The diatom genus Gomphonema Ehrenberg in India: Checklist and description of three new species

We have compiled a checklist of Gomphonema Ehrenberg taxa reported previously from India. From forty-nine references, over 100 Gomphonema taxa have been reported, including 39 new taxon descriptions. In addition to these previous reports of Gomphonema taxa, we describe three new species. G. gandhii Karthick & Kociolek, sp. nov., G. difformum Karthick & Kociolek, sp. nov. and G. diminutum Karthick & Kociolek, sp. nov., all from hill streams of Western Ghats, India. Frustule morphology, as studied in light and scanning electron microscopy, is compared with that of other recently described Gomphonema species from Africa and Asia. All three Indian species have distinctly dilated proximal raphe ends, in addition to differentiated apical pore fields, septa, pseudosepta and a round external stigma! opening. Gomphonema gandhii is linear-lanceolate-clavate, has a wide axial area, and is 19-51 mu m long, 3-7 mu m broad. Gomphonema difformum is smaller than G. gandhii, and has a hyaline area around the headpole. Gomphonema diminuta is much smaller and narrower than the other two species. These species are distinct from their closest congeners by their sizes, shape and structure of the head pole, and striae densities. All these species were described from low nutrient, neutral, low ionic content streams of Western Ghats. As most other species described from tropical region these three species appear to be endemic to India. Moreover, within India they have hitherto only been found in Western Ghats, one of the twelve biodiversity hotspots of the World.

Two Dimensional Principal Component Analysis for Online Tamil Character Recognition

This paper presents a new application of two dimensional Principal Component Analysis (2DPCA) to the problem of online character recognition in Tamil Script. A novel set of features employing polynomial fits and quartiles in combination with conventional features are derived for each sample point of the Tamil character obtained after smoothing and resampling. These are stacked to form a matrix, using which a covariance matrix is constructed. A subset of the eigenvectors of the covariance matrix is employed to get the features in the reduced sub space. Each character is modeled as a separate subspace and a modified form of the Mahalanobis distance is derived to classify a given test character. Results indicate that the recognition accuracy using the 2DPCA scheme shows an approximate 3% improvement over the conventional PCA technique.

Online character recognition using regression techniques

This paper introduces a scheme for classification of online handwritten characters based on polynomial regression of the sampled points of the sub-strokes in a character. The segmentation is done based on the velocity profile of the written character and this requires a smoothening of the velocity profile. We propose a novel scheme for smoothening the velocity profile curve and identification of the critical points to segment the character. We also porpose another method for segmentation based on the human eye perception. We then extract two sets of features for recognition of handwritten characters. Each sub-stroke is a simple curve, a part of the character, and is represented by the distance measure of each point from the first point. This forms the first set of feature vector for each character. The second feature vector are the coeficients obtained from the B-splines fitted to the control knots obtained from the segmentation algorithm. The feature vector is fed to the SVM classifier and it indicates an efficiency of 68% using the polynomial regression technique and 74% using the spline fitting method.

Comparison of AM-FM Based Features For Robust Speech Recognition

Effective feature extraction for robust speech recognition is a widely addressed topic and currently there is much effort to invoke non-stationary signal models instead of quasi-stationary signal models leading to standard features such as LPC or MFCC. Joint amplitude modulation and frequency modulation (AM-FM) is a classical non-parametric approach to non-stationary signal modeling and recently new feature sets for automatic speech recognition (ASR) have been derived based on a multi-band AM-FM representation of the signal. We consider several of these representations and compare their performances for robust speech recognition in noise, using the AURORA-2 database. We show that FEPSTRUM representation proposed is more effective than others. We also propose an improvement to FEPSTRUM based on the Teager energy operator (TEO) and show that it can selectively outperform even FEPSTRUM

Flux Partitioning of Species Approach in Binary Interdiffusion Process

Validation of the flux partitioning of species model has been illustrated. Various combinations of inequality expression for the fluxes of species A and B in two successively grown hypothetical intermetallic phases in the interdiffusion zone have been considered within the constraints of this concept. Furthermore, ratio of intrinsic diffusivities of the species A and B in those two phases has been correlated in four different cases. Moreover, complete and or partial validation or invalidation of this model with respect to both the species, has been proven theoretically and also discussed with the Co-Si system as an example.

