Document Image Segmentation and Compression using Artificial Neural Networks and Evolutionary Methods

The work is intended to study the following important aspects of document image processing and develop new methods. (1) Segmentation ofdocument images using adaptive interval valued neuro-fuzzy method. (2) Improving the segmentation procedure using Simulated Annealing technique. (3) Development of optimized compression algorithms using Genetic Algorithm and parallel Genetic Algorithm (4) Feature extraction of document images (5) Development of IV fuzzy rules. This work also helps for feature extraction and foreground and background identification. The proposed work incorporates Evolutionary and hybrid methods for segmentation and compression of document images. A study of different neural networks used in image processing, the study of developments in the area of fuzzy logic etc is carried out in this work

Studies on the microbial diseases of dendrobium hybrid (orchids)

There is no baseline data available at present on the nature of various diseases that occur in a orchid population, under cultivation, in any commercial orchid farm maintained by small scale entrepreneurs who invest considerable amount of money, effort and time. The available data on type of disease symptoms, causative agent, , nature of pathogens, as to bacteria or ftmgi or any other biological agents, and their source, appropriate and effective control measures could not be devised, for large scale implementation and effective management, although arbitrary methods are being practiced by very few farms. Further influence of seasonal variations and environmental factors on disease outbreak is also not scientifically documented and statistically verified as to their authenticity. In this context, the primary objective of the present study was to create a data bank on the following aspects 1. Occurrence of different disease symptoms in Dendrobium hybrid over a period of one year covering all seasons 2. Variations in the environmental parameters at the orchid farms 3. Variations in the characteristics of water used for irrigation in the selected orchid farm 4. Microbial population associated with the various disease symptoms 5. Isolation and identification of bacteria isolated from diseased plants 6. Statistical treatment of the quantitative data and evolving statistical model

Feature Extraction Methods Based on Linear Predictive Coding and Wavelet Packet Decomposition for Recognizing Spoken Words in Malayalam

Speech signals are one of the most important means of communication among the human beings. In this paper, a comparative study of two feature extraction techniques are carried out for recognizing speaker independent spoken isolated words. First one is a hybrid approach with Linear Predictive Coding (LPC) and Artificial Neural Networks (ANN) and the second method uses a combination of Wavelet Packet Decomposition (WPD) and Artificial Neural Networks. Voice signals are sampled directly from the microphone and then they are processed using these two techniques for extracting the features. Words from Malayalam, one of the four major Dravidian languages of southern India are chosen for recognition. Training, testing and pattern recognition are performed using Artificial Neural Networks. Back propagation method is used to train the ANN. The proposed method is implemented for 50 speakers uttering 20 isolated words each. Both the methods produce good recognition accuracy. But Wavelet Packet Decomposition is found to be more suitable for recognizing speech because of its multi-resolution characteristics and efficient time frequency localizations

A Novel Fast Hybrid Cryptographic System: MARS4

A novel and fast technique for cryptographic applications is designed and developed using the symmetric key algorithm “MAJE4” and the popular asymmetric key algorithm “RSA”. The MAJE4 algorithm is used for encryption / decryption of files since it is much faster and occupies less memory than RSA. The RSA algorithm is used to solve the problem of key exchange as well as to accomplish scalability and message authentication. The focus is to develop a new hybrid system called MARS4 by combining the two cryptographic methods with an aim to get the advantages of both. The performance evaluation of MARS4 is done in comparison with MAJE4 and RSA.

DNA Sequence Representation methods

DNA sequence representation methods are used to denote a gene structure effectively and help in similarities/dissimilarities analysis of coding sequences. Many different kinds of representations have been proposed in the literature. They can be broadly classified into Numerical, Graphical, Geometrical and Hybrid representation methods. DNA structure and function analysis are made easy with graphical and geometrical representation methods since it gives visual representation of a DNA structure. In numerical method, numerical values are assigned to a sequence and digital signal processing methods are used to analyze the sequence. Hybrid approaches are also reported in the literature to analyze DNA sequences. This paper reviews the latest developments in DNA Sequence representation methods. We also present a taxonomy of various methods. A comparison of these methods where ever possible is also done

Hybrid Multiferroic Nanostructure with Magnetic−Dielectric Coupling

The development of methods to economically synthesize single wire structured multiferroic systems with room temperature spin−charge coupling is expected to be important for building next-generation multifunctional devices with ultralow power consumption. We demonstrate the fabrication of a single nanowire multiferroic system, a new geometry, exhibiting room temperature magnetodielectric coupling. A coaxial nanotube/nanowire heterostructure of barium titanate (BaTiO3, BTO) and cobalt (Co) has been synthesized using a template-assisted method. Room temperature ferromagnetism and ferroelectricity were exhibited by this coaxial system, indicating the coexistence of more than one ferroic interaction in this composite system