i-manager Publications

i-manager's Journal on Electronics Engineering (JELE)

Current Issue Vol. 14 Issue 2

An analytical approach to Network with IoT security using NodeMCU access point- Prevention Technique
AUDIO CRYPTOGRAPHY USING AES ALGORITHM TECHNIQUE
ULTRA WIDEBAND TECHNOLOGY
Smart Rover-Voice Control Bluetooth Control Robot to Avoid Obstacles
A Review of Quality Assurance Texture for Highly Pigmented Iris Recognition Using an Optimal Wavelength Band
FEATURE EXTRACTION AND CLASSIFICATION OF DIFFERENT HAND MOVEMENTS FROM THE EMG SIGNAL USING LINEAR DISCRIMINANT ANALYSIS CLASSIFIER

Most Cited

Volume 3 Issue 2 December - February 2013

Research Paper

RFID Reader Range Extension Using Magnetodielectric Material – An Extended Version

Wen-Cheng Lai* , An-Cheng Sun, Nan-Wei Chen, Ching-Wen Hsue, Luis Galarza

Department of Electronics Engineering, National Taiwan University of Science and Technology, and Department of Engineering, Ming Chi University of Technology, Taipei, Taiwan.

Department of Chemical Engineering & Materials Science, Yuan Ze University, Jhongli, Taiwan.

Department of Communications Engineering, Yuan Ze University, Jhongli, Taiwan.

Department of Electronics Engineering, National Taiwan University of Science and Technology.

* Department of Electrical and Computer Engineering, Florida International University.

Lai, W., Sun, A., Chen, N., Hsue, C., and Galarza, L. (2013). RFID Reader Range Extension Using Magnetodielectric Material – An Extended Version. i-manager’s Journal on Electronics Engineering, 3(2), 1-6. https://doi.org/10.26634/jele.3.2.2116

Abstract

Radio-frequency identification (RFID) allows for data transfer between tag objects to a reader via radio frequency electromagnetic fields. RFID tags are utilized by many industries for identification and tracking purposes. The RFID reader does not require line of sight to scan for data unlike bar codes and can track tags which can be embedded in objects many meters away. To this end, this paper presents a straightforward and practical approach for extending the range of RFID readers. It is shown that the range of a reader can easily be extended with a magnetodielectric slab attached to the antenna. The proper placement of the slab enhances the reader’s range by increasing the antenna’s gain. The effectiveness of the proposed implementation for the extension of the reader’s range with the magnetodielectric material has been experimentally tested. The experimental results verify the viability of the approach and show an increase of about 70% for the reader’s range.

References

[1]. Evdokimov, S., and G¨unther, O. (2007). Practical Access Control Management for Outsourced EPC-related Data in RFID-enabled Supply Chain, IEEE International Conference on e-Business Engineering, 331 – 336, Hong Kong.

[2]. Rajparthiban, R., Aravind, C.V., Kannan. (2009). “Development of an Active RFID Communicator for Automatic Control Applications,” 2009. 5th, International Colloquium on Signal Processing & Its Applications (CSPA), 276 – 277, Kuala Lumpur.

[3]. Xin, C. (2009). RFID Applications in Retail Industry, 2009, ISECS International Colloquium on Computing, Communication, Control, and Management, 395 – 398, Sanya.

[4]. Yan, B., and Lee, D. (2009). Application of RFID in Emu Breeding Management Information System, 2009 ISECS International Colloquium on Computing, Communication, Control, and Management, 170 – 173, Sanya.

[5]. Du, B., Wang, D., and Ju, S. (2009). Access Control for OSGi-Based Reconfigurable RFID Middleware, 2009 Fourth International Conference on Computer Sciences and Convergence Information Technology, 1010 – 1014, Seoul.

[6]. Lee, D., Kim, S., Kim, H., and Park, N. (2010). Mobile Platform for Networked RFID Applications, 2010 Seventh International Conference on Information Technology, 625 – 630, Las Vegas, NV.

[7]. http://www.ieice.org/eng/shiori/index.html

[8]. Bouet, M., and Pujolle, G. (2009). L-VIRT: a 3-D Range-Free Localization Method for RFID Tags Based on Virtual Landmarks and Mobile Readers, Consumer Communications and Networking Conference (CCNC 2009. 6th IEEE), 1 – 5, Las Vegas, NV.

