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- Logic Programming Tools for Dynamic Content Generation and Internet Data Mining
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The phenomenal growth of Information Technology requires us to elicit, store and maintain huge volumes of data. Analyzing this data for various purposes is becoming increasingly important. Data mining consists of applying data analysis and discovery algorithms that under acceptable computational efficiency limitations, produce a particular enumeration of patterns over the data. We present two techniques based on using Logic programming tools for data mining. Data mining analyzes data by extracting patterns which describe its structure and discovers co-relations in the form of rules. We distinguish analysis methods as visual and non-visual and present one application of each. We explain that our focus on the field of Logic Programming makes some of the very complex tasks related to Web based data mining and dynamic content generation, simple and easy to implement in a uniform framework.
- The Role of Intelligent Mobile Agents in Network Management and Routing
- In this research, the application of intelligent mobile agents to the management of distributed network environments is investigated. Intelligent mobile agents are programs which can move about network systems in a deterministic manner in carrying their execution state. These agents can be considered an application of distributed artificial intelligence where the (usually small) agent code is moved to the data and executed locally. The mobile agent paradigm offers potential advantages over many conventional mechanisms which move (often large) data to the code, thereby wasting available network bandwidth. The performance of agents in network routing and knowledge acquisition has been investigated and simulated. A working mobile agent system has also been designed and implemented in JDK 1.2.
- Memory Management and Garbage Collection Algorithms for Java-Based Prolog
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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.
- CMOS Active Pixel Sensors for Digital Cameras: Current State-of-the-Art
- Image sensors play a vital role in many image sensing and capture applications. Among the various types of image sensors, complementary metal oxide semiconductor (CMOS) based active pixel sensors (APS), which are characterized by reduced pixel size, give fast readouts and reduced noise. APS are used in many applications such as mobile cameras, digital cameras, Webcams, and many consumer, commercial and scientific applications. With these developments and applications, CMOS APS designs are challenging the old and mature technology of charged couple device (CCD) sensors. With the continuous improvements of APS architecture, pixel designs, along with the development of nanometer CMOS fabrications technologies, APS are optimized for optical sensing. In addition, APS offers very low-power and low-voltage operations and is suitable for monolithic integration, thus allowing manufacturers to integrate more functionality on the array and building low-cost camera-on-a-chip. In this thesis, I explore the current state-of-the-art of CMOS APS by examining various types of APS. I show design and simulation results of one of the most commonly used APS in consumer applications, i.e. photodiode based APS. We also present an approach for technology scaling of the devices in photodiode APS to present CMOS technologies. Finally, I present the most modern CMOS APS technologies by reviewing different design models. The design of the photodiode APS is implemented using commercial CAD tools.
- A nano-CMOS based universal voltage level converter for multi-VDD SoCs.
- Power dissipation of integrated circuits is the most demanding issue for very large scale integration (VLSI) design engineers, especially for portable and mobile applications. Use of multiple supply voltages systems, which employs level converter between two voltage islands is one of the most effective ways to reduce power consumption. In this thesis work, a unique level converter known as universal level converter (ULC), capable of four distinct level converting operations, is proposed. The schematic and layout of ULC are built and simulated using CADENCE. The ULC is characterized by performing three analysis such as parametric, power, and load analysis which prove that the design has an average power consumption reduction of about 85-97% and capable of producing stable output at low voltages like 0.45V even under varying load conditions.
- FPGA Implementations of Elliptic Curve Cryptography and Tate Pairing over Binary Field
- 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.
- Split array and scalar data cache: A comprehensive study of data cache organization.
- 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 at L-1 caches, further power reduction is achieved by partially or completely shutting down L-2 data or L-2 instruction caches. The saving in cache sizes resulting from these designs can be used for other processor activities including instruction and data prefetching, branch-prediction buffers. The potential benefits of such techniques for embedded applications have been evaluated in my work. I also explore how my cache organization performs for non-numeric data structures. I propose a novel idea called "Data flattening" which is a profile based memory allocation technique to compress sparsely scattered pointer data into regular contiguous memory locations and explore the potentials of my proposed Spit cache organization for data treated with data flattening method.
