Multirate Setting Research Articles

Design of an optimal preview controller for linear time-varying discrete systems in a multirate setting

This paper is concerned with preview control problems for linear time-varying discrete systems in a multirate setting. First, by using the discrete lifting technique, the multirate time-varying discrete system is converted to a formal single-rate system. Then, by applying the standard linear quadratic (LQ) preview control method, we construct the expanded error system, and the optimal preview control model of the common time-varying discrete system is obtained. The optimal control input of the expanded error system is obtained by using the outcome of optimal control theory on time-varying systems. The controller with preview action is obtained when we transfer our conclusion into the original system. Finally, a numerical example is included to illustrate the validity of the proposed method.

Optimal preview control for discrete-time descriptor causal systems in a multirate setting

The optimal preview control problem is proposed for discrete-time descriptor causal systems in a multirate setting. First, according to the characteristics of causal systems, the descriptor system is transformed into an algebraic equation and a normal system represented by a dynamic equation. Second, by using the discrete lifting technique, the normal multirate system is converted to a single-rate system. Then by making use of the standard linear qudratic (LQ) preview control method, we construct the augmented error system, and the optimal preview control law of the augmented error system is obtained. The control law for the augmented system is then transformed to the optimal preview control law of the descriptor causal system in a multirate setting. The stabilisability and detectability of the lifted single-rate system and the augmented error system are discussed in detail. The effectiveness of the proposed method is shown by simulation.

Optimizing Multi-Rate Peer-to-Peer Video Conferencing Applications

We consider multi-rate peer-to-peer multiparty video conferencing applications, where different receivers in the same group can receive videos at different rates using, for example, scalable layered coding. The quality of video received by each receiver can be modeled as a concave utility function of the video bitrate. We study and address the unique challenges introduced by maximizing utility in the multi-rate setting as compared to the single-rate case. We first determine an optimal set of tree structures for routing multi-rate content using scalable layered coding. We then develop Primal and Primal-dual based distributed algorithms to maximize aggregate utility of all receivers in all groups by multi-tree routing and show their convergence. These algorithms can be easily implemented and deployed on today's Internet. We have built a prototype video conferencing system to show that this approach converges to optimal bitrates to improve user experience and offers automatic adaptation to network conditions and user preferences.

Measuring the effects of adaptive multirate (AMR) codecs on formant tracker performance.

Several approaches to forensic speaker comparison rely on formant center‐frequency measurements as features due to their rather straightforward interpretation as resonance frequencies of the cavities of the human vocal tract. Formant tracking algorithms, mostly based on linear predictive coding, are commonly used for automatic extraction. Telephone conversations constitute a substantial amount of forensic material, which increasingly involves wireless communication channels instead of landline transmission. The effects and limitations introduced by the adaptive multirate (AMR) set of codecs that is used for speech transmission in GSM and UMTS networks are therefore of special interest in forensic settings. To evaluate the extent of the effects that are caused solely by the codecs, speech recordings were en‐ and de‐coded with the different bitrate levels provided by the AMR narrowband codec. The formant frequencies of vowel segments were extracted using different trackers and settings. The preliminary results suggest partial shifts in frequency depending on codec level and individual speakers, but no consistent trend emerges.

M4ARC: multi-model–multi-mode adaptive resonant control for dynamically loaded flexible beamstructures

A hybrid multi-model–multi-mode adaptive resonant control(M4ARC) approach is proposed for dynamically loaded flexible beam structures to provide superiorvibration damping performance as compared to its fixed-model and adaptive counterparts.The proposed approach uses a configurable controller, the parameters of which areupdated using a fast and accurate on-line frequency identification method forN modesof interest. This method incorporates a simple supervision scheme that selects between the output ofan N-mode filter bank (representing the multiple-fixed-model set) and the output of an estimator bank(representing the accurate model of the plant). The estimator bank comprises a multi-rate set of parallelN second-order recursive-least-squares estimators to achieve rapid parameter convergence.While the estimators are still in transition, the supervisor provides the configurablecontroller with an intermediate set of frequencies that correspond to the closest fixedmodel. Once the estimators converge, the supervisor selects the estimated frequency set toprovide the configurable controller with an accurate representation of the currentplant. This supervisor scheme significantly reduces the computational complexityas compared with existing counterparts. Experiments reveal that the proposedM4ARC approach offers the best compromise in terms of adapting to sudden and highly variableloading condition changes (within a bounded domain) while, at the same time, achievingfast transient performance.

OPTIMAL SENSOR NETWORK DESIGN FOR MULTIRATE SYSTEMS

A methodology for determining optimal sensor network design for multirate systems is presented. This methodology is based on generating trade-off (Pareto optimal) solutions between the quality of state estimation and the total measurement cost associated with the sensor network. Variances associated with the states estimated using a Kalman filter in a multirate setting are used to calculate the state estimation quality. The resulting multiobjective optimization problem is solved using the well known non-dominated sorting genetic algorithm-II. The resulting solutions can be then further analyzed by the process designer for determining the optimal sensor network. The methodology is demonstrated using simulations on the benchmark quadruple tank set up (Johansson, 2000).