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Turbulence is very difficult to define succinctly except by its contrast with laminar flow and there is no universally accepted model to describe quantitatively the details of medium. If it is difficult to understand about turbulence then it�s also difficult to understand scattering of sound, i.e., sodar signal by turbulence. For the purpose of this analytical study, it is assumed that turbulence in the lower atmosphere with some considerations and developed mathematical algorithms and observed different plots to estimate the scope of the sound scattering in homogenous, isotopic and other mediums. All these mathematical algorithms and plots has developed in the scientific software MATLAB. These results are compared with the early studies and concludes that past and present studies found similar predictions for the scattering of sodar signal by turbulence.

Atmosphere, Flow, Homogenous, Isotopic, Laminar, MATALB, Scattering, SODAR, Turbulence

[1]. Beran, D. W.; Reynolds, R. M.; and Gething, J. T.: Sound Attenuation in the Free Atmosphere. Publ. No. 17, Project EAR, Rep. No. V, Meteorol. Dep., Univ. of Melbourne, Dec. 1970.
[2]. Beran, Donald W.: Turbulence Detection. Ph. D. Thesis, Univ. of Melbourne, July 1970.
[3]. Beran, D. W.; and Gething, J. T.: Use of a Sailplane in Measuring Acoustic Attenuation in the Atmosphere. Aero-Revue, Issue 2, Feb. 1972, pp. 93-95.
[4]. Chernov, Lev A. (R. A. Silverman, transl.): Wave Propagation in a Random Medium. McGraw-Hill Book Co., Inc., c.1960.
[5]. Coles, G. M.: Atmospheric Absorption of Noise. Aerodynamic Noise, Univ. of Toronto Press, c.1969, pp. 209-227.
[6]. Cole, John E., m; and Dobbins, Richard A.: Propagation of Sound through Atmospheric Fog. Atmos. Sci., vol. 27, no. 3, May 1970, pp. 426-434.
[7]. Delsasso, L. P.; and Leonard, R. W.: The Attenuation of Sound in the Atmosphere. Summary Report U.S. Air Force Contract W-28-099-AC-228, Univ. of California, Feb. 25, 1953.
[8]. Evans, L. B.; and Sutherland, L. C.: Absorption of Sound in Air. WR 70-14 (Contract DAH C04-69C 0088), Wyle Labs., July 1970.
[9]. Harris, Cyril M.: Absorption of Sound in Air in the Audio-Frequency Range.J. Acoustic. Soc. Amer., vol. 35, no. 1, Jan. 1963, pp. 11-17.
[10]. Harris, Cyril M.: On the Absorption of Sound in Humid Air at Reduced Pressures. J. Acoust. Soc. Amer., vol. 43, no. 3, Mar. 1968, pp. 530-532.
[11]. Ingard, Uno: The Physics of Outdoor Sound. Proceedings of the Fourth Annual National Noise Abatement Symposium, Vol. 4, Oct. 23-24, 1953, pp. 11-25.
[12]. Ingard, Uno: A Review of the Influence of Meteorological Conditions on Sound Progation.J. Acoust. Soc. Amer., vol. 25, no. 3, May 1953, pp. 405-411.
[13]. Ingard, Uno; and Oleson, Stanley K.: Measurements of Sound Attenuation in the Atmosphere. AFCRL-TR-60-431, U.S. Air Force, Nov. 4, 1960.
[14]. Ingard, Uno; and Wiener, Francis M.: On the Scattering of Sound by Atmospheric Turbulence. J. Acoust. Soc. Amer., vol. 30, no. 7, July 1958, p. 670.
[15]. Journal of Research in Computer and Communication Technology,(IJRCCT) Vol 2, Issue 12, December-2013;ISSN(O): 2278-5841.
[16]. Knudsen, Vern 0: The Absorption of Sound in Air, in Oxygen, and in Nitrogen -Effects of Humidity and Temperature. J. Acoustic. Soc. Amer., vol. V, no. 2, Oct. 1933, pp. 112-121.
[17]. Kallistratova, M. A.; and Tatarskii, V. I.: Accounting for Wind Turbulence in the Calculation of Sound Scattering in the Atmosphere. Soviet Physics - Acoustics, vol. 6, no. 4, Apr.-June 1961, pp. 503-505.
[18]. Kallistratova, M. A.: Experimental Investigation of Sound Wave Scattering in the Atmosphere. FTD-TT-63-447, U.S. Air Force, June 1963. (Available from DDC as AD 412 821.)
[19]. M. Hareesh Babu; M. Bala naga bhushanamu; M. Purnachandra Rao. �2KHz Mono-pulse acoustic signal generation for SODAR application using PC sound card and MATLAB graphical user interface� ; International
[20]. M. Hareesh Babu; M. Bala naga bhushanamu; M. Purnachandra Rao. �Design & Simulation of Graphical User Interface for SODAR system using VC++�; International Journal of Innovative Research & Development; (IJIRD) Vol2; Issue 12 (Special Issue), December-2013;ISSN(O): 2278-0211.
[21]. Sabine, H. J.; Raelson, V. J.; and Burkhard, M. D.: Sound Propagation Near the Earth�s Surface as Influenced by Weather Conditions. WADC Tech. Rep. 57-353, Part III, U.S. Air Force, Jan. 1961.

