Malur K. Sundareshan

Adaptive image contrast enhancement based on human visual properties

Existing methods for image contrast enhancement focus mainly on the properties of the image to be processed while excluding any consideration of the observer characteristics. In several applications, particularly in the medical imaging area, effective contrast enhancement for diagnostic purposes can be achieved by including certain basic human visual properties. Here the authors present a novel adaptive algorithm that tailors the required amount of contrast enhancement based on the local contrast of the image and the observers Just-Noticeable-Difference (JND). This algorithm always produces adequate contrast in the output image, and results in almost no ringing artifacts even around sharp transition regions, which is often seen in images processed by conventional contrast enhancement techniques. By separating smooth and detail areas of an image and considering the dependence of noise visibility on the spatial activity of the image, the algorithm treats them differently and thus avoids excessive enhancement of noise, which is another common problem for many existing contrast enhancement techniques. The present JND-Guided Adaptive Contrast Enhancement (JGACE) technique is very general and can be applied to a variety of images. In particular, it offers considerable benefits in digital radiography applications where the objective is to increase the diagnostic utility of images. A detailed performance evaluation together with a comparison with the existing techniques is given to demonstrate the strong features of JGACE.

Identification and decentralized adaptive control using dynamical neural networks with application to robotic manipulators

Efficient implementation of a neural network-based strategy for the online adaptive control of complex dynamical systems characterized by an interconnection of several subsystems (possibly nonlinear) centers on the rapidity of the convergence of the training scheme used for learning the system dynamics. For illustration, in order to achieve a satisfactory control of a multijointed robotic manipulator during the execution of high speed trajectory tracking tasks, the highly nonlinear and coupled dynamics together with the variations in the parameters necessitate a fast updating of the control actions. For facilitating this requirement, a multilayer neural network structure that includes dynamical nodes in the hidden layer is proposed, and a supervised learning scheme that employs a simple distributed updating rule is used for the online identification and decentralized adaptive control. Important characteristic features of the resulting control scheme are discussed and a quantitative evaluation of its performance in the above illustrative example is given.

Numerical methods for modeling computer networks under nonstationary conditions

Numerical techniques for modeling computer networks under nonstationary conditions are discussed, and two distinct approaches are presented. The first approach uses a queuing theory formulation to develop differential equation models which describe the behavior of the network by time-varying probability distributions. In the second approach, a nonlinear differential equation model is developed for representing the dynamics of the network in terms of time-varying mean quantities. This approach allows multiple classes of traffic to be modeled and establishes a framework for the use of optimal control techniques in the design of network control strategies. Numerical techniques for determining the queue behavior as a function of time for both approaches are discussed and their computational advantages are contrasted with simulation. >

Exponential stability and a systematic synthesis of a neural network for quadratic minimization

Abstract A continuous-time network with piecewise linear neuron input-output characteristics is proposed for optimization applications. Certain qualitative properties of the network of fundamental importance in these applications, such as the uniqueness of equilibrium conditions and the global exponential stability with any arbitrarily prescribed degree of this equilibrium, are analytically investigated. Deriving guidance from the obtained analytical results, a systematic synthesis procedure is outlined for identifying the network parameters and the bias inputs to employ the neural network for efficiently solving the important class of optimization problems where the objective is to minimize a specified quadratic function in the decision variables. For demonstrating the versatility of the solution procedure, three illustrative applications, namely synthesis of a class of spatial filters popularly employed in image recognition, the design of an associative memory by a master-slave formulation and the estimation of parameters of a linear system by a least squares procedure are outlined and the superiority of the present approach over the existing results is indicated. Some of the present results concerning the characterization of the network equilibrium conditions and the network scaling for confining the equilibrium to desired operational ranges are of basic interest and are useful in other applications of the neural network besides the specific applications to solve optimization problems discussed in this paper.

Design of decentralized observation schemes for large-scale interconnected systems: some new results

A new constructive procedure for the design of a decentralized observation scheme for large-scale interconnected systems is presented. The design procedure is based on a stability result that employs the notion of block diagonal dominance in matrices. The obtained algorithm is shown to considerably improve upon the existing results for the decentralized observer design problem. A major contribution of this paper is the demonstration of how the observer gains can be tailored to the existing interconnection pattern within the overall system. Although the present results are developed in the context of decentralized observation, they can be extended to the design of decentralised stabilization algorithms and to the design of decentralized model reference adaptive identification schemes.

