iosr-JECE   Volume-2 ~ Issue-6

Abstract:In this paper Electromagnetic Band Gap structure with microstrip antenna has been proposed. Many small and compact periodic EBGs have been investigated to design the antenna. Simulation is done on the HFSS software tool. The antenna is designed on the 21x31mm FR4 substrate with the dielectric constant of 4.4. The designed antenna is designed to resonate at 8GHz frequency. The designed antenna is having the peak return loss of 49.87dB at 7.7GHz frequency with the bandwidth of 4.7GHz from 5.3GHz to 10GHz, thus has the wide range of applications in ultra wideband communication.
Keywords– EBG, microstrip antenna, patch antenna, UWB, wideband antenna.

[[1]. Y. Fu and N. Yuan, "Elimination of scan blindness in phased array of microstrip patches using electromagnetic bandgap materials," IEEE Antennas Wireless Propag. Lett., vol. 3, pp. 64–65, 2004.
[2]. J. S. Colburn and Y. Rahmat-Samii, "Patch antennas on externally perforated high dielectric constant substrates," IEEE Trans. Antennas Propag., vol. 47, no. 12, pp. 1785–1794, Dec. 1999.
[3]. Pirhadi, A., M. Hakkak, and F. Keshmiri, "Bandwidth enhancement of the probe fed microstrip antenna using frequency selective surface as electromagnetic bandgap superstrate," Progress In Electromagnetics Research, PIER 61, 215–230, 2006.
[4]. Pirhadi, A., F. Keshmiri, and M. Hakkak, "Design of dualband low profile high directive EBG resonator antenna, using single layer frequency selective surface superstrate," IEEE APS/USNC/URSI Int. Symp., Albuquerque, New Mexico, USA, July 9–14, 2006.
[5]. Lee, Y. J., J. Yeo, R. Mittra, and W. S. Park, "Application of electromagnetic bandgap (EBG) superstrates with controllable defects for a class of patch antennas as spatial angular filters," IEEE Trans. Antennas and Propag., Vol. AP-53, No. 1, 224–234, Jan. 2005.
[6]. G. Cakir and L. Sevgi, "Design of a novel microstrip electromagnetic band-gap (EBG) structure," Microw. Opt.Technol Lett., vol. 46, pp. 399–401, 2005.
[7]. F. Yang and Y. Rahmat-Samii, "Microstrip antennas integrated with electromagnetic band-gap (EBG) structures: A low mutual coupling design for array applications," IEEE Trans. Antennas Propag., vol. 51, no. 10, pp. 2936–2946, Oct. 2003.
[8]. Ansoft Corporation, HFSS 14.

Abstract: This work proposes MARA, a joint method for automatic rate selection and route quality evaluation in Wireless Mesh Networks. This method targets at avoiding the problem of inaccurate link quality estimates, common to main existing proposals of multi-hop wireless routing metrics. In this proposal, the statistics collected by the routing protocol are used by the rate adaptation algorithm to compute the best rate for each wireless link. This coordinated decision aims at providing better routing and rate choices. Simulation results indicate that MARA leads to an overall improvement in network performance.

Key Words:Wireless Mesh Networks, Routing Metrics, Rate Adaptation Algorithms.

[1] M. E. M. Campista, D. G. Passos, P. M. Esposito, I. M. Moraes,C. V. N. de Albuquerque, D. Muchaluat-Saade, M. G. Rubinstein, L. H.M. K. Costa, and O. C. M. B. Duarte, "Routing metrics and protocolsfor wireless mesh networks," IEEE Network, vol. 22, no. 1, pp. 6–12,Jan./Feb. 2008.
[2] K. Ramachandran, H. Kremo, M. Gruteser, P. Spasojevic, and I. Seskar,"Scalability analysis of rate adaptation techniques in congested IEEE802.11 networks: An orbit testbed comparative study," in WoWMoM2007, Jun. 2007, pp. 1–12.
[3] J. P. Pavon and S. Choi, "Link adaptation strategy for IEEE 802.11WLAN via received signal strength measurement," in ICC '03, vol. 2, 2003, pp. 1108–1113.
[4] C. E. Koksal and H. Balakrishnan, "Quality-aware routing metrics fortime-varying wireless mesh networks," IEEE Journal on Selected Areas in Communications, vol. 24, no. 11, pp. 1984–1994, Nov. 2006.
[5] R. Draves, J. Padhye, and B. Zill, "Routing in multi-radio, multi-hop wireless mesh networks," in ACM International Conference on MobileComputing and Networking (MobiCom), Sept. 2004, pp. 114–128.
[6] Special Interest research Group on NETworking (SIGNET). (2008, Jun.) dei80211mr: a new 802.11 implementation for ns 2. [Online]. Available: http://www.dei.unipd.it/wdyn/?IDsezione=5090

