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Volume-5 Issue-4: Published on September 30, 2016
04
Volume-5 Issue-4: Published on September 30, 2016

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S. No

Volume-5 Issue-4, September 2016, ISSN:  2277-3878 (Online)
Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. 

Page No.

1.

Authors:

Th. Rupachandra Singh, Irengbam Tilokchan Singh, Tejmani Sinam

Paper Title:

Analysis of Skype and its Detection

Abstract:  This paper gives a complete analysis of Skype Traffic. Based on the analysis of Skype Traffic, we proposed a heuristic based detection method which classified the Skype Signaling and Skype Media Traffic. We properly categorized the Skype Media traffic as audio or video conversation. In this paper, we also propose a novel approach to identify VoIP Network Traffic in the first few seconds of initial state of communication. The proposed classifier works with Machine Learning Techniques based on the statistical features. The experimental results show that the proposed method can achieve over 99% accuracy for all testing dataset.

Keywords:
 Skype; Network Traffic Analysis; Traffic Classification; Machine Learning


References:

1.       S. Sen, O. Spatscheck, and D. Wang, “Accurate, scalable in- network identification of p2p traffic using application signatures,” in Proceedings of the 13th International Conference on World Wide Web. New York, NY, USA: ACM, 2004, pp. 512–521.
2.       “l7-filter application layer packet classifier for linux,” 2009, http:  //l7filter.sourceforge.net.

3.       T. Sinam, I. T. Singh, P. Lamabam, and N. N. Devi, “An efficient technique for detecting skype flows in udp media streams,” in Advanced Networks and Telecommuncations Systems (ANTS), 2013 IEEE International Conference, Dec 2013, pp. 1–6.

4.       T. Sinam, I. T. Singh, P. Lamabam, N. N. Devi, and S. Nandi, “A technique for classification of voip flows in udp media streams using voip signalling traffic,” in Advance Computing Conference (IACC), 2014 IEEE International, Feb 2014, pp. 354–359.

5.       T. Sinam, N. N. Devi, P. Lamabam, I. T. Singh and S. Nandi, “Early Detection of VoIP Network Flows based on Sub-Flow Statistical Characteristics of Flows using Machine Learning Techniques,” in Advanced Networks and Telecommuncations Systems (ANTS), 2014 IEEE International Conference, Dec 2014.

6.       L. Grimaudo, M. Mellia, E. Baralis, and R. Keralapura, “Select: Self- learning classifier for internet traffic,” IEEE Transactions on Network and Service Management, vol. 11, no. 2, pp. 144–157, 2014.

7.       T. T. Nguyen and G. Armitage, “A survey of techniques for internet traffic classification using machine learning,” Commun. Surveys Tuts., vol. 10, no. 4, pp. 56–76, Oct. 2008.

8.       J. Chandrakant and D. Lokhande Shashikant, “Analysis of early traffic processing and comparison of machine learning algorithms for real time internet traffic identification using statistical approach,” in Advanced Computing, Networking and Informatics- Volume 2, ser. Smart Innovation, Systems and Technologies, M. Kumar
Kundu, D. P. Mohapatra, A. Konar, and A. Chakraborty, Eds. Springer International Publishing, 2014, vol. 28, pp. 577–587.

9.       R. Yan and R. Liu, “Principal component analysis based network traffic classification,” JCP, vol. 9, no. 5, pp. 1234–1240, 2014.

10.    J. M. Reddy and C. Hota, “P2p traffic classification using ensemble learning,” in Proceedings of the 5th IBM Collaborative Academia Research Exchange Workshop, ser. I
CARE ’13. New York, NY, USA: ACM, 2013, pp. 14:1–14:4.

11.    M. Korczynski and A. Duda, “Markov chain fingerprinting to classify encrypted traffic,” in IEEE Conference on Computer Communikations, INFOCOM , Toronto, Canada, April 27 - May 2, 2014. IEEE, 2014, pp. 781–789.

12.    “libsvm-3.0,” http://www.csie.ntu.edu.tw/cjli n/libsvm/.

