Design And Development Of A Broadband Erbium Doped Fiber Amplifiers

This thesis presents the research work that was carried out on the development, characterization and analysis of broadband Erbium doped silica fiber amplifier (B-EDF). Erbium doped fiber amplifiers provide advantages over regenerative repeaters as well as other amplification systems. For example, better crosstalk characteristics, higher power operation, lower insertion loss than semiconductor laser amplifiers, higher efficiency than Raman amplifiers and low noise figure than Brillounamplifiers. In addition, EDFAs are the only amplifier, that can be used as both distributed and lumped amplifier in telecommunications. Currently, optical communication technology is moving from point-to-point systems to optical networking. The exponential growth in data communications and the internet places urgent demands on high-capacity communication networks. To increase the total capacity, amplifier bandwidth has commanded much attention. However, the spread and expansion of dense wavelength division multiplexing (DWDM) systems are keeping pace with various technology developments of optical amplifiers and, in particular, the bandwidth broadening of EDFAs. For this thesis, a novel EDFA structure is developed to increase the amplifier bandwidth by combining the conventional band and long wavelength band (C+L). This will offer more efficient use of optical fiber networks and it will satisfy the demand of higher transmission capacity

Ontology specific visual canvas generation to facilitate sense-making-an algorithmic approach

Ontologies are domain-specific conceptualizations that are both human and machine-readable. Due to this remarkable attribute of ontologies, its applications are not limited to computing domains. Banking, medicine, agriculture, and law are a few of the non-computing domains, where ontologies are being used very effectively. When creating ontologies for non-computing domains, involvement of the non-computing domain specialists like bankers, lawyers, farmers become very vital. Hence, they are not semantic specialists, particularly designed visualization assistance is required for the ontology schema verifications and sense-making. Existing visualization methods are not fine-tuned for non-technical domain specialists and there are lots of complexities. In this research, a novel algorithm capable of generating domain specialists’ friendlier visualization canvas has been explored. This proposed algorithm and the visualization canvas has been tested for three different domains and overall success of 85% has been yielded

Performance analysis in text clustering using k-means and k-medoids algorithms for Malay crime documents

Few studies on text clustering for the Malay language have been conducted due to some limitations that need to be addressed. The purpose of this article is to compare the two clustering algorithms of k-means and k-medoids using Euclidean distance similarity to determine which method is the best for clustering documents. Both algorithms are applied to 1000 documents pertaining to housebreaking crimes involving a variety of different modus operandi. Comparability results indicate that the k-means algorithm performed the best at clustering the relevant documents, with a 78% accuracy rate. K-means clustering also achieves the best performance for cluster evaluation when comparing the average within-cluster distance to the k-medoids algorithm. However, k-medoids perform exceptionally well on the Davis Bouldin index (DBI). Furthermore, the accuracy of k-means is dependent on the number of initial clusters, where the appropriate cluster number can be determined using the elbow method

The development of an ontology model for early identification of children with specific learning disabilities

Ontology-based knowledge representation is explored in special education environment as not much attention has been given to the area of specific learning disabilities such as dyslexia, dysgraphia and dyscalculia. Therefore, this paper aims to capture the knowledge in special education domain, represent the knowledge using ontology-based approach and make it efficient for early identification of children who might have specific learning disabilities. In this paper, the step-by-step development process of the ontology is presented by following the five phases of ontological engineering approach, which consists of specification, conceptualization, formalization, implementation, and maintenance. The details of the ontological model’s content and structure is built and the applicability of the ontology for early identification and recommendation is demonstrated

A computational analysis of short sentences based on ensemble similarity model

The rapid development of Internet along with the wide use of social media applications produce huge volume of unstructured data in short text form such as tweets, text snippets and instant messages. This form of data rarely contains repeated word. It presents challenge in sentences similarity analysis as the standard text similarity models merely rely on the number of word occurrence, often resulting unreliable similarity value. Besides, the use of abbreviation, acronyms, slang, smiley, jargon, symbol or non-standard short form also contributes to the difficulty in similarity analysis. Thus, an extended ensemble similarity model approach is proposed. An experimental study has been conducted using datasets of English short sentences. The findings are very encouraging in improving the similarity value for short sentences