[9]. Abbak, M., and Tekin, I. (2008). Microstrip Patch Antenna Array for Range Extension of RFID Applications, Antennas and Propagation Society International Symposium (AP-S 2008. IEEE), 1-4, Fac. of Eng. & Natural Sci., Sabanci Univ., Istanbul.

[10]. Hsue, C-W., and Lai, W-C. (2012). RFID Reader Range Extension using Magnetodielectric Material, 2012 3rd International Innovation and Invention Conference (IIIC), 95-96, Taipei, Taiwan.

[11]. Lai, W-C., Sun, A-C., Chen, N-W., and Hsue, C-W. (2011). Gain enhancement of planar monopole with magnetodielectric material, Progress in Electromagnetics Research, Vol. 21, 179-190.

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Research Paper

An Approach for the Segmentation of Satellite Images Using Moving KFCM and Naive Bayes Classifier

S. Praveena* , S.P. Singh, I.V. Muralikrishna*

- Department of Electronics and Communications Engineering, M.G.I.T, Hyderabad, A.P, India.

Rtd. Professor, JNTUH, Hyderabad, A.P, India.

Praveena, S., Singh, S.P., and Muralikrishna, I.V. (2013). An Approach for the Segmentation of Satellite Images Using Moving KFCM and Naive Bayes Classifier. i-manager’s Journal on Electronics Engineering, 3(2), 7-15. https://doi.org/10.26634/jele.3.2.2117

Abstract

This paper presents an improvised Moving kernel based fuzzy C-means(MKFCM) for land-cover mapping of trees, shade, building and road. It starts with the single step preprocessing procedure in which first the input image is passed through a median filter to reduce the noise and get a better image fit for segmentation. The pre-processed image is segmented using the Moving KFCM algorithm and classified using Bayesian classifier with kernel Distribution type. KFCM with moving property is used to improve the object segmentation in satellite images. Simulation results show that classification accuracy for different regions using Moving KFCM is better than moving k means using Naive Bayes classifier with four different kernels.

References

[1]. Kevin Tansey, Ian Chambers, Andrew Anstee, Anthony Denniss and Alistair Lamb. (2009). "Object-oriented classification of very high resolution airborne imagery for the extraction of hedgerows and field margin cover in agricultural areas", Applied Geography, Vol. 29, No. 2, pp. 145-157.

[2]. James A., Shine and Daniel B., Carr. (2002). "A Comparison of Classification Methods for Large Imagery Data Sets", JSM 2002 Statistics in an ERA of Technological Change-Statistical computing section, New York City, pp. 3205-3207.

[3]. Govender, M., Chetty, K., Naiken, V., and Bulcock, H. (2008). "A comparison of satellite hyperspectral and multispectral remote sensing imagery for improved classification and mapping of vegetation", Water SA, Vol. 34, No. 2, pp. 147-154.

[4]. Argialas, D., and C. Harlow. (1990). “Computational Image Interpretation Models: An Overview and a Perspective” Photogrammetric Engineering and Remote Sensing, Vol. 56, No 6, June, pp. 871-886.

[5]. Balazs Dezso, Istvan Fekete, David Gera, Roberto Giachetta and Istvan Laszlo, (2012). "Object-Based Image Analysis In Remote Sensing Applications Using Various Segmentation Techniques", Annales Univ. Sci. Budapest., Sect. Comp. 37 103–120.

[6]. J. Osman, J. Inglada and E. Christophe. "Interactive Object Segmentation in High Resolution Satellite”.

[7]. De Moraes R.M. (2004). “An Analysis of The Fuzzy Expert Systems Architecture For Multispectral Image Classification Using Mathematical Morphology Operators”, International Journal of Computational Cognition, Vol. 2, No. 2, pp. 35–69, June.

[8]. Smits P.C., & A. Annoni. (1999). “Towards operational knowledge based remote-sensing image analysis”, Pattern Recognition Letters, Vol. 20, pp. 1415 1422.

[9]. Angelos Tzotsos and D. Argialas. (2008). “A Support Vector Machine Approach For Object Based Image Analysis”, Lecture Notes in Geoinformation and Cartography, pp. 663-677.

[10]. Ashwini T. Sapkal, Chandraprakash Bokhare and N. Z. Tarapore. (2006). “Satellite Image Classification using the Back Propagation Algorithm of Artificial Neural Network”.

