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In the Modern world, the propensity of the detecting most Salient objects are trending on a large scale. In the era of Computer and science and specially in the Image Processing, we tend to find out most feasible techniques which can extract the most relevant and salient features of the selected image database. We are provided with a few most usable image retrieval methods however still we are not satisfied with the output extracted from the used method. With the advancement of the technology and the Image processing techniques, we have new methods to highlight the Salient features. One of them is Graph based Visual Saliency. GBVS is the technique which produces the salient features in a very accurate and faster way and in an elaborated way. It produces the data in the activation map and then extracts the features from the original image. We have here used a few different images and using our proposed method tried to depict the results in a graphical and pictorial way. Our effort main motive is to highlight the features of an image in a wider manner. In this paper, we would learn how to show Salient part of an image but in a large scale. In this paper, it shows 80% Salient part of an image of GBVS.

Image Processing, Image Retrieval, Shape, Color, Graph based visual saliency, Content Based Image retrieval

[1]L. Itti, C. Koch, E. Niebur, et al.“A saliency-based search mechanism for overt and covert shifts of visual attention”1999.
[2]Hans-Christoph Nothdurft* “Salience from feature contrast: temporal properties of saliency mechanisms” May 1999.
[3]RitendraDattaJia Li James Z. Wang “Content-Based Image Retrieval Approaches and Trends of the New Age”2005.
[4]D.Walther and C. Koch. “Modeling attention to salient proto-objects” Published by Elsevier Ltd 2006.
[5] Kanakam Siva Ram Prasad, "New Non-Parametric Model for Automatic Annotations of Images in Annotation Based Image Retrieval", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.16-21, 2017.
[6]Tamura,Hideyuki,Mori,Ymawaki,Shunji,Takashi,“Textual Features corresponding to visual perception”,Vol 8,Isssue 6, Systems Man And Cybernetics,in IEEE transactions,2007.
[7]Mustafa Ozden, EdizPolat, “A colour image segmentation approach for content-based image retrieval, Pattern Recognition”2007.
[8]WeilongHoua, XinboGaoa, Dacheng Taob1*, XuelongLic “Visual Saliency Detection Using Information Divergence”2007.
[9] A. Agarwal, S.S. Bhadouria, "An Evaluation of Dominant Color descriptor and Wavelet Transform on YCbCr Color Space for CBIR", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.56-62, 2017.
[10]Lin Zhang, ZhongyiGu, and Hongyu Li*1“A novel saliency detection method by combining simple priors” 978-1-4799-2341-0/13/$31.00 IEEE ©2013.
[11]Qiong Yan Li Xu Jianping Shi JiayaJia “Hierarchical Saliency Detection”2013.
[12]Anita N. Ligade, Manisha R. Patil “Content Based Image Retrieval Using Interactive Genetic Algorithm with Relevance Feedback” (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) ,2014.
[13]ChestiAltaff Hussain, S.AranaMasthani “Robust pre-processing technique based on saliency detection for content based image retrieval systems” Volume 85 published by Elsevier,2016.

Bhagya Laxmi, Pawan Kumar Mishra, "Usage of GBVS in Image Processing to Retrieve the Images," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.138-142, 2018.

Closed itemset mining plays an important role in mining compressed representations or summaries of the set of frequent patterns. It uses the support information of itemsets and the superset–subset relationship among itemsets for removing redundancy. Closed itemset is always smaller or equal in cardinality comparing to other concise representation such as frequent free sets and generators. But, the compression using this closed-pattern approach may not be very effective, since slightly different count often exists between super and sub patterns. This paper focuses to compare two closed itemset algorithms such as FPclose and CFPtree-closed and a frequent itemset algorithm NCFPGEN for studying and determining the performance of these algorithms in the execution time aspect.

