Dynamic memory management required by allocation-intensive (i.e., Object Oriented and linked data structured) applications has led to a large number of research trends. Memory performance due to the cache misses in these applications continues to lag in terms of execution cycles as ever increasing CPU-Memory speed gap continues to grow. Sophisticated prefetcing techniques, data relocations, and multithreaded architectures have tried to address memory latency. These techniques are not completely successful since they require either extra hardware/software in the system or special properties in the applications. Software needed for prefetching and data relocation strategies, aimed to improve cache performance, pollutes the cache so that the technique itself becomes counter-productive. On the other hand, extra hardware complexity needed in multithreaded architectures decelerates CPU's clock, since "Simpler is Faster." This dissertation, directed to seek the cause of poor locality behavior of allocation--intensive applications, studies allocators and their impact on the cache performance of these applications. Our study concludes that service functions, in general, and memory management functions, in particular, entangle with application's code and become the major cause of cache pollution. In this dissertation, we present a novel technique that transfers the allocation and de-allocation functions entirely to a separate processor residing in …

Peptide mass fingerprinting (PMF) was the first automated method for protein identification in proteomics, and it remains in common usage today because of its simplicity and the low equipment costs for generating fingerprints. However, one of the problems with PMF is its limited specificity and sensitivity in protein identification. Here I present a method that shows potential to significantly enhance the accuracy of peptide mass fingerprinting, using a machine learning approach based on a hidden Markov model (HMM). This method is applied to improve differentiation of real protein matches from those that occur by chance. The system was trained using 300 examples of combined real and false-positive protein identification results, and 10-fold cross-validation applied to assess model discrimination. The model can achieve 93% accuracy in distinguishing correct and real protein identification results versus false-positive matches. The receiver operating characteristic (ROC) curve area for the best model was 0.833.

The electronic voting machine (EVM) plays a very important role in a country where government officials are elected into office. Throughout the world, a specific operating system that tends to the specific requirement of the EVM does not exist. Existing EVM technology depends upon the various operating systems currently available, thus ignoring the basic needs of the system. There is a compromise over the basic requirements in order to develop the systems on the basis on an already available operating system, thus having a lot of scope for error. It is necessary to know the specific details of the particular device for which the operating system is being developed. In this document, I evaluate existing EVMs and identify flaws and shortcomings. I propose a solution for a new operating system that meets the specific requirements of the EVM, calling it Voting Operating System (VOS, pronounced 'voice'). The identification technique can be simplified by using the fingerprint technology that determines the identity of a person based on two fingerprints. I also discuss the various parts of the operating system that have to be implemented that can tend to all the basic requirements of an EVM, including implementation of the memory manager, …

Online airline ticket booking system is one of the essential applications of E-commerce. With the development of Internet and security technology, more and more people begin to consume online, which is more convenient and personal than traditional way. The goal of this system is to make people purchase airline tickets easily. The system is written in JAVATM. Chapter 1 will introduce some basic conception of the technologies have been used in this system. Chapter 2 shows how the database and the system are designed. Chapter 3 shows the logic of the Web site. In Chapter 4 the interface of the system will be given. Chapter 5 tells the platform of this system.

A co-location pattern is a set of spatial features frequently located together in space. A frequent pattern is a set of items that frequently appears in a transaction database. Since its introduction, the paradigm of frequent pattern mining has undergone a shift from candidate generation-and-test based approaches to projection based approaches. Co-location patterns resemble frequent patterns in many aspects. However, the lack of transaction concept, which is crucial in frequent pattern mining, makes the similar shift of paradigm in co-location pattern mining very difficult. This thesis investigates a projection based co-location pattern mining paradigm. In particular, a FP-tree based co-location mining framework and an algorithm called FP-CM, for FP-tree based co-location miner, are proposed. It is proved that FP-CM is complete, correct, and only requires a small constant number of database scans. The experimental results show that FP-CM outperforms candidate generation-and-test based co-location miner by an order of magnitude.

