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Volume-2 Issue-2: Published on January 15, 2014
Volume-2 Issue-2: Published on January 15, 2014

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Volume-2 Issue-2, January 2014, ISSN: 2319–6386 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

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S. R. Patil, Prachi Shewale, Aditi Agrawal, Vandana Choudhari, Balika Doke

Paper Title:

Real Time Data Processing for Detection of Apnea using Android Phone

Abstract: Sleep apnea (or sleep apnoea in British English) is a type of sleep disorder characterized by pauses in breathing or instances of shallow or infrequent breathing during sleep. Each pause in breathing, called an apnea, can last from at least ten seconds to several minutes, and may occur 5 to 30 times or more an hour. Similarly, each abnormally shallow breathing event is called a hypopnea. Sleep apnea is often diagnosed with an overnight sleep test called a polysomnogram, or "sleep study". The final diagnosis of sleep apnea is established by an overnight polysomnography (PSG) that involves the recording and the studying of several neurologic and cardio-respiratory signals. Those PSGs are carried out in sleep laboratories with attending systems and specialized staff. Because these studies are expensive, it is very relevant to find reliable diagnostic alternatives using fewer biological signals and providing a high level of usability. Identifying the presence of sleep apneas from blood oxygen saturation signal fragments taken from pulsioximetry systems (SPO2). In order to build the classifier, all the methods with which we worked were trained and tested with annotated SpO2 signals available in the Apnea-ECG Database. Another additional requirement we considered was that the classifier should run in real time using, at each particular moment, past information in the SpO2 signal and not information contained in the whole signal. Moreover, we implemented a monitoring system that detects apneic events in real time while the patient is sleeping, which can be sometimes used as a valid alternative to PSGs. This monitoring system constitutes of a desktop application consisting historical database and a mobile device in which our apnea classifier runs performing a local real-time analysis that allows the system to take an active role in the monitoring process. This system can also record patients’ nocturnal pulsioximetry and send data to a specific health center to be evaluated by qualified medical staff.

Data mining, real-time monitoring, sleep apnea and hypopnea syndrome (SAHS) detection, SpO2 signal analysis.


1.        Burgos, A. ,et al, ”Real time detection of apnea on pda” IEEE Transactions On Information Technology In Biomedicine, Vol. 14, No. 4, July 2010.
2.        Boyle, J. ;et al, “Automatic Detection of Respiration Rate From Ambulatory Single-Lead ECG”, IEEE Transactions On Information Technology In Biomedicine, Vol. 13, No. 6, November 2009.

3.        Morillo, D.S. ;et al,” An Accelerometer-Based Device for Sleep Apnea Screening” Information Technology in Biomedicine, IEEE Transactions on  (Volume:14 ,  Issue: 2),March 2010.

4.        Bsoul, M. ;et al, “Apnea MedAssist: Real-time Sleep Apnea Monitor Using Single-Lead ECG”, Information Technology in Biomedicine, IEEE Transactions on  (Volume:15 ,  Issue: 3 ),May 2011.

5.        Koley, B. ; “Adaptive classification system for real-time detection of apnea and hypopnea events”, Point-of-Care Healthcare Technologies (PHT), 2013 IEEE).

6.        Schluter, T.; “An Approach for Automatic Sleep Stage Scoring and Apnea-Hypopnea Detection.” Data Mining (ICDM), 2010 IEEE 10th International Conference in DEC 2010.

7.        Pantelopoulos, A.; “A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis”, IEEE transactions on systems, man, and cybernetics—part c: applications and reviews, vol. 40, no. 1, January 2010

8.        Martin O. Mendez;et al, “Sleep Apnea Screening by Autoregressive Models From a Single ECG Lead”, IEEE Transactions On Biomedical Engineering, Vol. 56, No. 12, December 2009

9.        Almazaydeh, L.; et al, “Detection of obstructive sleep apnea through ECG signal features.”, Electro/Information Technology (EIT), 2012 IEEE International Conference on May 2011.

