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

Design And Development Of A Named Entity Based Question Answering System For Malayalam Language

This is a Named Entity Based Question Answering System for Malayalam Language. Although a vast amount of information is available today in digital form, no effective information access mechanism exists to provide humans with convenient information access. Information Retrieval and Question Answering systems are the two mechanisms available now for information access. Information systems typically return a long list of documents in response to a user’s query which are to be skimmed by the user to determine whether they contain an answer. But a Question Answering System allows the user to state his/her information need as a natural language question and receives most appropriate answer in a word or a sentence or a paragraph. This system is based on Named Entity Tagging and Question Classification. Document tagging extracts useful information from the documents which will be used in finding the answer to the question. Question Classification extracts useful information from the question to determine the type of the question and the way in which the question is to be answered. Various Machine Learning methods are used to tag the documents. Rule-Based Approach is used for Question Classification. Malayalam belongs to the Dravidian family of languages and is one of the four major languages of this family. It is one of the 22 Scheduled Languages of India with official language status in the state of Kerala. It is spoken by 40 million people. Malayalam is a morphologically rich agglutinative language and relatively of free word order. Also Malayalam has a productive morphology that allows the creation of complex words which are often highly ambiguous. Document tagging tools such as Parts-of-Speech Tagger, Phrase Chunker, Named Entity Tagger, and Compound Word Splitter are developed as a part of this research work. No such tools were available for Malayalam language. Finite State Transducer, High Order Conditional Random Field, Artificial Immunity System Principles, and Support Vector Machines are the techniques used for the design of these document preprocessing tools. This research work describes how the Named Entity is used to represent the documents. Single sentence questions are used to test the system. Overall Precision and Recall obtained are 88.5% and 85.9% respectively. This work can be extended in several directions. The coverage of non-factoid questions can be increased and also it can be extended to include open domain applications. Reference Resolution and Word Sense Disambiguation techniques are suggested as the future enhancements

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.

Professional Development of Academic Library Professionals in Kerala

The paper aims to bring out the problems and prospects of the professional development opportunities of academic library professionals in the Universities in Kerala. The study is a part of research undertaken to survey the professional development activities and educational needs of library professionals in the major Universities of Kerala because of the developments in Information communication technology. The study recommends methods for improving the knowledge/skills of library professionals. The aim of the study is to evaluate the professional development activities of Library professionals and their attitude towards continuing education programmes. In order to achieve the objectives of the study a survey was conducted with the help of structured questionnaires distributed to 203 library professionals in seven major universities in Kerala, (South India) of which 185 questionnaires were returned. Results of the analysis show that majority of the professionals have pursued higher degrees in library science or IT allied courses after entering the profession, and that they have a positive attitude towards participation in training programmes and workshops. The results show that developments in ICT have a positive influence on majority of library professionals‘ attitude towards continuing education programmes.

Design, Development and Realization of Quadratic Volterra Filters for Selected Applications

The basic concepts of digital signal processing are taught to the students in engineering and science. The focus of the course is on linear, time invariant systems. The question as to what happens when the system is governed by a quadratic or cubic equation remains unanswered in the vast majority of literature on signal processing. Light has been shed on this problem when John V Mathews and Giovanni L Sicuranza published the book Polynomial Signal Processing. This book opened up an unseen vista of polynomial systems for signal and image processing. The book presented the theory and implementations of both adaptive and non-adaptive FIR and IIR quadratic systems which offer improved performance than conventional linear systems. The theory of quadratic systems presents a pristine and virgin area of research that offers computationally intensive work. Once the area of research is selected, the next issue is the choice of the software tool to carry out the work. Conventional languages like C and C++ are easily eliminated as they are not interpreted and lack good quality plotting libraries. MATLAB is proved to be very slow and so do SCILAB and Octave. The search for a language for scientific computing that was as fast as C, but with a good quality plotting library, ended up in Python, a distant relative of LISP. It proved to be ideal for scientific computing. An account of the use of Python, its scientific computing package scipy and the plotting library pylab is given in the appendix Initially, work is focused on designing predictors that exploit the polynomial nonlinearities inherent in speech generation mechanisms. Soon, the work got diverted into medical image processing which offered more potential to exploit by the use of quadratic methods. The major focus in this area is on quadratic edge detection methods for retinal images and fingerprints as well as de-noising raw MRI signals

Development of Directionally Adaptive Techniques for Single Image Super-Resolution

