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64
, G.Shyam Sundhar
"... Abstract: It is used to separate background from main structures in images, drawings, and paintaings.images consists of main structure and the texture. our new relative total variation method which will be presented in this paper is used to eliminate the complete texture based on different properti ..."
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Abstract: It is used to separate background from main structures in images, drawings, and paintaings.images consists of main structure and the texture. our new relative total variation method which will be presented in this paper is used to eliminate the complete texture based on different properties between the texture and structure..By performing the no of iterations we can get the effective structure. This is also used to extracting the boundries of the image by varying the values of the different parameters. We propose new algorithm which capture the essential difference of these two types of visual forms, and develop an efficient optimization system to extract main structures. The new variation measures are validated on millions of sample patches. Our approach finds a number of new applications to manipulate, render, and reuse the immense number of “structure with texture ” images and drawings that were traditionally difficult to be edited properly.,
Distributed recursive parameter estimation in parametrized linear statespace models. submitted
, 2008
"... We consider a network of sensors deployed to sense a spatiotemporal field and estimate a parameter of interest. We are interested in the case where the temporal process sensed by each sensor can be modeled as a statespace process that is perturbed by random noise and parametrized by an unknown par ..."
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Cited by 32 (2 self)
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We consider a network of sensors deployed to sense a spatiotemporal field and estimate a parameter of interest. We are interested in the case where the temporal process sensed by each sensor can be modeled as a statespace process that is perturbed by random noise and parametrized by an unknown parameter. To estimate the unknown parameter from the measurements that the sensors sequentially collect, we propose a distributed and recursive estimation algorithm, which we refer to as the incremental recursive prediction error algorithm. This algorithm has the distributed property of incremental gradient algorithms and the online property of recursive prediction error algorithms. We study the convergence behavior of the algorithm and provide sufficient conditions for its convergence. Our convergence result is rather general and contains as special cases the known convergence results for the incremental versions of the leastmean square algorithm. Finally, we use the algorithm developed in this paper to identify the source of a gasleak (diffusing source) in a closed warehouse and also report numerical simulations to verify convergence. I.
Distributed and nonautonomous power control through distributed convex optimization
 IEEE INFOCOM
"... Abstract — We consider the uplink power control problem where mobile users in different cells are communicating with their base stations. We formulate the power control problem as the minimization of a sum of convex functions. Each component function depends on the channel coefficients from all the ..."
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Cited by 16 (5 self)
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Abstract — We consider the uplink power control problem where mobile users in different cells are communicating with their base stations. We formulate the power control problem as the minimization of a sum of convex functions. Each component function depends on the channel coefficients from all the mobile users to a specific base station and is assumed to be known only to that base station (only CSIR). We then view the power control problem as a distributed optimization problem that is to be solved by the base stations and propose convergent, distributed and iterative power control algorithms. These algorithms require each base station to communicate with the base stations in its neighboring cells in each iteration and are hence nonautonomous. Since the base stations are connected through a wired backbone the communication overhead is not an issue. The convergence of the algorithms is shown theoretically and also verified through numerical simulations. I.
On the path coverage properties of random sensor networks
 IEEE Trans. Mob. Comput
, 2007
"... Abstract—In a sensor network, the points in the operational area that are suitably sensed are a twodimensional spatial coverage process. For randomly deployed sensor networks, typically, the network coverage of twodimensional areas is analyzed. However, in many sensor network applications, e.g., t ..."
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Cited by 11 (1 self)
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Abstract—In a sensor network, the points in the operational area that are suitably sensed are a twodimensional spatial coverage process. For randomly deployed sensor networks, typically, the network coverage of twodimensional areas is analyzed. However, in many sensor network applications, e.g., tracking of moving objects, the sensing process on paths, rather than in areas, is of interest. With such an application in mind, we analyze the coverage process induced on a onedimensional path by a sensor network that is modeled as a twodimensional Boolean model. In the analysis, the sensor locations form a spatial Poisson process of density and the sensing regions are circles of i.i.d. random radii. We first obtain a strong law for the fraction of a path that is ksensed, i.e., sensed by ð kÞ sensors. Asymptotic pathsensing results are obtained under the same limiting regimes as those required for asymptotic coverage by a twodimensional Boolean model. Interestingly, the asymptotic fraction of the area that is 1sensed is the same as the fraction of a path that is 1sensed. For k 1, we also obtain a central limit theorem that shows that the asymptotics converge at the rate of ð1=2Þ for k 1. For finite networks, the expectation and variance of the fraction of the path that is ksensed is obtained. The asymptotics and the finite network results are then used to obtain the critical sensor density to ksense a fraction k of an arbitrary path with very high probability is also obtained. Through simulations, we then analyze the robustness of the model when the sensor deployment is nonhomogeneous and when the paths are not rectilinear. Other path coverage measures like breach, support, “length to first sense, ” and sensing continuity measures like holes and clumps are also characterized. Finally, we discuss some generalizations of the results like characterization of the coverage process of mdimensional “straight line paths ” by ndimensional, n> m, sensor networks. Index Terms—Sensor networks coverage, path tracking, Boolean models, exposure. Ç 1
IndoWordnet
"... India is a multilingual country where machine translation and cross lingual search are highly relevant problems. These problems require large resources like wordnets and lexicons of high quality and coverage. Wordnets are lexical structures composed of synsets and semantic relations. Synsets are s ..."
