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Full-Text Articles in Engineering

Implementation Of Adaptive Critic-Based Neurocontrollers For Turbogenerators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley Jan 2003

Implementation Of Adaptive Critic-Based Neurocontrollers For Turbogenerators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents the design and practical hardware implementation of optimal neurocontrollers that replace the conventional automatic voltage regulator (AVR) and the turbine governor of turbogenerators on multimachine power systems. The neurocontroller design uses a powerful technique of the adaptive critic design (ACD) family called dual heuristic programming (DHP). The DHP neurocontroller's training and testing are implemented on the Innovative Integration M67 card consisting of the TMS320C6701 processor. The measured results show that the DHP neurocontrollers are robust and their performance does not degrade unlike the conventional controllers even when a power system stabilizer (PSS) is included, for changes in …

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An Industrial Food Processing Plant Automation Using A Hybrid Of Pi And Fuzzy Logic Control, Ganesh K. Venayagamoorthy, D. Naidoo, P. Govender Jan 2003

An Industrial Food Processing Plant Automation Using A Hybrid Of Pi And Fuzzy Logic Control, Ganesh K. Venayagamoorthy, D. Naidoo, P. Govender

Electrical and Computer Engineering Faculty Research & Creative Works

An industrial food processing plant consisting of nonlinear dynamics requires pressure and temperature controllers to be nonlinear. In this paper, nonlinear controllers are designed using fuzzy logic to augment the conventional pressure and temperature PI controllers for an industrial food processing plant. Simulation results are presented to show improvement in the plant response with the hybrid controllers.

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Adaptive Neural Network Based Power System Stabilizer Design, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch Jan 2003

Adaptive Neural Network Based Power System Stabilizer Design, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Power system stabilizers (PSS) are used to generate supplementary control signals for the excitation system in order to damp the low frequency power system oscillations. To overcome the drawbacks of conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, this paper presents an indirect adaptive neural network based power system stabilizer (IDNC) design. The proposed IDNC consists of a neuro-controller, which is used to generate a supplementary control signal to the excitation system, and a neuro-identifier, which is used to model the dynamics of the power system and to adapt the …

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Dual Heuristic Programming Excitation Neurocontrol For Generators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley Jan 2003

Dual Heuristic Programming Excitation Neurocontrol For Generators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

The design of nonlinear optimal neurocontrollers that replace the conventional automatic voltage regulators for excitation control of turbogenerators in a multimachine power system is presented in this paper. The neurocontroller design is based on dual heuristic programming (DHP), a powerful adaptive critic technique. The feedback variables are completely based on local measurements from the generators. Simulations on a three-machine power system demonstrate that DHP-based neurocontrol is much more effective than the conventional proportional-integral-derivative control for improving dynamic performance and stability of the power grid under small and large disturbances. This paper also shows how to design optimal multiple neurocontrollers for …

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Automated Segmentation Of Radiodense Tissue In Digitized Mammograms Using A Constrained Neyman-Pearson Classifier, Jeremy Thomas Neyhart Dec 2002

Automated Segmentation Of Radiodense Tissue In Digitized Mammograms Using A Constrained Neyman-Pearson Classifier, Jeremy Thomas Neyhart

Theses and Dissertations

Breast cancer is the second leading cause of cancer related mortality among American women. Mammography screening has emerged as a reliable non-invasive technique for early detection of breast cancer. The radiographic appearance of the female breast consists of radiolucent (dark) regions and radiodense (light) regions due to connective and epithelial tissue. It has been established that the percentage of radiodense tissue in a patient's breast can be used as a marker for predicting breast cancer risk. This thesis presents the design, development and validation of a novel automated algorithm for estimating the percentage of radiodense tissue in a digitized mammogram. …

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Adaptive Neural Network Identifiers For Effective Control Of Turbogenerators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Ronald G. Harley, Donald C. Wunsch Aug 2002

Adaptive Neural Network Identifiers For Effective Control Of Turbogenerators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Ronald G. Harley, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

This paper provides a novel method for nonlinear identification of multiple turbogenerators in a five-machine 12-bus power system using continually online trained (COT) artificial neural networks (ANNs). Each turbogenerator in the power system is equipped with all adaptive ANN identifier, which is able to identify/model its particular turbogenerator and rest of the network to which it is connected from moment to moment, based on only local measurements. Each adaptive ANN turbogenerator can be used in the design of a nonlinear controller for each turbogenerator in a multimachine power system. Simulation results for the adaptive ANN identifiers are presented

