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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 4 June - August 2013

Article

World's Quietest Room: Anechoic chambers for EMC Testing

Lipi Chhaya*

Assistant Professor, Department of ECE,IDS, Nirma University, Ahmedabad, Gujarat, India.

Chhaya, L., (2013). World's Quietest Room:anechoic Chambers For Emc Testing. i-manager’s Journal on Electronics Engineering, 3(4), 1-3. https://doi.org/10.26634/jele.3.4.2391

Abstract

As we are dealing with the number of electronic gadgets and equipments in our daily life, apparently we are living in the world of waves. Electromagnetic energy travels in waves and spans a broad spectrum from very long radio waves to very short gamma rays. An electromagnetic spectrum is the range consisting of all possible frequencies of electromagnetic radiation. Electromagnetic interference is caused by electromagnetic radiation. Electromagnetic interference is a disturbance which may result into malfunctioning of equipments and health hazards to human beings. Electromagnetic compatibility is a phenomenon to describe immunity of equipment or component in electromagnetic environment. Anechoic chambers are used for electromagnetic compatibility testing. This paper describes adverse effects of electromagnetic interference and design of anechoic chamber. It provides an overview of EMI, EMC & anechoic chamber used for EMC testing and measurement to engineers and readers unfamilier with this technology

References

[1]. Facilities and research. (n.d.). Retrieved from www.orfieldlabs.com/researchtour4.htm

[2]. Monographs and supplements. (n.d.). Retrieved from http://www.iarc.fr

[3]. ICNIRP guidelines for limiting exposure to time varying electric, magnetic and electromagnetic fields (up to300ghz). (1998). Retrieved from http://ww.icnirp.de/documents/emfgdl.pdf

[4]. Anechoic chambers. (n.d.). Retrieved from www.frankoniagroup.com

[5]. http://www.tee.tokin.jp/eng/pdf/ANECHOIC_CHAMBERS. pdf

[6]. www.inycom.es/.../frankonia-camaras_anecoicas_ y_apantalladas_2012.pdf

[7]. http://appadvice.com/appnn/2010/07/test-iphone

[8]. Wiles, M. & Vince, R. (2010). (n.d.). Choosing the right chamber for your test requirements, EMC Directory & Design Guide, Retrieved from http://www.ets-lindgren.com/pdf/item_mwvr_0510.pdf

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Research Paper

A Highly Durable Double Edge Triggered D-FlipFlop Based Shift Registers using 10nM CNTFETTechnology

Ravi T.* , Kannan .V**

* Research Scholar, Sathyabama University, Chennai.

** Principal, Jeppiaar Institute of Technology, Kunnam, Chennai.

Ravi, T., and Kannan, V., (2013). A Highly Durable Double Edge Triggered D-flip Flop Based Shift Registers Using 10nm Cntfet Technology. i-manager’s Journal on Electronics Engineering, 3(4), 4-12. https://doi.org/10.26634/jele.3.4.2392

Abstract

In this paper, a highly durable Double Edge Triggered D-Flip Flop based Serial in Serial Out (SISO), Serial in Parallel Out (SIPO), Parallel In Serial Out (PISO) and Parallel In Parallel Out (PIPO) shift registers are designed using 10nM Carbon Nano Tube Field Effect Transistor (CNTFET) technology. The CNTFET is a cutting edge device to overcome the current CMOS technology for its excellent electrical properties. To evaluate the durability, shift registers using CNTFET are analysed and the transient analysis are depicted. Power consumption, delay, PDP, rise time and fall time are evaluated using HSPICE with 1GHz operating frequency.

References

[1]. Anisur Rahman, Jing Guo, Supriyo Datta, and Mark S. Lundstrom. (September 2003). “Theory of ballistic nanotransistors”. Electron Devices, IEEE, 50(9):1853–1864.

[2]. Dang .T., L. Anghel, R. Leveugle, “CNTFET Basics and Simulation” IEEE 2006.ISBN: 0-7803-9726-6,pp: 28 - 33.