M.H. Babu, D.S.S.N. Raju, A. Sarvani, M.P. Rao, "Scattering of Sodar Signal by Turbulence in Homogenous, Isotopic and other Mediums," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.1-5, 2014.

Web Mining is an interesting domain in information processing that includes a large variety of applications i.e. recommendation system design, next user web page prediction, navigational pattern analysis and others. In this paper a new hybrid clustering algorithm is proposed and implemented using Genetic algorithm and K-NN algorithm and the implementation of desired algorithm is given using a web recommendation system which analyze user navigational pattern from web server access log file and recommends the next user web page. The performance of the designed system is evaluated and listed in this paper. According to the results, the proposed hybrid approach is efficient and effective for the given application domain.

Recommendation Systems; k-NN; Genetic Algorithm; Clustering

[1] Jaideep Shrivastava,Robert Cooley,�Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data� SIGKDD Explorations Copyright@1999 ACM SIGKDD Jan 2000 Volume1 Issue 2.
[2] L.K. Joshila Grace,V.Maheswari, Dhinaharan Nagamalai,�Analysis of web logs and web user in web mining�, International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.1, January 2011
[3] Osama Abu Abbas,�Comparision between Data Clustering Algorithms�,The International Arab Journal of Information Technology, Vol 5, No.3, July 2008.
[4] C.P.Sumathi et. al,�Automatic Recommendation of Web Pages in Web usage mining� International Journal on Computer Science and Engineering Vol. 02, No. 09, 2010, 3046-3052
[5] Tapas Kanungo, Nathan S. Netanyahu, �An Efficient k-Means Clustering Algorithm: Analysis and Implementation� IEEE transactions on pattern analysis and machine intelligence, Vol. 24, no. 7, July 2002
[6] Hassan H. Malik, and John R. Kender, �Classification by Pattern-Based Hierarchical Clustering�, Department of Computer Science, Columbia University, New York, NY 10027, USA{hhm2104, jrk}@cs.columbia.edu
[7] L�szl� Kozma Lkozma@cis.hut.fi,�k Nearest Neighbors algorithm� Helsinki University of Technology T-61.6020 Special Course in Computer and Information Science 20. 2. 2008
[8] Olga Georgiou,Nicolas Tsapatsoulis, �Improving the Scalability of Recommender Systems by Clustering Using Genetic Algorithms�, Volume 6352, 2010, pp 442-449 @Springer-Verlag Berlin Heidelberg ICANN 2010
[9] Ujjwal Maulik,Sanghamitra Bandyopadhyay, �Genetic algorithm based clustering technique�,PII: S 0 0 3 1 - 3 2 0 3 ( 9 9 ) 0 0 1 3 7 � 5@2000 Pattern Recognition Society. Published by Elsevier Science Ltd.
[10] Petra Kudov�a,�Clustering Genetic Algorithm�,18th International Workshop on Database and Expert Systems Applications DOI 10.1109/DEXA.2007.65

A. Shrivastava, S. Rajawat, "An Implementation of Hybrid Genetic Algorithm for Clustering based Data for Web Recommendation System," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.6-11, 2014.

Testing is done to find out any errors in the applications and to ensure that they are fit for use. Ordinarily, teams put in their best efforts to find and fix as many bugs as possible. Sometimes due to factors such as lack of exhaustive test cases & build deadlines extensive testing is not done. Also, missing test cases in terms of complex systems due to human errors is very much possible. The post production errors are not catastrophic when the applications are meant for non-critical purposes. But, in life critical applications such as aerospace & medicine, fully comprehensive testing needs to be performed. The success of stopping a bug leakage in release phase depends considerably on the test cases used to perform the testing. Effective set of test cases should be designed to enable detection of maximum number of errors. This paper proposes Test Matrix technique & Truth Table techniques as profound testing mechanisms for complex test flows and inputs.