[11]. Sonka M., Hlavac V. and Boyle R. (1999). “Image processing, Analysis and Machine Vision”, Second Edition, Brooks/Cole Publishing Company.

[12]. Roger Trias-Sanza, Georges Stamon and Jean Louchet. (2008). "Using colour, texture, and hierarchial segmentation for high-resolution remote sensing ", ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 63, No. 2, pp. 156-168.

[13]. Trias-Sanz, R. (2005). "A metric for evaluating and comparing hierarchical and multi-scale image segmentations", in Proceedings of IEEE International Geoscience and Remote Sensing Symposium, (IGARSS 05), Vol. 8, pp. 5647-5650.

[14]. Burnett C., and Thomas Blaschke. (2003). "A multi-scale segmentation/object relationship modelling methodology for landscape analysis", Ecological Modelling (Landscape Theory and Landscape Modelling), Vol. 168, No. 3, pp. 233-249.

[15]. PCI Enterprises Inc., (1997). "SAR Radiometric Correction", Radar Soft Course notes. pp. 80-91.

[16]. Young, S. S., and Wang, C. Y. (2001). "Land-cover change analysis of China using global-scale Pathfinder AVHRR Landcover (PAL) data", International Journal of Remote Sensing, Vol. 22, No. 8, pp. 1457-1477.

[17]. Lillesand, T. M., and Kiefer, R. W. (1994). “Remote Sensing and Image Linter Predation”, John Wiley and Sons Press.

[18]. Tso B., and Mather P. M. (2001). "Classification Methods for Remotely Sensed Data", Taylor and Francis, London.

[19]. Chang C. I. (2001). "Hyperspectral Imaging: Techniques for Spectral Detection and classification", Kluwer Academic Publishers.

[20]. Varshney, P. K., and Arora, M. K. (2004). "Advanced Image Processing Techniques for Remote Sensed Hyperspectral Data", Springer-Verlag, Germany.

[21]. Lu, D., and Weng, Q. (2007). "A survey of image classification methods and techniques for improving classification performance", International Journal of Remote Sensing, Vol. 28, No. 5, pp. 823-870.

[22]. Jasinski, M. F., (1996). "Estimation of subpixel vegetation density of natural regions using satellite multispectral imagery", IEEE Transactions on Geoscience and Remote Sensing, Vol. 34, pp. 804-813.

[23]. Landgrebe, D. (2003). “Signal Theory Methods in Multispectral Remote Sensing” John Wiley and Sons, Hoboken, NJ.

[24]. Haralick R. M., Shanmugam K., and Dinstein, I., (1973). "Texture features for image classification", IEEE Transactions on Systems, Man and Cybernetics, Vol. 8, No. 6, pp. 610-621.

[25]. Tuominen, S., Pekkarinen, A. (2005). "Performance of different spectral and textural aerial photograph features in multi-source forest inventory", Remote Sensing of Environment, Vol. 94, No. 2, pp. 256-268.

[26]. Gidudu Anthony, Hulley Gregg, and Marwala Tshilidzi, (2008). “An Svm Multiclassifier Approach to Land Cover Mapping ”, In Proceedings of ASPRS Annual Conference.

[27]. D Chutia and DK Bhattacharyya. (2010). “Extraction Of Linear Features From High Resolution Indian Satellite Imageries, (IJCSE) International Journal on Computer Science and Engineering, Vol. 02, No. 04, pp.1223-1227.

[28]. Alberto Fernándeza, María José del Jesusb, and Francisco Herreraa. (2009). 'Hierarchical fuzzyrule based classification systems with genetic rule selection for imbalanced data-sets", International Journal of Approximate Reasoning, Vol. 50, No.3, pp.561–577.

[29]. Yousefi, Mirhassani S.M., Hosseini M.M., and Rastegar Fatemi M.J. (2011). “A novel fuzzy based method for 3D buildings modeling in urban satellite imagery", In Proceedings of IEEE Conference on Open Systems (ICOS), pp.190-195.

[30]. So-Ra Kim, Woo-Kyun Lee, Doo-Ahn Kwak, Greg S. Biging, Peng Gong, Jun-Hak Lee and Hyun-Kook Cho. (2011). "Forest Cover Classification by Optimal Segmentation of High Resolution Satellite Imagery", Sensors, Vo1-11, No. 2, pp. 1943-1958.

[31]. Zhao Meng and Bai Xiao. (2011). "High-resolution satellite image classification and segmentation using Laplacian graph energy", In Proceeding of IEEE International Geoscience and Remote Sensing Symposium, pp. 605-608.