13.    N. Cristianini and J. Shawe-Taylor, An Introduction to support Vector Machines and other Kernel-based Learning Methods. Cambridge University Press, 2003.

14.    H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and techniques. Elsevier Inc., 2005.

15.    J. Han and M. Kamber, Data Mining: Concepts and Techniques. Elsevier Inc., 2006.

16.    T. Karagiannis, A. Broido, N. Brownlee, K. C. Claffy, and M. Faloutsos, “Is p2p dying or just hiding?” in Proceedings of the GLOBECOM 2004 Conference. IEEE Computer Society Press, November 2004.

17.    P. Haffner, S. Sen, O. Spatscheck, and D. Wang, “Acas: Automated construction of application signatures,” in Proceedings of the 2005 ACM SIGCOMM Workshop on Mining Network Data, ser. MineNet ’05. New York, NY, USA: ACM, 2005, pp. 197–202.

18.    J. Erman, A. Mahanti, M. F. Arlitt, I. Cohen, and C. L. Williamson, “Semi-supervised network traffic classification,” in SIGMETRICS, 2007, pp. 369–370.

19.    J. Erman, M. Arlitt, and A. Mahanti, “Traffic classification using clustering algorithms,” in Proceedings of the 2006 SIGCOMM Workshop on Mining Network Data, ser. MineNet ’06. New York, NY, USA: ACM, 2006, pp. 281–286.

20.    Y. Wang, Y. Xiang, and S.-Z. Yu, “An automatic application signature construction system for unknown traffic.” Concurrency and Computation: Practice and Experience, vol. 22, no. 13, pp. 1927–1944.

21.    X. Li, F. Qi, D. Xu, and X. Qiu, “An internet traffic classification method based on semi-supervised support vector machine.” in ICC. IEEE, 2011, pp. 1–50.

22.    T. N. Thuy T. and G. Armitage, “Training on multiple sub-flows to optimise the use of machine learning classifiers in real-world ip networks,” in in Proceedings of the IEEE 31st Conference on Local Computer Networks, 2006.

23.    S. Zander, T. T. T. Nguyen, and G. J. Armitage, “Sub-flow packet sampling for scalable ml classification of interactive traffic,” in LCN, 37th Annual IEEE Conference on Local Computer Networks. Clearwater Beach, FL, USA: IEEE, October 22-25 2012, pp. 68–75.

24.    G. Xie, M. Iliofotou, R. Keralapura, M. Faloutsos, and A. Nucci, “Sub-flow: Towards practical flow-level traffic classification,” in Proceedings of the IEEE INFOCOM. Orlando, FL, USA: IEEE, March 25-30 2012, pp. 2541–2545.

25.    Este, F. Gringoli, and L. Salgarelli, “On the stability of the information carried by traffic flow features at the packet level,” SIGCOMM Comput. Commun. Rev., vol. 39, no. 3, pp. 13–18, Jun. 2009.

26.    L. Peng, H. Zhang, B. Yang, and Y. Chen, “Feature evaluation for early stage internet traffic identification,” in Algorithms and Architectures for Parallel Processing, ser. Lecture Notes in Computer Science, X.-h. Sun, W. Qu, I. Stojmenovic, W. Zhou, Z. Li, H. Guo, G. Min, T. Yang, Y. Wu, and L. Liu, Eds. Springer International Publishing, 2014, vol. 8630, pp. 511–525.

27.    “Tstat - skype traces,” http://tstat.tlc.polito.it/ traces-skype.shtml.

28.    “Tstat - tcp statistic and analysis tool,” http://tstat.tlc.polito.it/index. shtml.

29.    F. Gringoli, L. Salgarelli, M. Dusi, N. Cascarano, F. Risso, and kc Claffy, “Gt: picking up the truth from the ground for internet traffic,” Computer Communication Review, vol. 39, no. 5, pp. 12–18, 2009.

30.    “Napatech,” http://www.napatech.com/.

31.    “Weka3.6.2,” 2011, http://www.cs.waikato.ac. nz /ml/weka.

32.    www.halcyon.com/pub/journals/21ps03-vidmar


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2.