- Resource Efficient and Scalable Routing using Intelligent Mobile Agents
- Many of the contemporary routing algorithms use simple mechanisms such as flooding or broadcasting to disseminate the routing information available to them. Such routing algorithms cause significant network resource overhead due to the large number of messages generated at each host/router throughout the route update process. Many of these messages are wasteful since they do not contribute to the route discovery process. Reducing the resource overhead may allow for several algorithms to be deployed in a wide range of networks (wireless and ad-hoc) which require a simple routing protocol due to limited availability of resources (memory and bandwidth). Motivated by the need to reduce the resource overhead associated with routing algorithms a new implementation of distance vector routing algorithm using an agent-based paradigm known as Agent-based Distance Vector Routing (ADVR) has been proposed. In ADVR, the ability of route discovery and message passing shifts from the nodes to individual agents that traverse the network, co-ordinate with each other and successively update the routing tables of the nodes they visit.
- Performance Analysis of Wireless Networks with QoS Adaptations
- The explosive demand for multimedia and fast transmission of continuous media on wireless networks means the simultaneous existence of traffic requiring different qualities of service (QoS). In this thesis, several efficient algorithms have been developed which offer several QoS to the end-user. We first look at a request TDMA/CDMA protocol for supporting wireless multimedia traffic, where CDMA is laid over TDMA. Then we look at a hybrid push-pull algorithm for wireless networks, and present a generalized performance analysis of the proposed protocol. Some of the QoS factors considered include customer retrial rates due to user impatience and system timeouts and different levels of priority and weights for mobile hosts. We have also looked at how customer impatience and system timeouts affect the QoS provided by several queuing and scheduling schemes such as FIFO, priority, weighted fair queuing, and the application of the stretch-optimal algorithm to scheduling.
- Intelligent Memory Manager: Towards improving the locality behavior of allocation-intensive applications.
- 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 chip with DRAM (Intelligent Memory Manager). Our empirical results show that, on average, 60% of the cache misses caused by allocation and de-allocation service functions are eliminated using our technique.
- Mobile agent security through multi-agent cryptographic protocols.
- An increasingly promising and widespread topic of research in distributed computing is the mobile agent paradigm: code travelling and performing computations on remote hosts in an autonomous manner. One of the biggest challenges faced by this new paradigm is security. The issue of protecting sensitive code and data carried by a mobile agent against tampering from a malicious host is particularly hard but important. Based on secure multi-party computation, a recent research direction shows the feasibility of a software-only solution to this problem, which had been deemed impossible by some researchers previously. The best result prior to this dissertation is a single-agent protocol which requires the participation of a trusted third party. Our research employs multi-agent protocols to eliminate the trusted third party, resulting in a protocol with minimum trust assumptions. This dissertation presents one of the first formal definitions of secure mobile agent computation, in which the privacy and integrity of the agent code and data as well as the data provided by the host are all protected. We present secure protocols for mobile agent computation against static, semi-honest or malicious adversaries without relying on any third party or trusting any specific participant in the system. The security of our protocols is formally proven through standard proof technique and according to our formal definition of security. Our second result is a more practical agent protocol with strong security against most real-world host attacks. The security features are carefully analyzed, and the practicality is demonstrated through implementation and experimental study on a real-world mobile agent platform. All these protocols rely heavily on well-established cryptographic primitives, such as encrypted circuits, threshold decryption, and oblivious transfer. Our study of these tools yields new contributions to the general field of cryptography. Particularly, we correct a well-known construction of the encrypted circuit and give one of the first provably secure implementations of the encrypted circuit.
- Web-based airline ticket booking system.
- 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.
- Adaptive Planning and Prediction in Agent-Supported Distributed Collaboration.
- 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.
- Optimal Access Point Selection and Channel Assignment in IEEE 802.11 Networks
- 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 optimization algorithm assigns channels in such a way that minimizes co-channel and adjacent channel interference resulting in higher throughput.