[32]. R. Gaetano, J. Zerubia, G. Scarpaand G. Poggi, (2011). "Morphological road segmentation in urban areas from high resolution satellite images", In Proceeding of 17th International Conference on Digital Signal Processing (DSP), pp. 1-8.

[33]. Liang Hong and Kun Yang. (2011). "A novel segmentation method of high resolution remote sensing image based on multi-feature object-oriented Markov random fields model", In Proceeding of International Conference on Remote Sensing, Environment and Transportation Engineering, pp. 8019-8024.

[34]. George H. John and Pat Langley. (1995). “Estimating Continuous Distributions in Bayesian Classifiers,” Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence. pp. 338-345. Morgan Kaufmann, San Mateo.

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Research Paper

Robust Sliding Mode Control for Time Delay Systems: An Application to Load Frequency Control

K. Naresh* , Y. Srinivasa Rao, M. Kiran Kumar*

-- Department of Electrical and Electronics Engineering, K.L. University, Vaddeswaram, India.

Naresh, K., Rao, Y.S., and Kumar, M.K. (2013). Robust Sliding Mode Control for Time Delay Systems: an Application to Load Frequency Control. i-manager’s Journal on Electronics Engineering, 3(2), 16-26. https://doi.org/10.26634/jele.3.2.2118

Abstract

This thesis presents robust sliding mode control for a class of linear continuous time delay systems with mismatched parametric uncertainties. The problem of delay in-dependent as well as delay dependent stability analysis has been studied and subsequently robust control with sliding mode control are also investigated for uncertain time delay systems. Delay independent as well as delay dependent sufficient conditions for the existence of linear sliding surface which are in terms of linear matrix inequality (LMI) frame work are studied, based on which the design of reaching motion controller is also studied. The objective of the present work is to implement the delay dependent stability analysis of LMI approach with sliding mode control to uncertain power system model with communication delay for load frequency control. The reaching motion controller is switched to two other control strategies namely, (i) equivalent Control Law and (ii) State Feedback Law.

References

[1]. Yuanqing Xia, & Yingmin Jia. (2003). “Robust sliding mode control for Uncertain Time-delay systems: An LMI approach,” IEEE Trans. Automat. Contr, Vol 42, NO 6, June.

[2]. Yuanqing Xia, Guo-Ping Liu, Peng Shi, Jie Chen and David Rees. (2008). “Robust delay-dependent sliding mode control for uncertain time-delay systems,” International Journal of Robust and Nonlinear Control.

[3]. Weibing Gao, (1993). “Variable structure control of non-linear systems: A New approach,” IEEE Trans on Industrial Electronics, Vol 40, No 1, Feb.

[4]. Boyd. S., Ghaoui L. El., Feron E. and Balakrishnan V. (1994). “LMI in systems and control theory”, Vol15, SIAM, Philadelphia.

[5]. Li Xi and Souza Ce.de. (1997). “Criteria for robust stability and stabilization of uncertain linear systems with state delays”, Automica, Vol33, No.9, 1657-1662.

[6]. Christopher Edwards and Sarah K . Spurgeon. Sliding Mode Control: Theory and Applications.

[7]. V.I. Utkin. Sliding Modes and their Application in Variable structure Systems, MIR Publishers Moscow.

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Research Paper

Piezoresistive Type Biosensor using a MEMS Cantilever for Detection of Tuberculosis

P. Sangeetha* , A. Vimala Juliet**

* Research Scholar, Sathyabama University, Chennai, Tamil Nadu, India.

** SRM University, Chennai, Tamil Nadu, India.

Sangeetha, P., and Juliet, A.V. (2013). Piezoresistive Type Biosensor Using A MEMS Cantilever for Detection of Tuberculosis. i-manager’s Journal on Electronics Engineering, 3(2), 27-32. https://doi.org/10.26634/jele.3.2.2119

Abstract

This paper deals with MEMS cantilever biosensor which functions as a piezoresistive sensor to detect tuberculosis. This piezoresistive sensor is based on measuring the change in resistance of the piezoresistive material due to the mechanical vibration of a cantilever beam. Cantilever surface is coated with antibody specific to TB antigen 85 complex. Patient blood sample is introduced on the cantilever if TB antigen is present in the blood sample that antigen binds with the antibody causing strain on the cantilever. The applied strain causes changes in the resistance of the piezoresistive material enabling the measurement of input variable. Here the strain is due antigen antibody binding on the cantilever surface. Bending of the cantilever due to the strain indicates the presence of the disease. The deflection of the microcantilever would be measured in terms of piezoresistive changes by implanting boron at the anchor point.