Testing, Test Matrix, Truth Table

[1] Software Testing, http://en.wikipedia.org/wiki/Software_testing, 2014
[2] Eldh S, Hansson H, Punnekkat S, �Analysis of Mistakes as a Method to Improve Test Case Design�, IEEE Fourth International conference on Software Testing, Verification and Validation (ICST), E-ISBN: 978-0-7695-4342-0, Page No (70-79), March 21-25, 2011
[3] What is the procedure to write an effective test case?, http://www.bayt.com/en/specialties/q/8821/what-is-the-procedure-to-write-an-effective-test-case/, 2014
[4] Boundary Value Analysis & Equivalence Class Partitioning with Simple Example, http://www.softwaretestingclass.com/boundary-value-analysis-and-equivalence-class-partitioning-with-simple-example/, 2014
[5] Formal Methods for Life Critical Software, http://shemesh.larc.nasa.gov/fm/papers/Butler-1993-Formal-Methods-For-Life-Critical-Software.pdf, 2014
[6] Definition of Matrix, http://chortle.ccsu.edu/vectorlessons/vmch13/vmch13_2.html, 2014
[7] Truth Table, http://en.wikipedia.org/wiki/Truth_table, 2014
[8] Logical Operations & Truth Tables, http://kias.dyndns.org/comath/21.html, 2014

H. Joshi, R.L. Chowdary, H. Varma, "Test Case Design for Critical Systems using Test Matrix and Truth Table," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.12-15, 2014.

DNA computing is a type of computational technology that uses DNA, biochemistry and molecular biology, in place of the fixed silicon-based chips. The emphasis of DNA computing lies in the fact that DNA molecules can store huge information than any existing conventional computer chip.DNA computing use different techniques for computational analysis. Research and growth in this area concerns theory, practical�s, and applications of DNA computing. This paper reviews DNA computing, also known as molecular computing is a new approach to especially parallel computation and recent developments in DNA computing.

DNA Computing, Encryption, Security, Quantum Computing

[1]. Grasha Jacob, �An Encryption Scheme with DNA Technology and JPEG Zigzag Coding for Secure Transmission of Images�, arXiv preprint arXiv:1305.1270 May 2013.
[2]. Grasha Jacob, et. al, 2013, in �DNA based Cryptography: An Overview and Analysis�, International Journal of Emerging Sciences, ISSN: 2222-4254, Page No.(36-42), March 2013.
[3]. Amish S Desai, �Xml security using DNA Technology� , International Journal of Engginering Research & Technology, Pages(25-26), Jan 2013.
[4]. Asha Cherian, �A Survey on different DNA Cryptographic Methods� , International Journal of Science and Research (IJSR), ISSN: 2319-7064, Vol-02 ,Issue-04, April 2013.
[5]. Komal Kumbharkar, �An improved Symmetric key cryptography with DNA based strong cipher� , international journal of advanced and innovative research, ISSN: 2278-7844,Vol-02, Issue-03,2013.
[6]. Kritika Gupta, � DNA Based Cryptographic Techniques: A Review�, International Journal of Advanced Research in Computer Science and Software Engineering 3,2013.
[7]. Er.Ranu Soni, �Innovative field of cryptography: DNA cryptography�, International Conference on Information Technology Convergence and Services,2012.
[8]. Bibhash Roy, �An improved Symmetric key cryptography with DNA Based strong cipher�, IEEE Explorer, ISBN: 978142449189-6, Page No(1-5),Jan 24-25, 2011.
[9]. Radu Terec, �DNA Security using Symmetric and Asymmetric Cryptography�, International Journal of New Computer Architectures and their Applications (IJNCAA), Page No. (34-51), 2011.
[10]. A. Leier, �Cryptography with DNA binary strands,� Biosystems 57, Page No. (13�22), Jan 14, 2000.
[11]. Molecular Structure of DNA, http://www.chemguide.co.uk/organicprops/aminoacids/dna1.html, Dec.2013.

M. Rani, S. Jain, "DNA Computing and Recent Developments," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.16-19, 2014.

A wireless Adhoc networks is the collection of wireless nodes that can co-operate by forwarding packets for each other to allow nodes to communicate directly. The deployment of Ad-hoc networks in security- and- safety in critical environments requires secure communication primitives. As WSN�s become more and more crucial to everyday functioning of the people. Securing in wireless ad-hoc networks is a challenging task. Low power wireless networks are an existing research direction in routing and security. This paper discusses a wide variety of vulnerabilities while routing and different existing securities to mitigate them.

