Voice over IP

The area that this thesis covers is Voice over IP (or IP Telephony as it is sometimes called) over Private networks and not over the Internet. There is a distinction to be made between the two even though the term is loosely applied to both. IP Telephony over Private Networks involve calls made over private WANs using IP telephony protocols while IP Telephony over the Internet involve calls made over the public Internet using IP telephony protocols. Since the network is private, service is reliable because the network owner can control how resources are allocated to various applications, such as telephony services. The public Internet on the other hand is a public, largely unmanaged network that offers no reliable service guarantee. Calls placed over the Internet can be low in quality, but given the low price, some find this solution attractive. What started off as an Internet Revolution with free phone calls being offered to the general public using their multimedia computers has turned into a telecommunication revolution where enterprises are beginning to converge their data and voice networks into one network. In retrospect, an enterprise\u27s data networks are being leveraged for telephony. The communication industry has come full circle. Earlier in the decade data was being transmitted over the public voice networks and now voice is just another application which is/will be run over the enterprises existing data networks. We shall see in this thesis the problems that are encountered while sending Voice over Data networks using the underlying IP Protocol and the corrective steps taken by the Industry to resolve these multitudes of issues. Paul M. Zam who is collaborating in this Joint Thesis/project on VoIP will substantiate this theoretical research with his practical findings. On reading this paper the reader will gain an insight in the issues revolving the implementation of VoIP in an enterprises private network as well the technical data, which sheds more light on the same. Thus the premise of this joint thesis/project is to analyze the current status of the technology and present a business case scenario where an organization will be able to use this information

A Hierarchical, Fuzzy Inference Approach to Data Filtration and Feature Prioritization in the Connected Manufacturing Enterprise

The current big data landscape is one such that the technology and capability to capture and storage of data has preceded and outpaced the corresponding capability to analyze and interpret it. This has led naturally to the development of elegant and powerful algorithms for data mining, machine learning, and artificial intelligence to harness the potential of the big data environment. A competing reality, however, is that limitations exist in how and to what extent human beings can process complex information. The convergence of these realities is a tension between the technical sophistication or elegance of a solution and its transparency or interpretability by the human data scientist or decision maker. This dissertation, contextualized in the connected manufacturing enterprise, presents an original Fuzzy Approach to Feature Reduction and Prioritization (FAFRAP) approach that is designed to assist the data scientist in filtering and prioritizing data for inclusion in supervised machine learning models. A set of sequential filters reduces the initial set of independent variables, and a fuzzy inference system outputs a crisp numeric value associated with each feature to rank order and prioritize for inclusion in model training. Additionally, the fuzzy inference system outputs a descriptive label to assist in the interpretation of the feature’s usefulness with respect to the problem of interest. Model testing is performed using three publicly available datasets from an online machine learning data repository and later applied to a case study in electronic assembly manufacture. Consistency of model results is experimentally verified using Fisher’s Exact Test, and results of filtered models are compared to results obtained by the unfiltered sets of features using a proposed novel metric of performance-size ratio (PSR)

Conformance Checking and Simulation-based Evolutionary Optimization for Deployment and Reconfiguration of Software in the Cloud

Many SaaS providers nowadays want to leverage the cloud's capabilities also for their existing applications, for example, to enable sound scalability and cost-effectiveness. This thesis provides the approach CloudMIG that supports SaaS providers to migrate those applications to IaaS and PaaS-based cloud environments. CloudMIG consists of a step-by-step process and focuses on two core components. (1) Restrictions imposed by specific cloud environments (so-called cloud environment constraints (CECs)), such as a limited file system access or forbidden method calls, can be validated by an automatic conformance checking approach. (2) A cloud deployment option (CDO) determines which cloud environment, cloud resource types, deployment architecture, and runtime reconfiguration rules for exploiting a cloud's elasticity should be used. The implied performance and costs can differ in orders of magnitude. CDOs can be automatically optimized with the help of our simulation-based genetic algorithm CDOXplorer. Extensive lab experiments and an experiment in an industrial context show CloudMIG's applicability and the excellent performance of its two core components

Forecasting CO2 Sequestration with Enhanced Oil Recovery

The aim of carbon capture, utilization, and storage (CCUS) is to reduce the amount of CO2 released into the atmosphere and to mitigate its effects on climate change. Over the years, naturally occurring CO2 sources have been utilized in enhanced oil recovery (EOR) projects in the United States. This has presented an opportunity to supplement and gradually replace the high demand for natural CO2 sources with anthropogenic sources. There also exist incentives for operators to become involved in the storage of anthropogenic CO2 within partially depleted reservoirs, in addition to the incremental production oil revenues. These incentives include a wider availability of anthropogenic sources, the reduction of emissions to meet regulatory requirements, tax incentives in some jurisdictions, and favorable public relations. The United States Department of Energy has sponsored several Regional Carbon Sequestration Partnerships (RCSPs) through its Carbon Storage program which have conducted field demonstrations for both EOR and saline aquifer storage. Various research efforts have been made in the area of reservoir characterization, monitoring, verification and accounting, simulation, and risk assessment to ascertain long-term storage potential within the subject storage complex. This book is a collection of lessons learned through the RCSP program within the Southwest Region of the United States. The scope of the book includes site characterization, storage modeling, monitoring verification reporting (MRV), risk assessment and international case studies