- Evaluating the Scalability of SDF Single-chip Multiprocessor Architecture Using Automatically Parallelizing Code
- 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 multiprocessor constructed from simpler processors and execute the automatically parallelizing application on the single-chip multiprocessor. SDF has many desirable features such as high throughput, scalability, and low power consumption, which meet the requirements of the next generation of CPU design. Compared with superscalar, VLIW (very long instruction word), and SMT (simultaneous multithreading), the experiment results show that for application with very little parallelism SDF is comparable to other architectures, for applications with large amounts of parallelism SDF outperforms other architectures.
- Voting Operating System (VOS)
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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, process manager and file system of the proposed operating system.
- Peptide-based hidden Markov model for peptide fingerprint mapping.
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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.
- An Empirical Evaluation of Communication and Coordination Effectiveness in Autonomous Reactive Multiagent Systems
- 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.
- Procedural content creation and technologies for 3D graphics applications and games.
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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 rely less on human intervention but rather more since they will depend heavily on direction from a human in order to synthesize the desired content. This research draws heavily from the entertainment software domain but the research is broadly relevant to 3D graphics applications in general.
- FP-tree Based Spatial Co-location Pattern Mining
- 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.
- A Minimally Supervised Word Sense Disambiguation Algorithm Using Syntactic Dependencies and Semantic Generalizations
- Natural language is inherently ambiguous. For example, the word "bank" can mean a financial institution or a river shore. Finding the correct meaning of a word in a particular context is a task known as word sense disambiguation (WSD), which is essential for many natural language processing applications such as machine translation, information retrieval, and others. While most current WSD methods try to disambiguate a small number of words for which enough annotated examples are available, the method proposed in this thesis attempts to address all words in unrestricted text. The method is based on constraints imposed by syntactic dependencies and concept generalizations drawn from an external dictionary. The method was tested on standard benchmarks as used during the SENSEVAL-2 and SENSEVAL-3 WSD international evaluation exercises, and was found to be competitive.
- Planning techniques for agent based 3D animations.
- The design of autonomous agents capable of performing a given goal in a 3D domain continues to be a challenge for computer animated story generation systems. We present a novel prototype which consists of a 3D engine and a planner for a simple virtual world. We incorporate the 2D planner into the 3D engine to provide 3D animations. Based on the plan, the 3D world is created and the objects are positioned. Then the plan is linearized into simpler actions for object animation and rendered via the 3D engine. We use JINNI3D as the engine and WARPLAN-C as the planner for the above-mentioned prototype. The user can interact with the system using a simple natural language interface. The interface consists of a shallow parser, which is capable of identifying a set of predefined basic commands. The command given by the user is considered as the goal for the planner. The resulting plan is created and rendered in 3D. The overall system is comparable to a character based interactive story generation system except that it is limited to the predefined 3D environment.
- Capacity and Throughput Optimization in Multi-cell 3G WCDMA Networks
- User modeling enables in the computation of the traffic density in a cellular network, which can be used to optimize the placement of base stations and radio network controllers as well as to analyze the performance of resource management algorithms towards meeting the final goal: the calculation and maximization of network capacity and throughput for different data rate services. An analytical model is presented for approximating the user distributions in multi-cell third generation wideband code division multiple access (WCDMA) networks using 2-dimensional Gaussian distributions by determining the means and the standard deviations of the distributions for every cell. This model allows for the calculation of the inter-cell interference and the reverse-link capacity of the network. An analytical model for optimizing capacity in multi-cell WCDMA networks is presented. Capacity is optimized for different spreading factors and for perfect and imperfect power control. Numerical results show that the SIR threshold for the received signals is decreased by 0.5 to 1.5 dB due to the imperfect power control. The results also show that the determined parameters of the 2-dimensional Gaussian model match well with traditional methods for modeling user distribution. A call admission control algorithm is designed that maximizes the throughput in multi-cell WCDMA networks. Numerical results are presented for different spreading factors and for several mobility scenarios. Our methods of optimizing capacity and throughput are computationally efficient, accurate, and can be implemented in large WCDMA networks.
- Automated Defense Against Worm Propagation.