10.     Al-Ashmouny, K.M.; et al,” Sleep Apnea Detection and Classification Using Fuzzy Logic: Clinical Evaluation Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 2005. 27th Annual International Conference of the Jan 2006.




Hossein Niavand, MojtabaTajeri

Paper Title:

Statistical Control Process: A Historical Perspective

Abstract: One of the most powerful tools in thestatistical quality process is the statisticalmethods.First developed in the 1920’s byWalter Shewhart, the control chart found widespread use during World War II and has been employed, with various modifications, ever since. There are many processes in which thesimultaneous monitoring or control of two ormore quality characteristics is necessary. Reviewing statistical process control tools and providing a description about necessity of using these tools for improving the production process and providing some applications of statistical process control.This study covers both the motivation for statistical quality process and a discussion of some of the techniques currently available. The emphasis focuses primarily on the developments occurring since the mid-980’s.

Statistics, quality control, shewhartchart, control chart, praetorchart, diagram.


1.        BamniMoghadam M. (2006), Statistical Quality Control, Volume I, Second Edition, published by Payam Noor University, Tehran
2.        BamniMoghadam M. and Movahedi M. (2010), Planning, Controlling and Improving the quality, Volume 1, First Edition, Zeytoon Publication, Tehran

3.        Montgomery, Douglas C. (1998). Statistical Quality Control, RasoulNoorossana (Translator), translated from the English version, Volume 1, Second Edition, University of Science and Technology, Tehran.

4.        Statistical Terms and Words, Persian - English and English - Persian, Volume 1, Third Edition, Third Publication, Institute of Statistics, Tehran 2005.

5.        Montgomery, D.C. (2001): Introduction to statistical quality control. New York, NY ,John Wiley and Sons

6.        Duncan, A. J. (1986), " Quality Control and Industrial Statistics," Homewood, IL, Richard Irwin

7.        Holland, J. H. (1975), "Adaptation in Natural and Artificial Systems," Ann Arbor, MI, University of Michigan press

8.        Juran, J. M. and Gryna, F. M. (1980), " Quality Planning and Analysis, "McGraw Hill, New York

9.        Rao, S. S. (1996), "Engineering Optimization: Theory and practice," New York, NY, John Wiley and Sons.

10.     Taguchi, G. (1986), "Introduction to Quality Engineering," Asian Productivity Organization, UNIPUB, White Plains, NY.

11.     Woodall, W. H. and Montgomery, D. C. (1999), "Research Issues and Ideas in Statistical Process Control," Journal of Quality Control, 31, 376-386.

12.     Dorris,A. L., and B.J. Foote (1978). "Inspection Error and Statistical Quality Control:A Survey, "AIIE Transactions,Vol.10.

13.     Shewhart, W. A., (1931). "Economic Control of Quality of Manufactured Product,"Van Nostrand, New York.

14.     Shewhart, W. A., and Deming, W. E. (1939). "Statistical Methods from the Viewpoint of Quality Control,"Graduate School, Department of Agriculture, Washungton, DC.

15.     Ishikawa, K. (1968), "Education and Training of Quality Control in Japanese Industry,"Tokyo,pp. 423-26

16.     Deming,W .E .(1994), "Transcript of Speech to GAO Roundtable on Product Quality-Japan vs. the United States," Quality Progress, Vol. 27, No.3, pp. 39-44.




Muthulakshmi G, Revathi S

Paper Title:

VLSI Implementation of Delayed LMS Adaptive Filter with Efficient Area-Power-Delay

Abstract: In this paper, we present an efficient architecture for the implementation of a delayed least mean square Adaptive filter. For achieving lower adaptation-delay and area-delay-power, we use a novel partial product generator and propose an optimized balanced pipelining across the time-consuming combinational blocks of the structure. From synthesis results, we find that the proposed design with less area-delay product (ADP) and less energy-delay product (EDP) than the best of the existing systolic structures, for various filter lengths. We propose an efficient fixed-point implementation scheme in the proposed architecture. We present here the optimization of design to reduce the number of pipeline delays along with the area, sampling period, and energy consumption. The proposed design is found to be more efficient in terms of the power-delay product (PDP) and energy-delay product (EDP) compared to the existing structures.

