Volume-3 , Issue-9 , Sep - 2015 Go Back

Image processing plays an important role in medical science for the analysis of heart attack and brain stroke. During the capturing of medical image some noise is induced and makes medical image blurred and unclear. So image de-nosing process is required to make the image noise free .In this paper we propose an image de-nosing method using subtraction transform and RBF neural network. The subtraction transform used basically in the field of voice noise reduction. the RBF neural network model is very efficient due to single layer network. The process of CT and MRI gets the high component value of noise in environment. For the reduction of these noise we have used spectral subtraction de-noising method. The spectral substation method is well recognized method for voice noise reduction. In spectral subtraction method the local noise component value are not considered. In this paper, we discuss image de-nosing methodology based on RBF neural network model comprised of radial biases neural network (RBF). The image features are extracted from the image using SSD function. RBF acts as a clustering mechanism that projects N-dimensional features from the SSD function into an M-dimensional feature space. The resulting vectors are fed into an RBF that categorizes them onto one of the relearned noise classes.

Medical Image, Noise, Subtraction Transform, RBF

[1]. M. Arcan, Paul A. Bottomley, Abdel-Monem and M. El-Sharkawy” de-noising mri using spectral subtraction” in ieee transactions on biomedical engineering, vol. 60, no. 6, june 2013.
[2]. Zhong, H., C. Yang and X. Zhang“A new weight for nonlocal means de-noising using method noise.” IEEE Signal Process. Lett, 2012.
[3]. M. A. Erturk, p. A. Bottomley, and a.-m. M. El-sharkawy, “spectral subtraction De-noising of mri,” in proc. 6th cairo int. Biomed. Eng. Conf., Cairo, egypt, 2012, Pp. 138–141.
[4]. Anupama, P., S.P. Kumar, B. Sudharshan and N. Pradhan” . A review of different image de-noising methods.” Int. J. Innov. Res. Dev, 2012. Pp 533-545.
[5]. Kadam, D.B., S.S. Gade, M.D. Uplane and R.K. Prasad” Neural network based brain tumor detection using MR images”. Int. J. Comp. Sci. Commun., 2011. Pp 325-231.
[6]. Cui, W., Y. Wang, Y. Fan, Y. Feng and T. Lei” Localized FCM clustering with spatial information for medical image segmentation and bias field estimation. “Int. J. Biomed. Imag. 2013.
[7]. Dahab, D.A., S.S.A. Ghoniemy and G.M. Selim” Automated brain tumor detection and identification using image processing and probabilistic neural network techniques.” Int. J. Comp. Image Process. Visual Commun., 1: 1-8,2013.
[8]. Kharat, K.D., P.P. Kulkarni and M.B. Nagori” Brain tumor classification using neural network based methods”in Int. J. Comput. Sci. Inform., 1: 85- 90.2012.
[9]. Xilin Liu, Zhengwei Ying, ShufangQiu “A Fourth-Order Partial Differential Equations Method Of Noise Removal” 4th International Congress on Image and Signal Processing, IEEE 2011.
[10]. JenyRajan, K. Kannan, M.R. Kaimal “An Improved Hybrid Model for Molecular Image De-noising” An Improved Hybrid Model for Molecular Image De-noising, Vol- 31, 2008. Pp 71-79.
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[12]. Jian Bai ,Xiang-Chu Feng “Fractional-Order Anisotropic Diffusion for Image De-noising” TRANSACTIONS ON IMAGE PROCESSING, IEEE, VOL. 16, 2007. Pp 2492-2503.
[13]. Marius Lysaker, Xue-Cheng Tai, “Iterative Image Restoration Combining Total Variation Minimization and a Second-Order Functional” Springer: International Journal of Computer Vision, Volume 66, Issue 1, pp 5-18, January 2006, DOI:10.1007/s11263-005-3219-7, Print ISSN:0920-5691, Online ISSN:1573-1405.
[14]. Ben Arfia, F. “Choosing interpolation RBF function in image filtering with the Bidimentional Empirical Modal Decomposition ” in IEEE 2014

Ankur Mudgal and Rajdeep Singh, "A Hybrid Method of Medical Image De-nosing Using Subtraction Transform and Radial Biases Neural Network," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.54-59, 2015.

