth Abraham, Sunu Ann Thomas, "Optimization of delay and temperature for improved design flow in 3D IC," International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.132-136, 2016.

This paper proposes the design and simulation of a DC-DC Boost converter employing PID controller, enhancing overall performance of the system. The main objective of a DC- DC converter is to maintain a constant output voltage despite variations in input/source voltage, components and load current. Designers aim to achieve better conversion efficiency, minimized harmonic distortion and improved power factor while keeping size and cost of converter within acceptable range. A simple PID (Proportional, Integral and Derivative) controller has been applied to a conventional Boost converter and tested in MATLAB-Simulink environment achieving improved voltage regulation. The proposed closed loop implementation of the converter maintains constant output voltage despite changes in input voltage and significantly reduces overshoot thereby improving the efficiency of the converter. The output of this investigation has the potential to contribute in a significant way in electric vehicles, industry, communication and renewable energy sectors.

DC-DC converter; voltage regulation; Boost converter; overshoot; PID; Block Diagram Reduction; stability

[1] Kumar, J. Sai, and Tikeshwar Gajpal. "A Multi Input DC-DC Converter for Renewable Energy Applications." (2016).
[2] Ortiz, G., J. Biela, D. Bortis, and J. W. Kolar. "1 Megawatt, 20 kHz, isolated, bidirectional 12kV to 1.2 kV DC-DC converter for renewable energy applications." In Power Electronics Conference (IPEC), 2010 International, pp. 3212-3219. IEEE, 2010.
[3] Wuhua, Xiaodong Lv, Yan Deng, Jun Liu, and Xiangning He. "A review of non-isolated high step-up DC/DC converters in renewable energy applications." In Applied Power Electronics Conference and Exposition, 2009. APEC 2009. Twenty-Fourth Annual IEEE, pp. 364-369. IEEE, 2009.
[4] Koutroulis, Eftichios, and Kostas Kalaitzakis. "Design of a maximum power tracking system for wind-energy-conversion applications." IEEE transactions on industrial electronics 53, no. 2 (2006): 486-494.
[5] Ferdous, S. M., Mohammad Abdul Moin Oninda, Golam Sarowar, Kazi Khairul Islam, and Md Ashraful Hoque. "Non-isolated single stage PFC based LED driver with THD minimization using Cúk converter." In Electrical and Computer Engineering (ICECE), 2016 9th International Conference on, pp. 471-474. IEEE, 2016.
[6] Yang, Bo, Fred C. Lee, A. J. Zhang, and Guisong Huang. "LLC resonant converter for front end DC/DC conversion." In Applied Power Electronics Conference and Exposition, 2002. APEC 2002. Seventeenth Annual IEEE, vol. 2, pp. 1108-1112. IEEE, 2002.
[7] Wei, Huai, and Issa Batarseh. "Comparison of basic converter topologies for power factor correction." In Southeastcon`98. Proceedings. IEEE, pp. 348-353. IEEE, 1998.
[8] Daniele, Matteo, Praveen K. Jain, and Geza Joos. "A single-stage power- factor-corrected AC/DC converter." IEEE Transactions on Power Electronics 14, no. 6 (1999): 1046-1055.
