The e-Learning refers to the use of networking technologies to create, foster, deliver and facili... more The e-Learning refers to the use of networking technologies to create, foster, deliver and facilitate learning anytime, anywhere. This chapter discusses our research on personalization of e-Learning content based on the learner’s profile. After justifying the feasibility of using mobile agents in distributed computing systems for information retrieval, processing and mining, the authors deal with the relevance of mobile agents in e-Learning domain. The chapter discusses the proposed Case-Based Reasoning (CBR) as an approach to context-aware adaptive content delivery. Different parameters like technological, cultural and educational background of a learner are taken as the basis for forming the case-base that determines the type of content to be delivered. Along with the CBR, a diagnostic assessment to gauge an insight into the student’s current skills is done to determine the type of content to deliver. The implementation observations of such implementation vis-à-vis traditional e-L...
Abstract. Group communication in wireless multimedia networks must consider the Quality of Servic... more Abstract. Group communication in wireless multimedia networks must consider the Quality of Service (QoS) parameters for efficient and quality aware multicast route computation. QoS parameters such as bandwidth, channel reliability, buffers, delays, jitters, etc. play a vital role in wireless multimedia networks. Multicast tree development for group communication must press for more than one or two QoS parameters for better services. This paper proposes a scheme for constructing a multicast tree based on a spanning tree by employing a fuzzy controller. Fuzzy controller uses three fuzzy input parameters namely, link bandwidth, link delay and link reliability for the construction of multicast spanning tree. The scheme is simulated to test its effectiveness in terms of multicast tree computation time, iterations required for multicast tree computation, packet delay and packet delivery ratio.
2006 IEEE International Conference on Industrial Technology, 2006
Mobile networks that handle multicast communication services such as video conferencing, audio-co... more Mobile networks that handle multicast communication services such as video conferencing, audio-conferencing, collaboration works, etc., require a kind of reliable and guaranteed point-to-multipoint communications. A multicast tree provides an efficient connectivity between the multicast mobile group members through base stations (point of attachment). Mobility of the hosts of a group necessitates maintenance of service quality multicast routes. This position paper
Group communication in wireless multimedia networks must consider the Quality of Service (QoS) pa... more Group communication in wireless multimedia networks must consider the Quality of Service (QoS) parameters for efficient and quality aware multicast route computation. QoS parameters such as bandwidth, channel reliability, buffers, delays, jitters, etc. play a vital role in wireless multimedia networks. Multicast tree development for group communication must press for more than one or two QoS parameters for better services. This paper proposes a scheme for constructing a multicast tree based on a spanning tree by employing a fuzzy controller. Fuzzy controller uses three fuzzy input parameters namely, link bandwidth, link delay and link reliability for the construction of multicast spanning tree. The scheme is simulated to test its effectiveness in terms of multicast tree computation time, iterations required for multicast tree computation, packet delay and packet delivery ratio.
[Proceedings] 1991 IEEE International Joint Conference on Neural Networks, 1991
A neural learning and adaptive scheme, called inverse-dynamics adaptive control (IDAC) is present... more A neural learning and adaptive scheme, called inverse-dynamics adaptive control (IDAC) is presented. The IDAC scheme provides a learn-while-functioning capability. The error signal, defined as a difference between the desired and the actual outputs, modifies the controller weights until the controller structure becomes an approximate inverse-dynamics model of the process under control, making the transfer function from output-to-input unity. The necessary learning and adaptive algorithm is derived, and the computer simulation results to evaluate the performance of the IDAC algorithm are presented.<<ETX>>
[Proceedings 1992] IJCNN International Joint Conference on Neural Networks, 1992
The performance evaluation of a dynamic neural unit (DNU) in the control of linear and nonlinear ... more The performance evaluation of a dynamic neural unit (DNU) in the control of linear and nonlinear dynamical systems is described. The architecture of the dynamic neural unit embodies feedforward and feedback flow of signals weighted by the synaptic weights in a dynamical structure. Because of the dynamical nature of the neuron, it can be trained to learn and control unknown linear dynamical systems. Nonlinear functions can be approximated using multistage dynamic neural units, and hence can be trained to control nonlinear dynamical systems. The DNUs not only emulate, to some extent, the learning and control actions of the biological neurons, but also have the potential of a parallel-distributed intelligent control scheme for a large-scale complex dynamic system.<<ETX>>
Neural networks potentially offer a general framework for modeling and control of nonlinear syste... more Neural networks potentially offer a general framework for modeling and control of nonlinear systems. The conventional neural network models are a parody of biological neural structures, and have the disadvantage of very slow learning. In this paper, we develop a dynamic neural network structure which is based upon the collective computation of subpopulation of neurons, thus different from the conventionally assumed structure of neural networks. The architecture and the learning algorithm to modify weights of the proposed neural model are elucidated. Three applications of this dynamic neural network, namely (i) functional approximation, (ii) control of unknown nonlinear dynamic systems, and (iii) coordination and control of multiple systems, are described through computer simulations.<<ETX>>
