Volume-6 Issue-11

S. No

Volume-6 Issue-11, July 2017, ISSN:  2278-3075 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Umer Farooq, Sachin Talan, Ajay Kumar

Paper Title:

Maximizing SNR in A CDMA System

Abstract: Signal to Noise Ratio (SNR) is an important index for wireless communications. There are many methods for increasing SNR. In CDMA systems, spreading sequences are used. To increase SNR, we have to improve spreading sequences. In classical approaches, the expression of SNR is not differentiable in terms of the parameter of the spreading sequences even in no fading situations. Thus, we express it as the differentiable form and construct the non-linear programing for maximizing SNR. In particular, we solve the problem of maximizing SNR numerically by obtaining spreading sequences whose SNR is guaranteed to be high. Also we use MATLAB programming for the same.

Keywords: MATLAB, Noise Ratio (SNR), CDMA, differentiable.

References:
1.       D. V. Sarwate,”Bounds on cross correlation and autocorrelation of sequences”, IEEE Transactions on Information Theory
2.       L. R. Welch,”Lower bounds on the maximum cross correlation of signals,
3.       R. Gold, ”Optimal binary sequences for spread spectrum multiplexing
4.       G. Heidari-Bateni and C. D. McGillem. ”A chaotic direct-sequence spread-spectrum communication system.” IEEE Transactions on communications.
5.       K.S. Halle, C.W. Wu, M. Itoh and L.O. Chua. ”Spread spectrum communication through modulation of chaos.” International Journal of Bifurcation and Chaos 3.02 (1993): 469-477
6.       Y. Soobul, K. Chady and H. C.S. Rughooputh. ”Digital chaotic coding and modulation in CDMA.” Africon Conference in Africa, 2002. IEEE AFRICON. 6th. Vol. 2. IEEE, 2002.
Spreading sequences using periodic orbits of chaos for CDMA, Electronics Letters 35.7 (1999), 545-546.
7.       C.C. Chen, K. Yao, K. Umeno and E. Biglieri, ”Design of spread spectrum sequences using chaotic dynamical systems and ergodic theory.” IEEE Transactions on Circuits and Systems I: Fundamental Theory
8.       C. E. Shannon, ”A mathematical theory of communication,” Bell System Technical Journal, Volume 27, Issue 3, 379-423 (1948).
9.       J. Proakis, ”Digital Communications. 1995”, McGraw-Hill, New York. [3] S. Verdu and S. Shamai, ”Spectral efficiency of CDMA with random spreading.” IEEE Transactions on Information theory 45.2 (1999): 622
10.    T.S.Rappaport. Wireless Communications –principles and practices, 2nd Edition, (2002 by Prentice-Hall), Upper Saddle River, New Jersey. Ch.5. 2. H. Taub, D. L. Schilling and G. saha, ―Principles of communication systems‖, third edition,(2008 by Tata McGraw-Hill), ch.15. 3. `Mabula Martina Cheeli, ―CDMA network simulator for wireless
communication applications‖, Graduation Degree Thesis, Department of Electrical Engineering, University of Cape Town 2006.
11.    Rpke, T. H. Christensen, and J. O. Jensen, “Information and communication technologies a new round of household electrification,” Energy Policy, vol. 38, no. 4, pp. 1764 – 1773, 2010.
12.    Y. Park, J. Ha, S. Kuk, H. Kim, C. J. Liang, and J. Ko, “A feasibility study and development framework design for realizing smartphone based vehicular networking systems,” IEEE Transactions on Mobile Computing, vol. 13, no. 11, pp. 2431– 2444, Nov. 2014.
13.    C. C. Huang, P. Y. Lee, and P. Y. Chen, “Implementation of a smartphone based portable doppler flowmeter,” in Proc. IEEE International Ultrasonics Symposium, Oct. 2011, pp. 1056–1059.
14.    Rahmati and L. Zhong, “Studying smartphone usage: Lessons from a four-month field study,” IEEE Transactions on Mobile Computing, vol. 12, no. 7, pp. 1417–1427, July 2013.
15.    M. Sauter, 3G, 4G and Beyond: Bringing Networks, Devices and the Web Together, Wiley, 2012.
16.    J. Proakis and M. Salehi, Digital Communications, McGraw-Hill Education, 2007.
17.    S. Dang, J. P. Coon, and D. Simmons, “Combined bulk/per-tone relay selection in two-hop OFDM systems,” Wireless Communications Letters, IEEE, 2015 (under review).
18.    Z. Ma, A. Gholamzadeh, B. Tang, S. Dang, and S. Yang, “Matlab based simulation of the efficiency of the complex ofdm on power line communication technology,” in Proc. Fourth International Conference on Instrumentation and Measurement, Computer, Communication and Control, Sept. 2014, pp. 374–378.