[3]. Dang.T., Anghel, Leveugle, (2006). “CNTFET Basics and Simulation”, IEEE Trans. Elec.dev.

[4]. Dielectric Constants of Common Materials “http://www.flowmeterdirectory.com/dielectric_ constant_ 01. html”

[5]. Fabien Pregaldiny et.al., (2006). “Design Oriented Compact Models for CNTFETs”, IEEE Trans. Elec. dev.

[6]. http://www.zdnet.com/ibm-packs-10000-carbon-nanotubetransistors-onto-single-chip-7000006512/

[7]. Imran Ahmed Khan, Danish Shaikh, Mirza Tariq Beg. (2012). “2 Ghz Low Power Double Edge Triggered flip- flop in 65nm CMOS Technology,” IEEE Conference. ISBN-978-1-4673-1317-9,pp-1-5.

[8]. Jie Deng, H.-S.P. Wong, (Dec 2007). “A Compact SPICE Model for Carbon- Nanotube Field-Effect Transistors Including Nonidealities and Its Application—Part II: Full Device Model and Circuit Performance Benchmarking,” in Electron Devices, IEEE Journal of, Volume 54, Issue 12, pp.

[9]. Jie Deng, H.-S.P. Wong, (Dec 2007). “A Compact SPICE Model for Carbon- Nanotube Field-Effect Transistors Including Nonidealities and Its Application -Part I: Model of the Intrinsic Channel Region,” in Electron Devices, IEEE Journal of, Volume 54, Issue 12, pp. 3186 – 3194.

[10]. Liu, I. O’Connor, D. Navarro, F. Gaffiot, “Design of a Novel CNTFET-based Reconfigurable Logic Gate”, IEEE Computer Society Annual Symposium on VLSI(ISVLSI'07). ISBN: 0-7695-2896-1,pp: 285 – 290.

[11]. Michael J. O’Connell, (2006). “Carbon nanotubes – properties and applications”, Taylor & Francis Group, LLC., pp – 84 –114.

[12]. Pedram. M., (Jan. 1996). “Power minimization in IC Design: Principles and applications,” ACM Transactions on Design Automation of Electronic Systems, vol. 1, pp.3-56.

[13]. Peiyi Zhao, Jason McNeely, Pradeep Golconda and Jianping Hu. (2008). “Low Power Design of Double-Edge Triggered Flip-Flop by Reducing the Number of Clocked Transistors”. IEEE Conference, ISBN- 978-1-4244-1708-7,pp. 343- 347.

[14]. Phaedon Avouris, Joerg Appenzeller, Richard Martel, and Shalom J. Wind. (November 2003). “Carbon nanotube electronics”. Proceedings of the IEEE, 91(11):1772–84.

[15]. Ravi. T, Irudaya Praveen. D and Kannan. V, (January 2013). “Design and Analysis of High Performance Double Edge Triggered D-Flip Flop,” International Journal of Recent Technology and Engineering (IJRTE), Volume-1, Issue-6, pp. 139-142.

[16]. Saeeid Tahmasbi Oskuii. (Dec 2003). “Comparative study on low-power high-performance flip-flops”. Master Thesis, Division of Electronic Devices, Linkoping University.

[17]. Stanford University CNFET Model website, http://nano.stanford.edu/model.php

[18]. Teng Wang, Martin Jonsson, Eleanor E.B. Campbell and Johan Liu, (2006). “Development of Carbon Nanotube Bumps for Ultra Fine Pitch Flip Chip Interconnection” Electronics System integration Technology conference Dresden, Germany-IEEE,ISBN: 1-4244-0552-1,pp- 892 - 895.

[19]. Troy A. Johnson P and Ivan S. Kourtev, (2001). “A Single Latch, High Speed Double-Edge-triggered flip-flop (DETFF)” IEEE, in press.