Adaptive filters, Adder tree optimization, fixed-point arithmetic, least mean square (LMS) algorithms.


1.        B. Widrow and S. D. Stearns, Adaptive Signal Processing Englewood Cliffs, NJ, USA: Prentice-Hall, 1985.
2.        S. Haykin and B. Widrow, Least-Mean-Square Adaptive Filters  Hoboken,  NJ,  USA:  Wiley,  2003.

3.        M. D. Meyer and D. P.  Agrawal, “A  modular pipelinedImplementation of a delayed   LMS    transversal adaptive Filter,” in Proc. IEEE Int. Symp. Circuits Syst., May 1990,pp. 1943–1946.

4.        G. Long, F. Ling, and J. G. Proakis, “The LMS algorithmWith  delayed     coefficient    adaptation,” IEEE Trans.Acoust. Speech, Signal Process.  vol.37, no. 9,   pp.1397–1405, Sep. 1989.

5.        G. Long, F. Ling, and J. G. Proakis, “Corrections to ‘The LMS algorithm  with   delayed  coefficient adaptation’,” IEEE Trans. Signal Process.,vol. 40, no. 1, pp. 230–232,Jan. 1992.

6.        H. Herzberg   and     R.   Haimi-Cohen,   “A systolic arrayRealization of an LMS adaptive filter and the  effects   of delayed adaptation,” IEEE Trans.Signal Process., vol. 40,no. 11, pp. 2799–2803, Nov. 1992.

7.        M. D. Meyer and D. P. Agrawal,   “A high sampling rate delayed LMS filter  architecture, ”  IEEE    Trans. Circuits Syst. II, Analog Digital Signal Process, vol. 40, no. 11, pp. 727–729, Nov. 1993.

8.        S.    Ramanathan   and    V.   Visvanathan, “A    systolic  architecture for  LMS  adaptive  filtering   with    minimal adaptation  delay,” in Proc. Int. Conf.  Very Large  ScaleIntegr. (VLSI) Design, Jan. 1996, pp. 286–289.

9.        Y. Yi, R. Woods, L.-K. Ting, and C. F. N. Cowan, “High Speed  FPGA- based  implementations of  delayed- LMS filters,” J . Very  Large   Scale   Integr.    (VLSI)    Signal Process., vol. 39, nos. 1–2, pp. 113–131, Jan. 2005.

10.     L.  D.  Van   and   W. S.  Feng,   “An   efficient  systolic architecture  for  the   DLMS  adaptive   filter   and   its applications,”  IEEE   Trans.   Circuits Syst. II,   AnalogDigital Signal  Process., vol. 48, no. 4, pp. 359–366, Apr.  2001.

11.     L.K.  Ting, R.  Woods,  and   C. F. N.   Cowan,  “VirtexFPGA  implementation  of  a  pipelined  adaptive  LMSPredictor  for  electronic   support   measures  receivers,” IEEE  Trans. Very  Large Scale Integr. (VLSI) Syst.,vol. 13, no. 1, pp. 86–99, Jan. 2005.

12.     P. K.  Meher  and  M. Maheshwari,  “A  high-speed  FIR Adaptive  filter  architecture  using  a  modified  delayed LMS algorithm,” in Proc. IEEE Int. Symp. Circuits Syst.,May 2011, pp. 121–124.

13.     P. K. Meher and S. Y. Park, “Low adaptation-delay LMSAdaptive filter part-I: Introducing a novel multiplicationcell,” in Proc. IEEE Int.  Midwest Symp.  Circuits  Syst.,Aug. 2011, pp. 1–4.

14.     P. K. Meher and S. Y. Park, “Low adaptation-delay LMS adaptive  filter  part- II:  An  optimized  architecture,”  in Proc.  IEEE  Int.  Midwest   Symp.  Circuits  Syst.,  Aug.2011,  pp. 1–4.