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Worms have caused significant destruction over the last few years. Network security elements such as firewalls, IDS, etc have been ineffective against worms. Some worms are so fast that a manual intervention is not possible. This brings in the need for a stronger security architecture which can automatically react to stop worm propagation. The method has to be signature independent so that it can stop new worms. In this thesis, an automated defense system (ADS) is developed to automate defense against worms and contain the worm to a level where manual intervention is possible. This is accomplished with a two level architecture with feedback at each level. The inner loop is based on control system theory and uses the properties of PID (proportional, integral and differential controller). The outer loop works at the network level and stops the worm to reach its spread saturation point. In our lab setup, we verified that with only inner loop active the worm was delayed, and with both loops active we were able to restrict the propagation to 10% of the targeted hosts. One concern for deployment of a worm containment mechanism was degradation of throughput for legitimate traffic. We found that with proper intelligent algorithm we can minimize the degradation to an acceptable level.
- System and Methods for Detecting Unwanted Voice Calls
- Voice over IP (VoIP) is a key enabling technology for the migration of circuit-switched PSTN architectures to packet-based IP networks. However, this migration is successful only if the present problems in IP networks are addressed before deploying VoIP infrastructure on a large scale. One of the important issues that the present VoIP networks face is the problem of unwanted calls commonly referred to as SPIT (spam over Internet telephony). Mostly, these SPIT calls are from unknown callers who broadcast unwanted calls. There may be unwanted calls from legitimate and known people too. In this case, the unwantedness depends on social proximity of the communicating parties. For detecting these unwanted calls, I propose a framework that analyzes incoming calls for unwanted behavior. The framework includes a VoIP spam detector (VSD) that analyzes incoming VoIP calls for spam behavior using trust and reputation techniques. The framework also includes a nuisance detector (ND) that proactively infers the nuisance (or reluctance of the end user) to receive incoming calls. This inference is based on past mutual behavior between the calling and the called party (i.e., caller and callee), the callee's presence (mood or state of mind) and tolerance in receiving voice calls from the caller, and the social closeness between the caller and the callee. The VSD and ND learn the behavior of callers over time and estimate the possibility of the call to be unwanted based on predetermined thresholds configured by the callee (or the filter administrators). These threshold values have to be automatically updated for integrating dynamic behavioral changes of the communicating parties. For updating these threshold values, I propose an automatic calibration mechanism using receiver operating characteristics curves (ROC). The VSD and ND use this mechanism for dynamically updating thresholds for optimizing their accuracy of detection. In addition to unwanted calls to the callees in a VoIP network, there can be unwanted traffic coming into a VoIP network that attempts to compromise VoIP network devices. Intelligent hackers can create malicious VoIP traffic for disrupting network activities. Hence, there is a need to frequently monitor the risk levels of critical network infrastructure. Towards realizing this objective, I describe a network level risk management mechanism that prioritizes resources in a VoIP network. The prioritization scheme involves an adaptive re-computation model of risk levels using attack graphs and Bayesian inference techniques. All the above techniques collectively account for a domain-level VoIP security solution.
- High Performance Architecture using Speculative Threads and Dynamic Memory Management Hardware
- With the advances in very large scale integration (VLSI) technology, hundreds of billions of transistors can be packed into a single chip. With the increased hardware budget, how to take advantage of available hardware resources becomes an important research area. Some researchers have shifted from control flow Von-Neumann architecture back to dataflow architecture again in order to explore scalable architectures leading to multi-core systems with several hundreds of processing elements. In this dissertation, I address how the performance of modern processing systems can be improved, while attempting to reduce hardware complexity and energy consumptions. My research described here tackles both central processing unit (CPU) performance and memory subsystem performance. More specifically I will describe my research related to the design of an innovative decoupled multithreaded architecture that can be used in multi-core processor implementations. I also address how memory management functions can be off-loaded from processing pipelines to further improve system performance and eliminate cache pollution caused by runtime management functions.
