iosr-JCE   Volume-17 ~ Issue-5 ~ Sep-Oct 2015

Abstract: This paper develops an approach for face feature representation under variant resolution condition with normalized face distribution. In the approach of face feature extraction, SVD based approach for Eigen space representation is been used. To optimize the feature representation under various input conditions, a novel approach for feature representationunder variant scales and its recognition is proposed. The effect of scalingover different resolutions and variation in face feature distribution is evaluated. A normalization approach to face feature representation with scale mapped feature is presented. The simulation observations under variant scaling condition on YALE face dataset is carried out. An improvementin the recognition accuracy under these variant conditions was observed.

Index Term – Face Recognition, Feature representation, Normalization, Scale mapped coding.

[1]. S.-W Lee. and J. Park, ―Low resolution face recognition based on support vector data description,‖ Pattern Recognit., vol. 39, no. 9, pp. 1809–1812, Sep. 2006.
[2]. O. Sezer, Y. Altunbasak, and A. Ercil, ―Face recognition with independent component-based super-resolution,‖ in Proc. SPIE Visual Commun. Image Process., vol. 6077. San Francisco, CA, 2006, pp. 52–66.
[3]. J. Lu, X. Yuan, and T. Yahagi, ―A method of face recognition based on fuzzy c-means clustering and associated sub-NNs,‖ IEEE Trans. Neural Netw., vol. 18, no. 1, pp. 150–160, Jan. 2007.
[4]. K.-C. Kwak and W. Pedrycz, ―Face recognition using an enhanced independent component analysis approach,‖ IEEE Trans. Neural Netw., vol. 18, no. 2, pp. 530–541, Mar. 2007.
[5]. F. Lin, J. Cook, V. Chandran, and S. ORidharan, ―Face recognition from super-resolved images,‖ in Proc. 8th Int. Symp. Signal Process. Appl., vol. 2. Sydney, NSW, Australia, Aug. 2005, pp. 667–670.

Abstract: With technology advancement, Internet Of things as Next Gen of communication mode. Wearable electrical and electronic and sand printing on paper and wearable material will create people and devices inter and intra connected .IOT will be soon controlling the devices for energy saving , defect tracking , medicine and wellness and also as E-waste disposal management. The article discusses and shares the new approaches for E-waste management that can be adopted by countries, with advancement of electronic and telecommunication industry, which forms the collaborative cross technology-industry model to address the issue globally.

[1]. What is Green IT , why you should care : http: /www.smallbusinesscomputing.com/testdrive/article.php/3855806/What-Is-Green-IT-and-Why-Should-You-Care.htm By Laurie Mc Cabe Dec30-2009
[2]. AT&T 2020 report
[3]. https://itunews.itu.int/En/4850-E-waste-and-the-Internet-of-Things.note.aspx Last referenced on 17-June
[4]. The Open Interconnect Consortium Is Being Founded By Leading Technology Companies With The Goal Of Defining The Connectivity Requirements And Ensuring Interoperability Of The Billions Of Devices That Will Make Up The Emerging Internet Of Things (Iot).
[5]. http://www.cio.com/article/2872574/it-industry/5-key-challenges-facing-the-industrial-internet-of-things.html "Key Challenges Facing the Industrial Internet of Things"

Abstract: The cloud offers data processing, data centers to process and preserve the transactional data of the clients. Dynamic capacity provisioning is promising approach for reducing energy consumption by dynamically changing number of progressive machines for contest resource condition .In processing the persistence management the data store is identified as more a power consuming process. We can reduce the power consumptions through on-demand allocation and activation of the machines as per the requests. Through there are many recent studies, but they cannot tolerate the variations in the available infrastructures and the requirements found in fabrications.

[1]. Googleclusterdata - traces of google workloads, http://code.google.com/p/googleclusterdata/.
[2]. R. Boutaba, L. Cheng, and Q. Zhang. "On cloud computational models and the heterogeneity challenge," J. Internet Services and Applications vol.3, pp.77-86, 2012.
[3]. Y. Chen, A. Das, W. Qin, A. Sivasubramaniam, Q. Wang, and N. Gautam." Managing server energy and operational costs in hosting centers," In ACM SIGMETRICS Performance Evaluation Review, volume 33, 2005.
[4]. G. Jung, M. A. Hiltunen, K. R. Joshi, R. D. Schlichting, and C. Pu. "Mistral: Dynamically managing power, performance, and adaptation cost in cloud infrastructures,". In IEEE ICDCS, 2010.

