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This paper tackles a power minimization problem of interleave-division multiple-access (IDMA) systems over a fading multiple-access channel. The problem is minimizing the total power received by the receiver while keeping the bit error rates (BERs) of all users below a predefined value. The original formulation of the problem has highly nonlinear and implicitly defined functions, which render most existing optimization methods incapable. A new formulation is proposed in this paper, whose solution can effectively be obtained by a branch-and-bound (B&B) technique. An algorithm is devised based on B&B, and its effectiveness is also demonstrated by numerical experiments of systems with a moderate numbers of users. [ABSTRACT FROM AUTHOR]

In this paper, we propose a fast and efficient adaptive transmission algorithm for the Mobile WiMAX system. In our algorithm, we select the position of the boundary between the downlink and uplink subframes, the positions of the service, flows within the frame, the coding and modulation schemes, and the transmission power values. Our algorithm gains high spectral efficiency, satisfies quality-of-service (QoS) requirements for the service flows, and has low computational complexity. The algorithm includes a frame boundary position optimization procedure, downlink and uplink optimization procedures, as well as a procedure that selects a transmission power value that enables satisfaction of the QoS requirements. All these procedures are novel, efficient, and fast. We evaluate the adaptive transmission algorithm by performing a system-level simulation of the Mobile WiMAX system with multisector topology. We show that our algorithm has high spectral efficiency and low computational complexity, enabling QoS provisioning. [ABSTRACT FROM AUTHOR]

This paper proposes a novel estimator named minimum-variance pseudo-unbiased reduced-rank estimator (MVPURE) for the linear regression model, designed specially for the case where the model matrix is ill-conditioned and the unknown deterministic parameter vector to be estimated is subjected to known linear constraints. As a natural generalization of the Gauss-Markov (BLUE) estimator, the MV-PURE estimator is a solution of the following hierarchical nonconvex constrained optimization problem directly related to the mean square error expression. In the first-stage optimization, under a rank constraint, we minimize simultaneously all unitarily invariant norms of an operator applied to the unknown parameter vector in view of suppressing bias of the proposed estimator. Then, in the second-stage optimization, among all pseudo-unbiased reduced-rank estimators defined as the solutions of the first-stage optimization, we find the one achieving minimum variance. We derive a closed algebraic form of the MV-PURE estimator and show that well-known estimators-the Gauss-Markov (BLUE) estimator, the generalized Marquardt's reduced-rank estimator, and the minimum-variance conditionally unbiased affine estimator subject to linear restrictions-are all special cases of the MV-PURE estimator. We demonstrate the effectiveness of the proposed estimator in a numerical example, where we employ the MV-PURE estimator to the ill-conditioned problem of reconstructing a 2-D image subjected to linear constraints from blurred, noisy observation. This example demonstrates that the MV-PURE estimator outperforms all aforementioned estimators, as it achieves smaller mean square error for all values of signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

The economic dispatch has the objective of generation allocation to the power generators in such a manner that the total fuel cost is minimized while all operating constraints are satisfied. Conventional optimization methods assume generator cost curves to be continuous and monotonically increasing, but modern generators have a variety of nonlinearities in their cost curves making this assumption inaccurate, and the resulting approximate dispatches cause a lot of revenue loss. Evolutionary methods like particle swarm optimization perform better for such problems as no convexity assumptions are imposed, but these methods converge to sub-optimum solutions prematurely, particularly for multimodal problems. To handle the problem of premature convergence, this paper proposes to apply a novel self-organizing hierarchical particle swarm optimization (SOH̱PSO) for the nonconvex economic dispatch (NCED). The performance further improves when time-varying acceleration coefficients are included. The results show that the proposed approach outperforms previous methods for NCED. [ABSTRACT FROM AUTHOR]

Critical dimension (CD) variation caused by defocus is largely systematic with dense lines ‘smiling’ through focus while isolated lines ‘frown.’ In this paper, we propose a new design methodology that allows explicit compensation of focus-dependent CD variation, in particular, either within a cell (self-compensated cells) or across cells in a critical path (self-compensated design). By creating iso and dense variants for each library cell, we can achieve designs that are more robust to focus variation. Optimization with a mixture of dense and iso cell variants is possible, both for area and leakage power in timing constraints (critical delay), with the latter an interesting complement to existing leakage-reduction techniques, such as dual-Vth. We implement both a heuristic and mixed-integer linear-programming (MILP) solution methods to address this optimization and experimentally compare their results. Results indicate that designing with a self-compensated cell library incurs 12% area penalty and 6% leakage increase over a baseline library while compensating for focus-dependent CD variation (i.e., the design meets timing constraints across a large range of focus variation). We observe 27% area penalty and 7% leakage increase at the worst case defocus condition using only single-pitch cells. The area penalty of circuits after using both the heuristic and MILP optimization approaches is reduced to 3% while maintaining timing. We also apply the optimization to leakage, which traditionally shows very large variability due to its exponential relationship with gate CD. We conclude that a mixed iso/dense library that is combined with a sensitivity-based optimization approach yields much better area/timing/leakage tradeoffs than using a self-compensated cell library alone. Self-compensated designs show 25% less leakage power on average at the worst defocus condition compared to a design employing a conventional library for the benchmarks studied. [ABSTRACT FROM AUTHOR]