- Automated Syndromic Surveillance using Intelligent Mobile Agents
- Current syndromic surveillance systems utilize centralized databases that are neither scalable in storage space nor in computing power. Such systems are limited in the amount of syndromic data that may be collected and analyzed for the early detection of infectious disease outbreaks. However, with the increased prevalence of international travel, public health monitoring must extend beyond the borders of municipalities or states which will require the ability to store vasts amount of data and significant computing power for analyzing the data. Intelligent mobile agents may be used to create a distributed surveillance system that will utilize the hard drives and computer processing unit (CPU) power of the hosts on the agent network where the syndromic information is located. This thesis proposes the design of a mobile agent-based syndromic surveillance system and an agent decision model for outbreak detection. Simulation results indicate that mobile agents are capable of detecting an outbreak that occurs at all hosts the agent is monitoring. Further study of agent decision models is required to account for localized epidemics and variable agent movement rates.
- The enhancement of machine translation for low-density languages using Web-gathered parallel texts.
- The majority of the world's languages are poorly represented in informational media like radio, television, newspapers, and the Internet. Translation into and out of these languages may offer a way for speakers of these languages to interact with the wider world, but current statistical machine translation models are only effective with a large corpus of parallel texts - texts in two languages that are translations of one another - which most languages lack. This thesis describes the Babylon project which attempts to alleviate this shortage by supplementing existing parallel texts with texts gathered automatically from the Web -- specifically targeting pages that contain text in a pair of languages. Results indicate that parallel texts gathered from the Web can be effectively used as a source of training data for machine translation and can significantly improve the translation quality for text in a similar domain. However, the small quantity of high-quality low-density language parallel texts on the Web remains a significant obstacle.
- Occlusion Tolerant Object Recognition Methods for Video Surveillance and Tracking of Moving Civilian Vehicles
- Recently, there is a great interest in moving object tracking in the fields of security and surveillance. Object recognition under partial occlusion is the core of any object tracking system. This thesis presents an automatic and real-time color object-recognition system which is not only robust but also occlusion tolerant. The intended use of the system is to recognize and track external vehicles entered inside a secured area like a school campus or any army base. Statistical morphological skeleton is used to represent the visible shape of the vehicle. Simple curve matching and different feature based matching techniques are used to recognize the segmented vehicle. Features of the vehicle are extracted upon entering the secured area. The vehicle is recognized from either a digital video frame or a static digital image when needed. The recognition engine will help the design of a high performance tracking system meant for remote video surveillance.
- Bayesian Probabilistic Reasoning Applied to Mathematical Epidemiology for Predictive Spatiotemporal Analysis of Infectious Diseases
- Abstract Probabilistic reasoning under uncertainty suits well to analysis of disease dynamics. The stochastic nature of disease progression is modeled by applying the principles of Bayesian learning. Bayesian learning predicts the disease progression, including prevalence and incidence, for a geographic region and demographic composition. Public health resources, prioritized by the order of risk levels of the population, will efficiently minimize the disease spread and curtail the epidemic at the earliest. A Bayesian network representing the outbreak of influenza and pneumonia in a geographic region is ported to a newer region with different demographic composition. Upon analysis for the newer region, the corresponding prevalence of influenza and pneumonia among the different demographic subgroups is inferred for the newer region. Bayesian reasoning coupled with disease timeline is used to reverse engineer an influenza outbreak for a given geographic and demographic setting. The temporal flow of the epidemic among the different sections of the population is analyzed to identify the corresponding risk levels. In comparison to spread vaccination, prioritizing the limited vaccination resources to the higher risk groups results in relatively lower influenza prevalence. HIV incidence in Texas from 1989-2002 is analyzed using demographic based epidemic curves. Dynamic Bayesian networks are integrated with probability distributions of HIV surveillance data coupled with the census population data to estimate the proportion of HIV incidence among the different demographic subgroups. Demographic based risk analysis lends to observation of varied spectrum of HIV risk among the different demographic subgroups. A methodology using hidden Markov models is introduced that enables to investigate the impact of social behavioral interactions in the incidence and prevalence of infectious diseases. The methodology is presented in the context of simulated disease outbreak data for influenza. Probabilistic reasoning analysis enhances the understanding of disease progression in order to identify the critical points of surveillance, control and prevention. Public health resources, prioritized by the order of risk levels of the population, will efficiently minimize the disease spread and curtail the epidemic at the earliest.
