The traditional approach to a digital investigation when a computer system is encountered in a running state is to remove the power, image the machine using a write blocker and then analyse the acquired image. This has the advantage of preserving the contents of the computer’s hard disk at that point in time. However, the disadvantage of this approach is that the preservation of the disk is at the expense of volatile data such as that stored in memory, which does not remain once the power is disconnected. There are an increasing number of situations where this traditional approach of ‘pulling the plug’ is not ideal since volatile data is relevant to the investigation; one of these situations is when the machine under investigation is using encryption. If encrypted data is encountered on a live machine, a live investigation can be performed to preserve this evidence in a form that can be later analysed. However, there are a number of difficulties with using evidence obtained from live investigations that may cause the reliability of such evidence to be questioned. This research investigates whether digital evidence obtained from live investigations involving encryption can be considered to be reliable. To determine this, a means of assessing reliability is established, which involves evaluating digital evidence against a set of criteria; evidence should be authentic, accurate and complete. This research considers how traditional digital investigations satisfy these requirements and then determines the extent to which evidence from live investigations involving encryption can satisfy the same criteria. This research concludes that it is possible for live digital evidence to be considered to be reliable, but that reliability of digital evidence ultimately depends on the specific investigation and the importance of the decision being made. However, the research provides structured criteria that allow the reliability of digital evidence to be assessed, demonstrates the use of these criteria in the context of live digital investigations involving encryption, and shows the extent to which each can currently be met.

Indiana University-Purdue University Indianapolis (IUPUI) / LBM-IB method is useful and popular simulation technique that is adopted ubiquitously to solve Fluid-Structure interaction problems in computational fluid dynamics. These problems are known for utilizing computing resources intensively while solving mathematical equations involved in simulations. Problems involving such interactions are omnipresent, therefore, it is eminent that a faster and accurate algorithm exists for solving these equations, to reproduce a real-life model of such complex analytical problems in a shorter time period. LBM-IB being inherently parallel, proves to be an ideal candidate for developing a parallel software. This research focuses on developing a parallel software library, LBM-IB based on the algorithm proposed by [1] which is first of its kind that utilizes the high performance computing abilities of supercomputers procurable today. An initial sequential version of LBM-IB is developed that is used as a benchmark for correctness and performance evaluation of shared memory parallel versions. Two shared memory parallel versions of LBM-IB have been developed using OpenMP and Pthread library respectively. The OpenMP version is able to scale well enough, as good as 83% speedup on multicore machines for <=8 cores. Based on the profiling and instrumentation done on this version, to improve the data-locality and increase the degree of parallelism, Pthread based data centric version is developed which is able to outperform the OpenMP version by 53% on manycore machines. A distributed version using the MPI interfaces on top of the cube based Pthread version has also been designed to be used by extreme scale distributed memory manycore systems.

<p>  </p> <p>Many machine learning models exist for battery management systems to utilize. Few have been shown to work. This work focuses on gathering data from cycling battery packs and sending this data directly to machine learning models built off robust datasets for applying the resulting predicted values and outputs directly on top of real-time systems. A parasitic sensor network was created composed of a main microcontroller, a host CPU, and various sensors including resistance temperature detection devices (RTDs), a voltage measurement circuit, current measurement circuit, and an accelerometer/gyroscope. The resulting network was integrated parasitically with a 4-cell 18650 SONY VTC6 battery pack, then tested both on-ground and in-flight with a commercial quadcopter. Real-time data for the battery pack with four cells in series was gathered. This real-time data stream was then integrated with data-driven neural network algorithms trained on various 18650 datasets and a real physical model to finalize the “AI BMS”. Using the power of non-linear models to infer battery health impacts not normally considered in battery management systems, the “AI BMS” was able to use low-fidelity real-time data in conjunction with a powerful multi-faceted model to make predictive decisions about battery health characteristics on top of normal system operations.</p>

In this thesis, frontal subroutines are implemented to a plane frame analysis program for execution on the IBM PC. The resulting program solves for the unknown joint displacements of frame structures with large numbers of degrees of freedom by utilizing a peripheral back-up storage; which can not be analyzed directly in core. A comparison of the frontal solver and the out-of-core band solver is presented. / M.S.