frequent itemset, closed itemset

[1] J. Han, J. Pei, and Y. Yin, “Mining frequent patterns without candidate generation”, In Proceedings of ACM SIGMOD’00, pp 1–12, May 2000.
[2] Pasquier N, Bastide Y, Taouil R and Lakhal, “Pruning Closed Itemset Lattices for Association Rules”, Proc. BDA conf., pp 177-196, 1998.
[3] Pasquier N, Bastide Y, Taouil R and Lakhal L, “Efficient Mining of Association Rules using Closed Itemset Lattices”, Information Systems, vol 24, No 1, pp 25-46,1999.
[4] Pasquier N, Bastide Y, Taouil R and Lakhal, “ Discovering frequent closed itemsets for association rules”, In: Proc 7th Int Conf on Database Theory (ICDT’99), Jerusalem, Israel, pp 398–416,1999.
[5] Zaki M, “Generating non-redundant association rules”, In: Proc 2000 ACM SIGKDD Int Conf Knowledge Discovery in Database (KDD’00), Boston, USA, pp 34–43, 2000.
[6] J. Pei, J. Han, and R. Mao, “CLOSET: An efficient algorithm for mining frequent closed itemsets”,In ACM SIGMOD’00 Workshop on Research Issues in Data Mining and Knowledge Discovery, pp 21–30, 2000.
[7] J. Wang, J. Han and J. Pei, “Closet+: Searching for the best strategies for mining frequent closed itemsets,” in Proc. KDD, New York, NY, USA, pp. 236–245, 2003.
[8] Ng R, Lakshmanan LVS, Han J and Pang A, “Exploratory mining and pruning optimizations of constrained associations rules”, In: Proc 1998 ACM-SIGMOD Int Conf Management of Data(SIGMOD’98), Seattle, USA, pp 13–24, 1998.
[9] Lakshmanan LVS, Ng R, Han J and Pang A, “Optimization of constrained frequent set queries with2-variable constraints”, In: Proc 1999 ACM-SIGMOD Int Conf Management of Data (SIGMOD’99),Philadelphia, USA, pp 157–168,1999.
[10] Pei J, Han J and Lakshmanan LVS, “Mining frequent itemsets with convertible constraints”, In: Proc 2001Int Conf Data Engineering (ICDE’01), Heidelberg, Germany, pp 433–332, 2001.
[11] R. Srikant, Q. Vu, R. Agrawal, Mining association rules with item constraints, in: Proceedings of the 3rd ACMSIGKDD Conference, 1997, pp. 67–73.
[12] Gosta Grahne and Jianfei Zhu, “Fast Algorithms for Frequent Itemset Mining Using FP-Trees”, IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 10, October 2005.
[13] Calders and B. Goethals., “Mining all non-derivable frequent itemsets”, In Proc. of 2002 European Conf. on Principles of Data Mining and Knowledge Discovery(PKDD’02), pp 74–85, 2002.
[14] Y. Bastide, N. Pasquier, R. Taouil, G. Stumme, and L.Lakhal, “Mining minimal non-redundant association rules using frequent closed itemsets,” in Proc. 1st Int. Conf. CL, London, U.K., pp. 972–986, 2000.
[15] A.Bykowski and C. Rigotti, “A condensed representation to find frequent patterns,” in Proc. PODS, New York, NY, USA, pp. 267-273, 2001.
[16] J.F. Boulicaut, A. Bykowski, and C. Rigotti, “Free-sets: A condensed representation of boolean data for the approximation of frequency queries,” Data Mining Knowl. Discov., vol. 7, no. 1, pp.5–22, 2003.
[17] R. J. Bayardo, “Efficiently mining long patterns from databases,”in Proc. SIGMOD, New York, NY, USA, pp. 85–93, 1998.
[18] J. Wang, J. Han, Y. Lu, and P. Tzvetkov, “TFP: An efficient algorithm for mining top-k frequent closed itemsets,” IEEE Trans. Knowl. Data Eng., vol. 17, no. 5, pp. 652–664, May 2005.
[19] D. Xin, H. Cheng, X. Yan, and J. Han, “Extracting redundancy aware top-k patterns,” in Proc. KDD, Philadelphia, PA, USA, pp.444–453,2006.
[20] G. Liu, H. Lu, and J. X. Yu, “CFP-tree: A compact disk-based structure for storing and querying frequent itemsets,” Inf. Syst., vol. 32, no. 2, pp. 295–319, 2007.
[21] R.Prabamanieswari, D.S.Mahendran, T.C. Raja Kumar, “A Survey on Concise and Lossless Representation of Frequent Pattern Sets”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 4, Issue 9, September 2015.
[22] Caiyan Dai and Ling Chen, “An Algorithm for Mining Frequent Closed Itemsets with Density from Data Streams”, International Journal of Computer Sciences and Engineering, Vol. 4(2), pp. 40-48, Feb 2016.
[23] R.Prabamanieswari, D.S.Mahendran, T.C. Raja Kumar, “NCFP-tree: A Non-Recursive Approach to CFPtree using Single Conditional Database”, International Journal for Research in Applied Science & Engineering Technology (IJRASET), Volume 5 Issue XI November 2017.

R.Prabamanieswari, D.S.Mahendran, T.C. Raja Kumar, "Comparative Study of FPclose, CFPtree-closed and NCFPGEN Algorithms," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.143-148, 2018.

Cloud computing demand is expanding because of which it is essential to redress benefits within the sight of faults too. The Resources in cloud computing can be progressively scaled that too in a manner that is cost effective. Adaptation to non-critical failure is the way toward discovering faults and failures in a system. Mobile gadgets are portrayed by constrained and non-adaptable assets, for example, low handling power, inadequate essential memory and short battery life. Asset concentrated applications, for example, increased reality, counterfeit consciousness, simulated vision, protest following, picture handling and common dialect preparing are getting to be famous. These applications cannot be executed adequately on asset requirement mobile gadgets. Over the most recent couple of years impressive research has been done on Mobile Cloud Computing to expand the abilities of mobile gadgets. Different models and structures have been proposed to offload asset escalated parts of uses to cloud for productive execution. The system should work appropriately even if there is chance of failure happens or hardware failure or programming failure. Failure ought to be overseen viably for dependable Cloud Computing. It will likewise guarantee accessibility and robustness. This paper depicts a profile based approach for energy efficient mobile cloud computing environment.

