The paper proposes a novel multi-modal document image retrieval framework by exploiting the information of text and graphics regions. The framework applies multiple kernel learning based hashing formulation for generation of composite document indexes using different modalities. The existing multimedia management methods for imaged text documents have not addressed the requirement of old and degraded documents. In the subsequent contribution, we propose novel multi-modal document indexing framework for retrieval of old and degraded text documents by combining OCR'ed text and image based representation using learning. The evaluation of proposed concepts is demonstrated on sampled magazine cover pages, and documents of Devanagari script.

The paper presents a novel feature based indexing scheme for image collections. The scheme presents the extension of distance based hashing to kernel space for generating the indexing structure based on similarity in kernel space. The objective of the scheme is to incorporate multiple features for defining the image indexing space using the concept of multiple kernel learning. However, the indexing problems are defined with unique learning objective; therefore, a novel application of genetic algorithm is presented for the optimization task. The extensive evaluation of the proposed concept is performed for developing word based document indexing application of Devanagari, Bengali, and English scripts. In addition, the efficacy of the proposed concept is shown by experimental evaluations on handwritten digits and natural image collection.

In this paper, we propose a novel framework for segmentation of documents with complex layouts. The document segmentation is performed by combination of clustering and conditional random fields (CRF) based modeling. The bottom-up approach for segmentation assigns each pixel to a cluster plane based on color intensity. A CRF based discriminative model is learned to extract the local neighborhood information in different cluster/color planes. The final category assignment is done by a top-level CRF based on the semantic correlation learned across clusters. The proposed framework has been extensively tested on multi-colored document images with text overlapping graphics/image.

We present a novel word image based document indexing scheme by combination of string matching and hashing. The word image representation is defined by string codes obtained by unsupervised learning over graphical primitives. The indexing framework is defined by distance based hashing function which does the object projection to hash space by preserving their distances. We have used edit distance based string matching for defining the hashing function and for approximate nearest neighbor based retrieval. The application of the proposed indexing framework is presented for two document image collections belonging to Devanagari and Bengali script.

Indexing and retrieval performance over digitized document collection significantly depends on the performance of available Optical Character Recognition (OCR). The paper presents a novel document indexing framework which attends the document digitization errors in the indexing process to improve the overall retrieval accuracy. The proposed indexing framework is based on topic modeling using Latent Dirichlet Allocation (LDA). The OCR's confidence in correctly recognizing a symbol is propagated in topic learning process such that semantic grouping of word examples carefully distinguishes between commonly confusing words. We present a novel application of Lucene with topic modeling for document indexing application. The experimental evaluation of the proposed framework is presented on document collection belonging to Devanagari script.

The present work is part of ongoing effort to improve the performance of Gujarati character recognition. In the recent advancement in kernel methods, the novel concept of multiple kernel learning(MKL) has given improved results for many problems. In this paper, we present novel application of MKL for Gujarati character recognition. We have applied three different feature representations for symbols obtained after zone wise segmentation of Gujarati text. The MKL based classification is proposed, where the MKL is used for learning optimal combination of different features for classification. In addition MKL based classification results for different features is also presented. The multiclass classification is performed in Decision DAG framework. The comparison results in 1-Vs-1 framework and using KNN classifier is also presented. The experiments have shown substantial improvement in earlier results.

The paper presents three novel features for handwritten data based identity recognition. A novel framework for combining the features for identification is presented. The framework combines the features in kernel space in MKL based framework. The application of features individually and in combination is presented for writer recognition and signature verification. The writer recognition results have been presented for Devanagari script input and signature verification results have been presented for open dataset [1]. The experiments have shown encouraging results.

The paper presents application of multiple features for word based document image indexing and retrieval. A novel framework to perform Multiple Kernel Learning for indexing using the Kernel based Distance Based Hashing is proposed. The Genetic Algorithm based framework is used for optimization. Two different features representing the structural organization of word shape are defined. The optimal combination of both the features for indexing is learned by performing MKL. The retrieval results for document collection belonging to Devanagari script are presented.

