Ali Zilouchian

Recovery of missing speech packets using the short-time energy and zero-crossing measurements

A waveform substitution technique using interpolation based on the slowly varying speech parameters of short-time energy and zero-crossing information is developed for a packetized speech communication system. The system uses 64-kb conventional pulse code modulation (PCM) for encoding and takes advantage of active talkspurts and silence intervals to increase the efficiency of utilizing a digital link. The short-time energy and information on the zero-crossings needed for the purpose of determining talkspurts are transmitted in a preceding packet. Hence, when a packet is pronounced lost, its envelope and frequency characteristics are obtained from a previous packet and used to synthesize a substitution waveform which is free of annoying sounds that are due to abrupt changes in amplitude. >

Automation and process control of reverse osmosis plants using soft computing methodologies

At a time of intensive demand for producing fresh water at a reasonable cost, addressing automation, process control and cost optimization of desalination plants have become increasingly evident. Large-scale direct seawater reverse osmosis (RO) plants must perform at high standards due to the increasing cost of high quality water production, high equipment utilization, and rising government regulations on labor protection and the environment. In this keynote presentation, the recent innovation and technological advances in the design and implementation of soft computing methodologies for desalination processes are addressed. Such advances are mainly due to the recent developments of intelligent control design approaches for the integration of sensory information, computation, human reasoning and decision making. The principal partners in such an intelligent system include fuzzy logic (FL), neural network (NN), generic algorithms (GA) and probabilistic reasoning (PR). Various issues which is related to the design and implementation of soft computing methodologies including the trade-off between tolerance, precision and uncertainty are also addressed. As a case study, the design and implementation of an intelligent system for a direct seawater RO system located near Atlantic Ocean at Boca Raton, Florida is presented. The operation of the prototype plant indeed demonstrated the effective and optimum performance of the design for two types of membrane modules, spiral wound (SW) and hollow fine fiber (HFF), under forced diverse operating conditions. The system has achieved a constant recovery of 30% and salt passage of 1.026% while salt concentration of six major salts were kept below their solubility limits at all time. The implementation of the proposed intelligent control methodology has achieved a 5% increase in availability and reduction in manpower requirements as well as reduction in overall chemical consumption of the plant. Therefore, it is believed that the cost of producing water can be decreased using the proposed fully automated control strategy.

Model reduction of discrete linear systems via frequency-domain balanced structure

In this paper, a novel model reduction technique for discrete linear time invariant systems is presented. The proposed technique is based on a conceptual viewpoint of the controllability and the observability Grammian balancing of a discrete system in an arbitrary frequency range of operation. It could be viewed as the generalization of the Moores balance structure approach in a specific frequency range of operation. Two modified Lyapunov equations are derived for the proposed frequency-domain balancing. Various properties of the reduced model such as controllability, observability, stability, its uniqueness, and the error bound are examined. A comparison study of the proposed method with the Moores time domain technique is presented using a sixth-order digital filter.

Principle of frequency-domain balanced structure in linear systems and model reduction

Abstract In this paper a novel model reduction technique for linear time-invariant systems is presented. The proposed technique is based on a conceptual viewpoint regarding the balancing of the controllability and observability Gramians of a multivariable system in a given range of frequency. The conditions for the stability of the reduced model are also provided. From a real-time applicability viewpoint, the frequency-domain balanced structure provides an attractive approach to the model reduction of large scaled systems. The simulation results establish the effectiveness of this proposed method compared to the effectiveness of existing techniques.

A coefficient sensitivity bound in 2-D state-space digital filtering

For a SISO two-dimensional (2-D) state-space digital filter, a general upper bound on the sensitivity measurement of the filters eigenfunctions is proposed. The bound for both equal and unequal word length registers of an optimal realization [11], [12] is calculated. It is shown that for a scaled equal wordlength realization, the bound has its minimum value.

Application of Fuzzy Logic for the Solution of Inverse Kinematics and Hierarchical Controls of RoboticManipulators

In this paper, hierarchical control techniques is used for controlling a robotic manipulator. The proposed method is based on the establishment of a non-linear mapping between Cartesian and joint coordinates using fuzzy logic in order to direct each individual joint. The hierarchical control will be implemented with fuzzy logic to improve the robustness and reduce the run time computational requirements. Hierarchical control consists of solving the inverse kinematic equations using fuzzy logic to direct each individual joint. A commercial Microbot with three degrees of freedom is utilized to evaluate this methodology. A decentralized fuzzy controller is used for each joint, with a Fuzzy Associative Memories (FAM) performing the inverse kinematic mapping in a supervisory mode. The FAM determines the inverse kinematic mapping which maps the desired Cartesian coordinates to the individual joint angles. The individual fuzzy controller for each joint generates the required control signal to a DC motor to move the associated link to the new position. The proposed hierarchical fuzzy controller is compared to a conventional controller. The simulation experiments indeed demonstate the effectiveness of the proposed method.

Adaptive receptive fields for radial basis functions

We propose a network architecture based on adaptive receptive fields and a learning algorithm that combines both supervised learning of centers and unsupervised learning of output layer weights. This algorithm causes each group of radial basis functions to adapt to regions of the clustered input space. Networks produced by this algorithm appear to have better generalization performance on prediction of non-linear input-output mappings than corresponding backpropagation algorithms and requires a fraction of the number of connection weights required by fixed center radial basis. For a test problem of predicting product quality of a reverse osmosis desalination plant, the network learns much faster than a three-layer perceptron trained with back-propagation, but requires additional computational burden.

Model reduction of large scaled systems via frequency-domain balanced structures

A model reduction technique for linear time-invariant systems is presented. The proposed technique is based on a conceptual view point regarding the balancing of the controllability and observability grammians of a multivariable system in a given frequency range of operation. The necessary, as well as sufficient conditions for the stability of the reduced model are provided. It is shown that from the real-time systems applicability view point, the frequency-domain balanced structure is a natural as well as a desired approach to model reduction of large scaled systems. The simulation results have indeed demonstrated the effectiveness of the proposed method in comparison with the existing proposed techniques.

Balanced structures and model reduction of unstable systems

It is shown that B.C. Moores balancing model (1981) can be extended to unstable systems by utilizing two weighted Grammian matrices. It is proposed that the J omega axis can be shifted to handle unstable systems. The balanced approach involves the solution of two modified Lyapunov equations. Two numerical examples are given to illustrate the results.<<ETX>>

Solutions of kinematics of robot manipulators using a Kohonen self-organizing neural network

Kohonen self-organizing neural network is used to solve the forward kinematics problems of robot manipulators. Through competition learning, neurons learn their distribution in the training phase. In sequel, the nonlinear mapping has been obtained by proper calibration of training results. The proposed method is based on the unsupervised learning which does not rely on the knowledge of process model information. Simulation results have effectiveness of the proposed method for a two degree planar robot manipulator.