Cloud Computing, Faults, Failures, Energy Consumption

[1] T. H. Noor, Q. Z. Sheng, L. Yao, S. Dustdar, and A. H. H. Ngu, “CloudArmor: Supporting Reputation-Based Trust Management for Cloud Services,” IEEE Trans. Parallel Distrib. Syst., vol. 27, no. 2, pp. 367–380, Feb. 2016.
[2] P. Zhang, S. Hu, J. He, Y. Zhang, G. Huang, and J. Zhang, “Building cloud-based healthcare data mining services,” Proc. - 2016 IEEE Int. Conf. Serv. Comput. SCC 2016, pp. 459–466, 2016.
[3] Z. Amin, “Review on Fault Tolerance Techniques in Cloud Computing,” vol. 116, no. 18, pp. 11–17, 2015.
[4] D. Singh, J. Singh, and A. Chhabra, “High availability of clouds: Failover strategies for cloud computing using integrated checkpointing algorithms,” Proc. - Int. Conf. Commun. Syst. Netw. Technol. CSNT 2012, pp. 698–703, 2012.
[5] B. Nicolae, “BlobCR : Efficient Checkpoint-Restart for HPC Applications on IaaS Clouds using Virtual Disk Image Snapshots.”
[6] B. H. Oh and J. Lee, “Constraint-based proactive scheduling for MPTCP in wireless networks,” Comput. Networks, vol. 91, pp. 548–563, 2015.
[7] T. Hirofuchi, H. Nakada, S. Itoh, and S. Sekiguchi, “Reactive Consolidation of Virtual Machines Enabled by Postcopy Live Migration,” pp. 11–18, 2011.
[8] J. Hursey, J. M. Squyres, T. I. Mattox, and A. Lumsdaine, “The Design and Implementation of Checkpoint / Restart Process Fault Tolerance for Open MPI ∗.”
[9] B. Schroeder and G. a. Gibson, “Disk failures in the real world: What does an MTTF of 1,000,000 hours mean to you,” Conf. File Storage Technol., no. September, pp. 1–16, 2007.
[10] G. Aupy, A. Benoit, M. E. M. Diouri, O. Glück, and L. Lefèvre, “Energy-Aware Checkpointing Strategies,” pp. 279–317, 2015.
[11] W. Chen, S. Member, R. Ferreira, E. Deelman, and T. Fahringer, “Dynamic and Fault-Tolerant Clustering for Scientific Workflows,” vol. 7161, no. FEBRUARY, pp. 1–14, 2015.
[12] K. Akherfi, M. Gerndt, and H. Harroud, “Mobile cloud computing for computation offloading : Issues and challenges,” Appl. Comput. Informatics, 2017.
[13] X. Chen, “Decentralized Computation Offloading Game for Mobile Cloud Computing,” IEEE Trans. Parallel Distrib. Syst., vol. 26, no. 4, pp. 974–983, Apr. 2015.
[14] J. Xu and S. Pears, “A Dynamic Shadow Approach to Fault-Tolerant Mobile Agents in an Autonomic Environment,” Real-Time Syst., vol. 32, no. 3, pp. 235–252, Mar. 2006.
[15] X. Cui, B. Mills, T. Znati, and R. Melhem, “Shadow replication: An energy-aware, fault-tolerant computational model for green cloud computing,” Energies, vol. 7, no. 8, pp. 5151–5176, 2014.
[16] A. Tchana, L. Broto, and D. Hagimont, “Fault Tolerant Approaches in Cloud Computing Infrastructures,” no. c, pp. 42–48, 2012.
[17] A. Bala and I. Chana, “Fault Tolerance-Challenges , Techniques and Implementation in Cloud Computing Fault Tolerance- Challenges , Techniques and Implementation in Cloud Computing,” no. January 2012, 2015.
[18] D. Terry, “Toward a New Approach to IoT Fault Tolerance,” Computer (Long. Beach. Calif)., vol. 49, no. 8, pp. 80–83, 2016.
[19] I. P. Egwutuoha, S. Cheny, D. Levy, B. Selic, and R. Calvo, “Energy efficient fault tolerance for high performance computing (HPC) in the cloud,” IEEE Int. Conf. Cloud Comput. CLOUD, pp. 762–769, 2013.
[20] V. M. Sivagami, “Survey on Fault Tolerance Techniques in Cloud Computing Environment,” no. 9, pp. 419–425, 2015.
[21] G. A. Kaur and S. Sharma, “Fault Tolerance in Cloud Computing : A Review,” vol. 8491, pp. 94–98, 2017.
[22] S. Choi, K. Chung, and H. Yu, “Fault tolerance and QoS scheduling using CAN in mobile social cloud computing,” 2013.
[23] C. A. Chen, M. Won, R. Stoleru, and G. G. Xie, “Energy-efficient fault-tolerant data storage and processing in mobile cloud,” IEEE Trans. Cloud Comput., vol. 3, no. 1, pp. 28–41, 2015.
[24] R. Jhawar, V. Piuri, and M. Santambrogio, “A Comprehensive Conceptual System-Level Approach to Fault Tolerance in Cloud Computing,” pp. 0–4, 2012.
[25] S. K. Suman, “Effective Fault Tolerance Management in Cloud Computing,” vol. 6, no. 3, pp. 192–198, 2016.
[26] D.-W. Sun, G.-R. Chang, S. Gao, L.-Z. Jin, and X.-W. Wang, “Modeling a Dynamic Data Replication Strategy to Increase System Availability in Cloud Computing Environments,” J. Comput. Sci. Technol., vol. 27, no. 2, pp. 256–272, 2012.
[27] K. Ranganathan, A. Iamnitchi, and I. Foster, “Improving data availability through dynamic model-driven replication in large peer-to-peer communities,” 2nd IEEE/ACM Int. Symp. Clust. Comput. Grid, CCGrid 2002, pp. 0–5, 2002.
[28] D. Boru, D. Kliazovich, F. Granelli, P. Bouvry, and A. Y. Zomaya, “Energy-efficient data replication in cloud computing datacenters,” Globecom Work. (GC Wkshps), 2013 IEEE, pp. 446–451, 2013.
[29] W. Lang, J. M. Patel, and J. F. Naughton, “On energy management, load balancing and replication,” ACM SIGMOD Rec., vol. 38, no. 4, p. 35, 2010.
[30] J. Wang, X. Yao, and H. Zhu, “Exploiting in-memory and on-disk redundancy to conserve energy in storage systems,” IEEE Trans. Comput., vol. 57, no. 6, pp. 733–747, 2008.
[31] B. Mills, T. Znati, R. Melhem, and K. B. Ferreira, “Shadow Computing An Energy-Aware Resiliency Scheme for High Performance Computing.”
[32] M. Mishra and A. Das, “Dynamic resource management using virtual machine migrations,” IEEE Commun. Mag., vol. 50, no. September, pp. 34–40, 2012.
[33] K. Hwang, X. Bai, Y. Shi, M. Li, W.-G. Chen, and Y. Wu, “Cloud Performance Modeling with Benchmark Evaluation of Elastic Scaling Strategies,” IEEE Trans. Parallel Distrib. Syst., vol. 27, no. 1, pp. 130–143, Jan. 2016.
[34] H. Wang, Z. Kang, and L. Wang, “Performance-Aware Cloud Resource Allocation via Fitness-Enabled Auction,” IEEE Trans. Parallel Distrib. Syst., vol. 27, no. 4, pp. 1160–1173, Apr. 2016.
[35] K. Yang and X. Jia, “An Efficient and Secure Dynamic Auditing Protocol for Data Storage in Cloud Computing,” IEEE Trans. Parallel Distrib. Syst., vol. 24, no. 9, pp. 1717–1726, Sep. 2013.
Authors Profile

Rajeev Kumar Bedi is currently working as Assistant Professor in Beant College of Engineering and Technology, Gurdaspur (Punjab). He has completed B.Tech and Post graduation M.Tech from Punjab Technical University, Jalandhar. He is currently persuing Ph.D from Punjabi University, Patiala. He has published papers in reputed journals including Springers, Elsevier etc. His main Research work focusses on Cloud Computing, Mobile Cloud Computing and Wireless Netwok.