Abstract:Biometric identification systems, which use physical features to check a person's identity, ensures much greater security than password and number systems. Multibiometric system is being increasingly deployed in much large scale application because they provide lower error rate, large population coverage compared to unibiometric. In this paper, multibiometric identification system aim to fuse iris n fingerprint traits. During enrollment stage system generate iris n fingerprint template separately n stored in database. Approach for fingerprint recognition is to extract minutiae from fingerprint images. It makes possible to achieve highly robust fingerprint recognition for low-quality fingerprints. During iris recognition images are segmented, normalized and features are extract by using Log-Gabor filter. Finally matching is done with help of hamming-distance. Once both iris n fingerprint template are match separately scores are combined by using sum rule-based score level fusion which increase the recognition rate. Thus improve system accuracy and dependability.
Keywords- biometric, minutiae extraction, Log-Gabor filter, sum-rule based score level fusion, identification system

[1] K. Nandakumar and A. K. Jain, ―Multibiometric based on feature-level fusion,‖ in Proc. IEEE 2nd Int. Conf. on information, and Security,vol.7,no.1, Feb.2012.
[2] Norman Poh, Josef Kittler ―A Unified Framework for Biometric Expert Fusion Incorporating Quality Measures,‖ IEEE Transactions on pattern analysis and machine intelligence, VOL. 34, NO. 1, January 2012
[3] A.Gunatilaka and Brian A., ―Feature level and decision level fusion of noncoincidently sampled sensor for land mine detection,‖ in
Proc. IEEE 2nd Int. Conf. on information, and Security,vol.7,no.1, Feb.2012.
[4] A. Nagar, S. Rane, and A. Vetro, ―Privacy and security of features extracted from minutiae aggregates,‖ in Proc. IEEE Int. Conf.
Acoustics, Speech and Signal Processing, Dallas, TX, Mar. 2010, pp. 524–531.
[5] T. Ignatenko and F. M. J. Willems, ―Biometric systems: Privacy and secrecy aspects,‖ IEEE Trans. Inf. Forensics Security, vol 4,
no. 4, pp. 956–973, Dec. 2009.
[6] R. Snelick, U. Uludag, A. Mink, M. Indovina, A. Jain, Large-scale evaluation of multimodal biometric authentication using state-ofthe- art systems, IEEE Trans- actions on Pattern Analysis and Machine Intelligence 27 (3) (2005) 450–455.
[7] K. Veeramachaneni, L. A. Osadciw, and P. K. Varshney, ―An adaptive multimodal biometric management algorithm,‖ IEEE Trans.
Syst., ManCybern. C, Appl. Rev., vol. 35, no. 3, pp. 344–356, Aug. 2005.
[8] M. Vatsa, R. Singh, and A. Noore, ―Improving iris recognition performance using segmentation, quality enhancement, match score
fusion, and indexing,‖ IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 38, no. 4, pp. 1021–1035, Aug. 2008.
[9] K. Miyazawa, K. Ito, T. Aoki, K. Kobayashi, and H. Nakajima, ―An effective approach for iris recognition using phase-based image matching,‖ IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 10, pp. 1741–1756,Oct. 2008.
[10] W. W. Boles and B. Boashash, ―A human identification technique using images of the iris and wavelet transform,‖ IEEE Trans.
Signal Process.,vol. 46, no. 4, pp. 1185–1188, Apr. 1998.

Abstract:Image enhancement is used to produce high quality pictures like Medical images. As the quality of the image is very much depends on environmental effects like light, weather or equipment that we used to capture the picture, images may loose important information which is required to enhance the quality of an image. So we have many techniques to recover the lost data to improve the quality of the picture . In this paper, we are using non linear enhancement technique to increase the contrast level of an image like wavelet transform and Histogram Equalization. In this experiment we found that this method enhance the contrast level of an image. Keywords: Image enhancement, Histogram equalization , Discrete wavelet transform (DWT),PSNR,MSE

