Irrigation along River Tatsawarki involves the application of stream water and/or groundwater from tube wells believed to be recharged by the stream that flows through industrialized and residential areas. In the use of these sources of water for irrigation, of major concern is the concentration of heavy metals that are neither rapidly removed, nor readily detoxified by metabolic activities. Soil samples are collected from three irrigated farmlands (A, B and C) along the river banks and control samples (D) were also collected at the control farms located away from the river bank. The samples were collected at 0 – 15cm depth, placed in polyethylene bags and taken to laboratory for analysis. Two samples were collected from each zone, one from farmland using surface water as the only source of irrigation water, and another sample from farmland using groundwater as its sole source of irrigation water. The samples were analysed for pH, texture, and heavy metals (Cr, Cu, Co, Cd, Fe, Zn, Mn, and Pb). It was found that Irrigation with the polluted water has a significant effect on the soils heavy metals content. This eventually affects the groundwater as the soils were prone to high metals mobility because of its acidic nature and very low clay content which increases the porosity and poor retention capacity. In comparison with the EU limits only cadmium (Cd) was found to be out of the set limits. It was concluded that heavy metals accumulate in the soils and if the source of pollution is not checked, it will gradually reach and subsequently pass, the standard limit set. Urgent steps required to control this gradual accumulation is recommended.

[1]. Udosen, E.D., "Determination of trace metals and fluxes in sediments along a segment of Qua Ibeo River in Southern Nigeria‟, Journal of Natural and Applied Sciences, 2: pp82-90, 2006.
[2]. Yang, H. and Rose, N.L "Trace element pollution records in some UK lake sediment, their history, influence factor and regional differences‟, Environmental International, 31:pp63-75, 2005.
[3]. WHO, The World Health Report 2003: Shaping the Future, World Health Organization, 1211Geneva 27, Switzerland, 2003.
[4]. Akan, J.C. Abdulrahman, F.I. Ogugbuaja, V.O and Reuben, K.D., "Study of the Physicochemical Pollutants in Kano Industrial Areas, Kano State, Nigeria‟ Journal of Applied Sciences in Environmental Sanitation. Vol. 4 No. 2, pp 89-102, 2009.
[5]. Khan, S.M. Zaidi, A. Wani, P.A. Mohammad, O. , "Role of plant growth promoting rhizobacteria in the remediation of metal contaminated soils‟, Environmental Chemistry Letters, Vol 7. No 1, p1-19, 2006.....

"Science Of Time And The Theory Of Everything" by author Bhausaheb Bhosle. It isa theory which explains Astronomy, Genetics and everything.

Black hole, Frequency, Galactic egg, Space Time constants, Theory Of Everything.

[1] Ancient book Surya-Siddhanta, Rig Veda.

Wireless Sensor Networks provides unauthorized person to make use of traffic information located on monitoring of the objects. These sensor networks locate the hotspot incident that causes the inconsistency in the network traffic prototype due to the large volume of packets originating from a small area. We make use of a adversary model assuming that the challenges of the network traffic in multiple areas are monitored, quite than the entire network in one location. In this model, a novel attack call the hotspot location where we use the traffic analysis techniques to locate hotspots. We proposed a cloud based scheme for providing defensive source nodes area privacy, besides from Hotspot-Location attack by developing a cloud with an uneven shapes fake to the traffic, detain the inconsistency in the traffic pattern and locate the source node in which the node forming the cloud. In order to reduce the vigorous cost, the clouds activities during data transmission are maintained and the junction of the clouds with a largest complex cloud are determined to decrease the number of fake packets and also rise privacy safeguarding of the network.

Wireless Sensor Network, Hotspot, Privacy-Preserving Location Monitoring, Back Tracing Attacks, Adversary modal, Network Modal.