The proposed work demonstrates the automation of students identification, bio-data and attendance maintenance which is one of the important tasks in college. But in case of large number it becomes difficult and clumsy. So to automate it, RFID’s are used in this project we perform different functions like Identification of students, Attendance details with timing information. To detect the RFID tags, a software program is developed in Intel Atom Board with ARM 7Processor supporting Linux environment and Python Script along with GSM Modem and RF module detects the RFID tags. Whenever the RF tag comes into the vicinity of the RF reader, the GSM Modem sends SMS to the particular Parents. The main advantage of implementing student identification using Intel Atom Board is that the digital output can be directly connected to any P.C. and can monitor it continuously.

RFID, Intel Atom Board, Linux, Python, ARM 7, GSM

[1] Ononiwu G, Chiagozie, Okorafor G. Nwaji. “Radio Frequency Identification (RFID) Based Attendance System with Automatic Door Unit”. In Academic Research International, ISSNL-L: 2223-9553. 2012; 2(2).
[2] Zatin Singhal and Rajneesh Kumar Gujral. “Anytime Anywhere- Remote Monitoring of Attendance System based on RFID using GSM Network”. In International Journal of Computer Applications (0975–8887). 2012; 39(3).
[3] Herdawatie Abdul Kadir, Mohd Helmy Abd. Wahab, Zarina Tukiran, Ariffin Abdul Mutalib. “Tracking Student Movement using Active RFID”. In 9th WSEAS International Conference, ISSN: 1790-5117.
[4] Elisabeth Ilie-Zudor, Zsolt Kemeny, Peter Egri, Laszlo Monostori. “The RFID Technology and its Current Applications” in MITIP-2006.
[5] Mohd. Firdaus Bin Mahyidin. “Student Attendance Using RFID System”. In University Malaysia, Pahang, May-2008.
[6] L. Sandip, “RFID Sourcebook”, IBM Press, USA, (2005) ISBN: 0-13-185137-3
[7] E. Zeisel & R. Sabella, “RFID+”, Exam Cram, (2006), ISBN: 0-7897-3504-0.

Suresh Cherukumalli and A. Sudhir Babu, "Design and Development of Student Attendance Management System Using Intel Atom Processor," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.60-64, 2015.

Detection of rising topics is currently receiving revived interest impressed by the rapid climb of social networks. during this context, Conventional-term-frequency-based approaches might not be acceptable, as a result of the data changed in social-network posts embody not solely text however conjointly pictures, URLs, and videos. We have a tendency to specialize in emergence of topics signaled by social aspects of those networks. Specifically, we have a tendency to specialize in mentions of user links between users that are generated dynamically (intentionally or unintentionally) through replies, mentions, and retweets. we have a tendency to propose to notice the emergence of a replacement topic from the anomalies measured through the model and propose a chance model of the mentioning behavior of a social network user, and aggregating anomaly scores from many users, we have a tendency to show that we will notice rising topics solely supported the reply/mention relationships in social-network posts. we have a tendency to gathered from Twitter and incontestable the technique in many real knowledge sets. The experiments show that the projected mention-anomaly-based approaches will notice new topics a minimum of as early as text-anomaly-based approaches, and in some cases abundant earlier once the subject is poorly known by the matter contents in posts.

TDT, Anomaly, SDNML, DTO

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[14] T. Roos and J. Rissanen, “On Sequentially Normalized Maximum Likelihood Models,” Proc. Workshop Information Theoretic Methods in Science and Eng., 2008.
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A Sasikanth and S Venkata Ramana, "Inventing Rising Topics in Social Networks through Link-Anomaly Detection," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.65-70, 2015.

A vehicle theft is becoming very common nowadays, which is one of the main issues for a person having car or bike. In this technical paper work; the design of a fingerprint and RFID tag based authentication for a vehicle is explained. Fingerprint identification gives the biometric based authentication and RFID tag gives a keyless authentication for a vehicle. So inorder to avoid vehicle theft the proposed system is designing a keyless authentication system for a vehicle instead of going with key based authentication system; it also provides a biometric based authentication which is a fingerprint of a person. A person, who wishes to drive the vehicle, first step is to verify with their fingerprint whether the person who wish to Drive the vehicle is allowed to drive or not; by checking the data base, once verification done then ignition unit of vehicle will starts automatically. If the person is not valid in the Fingerprint Module data base then the vehicle will not get started.