1] Mr. Salem Saleh Al-amri1,Dr.N.V.Kalyankar2,Dr.S.D.Khamitkar3 , Linear and Non linear Contrast Enhancement Image , IJCSNS
International Journal of Computer Science and Network Security, VOL.10 No.2, February 2010.
[2] Sos S. Agaian, Blair Silver and L. Karen A. Panetta, ,Transform coefficient histogram-based image enhancement algorithms using
contrast entropy, IEEE Trans. On Image Process, vol. 16,no. 3, Mar. 2007.
[3] Lu, J., Healy, D.M., and Weaver, J.B.: ,Contrast enhancement of medical images using multi -scale edge representation, Opt. Eng., 1994, 33, (7), pp. 2151–2161
[4] Yang, G., and Hansell, D.M.: ,CT image enhancement with wavelet analysis for the detection of small airways disease, IEEE Trans
Med. Imaging, 1997, 16, (6), pp. 953–961
[5] Hasan Demirel and Gholamreza Anbarjafar, Discrete Wavelet Transform-Based Satellite Image Resolution Enhancement, IEEE
Transactions On Geoscience And Remote Sensing, Vol. 49, No. 6, June 2011
[6] R. C. Gonzalez and R. E. Woods, Digital Image Processing (2nd Edi- tion). Prentice Hall, January 2002.
[7] Mallat, S.G.: , Multifrequency channel decompositions of image and wavelet models , IEEE Trans. Acoust. Speech Signal Process.,
1989, 37, (12), pp. 2091–2110
[8] Akhil Pratap Singh, Agya Mishra,, Wavelet Based Watermarking On Digital Image, Akhil Pratap Singh Et. Al../ Indian Journal Of
Computer Science And Engineering Vol 1 No 2, 86-91.

Abstract: Fingerprint recognition is one of the most popular methods used for identification with greater degree of success. The fingerprint has unique characteristics called minutiae, which are points where a curve track finishes, intersect or branches off. In this work a novel method for Fingerprint recognition is considered using a combination of Fast Fourier Transform (FFT) and Gabor Filters to enhancement the image. The proposed method involves combination of Gabor filter and Frequency domain filtering for enhancing the fingerprint. With eight different orientations of Gabor filter, features of the fingerprint are extracting and are combined. In Frequency domain filtering, the fingerprint image is subdivided into 32*32 small frames. Features are extracted from these frames in frequency domain. Final enhanced fingerprint is obtained the results of Gabor filter and frequency domain filtering. Binarization and Thinning follows next where the enhanced fingerprint is converted to binary and the ridges are thinned to one pixel width. This helps in extracting the Minutiae parts (ridge bifurcation and ridge endings). Euclidian distance method used for performance of recognition. The overall recognition rate for the proposed method is 95% obtained as which is much better compared to histogram method where the recognition rate is 64%. This project is implemented using MATLAB.
Keywords - Gabor filter, fingerprint, FFT, Minutia, AFIS.

[1] Leung, M. Engeler, and Frank, "Fingerprint image processing using neural network". Proceedings of the IEEE conf. on Computer and
CommunicationSystems, pp. 671-774, 1990.
[2] Louisa Lam,Seong Whan Lee "Thinning Methodologies A ComprehensiveSurvey", IEEE Transactions on Pattern Analysis and Machine Intelligence", Vol.14, no. 9, pp. 869-885, 1992.
[3] Dario Maio and Davide Maltoni "Direct Gray-Scale Minutiae Detection in Fingerprints", IEEE Transactions on pattern Analysis and Machine Intelligence, Vol. 19, no. 1, pp. 27-40, 1997.
[4] Ton van der Putte and Jeroen Keuning "Biometrical Fingerprint Recognition", Fourth Working Conference on Smart Card Research
and Advanced Applications, pp 289-303, 2000.
[5] Zsolt Miklo, Kova CS Vajna "A Fingerprint Verification System Based onTriangular Matching and Dynamic Time Warping", IEEE Transactions on PatternAnalysis and machine Intelligence, Vol. 22, NO. 11, pp. 1266-1276, 2000.
[6] A.K.Jain, R.Bolle, and S.Pankanti, "Biometrics Personal Identification inNetworked Society", Kluwer Academic Publishers New
York, Boston, Dordrecht,London, Moscow, pp. 1-64, 2002.
[7] Bolle, R.,Serior, A.Ratha, and Pankanti, "Fingerprint minutiae a constructivedefinition". Proceedings of the workshop on Biometric
Authentication, PP, 58–66(2002)
[8] Milene Arantes, Alessandro Noriaki Ide "A System ForFingerprint MinuteClassification and Recognition" Proceedings of the IEEE
International Conference on Neural Information Processing, Vol. 5, pp. 2474-2478, 2002.