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2.

Authors:

Taranath N.L., Shanthakumar B. Patil, Premajyothi Patil, C. K. Subbaraya

Paper Title:

Knowledge Based MDSS using Data Mining

Abstract:  Medical Decision Support System (MDSS) links the patient information to promising diagnostic and treatment paths. It can be built either as Knowledge-based system or Learning-based system. Knowledge-based systems are human-engineered maps from best medical practices and patient data will be recommended. Learning-based systems derive the mapping techniques from data mining, statistical approaches and machine learning techniques. An Integrated decision support system integrates both Knowledge-based and Learning-based systems to provide a robust solution to the information challenge in the presence of partial information. In this work, we design a framework and concrete implementation of Integrated Medical Decision Support System to assist the Doctors in clinical decisions regarding the prescription of drugs.  It uses the Knowledge base for prescribing the drugs to the patients, however if the available data is partial it employs the machine learning techniques to answer the query. It is suitable for many different healthcare settings and many different users. The framework is query-based and it can be adapted for use with many different end-user interfaces.

Keywords:  Artificial Intelligence, Data Mining, Learning based Systems, Knowledge based Systems.

References:
1.       M.M.Abbasi and S. Kashiyarndi, “Clinical Decision Support Systems: A discussion on different methodologies used in Health Care”, International Journal of Computer Science and Information Security, Vol. 8, No. 4, pages 249-256, 2010.
2.       M. Frize and R. Walker, “Clinical decision-support systems for intensive care units using case-based reasoning”, Medical engineering & physics, Vol. 22, No.9, pages 671-677, 2006.
3.       Y. Ye and S. J. Tong, “A Knowledge-Based Variance Management System for Supporting the Implementation of Clinical Pathways”, Management and Service Science, IEEE-2009, pages 1-4, 2009.
4.       M. Goadrich, L. Oliphant and J. Shavlik, “Learning Ensembles of First-Order Clauses for Recall-Precision Curves: A Case Study in Biomedical Information Extraction”, Proc. 14th Int’l Conf. on Inductive Logic Programming, pages 211-214, 2004.
5.       T. Mitsumori, M. Murata, Y. Fukuda, K. Doi and H. Doi, “Extracting Protein-Protein Interaction Information from Biomedical Text with SVM ”, IEICE Trans. Information and Systems, Vol. E89D, No. 8, pages 2464-2466, 2006.
6.       Oana Frunza, Diana Inkpen and Thomas Tran, “A Machine Learning Approach for Identifying Disease-Treatment Relations in Short Texts”, IEEE Vol. 23, No. 6, pages 246
246, June 2011.
7.       M. Zhu, Z. Zhang, J. Hirdes and P. Stolee, “Using machine learning algorithms to guide rehabilitation planning for home care clients”, BMC medical informatics and decision making, Vol. 7, Issue 1, pages  41-43, 2007.
8.       D. Rossille, J. Lauren and A. Burgun, “Modeling a decision-support system for oncology using rule-based and case-based reasoning methodologies”, International Journal of Medical Information, pages 299-306, 2005.
9.       Y. Ye and Z. Jiang, “A Semantics Based Clinical Pathway Workflow and Variance Management Framework”, Service Operation and Logistica and Informatics, IEEE, pages 758-763, 2008.
10.    Avrilia Floratou, Sandeep Tata, and Jignesh M. Patel, Member, IEEE, “Efficient and Accurate Discovery of Patterns in Sequence Data Sets”, IEEE Transactions On Knowledge and Data Engineering, Vol. 23, No. 8, pages 30-37 August 2011.
11.    P. Patel, E. Keogh, J. Lin and S. Lonardi, “Mining Motifs in Massive Time Series Databases”, Proc. of  IEEE Int’l Conf. Data Mining (ICDM), pages 370-377, 2002.
12.    X. Garg, N. K. J. Adhikari and H. McDonald, “Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes: A Systematic Review”, JAMA, pages 1223-1238, 2005.
13.    L. Lin, P. Hu, O. R. Liu Sheng, “A decision support system for lower back pain diagnosis: Uncertainly management and clinical evaluation”, Decision Support Systems, pages 1152 -1169, 2006.
14.    M. Frize, C. M. Ennett, M. Stevenson and H. Trigg, “Clinical decision support system for intensive care units: using artificial neural networks”, Medical Engineering & Physics, pages 217-225, 2001.
15.    D. J. Spiegel halter and R. P. Knill-jones, “Statistical and Knowledge-based Approaches to Clinical Decision-support Systems with an Application in Gastroenterology”, J. R. Statist. Soc., pages 55-77, 2004.
16.    E. Sivasankar and R. S. Rajesh, “Knowledge Discovery in Medical Datasets Using a Fuzzy Logic rule based Classifier”, IEEE International Conference on Electronic Computer Technology, pages 208-213, 2010.