Security, Vulnerabilities, WSN, Ad-Hoc Networks,Routing, Packet Forwarding

[1]. C. Perkins and P. Bhagwat, �Highly dynamic destination-sequenced distance vector routing (DSDV) for mobile computers,� in ACM SIGCOMM, pp. 234�244, Aug. 1994.
[2]. T. Clausen and P. Jacquet, �Optimized link state routing protocol (OLSR).� IETF Request for Comments 3626, 2003.
[3]. T. Clausen, G. Hansen, L. Christensen, and G. Behrmann, �The optimized link state routing protocol, evaluation through experiments and simulation,� in IEEE Symposium on Wireless Personal Mobile Communications, Sept. 2001.
[4]. L. Viennot, �Complexity results on election of multipoint relays in wireless networks,� tech. rep., INRIA, France, 1998.
[5]. C. Perkins, E. Belding-Royer, and S. Das, �Ad hoc on-demand distance vector (AODV) routing.� IETF Request for Comments 3561, 2003.
[6]. D. Johnson, D. Maltz, and J. Broch, DSR: The Dynamic Source Routing Protocol for Multihop Wireless Ad Hoc Networks, ch. 5, pp. 139�172. Addison-Wesley, 2001.
[7]. J. Hightower and G. Borriello, �Location systems for ubiquitous computing,� IEEE Computer, vol. 34, no. 8, pp. 57�66, Aug. 2001.
[8]. S. Basagni, I. Chlamtac, V. R. Syrotiuk, and B. A.Woodward, �A distance routing effect algorithm for mobility (DREAM),� in ACM International Conference on Mobile Computing and Networking (MobiCom), (Dallas, USA), pp. 76�84, Oct. 1998.
[9]. S. Basagni, I. Chlamtac, and V. R. Syrotiuk, �Geographic messaging in wireless ad hoc networks,� in Annual IEEE International Vehicular Technology Conference, (Houston, USA), pp. 1957�1961, May 1999. 32 Rubinstein et al.
[10]. J. Li, J. Jannotti, D. De Couto, D. Karger, and R. Morris, �A scalable location service for geographic ad-hoc routing,� in ACM International Conference on Mobile Computing and Networking (MobiCom), (Boston, USA), pp. 120�130, Aug. 2000.
[11]. R. Morris, F. Kaashoek, D. Karger, D. Aguayo, J. Bicket, S. Biswas, D. De Couto, and J. Li, �The grid ad hoc networking project.� http://pdos.csail.mit.edu/grid/, 2003.
[12]. Byran Parno, Mark Luk, Evan Gaustad, and Aridane Perrig, Secure sensor network routing: A clean-slate approach, CoNEXT, 2006
[13]. Y.-C. Hu, D. B. Johnson, and A. Perrig, �SEAD: Secure efficient distance vector routing for mobile wireless ad hoc networks,� in IEEE Workshop on Mobile Computing Systems and Applications (WMCSA), pp. 3�13, June 2002.
[14]. P. Papadimitratos and Z. Hass, �Secure routing for mobile ad hoc networks,� in SCS Communication Networks and Distributed Systems Modeling and Simulation Conference (CNDS), Jan. 2002.
[15]. Y.-C. Hu, A. Perrig, and D. B. Johnson, �Ariadne: A secure on-demand routing protocol for ad hoc networks,� in ACM International Conference on Mobile Computing and Networking (MobiCom), pp. 12�23, Sept. 2002.
[16]. M. G. Zapata and N. Asokan, �Securing ad hoc routing protocols,� in ACM Workshop on Wireless Security (WiSe), pp. 1�10, Sept. 2002.
[17]. Neeraj Kumar Pandey and Amit Kumar Mishra, "An Augmentation in a Readymade Simulators Used for MANET Routing Protocols: Comparison and Analysis", International Journal of Computer Sciences and Engineering, Volume-02, Issue-03, Page No (60-63), Mar -2014.

S.B. Kolla, B.B.K. Prasad, "A Survey of Source Routing Protocols, Vulnerabilities and Security In Wireless Ad-hoc Networks," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.20-25, 2014.

This paper is based on the development of fuzzy Logic based edge detection techniques in digital images. The proposed technique used Sobel operator, Zernike moment operator, and Fuzzy inference system in combination for edge detection purpose. Edge is a boundary between two homogeneous regions. Edge detection refers to the process of identifying and locating sharp discontinuities in an image. In this paper, the main aim of edge detection technique is to filter out unnecessary, false and double edges in the final image.