Abstract:Texture is defined as the regular repetition of an element or pattern on a surface. Texture is an important property of surfaces which characterizes the nature of the surface. An important task in image processing and machine vision is the task of segmenting the texture in an image. Texture segmentation is the process of partitioning an image into regions based on their texture. In this project we would like to propose new approach using multichannel Gabor filters for the segmentation o f colour and grayscale multi textured images. The proposed method includes the design of the constituent Gabor filters. Based on these new Gabor filters, we develop new algorithms for the texture segmentation.
Keywords - clustering, Gabor filter, Multichannel, segmentation, Texture

[1] Todd R. Reed and J. M. H. Du Buf, "A review of recent texture segmentation and feature extraction techniques", Computer Vision, Graphics, and Image Processing, Vol. 57, no.3, pp. 359-372, 1993.
[2] A. K. Jain, Farshid Farrokhnia, "Unsupervised texture segmentation using Gabor filters", Pattern Recognition", Vol. 4, no. 12, pp.
1167- 1186, 1991.
[3] A. C. Bovik, M. Clark, W. S. Geisler, "Multichannel texture analysis using localized spatial filters", IEEE trans. On pattern analysis and machine intelligence, Vol. 21, no. 1, pp. 55-73, 1990.
[4] J. Malik, P. Perona, "Preattentive texture discrimination with early vision mechanisms", J. Opt. Soc. Am. A, Vol. 7, pp 923-932, 1990.
[5] M. R. Turner, "Texture discrimination by Gabor functions", "Biological Cybernetics", Vol. 55, pp. 71-82, 1986.
[6] D. Clausi, M. Ed Jernigan, "Designing Gabor filters for optimal texture separability", PatternRecognition, vol. 33, pp. 1835-1849, 2000.
[7] K. R. Castleman, "Digital Image Processing," Prentice Hall, 1996.
[8] M. Clark, A. Bovik, W. Geisler, "Texture segmentation using a class of narrowband filters", IEEE CH2396-0, 1987.
[9] A. K. Jain, F. Farrokhnia, "Unsupervised texture segmentation using Gabor filters", Pattern Recognition, vol. 24, no. 12, pp. 1167-1186, 1991.
[10] P. Brodatz, "Textures: A Photographic Album for Artists and Designers", Dover, New York, 1966.

Abstract : This paper introduces the system design and implementation of a true-color light emitting diode (LED) display system. The improvement of LED (Light Emitting Diode) technology, the LED display system for video has attracted public attention. True color is a method of representing and storing graphical image information in an RGB color space such that a very large number of colors, shades, and hues can be displayed in an image, such as in high quality photographic images or complex graphics. We used the STM32F207MicroController for controlling the LED display system as it has advantages of processing fast video frames [2]. The Digital video is extracted and is converted from video signal to the LED display signal by circuit implementation. The driving circuits receives LED display signals after conversion, and assigns them to LED pixel, and drive the LED screen to display in real time.
Keywords- LED video display controller, Gamma correction, LED driver

Books:
[1] NI Vision IMAQ Vision Concepts Manual, January 2005, 372916D-01
Website:
[2]STM32F207Datasheethttp://www.st.com/internet/com/TECHNICAL_RESOURCES/TECHNICAL_ LITERATURE/DATASHEET
[3] MBI5030 Datasheet: http://www.kingelectronics.com/images/products/MBI5030PreliminaryDatasheetV2.00-English
[4] Gamma Correction: http://www.cgsd.com/papers/gamma_intro.html Thesis:
[5] Mo X, Zhang Y. Consecutive PWM driving video LED display system. Proceedings of International Symposium on Circuits and
Systems,IEEE; 1997 Jun 09; Hong Kong; 1997.p. 1437-1439
[6] Abramov V, Puisha A, et.al. LED modules for large screens. Journal of Optical Technology 2003; 70(7): 492-494.
[7] ZHANG Dan-tong, The design of the large-scr en led lattice display system based on FPGA, Microcomputer
Information,2009,(17)250- 251 306
[8] XIONG Yukai, and WU Guangmin, Design and Implementation of the Embedded Grand LED Screen, Modern Electronics
Technique,2006,(22) 43-45.

