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Palanivel, S.
- First Report of Two Mymarid Genera, Cleruchus Enock and Kikiki Huber and Beardsley (Hymenoptera: Mymaridae) from India
Authors
1 Department of Entomology, Faculty of Agriculture, Annamalai University, Chidambaram 608 002, Tamil Nadu, IN
Source
Journal of Biological Control, Vol 27, No 2 (2013), Pagination: 81-82Abstract
Two genera of fairyfly wasps, Cleruchus Enock and Kikiki Huber & Beardsley, are first reported from India and notes on their diagnosis are given.Keywords
Hymenoptera, Mymaridae, Cleruchus, Kikiki, First Report.Full Text
References
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- Huber JT, Beardsley JW. 2000. A new genus of fairyfly, Kikiki, from the Hawaiian Islands (Hymenoptera: Mymaridae). Proc Hawaiian Ent Soc. 34: 66–67.
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- Triapitsyn SV. 2002. Review of the Mymaridae (Hymenoptera: Chalcidoidea) of Primorskii Krai: Genera Cleruchus Enock and Stethynium Enock. Far Eastern Entomol. 122: 1–13.
- Triapitsyn SV, Moraal LG. 2008. Two new species of Cleruchus (Hymenoptera: Mymaridae) from The Netherlands and California, USA, apparently associated with Ciidae (Coleoptera) in bracket fungi. Entomologische Berichten, Amsterdam 68(2): 63.
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- Morphology and Taxonomy of Oscillatoria princeps Vaucher Ex Gomont (Oscillatoriales, Oscillatoriaceae)
Authors
1 Dept of Plant Biology and Plant Biotechnology, Guru Nanak College, Chennai 600042, IN
Source
Indian Journal of Education and Information Management, Vol 5, No 1 (2016), Pagination: 1-5Abstract
Background: Oscillatoria princeps has been reported from various parts of India for its diversity but not studied in detail about morphology and reproduction hence we try to fill that lacuna.
Methods: Samples were collected from a stream near Kanyakumari district, Tamil Nadu. One part was preserved in 4% formalin, while the other part was brought to unialgal culture by streaking method. The cultures were grown and maintained in ASN III (-NaCl) medium at the photoperiodic culture racks with 8 hours light and 16 hours dark. Semi-permanent slides were prepared and Leica EC3 Microsystems was used for observation and documentation.
Findings: The alga was dark blue green in colour, growing in clusters at the bottom of the stream. Individual filaments were blue green to olive green in colour. Mature trichome straight, cells much broader than long with distinct cross walls. Apical cells hemispherical with keritomized content. Individual cells were round in shape and the size varies from 57.60μm to 69.05μm in width and 5.20μm to 9.55μm in length thus the ratio of length and breadth as 1:8. When such cells were stacked one above the other it gives "stack of poker chips" appearance, characteristic feature of the species. Notches were found at the inner side of the cell corresponding to the slit on the trichome. When such cells were stacked one above the other, it results in the crack like appearance on the trichome. One to three notches/slits were observed in the present study. Reproduction both by fragmentation and hormogonia (formation of separation disc) was observed.
Application/Improvements: The new finding like cracks/ slit like structure on trichome can be taken for further studies. The microphotographs will enable the researchers to have better understanding.
Keywords
Cyanobacteria, Oscillatoria princeps, Kanyakumari, BGA, Hormogonia and Necridia.Full Text
References
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- A New Approach for Coding of Speech Signals using Auto Associative Neural Networks
Authors
Source
Digital Signal Processing, Vol 5, No 6 (2013), Pagination: 211-215Abstract
Digital Speech coding is a procedure to represent a digitized speech signal using as few bits as possible, maintaining the speech quality and its intelligibility at the same time. In this paper a new direction in research on speech coding using auto associative neural networks (AANN) is discussed. The AANN acts as a combination of encoder and decoder. The feature extractor extracts the necessary features from the input speech. Instead of coding the speech signal the Linear Predictive coefficients (LPC) and discrete cosine transform (DCT) features of the speech signal which acts as the compressed value of the speech, is passed to the neural network. The signal reconstructor reconstructs the signal based on the decompressed features and the weight matrix. Different features are extracted and the results are compared. The signal to noise ratio (SNR) shows the efficiency of the algorithm. Some of the applications for which this coder is suitable are videoconferencing, streaming audio, archival, and messaging.Keywords
Auto Associative Neural Networks, Discrete Cosine Transform, Linear Predictive Coefficients, Speech Coding.