Edge Detection, Fuzzy Logic, Image Processing, Fuzzy Infrence System, Zernike Moment

[1]. Ying-Dong, Q., Cheng-Song, C., San-Ben, C., & Jin-Quan, L. (2005). A fast subpixel edge detection method using Sobel Zernike moments operator. Image and Vision Computing, 23(1), 11-17.
[2]. Da, F., & Zhang, H. (2010). Sub-pixel edge detection based on an improved moment. Image and Vision Computing, 28(12), 1645-1658.
[3]. http://en.wikipedia.org/wiki/Sobel_operator.
[4]. Ziou, D., & Tabbone, S. (1998). Edge detection techniques-an overview. Pattern Recognition And Image Analysis C/C Of Raspoznavaniye Obrazov I Analiz Izobrazhenii, 8, 537-559.
[5]. Gao, W., Zhang, X., Yang, L., & Liu, H. (2010, July). An improved Sobel edge detection. In Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on (Vol. 5, pp. 67-71). IEEE.
[6]. Ghosal, S. Zernike Moment-Based Subpixel Edge Detection.
[7]. Aborisade, D. O. (2011). Novel Fuzzy logic Based Edge Detection Technique.International Journal of Advanced Science and Technology, 29, 75-82.
[8]. Jain, S. (2011). Edge Detection using Evolutionary Algorithms.
[9]. Kaur, A., & Singh, C. Sub-Pixel Edge Detection Using Pseudo Zernike Moment.
[10]. Alshennawy, Abdallah A., and Ayman A. Aly. "Edge Detection in Digital Images Using Fuzzy Logic Technique." Proceedings of World Academy of Science: Engineering & Technology 51 (2009).
[11]. Kaur, Natinder, and Deepak Sharma. "Edge Detection Using Fuzzy Logic."
[12]. Aborisade, David O. "Novel Fuzzy logic Based Edge Detection Technique." International Journal of Advanced Science & Technology 29 (2011).
[13]. Alshennawy, Abdallah A., and Ayman A. Aly. "Edge Detection in Digital Images Using Fuzzy Logic Technique." Proceedings of World Academy of Science: Engineering & Technology 51 (2009).
[14]. Senthilkumaran, N., and R. Rajesh. "Edge detection techniques for image segmentation�a survey of soft computing approaches." International Journal of Recent Trends in Engineering 1.2 (2009): 250-254.

J. Bala, R. Dhir, "A Novel Hybrid Technique for Sub-pixel Edge Detection using Fuzzy Logic and Zernike Moment," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.26-31, 2014.

with recent advances in imaging and computer graphics technologies, HDR images are becoming more widely available. To display high dynamic range (HDR) images onto conventional displayable devices that have low dynamic range (LDR) such as monitors and printers, an increasing number of tone mapping operators (TMOs) that convert HDR to LDR images have been developed. In order to assess the quality of several TMO, an objective quality assessment algorithm named Tone Mapped image Quality Index (TMQI) is proposed for tone mapped images. Initially the HDR image is generated for which the three low, Mid and high exposure images are subjected to HDR image generation and the created HDR image is stored in .hdr format which serves as the input to Tone mapped Images Quality Index Algorithm (TMQI). It combines a multi-scale signal fidelity measure based on a modified structural similarity (SSIM) index and a naturalness measure based on intensity statistics of natural images. It converts high dynamic range (HDR) to low dynamic range (LDR) images and also generate the multi scale structural fidelity measure. Then the laplacian pyramid is computed for Exposure images. Finally the structural fidelity and laplacian pyramid images are fused which produces the tone mapped images.

Dynamic Range; Tone Mapping; Quality Assessment; Structural Similarity

[1]. E. Reinhard, G. Ward, S. Pattanaik, P. Debevec, W. Heidrich, and K. Myszkowski, �High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting� San Mateo, CA: Morgan Kaufmann, page no(187-219),2010.
[2]. E. Reinhard, M. Stark, P. Shirley, and J. Ferwerda, �Photographic tone reproduction for digital images,� in Proc. 29th Annu. Conf. Comput. Graph. Interact. Tech., vol. 21. 2002, pp. 267�276.
[3]. G. W. Larson, H. Rushmeier, and C. Piatko, �A visibility matching tone reproduction operator for high dynamic range scenes,� IEEE Transaction Visual Computer. Graphics, vol. 3, no. 4, page no 291�306, Oct.�Dec. 1997.
[4]. F. Drago, K. Myszkowski, T. Annen, and N. Chiba, �Adaptive logarithmic mapping for displaying high contrast scenes,� Comput. Graph. Forum, vol. 22, no. 3, pp. 419�426, 2003.
[5]. R. Fattal, D. Lischinski, and M. Werman, �Gradient domain high dynamic range compression,� in Proceedings 29th Annual Conference Computer Graphics Interaction Technology, page no: 249�256, 2002.
[6]. Huei-Yung Lin, Xin-Han Chou �An Image Quality Assessment Technique using Defocused Blur as Evaluation Metric� in International Computer Science and Engineering VISAPP (1) 2013: 101-104.
[7]. A. J. Kuang, H. Yamaguchi, G. M. Johnson, and M. D. Fairchild, �Testing HDR image rendering algorithms,� in Proceedings IST/SID Color Image. Conference on 2004, page no: 315�320.
[8]. P. Ledda, A. Chalmers, T. Troscianko, and H. Seetzen, �Evaluation of tone mapping operators using a high dynamic range display,� ACM Transaction Graphics vol. 24, no. 3, page no. 640�648, 2005.
[9]. A. Yoshida, V. Blanz, K. Myszkowski, and H. Seidel, �Perceptual evaluation of tone mapping operators with real-world scenes,� Proceedings SPIE, Human Visual Electron. Image, vol. 5666, page no:192�203, Jan 2005.
[10]. M. ˇ Cad�k, M. Wimmer, L. Neumann, and A. Artusi, �Image attributes and quality for evaluation of tone mapping operators,� in Proceedings 14th Pacific Conference Computer Graphic, page no. 35�44, April 2006.

