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13
Sensor Selection via Convex Optimization
 IEEE Transactions on Signal Processing
, 2009
"... We consider the problem of choosing a set of k sensor measurements, from a set of m possible or potential sensor measurements, that minimizes the error in estimating some parameters. Solving this problem by evaluating the performance for each of the(m k possible choices of sensor measurements is not ..."
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Cited by 96 (2 self)
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We consider the problem of choosing a set of k sensor measurements, from a set of m possible or potential sensor measurements, that minimizes the error in estimating some parameters. Solving this problem by evaluating the performance for each of the(m k possible choices of sensor measurements is not practical unless m and k are small. In this paper we describe a heuristic, based on convex optimization, for approximately solving this problem. Our heuristic gives a subset selection as well as a bound on the best performance that can be achieved by any selection of k sensor measurements. There is no guarantee that the gap between the performance of the chosen subset and the performance bound is always small; but numerical experiments suggest that the gap is small in many cases. Our heuristic method requires on the order of m3 operations; for m = 1000 possible sensors, we can carry out sensor selection in a few seconds on a 2 GHz personal computer. 1
Multimodal fusion for multimedia analysis: a survey
, 2010
"... This survey aims at providing multimedia researchers with a stateoftheart overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several c ..."
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Cited by 58 (1 self)
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This survey aims at providing multimedia researchers with a stateoftheart overview of fusion strategies, which are used for combining multiple modalities in order to accomplish various multimedia analysis tasks. The existing literature on multimodal fusion research is presented through several classifications based on the fusion methodology and the level of fusion (feature, decision, and hybrid). The fusion methods are described from the perspective of the basic concept, advantages, weaknesses, and their usage in various analysis tasks as reported in the literature. Moreover, several distinctive issues that influence a multimodal fusion process such as, the use of correlation and independence, confidence level, contextual information, synchronization between different modalities, and the optimal modality selection are also highlighted. Finally, we present the open issues for further research in the area of multimodal fusion.
Optimal sensor selection for discrete event systems with partial observation
 IEEE TRANSACTIONS ON AUTOMATIC CONTROL
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A design methodology for selection and placement of sensors in multimedia surveillance systems
 in: VSSN ’06: Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks, ACM
, 2006
"... This paper addresses the problem of how to select the optimal number of sensors and how to determine their placement in a given monitored area for multimedia surveillance systems. We propose to solve this problem by obtaining a novel performance metric in terms of a probability measure for accomp ..."
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Cited by 12 (1 self)
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This paper addresses the problem of how to select the optimal number of sensors and how to determine their placement in a given monitored area for multimedia surveillance systems. We propose to solve this problem by obtaining a novel performance metric in terms of a probability measure for accomplishing the task as a function of set of sensors and their placement. This measure is then used to find the optimal set. The same measure can be used to analyze the degradation in system’s performance with respect to the failure of various sensors. We also build a surveillance system using the optimal set of sensors obtained based on the proposed design methodology. Experimental results show the effectiveness of the proposed design methodology in selecting the optimal set of sensors and their placement.
Goaloriented optimal subset selection of correlated multimedia streams
 ACM Transactions on Multimedia Computing, Communications, and Applications
"... A multimedia analysis system utilizes a set of correlated media streams, each of which, we assume, has a confidence level and a cost associated with it, and each of which partially helps in achieving the system goal. However, the fact that at any instant, not all of the media streams contribute towa ..."
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Cited by 10 (2 self)
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A multimedia analysis system utilizes a set of correlated media streams, each of which, we assume, has a confidence level and a cost associated with it, and each of which partially helps in achieving the system goal. However, the fact that at any instant, not all of the media streams contribute towards a system goal brings up the issue of finding the best subset from the available set of media streams. For example, a subset of two video cameras and two microphones could be better than any other subset of sensors at some time instance to achieve a surveillance goal (e.g. event detection). This article presents a novel framework that finds the optimal subset of media streams so as to achieve the system goal under specified constraints. The proposed framework uses a dynamic programming approach to find the optimal subset of media streams based on three different criteria: first, by maximizing the probability of achieving the goal under the specified cost and confidence; second, by maximizing the confidence in the achieved goal under the specified cost and probability with which the goal is achieved; and third, by minimizing the cost to achieve the goal with a specified probability and confidence. Each of these problems is proven to be NPComplete. From an AI point of view, the solution we propose is heuristicbased, and for each criterion, utilizes a heuristic function which for a given problem, combines optimal solutions of smallsized subproblems to yield a potential nearoptimal solution to the original problem. The proposed framework allows for a tradeoff among the aforementioned three criteria, and offers the flexibility to compare whether any one set of media streams of low cost would be better than any other set of higher cost, or whether any one set of media streams of high confidence would be better than any other set of low confidence. To show the utility of our framework,
Experiential sampling on multiple data streams
 IEEE Trans. Multimedia
, 2006
"... Abstract—Multimedia systems must deal with multiple data streams. Each data stream usually contains significant volume of redundant noisy data. In many realtime applications, it is essential to focus the computing resources on a relevant subset of data streams at any given time instant and use it t ..."