Authors:

K.A.M. Sajad Hyder, M. Vanitha

Paper Title:

Segmentation of Liver and Retrieval Procedure by Feature Extraction for CT-Scan Abdominal Image Processing

Abstract:  Liver is an important vital organ. In these days medical professionals utilize CT scan abdominal images for the diagnosis of liver disorders. There arise the problem of liver segmentation and image processing for recapturing the matching image of predetermined image of liver ill state. In this present work a new way of methodology has been introduced for the pulling out liver image in a large dataset of abdominal scanned images. Further the segmented liver images are preprocessed for the feature extractions of Shape, Intensity and Texture. An automatic system of Least Distance Method (LDM) is used for the recalling of image is run into the system. There is a significant speed and accuracy have been notified by this LDM. The above results are discussed with earlier related works and concluded with the application in clinical practice.

Keywords:
Automatic retrieval technique, Digital image processing, Liver Segmentation, Least Distance method for recapturing matching image.


References:

1.    R. Punia and S. Singh, “Review on Machine Learning Techniques for Automatic Segmentation of Liver Images,” International Journal of Advanced Research in
Computer Science and Software Engineering, Vol. 3, No. 4, 2013, pp. 666-670

2.    M. Erdt, et al., “Fast Automatic Liver Segmentation Combining Learned Shape Priors with Observed Shape Deviation,” Computer-Based Medical Systems, 2010, pp. 249-254

3.    M. Sammouda, et al., “Tissue Color Images Segmenta-tion Using Artificial Neural Networks,” Biomedical Im-aging: Nano to Macro, 2004

4.    G.G. Rajput and Anand M.Chavan (2016) “Atomic Detection of Abnormalities Associated with Abdomen and Liver Images : A survey on Segmentation methods”, International Journal of Computer Applications, Volume 140-No.4, pp. 1 to 8

5.    Lav R.Varshney (2002), “Abdominal Organ Segmentation in CT-Scan Images : A Survey”, International Journal of Information Technology, Volume 100. pp. 200 to 215

6.    Luo et. al., (2014), “Review on the methods of Automatic Liver Segmentation from Abdominal Images” Journal of Computer and Communications, 2, pp. 1-7

7.    X. Zhang, et al., “Automatic Liver Segmentation Using a Statistical Shape Model With Optimal Surface Detection,” IEEE Transaction on Biomedical Engineering, Vol. 57, No. 10, 2010, pp. 2611-2626

8.    M. Erdt, et al., “Fast Automatic Liver Segmentation Combining Learned Shape Priors with Observed Shape Deviation,” Computer-Based Medical Systems, 2010, pp.
249-254

9.    H. Badakhshannoory and P. Saeedi, “A Model-Based Validation Scheme for Organ Segmentation in CT Scan Volumes,” IEEE Transaction on Biomedical Engineering, 2009, pp. 2681-2693


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3.

Authors:

Sourav Sarkar, J. Shah, R. K. Kotnala, M. C. Bhatnagar

Paper Title:

Effect of Zn and Mn Substitution on Structural, Dielectric, Magnetic and Optical Properties of Multiferroic CoFe2O4-BaTiO3 Core-Shell Type Composites

Abstract: In this paper, we have reported the synthesis of Zn and Mn substituted cobalt ferrite by chemical co-precipitation method and used it as core material in barium titanate sol to finally prepare core-shell type composite material. Amount of ferrite was varied in the final composite samples from 30% to 50%. X-ray diffraction show prominent spinel and perovskite peaks corresponding to ferrite and titanate phases respectively. HRTEM micrographs reveal core-shell type nature with presence of a well-defined interface. Our proposed substitutions increase the resistivity of pure cobalt ferrite by one order which has been verified through I-V measurement. SEM micrographs show dense microstructure and particle formation of both phases in the composites. Substitution of Zn at the site of Co is supported by the peak shift in Attenuated Total Reflection Fourier Transform Infrared (ATR-FTIR) spectroscopy. Maxwell Wagner relaxation phenomena at the interface and hopping conduction in ferrites explain both frequency and temperature variation of dielectric parameters. Substitution of Zn and Mn result in super-paramagnetic type behavior with coercively  few Oe and very negligible remnant magnetization (MR). Photoluminescence (PL) spectra show slight decrease in energy band gap of ferrite as a result of these substitutions.