Mrs. Vandana is currently pursuing M.Tech (CSE) from Beant College of Engineering and Technology (BCET), Gurdaspur under IKG Punjab Tachnical University, Jalandhar, Punjab.
Dr. Sunil Kumar Gupta did B.E. in Computer Science from Gorakhpur University, Gorakhpur, India in 1988, and M.S. in 1991 and completed Ph.D. in Computer Science from Kurukshetra University, Kurukshetra, India. He possesses 28 years of teaching experience. He has worked as teaching faculty in many reputed institutions in India including N.I.T., Hamirpur (HP). Presently, he is working as Associate Professor in Computer Science & Engg. Department at Beant College of Engineering and Technology, Gurdaspur (India).He has more than 40 research publications. His work is published and cited in highly reputed journals of Elsevier, Springer, Taylor and Francis and IEEE. His areas of interest include database management systems, distributed systems, cloud computing and mobile computing and security.

Rajeev Kumar Bedi, Vandana,Sunil Kumar Gupta, "Design of an Efficient Memory Management Model for Mobile Device Using C-Ram," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.149-156, 2018.

This paper proposes a design of rectangular slot antenna with defected ground structure (DGS) loaded. DGS is used for improving the parameters of microstrip antenna .The designed antenna has compact size of 33(L) ×35.5(W) ×1.6(H) mm3. The proposed antenna is designed and fabricated on glass epoxy substrate FR-4 with dielectric constant 4.4 and loss tangent 0.02.The proposed design has the return loss of less than -10dB and VSWR < 2 dB for desired operating bands. The antenna shown two distinct bands I from 3.0 GHz to 4.2 GHz, and II from 5.2 GHz to 6.2 GHz, which cover WiMAX (fc = 3.5 GHz), and WLAN (fc = 5.8 GHz) respectively. The measured results of the antenna can achieved a total bandwidth (2.1GHz -10.7GHz) 8.6GHz for UWB applications. The proposed antenna is simulated using CST V.12microwave studio software using 50Ω strip-feed line.

Defected Ground Structure (DGS), Ultra Wide Band (UWB), Worldwide Interoperability for Microwave Access (WiMAX), Wireless Local Area Network (WLAN), Voltage Standing Wave Ratio (VSWR)

[1] Sajjad UR Rahman and Majeed A. S. AlkanhalL, “Design and System Characterization of Ultra-Wideband Antennas with Multiple Band-Rejection”, IEEE Access 2017
[2] X. Gao and Z. Shen, “UHF/UWB Tag Antenna of Circular Polarization”, IEEE Trans Antennas propagation, Vol64, No.9, Sept 2016
[3] Kai Da Xu, Y.H. Zhang, Ronald J. Spiegel, Y. Fan, William T. Joines, Qing Huo Liu, “Design of a stub- loaded Ring-Resonator slot for Antenna Application” IEEE Trans Antennas Propag. Vol.63, No.2, Feb 2015
[4] Debdeep Sarkar, Kumar Vaibhav Srivastava and Kushmanda Saurav, “A compact Microstrip-Fed Triple Band Notched UWB Monopole Antenna”,IEEE Antennas and Wireless Propagation Letters,Vol.13,2014
[5] Foad Fereidoony, Somayyeh Chamaani, and Seyed A. Mirtaheri, “UWB Monopole Antenna with Stable Radiation Pattern and Low Transient Distortion” IEEE Antennas and Wireless propagation Letters, Vol.10, 2011
[6] Ahmed S. Elkorany, Ghidaa T. Ahmed, Demyana A. Saleeb “ADouble Rotated ESSRs” ADVANCED ELECTROMAGNETICS, Vol. 7, JAN 2018
[7] Hari Shankar Mewara , Deepak Jhanwar, Mahendra Mohan Sharma ,Jitendra Kumar Deegwal, “A Novel Hammer-Shaped UWB Antenna with Triple Notched-Band for Rejecting RLS, WLAN and XSCS bands” ADVANCED ELECTROMAGNETICS, Vol. 6, NO. 4, OCTOBER 2017
[8] Mohammad Alibakhshi – Kenari, Mohammad Naser – Moghadasi, R.A. Sadeghzadeh, Bal S.Virdee Ernesto Limiti. “Bandwidth Extension of Planar Antennas Using Embedded Slits for Reliable Multiband RF Communications”, International journal of electronics and communication, Vol.70, pp. 910-919, 2016.
[9] J-Y Sze, J-C, Wang and C-C Chang, “Axial-Ratio Bandwidth Enhancement of Asymmetric – CPW – Fed Circularly Polarized Square Slot Antenna”, Electronic Letters Vol.44,No 18, 28th August 2008.

Sandeep Kumar Singh, Himanshu Parashar, Rajendra Singh, Vepakomma Kavya, "Design of Compact UWB Rectangular Patch Antenna for WiMAX/WLAN Applications," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.157-160, 2018.

Use of cloud technology for different requirements of an organization is on increase. Number of companies like Amazon, Microsoft, Salesforce, etc. is leading the package of cloud services. The main objective of these companies is to ensure that right resources are assigned to clients so that the resources are not left underutilized. Cloud task scheduling is a key research area and every company is investing a lot into it to reduce the underutilization of resources and ensuring the tasks finish on time. Metaheuristic algorithms over time have been used extensively for this task. This paper analyzes the performance of two metaheuristic algorithms namely ACO & PSO for cloud task scheduling.

ACO, PSO, VM, SJF, IAAS, PAAS, SAAS, Data Centre, Cloud Computing, ETC

[1] Celesti, A., Fazio, M., Villari, M., Puliafito, A., “Virtual machine provisioning through satellite communications in federated cloud environments.” Futur Gener Comput Syst 28(1), 85–93 (2012)
[2] Buyya, R., Broberg, J., Goscinski, A. (eds.): “Cloud Computing, Principles and Paradigms”. Wiley, Hoboken (2011)
[3] M. Kalra, S. Singh, “A review of metaheuristic scheduling technique in cloud computing”, Cairo University, Egyptian Informatics Journal, Vol. 16, issue 3, pp 275-295, 2015.
[4] Anitha H M, P. Jayarekha , "Security Challenges of Virtualization in Cloud Environment", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.1, pp.37-43, 2018..
[5] M. Tawfeek, A. El-Sisi, A. Keshk, F. Torkey, “ Cloud Task Scheduling Based on Ant Colony Optimization” ,The International Arab Journal of Information Technology, Vol. 12, No. 2, March 2015.
[6] N. Siddique, H. Adeli, “Nature Inspired Computing: An Overview and Some Future Directions”, Cong Comput, Vol 7, 706-714, 2015.
[7] A. Xu, Y. Yang, Z. Mi, “Task Scheduling algorithm based on PSO in cloud environment”, IEEE 12th International Conference on Autonomic and Trusted Computing, 978-1-4673-7212-1, 2015.
[8] F. Ramezani, J. Lu, F. K. Hussain. “Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization”, International Journal on Parallel Programming, Volume 42, Issue 5, pp 739–754, 2014.
[9] Shruti, M. Sharma, “Task Scheduling and Resource Optimization in Cloud Computing using Deadline-Aware Particle Swarm Technique”, International Journal of Computer Science and Engineering”, Vol. 5, Issue 6, pp. 227-231, 2017.