[9] Rathi, Ms Kashmira, and Dr MS Ali. "Design and Simulation of PID Controller for Power Electronics Converter Circuits." International Journal Of Innovative And Emerging Research In Engineering 3, no. 2 (2016): 26-31.
[10] Dave, Mitulkumar R., and K. C. Dave. "Analysis of boost converter using pi control algorithms." International Journal of Engineering Trends and Technology 3.2 (2012): 71-73.
[11] Raviraj, V. S. C., and Paresh C. Sen. "Comparative study of proportional-integral, sliding mode, and fuzzy logic controllers for power converters." IEEE Transactions on Industry Applications 33, no. 2 (1997): 518-524.
[12] Meena, Rajendra. "Simulation study of boost converter with various control techniques." International Journal of Science and Research (IJSR) 3, no. 9 (2014): 74-79.
[13] W. Lu, S. Lang, L. Zhou, H. H. C. Iu and T. Fernando, "Improvement of Stability and Power Factor in PCM Controlled Boost PFC Converter With Hybrid Dynamic Compensation," in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 62, no. 1, pp. 320-328, Jan. 2015.
[14] Z. Guo, X. Ren, Y. Wu, Z. Zhang and Q. Chen, "A novel simplified variable on-time method for CRM boost PFC converter," 2017 IEEE Applied Power Electronics Conference and Exposition (APEC), Tampa, FL, USA, 2017, pp. 1778-1784.
[15] T. Meng; H. Ben; Y. Song, "Investigation and Implementation of a Starting and Voltage Spike Suppression Scheme for Three-Phase Isolated Full-Bridge Boost PFC Converter," in IEEE Transactions on Power Electronics , vol.PP, no.99, pp.1-1.
[16] I. H. Kim and Y. I. Son, "Regulation of a DC/DC Boost Converter Under Parametric Uncertainty and Input Voltage Variation Using Nested Reduced-Order PI Observers," in IEEE Transactions on Industrial Electronics, vol. 64, no. 1, pp. 552-562, Jan. 2017.
[17] J. Ma, M. Zhu, J. Zhang and X. Cai, "Improved asynchronous voltage regulation strategy of non-inverting Buck-Boost converter for renewable energy integration," 2015 IEEE 2nd International Future Energy Electronics Conference (IFEEC), Taipei, 2015, pp. 1-5.
[18] A. Singh, A. A. Milani and B. Mirafzal, "Voltage regulation in single- stage boost inverter for stand-alone applications," 2014 IEEE Applied Power Electronics Conference and Exposition - APEC 2014, Fort Worth, TX, 2014, pp. 3011-3016.
[19] O. Ibrahim, N. Z. Yahaya and N. Saad, "Comparative studies of PID controller tuning methods on a DC-DC boost converter," 2016 6th International Conference on Intelligent and Advanced Systems (ICIAS), Kuala Lumpur, 2016, pp. 1-5.
[20] L. Mitra and N. Swain, "Closed loop control of solar powered boost converter with PID controller," 2014 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES), Mumbai, 2014, pp. 1-5.
[21] P. Verma, N. Patel, N. K. C. Nair and A. Sikander, "Design of PID controller using cuckoo search algorithm for buck-boost converter of LED driver circuit," 2016 IEEE 2nd Annual Southern Power Electronics Conference (SPEC), Auckland, 2016, pp. 1-4.