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Development Of An Efficient Ti:Sapphire Laser Transmitter For Atmospheric Ozone Lidar Measurements, Khaled A. Elsayed Jul 2002

Development Of An Efficient Ti:Sapphire Laser Transmitter For Atmospheric Ozone Lidar Measurements, Khaled A. Elsayed

Electrical & Computer Engineering Theses & Dissertations

The impetus of this work was to develop an all solid-state Ti:sapphire laser transmitter to replace the current dye lasers that could provide a potentially compact, robust, and highly reliable laser transmitter for differential absorption lidar measurements of atmospheric ozone. Two compact, high-energy pulsed, and injection-seeded Ti:sapphire lasers operating at a pulse repetition frequency of 30 Hz and wavelengths of 867 nm and 900 nm, with M2 of 1.3, have been experimentally demonstrated and compared to model results. The Ti:sapphire lasers have shown the required output beam quality at maximum output pulse energy, 115 mJ at 867 nm and …

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Two Separate Continually Online-Trained Neurocontrollers For Excitation And Turbine Control Of A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley Jan 2002

Two Separate Continually Online-Trained Neurocontrollers For Excitation And Turbine Control Of A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents the design of two separate continually online trained (COT) neurocontrollers for excitation and turbine control of a turbogenerator connected to the infinite bus through a transmission line. These neurocontrollers augment/replace the conventional automatic voltage regulator and the turbine governor of a generator. A third COT artificial neural network is used to identify the complex nonlinear dynamics of the power system. Results are presented to show that the two COT neurocontrollers can control turbogenerators under steady-state as well as transient conditions and, thus, allow turbogenerators to operate more closely to their steady-state stability limits

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Comparison Of Heuristic Dynamic Programming And Dual Heuristic Programming Adaptive Critics For Neurocontrol Of A Turbogenerator, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley Jan 2002

Comparison Of Heuristic Dynamic Programming And Dual Heuristic Programming Adaptive Critics For Neurocontrol Of A Turbogenerator, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents the design of an optimal neurocontroller that replaces the conventional automatic voltage regulator (AVR) and the turbine governor for a turbogenerator connected to the power grid. The neurocontroller design uses a novel technique based on the adaptive critic designs (ACDs), specifically on heuristic dynamic programming (HDP) and dual heuristic programming (DHP). Results show that both neurocontrollers are robust, but that DHP outperforms HDP or conventional controllers, especially when the system conditions and configuration change. This paper also shows how to design optimal neurocontrollers for nonlinear systems, such as turbogenerators, without having to do continually online training of …

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Comparison Of Mlp And Rbf Neural Networks Using Deviation Signals For On-Line Identification Of A Synchronous Generator, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley Jan 2002

Comparison Of Mlp And Rbf Neural Networks Using Deviation Signals For On-Line Identification Of A Synchronous Generator, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper compares the performances of a multilayer perceptron network (MLPN) and a radial basis function network (RBFN) for the online identification of the nonlinear dynamics of a synchronous generator. Deviations of signals from their steady state values are used. The computational complexity required to process the data for online training, generalization and online global minimum testing are investigated by time-domain simulations. The simulation results show that, compared to the MLPN, the RBFN is simpler to implement, needs less computational memory, converges faster and global minimum convergence is achieved even when operating conditions change.

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Dual Heuristic Programming Excitation Neurocontrol For Generators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley Jan 2001

Dual Heuristic Programming Excitation Neurocontrol For Generators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

The design of optimal neurocontrollers that replace the conventional automatic voltage regulators for excitation control of turbogenerators in a multimachine power system is presented in this paper. The neurocontroller design is based on dual heuristic programming (DHP), a powerful adaptive critic technique. The feedback variables are completely based on local measurements from the generators. Simulations on a three-machine power system demonstrate that DHP based neurocontrol is much more effective than the conventional PID control for improving dynamic performance and stability of the power grid under small and large disturbances. This paper also shows how to design optimal multiple neurocontrollers for …

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A Nonlinear Voltage Controller With Derivative Adaptive Critics For Multimachine Power Systems, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley Jan 2001

A Nonlinear Voltage Controller With Derivative Adaptive Critics For Multimachine Power Systems, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

Based on derivative adaptive critics, a novel nonlinear optimal voltage/excitation control for a multimachine power system is presented. The feedback variables are completely based on local measurements. Simulations on a three-machine system demonstrate that the nonlinear controller is much more effective than the conventional PID controller equipped with a power system stabilizer for improving dynamic performance and stability under small and large disturbances.