D. Gupta, H.J.S. Sidhu, "Performance Evaluation of ACO and PSO Task Scheduling Algorithms in Cloud Environment," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.161-164, 2018.

Deep learning has shown great improvements in all the computer vision and image interpretation tasks. In this paper a fully automated deep model for human activity recognition has been proposed which do not include any prior knowledge. In the first step of the proposed method, model automatically learns all the temporal and spatial features for recognition. In the second stage of the method memory network which is recurrent in nature is used to classify the various human actions. The results obtained from the suggested method are compared with all the rage methods. Outcomes show that the suggested method has better accuracy as compared to various alternative techniques available.

MLP, LSTM, TDR

[1] [1] G. Antonini, M. Bierlaire, and M. Weber, “Discrete choice models of pedestrian walking behaviour”, Transportation Research Part B: Methodological, Vol:40, Issue:8, pp. 667–687, 2006.
[2] [2] J. Azorin-Lopez, M. Saval-Calvo, A. Fuster-Guillo, and A. Oliver-Albert, “ A predictive model for recognizing human behaviour based on trajectory representation”, Neural Networks (IJCNN), 2014 International Joint Conference, pp. 1494–1501. IEEE, 2014.
[3] [3] A. Krizhevsky, I. Sutskever, and Hinton, G. E.Hinton, “ Image Net classification with deep convolutional neural network”, Advances in Neural Information Processing Systems, pp.1097–1105, 2012.
[4] [4] A. Graves, A. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks”, IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 6645– 6649,2013.
[5] [5] D.Weinland, R.Ronfard, and Boyerc, E.Boyerc, “A survey of vision-based methods for action representation, segmentation and recognition”, Computer Vision and Image Understanding Vol:115, Issue:2, pp. 224–241,2011.
[6] [6] K.Cho and Chen, X.Chen, “ Classifying and visualizing motion capture sequences using deep neural networks”, International Conference on Computer Vision Theory and Applications, pp. 122–130,2014.
[7] [7] G. Lefebvre, S.Berlemont, F.Mamalet, and C. Garcia, “BLSTM-RNN based 3D gesture classification”, Proceedings of the International Conference on Artificial Neural Networks and Machine Learning, pp. 381–388,2013.
[8] [8] Y.Du, W.Wang and L.Wang, “Hierarchical recurrent neural network for skeleton based action recognition”, IEEE Conference on Computer Vision and Pattern Recognition, 1110–1118,2015.
[9] [9] V. Pham,T. Bluche, C. Kermorvant, and J. Louradour, “Dropout improves recurrent neural networks for handwriting recognition”, International Conference on Frontiers in Handwriting Recognition, pp. 285–290,2014.
[10] [10] D. Anguita, A. Ghio, L. Oneto, X. Parra and J. L. Reyes-Ortiz, “A Public Domain Dataset for Human Activity Recognition Using Smartphones”, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013.

P. Sharma, S. Chaudhary, Komal, "Human Activity Recognition Using LSTM Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.165-167, 2018.

This research paper is designed on a unimodal biometric system. This system is based on Face recognition. The proposed modal consists of two major processes, the enrollment and the recognition. The enrollment is used for acquiring the template features which are called as the training features. The recognition means the involvement of the method which identifies the feature vectors from the template features to which that specific class belongs. This process is called as the testing and accuracy is obtained from this process. The Histograms of Oriented Gradients (HOG) are used for extracting the face features. This technique is applied for identification of a person on KVKRG face database. In this experiment total 200 images were used. KVKR Face database is developed under UGC-SAP Phase I (which is the researchers own major contribution) having 10 poses of each subject. The highest recognition rate is obtained by Ensemble (Subspace Discriminate) that is 98.8% and Linear Discriminant that is 100%. The experimental result has shown that biometrics system record an improvement in the overall system performance. Its results are quick and accurate.

Biometrics, Face Recognition, Histogram of Oriented Gradients, Multimodal Biometrics, Verification