The recent transformation of consumer graphics (CG) cards into powerful 3D rendering processors is due in large measure to the success of game developers in delivering mass market entertainment software that feature highly immersive and captivating virtual environments. Despite this success, 3D CG application development is becoming increasingly handicapped by the inability of traditional content creation methods to keep up with the demand for content. The term content is used here to refer to any data operated on by application code that is meant for viewing, including 3D models, textures, animation sequences and maps or other data-intensive descriptions of virtual environments. Traditionally, content has been handcrafted by humans. A serious problem facing the interactive graphics software development community is how to increase the rate at which content can be produced to keep up with the increasingly rapid pace at which software for interactive applications can now be developed. Research addressing this problem centers around procedural content creation systems. By moving away from purely human content creation toward systems in which humans play a substantially less time-intensive but no less creative part in the process, procedural content creation opens new doors. From a qualitative standpoint, these types of systems will not …

Advances in integrated circuit technology continue to provide more and more transistors on a chip. Computer architects are faced with the challenge of finding the best way to translate these resources into high performance. The challenge in the design of next generation CPU (central processing unit) lies not on trying to use up the silicon area, but on finding smart ways to make use of the wealth of transistors now available. In addition, the next generation architecture should offer high throughout performance, scalability, modularity, and low energy consumption, instead of an architecture that is suitable for only one class of applications or users, or only emphasize faster clock rate. A program exhibits different types of parallelism: instruction level parallelism (ILP), thread level parallelism (TLP), or data level parallelism (DLP). Likewise, architectures can be designed to exploit one or more of these types of parallelism. It is generally not possible to design architectures that can take advantage of all three types of parallelism without using very complex hardware structures and complex compiler optimizations. We present the state-of-art architecture SDF (scheduled data flowed) which explores the TLP parallelism as much as that is supplied by that application. We implement a SDF single-chip …

Agents that act as user assistants will become invaluable as the number of information sources continue to proliferate. Such agents can support the work of users by learning to automate time-consuming tasks and filter information to manageable levels. Although considerable advances have been made in this area, it remains a fertile area for further development. One application of agents under careful scrutiny is the automated negotiation of conflicts between different user's needs and desires. Many techniques require explicit user models in order to function. This dissertation explores a technique for dynamically constructing user models and the impact of using them to anticipate the need for negotiation. Negotiation is reduced by including an advising aspect to the agent that can use this anticipation of conflict to adjust user behavior.

Designing 802.11 wireless networks includes two major components: selection of access points (APs) in the demand areas and assignment of radio frequencies to each AP. Coverage and capacity are some key issues when placing APs in a demand area. APs need to cover all users. A user is considered covered if the power received from its corresponding AP is greater than a given threshold. Moreover, from a capacity standpoint, APs need to provide certain minimum bandwidth to users located in the coverage area. A major challenge in designing wireless networks is the frequency assignment problem. The 802.11 wireless LANs operate in the unlicensed ISM frequency, and all APs share the same frequency. As a result, as 802.11 APs become widely deployed, they start to interfere with each other and degrade network throughput. In consequence, efficient assignment of channels becomes necessary to avoid and minimize interference. In this work, an optimal AP selection was developed by balancing traffic load. An optimization problem was formulated that minimizes heavy congestion. As a result, APs in wireless LANs will have well distributed traffic loads, which maximize the throughput of the network. The channel assignment algorithm was designed by minimizing channel interference between APs. The …

This thesis describes experiments designed to measure the effect of collaborative communication on task performance of a multiagent system. A discrete event simulation was developed to model a multi-agent system completing a task to find and collect food resources, with the ability to substitute various communication and coordination methods. Experiments were conducted to find the effects of the various communication methods on completion of the task to find and harvest the food resources. Results show that communication decreases the time required to complete the task. However, all communication methods do not fare equally well. In particular, results indicate that the communication model of the bee is a particularly effective method of agent communication and collaboration. Furthermore, results indicate that direct communication with additional information content provides better completion results. Cost-benefit models show some conflicting information, indicating that the increased performance may not offset the additional cost of achieving that performance.