In this paper we present a novel shape descriptor based on shape context, which in combination with hierarchical distance based hashing is used for word and graphical pattern based document image indexing and retrieval. The shape descriptor represents the relative arrangement of points sampled on the boundary of the shape of object. We also demonstrate the applicability of the novel shape descriptor for classification of characters and symbols. For indexing, we provide anew formulation for distance based hierarchical locality sensitive hashing. Experiments have yielded promising results.

In this paper, a Hamilton-Jacobi-Bellman (HJB) equation based optimal control algorithm is proposed for a bilinear system. Utilizing the Lyapunov direct method, the controller is shown to be optimal with respect to a cost functional, which includes penalty on the control effort and the system states. In the proposed algorithm, Neural Network (NN) is used to find approximate solution of HJB equation using least squares method. Proposed algorithm has been applied on bilinear systems. Necessary theoretical and simulation results are presented to validate proposed algorithm.

This paper develops an adaptive fuzzy controller for robot manipulators using a Markov game formulation. The Markov game framework offers a promising platform for robust control of robot manipulators in the presence of bounded external disturbances and unknown parameter variations. We propose fuzzy Markov games as an adaptation of fuzzy Q-learning (FQL) to a continuous-action variation of Markov games, wherein the reinforcement signal is used to tune online the conclusion part of a fuzzy Markov game controller. The proposed Markov game-adaptive fuzzy controller uses a simple fuzzy inference system (FIS), is computationally efficient, generates a swift control, and requires no exact dynamics of the robot system. To illustrate the superiority of Markov game-adaptive fuzzy control, we compare the performance of the controller against a) the Markov game-based robust neural controller, b) the reinforcement learning (RL)-adaptive fuzzy controller, c) the FQL controller, d) the Hinfintheory-based robust neural game controller, and e) a standard RL-based robust neural controller, on two highly nonlinear robot arm control problems of i) a standard two-link rigid robot arm and ii) a 2-DOF SCARA robot manipulator. The proposed Markov game-adaptive fuzzy controller outperformed other controllers in terms of tracking errors and control torque requirements, over different desired trajectories. The results also demonstrate the viability of FISs for accelerating learning in Markov games and extending Markov game-based control to continuous state-action space problems.

This paper proposes a novel control approach that incorporates a hybrid game strategy in Markov-game-based fuzzy control. Specifically, we aim at designing a ldquosafe and universally consistentrdquo controller that exhibits an ability to maintain performance against large disturbance and environment variations. The proposed hybrid control is a convex combination (based on experiential information) of ldquoa variation of cautious fictitious playrdquo approach and the ldquominimaxrdquo control approach implemented on a fuzzy Markov game platform. We show analytical convergence of Markov-game-based control in the presence of bounded external disturbances, and extend the analysis to show convergence of the proposed Markov-game-based hybrid control approach. Controller simulation and comparison against baseline Markov game fuzzy control and fuzzyQ-learning control on a highly nonlinear two-link robot brings out the superiority of the approach in handling severe environment and disturbance variations over different desired trajectories. This paper illustrates the possibility of obtaining ldquouniversal consistency,rdquo i.e., reasonable performance against severe environment and disturbance variations, by hybridizing ldquocautious fictitious playrdquo with ldquominimaxrdquo approaches in Markov-game-based control.

In this paper, we use the approach of adaptive critic design (ACD) for control, specifically, the action-dependent heuristic dynamic programming (ADHDP) method. A least squares support vector machine (SVM) regressor has been used for generating the control actions, while an SVM-based tree-type neural network (NN) is used as the critic. After a failure occurs, the critic and action are retrained in tandem using the failure data. Failure data is binary classification data, where the number of failure states are very few as compared to the number of no-failure states. The difficulty of conventional multilayer feedforward NNs in learning this type of classification data has been overcome by using the SVM-based tree-type NN, which due to its feature to add neurons to learn misclassified data, has the capability to learn any binary classification data without a priori choice of the number of neurons or the structure of the network. The capability of the trained controller to handle unforeseen situations is demonstrated.