- An Integrated Architecture for Ad Hoc Grids
- Extensive research has been conducted by the grid community to enable large-scale collaborations in pre-configured environments. grid collaborations can vary in scale and motivation resulting in a coarse classification of grids: national grid, project grid, enterprise grid, and volunteer grid. Despite the differences in scope and scale, all the traditional grids in practice share some common assumptions. They support mutually collaborative communities, adopt a centralized control for membership, and assume a well-defined non-changing collaboration. To support grid applications that do not confirm to these assumptions, we propose the concept of ad hoc grids. In the context of this research, we propose a novel architecture for ad hoc grids that integrates a suite of component frameworks. Specifically, our architecture combines the community management framework, security framework, abstraction framework, quality of service framework, and reputation framework. The overarching objective of our integrated architecture is to support a variety of grid applications in a self-controlled fashion with the help of a self-organizing ad hoc community. We introduce mechanisms in our architecture that successfully isolates malicious elements from the community, inherently improving the quality of grid services and extracting deterministic quality assurances from the underlying infrastructure. We also emphasize on the technology-independence of our architecture, thereby offering the requisite platform for technology interoperability. The feasibility of the proposed architecture is verified with a high-quality ad hoc grid implementation. Additionally, we have analyzed the performance and behavior of ad hoc grids with respect to several control parameters.
- Flexible Digital Authentication Techniques
- 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 user can prove his/her authenticity without revealing his/her identity. On the other hand, the Web site owner would like to prevent a bunch of users from sharing a single subscription while hiding behind user anonymity. The Web site should be able to detect these possible malicious behaviors, and exclude corrupted users from future service. This dissertation extensively discusses anonymous authentication techniques, such as group signature, direct anonymous attestation, and traceable signature. Three anonymous authentication schemes have been proposed, which include a group signature scheme with signature claiming and variable linkability, a scheme for direct anonymous attestation in trusted computing platforms with sign and verify protocols nearly seven times more efficient than the current solution, and a state-of-the-art traceable signature scheme with support for variable anonymity. These three schemes greatly advance research in the area of anonymous authentication. The authentication techniques presented in this dissertation are based on common mathematical and cryptographical foundations, sharing similar security assumptions. We call them flexible digital authentication schemes.
- Towards Communicating Simple Sentence using Pictorial Representations
- 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.
- A Dual Dielectric Approach for Performance Aware Reduction of Gate Leakage in Combinational Circuits
- Design of systems in the low-end nanometer domain has introduced new dimensions in power consumption and dissipation in CMOS devices. With continued and aggressive scaling, using low thickness SiO2 for the transistor gates, gate leakage due to gate oxide direct tunneling current has emerged as the major component of leakage in the CMOS circuits. Therefore, providing a solution to the issue of gate oxide leakage has become one of the key concerns in achieving low power and high performance CMOS VLSI circuits. In this thesis, a new approach is proposed involving dual dielectric of dual thicknesses (DKDT) for the reducing both ON and OFF state gate leakage. It is claimed that the simultaneous utilization of SiON and SiO2 each with multiple thicknesses is a better approach for gate leakage reduction than the conventional usage of a single gate dielectric (SiO2), possibly with multiple thicknesses. An algorithm is developed for DKDT assignment that minimizes the overall leakage for a circuit without compromising with the performance. Extensive experiments were carried out on ISCAS'85 benchmarks using 45nm technology which showed that the proposed approach can reduce the leakage, as much as 98% (in an average 89.5%), without degrading the performance.
- Using Reinforcement Learning in Partial Order Plan Space
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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.