References

[1]. E.L. Florin, V.T. Moy, H.E. Gaub. (1994). Adhesion forces between individual ligand–receptor pairs, Science, 264 415–417.

[2]. R. Ros, S. Falk, D. Anselmetti, M. Kubon, R. Scha¨fer, A. Plu¨ckthun, L. Tiefenauer. (1998). Antigen binding forces of individually addressed single-chain Fv antibody molecules, PNAS 95, 7402–7405.

[3]. F. Kienberger, G. Kada, H.J. Gruber, V.P. Pastushenko, C. Riener, M. Trieb, H.-G. Knaus, H. Schindler, P. Hinterdorfer. (2000). Recognition force spectroscopy studies of the NTA-His6 bond, Single Mol. 1, 59–65.

[4]. Tang, Y., Fang, J., Due, X., Ji, H.-F., Brown, G.M., and Thundat, T. (2004). “Detection of femtomolar concentrations of using a SiO2 microcantilever.” Anal. Chem. 76, 2478–2481.

[5]. Cornell researchers move beyond 'nano' to'atto' to build a scale sensitive enough to weigh a virus [Online]. Available:http://www.news.cornell.edureleases/April04/attograms.ws.html, Retrieved.

[6]. Thundat T., P. I. Oden, and R. J. Warmack. (1997). "Microcantilever Sensors,"Microscale Thermophysical Engineering, Vol. 1, pp. 185-199.

[7]. Saeidpourazar, R., and Jalili, N., (2008). “Towards Nanomechanical Cantilever-Based Control and Manipulation" in International Symposium on Flexible Automation (2008 ISFA), Atlanta, Georgia, June 23-26, 2008. on 09-09-08.

[8]. Fritz, J. et al., (2000). ''Translating Bimolecular Recognition Nanomechanics,'' Science, 288, pp. 316–318.

[9]. Raiteri, R. et al., (1999). ''Sensing of Biological Substances Based on the Bending of Micro fabricated Cantilevers,'' Sens. Actuators B, 61, pp. 213–217.

[10]. G.G. Stoney. (1909). “The tension of metallic film deposited by electrolysis,” Proceedings of Royal Society of London. Series A, Vol.82, pp.172-175.

[11]. Chui, B. et al., (1998). ''Independent Detection of Vertical and Lateral Forces With a Sidewall-Implanted Dual-Axis Piezoresistive Cantilever,'' Appl. Phys. Lett., 72(11) pp. 1388–1390.

[12]. Tortonese, M., Barrett, R., and Quate, C., (1993). ''Atomic Resolution With an Atomic Force Microscope Using Piezoresistive Detection,'' Appl. Phys. Lett., 62(8) pp. 834–836.

[13]. Gaucher, P. et al., (1998). ''Piezoelectric Bimorph Cantilever for Actuation and Sensing Applications,'' J. Phys. IV, 8, pp. 235–238.

[14]. Minne, S., Manalis, S., and Quate, C. (1995). ''Parallel Atomic Force Microscopy Using Cantilevers With Integrated Piezoresistive Sensors and Integrated Piezoelectric Actuators,'' Appl. Phys. Lett., 67(26) pp. 3918–3920.

[15]. Huang, Q., and Lee, N. (2000). ''A Simple Approach to Characterizing the Driving Force of Polysilicon Laterally Driven Thermal Microactuators,'' Sens. Actuators, A, A80 (3) pp. 267–272.

[16]. Attia, P. et al., (1998). ''Fabrication and Characterization of Electrostatically Driven Silicon Microbeams,'' Microelectron. J., 29, pp. 641–44.

[17]. Shiba, Y. et al., (1998). ''Capacitive Afm Probe for High Speed Imaging,'' Trans. of the IEE of Japan, 118E (12) pp. 647 50.

[18]. Oates M.R., Clarke W., Marsh E.M., Hage DS. (1998). Kinetic studies on the immobilization of antibodies to high-performance liquid chromatographic supports. Bioconjugate Chem, 9:459–65.