Super Resolution problem is an inverse problem and refers to the process of producing a High resolution (HR) image, making use of one or more Low Resolution (LR) observations. It includes up sampling the image, thereby, increasing the maximum spatial frequency and removing degradations that arise during the image capture namely aliasing and blurring. The work presented in this thesis is based on learning based single image super-resolution. In learning based super-resolution algorithms, a training set or database of available HR images are used to construct the HR image of an image captured using a LR camera. In the training set, images are stored as patches or coefficients of feature representations like wavelet transform, DCT, etc. Single frame image super-resolution can be used in applications where database of HR images are available. The advantage of this method is that by skilfully creating a database of suitable training images, one can improve the quality of the super-resolved image. A new super resolution method based on wavelet transform is developed and it is better than conventional wavelet transform based methods and standard interpolation methods. Super-resolution techniques based on skewed anisotropic transform called directionlet transform are developed to convert a low resolution image which is of small size into a high resolution image of large size. Super-resolution algorithm not only increases the size, but also reduces the degradations occurred during the process of capturing image. This method outperforms the standard interpolation methods and the wavelet methods, both visually and in terms of SNR values. Artifacts like aliasing and ringing effects are also eliminated in this method. The super-resolution methods are implemented using, both critically sampled and over sampled directionlets. The conventional directionlet transform is computationally complex. Hence lifting scheme is used for implementation of directionlets. The new single image super-resolution method based on lifting scheme reduces computational complexity and thereby reduces computation time. The quality of the super resolved image depends on the type of wavelet basis used. A study is conducted to find the effect of different wavelets on the single image super-resolution method. Finally this new method implemented on grey images is extended to colour images and noisy images

Design and Development of Compact Chipless RFID Tags with High Data Encoding Capacity

The main objective of this thesis is to develop a compact chipless RFID tag with high data encoding capacity. The design and development of chipless RFID tag based on multiresonator and multiscatterer methods are presented first. An RFID tag using using SIR capable of 79bits is proposed. The thesis also deals with some of the properties of SIR like harmonic separation, independent control on resonant modes and the capability to change the electrical length. A chipless RFID reader working in a frequency band of 2.36GHz to 2.54GHz has been designed to show the feasibility of the RFID system. For a practical system, a new approach based on UWB Impulse Radar (UWB IR) technology is employed and the decoding methods from noisy backscattered signal are successfully demonstrated. The thesis also proposes a simple calibration procedure, which is able to decode the backscattered signal up to a distance of 80cm with 1mW output power.

SHGs and Cooperatives in the Fishing Sector: An Inclusive Development Option for the Fisher folk of Kerala

The Kerala model of development mostly bypassed the fishing community, as the fishers form the main miserable groups with respect to many of the socio-economic and quality of life indicators. Modernization drive in the fishing sector paradoxically turns to marginalization drives as far as the traditional fishers in Kerala are concerned. Subsequent management and resource recuperation drives too seemed to be detrimental to the local fishing community. Though SHGs and cooperatives had helped in overcoming many of the maladies in most of the sectors in Kerala in terms of livelihood and employment in the 1980s, the fishing sector by that time had been moving ahead with mechanization and export euphoria and hence it bypassed the fishing sector. Though it has not helped the fishing sector in the initial stages, but because of necessity, it soon has become a vibrant livelihood and employment force in the coastal economy of Kerala. Initial success made it to link this with the governmental cooperative set up and soon SHGs and Cooperatives become reinforcing forces for the inclusive development of the real fishers.The fisheries sector in Kerala has undergone drastic changes with the advent of globalised economy. The traditional fisher folk are one of the most marginalized communities in the state and are left out of the overall development process mainly due to the marginalization of this community both in the sea and in the market due to modernization and mechanization of the sector. Mechanization opened up the sector a great deal as it began to attract people belonging to non-fishing community as moneylenders, boat owners, employers and middle men which often resulted in conflicts between traditional and mechanized fishermen. These factors, together with resource depletion resulted in the backwardness experienced by the traditional fishermen compared to other communities who were reaping the benefits of the overall development scenario.The studies detailing the activities and achievements of fisher folks via Self Help Groups (SHGs) and the cooperative movement in coastal Kerala are scant. The SHGs through cooperatives have been effective in livelihood security, poverty alleviation and inclusive development of the fisher folk (Rajasenan and Rajeev, 2012). The SHGs have a greater role to play as estimated fall in demand for marine products in international markets, which may result in reduction of employment opportunities in fish processing, peeling, etc. Also, technological advancement has made them unskilled to work in this sector making them outliers in the overall development process resulting in poor quality of physical and social infrastructure. Hence, it is all the more important to derive a strategy and best practice methods for the effective functioning of these SHGs so that the