- VLSI Architecture and FPGA Prototyping of a Secure Digital Camera for Biometric Application
- This thesis presents a secure digital camera (SDC) that inserts biometric data into images found in forms of identification such as the newly proposed electronic passport. However, putting biometric data in passports makes the data vulnerable for theft, causing privacy related issues. An effective solution to combating unauthorized access such as skimming (obtaining data from the passport's owner who did not willingly submit the data) or eavesdropping (intercepting information as it moves from the chip to the reader) could be judicious use of watermarking and encryption at the source end of the biometric process in hardware like digital camera or scanners etc. To address such issues, a novel approach and its architecture in the framework of a digital camera, conceptualized as an SDC is presented. The SDC inserts biometric data into passport image with the aid of watermarking and encryption processes. The VLSI (very large scale integration) architecture of the functional units of the SDC such as watermarking and encryption unit is presented. The result of the hardware implementation of Rijndael advanced encryption standard (AES) and a discrete cosine transform (DCT) based visible and invisible watermarking algorithm is presented. The prototype chip can carry out simultaneous encryption and watermarking, which to our knowledge is the first of its kind. The encryption unit has a throughput of 500 Mbit/s and the visible and invisible watermarking unit has a max frequency of 96.31 MHz and 256 MHz respectively.
- Resource Management in Wireless Networks
- A local call admission control (CAC) algorithm for third generation wireless networks was designed and implemented, which allows for the simulation of network throughput for different spreading factors and various mobility scenarios. A global CAC algorithm is also implemented and used as a benchmark since it is inherently optimized; it yields the best possible performance but has an intensive computational complexity. Optimized local CAC algorithm achieves similar performance as global CAC algorithm at a fraction of the computational cost. Design of a dynamic channel assignment algorithm for IEEE 802.11 wireless systems is also presented. Channels are assigned dynamically depending on the minimal interference generated by the neighboring access points on a reference access point. Analysis of dynamic channel assignment algorithm shows an improvement by a factor of 4 over the default settings of having all access points use the same channel, resulting significantly higher network throughput.
- Modeling Infectious Disease Spread Using Global Stochastic Field Simulation
- 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.
- Group-EDF: A New Approach and an Efficient Non-Preemptive Algorithm for Soft Real-Time Systems
- 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 efficient real-time scheduling algorithms. I present results comparing gEDF with other real-time algorithms including, EDF, best-effort, and guarantee scheme, by using randomly generated tasks with varying execution times, release times, deadlines and tolerances to missing deadlines, under varying workloads. Furthermore, I implemented the gEDF algorithm in the Linux kernel and evaluated gEDF for scheduling real applications.
- Natural Language Interfaces to Databases
- Natural language interfaces to databases (NLIDB) are systems that aim to bridge the gap between the languages used by humans and computers, and automatically translate natural language sentences to database queries. This thesis proposes a novel approach to NLIDB, using graph-based models. The system starts by collecting as much information as possible from existing databases and sentences, and transforms this information into a knowledge base for the system. Given a new question, the system will use this knowledge to analyze and translate the sentence into its corresponding database query statement. The graph-based NLIDB system uses English as the natural language, a relational database model, and SQL as the formal query language. In experiments performed with natural language questions ran against a large database containing information about U.S. geography, the system showed good performance compared to the state-of-the-art in the field.
- An Approach Towards Self-Supervised Classification Using Cyc
- 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.
- Comparative Study of RSS-Based Collaborative Localization Methods in Wireless Sensor Networks
- 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.
- CLUE: A Cluster Evaluation Tool
- 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.
- Mediation on XQuery Views
- The major goal of information integration is to provide efficient and easy-to-use access to multiple heterogeneous data sources with a single query. At the same time, one of the current trends is to use standard technologies for implementing solutions to complex software problems. In this dissertation, I used XML and XQuery as the standard technologies and have developed an extended projection algorithm to provide a solution to the information integration problem. In order to demonstrate my solution, I implemented a prototype mediation system called Omphalos based on XML related technologies. The dissertation describes the architecture of the system, its metadata, and the process it uses to answer queries. The system uses XQuery expressions (termed metaqueries) to capture complex mappings between global schemas and data source schemas. The system then applies these metaqueries in order to rewrite a user query on a virtual global database (representing the integrated view of the heterogeneous data sources) to a query (termed an outsourced query) on the real data sources. An extended XML document projection algorithm was developed to increase the efficiency of selecting the relevant subset of data from an individual data source to answer the user query. The system applies the projection algorithm to decompose an outsourced query into atomic queries which are each executed on a single data source. I also developed an algorithm to generate integrating queries, which the system uses to compose the answers from the atomic queries into a single answer to the original user query. I present a proof of both the extended XML document projection algorithm and the query integration algorithm. An analysis of the efficiency of the new extended algorithm is also presented. Finally I describe a collaborative schema-matching tool that was implemented to facilitate maintaining metadata.