[19]. Ziegler, C. (2004). “Cantilever-based biosensors,” Anal. Bioanal. Chem., 379: 946-959.

[20]. Madou, M.J. (2008). “Fundamentals of Microfabrication,” 2nd ed. CRC Press, Florida.

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Research Paper

Mems Mass Based Cantilever Gas Sensor Using Peizoresistive Principle

S. Subhashini* , A. Vimala Juliet**

* Research Scholar, Sathyabama University, Chennai, Tamilnadu, India.

** HOD, EIE, SRM University, Chennai, Tamilnadu, India.

Subhashini, S., and Juliet, A.V. (2013). MEMS Mass Based Cantilever Gas Sensor Using Piezoresistive Principle. i-manager’s Journal on Electronics Engineering, 3(2), 33-37. https://doi.org/10.26634/jele.3.2.2120

Abstract

Gas Pollutants like CO2, SO2, CO, CH4 ... are posing a threat to the human lives by inducing health problems. A check over these toxic gases around us is possible with the development of small handheld sensor devices. This paper deals with a MEMS based sensor using the principle of peizoresistivity. Here a simple MEMS structure was designed for providing the mechanical structure in the form of a cantilever. This structure having been released from the substrate will be free to move in its free end and immovable at the fixed end. Then the selectivity of the gas molecules is achieved by using the affinity chemicals, which would selectively adsorb the gas molecules, here the selectivity of CO2 will be dealt and it was achieved by using Tin Oxide. The Molecular weight of CO2 is 44.01g/mol and this results in the increase of cantilever stress causing a deflection at the free end and this inturn form a stress at the fixed end, this is measured using the peizoresistive material embedded on the cantilever. Thus the quantity of CO2 present in the atmosphere will determine the resistance and thereby giving an electrical output which could be easily conditioned based on our requirement.

References

[1]. Raiteri R., Massimo G., Hans-Jurgen B., Petr S. (2001). Micomecahnical Cantilever-Based Biosensors, Sensor Actuator B, 79, Pp. 115–126.

[2]. Kouravand S., Ghader R., Mehdi S., Ahmadali T. (2006). Mems Capacitive Micro Thermometer Based on Tip Deflection of Bimetallic Cantilever Beam, J. Sensor Transducers,70, Pp. 637–644.

[3]. Brugger J., Despont M., Rossel C., Rothuizen H., Vettiger P., Willemin M. (1999). Microfabricated Ultrasensitive Piezoresistive Cantilevers For Torque Magnetometry, Sensors Actuators, 73, Pp. 235–242.

[4]. Jiang Y.G., Ono T., Eshashi M. (2008). Fabrication of Piezoresistive Nanocantilevers For Ultra-Sensitive Force Detection, Meas. Sci. Technol., 19, Article No. 084011 (5 Pp) .

[5]. Calleja M., Nordstrom M., Alvarez M., Tamayo J., Lechuga L.M., Boisen A. (2005). Highly Sensitive Polymer-Based Cantilever-Sensors For Dna Detection, J. Ultramicrosc., 105, Pp. 215–222.

[6]. Wang Z., Yue R., Zhang R., Liu L. (2005). Design And Optimization of Laminated Piezoresistive Microcantilever Sensors, Sensors Actuators A, 120, Pp. 325–336.

[7]. Yu X., Zhang H., Li X., Li T., Zhang D. (2005). Design And Characterization of High-Sensitivity Cantilevers, Proc. Ieee Conf. On Sensors, Hyatt Regency Irvine, Orange County, Irvine, Ca, Usa, Pp. 588–591.

[8]. He J.H., & Li Y.F. (2006). High Sensitivity Piezoresistive Cantilever Sensor For Biomolecular Detection, J. Phys. Conf. Series, 34, Pp. 429–435.

[9]. Bhatti M.A., Lee C.X., Lee Y.Z., Ahmed N.A. (2007). Design And Finite Element Analysis of Piezoresistive Cantilever With Stress Concentration Holes, Second IEEE Conf. On Industrial Electronics And Applications, Shangari-La Hotel, Harbin, People's Republic of China, Pp. 1171–1174.

[10]. Chu Duc T., Creemer J.F., Sarro Pasqualina M. (2006). Lateral Nano- Newton Force-Sensing Piezoresistive Cantilever For Microparticle Handling', J. Micromech. Microeng., 16, Pp. 102–106.

