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1,674
Column vector
, 2011
"... Abstract—In this paper, a novel source/target localization approach is proposed using a number of sensors (surrounding or not surrounding one or more sources) to form a sparse large aperture array of known geometry. Under a large array aperture, the array response (manifold vector) obeys a spherica ..."
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Abstract—In this paper, a novel source/target localization approach is proposed using a number of sensors (surrounding or not surrounding one or more sources) to form a sparse large aperture array of known geometry. Under a large array aperture, the array response (manifold vector) obeys a
a, A Column Vector
"... and effectiveness of the network is highly dependant on its geographical coverage. However, in many applications the actual coverage cannot be guaranteed to meet the requirement due to the random sensor deployment. While existing methods tend to exploit mobility to relocate all the sensors to be eve ..."
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Cited by 1 (0 self)
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and effectiveness of the network is highly dependant on its geographical coverage. However, in many applications the actual coverage cannot be guaranteed to meet the requirement due to the random sensor deployment. While existing methods tend to exploit mobility to relocate all the sensors to be evenly distributed, a Wireless Arraybased Cooperative Sensing Model (WACSM) that makes use of a wireless group of densely located nodes, namely wireless array (WA), is proposed in this paper to improve the initial network coverage. A distributed WA formation algorithm is derived to group together the overly clustered nodes to jointly sense the environment without moving them apart. In addition, the nodes that are located within the cooperative sensing range of other WA’s are identified as redundant nodes. As a result, a better coverage can be achieved with less number of active nodes being involved in the network operation. The effectiveness of the proposed approach, in comparison with the traditional Boolean Sensing Model (BSM), is demonstrated by computer simulation studies.
Pseudoinverse Column vector
"... A modal analysis aims at the identification of the modal parameters of a test structure from the measured vibratory behaviour. Traditionally, both the input forces and the resulting responses are measured. However, in many applications it is not possible to measure (all) the input forces. During the ..."
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A modal analysis aims at the identification of the modal parameters of a test structure from the measured vibratory behaviour. Traditionally, both the input forces and the resulting responses are measured. However, in many applications it is not possible to measure (all) the input forces. During the last decade, two new classes of modal parameter estimation techniques have been developed to overcome this problem: the operational techniques and the combined experimentaloperational techniques. Operational modal analysis techniques can identify the modal parameters from the responses of the structure; they do not require the input forces. The combined experimentaloperational techniques, only require a part of the input forces to estimate the modal parameters. The work presented in this paper is part of the evaluation process of these new modal parameters estimation techniques; it compares the modal parameters provided by the operational and combined experimentaloperational modal analysis techniques with the modal parameters obtained with the experimental modal analysis technique.
Column Vector Matrix
"... In this paper, a blind spacetime receiver is proposed to handle point and diffused sources for asynchronous multipath DSCDMA systems. The receiver is based on a computationally efficient subspacetype algorithm for its joint spacetime channel estimation which is insusceptible to nearfar problems ..."
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In this paper, a blind spacetime receiver is proposed to handle point and diffused sources for asynchronous multipath DSCDMA systems. The receiver is based on a computationally efficient subspacetype algorithm for its joint spacetime channel estimation which is insusceptible to nearfar problems. The coherency between the sources is removed by a novel temporal smoothing technique operating in the transformed domain. Unlike many conventional DSCDMA receivers, the proposed formulation and approach is applicable even with the presence of cocode interferers. Furthermore it is robust to channel estimation errors in the event of any unidentified (incomplete) or erroneous (incorrect) channel parameter.
a;A Column Vector
"... Abstract—In this paper, the parameter C is introduced as a figure of merit for comparing the performances of practical directionfinding (DF) algorithms in terms of their superresolution capabilities. C takes values between 0 and 1, with higher values indicating better resolving capability and C = ..."
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Abstract—In this paper, the parameter C is introduced as a figure of merit for comparing the performances of practical directionfinding (DF) algorithms in terms of their superresolution capabilities. C takes values between 0 and 1, with higher values indicating better resolving capability and C = 1 denotes an algorithm with the theoretically ideal resolution performance. Analytical expressions for C can be derived for a number of DF algorithms. In this paper, three such expressions are derived for MUSIC, ‘optimal ’ Beamspace MUSIC and Minimum Norm. It is found that optimal beamspace MUSIC yields the smallest resolution separation, which can approach the ideal when incident signals have equal powers.
Column Vectorizing Algorithms for Support Vector Machines
"... Abstract—In this paper we present the vectorization method for support vector machines in a hybrid Data Mining and CaseBased Reasoning system which incorporates a vector model to help transfer textual information to numerical vector in order to make the real world information more adapted to the da ..."
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to the data mining engine. The main issue of implementing this approach is two algorithms; the discrete vectorization algorithm and continuous vectorization algorithm. According to the design of the hybrid system, the input information is the text table contains different kinds of columns which are stored
NOTATIONS A Scalar A Column Vector
"... Abstract—The problem of joint transmitter and receiver beamforming in the downlink DSCDMA over multipath fading channels is considered in this paper. The proposed investigation is based on the array manifold concept and thus, the spacetime properties of the channel can be fully exploited, playing ..."
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Abstract—The problem of joint transmitter and receiver beamforming in the downlink DSCDMA over multipath fading channels is considered in this paper. The proposed investigation is based on the array manifold concept and thus, the spacetime properties of the channel can be fully exploited, playing a crucial role in the operation of blind channel estimation and interference suppression techniques. The beamforming weights are designed to minimise the overall meansquarederror (MSE) of the system. An iterative solution to the optimisation problem is firstly proposed under the system framework. A closedform solution based on channel eigendecomposition is then proposed. The convergence of the iterative method to the closedform solution and the equivalence of the two methods are verified through numerical simulations. The performance of the proposed approach is also supported by some illustrative examples.
The Dantzig selector: statistical estimation when p is much larger than n
, 2005
"... In many important statistical applications, the number of variables or parameters p is much larger than the number of observations n. Suppose then that we have observations y = Ax + z, where x ∈ R p is a parameter vector of interest, A is a data matrix with possibly far fewer rows than columns, n ≪ ..."
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Cited by 879 (14 self)
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In many important statistical applications, the number of variables or parameters p is much larger than the number of observations n. Suppose then that we have observations y = Ax + z, where x ∈ R p is a parameter vector of interest, A is a data matrix with possibly far fewer rows than columns, n
For Most Large Underdetermined Systems of Linear Equations the Minimal ℓ1norm Solution is also the Sparsest Solution
 Comm. Pure Appl. Math
, 2004
"... We consider linear equations y = Φα where y is a given vector in R n, Φ is a given n by m matrix with n < m ≤ An, and we wish to solve for α ∈ R m. We suppose that the columns of Φ are normalized to unit ℓ 2 norm 1 and we place uniform measure on such Φ. We prove the existence of ρ = ρ(A) so that ..."
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Cited by 568 (10 self)
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We consider linear equations y = Φα where y is a given vector in R n, Φ is a given n by m matrix with n < m ≤ An, and we wish to solve for α ∈ R m. We suppose that the columns of Φ are normalized to unit ℓ 2 norm 1 and we place uniform measure on such Φ. We prove the existence of ρ = ρ(A) so
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