Fingerprint, RFID tag, Vehicle Security, Biometric, Ignition System, ARM-7

[1] Kresimir Delac, Mislav Gregic, “A Survey of Biometric Recognition Methods”, 46thInternational Symposium Electronic in Marine, ELMAR-2004, 16-18June 2004,Zadar, Croatia.
[2] Ahmed Saeed Alzahrani, “Security analysis of RFID based devices in educative environments,” Life Science journal 2014.
[3] http://www.biometricinfo.org/fingerprintrecognit ion.htm“Biometrics Information Resource”
[4] Omidiora E. O., Fakolujo O. A., Arulogun O. T., Aborisade D. O., (2011), A Prototype of a Fingerprint Based Ignition Systems in Vehicles, European Journal of Scientific Research, ISSN 1450-216X Vol.62 No.2 (2011), pp. 164-171.
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[6] http://auto.howstuffworks.com/ignitionsystem.htm, “How Automobile Ignition Systems Work”
[7] Anil K. Jain, Lin Hong, Sharat Pankanti, and Ruud Bolle, “An identity authentication system using fingerprints,”
[8] Emma Newham, “The biometric report,” SJB Services, 1995.
[9] Ahson, S. A., & Ilyas, M., (2008). RFID Handbook, Applications, Technology, Security, and Privacy, CRC Press, FL, USA, ISBN: 978-1- 4200-5499 6.
[10] Nordby, K. (2010). Conceptual Designing and Technology: Short-Range RFID as Design Material. The Oslo School of Architecture and Design, Oslo, Norway: International Journal of Design Vol.4 No.1, pp. 29-44.

Kadali Sridhar, K. Naga Divya and D. Sree Lakshmi, "A Fingerprint and RFID Tag Based Authentication System for Driving," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.71-76, 2015.

Mobile devices such as smart phones are gaining an ever-increasing popularity. The information generated by these sensors give opportunities to create subtle inferences concerning not solely individuals however additionally their surroundings. This paper studies however an untrusted human in mobile sensing will sporadically get desired statistics over the information contributed by multiple mobile users, while not compromising the privacy of every user. The present protocols like Min mixture and add mixture to get the add mixture, that employs an additive homomorphic secret writing and a completely unique key management technique to support massive plain-text house. They either require bidirectional communications between the aggregator and mobile users in every aggregation period, or have high-computation overhead. The paper proposes a replacement hid information aggregation theme that is homomorphic public secret writing system primarily based. The planned theme has three contributions. First, it's designed for a multi-application surroundings. the bottom station extracts application-specific information from aggregate cipher texts. Next, it mitigates the impact of compromising attacks in single application environments. Finally, it degrades the injury from unauthorized aggregations. Data as a Service model is planned during which, a shopper stores a info on an untrusted service supplier. Therefore, the client has got to secure their info through Privacy similarity (PH) schemes as a result of hydrogen ion concentration schemes keep utile properties than standard ciphers. Based on PH schemes, the provider can conduct aggregation queries and retrieve results. The proposed protocols are faster than existing solutions, and it has much lower communication overhead.

WSN, Data Aggregation, Mobile sensing, Embedded Sensor

[1] S.B.Eisenman, E.Miluzzo, N.D.Lane, R.A.Peterson, G. S.Ahn, and A.T.Campbell, “The Bike net Mobile Sensing System for Cyclist Experience Mapping,” Proc. ACM Fifth Int’l Conf. Embedded Networked Sensor Systems (SenSys ’07), pp. 87-101, 2007.
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[5] N.D.Lane, M.Mohammod, M.Lin, X.Yang, H.Lu, S.Ali, A.Doryab, E.Berke, T.Choudhury, and A.Campbell, “BeWell: A Smart phone Application to Monitor, Model and Promote Well-being,” Proc. Fifth Int’l ICST Conf. Pervasive Computing Technologies for Healthcare, 2011.
[6] Nicholas D.Lane, Emiliano Miluzzo, Hong Lu, Daniel Peebles Tanzeem Choudhury, and Andrew T. Campbell, “A Survey of Mobile Phone Sensing”, Comm. Mag., vol. 48, pp. 140–150, September 2010.
[7] M. Jawurek and F. Kerschbaum, “Fault-Tolerant Privacy-Preserving Statistics,” Proc. 12th Privacy Enhancing Technologies Symp. (PETS ’12), 2012.
[8] R.Norris, D.Carroll, and R.Cochrane, “The Effects of Physical Activity and Exercise Training on Psychological Stress and Well-being in an Adolescent Population”, Journal of Psychosomatic Research, vol. 36, no. 1, pp. 55–65, 1992.
[9] Q. Li and G. Cao, “Providing Privacy-Aware Incentives for Mobile Sensing,” Proc. IEEE PerCom, 2013.
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[11] E.G.Rieffel, J.Biehl, W.Van Melle, and A.J.Lee, “Secured Histories: Computing Group Statistics on Encrypted Data While Preserving Individual Privacy,” 2010.
[12] E.Shi, T.H.H.Chan, E.Rieffel, R.Chow, and D.Song, “Privacy-Preserving Aggregation of Time-Series Data,” Proc. Network and Distributed System Security Symp. (NDSS ’11), 2011.
[13] T.-H.H. Chan, E. Shi, and D. Song, “Privacy-Preserving Stream Aggregation with Fault Tolerance,” Proc. Sixth Int’l Conf. Financial Cryptography and Data Security (FC ’12), 2012.