A 15.5-mile portion of the I-85 high-occupancy vehicle (HOV) lane in the metropolitan area of Atlanta, GA was converted to a high-occupancy toll (HOT) lane as part of a federal demonstration project designed to provide a reliable travel option through this congested corridor. Results from the I-85 demonstration project provided insight into the results that may follow the Georgia Department of Transportation's planned implementation of a $16 billion HOT lane network along metropolitan Atlanta's other major roadways [2]. To evaluate the impacts of the conversion, it was necessary to measure changes in corridor travel speed, reliability, vehicle throughput, passenger throughput, lane weaving, and user demographics. To measure such performance, a monitoring project, led by the Georgia Institute of Technology collected various forms of data through on-site field deployments, GDOT video, and cooperation from the State Road and Toll Authority (SRTA). Changes in the HOT lane's speed, reliability or other performance measure can affect the demographic and vehicle characteristics of those who utilize the corridor. The purpose of this particular study was to analyze the changes to the vehicle characteristics by comparing vehicle occupancy, vehicle classifications, and vehicle registration data to their counterparts from before the HOV-to-HOT conversion. As part of the monitoring project, the Georgia Tech research team organized a two-year deployment effort to collect data along the corridor during morning and afternoon peak hours. One year of data collection occurred before the conversion date to establish a control and a basis from which to compare any changes. The second year of data collection occurred after the conversion to track those changes and observe the progress of the lane's performance. While on-site, researchers collected data elements including visually-observed vehicle occupancy, license plate numbers, and vehicle classification [25]. The research team obtained vehicle records by submitting the license plate tag entries to a registration database [26]. In previous work, vehicle occupancy data were collected independently of license plate records used to establish the commuter shed. For the analyses reported in this thesis, license plate data and occupancy data were collected concurrently, providing a link between occupancy records of specific vehicles and relevant demographic characteristics based upon census data. The vehicle records also provided characteristics of the users' vehicles (light-duty vehicle vs. sport utility vehicle, model year, etc.) that the researchers aggregated to identify general trends in fleet characteristics. The analysis reported in this thesis focuses on identifying changes in vehicle characteristics that resulted from the HOV-to-HOT conversion. The data collected from post-conversion are compared to pre-conversion data, revealing changes in vehicle characteristics and occupancy distributions that most likely resulted from the implementation of the HOT lane. Plausible reasons affecting the vehicle characteristics alterations will be identified and further demographic research will enhance the data currently available to better pinpoint the cause and effect relationship between implementation and the current status of the I-85 corridor. Preliminary data collection outliers were identified by using vehicle occupancy data. However, future analysis will reveal the degree of their impact on the project as a whole. Matched occupancy and license plate data revealed vehicle characteristics for HOT lane users as well as indications that the tested data collectors are predominantly synchronized when concurrently collecting data, resulting in an argument to uphold the validity of the data collection methods. Chapter two provides reasons for why HOT lanes were sought out to replace I-85's HOV lanes. Chapter two will also provide many details regarding how the HOT lanes function and it will describe the role the Georgia Institute of Technology played in the assessment the HOV-to-HOT conversion. Chapter three includes the methodologies used to complete this document while chapter four provides results and analysis for the one year period before the conversion and the one year period after the conversion.