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Cited by 6 (3 self)
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Abstract—Multimedia systems must deal with multiple data streams. Each data stream usually contains significant volume of redundant noisy data. In many realtime applications, it is essential to focus the computing resources on a relevant subset of data streams at any given time instant and use it to build the model of the environment. We formulate this problem as an experiential sampling problem and propose an approach to utilize computing resources efficiently on the most informative subset of data streams. In this paper, we generalize our experiential sampling framework to multiple data streams and provide an evaluation measure for this technique. We have successfully applied this framework to the problems of traffic monitoring, face detection and monologue detection. Index Terms—Dynamical systems, experiential computing, experiential sampling, sampling, visual attention. I.
Goal based optimal selection of media streams
 In IEEE International Conference on Multimedia and Expo
, 2005
"... A multimedia system utilizes a set of correlated media streams each of which partially help in achieving the system goal. However, since not all of the streams always contribute towards the goal, there is a need for determining the most informative subset from the available set of media streams at ..."
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Cited by 4 (2 self)
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A multimedia system utilizes a set of correlated media streams each of which partially help in achieving the system goal. However, since not all of the streams always contribute towards the goal, there is a need for determining the most informative subset from the available set of media streams at any instant. For example, a subset of two video cameras and two microphones could be better than any other subset of multimedia sensors at some time instance. This paper presents a novel framework to nd the optimal subset of media streams that achieves the system goal under specied constraints. The proposed framework uses a dynamic programming approach to nd the optimal subset of media streams based on two criteria; rst, by maximizing the probability of achieving the goal under the specied maximum cost, and second by minimizing the cost of using the streams so that the goal is achieved with a specied minimum probability. To show the utility of our framework, we provide the simulation results for hypothesis testing. 1.
Coverage Analysis of Mobile Agent Trajectory via StateBased Opacity FormulationsI
"... This paper performs coverage analysis of mobile agent trajectory utilizing discrete event system models and employing statebased notions of opacity. Nondeterministic finite automata with partial observation on their transitions are used to simultaneously capture both the kinematic model of a mobi ..."
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Cited by 2 (2 self)
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This paper performs coverage analysis of mobile agent trajectory utilizing discrete event system models and employing statebased notions of opacity. Nondeterministic finite automata with partial observation on their transitions are used to simultaneously capture both the kinematic model of a mobile agent and the information that becomes available by a set of sensors that are deployed in a given environment. The information provided by the set of sensors is analyzed to track the passage of a mobile agent through certain secret (strategic) locations at specific time instants using statespace notions of opacity, which arise naturally as the way to capture/analyze secrecy and privacy considerations in such settings and to answer related coverage questions. Realistic examples of twodimensional environments equipped with sensors monitoring the location of a mobile agent that follows a known kinematic model
MM000690 Experiential Sampling in Multimedia Systems 1
"... Abstract — Multimedia systems must deal with multiple data streams. Each data stream usually contains significant volume of redundant noisy data. In many realtime applications, it is essential to focus the computing resources on a relevant subset of data streams at any given time instant and use it ..."
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Abstract — Multimedia systems must deal with multiple data streams. Each data stream usually contains significant volume of redundant noisy data. In many realtime applications, it is essential to focus the computing resources on a relevant subset of data streams at any given time instant and use it to build the model of the environment. In this paper, we formulate this problem as an experiential sampling problem and propose an approach to utilize computing resources efficiently on the most informative subset of data streams. We generalize the notion of static visual attention to multimedia data streams in a dynamical systems setting. The goaldriven generalized attention is maintained by a sampling representation that uses the current context and past experience for attention evolution. We have developed the theoretical background, algorithms and an evaluation measure for this technique. We have successfully applied this framework to the problems of traffic monitoring, face detection and monologue detection.