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A. Nicodemus , J. Prasanth Jacob , B. Nagarajan

Abstract

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A. Nicodemus , B. Nagarajan , C. Narayanan , M. Varghese , K. Subramanian

Abstract

RAPD markers were used to study genetic variation in ten Teak (Tectona grandis L.f.) populations from Western Ghats and Central regions of India. Ten oligonucleotide primers resolved 90 amplification products of which 66 (73%) were polymorphic. The total genetic diversity detected within the species (H_sp) was 0.3 Average gene diversity (H₀) within different populations ranged from 0.185 to 0.261 (mean = 0.233). The Western Ghats populations had more diversity (H₀ =0.227 - 0.261) compared to those from Central India (H₀ =0.185-0.219). Partitioning of gel;etie diversity within and between populations showed that 78% of variation existing within populations and the rest between populations. A negative relationship was observed between latitude and within-population diversity. Nei's genetic distance between populations ranged from 0.053 to 0.264. Genetic distance. Tended to be low between populations from the same geographic region. The UPGMA dendrogram grouped the Western Ghats and Central Indian populations into two distinct clusters. Low intensity selection within populations is likely to capture a major portion of genetic diversity existing in Teak. The Western Ghats and Central Indian regions can be proposed as separate genecological zones for Teak. Future conservation strategies should aim at preserving both within and across population variation in Teak.

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A. K. Mandal , K. N. Subramanian , B. Nagarajan

Abstract

Dalbergia latifolia and Dalbergia sissoo are two of the important timber yielding leguminous trees of India. They are known to occur in decidnous and mixed deciduous forests. They are well adapted to water clogged soil. Both the species are very well known for their coppicing nature which indirectly helps in the propagation of the species. Not much information is available on the genetics, breeding, floral biology aspects of the species. Biotechnological manipulation in terms of tissue culture and protoplast studies have been made and both species have responded positively. It is emphasised that further approaches in disciplines such as cytogenetics, genetics, breeding and biotechnology shall pave the way for betterment of the species.

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B. Nagarajan ¹, G. B. Jaiprakashnarain ²

Affiliations
1 Dept. of Civil Engg., Bharath Univ., Selaiyur, IN
2 MNM Jain Engg. College, Thoraippakkam, Chennai, TN, IN

Abstract

An economic and novel design is made to remove biological contamination from drinking water. Its upper portion consists of a water filling chamber along with a basic sediment filter and a combination GAC filter impregnated with nano silver colloid. They are in the form of a cartridge and are secured to the bottom of the top chamber. The filter uses two mechanisms to disinfect the water. The first is by filtration; any harmful microorganisms or particles larger than 1 µ m are removed from the water. The second mechanism is by colloidal silver induced antibacterial action. The disinfection rate of this equipment largely depends on the one to one contact between the nano silver ion and the microbe. The microorganisms removal efficiency has been studied extensively and the results are very encouraging to offer a technology at an affordable price tag to the third world countries, where the water borne diseases are a threat to their everyday life. The outline of the study has been published (AWW, 2009).

Keywords

Drinking Water Filter, Disinfection, Nano Silver Colloid, Purification

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R. Athilakshmi ¹, Amitabh Wahi ², B. Nagarajan ³

Affiliations
1 Department of Computer Applications, Sri Krishna College of Engineering and Technology, Coimbatore-641042, IN
2 Department of Information Technology, Bannari Amman Institute of Technology, Sathyamangalam-638401, IN
3 Department of Computer Applications, Bannari Amman Institute of Technology, Sathyamangalam-638401, IN

Abstract

Image background segmentation is one of the most prominent steps in many applications of the image processing. Several algorithms exist for the background segmentation from the dynamic scenes of a video sequence. However, elimination of background from the static images still remains a challenging task. Although trivial background subtraction algorithms can execute quickly, they do not give useful results in most situations. This paper addresses the issue of identifying and segmenting theft images captured from the background regions of the image to alert the user. Objective is to segment foreground object from the background region. An alert signal is produced to enable the user to initiate suitable action.

Keywords

Gray Scale Image, Histogram, Intensity Threshold, Segmentation, Theft Detection.

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B. Nagarajan ¹, K. Sudha ², V. Ramya ²

Affiliations
1 Dept. of CA., Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, IN
2 Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu, IN

Abstract

Vehicle class identification is an important task in pattern recognition. Identifying vehicle with cluttered background affects accuracy of the overall system. Removal of cluttered background from images gives better results in vehicle class identification. In this paper, the cluttered background and mild occlusions are removed from the static images. Statistical features are extracted from the background removed images. The statistical feature of master image is correlated with the features of images from the standard UIUC (University of Illinois, Urbana-Champaign) database. This paper addresses the issues to identify vehicle class of real-world images containing side views of cars class with that of non-cars class. Critical evaluation of the proposed method has improved to an accuracy of 91.7%.

Keywords

Vehicle Identification, Cluttered Background Removal, Statistical Features, Occlusions, Correlation Coefficient.

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B. Nagarajan ¹, P. Balasubramanie ²

Affiliations
1 Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, IN
2 Kongu Engineering College, Perundurai, Tamilnadu, IN

Abstract

Object classification under partially cluttered background is a difficult task in still images. The challenging task in this problem is the classification of objects invariant to size and pose with partially cluttered environment containing natural scenes. This paper addresses the issues to classify sample objects from caltech-101 standard database containing airplanes and motorbikes. The threshold technique with background subtraction is used to segment the background region to extract the object of interest. The background segmented image with region of interest is divided into equal sized blocks of sub-images. The statistical features are extracted from each sub-block. The features of the objects are fed to the back-propagation neural classifier. Thus the performance of the neural classifier is compared with various categories of block size. Quantitative evaluation out perform the previous work in the literature with an improved results of 92.4% due to absence of occlusions. A critical evaluation of our approach under the proposed is presented.

Keywords

Background Segmentation, Neural Classifier, Object Classification, Statistical Features.

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