IEEE WESCANEX 93 Communications, Computers and Power in the Modern Environment - Conference Proceedings
The authors describe the use of a dynamic model of the biological neuron called the dynamic neura... more The authors describe the use of a dynamic model of the biological neuron called the dynamic neural unit (DNU) and examine briefly how it can be used in a channel equalization problem. The DNU, which comprises an infinite impulse response (IIR) structure followed by a nonlinear activation function, is used to obtain an inverse model of unknown channel dynamics. Once a unity mapping from output to input is achieved, the channel passes the source signal with almost no distortion to the receiver end. The DNU architecture and algorithm are described.<<ETX>>
Intelligent Robots and Computer Vision XII: Algorithms and Techniques, 1993
ABSTRACT By virtue of their functional approximation, learning and adaptive capabilities, the com... more ABSTRACT By virtue of their functional approximation, learning and adaptive capabilities, the computational neural networks can be suitably employed for learning robot coordinate transformations. The major drawback of conventional static feedforward neural networks based on back-propagation learning algorithm is in their very large convergence time for a given task. Any attempts to accelerate the learning process by increasing the values of learning constants in the algorithm often result in unstable systems. The intent of this paper is to describe a neural network structure called dynamic neural processor (DNP), and examine briefly how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the DNP, are described. Computer simulations are provided to demonstrate the effectiveness of the proposed learning scheme using the DNP.
2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT)
In this paper we are proposing a student performance evaluation method using Fuzzy Inference Syst... more In this paper we are proposing a student performance evaluation method using Fuzzy Inference System (FIS) for Network Analysis (NA) course studied by third semester Electronics and Communication Engineering students. This paper explains about importance of Bloom's levels in studying and developing critical thinking skills for NA course and designing scoring rubric by aligning the rubric criteria with Bloom's Taxonomy levels which are intern given as inputs to the FIS. The five inputs identify, understand, apply, analyze and design/create are fuzzified using Mamdani Fuzzy Inference System. With the help of fuzzy rules the predicted results are expressed in linguistic variables.
It has been demonstrated by many researchers that an unknown dynamic plant can be made to track a... more It has been demonstrated by many researchers that an unknown dynamic plant can be made to track an input command signal if the plant is preceded by a controller which approximates the inverse of the plant’s transfer function. Precascading a plant with its inverse model provides an unity mapping between the input and output signal space. This concept of inverse modeling has been referred to as adaptive inverse control. However, the concept of transfer function is limited to linear systems, and the control algorithms developed under this framework can not be extended to nonlinear systems. Due to the functional approximation and learning capabilities, the artificial neural networks can be employed to extend the concept of adaptive inverse control to nonlinear systems. In this paper, two dynamic neural structures, called recurrent neural network and dynamic neural processor, are used to coerce the nonlinear systems to follow the desired trajectories based on the principle of adaptive in...
Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)
A complex control system, in general, consists of two or more independently designed and mutually... more A complex control system, in general, consists of two or more independently designed and mutually affecting subsystems. Proper coordination and control of multiple subsystems is responsible for the overall functioning of the system. This necessitates the development of control schemes for multivariable systems. This is a formidable task; more so if the systems involved are nonlinear with unknown dynamics. Because of their parallelism, functional approximation and learning capabilities, artificial neural networks can be effectively employed to control multivariable systems. The intent of this paper is to describe a neural network called the dynamic neural processor (DNP), and to use this structure to control nonlinear multivariable systems. The DNP is a dynamic neural network developed based on the concept of neural subpopulations which is in sharp contrast with the conventionally assumed structure of artificial neural networks
The e-Learning refers to the use of networking technologies to create, foster, deliver and facili... more The e-Learning refers to the use of networking technologies to create, foster, deliver and facilitate learning anytime, anywhere. This chapter discusses our research on personalization of e-Learning content based on the learner’s profile. After justifying the feasibility of using mobile agents in distributed computing systems for information retrieval, processing and mining, the authors deal with the relevance of mobile agents in e-Learning domain. The chapter discusses the proposed Case-Based Reasoning (CBR) as an approach to context-aware adaptive content delivery. Different parameters like technological, cultural and educational background of a learner are taken as the basis for forming the case-base that determines the type of content to be delivered. Along with the CBR, a diagnostic assessment to gauge an insight into the student’s current skills is done to determine the type of content to deliver. The implementation observations of such implementation vis-à-vis traditional e-L...