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3.

Authors:

A. Albert Martin Ruban, R. Anandaraj, K. Selvakumar, N. Kannan

Paper Title:

Integrated Renewable Energy Sources with EMS using Fuzzy Control and WSN for Smart Grid Applications

Abstract: This paper deals with the integrated renewable energy sources with EMS using fuzzy control for smart grid applications. This paper comprised of power supply which obtains its power from the green energy resources, which includes solar, wind, and fuel cell.  The modeling of the above mentioned generating system and storage device was simulated by using MATLAB/ Simulink.  The RS 485 ZigBee network, a communication protocol employed to monitor and command the EMS.  The fuzzy employed to manage the battery.

Keywords:  EMS, Fuzzy control, Smart grid, Solar, Wind, Fuel cell, Zigbee, Renewable Energy.

References:
1.       H. Rongxian, L. zhiwen, C. yaoming, W. Fu and R.    Guoguang: “DC microgrid simulation test platform,” in proc. 9th Taiwan Power Electron, Conf., 2010, pp. 1361-1366.
2.       Christian Hicks: “The Smart grid Where we are Today and what the future holds,” 2012.
3.       Zaheer and S.N. Singh: “Modeling and Control of grid connected PV system A review,” volume.3 issue 3, March 2013.
4.       Alliance for Rural Electrification: “Hybrid power system based on renewable energies: A sustainable   and cost competitive solution for rural electrifications”.
5.       K. Yukita, Y. Shimuku, and Y. Goto, of Aichi Institute of Technology, “Study of Power system using DC and AC micro grid systems”.
6.       Carlo cecati, Costantino Citro, Pierluigi Siano: “Combined Operation of Renewable Energy Resources and Responsive Demand in a Smart grid.” IEEE transaction on sustainable energy, volume.2, no. 4, Oct. 2011.
7.       W. Baosheng: “A controllable rectifier rectifier wind and solar hybrid power system based on   digital signal processor developed,” M.S. thesis in electrical engineering, Southern Taiwan University.
8.       Jeermy Lagrose, Marcelo G. Simoes: “A Multi agent Fuzzy logic Based Energy Management of Hybrid Systems,” IEEE transactions on industrial applications, vol. 45, no. 6, Nov. 2009.
9.       Tarak Saluki, Moniur Bouzguenda, and Abel Gastli: “MATLAB/Simulink Based Modeling of Solar PV cells,” International Journals of Renewable Energy Research.
10.    Dominique Boukoungou, Zacharie Koaloga, Douatien Njomo, “Modelling and Simulation of PV considering Single diode equivalent Circuit Model in MATLAB,” ISSN. 2250-2459, Vol. 3, and issue. 3, March 2013.
11.    Monlay Tahar Lamchich and Nora Lachguer: “MATLAB Simulink as a simulation tool for wind generation based on DFIG”.
12.    Blaabjerg, Z. chen, R. Teoloreseu, and F. Lov: “Power Electronics in wind turbine systems,” IEEE 2006.
13.    Jay Verma, Yogesh Timari, and Anup Mishra: “Performance Analysis and Simulation of Wind Energy Conversion System connected with grid” International Journal of Research and Technology.
14.    EG & G Technical services, Inc: “Fuel cell Handbook” edition November 2004.
15.    Jay Taweer Pukur Shipan: “Modeling and Control of fuel cell system and fuel cell processors.”.
16.    Dr. Seyezhai and Dr. B. Mathur: “Mathematical modeling of PEM fuel cell,” IJCA 2011.
17.    Winkler: “Intelligent Energy Management of Electrical Power system with Distributed Feeling on the basis of forecasts of Demand and Generations.

9-14

4.

Authors:

B. Jaya Lakshmi, R. Ramana Reddy

Paper Title:

Implementation of Low Power and High Performance Adder Circuits

Abstract: Adders plays a crucial role for implementing the arithmetic operations in analog and digital circuits. These adders are widely used in arithmetic systems, DSP systems, etc. The proposed dual mode square adder is designed for low power and high performance. Different techniques like dual mode logic (DML) and dual mode addition (DMADD) are reported in open literature to consume low power and high speed. In this paper dual mode square adder which is a combination of DML and DMADD is implemented using static energy recovery full (SERF) adder. The performance of adder circuitsare compared with ripple carry adder using NAND gates. The Power dissipation of RCA using SERF adder is reduced by 42.62% compared to RCA using NAND gates and speed is increased by 82.3% using SERF Adder in dual mode square adder to RCA using NAND gates. Adders are implemented in mentor graphics tools in 130 nm technology.

Keywords:   DML, DMADD, SERF Adder, Ripple carry adder, dual mode square adder.