[20]. Wing-Shan Tam, Sik-Lam Siu, Chi-Wah Kok, and Hei Wong. (2010). “Double Edge-Triggered Half-Static Clock-Gated D-Type Flip-Flop”. IEEE International Conference of Electron Devices and Solid-State Circuits (EDSSC), ISBN:978-1-4244-9997-7, pp-1-4.

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Research Paper

Certain Investigation On Fully Automated Method To Segment And Measure The Volume Of Pleura Effusion On Ct Images

Punitha .M* , Vijaykumar J.**

** Assistant Professor, Department of ECE, Nandha College of Technology, Erode.

Punitha, M., and Vijaykumar, .J. (2013). Certain Investigation On Fully Automated Method To Segment And Measure The Volume Of Pleural Effusion On Ct Images. i-manager’s Journal on Electronics Engineering, 3(4), 13-18. https://doi.org/10.26634/jele.3.4.2393

Abstract

A pleural effusion is an abnormal amount of fluid around the lung. Pleural effusions can result from many medical conditions. Most pleural effusions are not serious by themselves, but some require treatment to avoid problems. The pleura is a thin membrane that lines the surface of the lungs and the inside of the chest wall outside the lungs. In pleural effusions, fluid accumulates in the space between the layers of pleura. Normally, only teaspoons of watery fluid are present in the pleural space, allowing the lungs to move smoothly within the chest cavity during breathing. Pleural effusion is an important biomarker for the diagnosis of many diseases. To develop an automated method to evaluate pleural effusion on CT scans, the measurement of which is prohibitively time consuming when performed manually. The method is based on parietal and visceral pleura extraction, active contour models, region growing. To take 2D gray scale image as input and segment the PE by using region growing method.

References

[1]. Rogowska. J (2000). “Overview and fundamentals of medical image segmentation,” in Handbook of Medical Imaging, Processing and Analysis, I. N. Bankman, Ed. New York, NY, USA: Academic, pp. 69– 85.

[2]. Hedlund. L.W Anderson. R P. Goulding. L J. Beck J.W Effmann. E.L and C. E. Putman, (1982). “Two methods for isolating the lung area of a CT scan for density information,” Radiology, vol. 144, pp. 353 357.

[3]. Qian. Y Zhang. C and Yang. X, (2008). “New method for quantification of pleural effusions from CT imaging,” in Proc. ISECS Int. Colloq. Comput. Commun. Control Manage, pp. 768–773.

[4]. Donohue. R Shearer. A and Bruzzi. J, (2009). “Constrained region-based segmentation of pleural effusion in thin-slice CT,” in Proc. IEEE 13th Int. Mach. Vis. Image Process. Conf., pp. 24–29.

[5]. Tran. B.Q Tajik. J.K Chiplunkar. R and Hoffman. E.A, (Oct 1996). “Lung volume control for quantitative X-ray CT,” Ann. Biomed. Eng., vol. 24, no. 1, p. S-66.

[6]. Cai. W Tabbara. M Takata. A Yoshida. H Harris. G.J Novelline R.A, (2009). “MDCT for automated detection and measurement of pneumothoraces in trauma patients,” Amer. J. Roentgenol., vol. 192, pp. 830–836.

[7]. Mergo. P.J Helmberger. T Didovic. J Cernigliaro. J Ros. P.S and Staab. E.V, (1999). “New formula for quantification of pleural effusions from computed tomography,” J. Thorac. Imag., vol. 14, pp. 122–125.

[8]. Lankisch. P and Becher. R, (1994). “Pleural effusions: A new negative prognostic parameter for acute pancreatitis,” Amer. J. Gastroenterol., vol. 89, pp. 1849–1851.

[9]. Fabijanska. A, (2009). Results of applying two-pass region growing algorithm for airway tree segmentation to MDCT chest scans from EXACT database. In Proc. of Second International Workshop on Pulmonary Image Analysis, pages 251–260.

[10]. Polak, M, Zhang, H. & PI, M. (2009). An evaluation metric for image segmentation of multiple objects. Image and Vision Computing, 27(8):1223-1227.