A Profilometric Approach to 3D Face Reconstruction and Its Application to Face Recognition

3D Face Recognition is an active area of research for past several years. For a 3D face recognition system one would like to have an accurate as well as low cost setup for constructing 3D face model. In this paper, we use Profilometry approach to obtain a 3D face model.This method gives a low cost solution to the problem of acquiring 3D data and the 3D face models generated by this method are sufficiently accurate. We also develop an algorithm that can use the 3D face model generated by the above method for the recognition purpose.

Online Handwriting Recognition of Tamil Script Using Fractal Geometry

We present a fractal coding method to recognize online handwritten Tamil characters and propose a novel technique to increase the efficiency in terms of time while coding and decoding. This technique exploits the redundancy in data, thereby achieving better compression and usage of lesser memory. It also reduces the encoding time and causes little distortion during reconstruction. Experiments have been conducted to use these fractal codes to classify the online handwritten Tamil characters from the IWFHR 2006 competition dataset. In one approach, we use fractal coding and decoding process. A recognition accuracy of 90% has been achieved by using DTW for distortion evaluation during classification and encoding processes as compared to 78% using nearest neighbor classifier. In other experiments, we use the fractal code, fractal dimensions and features derived from fractal codes as features in separate classifiers. While the fractal code is successful as a feature, the other two features are not able to capture the wide within-class variations.

Unrestricted Kannada online handwritten akshara recognition using SDTW

In this paper, we present an unrestricted Kannada online handwritten character recognizer which is viable for real time applications. It handles Kannada and Indo-Arabic numerals, punctuation marks and special symbols like $, &, # etc, apart from all the aksharas of the Kannada script. The dataset used has handwriting of 69 people from four different locations, making the recognition writer independent. It was found that for the DTW classifier, using smoothed first derivatives as features, enhanced the performance to 89% as compared to preprocessed co-ordinates which gave 85%, but was too inefficient in terms of time. To overcome this, we used Statistical Dynamic Time Warping (SDTW) and achieved 46 times faster classification with comparable accuracy i.e. 88%, making it fast enough for practical applications. The accuracies reported are raw symbol recognition results from the classifier. Thus, there is good scope of improvement in actual applications. Where domain constraints such as fixed vocabulary, language models and post processing can be employed. A working demo is also available on tablet PC for recognition of Kannada words.

Comparison of HMM and SDTW for Tamil handwritten character recognition

In this paper, we compare the experimental results for Tamil online handwritten character recognition using HMM and Statistical Dynamic Time Warping (SDTW) as classifiers. HMM was used for a 156-class problem. Different feature sets and values for the HMM states & mixtures were tried and the best combination was found to be 16 states & 14 mixtures, giving an accuracy of 85%. The features used in this combination were retained and a SDTW model with 20 states and single Gaussian was used as classifier. Also, the symbol set was increased to include numerals, punctuation marks and special symbols like $, & and #, taking the number of classes to 188. It was found that, with a small addition to the feature set, this simple SDTW classifier performed on par with the more complicated HMM model, giving an accuracy of 84%. Mixture density estimation computations was reduced by 11 times. The recognition is writer independent, as the dataset used is quite large, with a variety of handwriting styles.