Language can sometimes be an impediment in communication. Whether we are talking about people who speak different languages, students who are learning a new language, or people with language disorders, the understanding of linguistic representations in a given language requires a certain amount of knowledge that not everybody has. In this thesis, we propose "translation through pictures" as a means for conveying simple pieces of information across language barriers, and describe a system that can automatically generate pictorial representations for simple sentences. Comparative experiments conducted on visual and linguistic representations of information show that a considerable amount of understanding can be achieved through pictorial descriptions, with results within a comparable range of those obtained with current machine translation techniques. Moreover, a user study conducted around the pictorial translation system reveals that users found the system to generally produce correct word/image associations, and rate the system as interactive and intelligent.

Abstract This dissertation investigates authentication techniques in some emerging areas. Specifically, authentication schemes have been proposed that are well-suited for embedded systems, and privacy-respecting pay Web sites. With embedded systems, a person could own several devices which are capable of communication and interaction, but these devices use embedded processors whose computational capabilities are limited as compared to desktop computers. Examples of this scenario include entertainment devices or appliances owned by a consumer, multiple control and sensor systems in an automobile or airplane, and environmental controls in a building. An efficient public key cryptosystem has been devised, which provides a complete solution to an embedded system, including protocols for authentication, authenticated key exchange, encryption, and revocation. The new construction is especially suitable for the devices with constrained computing capabilities and resources. Compared with other available authentication schemes, such as X.509, identity-based encryption, etc, the new construction provides unique features such as simplicity, efficiency, forward secrecy, and an efficient re-keying mechanism. In the application scenario for a pay Web site, users may be sensitive about their privacy, and do not wish their behaviors to be tracked by Web sites. Thus, an anonymous authentication scheme is desirable in this case. That is, a …

Partial order planning is an important approach that solves planning problems without completely specifying the orderings between the actions in the plan. This property provides greater flexibility in executing plans; hence making the partial order planners a preferred choice over other planning methodologies. However, in order to find partially ordered plans, partial order planners perform a search in plan space rather than in space of world states and an uninformed search in plan space leads to poor efficiency. In this thesis, I discuss applying a reinforcement learning method, called First-visit Monte Carlo method, to partial order planning in order to design agents which do not need any training data or heuristics but are still able to make informed decisions in plan space based on experience. Communicating effectively with the agent is crucial in reinforcement learning. I address how this task was accomplished in plan space and the results from an evaluation of a blocks world test bed.

Existing cache organization suffers from the inability to distinguish different types of localities, and non-selectively cache all data rather than making any attempt to take special advantage of the locality type. This causes unnecessary movement of data among the levels of the memory hierarchy and increases in miss ratio. In this dissertation I propose a split data cache architecture that will group memory accesses as scalar or array references according to their inherent locality and will subsequently map each group to a dedicated cache partition. In this system, because scalar and array references will no longer negatively affect each other, cache-interference is diminished, delivering better performance. Further improvement is achieved by the introduction of victim cache, prefetching, data flattening and reconfigurability to tune the array and scalar caches for specific application. The most significant contribution of my work is the introduction of novel cache architecture for embedded microprocessor platforms. My proposed cache architecture uses reconfigurability coupled with split data caches to reduce area and power consumed by cache memories while retaining performance gains. My results show excellent reductions in both memory size and memory access times, translating into reduced power consumption. Since there was a huge reduction in miss rates …