Abstract. Group communication in wireless multimedia networks must consider the Quality of Servic... more Abstract. Group communication in wireless multimedia networks must consider the Quality of Service (QoS) parameters for efficient and quality aware multicast route computation. QoS parameters such as bandwidth, channel reliability, buffers, delays, jitters, etc. play a vital role in wireless multimedia networks. Multicast tree development for group communication must press for more than one or two QoS parameters for better services. This paper proposes a scheme for constructing a multicast tree based on a spanning tree by employing a fuzzy controller. Fuzzy controller uses three fuzzy input parameters namely, link bandwidth, link delay and link reliability for the construction of multicast spanning tree. The scheme is simulated to test its effectiveness in terms of multicast tree computation time, iterations required for multicast tree computation, packet delay and packet delivery ratio.
2006 IEEE International Conference on Industrial Technology, 2006
Mobile networks that handle multicast communication services such as video conferencing, audio-co... more Mobile networks that handle multicast communication services such as video conferencing, audio-conferencing, collaboration works, etc., require a kind of reliable and guaranteed point-to-multipoint communications. A multicast tree provides an efficient connectivity between the multicast mobile group members through base stations (point of attachment). Mobility of the hosts of a group necessitates maintenance of service quality multicast routes. This position paper
Group communication in wireless multimedia networks must consider the Quality of Service (QoS) pa... more Group communication in wireless multimedia networks must consider the Quality of Service (QoS) parameters for efficient and quality aware multicast route computation. QoS parameters such as bandwidth, channel reliability, buffers, delays, jitters, etc. play a vital role in wireless multimedia networks. Multicast tree development for group communication must press for more than one or two QoS parameters for better services. This paper proposes a scheme for constructing a multicast tree based on a spanning tree by employing a fuzzy controller. Fuzzy controller uses three fuzzy input parameters namely, link bandwidth, link delay and link reliability for the construction of multicast spanning tree. The scheme is simulated to test its effectiveness in terms of multicast tree computation time, iterations required for multicast tree computation, packet delay and packet delivery ratio.
[Proceedings] 1991 IEEE International Joint Conference on Neural Networks, 1991
A neural learning and adaptive scheme, called inverse-dynamics adaptive control (IDAC) is present... more A neural learning and adaptive scheme, called inverse-dynamics adaptive control (IDAC) is presented. The IDAC scheme provides a learn-while-functioning capability. The error signal, defined as a difference between the desired and the actual outputs, modifies the controller weights until the controller structure becomes an approximate inverse-dynamics model of the process under control, making the transfer function from output-to-input unity. The necessary learning and adaptive algorithm is derived, and the computer simulation results to evaluate the performance of the IDAC algorithm are presented.<<ETX>>
[Proceedings 1992] IJCNN International Joint Conference on Neural Networks, 1992
The performance evaluation of a dynamic neural unit (DNU) in the control of linear and nonlinear ... more The performance evaluation of a dynamic neural unit (DNU) in the control of linear and nonlinear dynamical systems is described. The architecture of the dynamic neural unit embodies feedforward and feedback flow of signals weighted by the synaptic weights in a dynamical structure. Because of the dynamical nature of the neuron, it can be trained to learn and control unknown linear dynamical systems. Nonlinear functions can be approximated using multistage dynamic neural units, and hence can be trained to control nonlinear dynamical systems. The DNUs not only emulate, to some extent, the learning and control actions of the biological neurons, but also have the potential of a parallel-distributed intelligent control scheme for a large-scale complex dynamic system.<<ETX>>
Neural networks potentially offer a general framework for modeling and control of nonlinear syste... more Neural networks potentially offer a general framework for modeling and control of nonlinear systems. The conventional neural network models are a parody of biological neural structures, and have the disadvantage of very slow learning. In this paper, we develop a dynamic neural network structure which is based upon the collective computation of subpopulation of neurons, thus different from the conventionally assumed structure of neural networks. The architecture and the learning algorithm to modify weights of the proposed neural model are elucidated. Three applications of this dynamic neural network, namely (i) functional approximation, (ii) control of unknown nonlinear dynamic systems, and (iii) coordination and control of multiple systems, are described through computer simulations.<<ETX>>