Mini Forest - An experiment to evaluate the adaptability of Western Ghats species for afforestation

Saplings of forty nine species of trees from Western Ghats forests were planted on a 1.5 hectare tract of Deccan plateau (in the campus of Indian Institute of Science, Bangalore) and their performance monitored for 23 years. The objective was to evaluate their adaptability to a habitat and conditions apparently alien to these species. The study was also meant to understand the linkages of these trees with the surrounding environment. Contrary to the belief that tree species are very sensitive to change of location and conditions, the introduced trees have grown as good as they would do in their native habitat and maintained their phenology. Further, they have grown in perfect harmony with trees native to the location. The results show that the introduced species are opportunistic and readily acclimatized and grew well overcoming the need for the edaphic and other factors that are believed to be responsible for their endemicity. Besides ex situ conservation, the creation of miniforest has other accrued ecosystem benefits. For instance, the ground water level has risen and the ambient temperature has come down by two degrees.

Molecular Identification and Phylogeny of Bactrocera Species (Diptera: Tephritidae)

Fruit flies that belong to the genus Bactrocera (Diptera: Tephritidae) are major invasive pests of agricultural crops in Asia and Australia. Increased transboundary movement of agricultural produce has resulted in the chance introduction of many invasive species that include Bactrocera mainly as immature stages. Therefore quick and accurate species diagnosis is important at the port of entry, where morphological identification has a limited role, as it requires the presence of adult specimens and the availability of a specialist. Unfortunately when only immature stages are present, a lacunae in their taxonomy impedes accurate species diagnosis. At this juncture, molecular species diagnostics based on COX-I have become handy, because diagnosis is not limited by developmental stages. Yet another method of quick and accurate species diagnosis for Bactrocera spp. is based on the development of species-specific markers. This study evaluated the utility of COX-I for the quick and accurate species diagnosis of eggs, larvae, pupae and adults of B. zonata Saunders, B. tau Walker, and B. dorsalis Hendel. Furthermore the utility of species-specific markers in differentiating B. zonata (500bp) and B. tau (220bp) was shown. Phylogenetic relationships among five subgenera, viz., Austrodacus, Bactrocera, Daculus, Notodacus and Zeugodacus have been resolved employing the 5' region of COX-I (1490-2198); where COX-I sequences for B. dorsalis Hendel, B. tau Walker, B. correcta Bezzi and B. zonata Saunders from India were compared with other NCBI-GenBank accessions. Phylogenetic analysis employing Maximum Parsimony (MP) and Bayesian phylogenetic approach (BP) showed that the subgenus Bactrocera is monophyletic.

Testing concordance in species boundaries using acoustic, morphological, and molecular data in the field cricket genus Itaropsis (Orthoptera: Grylloidea, Gryllidae: Gryllinae)

In most taxa, species boundaries are inferred based on differences in morphology or DNA sequences revealed by taxonomic or phylogenetic analyses. In crickets, acoustic mating signals or calling songs have species-specific structures and provide a third data set to infer species boundaries. We examined the concordance in species boundaries obtained using acoustic, morphological, and molecular data sets in the field cricket genus Itaropsis. This genus is currently described by only one valid species, Itaropsis tenella, with a broad distribution in western peninsular India and Sri Lanka. Calling songs of males sampled from four sites in peninsular India exhibited significant differences in a number of call features, suggesting the existence of multiple species. Cluster analysis of the acoustic data, molecular phylogenetic analyses, and phylogenetic analyses combining all data sets suggested the existence of three clades. Whatever the differences in calling signals, no full congruence was obtained between all the data sets, even though the resultant lineages were largely concordant with the acoustic clusters. The genus Itaropsis could thus be represented by three morphologically cryptic incipient species in peninsular India; their distributions are congruent with usual patterns of endemism in the Western Ghats, India. Song evolution is analysed through the divergence in syllable period, syllable and call duration, and dominant frequency.

Self assembly of bivalent glycolipids on single walled carbon nanotubes and their specific molecular recognition properties

Solubilization of single walled carbon nanotubes (SWNTs) in aqueous milieu by self assembly of bivalent glycolipids is described. Thorough analysis of the resulting composites involving Vis/near-IR spectroscopy, surface plasmon resonance, confocal Raman and atomic force microscopy reveals that glycolipid-coated SWNTs possess specific molecular recognition properties towards lectins.