Implementing a Prolog Runtime System in a language like Java which provides its own automatic memory management and safety features such as built--in index checking and array initialization requires a consistent approach to memory management based on a simple ultimate goal: minimizing total memory management time and extra space involved. The total memory management time for Jinni is made up of garbage collection time both for Java and Jinni itself. Extra space is usually requested at Jinni's garbage collection. This goal motivates us to find a simple and practical garbage collection algorithm and implementation for our Prolog engine. In this thesis we survey various algorithms already proposed and offer our own contribution to the study of garbage collection by improvements and optimizations for some classic algorithms. We implemented these algorithms based on the dynamic array algorithm for an all--dynamic Prolog engine (JINNI 2000). The comparisons of our implementations versus the originally proposed algorithm allow us to draw informative conclusions on their theoretical complexity model and their empirical effectiveness.

The World Wide Web is one of the largest sources of information; more and more applications are being developed daily to make use of this information. This thesis presents a multi-agent architecture that deals with some of the issues related to Internet data extraction. The primary issue addresses the reliable, efficient and quick extraction of data through the use of HTTP performance monitoring agents. A second issue focuses on how to make use of available data to take decisions and alert the user when there is change in data; this is done with the help of user agents that are equipped with a Defeasible reasoning interpreter. An additional issue is the visualization of extracted data; this is done with the aid of VRML visualization agents. The cited issues are discussed using stock portfolio management as an example application.

A mobile ad hoc network (MANET) is a collection of mobile platforms or nodes that come together to form a network capable of communicating with each other, without the help of a central controller. To avail the maximum potential of a MANET, it is of great importance to devise a routing scheme, which will optimize upon the performance of a MANET, given the high rate of random mobility of the nodes. In a MANET individual nodes perform the routing functions like route discovery, route maintenance and delivery of packets from one node to the other. Existing routing protocols flood the network with broadcasts of route discovery messages, while attempting to establish a route. This characteristic is instrumental in deteriorating the performance of a MANET, as resource overhead triggered by broadcasts is directly proportional to the size of the network. Bounded-dynamic source routing (B-DSR), is proposed to curb this multitude of superfluous broadcasts, thus enabling to reserve valuable resources like bandwidth and battery power. B-DSR establishes a bounded region in the network, only within which, transmissions of route discovery messages are processed and validated for establishing a route. All route discovery messages reaching outside of this bounded region are dropped, thus …

Ulcerative colitis (UC) is a chronic inflammatory disease characterized by periods of relapses and remissions affecting more than 500,000 people in the United States. To achieve the therapeutic goals of UC, which are to first induce and then maintain disease remission, doctors need to evaluate the severity of UC of a patient. However, it is very difficult to evaluate the severity of UC objectively because of non-uniform nature of symptoms and large variations in their patterns. To address this, in our previous works, we developed two different approaches in which one is using the image textures, and the other is using CNN (convolutional neural network) to measure and classify objectively the severity of UC presented in optical colonoscopy video frames. But, we found that the image texture based approach could not handle larger number of variations in their patterns, and the CNN based approach could not achieve very high accuracy. In this paper, we improve our CNN based approach in two ways to provide better accuracy for the classification. We add more thorough and essential preprocessing, and generate more classes to accommodate large variations in their patterns. The experimental results show that the proposed preprocessing can improve the overall accuracy …

With an ever-growing number of publications in the biomedical domain, it becomes likely that important implicit connections between individual concepts of biomedical knowledge are overlooked. Literature based discovery (LBD) is in practice for many years to identify plausible associations between previously unrelated concepts. In this paper, we present a new, completely automatic and interactive system that creates a graph-based knowledge base to capture multifaceted complex associations among biomedical concepts. For a given pair of input concepts, our system auto-generates a list of ranked subgraphs uncovering possible previously unnoticed associations based on context information. To rank these subgraphs, we implement a novel ranking method using the context information obtained by performing random walks on the graph. In addition, we enhance the system by training a Neural Network Classifier to output the likelihood of the two concepts being likely related, which provides better insights to the end user.

Wireless ad hoc networks are expected to play an important role in civilian and military settings where wireless access to wired backbone is either ineffective or impossible. Wireless sensor networks are effective in remote data acquisition. Congestion control and power consumption in wireless ad hoc networks have received a lot of attention in recent research. Several algorithms have been proposed to reduce congestion and power consumption in wireless ad hoc and sensor networks. In this thesis, we focus upon two schemes, which deal with congestion control and power consumption issues. This thesis consists of two parts. In the first part, we describe a randomization scheme for congestion control in dynamic source routing protocol, which we refer to as RDSR. We also study a randomization scheme for GDSR protocol, a GPS optimized variant of DSR. We discuss RDSR and RGDSR implementations and present extensive simulation experiments to study their performance. Our results indicate that both RGDSR and RDSR protocols outperform their non-randomized counterparts by decreasing the number of route query packets. Furthermore, a probabilistic congestion control scheme based on local tuning of routing protocol parameters is shown to be feasible. In the second part we present a simulation based performance study …

The resources (memory, power and bandwidth) are limited in wireless and mobile networks. Previous research has shown that the quality of service (QoS) of the mobile client can be improved through efficient resources management. This thesis contains two areas of research that are strongly interrelated. In the first area of research, we extended the MoSync Algorithm, a network application layer media synchronization algorithm, to allow play-out of multimedia packets by the base station upon the mobile client in a First-In-First-Out (FIFO), Highest-Priority-First (PQ), Weighted Fair-Queuing (WFQ) and Round-Robin (RR) order. In the second area of research, we make modifications to the DSR and TORA routing algorithms to make them energy aware routing protocols. Our research shows that the QoS of the mobile client can be drastically improved through effective resource allocation.