Abstract — The fundamental advantage of peer-to-peer (P2P) multimedia streaming applications is to leverage peer upload capacities to minimize bandwidth costs on dedicated streaming servers. The available bandwidth among peers is of pivotal importance to P2P streaming applications, especially as the number of peers in the streaming session reaches a very large scale. In this paper, we utilize more than 230 GB of traces collected from a commercial P2P streaming system, UUSee, over a four-month period of time. With such traces, we seek to thoroughly understand and characterize the achievable bandwidth of streaming flows among peers in large-scale real-world P2P live streaming sessions, in order to derive useful insights towards the improvement of current-generation P2P streaming protocols, such as peer selection. Using continuous traces over a long period of time, we explore evolutionary properties of inter-peer bandwidth. Focusing on representative snapshots of the entire topology at specific times, we investigate distributions of interpeer bandwidth in various peer ISP/area/type categories, and statistically test and model the deciding factors that cause the variance of such inter-peer bandwidth. Our original discoveries in this study include: (1) The ISPs that peers belong to are more correlated to inter-peer bandwidth than their geographic locations; (2) There exist excellent linear correlations between peer last-mile bandwidth availability and inter-peer bandwidth within the same ISP, and between a subset of ISPs as well; and (3) The evolution of inter-peer bandwidth between two ISPs exhibits daily variation patterns. Based on these insights, we design a throughput expectation index that facilitates highbandwidth peer selection without performing any measurements.

Peer-to-peer streaming has emerged as a killer application in today’s Internet, delivering a large variety of live multimedia content to millions of users at any given time with low server cost. Though successfully deployed, the efficiency and optimality of the current peer-to-peer streaming protocols are still less than satisfactory. In this thesis, we investigate optimizing solutions to enhance the performance of the state-of-the-art mesh-based peer-to-peer streaming systems, utilizing both theoretical performance modeling and extensive real-world measurements. First, we model peer-to-peer streaming applications in both the single-overlay and multi-overlay scenarios, based on the solid foundation of optimization and game theories. Using these models, we design efficient and fully decentralized solutions to achieve performance optimization in peer-to-peer streaming. Then, based on a large volume of live measurements from a commercial large-scale peer-to-peer streaming application, we extensively study the real-world performance of peer-to-peer streaming over a long period of time. Highlights of our measurement study include ii the topological characterization of large-scale streaming meshes, the statistical characterization

Abstract—Gas Metal Arc Welding (GMAW) processes is an important joining process widely used in metal fabrication industries. This paper addresses modeling and optimization of this technique using a set of experimental data and regression analysis. The set of experimental data has been used to assess the influence of GMAW process parameters in weld bead geometry. The process variables considered here include voltage (V); wire feed rate (F); torch Angle (A); welding speed (S) and nozzle-to-plate distance (D). The process output characteristics include weld bead height, width and penetration. The Taguchi method and regression modeling are used in order to establish the relationships between input and output parameters. The adequacy of the model is evaluated using analysis of variance (ANOVA) technique. In the next stage, the proposed model is embedded into a Simulated Annealing (SA) algorithm to optimize the GMAW process parameters. The objective is to determine a suitable set of process parameters that can produce desired bead geometry, considering the ranges of the process parameters. Computational results prove the effectiveness of the proposed model and optimization procedure. Keywords—Weld Bead Geometry, GMAW welding, Process parameters Optimization, Modeling, SA algorithm

We consider the specification of prior distributions for Bayesian model comparison, focusing on regression-type models. We propose a particular joint specification of the prior distribution across models so that sensitivity of posterior model probabilities to the dispersion of prior distributions for the parameters of individual models (Lindley’s paradox) is diminished. We illustrate the behavior of inferential and predictive posterior quantities in linear and log-linear regressions under our proposed prior densities with a series of simulated and real data examples.

In the last few years, some researchers have proposed the use of COSMIC-FFP for effort prediction of Web applications. It is widely recognized, that a measure can be accepted only if its usefulness has been proved through some empirical studies. In this paper, we reported on an empirical study carried out using an industrial dataset and compared the results obtained with a previous analysis based on Web applications developed by academic students. We used an adaptation of COSMIC-FFP specifically conceived for Web applications as size measure and the Ordinary Least Square Regression as modelling technique. This analysis had a twofold goal: to verify whether COSMIC-FFP can provide good estimations and to analyse possible differences/similarities in the empirical results obtained with the two different datasets.

statistical approach for predicting and

Abstract — This paper addresses modeling and optimization of process parameters in powder mixed electrical discharge machining (PMEDM). The process output characteristics include metal removal rate (MRR) and electrode wear rate (EWR). Grain size of Aluminum powder (S), concentration of the powder (C), discharge current (I) pulse on time (T) are chosen as control variables to study the process performance. The experimental results are used to develop the regression models based on second order polynomial equations for the different process characteristics. Then, a genetic algorithm (GA) has been employed to determine optimal process parameters for any desired output values of machining characteristics.