[1] W. Zhao, R. Chellappa, P. J. Phillips, & A. Rosenfeld, “Face recognition: A literature survey”, ACM Computing Surveys (CSUR), 35(4): 399-458, 2003.
[2] J. B. Li, S. C. Chu, J. S. Pan, & L. C. Jain, “Multiple Viewpoints Based Overview for Face Recognition”, Journal of Information Hiding and Multimedia Signal Processing, 3(4): 352-369, 2012.
[3] V. Blanz, and T. Vetter, “A Morphable Model For The Synthesis Of 3D Faces”, Proceedings of the 26th annual conference on Computer graphics and interactive techniques, pp. 187-194, 1999.
[4] H. Zhang, Y. Li, C. Wang, L. Wang, “Face Recognition across Poses Using Transformed Features”, IEEE, pp. 1-4, 2006.
[5] M. Kirby and L. Sirovich, “Application of the Karhunen Loeve Procedure for the Characterization of Human Faces”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(1):103–108, 1990.
[6] L. Wiskott, J. M. Fellous, N. Krger and C. Vonderd, “Face Recognition Across Pose”, International Conference on Automatic Face and Gesture Recognition, 2002.
[7] Laurenz Wiskott, Jean-Marc Fellous, and Norbert Kruger, “Face Recognition by Elastic Bunch Graph Matching” Technical Report IR-INI 96–08, April 1996.
[8] D. Gonzalez, and J. L. Alba-Castro, “Pose Correction and Subject-Specific Features for Face Authentication”, Proceedings of the 18th International Conference on Pattern Recognition, pp. 602–605, 2006.
[9] T.F. Cootes, C. J. Taylor, D. H. Cooper and J. Graham, “Active Shape Models their Training and Application”, Computer Vision and Image Understanding, Vol.61, No. pp. 38–59,1995.
[10] H. S. Lee, and D. Kim, “Generating frontal view face image for pose invariant face recognition”, Pattern Recognition Letters, Vol. 27, Issue 7, pp. 747–754,2006.
[11] T. Akimoto, Y. Suenaga, and R.S. Wallace, “Automatic creation of 3D facial models”, IEEE Computer Graphics and Applications, Vol. 13, No.3, pp. 16-22,1993.
[12] Ralph Gross, Iain Matthews, and Simon Baker “Eigen Light-Fields and Face Recognition Across Pose”, International Conference on Automatic Face and Gesture Recognition, 2002.
[13] A E. Hoerl, and R. W. Kennard, “Ridge regression: biased estimation of Nonorthogonal problems”, Technometrics,12, pp.55-67,1970.
[14] Ajinkya Patil, Mridang Shukla, “Implementation of Class Room Attendance System Based on Face Recognition Class”, IJAET (International Journal of Advances in Engineering and Technology), Vol. 7, Issue 3, July 2014.
[15] Naveed Khan Baloch, M. Haroon Yousaf, Wagar Ahmad, M. Iran Baig, “Algorithm for Efficient Attendance Management: Face Recognition based Approach”, IJCSI, Vol. 9, Issue 4, No I, July 2012.
[16] Yasaman Heydarzadeh, Abol Fazl ToroghiH aghighat, “An Efficient Face Detection Method using AdaBoost and Facial Parts", IJSSST.
[17] Navneet Dalal and Bill Triggs, “Histogram of Oriented Gradients for Human Detection”, IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 1063-6919/05, 2005.
[18] Wei Jia, Rong-Xiang Hu, Ying-Ke Lei, Yang Zhao, and Jie Gui, “Histogram of Oriented Lines for Palmprint Recognition”, IEEE Transaction on Systems, Man and Cybernetics: Vol. 44, No. 3, March 2014.
[19] Akram Alsubari, and R. J. Ramteke. “Extraction of Face and Palmprint Features based on LBP, HOG and Zernike Moments Extraction”, International Journal of Computer Applications 172(5):31-34, August 2017.

Pravin G. Sarpate, Ramesh R. Manza, "Extraction of Face Texture Features Based on Histograms of Oriented Gradients (HOG)," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.168-172, 2018.

In this paper, design and analysis of a coplanar waveguide (CPW) fed broadband square patch antenna is introduced. The proposed antenna consists of a square slot, L-shaped feed line, and with ‘T’ and inverted ‘T’ like structure which acts as tuning stubs. The Proposed antenna achieved IMT band (fc=2.8 GHz) and bandwidth of antenna is 2.51GHz from 1.25 GHz to 3.76 GHz. The structure of antenna occupies a small volume 60(L) × 60(W) × 0.76(H) mm3 and printed on FR-4 lossy (εr= 4.3) substrate. The proposed antenna is simulated using CST V.12 microwave studio, and the performance of the antenna parameters is measured and characterized in the terms of return loss, VSWR, radiation pattern and surface current density at the resonant frequencies respectively. The simulated results confirmed by the successful design, using 50Ω CPW-feed line of antenna with return loss <-10 dB and VSWR <2 dB.

Coplanar Waveguides (CPW), Microwave Integrated Circuits (MICs), Monolithic Microwave Integrated Circuits (MMICs), Voltage Standing Wave Ratio (VSWR), International Mobile Telecommunication (IMT), Industrial, Scientific, and Medical (ISM)

[1] Hai-Gao Xue , Xue-Xia Yang , “A Novel Microstrip – CPW Fed Planar Slot Antenna with Broadband and Circular Polarization.” IEEE transactions on Antennas & wireless propagation Vol. 14.2015.
[2] Li Li,Xiaoliang Zhang,X.Yin and Le Zhou, “A compact triple –Band printed monopole antenna for WLAN Wi MAX Applications.” IEEE transactions on Antennas & wireless propagation Vol. 15 .2016.
[3] Chien jen Wang and Chih-Hsing Chen, “CPW FED Stair – shaped slot antennas with Circular Polarisation.” IEEE transactions on Antennas & wireless propagation Vol. 57, No 8. August 2009.
[4] K.Y.Lam, K.M. Luk, K.F Lee, H.Wong, and K.B.Ng. “Small Circularly polarized U-slot wideband patch antenna,” IEEE Antennas Wireless Propag.Lett., vol. 10, pp 87-90 , 2011.
[5]J.S. Row, “The design of a squarer-ring slot antenna for Circular Polarization,” IEEE Trans.Antennas Propag. vol. 53, no.6, pp.1967- 1972. Jun.2005.
[6] Horng-Dean Chen, “ Broadband CPW –FED Square slot antenna with a widening tuning stub”.IEEE transactions on Antennas & propagation Vol. 51 No. 8-2003.
[7] Mohammad Alibakhshi – Kenari, Mohammad Naser – Moghadasi , R.A. Sadeghzadeh, Bal S.Virdee Ernesto Limiti. “Band width Extension of planar antennas using embedded slits for reliable multiband RF Communications”.International journal of electronics and communication 70(2016)910-919.
[8] J-Y Sze, J-C, Wang and C-C Chang, “Axial –Ratio bandwidth enhancement of asymmetric – CPW – fed
circularly polarized square slot antenna.”Electronic Letters Vol .44 No 18. 28th August 2008.

Sandeep Kumar Singh, Vepakomma Kavya, Rajendra Singh, Himanshu Parashar, "Broadband CPW-Fed L-Shaped Slot Antenna with T-Tuning Stub for IMT Applications," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.173-176, 2018.