References:
1.       Itamar LEVI, Amir Albeck, Alexandar Fish and Shumel Wimer, ”A novel low Energy and high performance dual mode sqaure adder”, IEEE Transcations on circiuts and systems, vol 61, no.11, november 2014.
2.       Bhogadi Namitha and U. Hari, “Design of DM2 adder with low energy and high speed”, IJCTA, pp.7401-7410, 2016.
3.       W.Shen, Y.Cai, X.Hong and J.Hu, ”An efficient gated clock tree design based on activity and register aware placement”, IEEE transcations on VLSI systems, vol.18, no.12, pp-1639-1648, 2010.
4.       B.R.Zeydel, D.Baran and V.G.Oklobzijia, “Energy efficient design methodlogies-High performance vlsi adders”, IEEE J.Solid state circiuts, vol.45, no.6, pp.1220-1233, 2010.
5.       J. Shinde and S. S. Salankar,“clock gating- A power optimizing technique for VLSI circuits”, in Proc.INDICON, pp.1-4, 2011.
6.       H. Q. Dao, B. R. Zeydel and V. G. Oklobzijia, “energy optimization of pipelined digital systems using circiut sizing and supply scaling”, IEEE transcations on VLSI systems, vol.14, no.2, pp.122-134, 2006.
7.       R. Brent and H. Kung, “A regualar layout for parallel adders “, IEEE transcations on computers, vol.C-31, no.3, pp.260-264, 1982.
8.       ParvathiMuddapu, N. Vasanthaand K. Satya Prasad, "Design of high speed-low power-high accurate (HS-LP-HA) adder." International Journal of Computer and Communication Engineering, no.5, 2013.
9.       Nirmal, Uma, Geetanjali Sharma and YogeshMisra, "A low power high speed adders using MTCMOS technique”,International Journal of Computational Engineering & Management, vol.13,2011.
10.    Bhattacharyya Partha, BijoyKundu, SovanGhosh, Vinay Kumar and AnupDandapat, "Performance analysis of a low-power high-speed hybrid 1-bit full adder circuit",IEEE Transactions on very large scale integration (VLSI) systems vol.23, no.10, pp.2001-2008, 2015.
11.    SaxenaPallavi, "Design of low power and high speed Carry Select Adder using Brent Kung adder,"VLSI Systems, Architecture, Technology and Applications (VLSI-SATA), pp.1-6, International Conference on. IEEE, 2015.

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5.

Authors:

Lalita Gupta, R K Baghel

Paper Title:

Development of Probe Type Moisture Meter for Quick Measurement of Grain Moisture in Sacks

Abstract:  In this paper, a method for measuring the moisture content of grain has been presented based on single chip microcomputer and capacitive sensor. The working principle of measuring moisture content is introduced and a concentric cylinder type of capacitive sensor is designed, the signal processing circuits of system are described in details. System is tested in practice and discussions are made on the various factors affecting the capacitive measuring of grain moisture based on the practical experiments, experiment results showed that the system has high measuring accuracy and good controlling capacity.

Keywords: Dielectric properties, moisture content, capacitive sensor, signal conditioning.

References:
1.    K.B. Kim, J. H. Kim, C. J. Lee, et al.. Simple instrument  for moisture measurement in grain by free- space microwave transmission. American Society of Agricultural and Biological Engineers, 2006, 49(4): 1089-1093
2.    W.C. Wang, Y. Z. Dai. A Grain Moisture Detecting System Based on Capacitive Sensor. International Journal of Digital Content Technology and its Applications 2011,5(3):203-209.
3.    S.S. Teng, X.C. Song. The design of an automatic temperature and moisture measuring system for grain storage. Industrial Instrumentation and Automation, 2000, (1):39-42.
4.    Z.T. Li, Y. Zhang, L.M. Zhang. On research of automatic control about moisture content for cereal grains. Journal of Shenyang Normal University,2008,26(1):79-81.
5.    W.D. Cheng, X.Y. Bai, X.Y. Wang, et al.. An on-line measurement and monitoring system of grain moisture during drying process. Transactions of the Chinese Society of Agricultural Machinery,2000,31(2): 53-55.
6.    B.F. ZHAI, H. WANG. Capacitive Measuring of Grain Moisture. Journal of Liaoning Institute of Technology, 2002,22(5):1:3
7.    L. Yang, Z.H. Mao, L.L. Dong. Development of plane polar probe of capacitive grain moisture sensor. Transactions of the CSAE, 2010,26(2):185-189
8.    Y.L. Ding. Grain's moisture teller based on capacitive sensor. Journal of Transducer Technology, 2003,22(4):54-56.
9.    Y.L. Zhang, W.P. Wang, C.Z. Zheng, et al.. Intelligent real-time on-line measuring system for moisture content during grain drying. Transactions of the CSAE,2007,23(9) 137-140.

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