[1] Timothy X Brown Jesse E. James.Jamming and Sensing of Encrypted Wireless Ad Hoc Networks.AmitaSethi University.
[2] Mr.PushphasChaturvediMr.Kunal Gupta.Detection and Prevention of various types of Jamming Attacks in Wireless Networks.Dept.Of Computer Science, Amity University.
[3] Neha Thakur. Introduction to Jamming Attacks and Prevention Techniques using Honeypots in Wireless Networks. Dept. of Software Engineering ,SRM University,Chennai, India.
[4] KwangsungJu and Kwangsue Chung . Jamming Attack Detection and Rate Adaptation Scheme for IEEE 802.11 Multi-hop Tactical Networks. Department of Communications Engineering Kwangwoon University, Seoul, Korea ksju@cclab.kw.ac.kr, kchung@kw.ac.kr.
[5] S. Periyanayagi and V. Sumathy.A Swarm Based Defense Technique for Jamming Attacks in Wireless Sensor Networks....

Wireless networks are built upon a shared medium that makes it easy for adversaries to launch jamming-style attacks. In wireless networks, the problem of selective jamming attacks is identified. In these attacks, the adversary is active only for a short period of time, selectively targeting messages of high importance. We illustrate the advantages of selective jamming in terms of network performance degradation and adversary effort by presenting two case studies: a selective attack on TCP and routing. We show that selective jamming attacks can be launched by performing real-time packet classification at the physical layer. To mitigate these attacks, we develop three schemes that prevent real-time packet classification by combining cryptographic primitives with physical-layer attributes. We analyse the security of our methods and evaluate their computational and communication overhead.

[1] Timothy X Brown Jesse E. James.Jamming and Sensing of Encrypted Wireless Ad Hoc Networks. Amita Sethi University.
[2] Mr. Pushphas Chaturvedi Mr. Kunal Gupta.Detection and Prevention of various types of Jamming Attacks in Wireless Networks. Dept. Of Computer Science, Amity University.
[3] Neha Thakur.Introduction to Jamming Attacks and Prevention Techniques using Honeypots in Wireless Networks. Dept. of Software Engineering ,SRM University,Chennai, India.
[4] Kwangsung Ju and Kwangsue Chung .Jamming Attack Detection and Rate Adaptation Scheme for IEEE 802.11 Multi-hop Tactical Networks. Department of Communications Engineering Kwangwoon University, Seoul, Korea.
[5] S. Periyanayagi and V. Sumathy.A Swarm Based Defense Technique for Jamming Attacks in Wireless Sensor Networks....

Ever since the first computer was constructed more than fifty years ago, the general trend has been towards smaller and smaller computers. the trend is in line with moore's law that states that the number of transistors on a semiconductor doubles roughly every 18months with a 50% reduction in area. Within the next twenty years a truly amazing milestone would be reached, transistors would have become the size of a few aggregates of atoms. Going smaller requires manipulating rules of quantum mechanics where the laws of classical physics breaks down. Not only do the laws of quantum mechanics allow us to build smaller computer but they also let usincreasethecomputational power exponentially. such a computer based on quantum mechanics is known as the quantum computer. In a quantum computer there would be no wires, no plugs and no transistors. Quantum computers which harness the power of atoms and molecules to perform memory and processing tasks have The potential to perform certain calculations billions of times faster than any silicon-based computer. This paper gives into insight into the quantum mechanics involved in designing quantum computers and various issues relating to quantum computers such as quantum entanglement and de-coherence. in this paper we discuss how non magnetic resonance (nmr) is used to detect qu-bits State in a quantum computer. a large number of quantum algorithms have been discovered and the Field of quantum cryptology is also budding. there is even a proposal to build a quantum neural network. neural networks are based on the same principle as a human mind. a sizable quantum neural network, if built, would behave just like the human mind but exponentially faster. the principle of quantum entanglement can form the basis for high-speed networking in the future. Quantum computers are surely going to revolutionize the field of computing.

Quantum Computers-A Revolution InComputing

[1]. Richard Feynman, ―Introduction To Quantum Computing‖, 1992
[2]. Peter Shor, ―The Power Of Quantum Computing‖, Springer Publications, 1995
[3]. Www.Vector.Cshl.Org/Quantum And Some Websites And Journals.