Keywords:
 Sol-gel process (A); Composites (B); Dielectric properties (C); Optical properties (C)


References:

1.       J. Ryu, S. Priya, K. Uchino, H.E. Kim, Magnetoelectric Effect in Composites of Magnetostrictive and Piezoelectric Materials,J. Electroceram, (2002) 107–119. doi:10.1023/A.
2.       G.V. Duong, R. Groessinger, R.S. Turtelli, Magnetoelectric properties of CoFe2O4 –BaTiO3 magnetoelectric composites, J. Magn. Magn. Mater. 316 (2007) 624–627. doi:10.1016/j.jmmm.2007.03.142.

3.       N.A. Spaldin, M. Fiebig, The Renaissance of Magnetoelectric Multiferroics, Science 309 (2005) 391–392. doi:10.1126/science.1113357.

4.       Corral-Flores, D. Bueno-Baques, R.F. Ziolo, Synthesis and characterization of novel CoFe2O4-BaTiO3 multiferroic core-shell-type nanostructures, Acta Mater. 58 (2010) 764–769. doi:10.1016/j.actamat.2009.09.054.

5.       G. V. Duong, R. Groessinger, M. Schoenhart, D. Bueno-Basques, The lock-in technique for studying magnetoelectric effect, J. Magn. Magn. Mater. 316 (2007) 390–393. doi:10.1016/j.jmmm.2007.03.185.

6.       Gupta, R. Chatterjee, Dielectric and magnetoelectric properties of BaTiO3-Co0.6Zn0.4Fe1.7Mn0.3O4 composite, J. Eur. Ceram. Soc. 33 (2013) 1017–1022. doi:10.1016/j.jeurceramsoc.2012.11.003.

7.       P. Zhu, Q. Zheng, R. Sun, W. Zhang, J. Gao, C. Wong, Dielectric and magnetic properties of BaTiO3/Ni0.5Zn0.5Fe2O4 composite ceramics synthesized by a co-precipitation process, J. Alloys Compd. 614 (2014) 289–296. doi:10.1016/j.jallcom.2014.06.065.

8.       G.S. Shahane, A. Kumar, M. Arora, R.P. Pant, K. Lal, Synthesis and characterization of Ni-Zn ferrite nanoparticles, J. Magn. Magn. Mater. 322 (2010) 1015–1019. doi:10.1016/j.jmmm.2009.12.006.

9.       K. Raidongia, A. Nag, A. Sundaresan, C.N.R. Rao, Multiferroic and magnetoelectric properties of core-shell CoFe2O4 @ BaTiO3 nanocomposites, Appl. Phys. Lett. 97 (2010) 2010–2012. doi:10.1063/1.3478231.

10.    R.K. Singh, A. Narayan, K. Prasad, R.S. Yadav, A.C. Pandey, A.K. Singh, L. Verma, R.K. Verma, Thermal, structural, magnetic and photoluminescence studies on cobalt ferrite nanoparticles obtained by citrate precursor method, J. Therm. Anal. Calorim. 110 (2012) 573–580. doi:10.1007/s10973-012-2728-1.

11.    S. Sarkar, M.C. Bhatnagar, Effect of Mn substitution on acetone and ammonia sensing in CoFe2O4nanoparticles, 2nd Int. Symp. Phys. Technol. Sensors, IEEE, 2015: pp. 253–256. doi:10.1109/ISPTS.2015.7220123.

12.    G. Vaidyanathan, S. Sendhilnathan, R. Arulmurugan, Structural and magnetic properties of Co1-xZnxFe2O4 nanoparticles by co-precipitation method, J. Magn. Magn. Mater. 313 (2007) 293–299. doi:10.1016/j.jmmm.2007.01.010.

