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

Trusted Computing capability has become ubiquitous these days, and it is being widely deployed into consumer devices as well as enterprise platforms. As the number of threats is increasing at an exponential rate, it is becoming a daunting task to secure the systems against them. In this context, the software integrity measurement at runtime with the support of trusted platforms can be a better security strategy. Trusted Computing devices like TPM secure the evidence of a breach or an attack. These devices remain tamper proof if the hardware platform is physically secured. This type of trusted security is crucial for forensic analysis in the aftermath of a breach. The advantages of trusted platforms can be further leveraged if they can be used wisely. RADIUM (Race-free on-demand Integrity Measurement Architecture) is one such architecture, which is built on the strength of TPM. RADIUM provides an asynchronous root of trust to overcome the TOC condition of DRTM. Even though the underlying architecture is trusted, attacks can still compromise applications during runtime by exploiting their vulnerabilities. I propose an application-level integrity measurement solution that fits into RADIUM, to expand the trusted computing capability to the application layer. This is based on the concept …

Accepted Manuscript version of an article introducing the innovative idea of the Internet-of-Agro-Things (IoAT) with an explanation of the automatic detection of plant disease for the development of Agriculture Cyber-Physical System (ACPS). An accuracy of 99.24% is achieved by the proposed plant disease prediction framework.

Computer security attacks take place due to the presence of vulnerabilities and bugs in software applications. Bugs and vulnerabilities are the result of weak software architecture and lack of standard software development practices. Despite the fact that software companies are investing millions of dollars in the research and development of software designs security risks are still at large. In some cases software applications are found to carry vulnerabilities for many years before being identified. A recent such example is the popular Heart Bleed Bug in the Open SSL/TSL. In today’s world, where new software application are continuously being developed for a varied community of users; it’s highly unlikely to have software applications running without flaws. Attackers on computer system securities exploit these vulnerabilities and bugs and cause threat to privacy without leaving any trace. The most critical vulnerabilities are those which are related to the integrity of the software applications. Because integrity is directly linked to the credibility of software application and data it contains. Here I am giving solution of maintaining web applications integrity running on RADIUM by using daikon. Daikon generates invariants, these invariants are used to maintain the integrity of the web application and also check the …

Optimization of relief networks in humanitarian logistics often exemplifies the need for solutions that are feasible given a hard constraint on time. For instance, the distribution of medical countermeasures immediately following a biological disaster event must be completed within a short time-frame. When these supplies are not distributed within the maximum time allowed, the severity of the disaster is quickly exacerbated. Therefore emergency response plans that fail to facilitate the transportation of these supplies in the time allowed are simply not acceptable. As a result, all optimization solutions that fail to satisfy this criterion would be deemed infeasible. This creates a conflict with the priority optimization objective in most variants of the generic vehicle routing problem (VRP). Instead of efficiently maximizing usage of vehicle resources available to construct a feasible solution, these variants ordinarily prioritize the construction of a minimum cost set of vehicle routes. Research presented in this dissertation focuses on the design and analysis of efficient computational methods for optimizing high-consequence variants of the VRP for relief networks. The conflict between prioritizing the minimization of the number of vehicles required or the minimization of total travel time is demonstrated. The optimization of the time and capacity constraints in …

Demand of automobiles are significantly growing despite various factors, steadily increasing the average number of vehicles on the road. Increase in the number of vehicles, subsequently increases the risk of collisions, characterized by the driving behavior. Driving behavior is influenced by factors like class of vehicle, road condition and vehicle maneuvering by the driver. Rapidly growing mobile technology and use of smartphones embedded with in-built sensors, provides scope of constant development of assistance systems considering the safety of the driver by integrating with the information obtained from the vehicle on-board sensors. Our research aims at learning a vehicle system comprising of vehicle, human and road by employing driving patterns obtained from the sensor data to develop better systems of safety and alerts altogether. The thesis focusses on utilizing together various data recorded by the in-built embedded sensors in a smartphone to understand the vehicle motion and dynamics, followed by studying various impacts of collision events, types and signatures which can potentially be integrated in a prototype framework to detect variations, alert drivers and emergency responders in an event of collision.

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.