This short correspondence is a reply to a recently published paper by Verlinde et al. in which the authors empirically compared fuzzy and nonfuzzy association analysis and, on the basis of their results, questioned the usefulness of a fuzzy approach. Although we highly welcome the critical examination of the topic and definitely agree that fuzzy extensions of existing methods call for a thorough justification, the empirical comparison presented in the aforementioned paper is in our opinion not objective and extensive enough to fully warrant the conclusions drawn from the results. Apart from some general comments on the claims raised in their paper, we present empirical results based on an alternative experimental setup that lead to different conclusions. [ABSTRACT FROM AUTHOR]

The generation expansion planning (GEP) problem is defined as the problem of determining WHAT, WHEN, and WHERE new generation units should be installed over a planning horizon to satisfy the expected energy demand. This paper presents a framework to determine the number of new generating units (e.g., conventional steam units, coal units, combined cycle modules, nuclear plants, gas turbines, wind farms, and geothermal and hydro units), power generation capacity for those units, number of new circuits on the network, the voltage phase angle at each node, and the amount of required imported fuel for a single-period generation expansion plan. The resulting mathematical program is a mixed-integer bilinear multiobjective GEP model. The proposed framework includes a multiobjective evolutionary programming algorithm to obtain an approximation of the Pareto front for the multiobjective optimization problem and analytical hierarchy process to select the best alternative. A Mexican power system case study is utilized to illustrate the proposed framework. Results show coherent decisions given the objectives and scenarios considered. Some sensitivity analysis is presented when considering different fuel price scenarios. [ABSTRACT FROM AUTHOR]

Liu and Tureli proposed a blind carrier frequency offset (CFO) estimation method for orthogonal frequency-division multiplexing (OFDM) systems, making use of null subcarriers. The optimal subcarrier placement that minimizes the Cramer-Rao bound (CRB) of the CFO estimation was reported by Ghogho et al. In this paper, we study the optimality of the null subcarrier placement from another perspective. We first show that the SNR of the CFO estimation using null subcarriers is a function of the null subcarrier placement. We then formulate the CFO-SNR optimization for the null subcarrier placement as a convex optimization problem for small CFO values and derive the optimal placement when the number of subcarriers is a multiple of the number of null subcarriers. In addition, we show that the SNR-optimal null subcarrier placement also minimizes the theoretical mean square error in the high SNR region. When the number of subcarriers is not a multiple of the number of null subcarriers, we propose a heuristic method for the null subcarrier placement that still achieves good performance in the CFO estimation. We also discuss the optimality of the null subcarrier placement in practical OFDM systems, where guard bands are required at both ends of the spectrum. [ABSTRACT FROM AUTHOR]

In this paper, the optimal carrier frequency offset (CFO) and channel estimator for an orthogonal frequency-division multiple-access (OFDMA) uplink based on a joint maximum- likelihood (ML) criterion is derived. Direct implementation of the resultant estimation scheme is challenging due to the need for a multidimensional exhaustive search in multi-CFO estimation. To solve this problem, an optimization theorem is exploited, and a computationally efficient method using an importance sampling technique is proposed. Without the need to provide an initial estimate, the proposed estimator guarantees the generation of the global optimal solution. Simulation results clearly verify the effectiveness of the proposed estimation scheme. [ABSTRACT FROM AUTHOR]

This paper describes an advanced coordination method for an optimized protection time grading based on a new nonstandard tripping characteristic for overcurrent protection relays. The intention is the highest possible reduction of tripping times for a selective fault clearing in distribution networks protected by overcurrent relays without communication links. The new nonstandard tripping characteristic will be described from its basic idea to its constraints of the optimization problem. The optimization is solved by the method of Lagrange generalized with the Karush-Kuhn-Tucker conditions and is aimed at selective fault tripping with shorter tripping times as standard characteristics and conventional coordination methods. Overcurrent relay coordination becomes a mathematical optimization task. A comparison with the standard characteristics and conventional coordination methods shows a notable advancement regarding their average and maximum tripping times which can be achieved using this new method. [ABSTRACT FROM AUTHOR]