13.    L. Zhao, H. Zhang, Y. Xing, S. Song, S. Yu, W. Shi, X. Guo, J. Yang, Y. Lei, F. Cao, Studies on the magnetism of cobalt ferrite nanocrystals synthesized by hydrothermal method, J. Solid State Chem. 181 (2008) 245–252. doi:10.1016/j.jssc.2007.10.034.

14.    S. Singhal, S. Bhukal, J. Singh, K. Chandra, S. Bansal, Optical, X-ray diffraction, and magnetic properties of the cobalt-substituted nickel chromium ferrites (CrCox Ni1-xFeO4, x = 0, 0.2, 0.4, 0.6, 0.8, 1.0) synthesized using sol-gel autocombustion method, J. Nanotechnol. 2011 (2011) 2–7. doi:10.1155/2011/930243.

15.    R.M. Mohamed, M.M. Rashad, F.A. Haraz, W. Sigmund, Structure and magnetic properties of nanocrystalline cobalt ferrite powders synthesized using organic acid precursor method, J. Magn. Magn. Mater. 322 (2010) 2058–2064. doi:10.1016/j.jmmm.2010.01.034.

16.    H. Deligöz, A. Baykal, M.S. Toprak, E.E. Tanriverdi, Z. Durmus, H. Sözeri, Synthesis, structural, magnetic and electrical properties of Co1-xZnxFe2O4 (x = 0.0, 0.2) nanoparticles, Mater. Res. Bull. 48 (2013) 646–654. doi:10.1016/j.materresbull.2012.11.032.

17.    S.J. Chang, W.S. Liao, C.J. Ciou, J.T. Lee, C.C. Li, An efficient approach to derive hydroxyl groups on the surface of barium titanate nanoparticles to improve its chemical modification ability, J. Colloid Interface Sci. 329 (2009) 300–305. doi:10.1016/j.jcis.2008.10.011.

18.    U.-Y. Hwang, H.-S. Park, K.-K. Koo, Behavior of Barium Acetate and Titanium Isopropoxide during the Formation of Crystalline Barium Titanate, Ind. Eng. Chem. Res. 43 (2004) 728–734. doi:10.1021/ie030276q.

19.    H. Reverón, C. Aymonier, A. Loppinet-Serani, C. Elissalde, M. Maglione, F. Cansell, Single-step synthesis of well-crystallized and pure barium titanate nanoparticles in supercritical fluids, Nanotechnology. 16 (2005) 1137–1143. doi:10.1088/0957-4484/16/8/026.

20.    Y. Wang, Y. Wang, W. Rao, M. Wang, G. Li, Y. Li, J. Gao, W. Zhou, J. Yu, Dielectric, ferromagnetic and ferroelectric properties of the (1 − x)Ba0.8Sr0.2TiO3–xCoFe2O4 multiferroic particulate ceramic composites, J. Mater. Sci. Mater. Electron. 23 (2012) 1064–1071. doi:10.1007/s10854-011-0548-x.

21.    I.H. Gul, A. Maqsood, M. Naeem, M.N. Ashiq, Optical, magnetic and electrical investigation of cobalt ferrite nanoparticles synthesized by co-precipitation route, J. Alloys Compd. 507 (2010) 201–206. doi:10.1016/j.jallcom.2010.07.155.

22.    Gupta, R. Chatterjee, Study of dielectric and magnetic properties of PbZr0.52Ti0.48O3-Mn0.3Co0.6Zn0.4Fe1.7O4 composite, J. Magn. Magn. Mater. 322 (2010) 1020–1025. doi:10.1016/j.jmmm.2009.12.007.

23.    V Shvartsman, F. Alawneh, P. Borisov, D. Kozodaev, D.C. Lupascu, Converse magnetoelectric effect in CoFe2O4 –BaTiO3 composites with a core–shell structure, Smart Mater. Struct. 20 (2011) 075006. doi:10.1088/0964-1726/20/7/075006.

24.    S. Singhal, T. Namgyal, S. Bansal, K. Chandra, Effect of Zn Substitution on the Magnetic Properties of Cobalt Ferrite Nano Particles Prepared Via Sol-Gel Route, J. Electromagn. Anal. Appl. 02 (2010) 376–381. doi:10.4236/jemaa.2010.26049.

