Single-rate Model Research Articles

The Medium Time Metric: High Throughput Route Selection in Multi-rate Ad Hoc Wireless Networks

Modern wireless devices, such as those that implement the 802.11abg standards, utilize multiple transmission rates in order to accommodate a wide range of channel conditions. The use of multiple rates presents a significantly more complex challenge to ad hoc routing protocols than the traditional single rate model. The hop count routing metric, which is traditionally used in single rate networks, is sub-optimal in multi-rate networks as it tends to select short paths composed of maximum length links. In a multi-rate network, these long distance links operate at the slowest available rate, thus achieving low effective throughput and reduced reliability due to the low signal levels. In this work we explore the lower level medium access control and physical phenomena that affect routing decisions in multi-rate ad hoc networks. We provide simulation results which illustrate the impact of these phenomena on effective throughput and show how the traditional minimum hop routing strategy is inappropriate for multi-rate networks. As an alternative, we present the Medium Time Metric (MTM) which avoids using the long range links often selected by shortest path routing in favor of shorter, higher throughput, more reliable links. Our experimental results with 802.11 g radios show that the Medium Time Metric achieves significantly higher throughput then alternative metrics. We observed up to 17 times more end-to-end TCP throughput than when the Min Hop or ETX metrics were used.

Parameter estimation of dual-rate stochastic systems by using an output error method

In this note, a new gradient-based recursive algorithm for estimating parameters of dual-rate sampled-data systems is presented. The basic idea is, by using an output error method, to identify the single-rate models and the unknown noise-free outputs (true outputs) directly from dual-rate input-output data. Further, convergence properties of the proposed algorithm in parameter and output estimation are analyzed; and the result is illustrated and tested with an experimental water-level system.

Combined parameter and output estimation of dual-rate systems using an auxiliary model

For a dual-rate sampled-data system, an auxiliary model based identification algorithm for combined parameter and output estimation is proposed. The basic idea is to use an auxiliary model to estimate the unknown noise-free output (true output) of the system, and directly to identify the parameters of the underlying fast single-rate model from the dual-rate input–output data. It is shown that the parameter estimation error consistently converges to zero under generalized or weak persistent excitation conditions and unbounded noise variance, and that the output estimates uniformly converge to the true outputs. An example is included.

Identification of dual‐rate systems based on finite impulse response models

AbstractTwo identification algorithms, a least squares and a correlation analysis based, are developed for dual‐rate stochastic systems in which the output sampling period is an integer multiple of the input updating period. The basic idea is to use auxiliary FIR models to predict unmeasurable noise‐free (true) outputs, and then use these and system inputs to identify parameters of underlying fast single‐rate models. The simulation results indicate that the proposed algorithms are effective. Copyright © 2004 John Wiley & Sons, Ltd.

Identification of dual-rate systems based on finite impulse response models

Two identification algorithms, a least squares and a correlation analysis based, are developed for dual-rate stochastic systems in which the output sampling period is an integer multiple of the input updating period. The basic idea is to use auxiliary FIR models to predict unmeasurable noise-free (true) outputs, and then use these and system inputs to identify parameters of underlying fast single-rate models. The simulation results indicate that the proposed algorithms are effective. Copyright © 2004 John Wiley & Sons, Ltd.

A Solution Concentration Model for Carbon-in-Pulp Simulation

This article describes a procedure for carbon-in-pulp (CIP) simulation based on a dual mass-transfer kinetic approach. The method attempts to overcome the short-comings of frequently used single-rate simulation techniques and at the same time, addresses the mathematical complexities of multirate modeling procedures. Operating parameters for a full-scale CIP plant were utilized to test the new simulation approach. It was found that this simplified approach, based entirely on solution concentration, provided a significant improvement on certain current single-rate expression models.

Rate matrices for analyzing large families of protein sequences.