A recurrent neural network-based adaptive variable structure model-following control of robotic manipulators

A novel scheme for integrating a neural network approach with an adaptive implementation of variable structure control for multijointed robotic manipulators in complex task executions is presented. The control strategy is developed within the general framework of nonlinear model-following control and attempts to minimize the total regulation time while ensuring a specified percentage of time on the sliding manifolds in order to exploit the disturbance attenuation features present during the sliding motions. These objectives are realized by tailoring an adaptation process that appropriately adjusts the controller gains to keep the motion on the sliding manifolds and also progressively updates the sliding manifold parameters. Rapid execution of the adaptation process is facilitated by a multilayer recurrent neural network. The resulting control scheme is decentralized, and permits design of independent joint controls. A quantitative performance evaluation of the neural network-based adaptive variable structure controller is given in several task scenarios, namely regulation, trajectory tracking and model-following.

Qualitative analysis and decentralized controller synthesis for a class of large-scale systems with symmetrically interconnected subsystems

Abstract A number of large-scale interconnected systems often encountered in practice are composed of subsystems with similar dynamics interconnected in a symmetrical fashion and the synthesis of controllers for such systems must exploit the special structural properties in order to avoid overly conservative designs and to take advantage of the possible beneficial effects of the interconnections. An analysis of some important qualitative properties of such symmetrically interconnected systems focussing on the spectrum characterization, controllability and observability, and the solutions of the algebraic Riccati equation and the matrix Lyapunov equation is conducted in this paper and procedures for constructing the solutions to the analysis problems at the overall system level from the computationally simple subsystem level solutons are developed. A decentralized controller design procedure is presented as an illustration of the utilization of the available structural information in addressing synthesis problems. Numerical examples are included to demonstrate the superiority of the presented designs over the use of existing approaches which do not take full advantage of the structural knowledge in these large-scale systems.

Equilibrium characterization of dynamical neural networks and a systematic synthesis procedure for associative memories

Several novel results concerning the characterization of the equilibrium conditions of a continuous-time dynamical neural network model and a systematic procedure for synthesizing associative memory networks with nonsymmetrical interconnection matrices are presented. The equilibrium characterization focuses on the exponential stability and instability properties of the network equilibria and on equilibrium confinement, viz., ensuring the uniqueness of an equilibrium in a specific region of the state space. While the equilibrium confinement result involves a simple test, the stability results given obtain explicit estimates of the degree of exponential stability and the regions of attraction of the stable equilibrium points. Using these results as valuable guidelines, a systematic synthesis procedure for constructing a dynamical neural network that stores a given set of vectors as the stable equilibrium points is developed.

Generation of Multilevel Control and Estimation Schemes for Large-Scale Systems: A Perturbational Approach

A new approach to the development of multilevel control and estimation schemes for large-scale systems with a major emphasis on the reliability of performance under structural perturbations is described. The study is conducted within a decomposition-decentralization framework and leads to simple and noniterative control and estimation schemes. The solution to the control problem involves the design of a set of locally optimal controllers for the individual subsystems in a completely decentralized environment and a global controller on a higher hierarchical level that provides corrective signals to account for the interconnection effects. Similar principles are employed to develop an estimation scheme, which consists of a set of decentralized optimal estimators for the subsystems, together with certain compensating signals for measurements. The principal feature of the paper is a detailed study of the system structure and the consequent classification of interconnection patterns into several interesting categories (beneficial, nonbeneficial, and neutral) based on their effects on decentralized control and estimation.

Secure communication in internet environments: a hierarchical key management scheme for end-to-end encryption

A hierarchical approach for key management is presented which utilizes the existing network specific protocols at the lower levels and protocols between authentication servers and/or control centers of different networks at the higher levels. Details of this approach are discussed for specific illustrative scenarios to demonstrate the implementation simplicity. A formal verification of the security of the resulting system in the sense of protecting the privacy of privileged information is also conducted by an axiomatic procedure utilizing certain combinatory logic principles. This approach is general and can be used for verifying the security of other existing key management schemes. >