Abstract: Recent advances in the communications and connectivity has encouraged the development and definition of various IEEE 802.11 series of standards, such as IEEE802.11a, b, g, n, and ac. Each of the standards is equipped with capabilities and features that suits the type of applications the standard is intended for. This paper provides a comparative study of these standards from the aspects of inception dates, evolution, implementation, operating conditions, and future of the IEEE 802.11a, b, g, n, and ac standards. This study also provide a summary of benefits and limitations of these standards.

Keywords: IEEE802.11, networking, communications, WiFi, WLAN, LAN

[1] Mobile subscriptions outlook, http://www.ericsson.com/res/docs/2015/ericsson-mobility-report-june-2015.pdf, Accessed August 22, 2015
[2] IEEE 802.11, https://en.wikipedia.org/wiki/IEEE_802.11, accessed August 22, 2015.
[3] E. Perahia and R. Stacey, ―Next Generation Wireless LANs: Throughput, Robustness, and Reliability in 802.11n‖, Cambridge University Press, Cambridge, UK,2008.
[4] Jagdish Rebello, ―WLAN: Differentiation Opportunities Emerge as 802.11n Rapidly Becomes Mainstream, http://www.isuppli.com/Abstract/WLAN-Differentiation-Opportunities-Emerge-as-802-11n-Rapidly-becomes Mainstream.pdf , accessed August 22, 2015.
[5] Rohde & Schwarz, ―802.11ac Technology Introduction White Paper,‖ March 2012. https://cdn.rohde-schwarz.com/pws/dl_downloads/dl_application/application_notes/1ma192/1MA192_7e_80211ac_technology.pdf

Abstract: The IEEE802.11ac specified a number of improvements over the IEEE802.11n such as: 80 MHz and (iii) multi-user MIMO on the downlink, these improvements mainly improve the per-link throughput on the downlink. In order to satisfy the requirements of future high density deployments of Wi-Fi, it is important to consider other metrics for system performance. This paper gives the most important feature technology comparison between IEEE802.11ac and IEEE802.11n in wire-less LAN technologies and how the IEEE802.11ac ad-vanes wireless LAN throughput beyond gigabit rate.

Keywords: IEEE802.11, networking, communications, WiFi, WLAN, LAN

[1] Mobile subscriptions outlook, http://www.ericsson.com/res/docs/2015/ericsson-mobility-report-june-2015.pdf, Accessed August 22, 2015
[2] IEEE 802.11, https://en.wikipedia.org/wiki/IEEE_802.11, accessed August 22, 2015.
[3] E. Perahia and R. Stacey, "Next Generation Wireless LANs: Throughput, Robustness, and Reliability in 802.11n", Cambridge University Press, Cambridge, UK,2008.
[4] Jagdish Rebello, "WLAN: Differentiation Opportunities Emerge as 802.11n Rapidly Becomes Mainstream, http://www.isuppli.com/Abstract/WLAN-Differentiation-Opportunities-Emerge-as-802-11n-Rapidly-becomes Mainstream.pdf , accessed August 22, 2015.
[5] Rohde & Schwarz, "802.11ac Technology Introduction White Paper," March 2012. https://cdn.rohde-schwarz.com/pws/dl_downloads/dl_application/application_notes/1ma192/1MA192_7e_80211ac_technology.pdf

Abstract:Image classification is among the complex processes which depend on several factors. In this paper, the most common existing methods for image classification are compared first and different structures of multilayer Perceptron (MLP) and Radial Basis neural networks (RBF) are then reviewed for classification. Finally, they are compared to non-neural algorithms. The function obtained by using the proposed method enjoys better results as compared to other methods and it has the best result among other methods with respect to accuracy with an average accuracy of 98.84%.
Keywords: Image Classification, MLP Neural Network, RBF, KNN