[11]. Rein, A, Hamida, A. B. & Benjelloun, M. (2009). An Optimal Unsupervised Satellite image Segmentation Approach Based on Pearson System and k-Means Clustering Algorithm Initialization, International Journal of Signal Processing.

[12]. Emerson Carlos Pedrino, José Hiroki Saito,Valentin Obac Roda, Marcelo Marinho. An FPGA-Based Region-Growing Architecture for Binary Images, IWSSIP 2010 - 17th International Conference on Systems, Signals and Image PA

[13]. Indra Kanta Maitra, Sanjay Nag and Prof. Samir K. Bandyopadhyay, (2011). Automated Digital Mammogram Segmentation For Detection Of Abnormal Masses Using Binary Homogeneity Enhancement Algorithm. Indian Journal of Computer Science and Engineering (IJCSE).

[14]. Jayant. N.S, (Sept. 1976). “Average and median-based smoothing techniques for improving digital speech quality in the presence of transmission errors,” IEEE Trans. Commun., vol. COM-24, pp. 1043-1045.

[15]. Porcel. J and Vives. M, (2003). “Etiology and pleural fluid characteristics of large and massive effusions,” Chest, vol. 124, pp. 978–983.

[16]. Arad. M Zlochiver. S Davidson. T Shoenfeld. Y Adunsky. A and Abboud. S, (2009). “The detection of pleural effusion using a parametric fit technique,” Physiol. Meas., vol. 30, pp. 421–428.

[17]. Zhang. L E. Hoffman. A and Reinhardt J.M, (Jan 2006). “Atlas-driven lung lobe segmentation in volumetric X-ray CT images,” IEEE Trans. Med. Imag., vol. 25, no. 1, pp. 1–16.

[18]. Zhou. X Hayashi. T Hara. T Fujita. H Yokoyama. R Kiryu. T and Hoshi. H, (2006). “Automatic segmentation and recognition of anatomical lung structures from high resolution chest CT images,” Comput. Med. Imag. Graph., vol. 30, pp. 299–313.

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Research Paper

Performance Comparison of Various Speckle noiseRemoval Filters for Ultrasound Images

P. Nirmaladevi* , R. Asokan, S.Govindaraj*, S. Usha****

Principal, Kongunadu College of Engineering and Technology, Namakkal, Thiruchirapalli.

* Final year postgraduate Student, School of Electrical Sciences, Department of Electrical and Electronics Engineering, Kongu Engineering College, Perundurai, Erode.

**** Assistant Professor, School of Electrical Sciences, Department of Electrical and Electronics Engineering, Kongu Engineering College, Perundurai, Erode.

Nirmaladevi, C., Asokan, R., Govindaraj, S., and Usha, S. (2013). Performance Comparison Of Various Speckle Noise Removal Filters For Ultrasound Images. i-manager’s Journal on Electronics Engineering, 3(4), 19-24. https://doi.org/10.26634/jele.3.4.2394

Abstract

In this paper, we propose a wavelet denoising algorithm based on adaptive wavelet thresholding technique for ultrasound images. The adaptive wavelet thresholding is a Bayesian frame work and wavelet coefficients are assumed to be General Gaussian Distribution function (GGD). The proposed algorithm performs better when compared with other shrinking technique like Visu shrink, Level shrink, and Sure shrink. This approach preserves edges and also the computational complexity is less. The image quality is measured using Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Universal Quality Index (UQI).

References

[1] Haryali Dhillon, Gagan Deep Jindal and Akshay Girdhar, (2011). “A New Threshold Technique for Despeckling Ultrasound Images” International Conference on Computational Techniques and Artificial Intelligence (ICCTAI'2011).

[2]. Mantosh Biswas and Hari Om, (2013). “An Image Denoising Threshold Estimation Method “Advances in Computer Science and its Applications (ACSA) 377Vol. 2, No. 3, ISSN 2166-2924.

[3]. D.L. Donoho, (May 1995). “De-noising by soft-thresholding”, IEEE Trans on Inform. Theo ry, Vol. 41. pp. 613-627.