IEEE WESCANEX 93 Communications, Computers and Power in the Modern Environment - Conference Proceedings
The authors describe the use of a dynamic model of the biological neuron called the dynamic neura... more The authors describe the use of a dynamic model of the biological neuron called the dynamic neural unit (DNU) and examine briefly how it can be used in a channel equalization problem. The DNU, which comprises an infinite impulse response (IIR) structure followed by a nonlinear activation function, is used to obtain an inverse model of unknown channel dynamics. Once a unity mapping from output to input is achieved, the channel passes the source signal with almost no distortion to the receiver end. The DNU architecture and algorithm are described.<<ETX>>
Intelligent Robots and Computer Vision XII: Algorithms and Techniques, 1993
ABSTRACT By virtue of their functional approximation, learning and adaptive capabilities, the com... more ABSTRACT By virtue of their functional approximation, learning and adaptive capabilities, the computational neural networks can be suitably employed for learning robot coordinate transformations. The major drawback of conventional static feedforward neural networks based on back-propagation learning algorithm is in their very large convergence time for a given task. Any attempts to accelerate the learning process by increasing the values of learning constants in the algorithm often result in unstable systems. The intent of this paper is to describe a neural network structure called dynamic neural processor (DNP), and examine briefly how it can be used in developing a learning scheme for computing robot inverse kinematic transformations. The architecture and learning algorithm of the proposed dynamic neural network structure, the DNP, are described. Computer simulations are provided to demonstrate the effectiveness of the proposed learning scheme using the DNP.
2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT)
In this paper we are proposing a student performance evaluation method using Fuzzy Inference Syst... more In this paper we are proposing a student performance evaluation method using Fuzzy Inference System (FIS) for Network Analysis (NA) course studied by third semester Electronics and Communication Engineering students. This paper explains about importance of Bloom's levels in studying and developing critical thinking skills for NA course and designing scoring rubric by aligning the rubric criteria with Bloom's Taxonomy levels which are intern given as inputs to the FIS. The five inputs identify, understand, apply, analyze and design/create are fuzzified using Mamdani Fuzzy Inference System. With the help of fuzzy rules the predicted results are expressed in linguistic variables.
It has been demonstrated by many researchers that an unknown dynamic plant can be made to track a... more It has been demonstrated by many researchers that an unknown dynamic plant can be made to track an input command signal if the plant is preceded by a controller which approximates the inverse of the plant’s transfer function. Precascading a plant with its inverse model provides an unity mapping between the input and output signal space. This concept of inverse modeling has been referred to as adaptive inverse control. However, the concept of transfer function is limited to linear systems, and the control algorithms developed under this framework can not be extended to nonlinear systems. Due to the functional approximation and learning capabilities, the artificial neural networks can be employed to extend the concept of adaptive inverse control to nonlinear systems. In this paper, two dynamic neural structures, called recurrent neural network and dynamic neural processor, are used to coerce the nonlinear systems to follow the desired trajectories based on the principle of adaptive in...
Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)
A complex control system, in general, consists of two or more independently designed and mutually... more A complex control system, in general, consists of two or more independently designed and mutually affecting subsystems. Proper coordination and control of multiple subsystems is responsible for the overall functioning of the system. This necessitates the development of control schemes for multivariable systems. This is a formidable task; more so if the systems involved are nonlinear with unknown dynamics. Because of their parallelism, functional approximation and learning capabilities, artificial neural networks can be effectively employed to control multivariable systems. The intent of this paper is to describe a neural network called the dynamic neural processor (DNP), and to use this structure to control nonlinear multivariable systems. The DNP is a dynamic neural network developed based on the concept of neural subpopulations which is in sharp contrast with the conventionally assumed structure of artificial neural networks
Uploads
Papers by Dr. D. H. Rao