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ABSTRACT: In this paper, the electromagnetic interaction between human skin and terahertz radiation is investigated through the double Debye parameters' extraction algorithm. The changes of skin content are contrasted at the frequencies below one terahertz(THz) but the recent approaches could provide only a rough estimation. We propose an global optimization based identification, which results in globally accurate estimators in the frequency range up to two THz, and thus supports the validity of Debye model for Terahertz wave's propagation and reflection in skin. Simulation results confirm our prominent methodology.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2012; 2012:5474-7.
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ABSTRACT: This paper discusses dynamic state estimation for nonlinear measurement model through distributed multisensor network under power constraints. For this scenario, we propose an optimized power allocation strategy based on semidefinite programming, that achieves minimum mean-squared error for the estimate subject to constraints on total transmit power. System nonlinearity is handled effectively with the help of distributed unscented Kalman filtering and linear fractional transformation. Furthermore, advantage of using multiple sensors over a single independent sensor is established through simulation results for tracking a maneuvering target.
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on; 06/2011 · 4.63 Impact Factor
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ABSTRACT: This paper deals with optimized training sequences to estimate multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) channel states in the presence of spatial fading correlations. The optimization criterion is the entropy minimization of the error between the high multi-dimensional and correlated channel state and its estimator. The globally optimized training sequences are exactly solved by a semi-definite programming (SDP) of tractable computational complexity O((M t (M t + 1)/2)<sup>2.5</sup>), where M t is the transmit antenna number. With new tight two-sided bounds for the objective function, the optimal value of the generic SDP can be approximately solved by the standard water-filling algorithm. Intensive simulation results are provided to illustrate the performance of our methods.
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on; 06/2011 · 4.63 Impact Factor
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ABSTRACT: This correspondence proposes an efficient semidefinite programming (SDP) method for the design of a class of linear phase finite impulse response triplet halfband filter banks whose filters have optimal frequency selectivity for a prescribed regularity order. The design problem is formulated as the minimization of the least square error subject to peak error constraints and regularity constraints. By using the linear matrix inequality characterization of the trigonometric semi-infinite constraints, it can then be exactly cast as a SDP problem with a small number of variables and, hence, can be solved efficiently. Several design examples of the triplet halfband filter bank are provided for illustration and comparison with previous works. Finally, the image coding performance of the filter bank is presented.
IEEE Transactions on Image Processing 03/2011; · 3.04 Impact Factor
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ABSTRACT: This correspondence proposes an efficient semidefinite programming (SDP) method for the design of a class of linear phase finite impulse response triplet halfband filter banks whose filters have optimal frequency selectivity for a prescribed regularity order. The design problem is formulated as the minimization of the least square error subject to peak error constraints and regularity constraints. By using the linear matrix inequality characterization of the trigonometric semi-infinite constraints, it can then be exactly cast as a SDP problem with a small number of variables and, hence, can be solved efficiently. Several design examples of the triplet halfband filter bank are provided for illustration and comparison with previous works. Finally, the image coding performance of the filter bank is presented.
IEEE Transactions on Image Processing 02/2011; 20(2):586-91. · 3.04 Impact Factor
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ABSTRACT: This paper presents a novel technique of allocating optimized power to wireless sensor nodes in a nonlinear measurement model. We consider the problem of distributed estimation of a random vector-valued parameter in an energy-constrained sensor network. Noise-corrupted local nonlinear observations are transmitted by spatially distributed sensor nodes towards fusion center where estimation of the vector parameter is carried out. In order to guarantee reliable communication, we minimize mean square error of this estimate subject to a constraint on total power consumed by the network. This optimization problem is then recast into a semi-definite program (SDP) which guarantees globally optimized values of the required power gains at sensor nodes. Estimation performance of this novel technique is demonstrated through examples of nonlinear models. Furthermore, for linear models the proposed strategy provides better performance when compared with the previous sub-optimial techniques.