R. Madhanmohan, G. Manju , "Assessing the Quality of Tone Mapped Images Based On Structural Similarity," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.32-37, 2014.

In recent years, network bandwidth and quality has been drastically improved in a speed even much faster than the enhancement of computer performance. Various communication and computing tasks in the fields can be integrated and applied in a distributed system in now a days. However, those resources are heterogeneous and dynamic in distributed systems connecting a broad range of resources. This study proposed a hybrid load balancing policy to maintain performance and stability of distributed system. Load balancing is found to reduce significantly the mean and standard deviation of job response times, especially under heavy and/or unbalanced workload. Network overload is one of the key challenges in wireless LANs (WLANs). This goal is classically achieved when the load of access points (APs) is balanced. Recent studies on operational WLAN have shown that AP load is often uneven allocation. To rectify such overload, more than a few load balancing schemes have been proposed. These methods are commonly required proprietary software or hardware at the end side for calculating the user-AP association. In this paper we present a new load balancing method by controlling the size of WLAN cells (i.e., AP�s coverage range), which is conceptually similar to cell breathing in cellular networks. This method does not require any modification to the users neither the wireless standard. It only requires the ability of dynamically changing the transmission power of the AP beacon messages. We build up a set of polynomial time algorithms that locate the optimal beacon power settings which minimize the load of the most congested AP. We also consider the problem of network-wide min-max load balancing.

WLAN IEEE 802.11 Network, Cell Breathing, Load Balancing, faireness, FTP Server, TCP protocol, RSSI

[1] Devesh Sharma, Prof. Subhash Patil, �Throughput Maximization in Wireless LAN with Load Balancing Approach and Cell Breathing", Int. J. Computer Technology & Applications,Vol 4(1),92-96 ,TIT Bhopal, 2013.
[2] Alatishe S. Adeyemi and Dike U. Ike ,"A Review of Load Balancing Techniques in 3GPP LTE System", International Journal of Computer Science Engineering(IJCSE),ISSN:2319-7323Vol.2 No.04,July 2013.
[3] Parminder Kaur, "on the cell breathing technique to reduce congestion applying bandwidth limitation", International Journal of Grid Computing & Applications (IJGCA) Vol.3, No.1, March 2012.
[4] Yigal Bejerano and Seung-Jae Han,�Cell Breathing Techniques for Load Balancing in Wireless LANs", Member, IEEE transactions on mobile computing, VOL. 8, NO. 6, JUNE 2009.
[5] Paramvir (Victor) Bahl, Mohammad T. Hajiaghayi, Member," Cell Breathing in Wireless LANs: Algorithms and Evaluation",IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 6, NO. 2, FEBRUARY 2007.
[6] Sandip Patil and Sandeep Vanjale, "A Survey on Malicious Access Point Detection Methods for Wireless Local Area Network", International Journal of Computer Sciences and Engineering, Volume-02, Issue-03, Page No (22-25), Mar -2014.

S. Kunal, N. Mohan, G. Kavita, K. Monali, D. Pooja, "Improving throughput in Wireless LAN using Load Balancing Approach," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.38-43, 2014.

A Mobile Ad-Hoc Network (MANET) is self-configuring network of mobile nodes connected by wireless links to form an arbitrary network topology without the use of existing Infrastructure. MANETs are characterized by multi-hop wireless connectivity, a frequently changing network topology and the need for efficient dynamic routing protocols plays an important role. Severe routing protocols targeted specifically at this environment have been developed and some performance simulations are made on a mature routing protocol i.e. Ad hoc On-Demand Distance Vector Routing. In this paper we perform extensive simulations using network simulator considering four performance metrics. For experiment purposes and to determine the impact of network size on the performance of this routing protocol we considered three different entity mobility models namely Random Direction (RD), Random Walk (RW) and Gauss Markov (GM) models with rectangular area sizes 1000 × 1000 m2. These three Mobility Models are selected to represent the possibility of practical applications in future. Our framework aims to evaluate the impact of different mobility models on the performance of MANET routing protocols.