With the ever-increasing quantity of text data from a variety of online sources, it is a significant task to categorize or classify these text documents into categories that are manageable and easy to understand. In our old world, learning has been studied either in the unsupervised paradigm which include clustering where all the data is unlabeled, or in the supervised paradigm which include classification where all the data is labeled. A supervised classification of text demands labeled instances which are often arduous, formidable, expensive, or time consuming to obtain. During the intervening time, unlabeled data may be relatively easy to collect, but there are a couple of ways to use them and this method often clusters blindly. Semi-supervised learning figure out this problem by using labeled data together with large amount of unlabeled data to build better classifiers. We also make contribution towards this goal along several dimensions. This paper presents a survey on semi supervised methods of text classification using several Methods.

Semi-supervised learning; Text Classification; Labeled data; unlabeled data.

[1] Zenglin Xu, Rong Jin, Kaizhu Huang, Michael R. Lyu, Irwin King "Semi-supervised Text Categorization by Active Search", Proceedings of the 17th ACM conference on Information and knowledge management, pp. 1517-1518, 2008.
[2] Shweta C. Dharmadhikari, Maya Ingle, Parag Kulkarni "A Novel Multi label Text Classification Model using Semi supervised learning", International Journal of Data Mining & Knowledge Management Process (IJDKP) Vol.2, No.4, pp. 11 – 20, July 2012.
[3] Xue Zhang and Wangxin Xiao "Active Semi-supervised Framework with Data Editing", International Conference on Systems and Informatics (ICSAI -2012), Vol. 9, No. 4, Special Issue, pp. 46 – 50, 2012.
[4] Rong Liu, Jianzhong Zhou, Ming Liu, "A Graph-based Semi-supervised Learning Algorithm for Web Page Classification*" IEEE 2006 Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) (Volume:2 ),pp. 856-860
[5] Hossein Tajari and Hamid Beigy "Gene Expression Based Classification using Iterative Transductive Support Vector Machine "International Journal of Machine Learning and Computing vol. 2, no. 1, pp. 76-81, 2012....

The aim of the project is to develop intelligent pillow to monitor the heart rate by using fiber optical sensor. The research is focused upon allowing more automation of patient care, an especially important matter for the elder population or bedridden patients, which is a rapidly growing fraction of much of the world population today. The fiber-optical sensor is built into pillow. Patients can determine their heart rate by just lying on the pillow and the device will not cause any disturbance to patients and at the same time still monitoring the heart rate. ECG electrodes and finger clip / ear lobe clip probes (using IR LED and photodiode technology) are commonly used for monitoring heart rate. Unfortunately, they are inconvenient and inadequate for long-term, everyday measurements. Fiber-optical sensing overcomes many of these challenges by using light rather than electricity and standard optical fiber in place of copper wire. Optical fibers are nonconductive, electrically passive, immune to electromagnetic interference (EMI)-induced noise, and able to transmit data over long distances with little or no loss in signal integrity. Using the concept of light and the benefits of using fiber optical sensors to detect the heart rate, PPG(photoplethysmogram) is being used in this project. A photoplethysmogram (PPG) is an optically obtained plethysmogram, a volumetric measurement of an organ. A PPG is often obtained by using a pulse oximeter which illuminates the skin and measures changes in light absorption (Shelley and Shelley, 2001).

fiber optical sensor, heart rate, IR LED, photodiode/phototransistor, photoplethymogram, PPG, pulse oximeter.

[1] Optical Fibre Sensors Embedded into technical Textile for Healthcare, "Optical fibre sensors for medical applications"
[2] Venu Gopal Madhav Annamdas1,2, "Review on Developments in Fiber Optical Sensors and Applications", International Journal of Materials Engineering 2011; 1(1): 1-16
[3] W B Spillman Jr1,2, M Mayer1, J Bennett1, J Gong1,K E Meissner1, B Davis1, R O Claus1, A A Muelenaer Jr1 and X Xu1, "A "smart‟ bed for non-intrusive monitoring of patient physiological factors", Received 24 December 2003, in final form 24 February 2004, Published 19 July 2004
[4] William Spillman, chair, Jimmy Ritter, Guy Indebetouw, "Dual Processing Spatially Distributed Integrating Fiber Optic Sensors for Non-intrusive Patient Monitoring", April 21 2005 Blacksburg, Virginia
[5] John Allen, "Photoplethysmography and its application in clinical physiological measurement", Received 8 October 2006, accepted for publication 24 January 2007, Published 20 February 2007, Regional Medical Physics Department, Freeman Hospital, Newcastle upon Tyne NE7 7DN, UK...