Signal Processing and Communication Systems (ICSPCS), 2010 4th International Conference on; 01/2011
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ABSTRACT: The amplify-and-forward (AF) relay beamforming problems are naturally formulated as indefinite quadratic (nonconvex) optimization programs. The typical methods for solving such optimization problems are to transform them into convex semi-definite programs (SDPs) with additional rank-one (nonconvex and discontinuous) constraints. The rank-one constraints are then dropped to obtain solvable SDP relaxed problems and randomization techniques are employed for seeking the feasible solutions to the original nonconvex optimization problems. In many conventional scenarios, the results from solving the rank-one relaxed SDP problems are enough to conclude the solutions since no rank-higher-than-one SDP resulting matrix can be observed. Through our simulations, we found that there are also many scenarios that the SDP solvers give high-rank solutions. Hence, in this paper the rank-one constraints are equivalently expressed as reverse convex constraints and are incorporated into the optimization problems. Then, we propose an efficient iterative algorithm for solving the nonsmooth reverse convex optimization problems.Our simulations show that our proposed approach yields nearly global optimal solutions.
Signal Processing and Communication Systems (ICSPCS), 2010 4th International Conference on; 01/2011
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ABSTRACT: The optimal beamforming for cognitive multicast transmission is nonconvex rank-one constrained optimization problem. For a solution, a popular method is the combination of relaxed convex semi-definite programming, where the rank-one constraint is dropped, and randomization. We show that in many cases, this method cannot give satisfactory solutions. As an initial step, we develop a simple alternative method, which gives much better solutions. Our simulation confirms this fact.
Vehicular Technology Conference Fall (VTC 2010-Fall), 2010 IEEE 72nd; 10/2010
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ABSTRACT: In this paper, the training sequence design for multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems under the minimum mean square error (MMSE) criterion is addressed. The optimal training sequence for channel estimation in spatially correlated MIMO-OFDM systems was not known for an arbitrary signal-to-noise ratio (SNR). Only one class of training sequences was proposed in the literature in which the power allocation is given only for the extreme conditions of low and high SNRs. The current paper presents a necessary and sufficient condition for the optimal training sequence, and reformulates the training design problem as a convex optimization problem whose optimal solution is efficiently solved. In addition, tight upper bounds for MMSE and resulting low complexity iterative algorithms with the closed-form expression in iterations to find the optimum training sequence are derived. Simulation results confirm the superiority of the proposed design over the existing one in terms of both MSE estimation and BER performance. The proposed methods are also shown to be robust with respect to the spatial correlation mismatch at the transmitter.
IEEE Transactions on Wireless Communications 10/2010; · 2.59 Impact Factor
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ABSTRACT: The cognitive beamforming problems are naturally formulated as indefinite quadratic (nonconvex) optimization programs. The typical methods for solving such optimization problems are to transform them into convex semi-definite programs (SDPs) with additional rank-one (nonconvex and discontinuous) constraints. The rank-one constraints are then dropped to obtain solvable SDP relaxed problems and randomization techniques are employed for seeking the feasible solutions to the original nonconvex optimization problems. In many practical cases, these approaches fail to deliver satisfactory solutions, i.e., their solutions are very far from the optimal ones. In contrast, in this paper the rank-one constraints are equivalently expressed as reverse convex constraints and are incorporated into the optimization problems. Then, we propose an efficient iterative algorithm for solving the nonsmooth reverse convex optimization problems. Our simulations show that our proposed approach yields nearly global optimal solutions with much less computational load as compared to the conventional one.
Communications and Electronics (ICCE), 2010 Third International Conference on; 09/2010
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ABSTRACT: A design method of a linear-phased, two-dimensional (2-D), two-fold symmetric circular shaped filter is presented in this paper. Although the proposed method designs a non-separable filter, its implementation has linear complexity. The shape of the passband and the stopband is expressed in terms of level sets of second order trigonometric polynomials. This enables the transformation of the filter specifications to a Semi-Definite Program (SDP) of moderate dimension. The proposed filter outperforms currently available filter design methods. We present a performance comparison, as well as a homomorphic processing image enhancement example to illustrate the effectiveness of this method.
Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on; 04/2010 · 4.63 Impact Factor
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ABSTRACT: This paper proposes a novel method to design exactly linear phase infinite impulse response half-band filters with arbitrary regularity. Broadly speaking, the design problem is formulated as a semi-infinite program, which is then turned into a semidefinite program of minimal order via a new linear matrix inequality characterization of convex hulls of trigonometric polynomials. In contrast to maximally flat approach, the proposed method allows direct control of various design parameters, which in turn enables the synthesis of filters with better transition response. The viability of the proposed method is demonstrated through several numerical examples.
Circuits and Systems II: Express Briefs, IEEE Transactions on 01/2009; · 1.41 Impact Factor
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ABSTRACT: The triplet halfband filter bank structure is well known to be efficient for the design and implementation of a class of biorthogonal wavelet filter banks. Previously, two extreme cases in filter characteristics including equiripple filters with no regularity and maximally flat filters with poor frequency selectivity have mainly been treated. This paper proposes an efficient semi-definite programming (SDP) method for the design of linear phase finite impulse response (FIR) triplet halfband filter banks whose filters have optimal frequency selectivity for a prescribed regularity order. By using the linear matrix inequality (LMI) characterization of the trigonometric semi-infinite constraints, the design problem can be exactly cast as an SDP problem with a small number of variables and, hence, can be solved efficiently. A design example of the triplet halfband filter bank with different regularity orders is provided to validate the proposed method. Finally, the image coding performance of the filter bank is presented.
Communications and Electronics, 2008. ICCE 2008. Second International Conference on; 07/2008
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ABSTRACT: This paper proposes a novel method to design exactly linear phase infinite impulse response half-band filters with ar-bitrary regularity. Broadly speaking, the design problem is for-mulated as a semi-infinite program, which is then turned into a semidefinite program of minimal order via a new linear matrix in-equality characterization of convex hulls of trigonometric polyno-mials. In contrast to maximally flat approach, the proposed method allows direct control of various design parameters, which in turn enables the synthesis of filters with better transition response. The viability of the proposed method is demonstrated through several numerical examples. Index Terms—Half-band, infinite impulse response (IIR), linear phase filters, semidefinite programming (SDP).
Circuits and Systems II: Express Briefs, IEEE Transactions on 01/2008; 55. · 1.41 Impact Factor
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ABSTRACT: This paper proposes a computationally efficient method for designing a class of triplet halfband filter banks. We show that the design of perfect reconstruction two-channel Alter banks with arbitrary regularity order can be precisely formulated as a semi-definite programming problem. We also show that the dual problem has a significant smaller number of variables and, hence, can be solved efficiently. The effectiveness of the proposed method is demonstrated by the design examples of filter banks with high filter order and arbitrary regularity order.
Communications and Information Technologies, 2007. ISCIT '07. International Symposium on; 11/2007
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ABSTRACT: The paper presents an efficient semidefinite programming (SDP) based design for prototype filters of cosine-modulated filter banks (CMFBs). We consider a class of near-perfect reconstruction CMFBs with the linear phase prototype filter, which structurally eliminates the amplitude overall distortion. The prototype filter design problem is then formulated into a convex semi-infinite programming problem. Furthermore, to handle the semi-infinite constraints, we use the linear matrix inequality (LMI) characterization of positive trigonometric polynomials to cast the semi-infinite programming problem into SDP one. Finally, convex duality is applied to transform the SDP into another SDP with the minimal number of additional variables, which is efficiently solved. An additional advantage of the proposed method is that we can precisely control the filter specifications.
Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on; 05/2007 · 4.63 Impact Factor
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ABSTRACT: The paper presents an efcient semidenite programming (SDP) based design for prototype lters of cosine-modulated lter banks (CMFBs). We consider a class of near-perfect reconstruction CMFBs with the linear phase prototype lter, which structurally eliminates the am-plitude overall distortion. The prototype lter design problem is then formulated into a convex semi-innite programming problem. Furthermore, to handle the semi-innite constraints, we use the lin-ear matrix inequality (LMI) characterization of positive trigonomet-ric polynomials to cast the semi-innite programming problem into SDP one. Finally, convex duality is applied to transform the SDP into another SDP with the minimal number of additional variables, which is efciently solved. An additional advantage of the proposed method is that we can precisely control the lter specications. Index Terms— cosine-modulated lter bank, semidenite pro-gramming, linear matrix inequality.
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ABSTRACT: It is well-known that the optimal beamforming problems for cognitive multicast transmission are indefinite quadratic (nonconvex) optimization programs. The conventional approach is to reformulate them as convex semi-definite programs (SDPs) with additional rank-one (nonconvex and discontinuous) constraints. The rank-one constraints are then dropped for relaxed solutions, and randomization techniques are employed for solution search. In many practical cases, this approach fails to deliver satisfactory solutions, i.e., its found solutions are very far from the optimal ones. In contrast, in this paper we cast the optimal beamforming problems as SDPs with the additional reverse convex (but continuous) constraints. An efficient algo-rithm of nonsmooth optimization is then proposed for seeking the optimal solution. Our simulation results show that the proposed approach yields almost global optimal solutions with much less computational load than the mentioned conventional one.
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ABSTRACT: This paper discusses dynamic state estimation for nonlinear measurement model through distributed multisensor network under power constraints. For this scenario, we propose an optimized power allocation strategy based on semidefinite pro-gramming, that achieves minimum mean-squared error for the estimate subject to constraints on total transmit power. System nonlinearity is handled effectively with the help of distributed unscented Kalman filtering and linear fractional transformation. Furthermore, advantage of using multiple sensors over a single independent sensor is established through simulation results for tracking a maneuvering target. Index terms. Nonlinear sensor network, unscented trans-formation, semi-definite programming, distributed linear frac-tional transformation filtering I. INTRODUCTION Wireless sensor network (WSN) is an ever emerging tech-nology which has the potential of being used in many appli-cations like environment sensing, traffic monitoring, military surveillance [1], [2], [6]. Nonlinear sensor networks (NSNs) practically arise when sensors' observations bear a nonlinear relationship with the parameter to be estimated. Typically such a network consists of multitude of tiny inexpensive sensor nodes deployed randomly or deterministically over an area of interest. Each node makes its own local observation indepen-dently, while a central unit, called fusion center (FC), monitors the entire network to ensure high-quality performance. Fusion of noisy observations from these spatially distributed nodes delivers a more reliable picture of the environment that is difficult to achieve with a single independent sensor. The sensor nodes are characterized by their low power profiles and limited computational capability. Their power requirements become pronounced when they have to transmit their observations towards FC through a noisy channel. There-fore, an algorithm must be found to achieve energy-efficient operation of sensor nodes. Based on the knowledge of unconditional mean of a random state variable, conditioning random observations, covariance of the conditioning random variable and cross-covariance of measurement and state variables, the FC can compute linear minimum mean square error (LMMSE) estimate using recursive Bayes' filtering. This works well for systems bearing linear relationships in state space as well as measurement model description. For nonlinear systems, we have to resort to some linearizing approximations to estimate the evolving state, since most of the physical systems involve nonlinear mappings.
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ABSTRACT: This paper deals with optimized training sequences to estimate multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) channel states in the presence of spatial fading correlations. The optimization criterion is the entropy minimization of the error between the high multi-dimensional and correlated channel state and its estimator. The globally optimized training sequences are exactly solved by a semi-definite programming (SDP) of tractable computational complexity O((Mt(Mt + 1)/2) 2.5), where Mt is the transmit antenna number. With new tight two-sided bounds for the objective function, the optimal value of the generic SDP can be approximately solved by the standard water-filling algorithm. Intensive simulation results are provided to illustrate the perfor-mance of our methods.