Mobile ad hoc networks (MANETs), Routing protocols, AODV and NS-2

[1] Dimitri Bertsekas and Robert Gallager, “Data Networks- 2nd Edition”, Prentice Hall, New Jersey, ISBN 0-13-200916-1.
[2] Jun-Zhao Sun, “Mobile Ad Hoc Networking: An Essential Technology for Pervasive Computing” International Conferences on Info-tech and Info-net Proceedings, ICII 2001, Volume 3, Beijing, pp 316-321.
[3] Krishan Kant Lavania, G. L. Saini, Kothari Rooshabh H., Yagnik Harshraj A. “Privacy Anxiety and Challenges in Mobile Ad Hoc Wireless Networks and its Solution”, International Journal of Scientific & Engineering Research, Volume 2, Issue 9, September-2011, ISSN 2229-5518, pp 173-177.
[4] Anastasi,G. E. Borgia, M. Conti, E. Gregori, 2003 “IEEE 802.11 Ad Hoc networks: performance measurements”, Proceedings of 23rd International Conference on Distributed Computing Systems, May 2003, pp 758 – 763.
[5] Neeraj Kumar Pandey and Amit Kumar Mishra, "An Augmentation in a Readymade Simulators Used for MANET Routing Protocols: Comparison and Analysis", International Journal of Computer Sciences and Engineering, Volume-02, Issue-03, Page No (60-63), Mar -2014.
[6] Nilesh P. Bobade, Nitiket N. Mhala, “ Performance Evaluation of Ad Hoc On Demand Distance Vector in MANETs with varying Network Size using NS-2 Simulation” , International Journal on Computer Science and Engineering, Volume 02, No. 08, 2010, pp. 2731-2735.
[7] Tracy Camp, Jeff Boleng and Vanessa Davies, “ A Survey of Mobility Models for Ad Hoc Networks Research”, Wireless Communication & Mobile Computing (WCMC): Special issue on Mobile Ad Hoc Networking:Research, Trends and Applications, vol. 2, no. 5, pp. 483-502, 2002.
[8] Fan Bai, Ahmed Helmy, “A Survey of Mobility Models in Wireless Ad hoc Networks”, in Wireless Ad Hoc and Sensor Networks, Chapter 1, Kluwer Academic Publishers, June 2004, pp. 1-291.
[9] BONNMOTION: “A mobility scenario generation and analysis tool”, University of Bonn, [Online]. Available: http://net.cs.uni-bonn.de/wg/cs/applications/bonnmotion/
[10] “The Network Simulator - ns-2,” available at http://www.isi.edu/nsnam/ns/ referred on March 2013.

Preeti, "Performance Evaluation of AODV with Varying Network Size in MANET Using Entity Models," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.44-48, 2014.

Shadow Elimination is the removal of shadow without disturbing the quality of the image from either indoor and outdoor scenes or any other scenes. This review covers up all the different techniques and algorithms used in shadow elimination process as well as gives a brief description of the advantage and disadvantage of each algorithm used; therefore making an easy platform for the researchers to work more precisely with image processing to yield better growth in science and technology especially in geostatistic.

