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In this paper, a new algorithm is proposed for the constrained control of weakly coupled nonlinear systems. The controller design problem is solved by solving Hamilton-Jacobi-Bellman(HJB) equation with modified cost to tackle constraints on the control input and unknown coupling. In the proposed controller design framework, coupling terms have been formulated as model uncertainties. The bounded controller...
The male urethra is divided into four segments 1. Posterior (a) Prostatic urethra – from the bladder neck to the site of “urogenital diaphragm.” (b) Membranous urethra – “urogenital diaphragm”. 2. Anterior (a) Bulbar urethra – from the distal margin of the urogenital diaphragm to penoscrotal junction. (b) Penile urethra – urethra that traverses the penile shaft including the glans.
The performance of models at different resolutions is compared in the context of an iterative protein structure refinement protocol. The models considered here consist of an all-atom model described with the CHARMM22 force field in combination with a distance-dependent dielectric implicit solvent approximation, a united-atom model described by the CHARMM19 force field in combination with the effective...
Least squares twin support vector machines (LSTSVM) [1] is a popular kernel-based SVM formulation for binary classification tasks. LSTSVM is an efficient algorithm to learn linear/nonlinear classification boundaries as it just requires solution of two linear systems of equations. LSTSVM has been applied to text categorization with simple bag-of-words representation and conventional feature selection...
A novel architecture of Phase and Frequency Detector is introducing in this paper. This Tri-state transmission gate PFD (Tt-PFD) is designed with transmission gates by using only 12 transistors and conveying functional characteristics of traditional PFD with an improved performance. Proposed PFD is used to generate a qualitative clock signal at high frequency and low Jitter Phase Locked Loop (PLL)...
Markov games can be used as a platform to deal with exogenous disturbances and parametric variations. In this work an attempt has been made to achieve a superior performance with fuzzy Markov game based control by hybridizing two game theory based approaches of ‘fictitious play’ and ‘minimax’. The work attempts a ‘safe yet consistent’ Markov game controller which advocates a minimax policy during...
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...
A new modelling method of image jacobian estimation is presented for uncalibrated visual servoing of robots, in which a kernel recursive least squares (KRLS) technique is used for non-linear mapping between target image features and robot joint angles, and an image jacobian expression is derived from the KRLS algorithm with gaussian kernel. The simulations of robot visual servoing with eye-in-hand...
We present a novel dance posture based annotation model by combining features using Multiple Kernel Learning (MKL). We have proposed a novel feature representation which represents the local texture properties of the image. The annotation model is defined in the direct a cyclic graph structure using the binary MKL algorithm. The bag-of-words model is applied for image representation. The experiments...
The paper presents a novel approach for event detection in sports videos by topic based graphical model learning. The characteristics features defining various sport events are extracted by contextual grouping of low-level video and audio features using topic modeling. Event detection is performed by learning the structure of context based distribution of characteristic features by CRF based graphical...
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...
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...
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...
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...
For sequential design processes, the min-max strategy minimizes the worst-case performance cost. This is a game against nature, where the agent attempts to minimize a specified cost criterion, while nature attempts to maximize it. In this paper, we formulate the problem of decision making under uncertainty as a game in which the opponent (nature) is “disinterested” and plays at random, while the agent...
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...
This paper presents a linear SVM (Support Vector Machine) Pyramidal Tree (SVMPT) for binary classification tasks. SVMPT is a modified version of SVM based Tree Type Neural Networks (SVMTNN), reported earlier in the literature [1]. Both the algorithms use parameter-less SVM proposed by Mangasarian [2] for learning in each node. While SVMTNN insists on 100 percent training accuracy, linear SVMPT uses...
Linear Support Vector Machines (SVMs) have been used successfully to classify text documents into set of concepts. With the increasing number of linear SVM formulations and decomposition algorithms publicly available, this paper performs a study on their efficiency and efficacy for text categorization tasks. Eight publicly available implementations are investigated in terms of Break Even Point (BEP),...
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...
Reinforcement learning (RL) is a popular learning paradigm to adaptive learning control of nonlinear systems, and is able to work without an explicit model. However, learning from scratch, i.e., without any a priori knowledge, is a daunting undertaking, which results in long training time and instability of learning process with large continuous state space. For physical systems, one must consider...
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