[4]. S. Grace Chang, Bin Yu, and Martin Vetterli, (2000). “Adaptive Wavelet Thresholding for Image Denoising and Compression”, IEEE Transactions on Image Processing, 9(9), pp. 1532 – 1546.

[5]. M. Miyahara, K. Kotani, and V.R. Algazi, (1998). “Objective Picture Quality Scale (PQS) for Image Coding”, IEEE Transactions on Communications, Vol. 46, No. 9, pp. 1215- 1226.

[6]. E. Simoncelli and E. Adelson, (Sept. 1996). “Noise removal via Bayesian wavelet coring,” Proc. IEEE Int. Conf. Image Processing, Vol. 1, pp. 379–382.

[7]. D. L. Donoho and I. M. Johnstone, (1995). “Adapting to unknown smoothness via wavelet shrinkage”, J. Amer. Statist. Assoc., Vol. 90, pp. 1200–1224.

[8]. M. I. H. Bhuiyan, M. O. Ahmad, and M. N. S. Swamy, (Apr. 227). “Wavelet-based despeckling of medical ultrasound images with the symmetric normal inverse Gaussian Prior,” in Proc. ICASSP, Vol. 1, pp. 721–724.

[9]. H. Om and M. Biswas, (2012). “An Improved Image Denoising Method based on Wavelet Thresholding,” Journal of Signal and Information Processing (USA), Vol. 3(1), pp. 109-116.

[10]. Karunesh K. Gupta' and Rajiv Gupta2, (2007). “Feature Adaptive Wavelet Shrinkage for Image Denoising”, IEEE - ICSCN, MIT Campus, Anna University, Chennai, India. Feb. 22-24, 2007. pp.81-85.

[11]. X. Zong, A. F. Laine, and E. A. Geiser, (1998). “Speckle reduction and contrast enhancement of echocardiograms via multi-scale nonlinear processing,” IEEE Trans. Med. Imaging Vol. 17, No. 4pp. 532–540.

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Research Paper

Detection of Unsupervised Changes in a Multi-temporal remote sensing Image

G. Kowsalya* , T.K. Revathi**

*-** Lecturer, Department of IT, Muthayammal Engineering College, Rasipuram, Namakkal, Tamilnadu, India.

Kowsalya, G., and Revathi, T.K. (2013). Detection Of Unsupervised Changes In A Multi-temporal Remote Sensing Image. i-manager’s Journal on Electronics Engineering, 3(4), 25-33. https://doi.org/10.26634/jele.3.4.2395

Abstract

Detecting of different kinds of changes in multitemporal remote sensing images. Two Multitemporal images, acquired over the same area at different times, are independently classified and land-cover transitions are estimated according to a pixel-by-pixel comparison. Unsupervised methods available in preceding pixel-by-pixel comparison methods are not effective in handling the images. In many real applications, it fails to collect ground truth information for multitemporal images and also loss of change information should takes place. To overcome the unsupervised framework is designed for detecting the changes in remote sensing images. Two different data sets having different property representations allow to easily display and understand changed information. Here, the information about multiple kinds of changes can be preserved as well as it is easy to visualize those images. It makes the process a better one in detecting maximum number of changes from the multi temporal image. The plan is to exploit the potentialities of the proposed technique in the context of more complex approaches to change detection like those exploit multi scale/multiresolution information intrinsically present in VHR images and the ones robust to registration noise.

References

[1]. A. Singh, (1989). “Digital change detection techniques using remotely-sensed data,” Int. J. Remote Sens., Vol. 10, No. 6, pp. 989–1003.

[2]. L. Bruzzone and S. B. Serpico, (Jul. 1997) “An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images,” IEEE Trans. Geosci. Remote Sens., Vol. 35, No. 4, pp. 858–867.

[3]. L. Bruzzone and M. Marconcini, (May 2010) “Domain adaptation problems: A DASVM classification technique and a circular validation strategy,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 32, No. 5, pp. 770–787.