We propose and study a new approach for the analysis of families of protein sequences. This method is related to the LogDet distances used in phylogenetic reconstructions; it can be viewed as an attempt to embed these distances into a multidimensional framework. The proposed method starts by associating a Markov matrix to each pairwise alignment deduced from a given multiple alignment. The central objects under consideration here are matrix-valued logarithms L of these Markov matrices, which exist under conditions that are compatible with fairly large divergence between the sequences. These logarithms allow us to compare data from a family of aligned proteins with simple models (in particular, continuous reversible Markov models) and to test the adequacy of such models. If one neglects fluctuations arising from the finite length of sequences, any continuous reversible Markov model with a single rate matrix Q over an arbitrary tree predicts that all the observed matrices L are multiples of Q. Our method exploits this fact, without relying on any tree estimation. We test this prediction on a family of proteins encoded by the mitochondrial genome of 26 multicellular animals, which include vertebrates, arthropods, echinoderms, molluscs, and nematodes. A principal component analysis of the observed matrices L shows that a single rate model can be used as a rough approximation to the data, but that systematic deviations from any such model are unmistakable and related to the evolutionary history of the species under consideration.

Application of dual-rate modeling to CCR octane quality inferential control

This paper discusses system identification of dual-rate systems. In particular, a dual-rate system with control interval T and output sampling interval nT is considered. At first the problem of identifying a fast single-rate model with sampling period T based on dual-rate input-output data is studied in the polynomial domain. Then the proposed algorithm is applied to model an industrial process. The estimated fas-rate model is then used in inferential control of the octane quality in a continuous catalytic reformer.

Identification of fast-rate models from multirate data

For a multirate sampled-data system consisting of a continuous-time process with or without a time delay, a sampler with period nT and a zero-order hold with period mT (m < n), we study the problem of identifying a fast single-rate model with sampling period mT based on multirate input-output data. This problem is solved in two steps: First, we identify a lifted state-space model for the multirate system by extending existing subspace identification algorithms to take into account the causality constraint in the lifted model; next, based on the lifted model, we extract a state-space model for the fast single-rate system. Such fast-rate models are useful for many applications such as inferential control. Other related topics discussed in the paper include observability of lifted models in the presence of time delay and time-delay estimation from multirate data. Finally, we apply and test the proposed algorithms to an experimental setup involving a continuously stirred tank heater.

98/02041 Black liquor recovery by pressurized, oxygen-blown gasification

Numerical simulations of the oxygen-blown coal gasification process inside a generic entrained-flow gasifier are carried out. The Eulerian–Lagrangian approach is applied to solve the Navier–Stokes equations and the particle dynamics. Seven species transport equations are solved with three heterogeneous global reactions and two homogeneous reactions. Finite rates are used for the heterogeneous solid-to-gas reactions. Both finite rate and eddy-dissipation combustion models are calculated for each homogeneous gas-to-gas reaction, and the smaller of the two rates is used. Four different devolatilization models are employed and compared. The Kobayashi model produces slower devolatilization rate than the other models. The constant rate model produces the fastest devolatilization rate. The single rate model and the chemical percolation model produce moderate and consistent devolatilization rate. Slower devolatilization rate produces higher exit gas temperature and higher CO and CO2 mass fractions, but lower H2 and heating value, and hence, achieves lower gasification efficiency. Combustion of volatiles is modeled with two-stage global reactions with an intermediate stage via benzene.Turbulence models significantly affect the simulated results. Among five turbulence models employed, the standard k–ε and the RSM models give consistent results. The time scale for employing stochastic time tracking of particles also affects simulated result. Caution has to be exerted to select the appropriate time constant value. Smaller particles have a higher surface/volume ratio and react faster than larger particles. However, large particles possessing higher inertia could impinge on the opposing jet and change the thermal-flow filed and the reaction rates.

Combining Data with Different Distributions of Among-Site Rate Variation

As the number of molecular studies continues to grow, so does the problem of how to analyze multiple data sets. The importance of this problem is indicated by the flurry of recent papers (reviewed by de Queiroz et alv 1995) addressing philosophical and practical considerations facing systematists fortunate enough to have several pertinent data sets on hand. Perhaps the most rigorous practical treatment was that of Bull et al. (1993), who argued against combining data when there is demonstrable heterogeneity among the different data sets in the processes governing character evolution. These authors showed that combining simulated data sets generated from the same topology but with different rates of evolution leads to less accurate estimation of phylogeny than does analysis of the more slowly evolving data set alone. Although the validity of these results is not in question for the models tested, the models used in these simulations may not be very realistic. Specifically, Bull et al. used two single-rate models to generate data sets, one with a uniform high rate and one with a uniform low rate of evolution. Chippindale and Wiens (1994) demonstrated that down-weighting rapidly evolving characters resulted in increased accuracy in combined analyses of