15.     K. K. Parhi, VLSI   Digital Signal  Processing    Systems: Design  and   Implementation.  New York,  USA: Wiley, 1999.

16.     C. Caraiscos and B. Liu, “A roundoff error analysis of theLMS adaptive algorithm,” IEEE Trans. Acoust., Speech, Signal Process., vol. 32, no. 1, pp. 34–41, Feb. 1984.

17.     R.  Rocher,  D.  Menard,  O.  Sentieys,  and    P.   Scalart,   “Accuracy evaluation of fixed-point LMS  algorithm,” inProc. IEEE Int. Conf. Acoust., Speech,  Signal  Process.,May 2004, pp. 237–240.




Poonam Bobade, Seematai Wadekar, Nisha Pagare, K. S. Warke

Paper Title:

Defeating Attacks in Cloud Computing

Abstract: As vulnerabilities keep increasing exponentially every year, the need to efficiently classify, manage, and analyze them also increases. As more and more users, becomes very important to have proper vulnerability management in cloud. In this paper presentation of vulnerability management framework for cloud computing is represented. Cloud computing is a new environment in computer oriented services. It is not an easy task to securely maintain all essential data where it has the need in many applications for clients in cloud. To maintain our data in cloud, it may not be fully trustworthy because client doesn’t have copy of all stored data. Therefore the security is the biggest problem of this system, because the services of cloud computing is based on the sharing. So, the preventive measures of, the different types of attacks in cloud computing services is described.

Cloud Computing, D-DOS, IP Spoofing, Malware, Security, Vulnerability.


1.        Zhifeng Xiao and Yang Xiao, senior member, IEEE Security and privacy in cloud computing, 2012.
2.        Amol Poman , Mahesh Gundras, Prashant Pujari ,“G Rahul Johari USIT, GGSIP University Sector 16-C Dwarka, India & Pankaj Sharma CERT -In   Ministry of communication & IT Govt. of India.A survey on Web application vulnerabilities (SQLIA, XSS) Exploitation and security Engine for SQL injection, 2012

3.        Farzad Sababhi Faculty of computer engineering Azad University Iran. Cloud computing Security Threats & Responses.2011

4.        Fog Computing: Mitigating Insider Data Theft Attacks in the Cloud  Salvatore J. Stolfo Computer Science Department Columbia University New York , NY, US, Malek Ben Salem Cyber  Security Laboratory Accenture Technology Labs Reston, VA, USA Angelo’s D. Keromytis Allure Security Technologies New York , NY, USA.

5.        Data Integrity Proofs in Cloud Storage Sravan Kumar R Software Engineering and Technology labs Infosys Technologies Ltd Hyderabad, India.Ashutosh Saxena Software Engineering and Technology labs Infosys Technologies Ltd Hyderabad, India.

6.        Prudent Practices for Designing Malware Experiments: Status Quoand outlook.  Christian Rossow  , Christian J. Dietrich, Chris Grier, Christian Kreibich, Vern Paxson , Norbert Pohlmann, Herbert Bos, Maarten van Steen.

7.        Preventing IP Source Address Spoofing: A Two-Level, State Machine-Based Method BI Jun, LIU Bingyang, WU Jianping   , SHEN Yan.

8.        A unified approach for detection and prevention of DDOS attacks using enhanced support vector machins and filtering  mechanisms T. Subbulakshmi, P. Parameswaran, C. Parthiban, M. Mariselvi, J. Adlene Anusha  and   G.Mahalakshmi  bed.

9.        Data Integrity Proofs in Cloud Storage.Sravan Kumar R, Ashutosh Saxena,978-1-4244-8953-4/11/$26.00c 2011 IEEE

10.     N. Gruschka, M. Jensen, “Attack Surfaces: A Taxonomy for Attacks on Cloud Services,” Cloud Computing, IEEE International Conference on, pp. 276-279, 2010 IEEE 3rd International Conference on Cloud  Computing, 2010.