- Tumor Diagnosis in MRI Brain Image using ACM Segmentation and ANN-LM Classification Techniques
Authors
1 Department of Computer Science and Engineering, Annamalai University, Chidambaram - 608002, Tamil Nadu, IN
2 Department of Computer Science and Engineering, P. S. R. Rengasamy College of Engineering for Women, Sivakasi - 626140, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 9, No 1 (2016), Pagination:Abstract
Background: Magnetic Resonance Images (MRI) is an important medical diagnosis tool for the detection of tumours in brain as it provides the detailed information associated to the anatomical structures of the brain. MRI images help the radiologist to find the presence of abnormal cell growths or tumours. MRI image analysis plays a vital role in diagnosis of brain tumours in the earlier stages and treatment of diseases. Methods: Therefore, this paper introduces an efficient MRI brain image analysis method, where, the MRI brain images are classified into normal, non cancerous (benign) brain tumour and cancerous (malignant) brain tumour. This proposed method follows four steps, 1. Pre-processing, 2. Segmentation, 3. Textural and shape feature extraction and 4. Classification. In this proposed MRI image analysis using the region based Active Contour Method (ACM) used for segmentation and Artificial Neural Network (ANN) based Levenberg-Marquardt (LM) algorithm used for classification process, which used to efficiently classify the MRI image as normal and Tumourous. Findings: The results revealed that the proposed MRI brain image tumour diagnosis process is accurate, fast and robust. The classifier based MRI brain image processing approach produced the best MRI brain image classification with use of feature extraction and segmentation results, in terms of accuracy. Best overall classification accuracy results were obtained using the given DioCom Images; The performance results proven that there is not sufficient result given to the classification process when it perform separately. With the use of ACM segmentation and feature extraction approaches, the proposed LM classification approach provides better classification accuracy than the existing approach. Application: The proposed MRI image based brain tumour analysis would efficiently deal with segmentation and classification process for brain tumour analysis with use of feature extraction methods, so this method can yield the better result of brain tumour diagnosis in advance where this method using in medical fields.
Keywords
Active Contour Method (ACM), Artificial Neural Network (ANN) based Levenberg-Marquardt (LM) Algorithm, Magnetic Resonance Images (MRI)Full Text
- Automatic Segmentation of Broadcast Audio Signals Using Auto Associative Neural Networks
Authors
1 Department of Computer Science and Engineering, Annamalai University, Tamil Nadu, IN
2 Department of Business Administration, Annamalai University, Tamil Nadu, IN
Source
ICTACT Journal on Communication Technology, Vol 1, No 4 (2010), Pagination: 187-190Abstract
In this paper, we describe automatic segmentation methods for audio broadcast data. Today, digital audio applications are part of our everyday lives. Since there are more and more digital audio databases in place these days, the importance of effective management for audio databases have become prominent. Broadcast audio data is recorded from the Television which comprises of various categories of audio signals. Efficient algorithms for segmenting the audio broadcast data into predefined categories are proposed. Audio features namely Linear prediction coefficients (LPC), Linear prediction cepstral coefficients, and Mel frequency cepstral coefficients (MFCC) are extracted to characterize the audio data. Auto Associative Neural Networks are used to segment the audio data into predefined categories using the extracted features. Experimental results indicate that the proposed algorithms can produce satisfactory results.Keywords
Linear Prediction Cepstral Coefficients, Mel Frequency Cepstral Coefficients, Auto Associative Neural Networks, Audio Segmentation, Audio Classification.- Algal Diversity of Arulmigu Sri Thiyagarajaswamy Temple Tank Thiruvottiyur, Chennai, Tamil Nadu, India
Authors
1 Department of Plant Biology and Plant Biotechnology, Guru Nanak College (GRI), Chennai – 600042, Tamil Nadu, IN
Source
Indian Journal of Science and Technology, Vol 10, No 13 (2017), Pagination:Abstract
Objectives: Arulmigu Sri Thiyagarajaswamy Temple, Thiruvottiyur, Tamil Nadu has a tank which is unexplored for its algal flora. Thus an attempt has been made to study the diversity of algae. Methods: Water samples were randomly collected in plastic bottles from different sites of the tank. Samples were also collected by scrapping the steps of the temple tank to get the epilithic algae. Habitat was checked for its pH and Temperature. Samples were brought to the laboratory and preserved in 4% formalin for further studies. Observation and documentation of the algae was done using HOVERLABS Research Photo Microscopic Unit. Findings: In the present study a total number of 28 genera 39 taxa belonging to the three different Classes namely Cyanophyceae, Chlorophyceae and Bacillariophyceae were recorded. Maximum number of 9 taxa was observed from the family Scenedesmaceae of Chlorophyceae. It was interesting to note that 16 families had only a single taxon. The result clearly shows that the temple tank has rich algal flora and greater diversity. Applications: This study divulges that the temple tanks are one of the best habitats for microalgae. Hence the algal diversity can be used for determining the pollution level of such tanks.Keywords
Bacillariophyceae, Chlorophyceae, Cyanophyceae, Temple Tank.Full Text
- HOG-based Emotion Recognition Using One-Dimensional Convolutional Neural Network
Authors
1 Department of Computer Science and Engineering, Annamalai University, IN