[11]. Chu Duc T., Creemer J.F., Sarro Pasqualina M. (2007). Piezoresistive Cantilever Beam For Force Sensing In Two Dimensions, IEEE Sensors J., 7, (1), Pp. 96–104.

[12]. Fuller L.F. (2007). Microelectronic Engineering', Department of Rochester, Institute of Technology, Available At: Http:Www.Rit. Edu/~Iffee/.

i-manager's Journal on Electronics Engineering (JELE)

Current Issue Vol. 14 Issue 2

Most Read

Most Cited

Volume 3 Issue 2 December - February 2013

RFID Reader Range Extension Using Magnetodielectric Material – An Extended Version

Wen-Cheng Lai* , An-Cheng Sun**, Nan-Wei Chen***, Ching-Wen Hsue****, Luis Galarza*****

Lai, W., Sun, A., Chen, N., Hsue, C., and Galarza, L. (2013). RFID Reader Range Extension Using Magnetodielectric Material – An Extended Version. i-manager’s Journal on Electronics Engineering, 3(2), 1-6. https://doi.org/10.26634/jele.3.2.2116

Abstract

References

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An Approach for the Segmentation of Satellite Images Using Moving KFCM and Naive Bayes Classifier

S. Praveena* , S.P. Singh**, I.V. Muralikrishna***

*-** Department of Electronics and Communications Engineering, M.G.I.T, Hyderabad, A.P, India. *** Rtd. Professor, JNTUH, Hyderabad, A.P, India.

Praveena, S., Singh, S.P., and Muralikrishna, I.V. (2013). An Approach for the Segmentation of Satellite Images Using Moving KFCM and Naive Bayes Classifier. i-manager’s Journal on Electronics Engineering, 3(2), 7-15. https://doi.org/10.26634/jele.3.2.2117

Abstract

References

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Robust Sliding Mode Control for Time Delay Systems: An Application to Load Frequency Control

K. Naresh* , Y. Srinivasa Rao**, M. Kiran Kumar***

*-**-*** Department of Electrical and Electronics Engineering, K.L. University, Vaddeswaram, India.

Naresh, K., Rao, Y.S., and Kumar, M.K. (2013). Robust Sliding Mode Control for Time Delay Systems: an Application to Load Frequency Control. i-manager’s Journal on Electronics Engineering, 3(2), 16-26. https://doi.org/10.26634/jele.3.2.2118

Abstract

References

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Piezoresistive Type Biosensor using a MEMS Cantilever for Detection of Tuberculosis

P. Sangeetha* , A. Vimala Juliet**

* Research Scholar, Sathyabama University, Chennai, Tamil Nadu, India. ** SRM University, Chennai, Tamil Nadu, India.

Sangeetha, P., and Juliet, A.V. (2013). Piezoresistive Type Biosensor Using A MEMS Cantilever for Detection of Tuberculosis. i-manager’s Journal on Electronics Engineering, 3(2), 27-32. https://doi.org/10.26634/jele.3.2.2119

Abstract

References

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Mems Mass Based Cantilever Gas Sensor Using Peizoresistive Principle

S. Subhashini* , A. Vimala Juliet**

* Research Scholar, Sathyabama University, Chennai, Tamilnadu, India. ** HOD, EIE, SRM University, Chennai, Tamilnadu, India.

Subhashini, S., and Juliet, A.V. (2013). MEMS Mass Based Cantilever Gas Sensor Using Piezoresistive Principle. i-manager’s Journal on Electronics Engineering, 3(2), 33-37. https://doi.org/10.26634/jele.3.2.2120

Abstract

References

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Wen-Cheng Lai* , An-Cheng Sun, Nan-Wei Chen, Ching-Wen Hsue, Luis Galarza

S. Praveena* , S.P. Singh, I.V. Muralikrishna*

- Department of Electronics and Communications Engineering, M.G.I.T, Hyderabad, A.P, India.

Rtd. Professor, JNTUH, Hyderabad, A.P, India.

K. Naresh* , Y. Srinivasa Rao, M. Kiran Kumar*

-- Department of Electrical and Electronics Engineering, K.L. University, Vaddeswaram, India.

* Research Scholar, Sathyabama University, Chennai, Tamil Nadu, India.

** SRM University, Chennai, Tamil Nadu, India.

* Research Scholar, Sathyabama University, Chennai, Tamilnadu, India.

** HOD, EIE, SRM University, Chennai, Tamilnadu, India.