R.Rathi priya and S.M.Jagatheesan, "Privacy-Aware Data Aggregation Mechanism for Mobile Sensors," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.77-80, 2015.

Huge amounts of data are required to build internet search engines and therefore large number of machines to process this entire data. The Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters of machines. The Hadoop having two modules: 1. Hadoop distributed file system and 2. MapReduce. The Hadoop distributed file system is different from the local normal file system. The hdfs can be implemented as single node cluster and multi node cluster. The large datasets are processed more efficiently by the multi node clusters. By increasing number of nodes the data will be processed faster than the fewer nodes.

BigData, HDFS, MapReduce, Namenode, Datanode, Jobtracker, Tasktracker

[1] Linthala Srinithya, Dr. G. Venkata Rami Reddy, “Performance Evaluation of Hadoop Distributed file System and Local File System” in IJSR ISSN: 2319-7064, Volume 3 Issue 9, September 2014.
[2] Liu Liu, Jiangtao Yin, Lixin Gao, “Efficient Social Network Data Query Processing on MapReduce” ACM August 16, 2013.
[3] E. Dede, M. Govindaraju, D. Gunter, R. Canon, L. Ramakrishnan,”Performance Evaluation of a MongoDB and Hadoop Platform for Scientific Data Analysis”.
[4] Christos Doulkeridis, Kjetil Norvag, “A Survey of Large-Scale Analytical Query Processing in MapReduce”.
[5] Stephen Kaisler, Frank Armour, J. Alberto Espinosa, William Money, “Big Data: Issues and Challenges Moving Forward”.
[6] Hadoop The Definitive Guide,©2012, Tom White.
[7] K.Udhaya Malar,D.Ragupathi and G.M.Prabhu, "The Hadoop Dispersed File system: Balancing Movability And Performance", IJCSE, Volume-2, Issue-9, september-2014.
[8] Apache Hadoop. http://hadoop.apache.org/ Tuesday, June 23, 2015.
[9] Hadoop multinode cluster configuration, http://hashprompt.blogspot.in/2014/06/multi-node-hadoop-cluster-on-ubuntu-1404.html, Wednesday, August 19, 2015.

D.Sudheer and A.Ramana Lakshmi, "Performance evaluation of Hadoop Distributed File System," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.81-86, 2015.

Several encryption schemes provide data confidentiality but at the same time functionality of the storage system is limited. A threshold proxy re-encryption scheme which is integrated with decentralized erasure code was proposed to formulate a secure storage system. This scheme provides robust and secure storage and retrieval and also lets the user forward the data to other users. The main technical contribution is that encoding operations over encrypted messages as well as forwarding operations over encoded and encrypted message are supported by this scheme.