- Energy-Aware Time Synchronization in Wireless Sensor Networks
- 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.
- Grid-based Coordinated Routing in Wireless Sensor Networks
- 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.
- Power-benefit analysis of erasure encoding with redundant routing in sensor networks.
- One of the problems sensor networks face is adversaries corrupting nodes along the path to the base station. One way to reduce the effect of these attacks is multipath routing. This introduces some intrusion-tolerance in the network by way of redundancy but at the cost of a higher power consumption by the sensor nodes. Erasure coding can be applied to this scenario in which the base station can receive a subset of the total data sent and reconstruct the entire message packet at its end. This thesis uses two commonly used encodings and compares their performance with respect to power consumed for unencoded data in multipath routing. It is found that using encoding with multipath routing reduces the power consumption and at the same time enables the user to send reasonably large data sizes. The experiments in this thesis were performed on the Tiny OS platform with the simulations done in TOSSIM and the power measurements were taken in PowerTOSSIM. They were performed on the simple radio model and the lossy radio model provided by Tiny OS. The lossy radio model was simulated with distances of 10 feet, 15 feet and 20 feet between nodes. It was found that by using erasure encoding, double or triple the data size can be sent at the same power consumption rate as unencoded data. All the experiments were performed with the radio set at a normal transmit power, and later a high transmit power.
- Timing and Congestion Driven Algorithms for FPGA Placement
- Placement is one of the most important steps in physical design for VLSI circuits. For field programmable gate arrays (FPGAs), the placement step determines the location of each logic block. I present novel timing and congestion driven placement algorithms for FPGAs with minimal runtime overhead. By predicting the post-routing timing-critical edges and estimating congestion accurately, this algorithm is able to simultaneously reduce the critical path delay and the minimum number of routing tracks. The core of the algorithm consists of a criticality-history record of connection edges and a congestion map. This approach is applied to the 20 largest Microelectronics Center of North Carolina (MCNC) benchmark circuits. Experimental results show that compared with the state-of-the-art FPGA place and route package, the Versatile Place and Route (VPR) suite, this algorithm yields an average of 8.1% reduction (maximum 30.5%) in the critical path delay and 5% reduction in channel width. Meanwhile, the average runtime of the algorithm is only 2.3X as of VPR.
- Comparison and Evaluation of Existing Analog Circuit Simulator using Sigma-Delta Modulator
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In the world of VLSI (very large scale integration) technology, there are many different types of circuit simulators that are used to design and predict the circuit behavior before actual fabrication of the circuit. In this thesis, I compared and evaluated existing circuit simulators by considering standard benchmark circuits. The circuit simulators which I evaluated and explored are Ngspice, Tclspice, Winspice (open source) and Spectre® (commercial). I also tested standard benchmarks using these circuit simulators and compared their outputs. The simulators are evaluated using design metrics in order to quantify their performance and identify efficient circuit simulators. In addition, I designed a sigma-delta modulator and its individual components using the analog behavioral language Verilog-A. Initially, I performed simulations of individual components of the sigma-delta modulator and later of the whole system. Finally, CMOS (complementary metal-oxide semiconductor) transistor-level circuits were designed for the differential amplifier, operational amplifier and comparator of the modulator.
- A Language and Visual Interface to Specify Complex Spatial Pattern Mining
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The emerging interests in spatial pattern mining leads to the demand for a flexible spatial pattern mining language, on which easy to use and understand visual pattern language could be built. It is worthwhile to define a pattern mining language called LCSPM to allow users to specify complex spatial patterns. I describe a proposed pattern mining language in this paper. A visual interface which allows users to specify the patterns visually is developed. Visual pattern queries are translated into the LCSPM language by a parser and data mining process can be triggered afterwards. The visual language is based on and goes beyond the visual language proposed in literature. I implemented a prototype system based on the open source JUMP framework.