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Cited by 8 (1 self)
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India is a multilingual country where machine translation and cross lingual search are highly relevant problems. These problems require large resources like wordnets and lexicons of high quality and coverage. Wordnets are lexical structures composed of synsets and semantic relations. Synsets are sets of synonyms. They are linked by semantic relations like hypernymy (isa), meronymy (partof), troponymy (mannerof) etc. IndoWordnet is a linked structure of wordnets of major Indian languages from IndoAryan, Dravidian and SinoTibetan families. These wordnets have been created by following the expansion approach from Hindi wordnet which was made available free for research in 2006. Since then a number of Indian languages have been creating their wordnets. In this paper we discuss the methodology, coverage, important considerations and multifarious benefits of IndoWordnet. Case studies are provided for Marathi, Sanskrit, Bodo and Telugu, to bring out the basic methodology of and challenges involved in the expansion approach. The guidelines the lexicographers follow for wordnet construction are enumerated. The difference between IndoWordnet and EuroWordnet also is discussed. 1.
Meaning and context in children’s understanding of gradable adjectives
 Journal of Semantics
, 2010
"... This paper explores what children and adults know about three specific ways that meaning and context interact: the interpretation of expressions whose extensions vary in different contexts (semantic context dependence); conditions on the felicitous use of expressions in a discourse context (presuppo ..."
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Cited by 9 (1 self)
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This paper explores what children and adults know about three specific ways that meaning and context interact: the interpretation of expressions whose extensions vary in different contexts (semantic context dependence); conditions on the felicitous use of expressions in a discourse context (presupposition accommodation) and informative uses of expressions in contexts in which they strictly speaking do not apply (imprecision). The empirical focus is the use of unmodified (positive form) gradable adjectives (GAs) in definite descriptions to distinguish between two objects that differ in the degree to which they possess the property named by the adjective. We show that by 3 years of age, children are sensitive to all three varieties of context– meaning interaction and that their knowledge of this relation with the definite description is appropriately guided by the semantic representations of the GA appearing in it. These findings suggest that children’s semantic representations of the GAs we investigated and the definite determiner the are adultlike and that they are aware of the consequences of these representations when relating meaning and context. Bolstered by adult participant responses, this work provides important experimental support for theoretical claims regarding the semantics of gradable predicates and the nature of different types of ‘interpretive variability’, specifically semantic context dependence v. pragmatic tolerance of imprecision. 1
Rates of Convergence for Greedy Gossip with Eavesdropping
"... Abstract — Greedy gossip with eavesdropping (GGE) is a randomized gossip algorithm that exploits the broadcast nature of wireless communications to converge rapidly on gridlike network topologies without requiring that nodes know their geographic locations. When a node decides to gossip, rather tha ..."
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Cited by 7 (4 self)
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Abstract — Greedy gossip with eavesdropping (GGE) is a randomized gossip algorithm that exploits the broadcast nature of wireless communications to converge rapidly on gridlike network topologies without requiring that nodes know their geographic locations. When a node decides to gossip, rather than choosing one of its neighbors randomly, it greedily chooses to gossip with the neighbor whose values are most different from its own. We assume that all transmissions are wireless broadcasts so that nodes can keep track of their neighbors’ values by eavesdropping on their communications. We have previously proved that GGE converges to the average consensus on connected network topologies. In this paper we study the rate of convergence of GGE, a nontrivial task due to the greedy, datadriven nature of the algorithm. We demonstrate that GGE outperforms standard randomized gossip, and we
Developing Verb Frames for Hindi
 In the Proc LREC
, 2008
"... This paper introduces an ongoing work on developing verb frames for Hindi. Verb frames capture syntactic commonalities of semantically related verbs. The main objective of this work is to create a linguistic resource which will prove to be indispensable for various NLP applications. We also hope thi ..."
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Cited by 5 (1 self)
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This paper introduces an ongoing work on developing verb frames for Hindi. Verb frames capture syntactic commonalities of semantically related verbs. The main objective of this work is to create a linguistic resource which will prove to be indispensable for various NLP applications. We also hope this resource to help us better understand Hindi verbs. We motivate the basic verb argument structure using relations as introduced by Panini. We show the methodology used in preparing these frames and the criteria followed for classifying Hindi verbs.
Distributed and Recursive Estimation
"... Abstract Estimation is a canonical problem in sensor networks. The intrinsic nature of sensor networks requires estimation algorithms based on sensor data to be distributed and recursive; such algorithms are studied in this chapter for the problem of (conditional) least squares estimation. The chapt ..."
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Abstract Estimation is a canonical problem in sensor networks. The intrinsic nature of sensor networks requires estimation algorithms based on sensor data to be distributed and recursive; such algorithms are studied in this chapter for the problem of (conditional) least squares estimation. The chapter is divided into three parts. In the first part, distributed and recursive estimation algorithms are developed for the nonlinear regression problem. In the second part, a distributed and recursive algorithm is designed to estimate the unknown parameter in a parametrized statespace random process. In the third part, the problem of identifying the source of a diffusion field is discussed as a representative application for the algorithms developed in the first two parts. 1
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