Proxy Re-Encryption, Decentralized Erasure Code, RSA, AES, Erasure Codes

[1] Introduction to Information Security, https://en.wikipedia.org/wiki/Information_security, Wednesday, July 1, 2015.
[2] Hsiao-Ying Lin, Member, IEEE, and Wen-Guey Tzeng, Member, IEEE.,‖ A Secure Erasure Code-Based Cloud Storage System with Secure Data Forwarding‖, IEEE transactions on parallel and distributed systems, vol. 23, no.6, pp.995-1003 , june 2012.
[3] Rachna Jain, Sushila Madan and Bindu Garg, “Analyzing Various existing Security Techniques to Secure Data Access in Cloud Environment”, Volume-3, Issue-1, January-2015.
[4] Evgeny Milanov ― The RSA Alorithm‖ 3 june, 2009.
[5] G. Ateniese, K. Fu, M. Green, and S. Hohenberger, ―Improved Proxy Re-Encryption Schemes with Applications to Secure Distributed Storage,‖ ACM Trans. Information and System Security, vol. 9, no. 1, pp. 1-30, 2006.
[6] G. Ateniese, K. Benson, and S. Hohenberger, ―Key-Private Proxy Re-Encryption,‖ Proc. Topics in Cryptology (CT-RSA), pp. 279-294, 2009.
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B.Anusha and S. Phani Praveen, "An Approach to provide Secure Storage using Self Destructing Erasure Codes," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.88-90, 2015.

The cloud platform requires an efficient computational infrastructure. On this platform a huge amount of data gets generated in a fraction of a second, therefore, traditional computing techniques are not enough. The Big Data provides an answer for such huge computing and also provides support to scale the storage according to the application’s need. Big Data is a new generation storage infrastructure (hardware and software). In this paper the Big Data environment is investigated and the comparative study is performed among most frequently used data retrieval techniques. In order to perform the comparative study, Pig and Hive of Hadoop technology are selected. These techniques provide efficient data processing ability. In order to perform comparative study Hadoop storage is prepared first and then with the help of MapReduce framework the Pig and Hive are configured. Additionally, for evaluating the efficiency of query execution in terms of processing time, a list of similar queries is prepared and for each query the experiment was performed. The result evaluation is done for both the techniques. It is observed that query processing time of the Hive is less as compared to the Pig for the selected new_songs dataset, but both the data models are working to achieve the different goals thus both the technologies are adaptable for different kinds of computer configuration.

Big Data; Hive; Pig; Performance Analysis; Data Processing; Query Execution Time

[1] Bharath Vissapragada, “Optimizing SQL Query Execution over Map-Reduce,” M.S. thesis, Dept Comp. Sc., Center for Data Engineering International Institute of Information Technology, Hyderabad, India, September 2014.
[2] Ammar Fuad, Alva Erwin, and Heru PurnomoIpung, “Processing Performance on Apache Pig, Apache Hive and MySQL Cluster,” International Conference on Information, Communication Technology and System, IEEE, 2014.
[3] F. Provost, T. Fawcett, “Data Science and its relationship to Big Data and data-driven decision making,” University of Massachusetts Amherst, DOI: 10.1089/big.2013.1508, March 2013.
[4] Changqing Ji, Yu Li, Wenming Qiu, Uchechukwu Awada, and Keqiu Li, “Big Data Processing in Cloud Computing Environments,” International Symposium on Pervasive Systems, Algorithms and Networks, IEEE, Dalian, China, 2012.
[5] Apache Hadoop, Available: http://wiki.apache.org/hadoop.
[6] Munesh Kataria, Ms.Pooja Mittal, “Big Data and Hadoop with Components like Flume, Pig, Hive and Jaql,” IJCSMC, Vol. 3, July 2014, pp. 759 – 765.
[7] Ashish Thusoo, Joydeep Sen Sarma, Namit Jain, Zheng Shao, Prasad Chakka, Ning Zhang, Suresh Antony, Hao Liu and Raghotham Murthy, “Hive – A Petabyte Scale Data Warehouse Using Hadoop,” ICDE Conference, IEEE, 2010.
[8] Ashish Thusoo, Joydeep Sen Sarma, Namit Jain, Prasad Chakka, Suresh Anthony, Hao Liu, Pete Wyckoff and Raghotham Murthy, “Hive – A Warehousing Solution Over a Map-Reduce Framework,” VLDB, ACM, Lyon, France, August 2009, pp. 24-28.
[9] Anja Gruenheid, Edward Omiecinski, and Leo Mark, “Query Optimization Using Column Statistics in Hive,” IDEAS, ACM, Lisbon, Portugal, September 2011, pp. 21-23.
[10] Meng-Ju Hsieh, Chao-Rui Chang, Li-Yung Ho, Jan-Jan Wu, and Pangfeng Liu, “SQLMR: A Scalable Database Management System for Cloud Computing,” DBLP, January 2011.
[11] Avrilia Floratou, Umar Farooq Minhas, and Fatma Ozcan, “SQL-on-Hadoop: Full Circle Back to Shared-Nothing Database Architectures,” Proceedings of the VLDB Endowment, Vol. 7, No. 12, 2014.
[12] Rakesh Kumar, Neha Gupta, Shilpi Charu, Somya Bansal, and Kusum Yadav, “Comparison of SQL with HiveQL,” International Journal for Research in Technological Studies, Vol. 1, Issue 9, August 2014.
[13] Sai Prasad Potharaju, Shanmuk Srinivas, Ravi Kumar Tirandasu, “Case Study of Hive Using Hadoop,” DBLP, Volume-1, Issue-3, 2014.
[14] Madhuri Srinivas Palle, Konisa Jyothsna and B. Anusha, “Analyzing Failures of a Semi-Structured Supercomputer Log File Efficiently by Using Pig on Hadoop,” International Journal of Computer Science and Engineering, Volume-2, Issue-1, 2014.
[15] Tak Lon Wu, Abhilash Koppula, and Judy Qiu, “Integrating Pig with Harp to Support Iterative Applications with Fast Cache and Customized Communication”, ACM, 2014.
[16] Gang Zhao, “A Query Processing Framework based on Hadoop,” International Journal of Database Theory and Application, Vol.7, No.4, 2014, pp. 261-272.
[17] James M. Harris, and Dr. Cynthia, and Z.F. Clark, “Strengthening Methodological Architecture with Multiple Frames and Data Sources,” Proceedings 59th ISI World Statistics Congress, Hong Kong, August 2013.
[18] J. Christy Jackson, V. Vijaya kumar, Md. Abdul Quadir, and C. Bharathi, “Survey on Programming Models and Environments for Cluster, Cloud, and Grid Computing that defends Big Data,” 2nd International Symposium on Big Data and Cloud Computing (ISBCC’15), ELSEVIER, 2015.
[19] Dataset that is used in this project, Available: https://github.com/jasondbaker/seis734.
[20] Radhiya A. Arsekar, Ankita V. Chikhale, Vaibhav T. Kamble and Vinayak N. Malavade, “Comparative Study of MapReduce and Pig in Big Data”, International Journal of Current Engineering and Technology, Vol.5, No.2, April 2015.