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Experimental Studies With Continually Online Trained Artificial Neural Network Identifiers For Multiple Turbogenerators On The Electric Power Grid, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley Jan 2001

Experimental Studies With Continually Online Trained Artificial Neural Network Identifiers For Multiple Turbogenerators On The Electric Power Grid, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing complexity of a modern power grid highlights the need for advanced system identification techniques for effective control of power systems. This paper provides a new method for nonlinear identification of turbogenerators in a 3-machine 6-bus power system using online trained feedforward neural networks. Each turbogenerator in the power system is equipped with a neuro-identifier, which is able to identify its particular turbogenerator and the rest of the network to which it is connected from moment to moment, based on only local measurements. Each neuro-identifier can then be used in the design of a nonlinear neurocontroller for each turbogenerator …

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A Continually Online Trained Neurocontroller For Excitation And Turbine Control Of A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley Jan 2001

A Continually Online Trained Neurocontroller For Excitation And Turbine Control Of A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

No abstract provided.

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Excitation And Turbine Neurocontrol With Derivative Adaptive Critics Of Multiple Generators On The Power Grid, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley Jan 2001

Excitation And Turbine Neurocontrol With Derivative Adaptive Critics Of Multiple Generators On The Power Grid, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

Based on derivative adaptive critics, neurocontrollers for excitation and turbine control of multiple generators on the electric power grid are presented. The feedback variables are completely based on local measurements. Simulations on a three-machine power system demonstrate that the neurocontrollers are much more effective than conventional PID controllers, the automatic voltage regulators and the governors, for improving the dynamic performance and stability under small and large disturbances

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A Committee Of Neural Networks For Automatic Speaker Recognition (Asr) Systems, Viresh Moonasar, Ganesh K. Venayagamoorthy Jan 2001

A Committee Of Neural Networks For Automatic Speaker Recognition (Asr) Systems, Viresh Moonasar, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

This paper describes how the results of speaker verification systems can be improved and made robust with the use of a committee of neural networks for pattern recognition rather than the conventional single-network decision system. It illustrates the use of a supervised learning vector quantization neural network as the pattern classifier. Linear predictive coding and cepstral signal processing techniques are utilized to form hybrid feature parameter vectors to combat the effect of decreased recognition success with increased group size (number of speakers to be recognized)

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Decentralized Online Neuro-Identification Of Turbogenerators In A Multi-Machine Power System, Ganesh K. Venayagamoorthy, Ronald G. Harley Jan 2000

Decentralized Online Neuro-Identification Of Turbogenerators In A Multi-Machine Power System, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a new and a novel technique based on Artificial Neural Networks (ANNs) for nonlinear model of turbogenerators in a multi-machine power system. Only local measurements are required by each ANN in this new method, and hence it is called decentralized neuro-identificiation. Each turbogenerator in the power system is quipped with an ANN which is able to identify (or model) its particular turbogenerator from moment to moment This information can then be used by a second ANN at each generator to enable effective control of the nonlinear non-stationary process under all operating conditions. Simulation results are presented in …

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Comparison Of Text-Dependent Speaker Identification Methods For Short Distance Telephone Lines Using Artificial Neural Networks, Ganesh K. Venayagamoorthy, N. Sundepersadh Jan 2000

Comparison Of Text-Dependent Speaker Identification Methods For Short Distance Telephone Lines Using Artificial Neural Networks, Ganesh K. Venayagamoorthy, N. Sundepersadh

Electrical and Computer Engineering Faculty Research & Creative Works

The transition to democracy in South Africa has brought with it certain challenges. The main challenge is to get rid of crime and corruption. The paper presents a technique to combat white-collar crime in telephone transactions by identifying and verifying speakers using artificial neural networks (ANNs). Results are presented to show that speaker identification is feasible and this is illustrated with two different types of ANN architectures and with two different types of characteristic features as inputs to ANNs.

