Background: Ensuring adequate depth of anesthesia during surgery is essential for anesthesiologists to prevent the occurrence of unwanted alertness during surgery or failure to return to consciousness. Since the purpose of using anesthetics is to affect the central nervous system, brain signal processing such as electroencephalography (EEG) can be used to predict different levels of anesthesia....

We consider the H∞ almost output synchronization and regulated output synchronization problem for heterogeneous directed networks with external disturbances where agents are introspective (i.e. agents have access to a part of their own state). A decentralized protocol is designed for each agent, without exchanging internal controller states with neighboring agents, to achieve output synchroniza...

fifty years ago warren and root have introduced the shape factor. this fundamental parameter for modeling of naturally fractured reservoirs has been discussed stormily ever since. different definitions for shape factor have been suggested which all of them are heuristically based. recently, heinemann and mittermeir mathematically derived - based on the dual-continuum theorem assuming pseudo-ste...

Due to the changes which may occur in their parameters, systems are usually demonstrated by some subsystems for different conditions. This paper employs Modified Petri Nets (MPN) to model theses subsystems and makes it simple to analyze them. In this method, first, the continuous transfer function is converted to a discrete transfer function and then, by MPN, system is modeled and analyzed. All...

In this paper a class of nonparametric transfer function models is proposed to model nonlinear relationships between 'input' and 'output' time series. The transfer function is smooth with unknown functional forms, and the noise is assumed to be a stationary autoregressive-moving average (ARMA) process. The nonparametric transfer function is estimated jointly with the ARMA parameters. By modelin...

Direct volume rendering has been an active area of research for over two decades. Transfer function design remains a difficult task since current methods, such as traditional 1D and 2D transfer functions are not always effective for all datasets. Various 1D or 2D transfer function spaces have been proposed to improve classification exploiting different aspects, such as using the gradient magnit...