25.    Y. Fu, H. Chen, X. Sun, X. Wang, Combination of cobalt ferrite and graphene: High-performance and recyclable visible-light photocatalysis, Appl. Catal. B Environ. 111-112 (2012) 280–287. doi:10.1016/j.apcatb.2011.10.009.

26.    N. V. Dang, T.L. Phan, T.D. Thanh, V.D. Lam, L. V. Hong, Structural phase separation and optical and magnetic properties of BaTi1-xMnxO3 multiferroics, J. Appl. Phys. 111 (2012). doi:10.1063/1.4725195.

27.    K. Vanheusden, W.L. Warren, C.H. Seager, D.R. Tallant, J.A. Voigt, B.E. Gnade, Mechanisms behind green photoluminescence in ZnO phosphor powders, J. Appl. Phys. 79 (1996) 7983. doi:10.1063/1.362349.

28.    Warren, W.L., Vanheusden, K., Dimos, D., Pike, G.E. and Tuttle, B.A., 1996. Oxygen vacancy motion in perovskite oxides. J. Am. Ceram. Soc. 79(2), pp.536-538.


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4.

Authors:

Naina Lohana, M. Mani Roja

Paper Title:

A Review of the Internet of Things

Abstract:  In this paper an effort is taken to review the concept of the Internet of Things (IoT). It has gained popularity in the recent years due to its wire-ranging applications. As the world moves towards a future with more and more devices linked to the Internet, this paper looks at the elements of IoT, its communication models, the challenges it faces and its applications.

Keywords:
IoT


References:

1.    M. Rouse Internet of  Things. Retreived from http://internetofthingsagenda.techtarget.com/definition/Internet-of-Things-IoT
2.    L. Atzori et al., The Internet of Things: A survey, Comput. Netw. (2010), doi:10.1016/j.comnet.2010.05.010

3.    K. L. Lueth, IoT Basics: Getting Started with the Internet of Things, IoT Analytics(2015). Retrieved from https://iot-analytics.com/product/whitepaper-iot-basics-getting-started-with-the-internet-of-things/

4.    K. Rose, S. Elridge, L. Chapin  The Internet of Things: An Overview, Internet Society (2015). Retrieved from http://www.internetsociety.org/doc/iot-overview

5.    D. Hamilton The Four Internet of Things Connectivity Models Explained. Retrieved from http://www.thewhir.com/web-hosting-news/the-four-internet-of-things-connectivity-models-explained

6.    T.T. Mulani, S.V. Pingle  Internet of Things, IRJMS Vol 2  Special Issue 1, March 2016.

7.    B. Katole, M. Sivapala, V. Suresh,Principle Elements and Framework of Internet of Things, Research Inventy: IJES Vol 3 Issue 5, July 2013.

8.    J. Gubbi, R. Buyya, S Marusic, M. Paluniswami, Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions,Future Generation Computer Systems Vol.29 Issue 7, September 2013.


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5.

Authors:

Ahmed H.Almutairi

Paper Title:

Laser Diode and Applications

Abstract: This paper shows how to use the P-N Junction to generate the Laser (Laser Diode) and how we use this laser Diode in many applications.

Keywords:
 Introduction, P-N Junction, Biased p-n Junction, Laser diodes, Turning semiconductor amplifiers into laser diodes, Applications of Laser Diodes, Conclusion and References.


References:

1.       J. Gowar, Optical Communication Systems, Prentice Hall, London, 1984.
2.       Kittel, Introduction to Solid State Physics, 6th Edition, Wiley, New York,1986.

3.       Reif, Fundamentals of Statistical and Thermal Physics, McGraw Hill, NewYork, 1965.

4.       Yariv, Introduction to Optical Electronics, 1st Edition, Holt Rinehart and Winston Inc., New York, 1971.

5.       M.I. Nathan, “Semiconductor Lasers,” Appl. Opt. 5, 1514–1528, 1966.

6.       H.C. Casey, Jr, and M.B. Panish, Heterostructure Lasers, Parts A and B, Academic Press, New York, 1978; see also M.B. Panish, “Heterostructure Injection Lasers,” Proc. IEEE, 64, 1512–1540, 1976.