11.     The Management of Security in Cloud Computing Ramgovind S, Eloff MM, Smith ESchool of Computing, University of South Africa, Pretoria, South Africa. 978-1-4244-5495-2/10/$26.00 ©2010 IEEE

12.     Security and Privacy Challenges in Cloud Computing Environments, Hassan Takabi and James B.D.Joshi Gail-Joon Ahn 1540-7993/10/$26.00 © 2010 IEEE




Sonal Dubey, R. K. Pandey, S. S. Gautam

Paper Title:

Development of Multimedia Fuzzy Based Diagnostic Expert System for Integrated Disease Management in Chickpea

Abstract: One of the most important branches of Artificial Intelligence are the expert systems. Expert systems are application oriented. . An expert system is a computer application that solves complicated problems that would otherwise require extensive human expertise. It can be operated by a less educated person or a layman in a particular field of knowledge. It uses the knowledge of the domain expert to form rules to assist in decision making depending on the inputs given by the user. Chickpea (Cicer arietinum L) is the second most important cool season legume crop. It is mainly grown in tropical, sub-tropical and temperate regions, as rainfed in semi arid regions. there is a tremendous scope for increasing the productivity of chickpea by reducing the production losses thereof caused by serious insect pests and diseases causing up to 100 % losses during epidemic years. . For better management of the pest, effective integrated disease and insect management techniques have to be followed for increasing crop production. Expert systems play an important role in supporting farmers to practice effective integrated disease and insect management techniques and taking decisions on crop protection where the experts are not available. Since Fuzzy logic can effectively handle vagueness and inperfect data, it is widely used in diagnosis of diseases in agriculture. This paper describes the fuzzy expert system for integrated disease management in chickpea taking into account the environmental factors like soil moisture, temperature, soil pH, relative humidity in the first step. In the second step identification based on symptoms and photos are taken into consideration and a conclusion is drawn about the diseases attacking the crop.

Chickpea, environmental factors fuzzy expert system, integrated disease management.


1.        Sonal Dubey, R.K. Pandey, S.S. Gautam “Literature Review on Fuzzy Expert System in Agriculture “ published in the International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-6, January 2013
2.        Paul Ho and  S M Lo Fuzzy expert systems and its future potential applications for general practice surveyors.

3.        Chen, C.L. and Chen, W.C. 1994. “Fuzzy Controller Design by Using Neural Network Techniques”. IEEE Transactions on Fuzzy Systems. 2(3):235-244.

4.        Ajith Abraham “Rule-based Expert Systems” Handbook of Measuring System Design, edited by Peter H. Sydenham and Richard Thorn  2005 John Wiley & Sons, Ltd.

5.        O. C. Agbonifo D. B. Olufolaji “A Fuzzy Expert System for Diagnosis and Treatment of Maize Plant Diseases”. International Journal of Advanced Research in Computer Science and Software Engineering  Volume 2, Issue 12, December 2012.

6.        Guo-Dong You, Ji-Sheng Li, Shi-Feng Yang, Xiu-Qing Wang and Yong Hou Study and Simulation on Fuzzy Control Model for Crop Disease Research Journal of Applied Sciences, Engineering and Technology 6(8): 1394-1401, 2013

7.        Fahad Shahbaz Khan , Saad Razzaq, Kashif Irfan, Fahad Maqbool, Ahmad Farid, Inam Illahi, Tauqeer ul amin “ Dr. Wheat: A Web-based Expert System for Diagnosis of Diseases and Pests in Pakistani Wheat”  Proceedings of the World Congress on Engineering 2008 Vol I.