Multicast services, assisted by special hardware, are being considered as a part of high-speed wide-area networks (WANs) in order to support new generations of multiuser applications. The paper describes a multicast service application for high-speed WANs which is capable of exploiting multicast hardware. Indeed, this research was conducted in the context of the spanning tree hardware structure of PARIS and of plaNET, the pioneering broadband experimental networks that predated ATM. The results of this research were also included in IBM's ATM, called networking broadband services (NBBS). We achieve modularity and low cost by assigning to distinct components the separate problems of: 1) naming groups; 2) finding group members in a network; 3) configuring multicast hardware; 4) delivering multicast messages in sequence. This modularity enables, for example, the multicast, on one hand, to a group to which the user initiates the joining (formed by using 1 and 2 above) and, on the other hand, to groups computed by the source. We give the overall organization of our service and then describe in detail the methods used to solve the first two of the subproblems.

This paper is concerned with the design of a neuro-adaptive trajectory tracking controller. The paper presents a new control scheme based on inversion of a feedforward neural model of a robot arm. The proposed control scheme requires two modules. The first module consists of an appropriate feedforward neural model of forward dynamics of the robot arm that continuously accounts for the changes in the robot dynamics. The second module implements an efficient network inversion algorithm that computes the control action by inverting the neural model. In this paper, a new extended Kalman filter (EKF) based network inversion scheme is proposed. The scheme is evaluated through comparison with two other schemes of network inversion: gradient search in input space and Lyapunov function approach. Using these three inversion schemes the proposed controller was implemented for trajectory tracking control of a two-link manipulator. Simulation results in all cases confirm the efficacy of control input prediction using network inversion. Comparison of the inversion algorithms in terms of tracking accuracy showed the superior performance of the EKF based inversion scheme over others.

Routing algorithms based on the distributed Bellman-Ford algorithm (DBF) suffer from exponential message complexity in some scenarios. We propose two modifications to the algorithm which result in a polynomial message complexity without adversely affecting the response time of the algorithm. However, the new algorithms may not compute the shortest path. Instead, the paths computed can be worse than the shortest path by at most a constant factor (>

An application for multicast service for high-speed WANs (wide area networks) which is capable of exploiting multicast hardware is described. Modularity and low cost area achieved by assigning to distinct components the separate problems of (1) naming groups, (2) finding group members in a network, (3) configuring multicast hardware, and (4) delivering multicast messages in sequence. The overall organization of the service is given, along with the methods used to solve the first two subproblems.>

Routing algorithms based on the distributed Bellman-Ford algorithm (DBF) suffer from exponential message complexity in some scenarios. Two modifications to the algorithm are proposed which result in polynomial message complexity without adversely affecting the response time of the algorithm. However, the new algorithms may not compute the shortest path. Instead, the paths computed can be worse than the shortest path by at most a constant factor (>

Routing a message in a network is efficient (in terms of weight of the path used to carry the message) when nodes know the full topology of the network. This may not be the case in large networks since a network may be composed of smaller autonomous pieces by design or by requirements on performance, with each piece having less than complete information about other pieces. We present a trade-off between the amount of topology information exchanged among these pieces and the efficiency of routing in the network. The large network that we study is a collection of networks connected by boundary nodes. Each boundary node knows the topology of its network and the connectivity of networks to each other. The question addressed here is how much topology information about each network should be distributed to other networks in order to achieve reasonably efficient routing.

A scheme for decentralized estimation based on the modeling of interaction in large dynamical systems is proposed. An example of a two-area power system is presented to illustrate the application of the proposed design procedure. Computer simulation results show that the performance of decentralized and centralized observers designed to optimize the same performance index is quite comparable.