[4]. L. Bruzzone, R. Cossu, and G. Vernazza, (Oct. 2004). “Detection of land-cover transitions by combining multidate classifiers,” Pattern Recognit. Lett., Vol. 25, No. 13, pp. 1491–1500.

[5]. P. R. Coppin, I. Jonckheere, and K. Nachaerts, (2004). “Digital change detection techniques,” Int. J. Remote Sens., Vol. 25, No. 9, pp. 1565–1596.

[6]. D. Lu, P. Mausel, E. Brondízio, and E. Moran, (2004). “Change detection techniques,” Int. J. Remote Sens., Vol. 25, No. 12, pp. 2365–2407.

[7]. S. Andra, O. Al-Kofahi, and A. J. Radke, B. Roysam, (Mar. 2005). “Image change detection algorithms: A systematic survey,” IEEE Trans. Image Process., Vol. 14, No. 3, pp. 294–307.

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

Current Issue Vol. 14 Issue 2

Most Read

Most Cited

Volume 3 Issue 4 June - August 2013

World's Quietest Room: Anechoic chambers for EMC Testing

Lipi Chhaya*

Assistant Professor, Department of ECE,IDS, Nirma University, Ahmedabad, Gujarat, India.

Chhaya, L., (2013). World's Quietest Room:anechoic Chambers For Emc Testing. i-manager’s Journal on Electronics Engineering, 3(4), 1-3. https://doi.org/10.26634/jele.3.4.2391

Abstract

References

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A Highly Durable Double Edge Triggered D-FlipFlop Based Shift Registers using 10nM CNTFETTechnology

Ravi T.* , Kannan .V**

* Research Scholar, Sathyabama University, Chennai. ** Principal, Jeppiaar Institute of Technology, Kunnam, Chennai.

Ravi, T., and Kannan, V., (2013). A Highly Durable Double Edge Triggered D-flip Flop Based Shift Registers Using 10nm Cntfet Technology. i-manager’s Journal on Electronics Engineering, 3(4), 4-12. https://doi.org/10.26634/jele.3.4.2392

Abstract

References

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Certain Investigation On Fully Automated Method To Segment And Measure The Volume Of Pleura Effusion On Ct Images

Punitha .M* , Vijaykumar J.**

** Assistant Professor, Department of ECE, Nandha College of Technology, Erode.

Punitha, M., and Vijaykumar, .J. (2013). Certain Investigation On Fully Automated Method To Segment And Measure The Volume Of Pleural Effusion On Ct Images. i-manager’s Journal on Electronics Engineering, 3(4), 13-18. https://doi.org/10.26634/jele.3.4.2393

Abstract

References

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Performance Comparison of Various Speckle noiseRemoval Filters for Ultrasound Images

P. Nirmaladevi* , R. Asokan**, S.Govindaraj***, S. Usha****

Nirmaladevi, C., Asokan, R., Govindaraj, S., and Usha, S. (2013). Performance Comparison Of Various Speckle Noise Removal Filters For Ultrasound Images. i-manager’s Journal on Electronics Engineering, 3(4), 19-24. https://doi.org/10.26634/jele.3.4.2394

Abstract

References

Full Article (HTML)

Pdf

Detection of Unsupervised Changes in a Multi-temporal remote sensing Image

G. Kowsalya* , T.K. Revathi**

*-** Lecturer, Department of IT, Muthayammal Engineering College, Rasipuram, Namakkal, Tamilnadu, India.

Kowsalya, G., and Revathi, T.K. (2013). Detection Of Unsupervised Changes In A Multi-temporal Remote Sensing Image. i-manager’s Journal on Electronics Engineering, 3(4), 25-33. https://doi.org/10.26634/jele.3.4.2395

Abstract

References

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* Research Scholar, Sathyabama University, Chennai.

** Principal, Jeppiaar Institute of Technology, Kunnam, Chennai.

P. Nirmaladevi* , R. Asokan, S.Govindaraj*, S. Usha****