8.        Silvia Maria Fonseca Silveira Massruhá, Raphael Fuini Riccioti, Helano Povoas Lima and Carlos Alberto Alves Meira “ DiagData: A Tool for Generation of Fuzzy Inference System” Journal of Environmental Science and Engineering B 1 (2012) 336-343 Formerly part of Journal of Environmental Science and Engineering, ISSN  934-8932

9.        Shikhar Kr. Sarma, Kh. Robindro Singh & Abhijeet Singh “An Expert System for diagnosis of diseases in Rice Plant” International Journal of Artificial Intelligence, Volume(1): Issue(1)

10.     Alper PAHSA “Morphological Image Processing With Fuzzy Logic “Havacilik Ve Uzay Teknolojileri Dergisi Ocak 2006 Cilt 2 Sayi 3 (27-34)




A. S. Devare, M. P. Wankhade

Paper Title:

Dynamic Channel Allocation Using ARS and BFS- CA in WMN

Abstract: Traditionally in wireless networks, nodes were operating with a single radio, due to the cost associated with having multiple radios on a node, which was high. Several methods were proposed which aimed to improve the network throughput, for single-radio wireless mesh networks. However, with lowering costs, it has become possible to equip a node with multiple radios. Having multiple radios on a node opens several possibilities and options as to how these radios can be utilized to improve some of the important characteristics of the nodes and the performance of the network. Several interesting studies have been performed on multi-radio nodes and have concluded that in some cases, having multiple radios can considerably improve the throughput and network performance. In this we use the concept of a multi-radio mesh node to analyze the performance of wireless mesh networks in different conditions with different channel assignment schemes. We look at new ways to try and improve the network throughput in wireless mesh networks performance, such as delay, bandwidth, probability of packet loss, delay variance (jitter), and throughput.

IEEE 802.11, multiradio wireless mesh networks(mr-WMNs), E-ARS, BFS-CA networks, wireless link failures.


1.        I. Akyildiz, X. Wang, and W. Wang, “Wireless mesh networks: A survey,” Comput. Netw., vol. 47, no. 4, pp. 445–487, Mar. 2005.
2.        K. Ramanchandran, E. Belding-Royer, and M. Buddhikot, “Interference- aware channel assignment in multi-radio wireless mesh networks,” in Proc. IEEE INFOCOM, Barcelona, Spain, Apr. 2006.

3.        M. Alicherry, R. Bhatia, and L. Li, “Joint channel assignment and routing for throughput optimization in multi-radio wireless mesh networks,” in Proc. ACM MobiCom, Cologne, Germany, Aug. 2005

4.        A. P. Subramanian, H. Gupta, S. R. Das, and J. Cao, “Minimum interference channel assignment in multiradio wireless mesh networks,”IEEE Trans. Mobile Comput., vol. 7, no. 12, pp. 1459–1473, Dec. 2008

5.        K.-H. Kim and K. G. Shin, “On accurate and asymmetry-aware measurement of link quality in wireless mesh networks,” IEEE/ACMTrans.Netw., vol. 17, no. 4, pp. 1172–1185, Aug. 2009.

6.        P. Kyasanur and N. Vaidya, “Capacity of multi-channel wireless networks:Impact of number of channels and interfaces,” in Proc. ACM MobiCom, Cologne, Germany, Aug. 2005, pp. 43–57.

7.        A. Brzezinski, G. Zussman, and E. Modiano, “Enabling distributed throughput maximization in wireless mesh networks: A partitioning approach,” in Proc. ACM MobiCom, Los Angeles, CA, Sep. 2006, pp.26–37.

8.        S. Chen and K. Nahrstedt, “Distributed quality-of-service routing in ad hoc networks,” IEEE J. Sel. Areas Commun., vol. 17, no. 8, pp.1488–1505, Aug. 1999

9.        P. Bahl, R. Chandra, and J. Dunagan. SSCH: Slotted Seeded Channel Hopping For Capacity Improvement in IEEE 802.11 Ad Hoc Wireless Networks. In ACM MobiCom, Philadelphia, PA, September 2004.

10.     R. Draves, J. Padhye, and B. Zill. Routing in Multi-radio, Multihop Wireless Mesh Networks. In ACM MobiCom, Philadelphia, PA ,September 2004.