Elliptic curve cryptography (ECC) is an alternative to traditional techniques for public key cryptography. It offers smaller key size without sacrificing security level. Tate pairing is a bilinear map used in identity based cryptography schemes. In a typical elliptic curve cryptosystem, elliptic curve point multiplication is the most computationally expensive component. Similarly, Tate pairing is also quite computationally expensive. Therefore, it is more attractive to implement the ECC and Tate pairing using hardware than using software. The bases of both ECC and Tate pairing are Galois field arithmetic units. In this thesis, I propose the FPGA implementations of the elliptic curve point multiplication in GF (2283) as well as Tate pairing computation on supersingular elliptic curve in GF (2283). I have designed and synthesized the elliptic curve point multiplication and Tate pairing module using Xilinx's FPGA, as well as synthesized all the Galois arithmetic units used in the designs. Experimental results demonstrate that the FPGA implementation can speedup the elliptic curve point multiplication by 31.6 times compared to software based implementation. The results also demonstrate that the FPGA implementation can speedup the Tate pairing computation by 152 times compared to software based implementation.

The Internet and networks in general have become essential tools for disseminating in-formation. Search engines have become the predominant means of finding information on the Web and all other data repositories, including local resources. Domain scientists regularly acquire and analyze images generated by equipment such as microscopes and cameras, resulting in complex image files that need to be managed in a convenient manner. This type of integrated environment has been recently termed a netcentric sci-entific research repository. I developed a number of data manipulation tools that allow researchers to manage their information more effectively in a netcentric environment. The specific contributions are: (1) A unique interface for management of data including files and relational databases. A wrapper for relational databases was developed so that the data can be indexed and searched using traditional search engines. This approach allows data in databases to be searched with the same interface as other data. Fur-thermore, this approach makes it easier for scientists to work with their data if they are not familiar with SQL. (2) A Web services based architecture for integrating analysis op-erations into a repository. This technique allows the system to leverage the large num-ber of existing tools by wrapping them …

The major sources of power dissipation in a nanometer CMOS circuit are capacitive switching, short-circuit current, static leakage and gate oxide tunneling. However, with the aggressive scaling of technology the gate oxide direct tunneling current (gate leakage) is emerging as a prominent component of power dissipation. For sub-65 nm CMOS technology where the gate oxide (SiO2) thickness is very low, the direct tunneling current is the major form of tunneling. There are two contribution parts in this thesis: analytical modeling of behavioral level components for direct tunneling current and propagation delay, and the reduction of tunneling current during behavioral synthesis. Gate oxides of multiple thicknesses are useful in reducing the gate leakage dissipation. Analytical models from first principles to calculate the tunneling current and the propagation delay of behavioral level components is presented, which are backed by BSIM4/5 models and SPICE simulations. These components are characterized for 45 nm technology and an algorithm is provided for scheduling of datapath operations such that the overall tunneling current dissipation of a datapath circuit under design is minimal. It is observed that the oxide thickness that is being considered is very low it may not remain constant during the course of fabrication. Hence …

Hard real-time systems in robotics, space and military missions, and control devices are specified with stringent and critical time constraints. On the other hand, soft real-time applications arising from multimedia, telecommunications, Internet web services, and games are specified with more lenient constraints. Real-time systems can also be distinguished in terms of their implementation into preemptive and non-preemptive systems. In preemptive systems, tasks are often preempted by higher priority tasks. Non-preemptive systems are gaining interest for implementing soft-real applications on multithreaded platforms. In this dissertation, I propose a new algorithm that uses a two-level scheduling strategy for scheduling non-preemptive soft real-time tasks. Our goal is to improve the success ratios of the well-known earliest deadline first (EDF) approach when the load on the system is very high and to improve the overall performance in both underloaded and overloaded conditions. Our approach, known as group-EDF (gEDF), is based on dynamic grouping of tasks with deadlines that are very close to each other, and using a shortest job first (SJF) technique to schedule tasks within the group. I believe that grouping tasks dynamically with similar deadlines and utilizing secondary criteria, such as minimizing the total execution time can lead to new and more …