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Two Separate Continually Online Trained Neurocontrollers For Excitation And Turbine Control Of A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley Jan 2000

Two Separate Continually Online Trained Neurocontrollers For Excitation And Turbine Control Of A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents the design of two separate continually online trained (GOT) artificial neural network (ANN) controllers for excitation and turbine control of a turbogenerator connected to the infinite bus through a transmission line. These neurocontrollers augment/replace the conventional automatic voltage regulator and the turbine governor of a generator. A third COT ANN is used to identify the complex nonlinear dynamics of the power system. Results are presented to show that the two COT ANN controllers can control turbogenerators under steady state as well as transient conditions and thus allow turbogenerators to operate more closely to their steady state stability …

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Adaptive Critic Based Neurocontroller For Turbogenerators With Global Dual Heuristic Programming, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley Jan 2000

Adaptive Critic Based Neurocontroller For Turbogenerators With Global Dual Heuristic Programming, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

Turbogenerators are nonlinear time varying systems. This paper presents the design of a neurocontroller for such a turbogenerator that augments/replaces the traditional automatic voltage regulator (AVR) and the turbine governor using a novel technique based on the adaptive critic designs (ACDs) with emphasis on global dual heuristic programming (GDHP). Simulation results are presented to show that the neurocontroller derived with the GDHP approach is robust and its performance is better when compared with that derived with other neural network technique, especially when system conditions and configuration changes.

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Comparison Of A Heuristic Dynamic Programming And A Dual Heuristic Programming Based Adaptive Critics Neurocontroller For A Turbogenerator, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley Jan 2000

Comparison Of A Heuristic Dynamic Programming And A Dual Heuristic Programming Based Adaptive Critics Neurocontroller For A Turbogenerator, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents the design of a neurocontroller for a turbogenerator that augments/replaces the conventional automatic voltage regulator and the turbine governor. The neurocontroller uses a novel technique based on the adaptive critic designs with emphasis on heuristic dynamic programming (HDP) and dual heuristic programming (DHP). Results are presented to show that the DHP based neurocontroller is robust and performs better than the HDP based neurocontroller, as well as the conventional controller, especially when the system conditions and configuration changes.

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A Performance Analysis Of Dynamic Routing Algorithms In An Iridium-Like Low Earth Orbit Satellite System, Stephen R. Pratt Mar 1999

A Performance Analysis Of Dynamic Routing Algorithms In An Iridium-Like Low Earth Orbit Satellite System, Stephen R. Pratt

Theses and Dissertations

This research presents a first of its kind comparative analysis of the Extended Bellman-Ford and Darting algorithms, using the Iridium low earth orbit (LEO) satellite system configuration for the simulation environment. The algorithms are compared to one another via discrete-event computer simulation and evaluated based on their ability to route real-time voice communications under low, medium, and high network loading conditions. The algorithms' ability to meet real-time voice constraints is evaluated with a full and degraded satellite constellation using an algorithmic satellite removal method. The investigation results indicate that both algorithms are suitable for use in a LEO environment and …

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A Cloud-Removal Algorithm For Ssm/I Data, David G. Long, Douglas L. Daum, Quinn P. Remund Jan 1999

A Cloud-Removal Algorithm For Ssm/I Data, David G. Long, Douglas L. Daum, Quinn P. Remund

Faculty Publications

Microwave radiometers, while traditionally utilized in atmospheric and oceanic studies, can also be used in land surface applications. However, the problem of undesirable atmospheric effects caused by clouds and precipitation must be addressed. In this paper, temporal composite surface brightness images are generated from special sensor microwave/imager (SSM/I) data with the aid of new algorithms to eliminate small-scale distortion caused by clouds or precipitation. Mean, second-highest value, modified maximum average (MMA), and hybrid compositing algorithms are compared. The effectiveness of each algorithm is illustrated through simulation and real data distribution analysis. The results show that the second-highest value algorithm is …

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Neurocontrol Of Turbogenerators With Adaptive Critic Designs, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley Jan 1999

Neurocontrol Of Turbogenerators With Adaptive Critic Designs, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents the design of a neuro-controller for a turbogenerator using a novel technique based on adaptive critic designs (ACD). This adaptive critic design based neuro-controller augments/replaces the traditional automatic voltage regulator (AVR) and the turbine governor of the generator. Simulation results are presented to show that neural network controllers with the ACD have the potential to control turbogenerators when system conditions and configuration changes.