Abstract: India being an agro-based economy, farmers experince alot of problem in detecting andpreventing diseases in fauna. So there is a necessacity in detecting diseases in fauna which proves to be effective and conviennent for researchers. Relying on pure naked-eye observation to detect and classify diseases can be very unprecise and cumbersome. The color and texture features are used to recognize and classify different agriculture/horticulture produce into normal and affected regions. The combinations of features prove to be very effective in disease detection. The experimental results indicate that proposed approach significantly enhances accuracy in automatic detection of normal and affected produce. This paper presents an effective method for detection of diseases in Malus Domestica using methods like K-meanclustering, color and texture analysis.
Keywords-CCM method, color & texture segmentation, K-mean clustering, Malus Domestica, nuclei segmentation

[1] Hillnhuetter, C. and A.-K. Mahlein, Early detection and localization of sugar beet diseases: new approaches, Gesunde Pfianzen
60 (4) (2008), pp. 143–149.
[2] Rumpf, T., A.-K. Mahlein, U. Steiner, E.-C. Oerke, H.-W. Dehne, L. Plumer, Early detection and classification of plant diseases
with Support Vector Machines based on hyper spectral reflectance, Computers and Electronics in Agriculture, Volume 74, Issue
1, October 2010, Pages 91-99, ISSN 0168- 1699, DOI: 10.1016/j.compag.2010.06.009.
[3] Mohammad Ei –Helly, Ahmed Rafea, Salwa Ei – Gamal And Reda Abd Ei Whab[2004] Integrating Diagnostic Expert System
With Image Processing Via Loosely Coupled Technique, Central Laboratory for Agricultural Expert System(CLAES).
[4] Panagiotis Tzionas, Stelios E. Papadakis and Dimitris Manolakis[2005] Plant leaves classification based on morphological
features and fuzzy surfaceselection technique, 5th International Conference ON Technology and Automation ICTA'05, Thessaloniki, Greece, pp.365-370,15-16 .
[5] Basvaraj.S.Anami1,J.D.Pujari2,Rajesh.Yakkundimath[2011] Identification and Classification of Normal and Affected
Agriculture/horticulture ProduceBased on Combined Color and Texture FeatureExtraction
[6] M.S. Prasad Babu and B. Srinivasa Rao[2007]Leaves Recognition Using Back Propagation Neural Network-Advice For Pest and
Disease Control On Crops, IndiaKisan.Net: Expert Advissory System.
[7] Santanu Phadikar & Jaya Sil[2008] Rice Disease dentification Using Pattern Recognition Techniques, Proceedings Of 11th
International Conference On Computer And Information Technology,25-27.
[8] Helmi Zulhaidi Mohd Shafri and Nasrulhapiza Hamdan[2009] Hyperspectral Imagery for Mapping Disease Infection in Oil Palm
Plantation Using Vegetation Indices and Red Edge Techniques, American Journal of Applied Sciences 6(6):1031- 1035.

Abstract:In rapidly developing market in personal communication systems (PCS), mobile satellite communications, direct broadband television (DBS) wireless local area networks (WLANs) suggest that demand for Microstrip antennas and array will increase even further. In this paper the gain of microstrip patch antenna is enhanced by using covered dielectric layer which is separated from feed patch by air as an another dielectric. Here two patches, one feed patch and one parasitic patch are used to enhance the gain and bandwidth and whole structure resonates at their resultant frequency which is in the ISM band. Air is used as dielectric between feed patch and ground plane as well as between feed patch and parasitic patch. The dimensions of feed patch are adjusted to achieve desired resonant frequency. In this microstrip antenna the coaxial probe feed technique is used for its simplicity. This antenna structure is simulated using zeland IE3D software package and effects of physical parameters are investigated. This work leads to conclusion that directivity, bandwidth and gain of microstrip antenna can be increased by covered dielectric and multidielectric structure with parasitic patch. The gain is found as 13.4 dbi and bandwidth as 220 MHz which is higher as compare to conventional rectangular patch antenna. Keywords: Gain, Directivity, Rectangular Microstip Antenna, Ie3d, VSWR.

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Abstract:This paper proposes a discrimination algorithm, which discriminates speech and data on a multiplexed input signal. Commercial communication networks may use single voice band channel for transmission of both speech and data. Also, for optimum utilization of channel, the pauses in voice signal are being utilized. At receiver side the speech and data should be separately extracted, in order to send information to the respective users. For above mentioned to happen with least error, sufficient measures are to be taken for identifying the type of the signal. The speech/data discriminator is the solution for above mentioned problem. This algorithm may also be useful in the analysis of intercepted signal, where speech/data discrimination may be performed to make sure that whether the communication channel carries data or voice. After discrimination, voice will be sent to voice codec and data to the data decoder for extraction of intelligence. In this paper we proposed a simple and low complexity algorithm for speech/data discrimination based on two parameters namely short time energy and zero crossing rates. This algorithm can serve the above two purposes and gives good results in discriminating between speech and data.
Keywords - Discrimination, intercepted signal, intelligence extraction.

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