In this paper we give an overview of the advances made in image and video filtering using fuzzy logic, at our Fuzziness and Uncertainty Modeling Laboratory. The fact that fuzzy techniques have found an interesting application field in image and video filtering is not a surprise: detecting whether a pixel is corrupted by noise and assessing the degree to which such a pixel is corrupted are intrinsically fuzzy processes,that come along with uncertainty (is the pixel noisy or not?)and imprecision (how noisy is it?).This paper proposes an intelligent Furry lmqe Fdter (FIF)to remove impulse noise.The filter including two processes, the ImeUigenr Fuizy Number Deciding (mVD) pmcp~s and fuzzy inJkencc pmcess, to filter impulse noise from heavily corrupted images efiiciently.mVD can automatically decide the number of furzy number based on image features to overcome the drawbacks of Adoptive Weighted Furzy Mean (AWFM) filter that must be defined by
domain expert Moreover,the fuzzy inference process refers the howledge base produced by IFND and fuzzy rule base that can improve the wealole ss of conventional filters in heavily corrupted condition.The intelligent FIF achieves better performance than the other filters based on the criteria of Mean Absolute Error (MAD,and Mean Square Error (MSE).By the experiments,FIF still keeps the high performance to filtering impulse noise from calor image.

Furzy number,AWFM filter,image processing, edge detection, impulse noise.

[1]. IC Anlrawq "Median filter ba& on fuzzy rules and isbapplication to image restoration," F- Se$ and Systems, vol. 77, pp. 343,1996.
[2]. X. Yang and PS Toh, "Adaptive fmy multilevel filter," IEEE Trans. on lmage Pmeeuing," vol. 4, no. 5, pp.680682, 1995.
[3]. F. Russo and G. Rampmi "A fuayowr for the enhancement of bluned and noisy image," IEEE Trans.onImagePmessing,vol.4.no.8,pp.l169-1174,1995.
[4]. Y. S. Chi and R Krishnapuram, "A robust approach to image enhanament based on fuzzy logic,"lEEE Trans.on Image Pmcessing, vol. 6, no. 6, pp, 808825, 1997.
[5]. R. C. Hardie and K. E. Bamer, "Rank conditioned rank selection filters for signal restaration:‟ IEEE Trans. On hage Processing, vol. 3, no. 2, pp. 192-2C6, 1994...

Visual Cryptography is a new Cryptography technique which is used to secure the images. In Visual Cryptography the Image is divided into parts called shares and then they are distributed to the participants. The Decryption side just stacking the share images gets the image. The initial model developed only for the bi-level or binary images or monochrome images. Later it was advanced to suit for the Colour Images means Gray Images and RGB/CMY Images. For the RGB/CMY Images different methods are developed based on the colour decomposition techniques. Pixel expansion and the quality of the reconstructed secret image has been a major issue of visual secret sharing (VSS) schemes. A number of probabilistic VSS schemes with minimum pixel expansion have been proposed for black and white (binary) secret images. This paper presents a (2, 2)-VSS scheme for gray scale of the shadow images (transparent shares) is based on the bit plane coding.

Visual Cryptography in Gray Scale Images

[1]. M. Noar and A. Shamir, "Visual cryptography," Advances in Cryptology - EUROCRYPT'94, pp. 1-12, 1995.
[2]. Mizuho Nakajima and Yasushi Yamaguchi, "Extended Visual Cryptography for Natural Images"
[3]. Young-Chang Hou, "Visual cryptography for color images," Pattern Recognition, Vol. 36, No. 7, pp. 1619-1629, 2003.
[4]. Z. Zhou, G.R. Arce and G. Crescenzo, "Halftone visual cryptography," IEEE Transactions on Image Processing, Vol. 15, No. 8, pp. 2441-2453, 2006.
[5]. Inkookang, G.R. Arce, and H.K. Lee, "Color Extended Visual Cryptography using Error Diffusion," 2009....