You: What Generation Y Thinks about Corporate Social Networking Applications[ns2 project]

This knowledge could then guide thedevelopment of appropriate strategies to improve transparency and could even indicate some fundamental limitationsFinally, the third goal of the paper is to investigate whether transparency is an independent property of the system, or if different output signals in the system can experience different levels of transparency. The significance of this goal is that it will help find out whether or not establishing the transparency in a set of output signals would also automatically ensure the transparency in other signals in the system, as well. You: What Generation Y Thinks about Corporate Social Networking Applications? In summary, the goals of this paper can be listed as follows. Development of a statistical transparency analysis method. Comparing the relative impact of Internet QoS versus distribution on transparency using the developed transparency analysis method on a sample ID-HILS system. Experimentally investigating whether transparency is an independent property of the system or an output-signal dependent property. Previous work on ID-HILS systems concentrated on investigating observer-based and observer-free alternatives to integrating HILS systems over the Internet, but did not address the aforementioned goals of this paper. For example, Compere et al. proposed an observer-based method to overcome the adverse affects of Internet QoS and distributed simulation and achieve a stable ID-HILS system Specifically, they successfully integrated a ride motion simulator in Warren, MI, with a hybrid power system in Santa Clara, CA, You: What Generation Y Thinks about Corporate Social Networking Applications?over the Internet. A rigorous transparency analysis, however, was not performed. A similar integration has been achieved by Ersal with an engine-in-the-loop simulation EILS setup in Ann Arbor, MI and the aforementioned ride motion simulator in Warren, MI but without relying on observers and using an event-based framework However, a rigorous experimental analysis of transparency was not feasible due to the infeasibility of directly coupling the two HILS setups. The paper explores its second and third research goals on a new ID-HILS system. Specifically, the Internet-distributed integration of the aforementioned EILS setup in Ann Arbor with simple vehicle and driver models in Warren is considered. In addition, the same event-based framework mentioned above is utilized. The transparency of this setup has been previously analyzed in simulation in which the EILS setup was modeled so that the only source of stochasticity was the Internet. This paper considers the actual hardware and the proposed statistical analysis method helps distinguish the variation introduced by the distribution over the Internet from the inherent variation in the hardware. The rest of the paper is organized as follows. Section first reviews the transparency metrics proposed in the literature and then presents the proposed statistical transparency analysis. Section illustrates on a simple example how significant the impact of distributing the simulation on transparency can be, You: What Generation Y Thinks about Corporate Social Networking Applications? even if there are no sampling or delay effects. This section motivates the special attention given in the paper to characterizing the effect of distributing the simulation. Section describes the ID-HILS setup considered in this study in detail. Section describes the four system configurations devised for this study, each aimed to capture the effect of a different source of variation. Section applies the proposed statistical transparency analysis to the ID-HIL setup and discusses the findings. Finally, Section summarizes the work, its conclusions, and original contributions. You: What Generation Y Thinks about Corporate Social Networking Applications? To quantify transparency, the literature presents many frequency-domain metrics within the context of telerobotics and haptics. In a linear analysis, for example, Lawrence defines the transmitted impedance ratio as the ratio of the transmitted impedance, Zt , over the impedance of the environment, Ze, in a teleoperation device