Anshu Choudhary and C.S. Satsangi, "Query Execution Performance Analysis of Big Data Using Hive and Pig of Hadoop," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.91-97, 2015.

This paper describes the techniques of image processing used for construction of 3D face models from a 2D input. 3D models play an important role in various fields. The advances in technologies have put a greater emphasis on efficient techniques of 2D to 3D conversion. 3D models can be developed manually or automatically. Automatic construction process faces certain problems such as identification of the subtle nuances present in the features of each individual. Also presenting a model which has the natural elements of each individual requires consideration. The process can be carried out in two phases; in the initial phase a 2D input is fed as input from which a new 3D model is registered by computing one-to-one correspondence to a 3D model present in a database of 3D models. Next the naturalness of the models is taken into consideration and the unlikely appearance factor is minimized. This paper discusses about the different techniques used in 2D to 3D conversion, how well they have contributed in increasing the efficiency of the process and what future research needs to be conducted.

3D modeling techniques, facial modeling, Image registration, photogrammetry, Morphological operation, virtual imaging, facial animation, computer vision, image metamorphosis, view interpolation, view synthesis, image warping

[1] Volker Blanz, Thomas Vetter, “A Morphable Model For The Synthesis Of 3D Faces” ,SIGGRAPH '99 Proceedings of the 26th annual conference on Computer graphics and interactive techniques, 1999
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Arib Patel, Rushabh Shah and Ruhina Karani, "2D to 3D Image Morphing Techniques," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.98-101, 2015.

The wireless network uses the communication protocols and it uses the air through the operation of the communication protocols. Wireless networks use a carrier sense protocol for the synchronization and these protocols are similar to the Ethernet standard. In this paper we have reviewed CSMA and RFID protocol. CSMA/CA is Carrier Sense Multiple Access/Collision avoidance protocol for carrier transmission in 802.11 networks. RFID is an enhancement of CSMA protocol. By using RFID protocol, collision and packet loss problem can be controlled.

RFID, CSMA/CA, Packet loss, Collision

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Iqbaljeet and Sonal Rana, "Clock Synchronization Technique- A Review," International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp.102-104, 2015.