Jeneesh Scaria, Preethy Sebastian, Susan V. Nanan, "Design and Implementation of Efficiency Improved DC-DC Boost Converter," International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.137-141, 2016.

The surest route from the supply node to the destination node that guarantees a high cease-to-stop throughput is the principle trouble of routing in multi-hop wireless network. As the surroundings is heterogeneous the issue seems to be a lot complicated, most of the answers cease with local most desirable due to the fact the ones algorithms often fail to make certain an quit to quit throughput. By considering spatial reusability of wireless media, the cease-to-give up throughput in wi-fi multi-hop far flung structures may be stronger hugely. To assist the argument, Spatial-reusability Aware Single-path Routing (SASR) algorithm is proposed and as compared with existing single direction routing protocol. The assessment showed that proposed protocol display full-size improvement in end-to-give up throughput in evaluation with existing protocols.

WSN, througput, optimization.

[1] W. Hu, J. Xie, and Z. Zhang, “Practical opportunistic routing in high-speed multi-rate Wireless mesh networks,” in Proc. 14th ACM Int. Symp. Mobile Ad Hoc Netw. Comput. 2013, pp. 127–136.
[2] N. M. Jones, B. Shrader, and E. Modiano, “Optimal routing and scheduling for a simple network coding scheme,” in Proc. INFOCOM, 2012, pp. 352–360.
[3] S. Zhao, L. Fu, X. Wang, and Q. Zhang, “Fundamental relationship between node density and delay in wireless ad hoc networks with unreliable links,” in Proc. 17th Annu. Int. Conf. Mobile Comput. Netw. 2011, pp. 337–348.
[4] R. P. Laufer, H. Dubois-Ferri_ere, and L. Kleinrock, “Multirateanypathrouting in wireless mesh networks,” in Proc. INFOCOM, 2009, pp. 37–45.
[5] Y. Lin, B. Li, and B. Liang, “Codeor: Opportunistic routing in wireless mesh networks with segmented network coding,” in Proc. IEEE Int. Conf. Netw. Protocols, 2008, pp. 13–22.
[6] G. Jakllari, S. Eidenbenz, N. W. Hengartner, S. V. Krishnamurthy, and M. Faloutsos, “Link. Positions matter: A noncommutative routing metric for a wireless mesh network,” in Proc. IEEE 27thConf. Comput. Commun., 2008, pp. 744–752.
[7] S. Chachulski, M. Jennings, S. Katti, and D. Katabi, “Trading structure for randomness in wireless opportunistic ro uting,” in Proc. SIGCOMM Conf. Appl., Technol., Archit. Protocols Comput. Commun., 2007, pp. 169–180.
[8] T.-S. Kim, J. C. Hou, and H. Lim, “Improving spatial reuse through tuning transmit power, carrier sense threshold, and data rate in multihop wireless networks,” in Proc. 12th Annu. Int. Conf. Mobile Comput. Netw., 2006, pp. 366–377.
[9] S. Biswas and R. Morris, “Exor: Opportunistic multi-hop routing for wireless networks,” in Proc. SIGCOMM Conf. Appl., Technol., Archit., Protocols Comput. Commun., 2005, pp. 133-144 .
[10] J. Padhye, S. Agarwal, V. N. Padmanabhan, L. Qiu, A. Rao, and B. Zill, “Estimation of link interference in static multi-hop wireless networks,” in Proc. Internet Meas. Conf., 2005, p 28.
[11] A. Adya, P. Bahl, J. Padhye, A. Wolman, and L. Zhou, “A multi-radio unification protocol for IEEE 802.11 wireless networks,” in Proc. 1st Int. Conf. Broadband Netw., 2004, pp. 344–345.
[12] R. Draves, J. Padhye, and B. Zill, “Routing in multi-radio, multihop wireless mesh networks,” in Proc. 10th Annu. Int. Conf. Mobile Comput. Netw., 2004, pp. 114–128.
[13] R. Cohen, and S. Havlin, (2003, Feb.). Scale-free networks are ultrasmall. Phys. Rev. Lett. [Online] 90, p. 058701.Available: http://link.aps.org/doi/10.1103/PhysRevLett.90.058701.
[14] D. S. J. D. Couto, D. Aguayo, J. C. Bicket, and R. Morris, “A high throughput path metric for multi-hop wireless routing,” in Proc. 9th Annu. Int. Conf. Mobile Comput. Netw., 2003, p 134–146.
[15] C. E. Perkins and E. M. Belding-Royer, “Ad-hoc on-demand distance vector routing,” in Proc. 2nd IEEE Workshop Mobile Comput.Syst. Appl., 1999, pp. 90–100.
[16] J. Broch, D. A. Maltz, D. B. Johnson, Y.-C. Hu, and J. G. Jetcheva, “A Performance Comparison of multi-hop wireless ad hoc network routing protocols,” in Proc. 4th Annu. ACM/IEEE Int. Conf. Mobile Comput. Newt., 1998, pp. 85–97.
[17] D. B. Johnson and D. A. Maltz, “Dynamic source routing in ad hoc wireless networks,” Mobile Comput., vol. 353, pp. 153– 181, 1996.
[18] C. E. Perkins and P. Bhagwat, “Highly dynamic destination sequenced distance-vector routing (DSDV) for mobile computers,” in Proc. Conf. Commun. Archit., Protocols Appl., 1994, pp. 234–244.

Vinodh P Vijayan, Neena Joseph, Neema George, Simy Mary Kurian, "Improving load balance in Wireless Network using Spatial Reusability," International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.142-147, 2016.