Abstract: In recent times, multidimensional arrays are extensively used to handle large amount of data in scientific research, engineering, and MOLAP applications. Most of the existing data structures are static in nature and cannot grow or shrink dynamically. The Traditional Multidimensional Array (TMA) is an efficient structure in terms of accessing the element of the array by straight computation of the addressing function, but TMA is not extendible during run time. There are some extendible data structures but they have the problem of address space overflow for large number of dimensions or length of dimension. In this paper we propose a new data structure, Extendible Karnaugh Array (EKA), to represent the multidimensional data. The main idea of this scheme is to represent n dimensional array by a set of two dimensional extendible arrays. The scheme can be extended in any direction during run time. We evaluate our proposed scheme by comparing for different operations with the Traditional Multidimensional Array (TMA). Our experimental result shows that the EKA scheme outperforms TMA.
Abstract: We describe a novel implementation scheme of multidimensional array for handling large scale high dimensional datasets. The scheme implements a dynamic multidimensional extendible array employing a set of two dimensional extendible arrays. The multidimensional arrays provide many advantages but it has some problems as well. The Traditional Multidimensional array is not dynamic extendible. Again, if the length of dimension and number of dimension of a multidimensional array is large then the address space for the array overflows soon. In this paper, we propose a solution against the essential problem of address space overflow for handling large scale multidimensional datasets using our implementation model. We evaluate our proposed scheme by comparing with Traditional Multidimensional Array (TMA) for different operations and find a reasonable delay on address space overflow with no significant performance degradation.
Abstract: The Internetpsilas excellent scalability and robustness result in part from the end-to-end nature of Internet congestion control. End-to-end congestion control algorithms alone however are unable to prevent the congestion collapse and unfairness created by applications that are unresponsive to network congestion. To address these maladies, we propose a modified Queue Management algorithm and Stream Control Transmission Protocol (SCTP) congestion control mechanism. SCTP mechanisms are based upon TCP congestion control principles. It allows network operators to simultaneously achieve high throughput and low average delay. One of the flaws of SCTP is-fast retransmission procedure is vulnerable to being mistakenly triggered multiple times leading to under utilization of the network during recovery and duplicate retransmissions of the lost packet. To solve these problems, we change in determining the congestion window while leaving the basic idea intact and then evaluate its performance using simulation. Simulation results show that our proposed method can effectively eliminates congestion collapse and give better performance than conventional procedure. Based on extensive simulation, we believe that our technique is sufficiently robust for deployment in routers.
Abstract: Automatic speaker recognition is a field of study attributed in identifying a person from a spoken phrase. The technique makes it possible to use the speaker’s voice to verify their identity and control access to the services such as biometric security system, voice dialing, telephone banking, telephone shopping, database access services, information services, voice mail, and security control for confidential information areas and remote access to the computers. This thesis represents a development of a Matlab based text dependent speaker recognition system. Mel Frequency Cepstrum Coefficient (MFCC) Method is used to extract a speaker’s discriminative features from the mathematical representation of the speech signal. After that Vector Quantization with VQ-LBG Algorithm is used to match the feature.
Abstract: Usually fuzzy system takes into account of expert knowledge base. But to design fuzzy models and controllers we encounter a major difficulty in the identification of an optimized fuzzy rule base as well as membership function shapes and types. In fact, it is more difficult and time consuming for the expert to define a complete rule sets for a complex system that uses a large number of parameters. On the contrary, evolutionary fuzzy system widely used for optimization and learning process in which the membership function, shapes, types, fuzzy rule sets including the number of rules inside it are evolved using an evolutionary algorithm. A new approach to discovery of fuzzy rules using evolutionary algorithms and a flexible encoding method is proposed in this paper. The benefits of this methodology are illustrated for the control of aircraft system that shows better performance rather than existing fuzzy expert systems.
