Community detection in opportunistic networks using memory-based cognitive heuristics
Thus, perfect transparency is achieved when this ratio is for all frequencies. Community detection in opportunistic networks using memory-based cognitive heuristicsThis impedance-based quantification of transparency has been used by other researchers, as well and also been extended to linear time-delay systems As a transparency metric for telesurgery, C¸ avus¸o˘glu et al. suggested a weighted norm of the sensitivity of the transmitted impedance to the environmental impedance whereWs is a is a frequency-dependent weighting function, and Ze is the nominal environment impedance Rather than using a ratio, De Gersem et al.
proposed to use a difference as a transparency metric Specifically, assuming that the environment can be described as a pure stiffness, they characterize transparency as where Wp is a low-pass weighting function implementing the performance bandwidth requirement, ke is the environment stiffness for an environment with negligible damping and inertial characteristics, and K and σ are constant factors for scaled telemanipulation and enhanced sensitivity, Community detection in opportunistic networks using memory-based cognitive heuristicsrespectively. Rather than using impedances,Yokokohji andYoshikawa proposed to use the correspondence between the position and force signals as a metric for transparency Specifically, they define the integrals where ωmax is the manipulation bandwidth of human operators, T is time constant of first order lag, and Gmp, Gsp , Gmf , and force, and slave force, respectively.
Community detection in opportunistic networks using memory-based cognitive heuristic
The index is zero, if themaster and slave positions forces perfectly match. Therefore, formaximum transparency, both indices are desired to be zero. Griffiths et al., on the other hand, proposed the distortion metric, which is defined as the difference between the desired and actual rendered dynamics in an haptic device normalized with respect to the desired dynamics where Rd is the desired dynamics and P is the actual rendered dynamics Therefore, maximum transparency is achieved when distortion is equal to zero. This metric allows to frame the problem as a general control configuration, a configuration in which the performance variable is different than the feedback variable, and takes advantage of the knowledge about the fundamental limitations of the general control configuration as first highlighted by Freudenberg These metrics are useful in linear, deterministic frameworks, but the nonlinear, stochastic nature of ID-HILS systems, in general, limits their utility. Therefore, a statistical characterization of transparency is proposed in the next section. Any HILS setup will have some inherent variation in it due to the hardware components involved. Distribution over the Internet will introduce additional variation.
It is important to know whether this additional variation is negligible or significant relative to the inherent variation in the output signals of interest. If it is negligible, then the system can be considered transparent with respect to the chosen outputs; otherwise there is a noticeable degradation in transparency. This section formalizes this statistical approach to characterizing transparency. Let n denote the number of different configurations of a system to be compared, each with a different level of distribution. To capture the inherent variation in the setup, one of these configurations needs to be the ideal case, direct integration without distribution and without the Internet. The remaining configurations need to capture the effects that are being analyzed for their impact on transparency. Community detection in opportunistic networks using memory-based cognitive heuristics Thus, to differentiate between the inherent variation in the setup, the variation due to distribution, and the variation due to the Internet QoS, for example, at least three different configurations need to be considered. These are the ideal case the case, which considers a distributed system, but with a negligible delay the Internet-distributed case that captures the impact of Internet QoS, as well. Community detection in opportunistic networks using memory-based cognitive heuristics