Shadow Elimination; Image processing; Algorithms; Features; Technology

[1]. https://reference.wolfram.com/mathematica/tutorial/ImageProcessing.htm
[2]. Alessandro Leone CD, and Francesco Buccolieri: A shadow elimination approach in video-surveillance context. Pattern Recognition Letters 2006, 27(5):345-355.
[3]. Sanjeev kumar Ak: Shadow detection and removal in colour images using matlab. International Journal of Engineering Science and Technology 2010, 2(9):4482-4486.
[4]. Shugen Wang YW: Shadow Detection and Compensation in High Resolution Satellite Image Based on Retinex. In: Image and Graphics, 2009 ICIG `09 Fifth International Conference. Xi`an, Shanxi 2009: 209 - 212
[5]. Andres Sanina CS, Brian C. Lovella: Shadow detection: A survey and comparative evaluation of recent methods. Pattern Recognition Letters 2011, 45(4):1684-1695.
[6]. A. Prati IM, M. Trivedi, and R. Cucchiara.: Detecting moving shadows: algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003, 25(7):918-923.
[7]. W. Hsieh W-FH, C.-J. Chang, Y.-S. Chen: Shadow elimination for effective moving object detection by Gaussian shadow modeling. Image and Vision Computing 2003, 21(6):505-516.
[8]. T. Horprasert DH, L. Davis: A statistical approach for real-time robust background subtraction and shadow detection. In: Frame-Rate Workshop: 1999: IEEE ICCV�99; 1999.
[9]. Bangyu Sun SL: Moving Cast Shadow Detection of Vehicle Using Combined Color Models. In: Pattern Recognition (CCPR): 2010; Chinese; 2010: 1-5.
[10]. H. Nicolas J-MP: Joint moving cast shadows segmentation and light source detection in video sequences. Signal Processing: Image Communication 2006, 21(1):22-43.
[11]. Y.-L. Tian ML, A. Hampapur: Robust and efficient foreground analysis for real-time video surveillance. In: Computer Vision and Pattern Recognition: 2005: IEEE Conference 2005.
[12]. Kamath S-CSCaC: Robust Background Subtraction with Foreground Validation for Urban Traffic Video. EURASIP Journal on Advances in Signal Processing 2005.
[13]. AVERY Ryan P GZ, YINHAI WANG ,NIHAN Nancy L.: An Investigation into Shadow Removal from Traffic Images. National Research Council 2007, 17: 70-77.
[14]. Fredembach CaF, G. D: Path-based shadow removal. In: 10th Congress of the International Colour Association Edited by 05 AC. Granada; 2005.
[15]. Yue Wang A SWA: SHADOW DETECTION OF URBAN COLOR AERIAL IMAGES BASED ON PARTIAL DIFFERENTIAL EQUATIONS.
[16]. Paul L. Rosin TE: Image Difference Threshold Strategies and Shadow Detection. BMVC `95 Proceedings of the 1995 British conference on Machine vision 1995:347-356.
[17]. Sanyam Anand RT: Detection and Removal of Cast Shadow from Digital GrayScale Images using Novel Fuzzy Technique. IJCST 2012, 3(2).
[18]. SUMIT KUMAR SINGH MS: AN ADAPTIVE BACKGROUND UPDATION AND GRADIENT BASED SHADOW REMOVAL METHOD. International Journal of Image Processing and Vision Sciences 2012, 2(1):2278 � 1110.
[19]. Barczak ALC: Experiments with Object Tracking in the Presence of Partial Occlusions.
[20]. Daisuke Miyazaki YM, Katsushi Ikeuchi Interactive Shadow Removal from a Single Image Using Hierarchical Graph Cut. Lecture Notes in Computer Science 2010, 5994:234-245.
[21]. M OAaS: From shape from shading to object recognition. Int J of Pattern Recognition and Artificial intelligence 1998, 12(7):969-984.
[22]. Yang Yijun ZRaJW: Detection of shadow areas from aerial imagery. Signal Processing 2002.
[23]. Shibam Das AA: A Review: Shadow Detection And Shadow Removal from Images. International Journal of Engineering Trends and Technology (IJETT) 2013, 4(5).
[24]. Yung WWLLCC-CPNHC: Highly accurate texture-based vehicle segmentation method. Optical Engineering 2004, 43(3).
[25]. Elena Salvador Ac, Touradj Ebrahimi: Cast shadow segmentation using invariant color features. Computer Vision and Image Understanding 2004, 95(2):238-259.
[26]. Sam-Yong Kim SYO: On road vehicle detection and tracking based on road context and the ambient lighting adaptive framework. International Journal of Imaging Systems and Technology 2007, 18(5-6):283-295.
[27]. www.cs.huji.ac.il/~danix/ShadowRemoval/.
[28]. Girisha R MS: Self Shadow Elimination Algorithm for Surveillance Videos using Inferential Difference in Mean Statistics. International Journal of Computer Applications 2010:507-824.
[29]. Jacques JCS, Jung, C.R. ,Raupp Musse: Background Subtraction and Shadow Detection in Grayscale Video Sequences. In: SIBGRAPI 2005. Edited by Processing CGaI. Brazilian 2005: 189 - 196.
[30]. AmirReza Shahtahmassebi NY, Ke Wang, Nathan Moore, Zhangquan Shen Review of shadow detection and de-shadowing methods in remote sensing. Chinese Geographical Science 2013, 23(4):403-420.
[31]. Sanjeev Kumar , Anupreet Kaur Alorithm For Shadow Detection In Real Colour Images. (IJCSE) Vol. 02, No. 07, 2010, 2444-2446.

N. Vincent, S. Mathew, "Shadow Detection: A Review of Various Approaches to Enhance Image Quality," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.49-54, 2014.