Reliable communication over the noisy channel has become one of the major concerns in the field of digital wireless communications. The low density parity check codes (LDPC) has gained lot of attention recently because of their excellent error-correcting capacity. It was first proposed by Robert G. Gallager in 1960. LDPC codes belong to the class of linear block codes. Near capacity performance is achievable on a large collection of data transmission and storage.In my thesis I have focused on hardware implementation of (3, 6) - regular LDPC codes. A fully parallel decoder will require too high complexity of hardware realization. Partly parallel decoder has the advantage of effective compromise between decoding throughput and high hardware complexity. The decoding of the codeword follows the belief propagation alias probability propagation algorithm in log domain. A 9216 bit, (3, 6) regular LDPC code with code rate ½ was implemented on FPGA targeting Xilinx Virtex 4 XC4VLX80 device with package FF1148. This decoder achieves a maximum throughput of 82 Mbps. The entire model was designed in VHDL in the Xilinx ISE 9.2 environment.

Vehicular ad-hoc networks (VANETs) are used for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. The IEEE 802.11p/WAVE (Wireless Access in Vehicular Environment) and with WAVE Short Messaging Protocol (WSMP) has been proposed as the standard protocol for designing applications for VANETs. This communication protocol must be thoroughly tested before reliable and efficient applications can be built using its protocols. In this paper, we perform on-road experiments in a variety of scenarios to evaluate the performance of the standard. We use commercial VANET devices with 802.11p/WAVE compliant chipsets for both BSM (basic safety messages) as well as video streaming applications using WSMP as a communication protocol. We show that while the standard performs well for BSM application in lightly loaded conditions, the performance becomes inferior when traffic and other performance metric increases. Furthermore, we also show that the standard is not suitable for video streaming due to the bursty nature of traffic and the bandwidth throttling, which is a major shortcoming for V2X applications.

Managing large-scale dynamical systems (e.g., transportation systems, complex information systems, and power networks, etc.) in real-time is very challenging considering their complicated system dynamics, intricate network interactions, large scale, and especially the existence of various uncertainties. To address this issue, intelligent techniques which can quickly design decision-making strategies that are robust to uncertainties are needed. This dissertation aims to conquer these challenges by exploring a data-driven decision-making framework, which leverages big-data techniques and scalable uncertainty evaluation approaches to quickly solve optimal control problems. In particular, following techniques have been developed along this direction: 1) system modeling approaches to simplify the system analysis and design procedures for multiple applications; 2) effective simulation and analytical based approaches to efficiently evaluate system performance and design control strategies under uncertainty; and 3) big-data techniques that allow some computations of control strategies to be completed offline. These techniques and tools for analysis, design and control contribute to a wide range of applications including air traffic flow management, complex information systems, and airborne networks.

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.

The widely used mobile phone, as well as its related technologies had opened opportunities for a complete change on how people interact and build relationship across geographic and time considerations. The convenience of instant communication by mobile phones that broke the barrier of space and time is evidently the key motivational point on why such technologies so important in people's life and daily activities. Mobile phones have become the most popular communication tools. Mobile phone technology is apparently changing our relationship to each other in our work and lives. The impact of new technologies on people's lives in social spaces gives us the chance to rethink the possibilities of technologies in social interaction. Accordingly, mobile phones are basically changing social relations in ways that are intricate to measure with any precision. In this dissertation I propose a socioscope model for social network, relationship and human behavior analysis based on mobile phone call detail records. Because of the diversities and complexities of human social behavior, one technique cannot detect different features of human social behaviors. Therefore I use multiple probability and statistical methods for quantifying social groups, relationships and communication patterns, for predicting social tie strengths and for detecting human behavior …

In order to run and manage resource-intensive high-performance applications, large-scale computing and storage platforms have been evolving rapidly in various domains in both academia and industry. The energy expenditure consumed to operate and maintain these cloud computing infrastructures is a major factor to influence the overall profit and efficiency for most cloud service providers. Moreover, considering the mitigation of environmental damage from excessive carbon dioxide emission, the amount of power consumed by enterprise-scale data centers should be constrained for protection of the environment.Generally speaking, there exists a trade-off between power consumption and application performance in large-scale computing systems and how to balance these two factors has become an important topic for researchers and engineers in cloud and HPC communities. Therefore, minimizing the power usage while satisfying the Service Level Agreements have become one of the most desirable objectives in cloud computing research and implementation. Since the fundamental feature of the cloud computing platform is hosting workloads with a variety of characteristics in a consolidated and on-demand manner, it is demanding to explore the inherent relationship between power usage and machine configurations. Subsequently, with an understanding of these inherent relationships, researchers are able to develop effective power management policies to optimize …

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.