Indiana University-Purdue University Indianapolis (IUPUI) / As our population ages, treating bone and joint ailments is becoming increasingly important. Both osteoporosis, a bone disease characterized by a decreased density of mineral in bone, and osteoarthritis, a joint disease characterized by the degeneration of cartilage on the ends of bones, are major causes of decreased movement ability and increased pain. To combat these diseases, many treatments are offered, including drugs and exercise, and much biomedical research is being conducted. However, how can we get the most out of the research we perform and the treatment we do have? One approach is through computational analysis and mathematical modeling. In this thesis, quantitative methods of analysis are applied in different ways to two systems: osteoporosis and osteoarthritis. A mouse model simulating osteoporosis is treated with salubrinal and knee loading. The bone and cell data is used to formulate a system of differential equations to model the response of bone to each treatment. Using Particle Swarm Optimization, optimal treatment regimens are found, including a consideration of budgetary constraints. Additionally, an in vitro model of osteoarthritis in chondrocytes receives RNA silencing of Lrp5. Microarray analysis of gene expression is used to further elucidate the mode of regulation of ADAMTS5, an aggrecanase associated with cartilage degradation, by Lrp5, including the development of a mathematical model. The math model of osteoporosis reveals a quick response to salubrinal and a delayed but substantial response to knee loading. Consideration of cost effectiveness showed that as budgetary constraints increased, treatment did not start until later. The quantitative analysis of ADAMTS5 regulation suggested the involvement of IL1B and p38 MAPK. This research demonstrates the application of quantitative methods to further the usefulness of biomedical and biomolecular research into treatment and signaling pathways. Further work using these techniques can help uncover a bigger picture of osteoarthritis's mode of action and ideal treatment regimens for osteoporosis.

Indiana University-Purdue University Indianapolis (IUPUI) / Heat generation is a major safety concern in the design and development of Li ion batteries (LIBs) for large scale applications, such as electric vehicles. The total heat generation in LIBs includes entropic heat, enthalpy, reaction heat, and heat of mixing. The main objective of this study is to investigate the influence of heat of mixing on the LIBs and to understand whether it is necessary to consider the heat of mixing during the design and development of LIBs. In the previous research, Thomas and Newman derived methods to compute heat of mixing in LIB cells. Their results show that the heat of mixing cannot be neglected in comparison with the other heat sources at 2 C rate. In this study, the heat of mixing in different materials, porosity, particle sizes, and charging/discharging rate was investigated. A COMSOL mathematical model was built to simulate the heat generation of LIBs. The LIB model was based on Newman’s model. LiMn2O4 and LiCoO2 were applied as the cathode materials, and LiC6 was applied as the anode material. The results of heat of mixing were compared with the other heat sources to investigate the weight of heat of mixing in the total heat generation. The heat of mixing in cathode is smaller than the heat of mixing in anode, because of the diffusivity of LiCoO2 is 1 ×10-13 m2/s, which is larger than LiC6's diffusivity 2.52 × 10-14 m2/s. In the comparison, the heat of mixing is not as much as the irreversible heat and reversible heat, but it still cannot be neglected. Finally, a special situation will be discussed, which is the heat of mixing under the relaxation status. For instance, after the drivers turn off their vehicles, the entropy, ix enthalpy and reaction heat in LIBs will stop generating, but the heat will still be generated due to the release of heat of mixing. Therefore, it is meaningful to investigate to see if this process has significant influence on the safety and cycle life of LIBs.

Indiana University-Purdue University Indianapolis (IUPUI) / In recent years, there have been a lot of technology innovations to automate the day to day processes done by every person. These days the automobile manufacturers introduce new features in their cars, in order to improve customer experience, like Adaptive cruise control, Parallel park assist, etc. The objective of this thesis is to model an automated parallel parking system and to simulate the system behavior, by taking into account the high level events which happen when a car is parallel parked. The tool used in this thesis to model and simulate the system is Hybrid Petri net (HPN), which is versatile to model the real life systems. Chapter 1 deals with a brief introduction of the related work in Hybrid Petri net modeling of real life systems, automatic parallel parking systems and how the concept for modeling the parallel parking system was developed. Chapter 2 deals with the general introduction about Discrete, Continuous and Hybrid Petri nets and their dynamics which are essential for understanding this thesis. Chapter 3 deals with the development of the model and the various stages in the model development. Errors encountered in each stage is briefly discussed and the improvements are discussed in the next stage of development. This chapter concludes with the final integrated model and operation of the model. Chapter 4 deals with the discussion of results obtained when the model is tested in MATLAB and SIMHPN (which is a Matlab embedded simulation program). The results are compared, the system behavior is observed and the purpose of the thesis is justified. In Chapter 5, a conclusion is provided to summarize the entire thesis.