Deforestation is the permanent ruin of forests in order to make the land available for other uses. Deforestation activities come in many ways including forest fire, logging, and mining. There is a need to detect deforestation to protect available resources. Satellites remote sensing provides fundamental information for accessing deforestation effect. In this paper, we proposed two method to detect deforestation 1.Extraction based RGB bands - with the help of clustering pixels combined with RGB color bands. Vegetation coverage estimated by scheming of RGB pixels .Region clustering were demonstrated to be more suitable for identifying the waste, sand, cultivated region. Classification accuracy of the features of interest varied from 80 to 95 percent.2.Decorrelation stretching- technique to improve the visual distinction of an image and representing the deforestation effect which understood by human through image. Finally, Histogram of an RGB bands was proposed to estimate the distribution of each pixel in an image which could give the clarity of vegetation coverage and make forest detection more efficient. The sample forest area around Madurai district was taken through the satellite and selected as a test site.

Satellite remote sensing, Vegetation coverage , Region clustering, Decorrelation stretching, Histogram

[1] Tucker, Compton J.,John RG Townshend. "Strategies for monitoring tropical deforestation using satellite data." International Journal of Remote Sensing 21.6-7 (2000): 1461-1471.
[2] Rachmawan, Irene Erlyn Wina, Yasushi Kiyoki, and Shiori Sasaki. "An automatic feature extraction method of satellite multispectral images for interpreting deforestation effects in soil degradation." International Journal on Advanced Science, Engineering and Information Technology 7.2 (2017): 676-687.
[3] Geyer, Philipp, Arno Schlüter, and Sasha Cisar. "Application of clustering for the development of retrofit strategies for large building stocks." Advanced Engineering Informatics 31 (2017): 32-47.
[4] Pan, Bin, et al. "A novel spectral-unmixing-based green algae area estimation method for GOCI data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10.2 (2017): 437-449.
[5] Badamasi, M. M., et al. "NDVI threshold classification and change detection of vegetation cover at the Falgore Game Reserve in Kano State, Nigeria." Sokoto Journal of the social sciences 2.2 (2010): 174-194.
[6] Emrah Hancer, Dervis Karaboga, “A Comprehensive Survey of Traditional, Merge-Split and Evolutionary Approaches Proposed for Determination of Cluster Number”, Swarm and Evolutionary Computation, S2210-6502(16)30047-5.
[7] Collins, Curtis A., David W. Wilkinson, and David L. Evans. "Multi-temporal analysis of landsat data to determine forest age classes for the mississippi statewide forest inventory~ preliminary results." Analysis of Multi-Temporal Remote Sensing Images, 2005 International Workshop on the. IEEE, 2005.
[8] Bosch, J. Mv, and J. D. Hewlett. "A review of catchment experiments to determine the effect of vegetation changes on water yield and evapotranspiration." Journal of hydrology 55.1-4 (1982): 3-23.
[9] Lingli Yu, Kaijun Zhou , Yongliang Yang, Haichu Chen,Bionic “RSTN invariant feature extraction method for image recognition and its application”, The Institute of Engineering and Technology. ISSN 1751-9659.
[10] Mello, Marcio Pupin, et al. "Spectral-Temporal Analysis by Response Surface applied to detect deforestation in the Brazilian Amazon." Analysis of Multi-temporal Remote Sensing Images (Multi-Temp), 2011 6th International Workshop on the. IEEE, 2011.
[11] S.P. Kullarkar, S.V. Jain “Review on Rain drop detection and removal using k-means clustering and Hough transformation” International Journal of Computer Sciences and Engineering, Vol.6(1), Jan 2017, E-ISSN: 2347-2693
[12] Lal, R. "Deforestation and land use effects on soil degradation and rehabilitation in western Nigeria. II. Soil chemical properties." Land Degradation & Development 7.2 (1996): 87-98.
[13] Glaser, Bruno, Johannes Lehmann, and Wolfgang Zech. "Ameliorating physical and chemical properties of highly weathered soils in the tropics with charcoal-a review." Biology and fertility of soils 35.4 (2002): 219-230.
[14] Downton, Mary W. "Measuring tropical deforestation: development of the methods." Environmental Conservation 22.3 (1995): 229-240.

B. Tamil Selvi, M. Parvathy, PL. Prabha, C.H. Sumalakshmi, "Deforestation Identification Model using RBC-Region Based Clustering," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.177-181, 2018.

A larger bandwidth is offered by optical fiber communication for data transmission at higher rates. The fiber channel is capable of transmitting data at the rate of terabits-per-second but for increasing data throughput, addition of extra fibers to the system is uneconomical. Erbium-Doped Fiber Amplifier (EDFA) is the optical amplifier that can amplify the multiplexed optical signals falling into its amplification band for long range optical communication networks. EDFA’s operational flexibility is also increased by reducing different losses that occur in optical fiber communication system. Besides these losses, optical communication system also suffers from dispersion and nonlinear impairments. For this purpose, Fiber Bragg Grating (FBG) is most widely used. In the present paper, performance analysis of optical link using EDFA and FBG has been explored in details. Also it focuses on different pumping techniques used to analyze the performance of EDFA and FBG with respect to various parameters- wavelengths, modulation formats and data rates.

EDFA, FBG, Return to Zero (RZ), Single mode fiber (SMF), Continuous wave laser (CW), Refractive index (RI),Non Return to Zero (NRZ), Wavelength Selector Coupler (WSC), Mach-ZehnderModulator (MZM)