Susceptibles-infectives-removals (SIR) and its derivatives are the classic mathematical models for the study of infectious diseases in epidemiology. In order to model and simulate epidemics of an infectious disease, a global stochastic field simulation paradigm (GSFS) is proposed, which incorporates geographic and demographic based interactions. The interaction measure between regions is a function of population density and geographical distance, and has been extended to include demographic and migratory constraints. The progression of diseases using GSFS is analyzed, and similar behavior to the SIR model is exhibited by GSFS, using the geographic information systems (GIS) gravity model for interactions. The limitations of the SIR and similar models of homogeneous population with uniform mixing are addressed by the GSFS model. The GSFS model is oriented to heterogeneous population, and can incorporate interactions based on geography, demography, environment and migration patterns. The progression of diseases can be modeled at higher levels of fidelity using the GSFS model, and facilitates optimal deployment of public health resources for prevention, control and surveillance of infectious diseases.

Wireless sensor networks are battery-powered ad-hoc networks in which sensor nodes that are scattered over a region connect to each other and form multi-hop networks. These nodes are equipped with sensors such as temperature sensors, pressure sensors, and light sensors and can be queried to get the corresponding values for analysis. However, since they are battery operated, care has to be taken so that these nodes use energy efficiently. One of the areas in sensor networks where an energy analysis can be done is routing. This work explores grid-based coordinated routing in wireless sensor networks and compares the energy available in the network over time for different grid sizes.

I present a time synchronization algorithm for wireless sensor networks that aims to conserve sensor battery power. The proposed method creates a hierarchical tree by flooding the sensor network from a designated source point. It then uses a hybrid algorithm derived from the timing-sync protocol for sensor networks (TSPN) and the reference broadcast synchronization method (RBS) to periodically synchronize sensor clocks by minimizing energy consumption. In multi-hop ad-hoc networks, a depleted sensor will drop information from all other sensors that route data through it, decreasing the physical area being monitored by the network. The proposed method uses several techniques and thresholds to maintain network connectivity. A new root sensor is chosen when the current one's battery power decreases to a designated value. I implement this new synchronization technique using Matlab and show that it can provide significant power savings over both TPSN and RBS.

Modern high performance computing is dependent on parallel processing systems. Most current benchmarks reveal only the high level computational throughput metrics, which may be sufficient for single processor systems, but can lead to a misrepresentation of true system capability for parallel systems. A new benchmark is therefore proposed. CLUE (Cluster Evaluator) uses a cellular automata algorithm to evaluate the scalability of parallel processing machines. The benchmark also uses algorithmic variations to evaluate individual system components' impact on the overall serial fraction and efficiency. CLUE is not a replacement for other performance-centric benchmarks, but rather shows the scalability of a system and provides metrics to reveal where one can improve overall performance. CLUE is a new benchmark which demonstrates a better comparison among different parallel systems than existing benchmarks and can diagnose where a particular parallel system can be optimized.

Due to the long duration required to perform manual knowledge entry by human knowledge engineers it is desirable to find methods to automatically acquire knowledge about the world by accessing online information. In this work I examine using the Cyc ontology to guide the creation of Naïve Bayes classifiers to provide knowledge about items described in Wikipedia articles. Given an initial set of Wikipedia articles the system uses the ontology to create positive and negative training sets for the classifiers in each category. The order in which classifiers are generated and used to test articles is also guided by the ontology. The research conducted shows that a system can be created that utilizes statistical text classification methods to extract information from an ad-hoc generated information source like Wikipedia for use in a formal semantic ontology like Cyc. Benefits and limitations of the system are discussed along with future work.

In this thesis two collaborative localization techniques are studied: multidimensional scaling (MDS) and maximum likelihood estimator (MLE). A synthesis of a new location estimation method through a serial integration of these two techniques, such that an estimate is first obtained using MDS and then MLE is employed to fine-tune the MDS solution, was the subject of this research using various simulation and experimental studies. In the simulations, important issues including the effects of sensor node density, reference node density and different deployment strategies of reference nodes were addressed. In the experimental study, the path loss model of indoor environments is developed by determining the environment-specific parameters from the experimental measurement data. Then, the empirical path loss model is employed in the analysis and simulation study of the performance of collaborative localization techniques.