[1] D. LU,Q. WENG, A survey of image classification methods and techniques for improving classification performance, International Journal of Remote Sensing 2007, 823–870.
[2] M.He, F. M.Imran, B. Belkacem, and Sh.Mei , IMPROVING HYPERSPECTRAL IMAGE CLASSIFICATION ACCURACY USING ITERATIVE SVM WITH SPATIAL-SPECTRAL INFORMATION, IEEE conference, 2013.
[3] J.Tin,Y.Kwok, Moderating the Outputs of Support Vector Machine Classifiers, IEEE Conference,1999.
[4] N.Verbiest, C.Cornelis, and R.Jensen, Fuzzy rough Positive region based Nearest Neighbour Classificaton , IEEE World congress on Computational Intelligence, 2012.
[5] G.P.Zhang, Neural Networks for Classification: A Survey ,IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, 2000.
[6] Simon Haykin, Multilayer Perceptrons (in Neural Networks, 2th ed. New Jersey, Prentice Hall, 1999),156-178.

Abstract:The project discussed here is a gesture controlled robot having two wheels on either side just like a car. The key features described in the project are i) controlling the movement of the wheels using an embedded system ii) using the concept of image processing hand gestures are analyzed and according to the gestures the robot moves. The embedded system used here is an Arduino Uno board. It is a microcontroller (Atmega328) based board having 14 digital pins (out of which some are PWM pins) and 6 analog pins. The Arduino have been programmed using JAVA. Two driving motors have been connected with each of the wheels and hence a motor driving circuit (IC no.-L293D) has been used. The gesture detection part has been done using PYTHON programming. The concept of image processing has been used for the detection.

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Abstract: This paper describes the Curvelets Transform method to get the significant results for the MRI images. To study and understanding of different kinds of MRI Images and formats like jpg, bmp, tiff, png and conversion of real MRI Images into the basic standards using convertor (Image Viewer). Implementation of Curvelets method is done on different image formats. Also the calculation of Mean square error (MSE), Peak signal-to-noise ratio (PSNR), time and the percentage improvement using Curvelets is done. Thus, the idea behind using this technique is to get better results in terms of quality and in removal of noise and blurring of MRI images. Our main objective is to obtain a high resolution with as much details as possible for the sake of diagnosis of MRI images. Curvelets Transform Method is used to get the significant and higher results.

Keywords: Curvelets, MRI Images, Mean Square Error (MSE), Peak signal-to-noise ratio (PSNR), Time and Percentage.

[1]. Mr. Alkeshkumar B Vaghela," Image Restoration Using Curvelets Transform", VOLUME – 02, ISSUE – 02, Nov 12 to Oct 13
[2]. Jean-Luc Starck, Emmanuel J. Candès, and David L. Donoho," The Curvelets Transform for Image Denoising "IEEE TRANSACTIONS ON IMAGE PROCESSING VOL. 11, NO. 6, June 2002.
[3]. Jianwei Ma and Gerlind Plonka," The Curvelets Transform", IEEE, March 2010.
[4]. Deepa M, "Wavelet and Curvelets based Thresholding Techniques for Image Denoising ",International Journal of Advanced Research in Computer Science and Electronics Engineering (IJARCSEE), Volume 1, Issue 10,Dec 2012. [10] Tutorial Directional Wavelet Transform: Curvelet.
[5]. Introduction to the Curvelet Transform By Zvi Devir and Yanai Shpinner

Abstract: This paper proposes a novel steganographic technique that embeds compressed payload image within cover image. The pixel adjustment of the cover image is done optimally using Fourier Transform, so that visual characteristics change of the cover image is kept as minimum. The work presents the application of Discrete Wavelet Transform (DWT) for image compression, Discrete Fourier Transform (DFT) as the efficient selection of pixel locations in cover image and suggests as a new approach to compressed image steganography. We apply the Fourier Transform to our cover image and calculate the coefficients less than or higher than a particular threshold value. These coefficients coordinates are stored in an array. Wavelet transform is applied to the payload image and the approximate coefficients are calculated. These coefficients replace the Fourier coefficients of the original image.