7.       C.A. Burrus and B.I. Miller, “Small-area, double heterostructure, aluminum gallium arsenide electroluminescent diode source for optical-fiber transmission lines,” Opt. Commun. 4, 307–309, 1971.

8.       Chandra and L.F. Eastman, “Rectification at n−nGaAs : (Ga,Al)As heterojunctions,” Electron. Lett., 15, 90–91, 1979.

9.       J.F. Womac and R.H. Rediker, “The graded-gap Alxga1 − xAs-GaAs heterojunction,” J. Appl. Phys. 43, 4129–4133, 1972.

10.    M.G. Bernard and G. Duraffourg, “Laser conditions in semiconductors,” Phys.Stat. Solids, 1, 699–703, 1961.

11.    I.F. Wu, I. Riant, J-M. Verdiell, and M. Dagenais, “Real-time in situ monitoring of antireflection coatings for semiconductor laser amplifiers by ellipsometry,” IEEE Photonics Technology Letters, 4, 991–993, 1992.

12.    R.L. Hartmann and R.W. Dixon, “Reliability of DH GaAs lasers at elevated temperatures, Appl. Phys. Lett. 26, 239–240, 1975.

13.    G.H. Olsen, C.J. Nuese, and M. Ettenberg, Appl. Phys. Lett. 34, 262–264,1979.354 Semiconductor Lasers

14.    Yonezu, I. Sakuma, K. Kobayashi, T. Kamejima, M. Unno, and Y. Nannichi, “A GaAs-AlxGa1−xAs double heterostructure planar stripe laser,” Japan J. Appl. Phys., 12, 1585–1592, 1973.

15.    Kressel, M. Effenberg, J.P. Wittke, and I. Ladany, “Laser diodes and LEDs for optical fiber communication,” in Semiconductor Devices for Optical Communication, H. Kressel, Ed., Springer-Verlag, New York, 1982.

16.    D.R. Scifres, R.D. Burnham and W. Streifer, “High Power Coupled Multiple Stripe Quantum Well Injection Lasers,” Appl. Phys. Lett., 41, 118–120 (1982).

17.    I.P. Kaminow, L.W. Stulz, J.S. Ko, A.G. Dentai, R.E. Nahory, J.C. DeWinter, and R.L. Hartman, “Low threshold IngaAsP ridge waveguide lasers at 1.3 μm,” IEEE J. Quant. Electron. QE-19, 1312–1319, 1983.

18.    Botez, “CW high-pressure single-mode operation of constricted double heterojunction AlgaAs lasers with a large optical cavity,” Appl. Phys. Lett. 36, 190–192, 1980.

19.    P.K. Cheo, Fiber Optics and Optoelectronics, 2nd Edition, Prentice Hall, Englewood Cliffs, New Jersey 1990; A. Yariv, Optical Electronics, 3rd Edition, Holt, Rinehart and Winston, New York, 1985.

20.    K. Aiki, M. Nakamura, J. Umeda, A. Yariv, A. Katziv, and H.W. Yen, “GaAs-GaAlAs distributed feedback laser with separate optical and carrier confinement,” Appl. Phys. Lett., 27, 145–146, 1975.

21.    D.F.Welch, R. Parke, A. Hardy, R.Waaits, W. Striefer, and D.R. Scifres.“High-power, 4W pulsed, grating-coupled surface emitting laser,” Electron. Lett. 25, 1038–1039, 1989.

22.    J.S. Mott and S.H. Macomber, “Two-dimensional surface emitting distributed feedback laser array, IEEE Photon. Technal. Lett. 1, 202–204, 2989.

23.    J.L. Jewel, “Microlasers,” Sci. Am., Nov. 1991, 86–94.

24.    Yariv, Quantum Electronics, 3rd Edition, Wiley, New York, 1989.


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