[1] V. Kumar, A.K. Jaiswal, M. Kumar, N. Agrawal, R. Sexena, “Design and Performance Analysis of Optical Transmission System”, IOSR Journal of Electronics and Communication Engineering,e-ISSN: 2250-3021,p- ISSN: 2278-8719, Vol. 4, Issue. 5, pp. 22-26, 2014.
[2] D. Sharma, “Design and Analysis of a Hybrid Optical Amplifier using EDFA and Raman Amplifier”,International Journal of Enhanced Research in Management and Computer Applications (ISSN: 2319-7471) , Vol. 5, Issue. 9, pp. 16-22, 2016.
[3] Payal, S. Kumar, D. Sharma, “Performance Analysis of NRZ and RZ Modulation Schemes in Optical Fiber Link Using EDFA”,International Journals of Advanced Research in Computer Science and Software Engineering (ISSN: 2277-128X),Vol.7, Issue.8, pp. 161-168, 2017.
[4] U.J. Sindhi , R.B. Patel, K.A. Mehta, M. Mishra, “Performance analysis of 32-channel WDM system using erbium doped fiber amplifier”,International journal of electrical and electronics engineering and telecommunication, pp. 45-49, 2013.
[5] D. Birdi, M. Singh, “Performance Analysis of EDFA Amplifier for DWDM System using RZ”,International Journal of Advanced Research in Electrical, Electronics and instrumentation Engineering, (ISSN(Print) :2320 3765 ISSN (Online): 2278 –887) Vol. 4, Issue.7, pp. 6592-6598, 2015.
[6] P. Shukla, K.P. Kaur, “Performance Analysis of EDFA for different Pumping Configurations at High Data Rate”, International Journal of Engineering and Advanced Technology,Online ISSN: 2249-4596), Vol. 2, Issue. 5, pp. 19-24, 2013.
[7] R. Deepa, R. Vijaya, “Influence of bidirectional pumping in high-power EDFA on single-channel, Multichannel and pulsed signal amplification,” pp. 20-26, 2008.
[8] Payal, S. Kumar , D. Sharma, “A Review of Optical Communication link design using EDFA," International Journal of All Research Education and Scientific Methods ( ISSN: 2455-6211), Vol. 5, Issue. 3, pp. 23-28, 2017.
[9] R. Mishra, N.K. Shukla, C.K. Dwivedi, “Performance Analysis and Implementation of Different Pumping Techniques on an EDFA Amplifier”, In the Proceedings of the 2017 IEEE International Conference on Sensing, Signal Processing and Security,pp. 39-44, 2017.
[10] S.R. Sharma, T. Sood, “Gain Flattening of EDFA using Hybrid EDFA/Raman Amplifier with Reduced Channel Spacing”, International Journal of Engineering Development and Research,Vol. 3, Issue.3, pp. 1-7, 2015.
[11] G. Kumar, R. Sadhu, N. Sangeetha, “Gain and Noise Figure Analysis of Erbium Doped Fiber Amplifier by Four Stage Enhancement and Analysis”,International Journal of Scientific and Research Publications, (ISSN 2250-3153) , Vol. 4, Issue. 4, pp.1-10, 2014.
[12] S. Srivastava, K.K. Upadhyay, N. Singh, “Design and performance analysis of dispersion managed system with RZ and NRZ modulation format”,In the Proceedings of the 2016 IEEE International Conference on Control, Computing, Communication and Materials, pp. 4673-9084-2/16/$31.00, 2016.
[13] S. Singh, M. Kour, T.S. Cheema, “Performance Analysis of Raman-EDFA and EDFA-YDFA Hybrid Amplifiers at 10Gbps in DWDM Optical Communication System,” pp. 1-6, 2017.
[14] G. Lvanovs, V. Bobrovs, S. Olonkins, A. Alsevska, L. Gegere, R. Parts, P. Gavars, G. Lauks, “Application of the erbium doped fiber amplifier (EDFA) in wavelength division multiplexing (WDM) transmission systems,”doi: 10.5897/IJPS2013, Vol. 9(5), pp. 91-101, 2014.
[15] S. Dev, S. Kumar, “Dispersion Compensation in Optical Fiber Communication using Bragg Grating- A Review,” International Journal of Electronics, Electrical and Computational System,Vol. 5, Issue. 9 , pp. 9-15, 2016.
[16] M.M. Werneck, F.V.B.D. Nazaré, “A Guide to Fiber Bragg Grating Sensors”, http://dx.doi.org/10.5772/54682, 2013.
[17] Q. Chen, P. Lu, “Fiber Bragg Gratings and their applications as temperature and humidity sensors”, ISBN:978-1-60456-907-0,pp. 235-260, 2008.
[18] F. Barón, G.A. Botero, F. Amortegui, D. Pastor, M. Varón, “Temperature measurements on overhead lines using Fiber Bragg Grating sensors”, IEEE International Instrumentation and Measurement Technology Conference,pp. 1-4,2017.
[19] C.C. Lee, K. Hung, W.M. Chan, Y.K. Wu, S.O. Choy, P. Kwok, “FBG sensor for physiologic monitoring in M-health application”, Asia Communications and Photonics Conference and Exhibition, Shanghai,doi: 10.1117/12.905505, pp.1-14, 2011.
[20] C. Wang, “Advanced Fiber Bragg Gratings for microwave photonics applications”, 14th International Conference on Optical Communications and Networks, China, doi: 10.1109,pp. 1-3, 2015.
[21] G. Allwood, S. Hinckley, G. Wild, “Optical Fiber Bragg Grating Based Intrusion Detection Systems for Homeland Security’, Sensors Applications Symposium ,pp. 66-70, 2013.
[22] Z. Chen, J. Hu, C. Yu, “Fiber senor for long-range and biomedical measurements”,(ICOCN) ,doi: 10.1109/ICOCN.2013.6617192, pp.1-4, 2013.
[23] N. Kumar, A.K. Jaiswal, M. Kumar, A. Kumar, K.N. Suman, “Analysis of Pulse Code Modulation Formats in High Speed Optical Transmission System Using FBG and EDFA”,IOSR Journal of Electronics and Communication Engineering, (e-ISSN: 2278-2834,p- ISSN: 2278-8735),Vol. 9, Issue. 1, Ver. VI , pp. 125-130, 2014.
[24] A. Bhardwaj, G. Gaurav, “Performance Analysis of 20Gbps Optical Transmission System Using Fiber Bragg Grating”,International Journal of Scientific and Research Publications, Vol. 5, Issue. 1, ISSN 2250-3153, pp 1-4, 2015.
[25] M. Kaur, H. Sarangal, “Simulation of Optical Transmission System to Compensate Dispersion Using Chirped Fiber Bragg Grating (FBG)”,International Journal of Advanced Research in Computer and Communication Engineering, (ISSN (online) 2278-1021,Vol. 4, Issue. 2 ,pp. 357-359, 2015.

Deepti, S. Kumar, Payal, "Optical Communication link design using EDFA with FBG : Theoretical Review," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.182-187, 2018.