[1] N.Wu and M.Hwang. "Data Hiding: Current Status and Key Issues," International Journal of Network Security, Vol.4, No.1, pp. 1-9, Jan.2007
[2] A High-Capacity Image Data Hiding Scheme Using Adaptive LSB Substitution Hengfu YANG, Xingming SUN,Guang SUN.
[3] C. Chan and L. M. Cheng, "Hiding data in images by simple LSB substitution," Pattern Recognition, pp. 469-474, Mar. 2004.
[4] R. Chandramouli and N. Memon, "Analysis of LSB based image steganography techniques," International Conference on Image Processing, Thessaloniki, Greece, pp. 1019-1022, 2001.
[5] Image compression using Haar wavelet transform, Colm Mulcahy,Ph.D

Abstract: Data mining is the extraction of the important patterns or information from large amount of data, which is used for decision making in future work. But the process of data collection and data dissemination may cause the serious damage for any organization, however may causes the privacy concerns. Sensitive or personal information of individuals, industries and organization must be kept private before it is shared or published. Thus privacy preserving data mining has become an important issue to efficiently hide sensitive information. Many numbers of methods and techniques have been proposed for privacy preserving data mining for hiding sensitive information. In this paper we provides our own overview which has taken from previous paper on privacy preserving data mining.

[1]. The free dictionary.Homepage on Privacy [Online]. Available: http://www.thefreedictionary.com/privacy.
[2]. M. Prakash, G. Singaravel, "A New Model for Privacy Preserving Sensitive Data Mining", in proceedings of ICCCNT
Coimbatore, India, IEEE 2012.
[3]. Chun-Wei Lin, Tzung-Pei Hong, Chia-Ching Chang, and Shyue-Liang Wang "A Greedy-based Approach for Hiding Sensitive
Itemsets by Transaction Insertion", Journal of Information Hiding and Multimedia Signal Processing, Volume 4, Number 4,
October 2013.
[4]. M.Mahendran, Dr.R.Sugumar, K.Anbazhagan, R.Natarajan "An Efficient Algorithm for Privacy Preserving Data Mining Using
Heuristic Approach", International Journal of Advanced Research in Computer and Communication Engineering, Vol. 1, Issue 9,
November 2012.
[5]. Chun-Wei Lin · Tzung-Pei Hong ·Kuo-Tung Yang ·Leon Shyue-LiangWang "The GA-based algorithms for optimizing hiding
sensitive itemsets through transaction deletion", Published online: 20 September 2014, © Springer Science+Business Media New
York 2014.

Abstract: Botanical evidences play a significant role in criminal investigations. These evidences help in linking a victim or a suspect to the crime scene and eventually help in correlating them. In present study, it was correlated that the botanical evidences found at the crime scene with the victim or suspect, whether the place of murder and the place where the dead body was found are same or not, using RAPD-PCR technique. The DNA extracted from the botanical evidences using CTAB method and the RAPD-PCR profile of the standard and the suspected samples were then matched by using TotalLab softwares, TotalLab Quant and Phoretix 1D, data analysis report. Out of three cases, in two cases, separated RAPD-PCR products were matched with the standard and the suspected sample, while in one case RAPD-PCR profile of both samples did not matched. Keywords: Botanical evidences, CTAB, RAPD-PCR, TotalLab.

[1] F. Bardakci, Random Amplified Polymorphic DNA (RAPD) Markers, Turkish Journal of Biology, 25, 2001, 185-196.
[2] K. J. Craft, J. D. Owens and M. V. Ashley, Application of Plant DNA Markers In Forensic Botany: Genetic Comparison of Quercus Evidence Leaves To Crime Scene Trees Using Microsatellites, Forensic Science International, 165, 2007, 64–70.
[3] A. Iqbal, I. Ahmad, H. Ahmad, M. S. Nadeem, M. Nisar and H. Riaz, An Efficient DNA Extraction Protocol for Medicinal Plants, International Journal of Biosciences; Vol. 3, No. 7, 2013, 30-35.
[4] J. Ward, R. Peakall; S. R. Gilmore and J. Robertson, A Molecular Identification System for Grasses: A Novel Technology for Forensic Botany, Forensic Science International, 152, 2005, 121–131.
[5] V. P. Deshmukh, P. V. Thakare, U. S. Chaudhari and P. A. Gawande, A Simple Method For Isolation Of Genomic DNA From Fresh and Dry Leaves Of Terminalia Arjuna (Roxb.) Wight and Argot, Electronic Journal of Biotechnology, Vol.10, No.3, 2007, ISSN: 0717-3458.
[6] S. Zidani, A. Ferchichi and M. Chaieb, Genomic DNA Extraction Method from Pearl Millet (Pennisetum Glaucum) Leaves, African Journal of Biotechnology Vol. 4 (8), 2005, 862-866.