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Experimental Studies With A Continually Online Trained Artificial Neural Network Controller For A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley Jan 1999

Experimental Studies With A Continually Online Trained Artificial Neural Network Controller For A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents the design of a continually online trained (COT) artificial neural network (ANN) controller for a laboratory turbogenerator system connected to the infinite bus through a transmission line in real time. Two COT ANNs are used for the implementation: one ANN to identify the complex nonlinear dynamics of the power system, and the other ANN to control the turbogenerator. Practical results are presented to show that COT ANN controllers can control turbogenerators under steady state as well as transient conditions in the laboratory environment

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Implementation Of An Adaptive Neural Network Identifier For Effective Control Of Turbogenerators, Ganesh K. Venayagamoorthy, Ronald G. Harley Jan 1999

Implementation Of An Adaptive Neural Network Identifier For Effective Control Of Turbogenerators, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper describes an on-line identification technique for modelling a turbogenerator system. The dynamics of a single turbogenerator infinite bus system are modelled using an adaptive artificial neural network identifier (AANNI) based on continual online training (COT). This paper goes further to show that multilayered perceptrons with deviation signals as inputs and outputs trained using the standard backpropagation algorithm retain past learned information despite COT. Simulation and practical results are presented.

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Speaker Identification Using A Combination Of Different Parameters As Feature Inputs To An Artificial Neural Network Classifier, Viresh Moonasar, Ganesh K. Venayagamoorthy Jan 1999

Speaker Identification Using A Combination Of Different Parameters As Feature Inputs To An Artificial Neural Network Classifier, Viresh Moonasar, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a technique using artificial neural networks (ANNs) for speaker identification that results in a better success rate compared to other techniques. The technique used in this paper uses both power spectral densities (PSDs) and linear prediction coefficients (LPCs) as feature inputs to a self organizing feature map to achieve a better identification performance. Results for speaker identification with different methods are presented and compared.

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A Continually Online Trained Artificial Neural Network Identifier For A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley Jan 1999

A Continually Online Trained Artificial Neural Network Identifier For A Turbogenerator, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing complexity of modern power systems highlights the need for advanced modelling techniques for effective control of power systems. This paper presents results of simulation and practical studies carried out on identifying the dynamics of a single turbogenerator connected to an infinite bus through a short transmission line, using a continually online trained (COT) artificial neural network (ANN).

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A Robust Artificial Neural Network Controller For A Turbogenerator When Line Configuration Changes, Ganesh K. Venayagamoorthy, Ronald G. Harley Jan 1999

A Robust Artificial Neural Network Controller For A Turbogenerator When Line Configuration Changes, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents the design of a robust controller for a turbogenerator. The robust controller is an artificial neural network (ANN) that is trained offline on a family of ANN models of the turbogenerator. This ANN controller augments/replaces the traditional automatic voltage controller (AVR) and the turbine governor of the generator. Simulation results are presented to show that the ANN controller is robust when the transmission line configuration changes.

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Adaptive Bootstrap Signal Separators For Bpsk/Qam-Modulated Wireless Cdma Systems In A Multipath Environment, Nico J.M. Van Waes Aug 1998

Adaptive Bootstrap Signal Separators For Bpsk/Qam-Modulated Wireless Cdma Systems In A Multipath Environment, Nico J.M. Van Waes

Dissertations

CDMA is an attractive multiple-access scheme, because of its potential capacity increase and its anti-multipath fading capability. For satisfactory performance, however, the effect of the "near-far" problem has to be resolved. This problem can be combated by using power-control, which, however, results in an overall reduction in communication ranges, and thus in a loss of capacity. Among other methods for mitigating the near-far problem is the use of decorrelating receivers, both of fixed type, which directly utilizes the cross-correlation of the users codes, and of adaptive type, which uses recursive algorithms that leads to signal decorrelation. Not to lessen the …

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