With growing market pressures and rising system complexities, automated system-level communication design with efficient design space exploration capabilities is becoming increasingly important. At the same time, customized network-oriented communication architectures become necessary in enabling a high-performance communication among the system components. To this end, corresponding communication design flows that are supported by efficient design automation techniques need to be developed. In this paper, we present a system-level design environment for the generation of bus-based system-on-chip architectures. Our approach supports a two-stage design flow using automated model refinement toward custom heterogeneous communication networks. Starting from an abstract specification of the desired communication channels, our environment automatically generates tailored network models at various levels of abstraction. At its core, an automatic layer-based refinement approach is utilized. We have applied our approach to a set of industrial-strength examples with a wide range of target architectures. Our experimental results show significant productivity gains over a traditional communication design, allowing early and rapid design space exploration. [ABSTRACT FROM AUTHOR]

Typical evolutionary algorithms (EAs) exploit the different space-search properties of variation operators such as crossover, mutation, and local optimization. There are also various operators in each element. This paper provides an extensive empirical study on the synergy among multiple crossover operators. We choose a number of different crossover operators in an EA and investigate whether or not their combinations outperform the sole usage of the best crossover operator. The traveling salesman problem and the graph bisection problem were chosen for experimentation. Strong synergy effects were observed in both problems. [ABSTRACT FROM AUTHOR]

This paper presents novel results on perfect reconstruction feedback quantizers (PRFQs), i.e., noise-shaping, predictive and sigma-delta A/D converters whose signal transfer function is unity. Our analysis of this class of converters is based upon an additive white noise model of quantization errors. Our key result is a formula that relates the minimum achievable MSE of such converters to the signal-to-noise ratio (SNR) of the scalar quantizer embedded in the feedback loop. This result allows us to obtain ana- lytical expressions that characterize the corresponding optimal filters. We also show that, for a fixed SNR of the scalar quantizer, the end-to-end MSE of an optimal PRFQ which uses the optimal filters (which for this case turn out to be HR) decreases exponentially with increasing oversampling ratio. Key departures from earlier work include the fact that fed back quantization noise is explicitly taken into account and that the order of the converter filters is not a priori restricted. [ABSTRACT FROM AUTHOR]

This paper deals with the design of a multicarrier multiple-input multiple-output system which performs nonlinear Tomlinson-Harashima precoding at the transmitter and linear equalization at the receiver. The goal is the joint optimization of the processing matrices at both ends of the wireless link, assuming a constraint on the overall transmit power. We consider different design criteria which require the maximization/minimization of objective functions that depend on the mean square errors over the individual data streams. A unified framework for the solution of a great number of optimization problems is developed, based on the concept of multiplicative Schur-convexity. We show that many widely used design criteria can be easily accommodated in our framework. Our analysis is centered on two theorems which, in the case of cooperative processing among the different subcarriers, provide closed-form solutions to a number of optimization problems. On the other hand, when an independent processing is assumed at each subcarrier closed-form solutions may not exist. Nonetheless, exploiting the results of the two theorems, we show that for a variety of cost functions the optimization problem can be efficiently solved. Theoretical analysis and computer simulations are used to assess the performance of the proposed schemes and make comparisons with linear transceiver architectures. It turns out that nonlinear precoding provides better results than linear prefiltering when the cost function is multiplicatively Schur-convex. In contrast, when the cost function is multiplicatively Schur-concave the nonlinear scheme is equivalent to the linear one. [ABSTRACT FROM AUTHOR]

Application of the network equivalent concept for external system representation in electromagnetic transient studies is well known. However, the challenge in application of an equivalent model, approximated by rational functions, is to guarantee passivity of the corresponding model. Therefore, there is a need to enforce passivity of the equivalent model. In this paper, the passivity enforcement problem is first formulated as a quadratic programming (QP) problem, and then solved based on an efficient implementation, using an infeasible-interior-point primal-dual method. An application of this method for passivity enforcement of a six-port admittance model (with large passivity violations) demonstrates its effectiveness and computational efficiency. [ABSTRACT FROM AUTHOR]

Due to technology scaling trends, the accurate and efficient calculations of the temperature distribution corresponding to a specific circuit layout and power density distribution will become indispensable in the design of high-performance very large scale integrated circuits. In this paper, we present three highly efficient thermal simulation algorithms for calculating the on-chip temperature distribution in a multilayered substrate structure. All three algorithms are based on the concept of the Green function and utilize the technique of discrete cosine transform. However, the application areas of the algorithms are different. The first algorithm is suitable for localized analysis in thermal problems, whereas the second algorithm targets full-chip temperature profiling. The third algorithm, which combines the advantages of the first two algorithms, can be used to perform thermal simulations where the accuracy requirement differs from place to place over the same chip. Experimental results show that all three algorithms can achieve relative errors of around 1% compared with that of a commercial computational fluid dynamic software package for thermal analysis, whereas their efficiencies are orders of magnitude higher than that of the direct application of the Green function method. [ABSTRACT FROM AUTHOR]

In this paper, we study the retiming problem of sequential circuits with net topology optimization. Both interconnect and gate delay are considered in retiming. Most previous retiming algorithms have assumed ideal conditions for the nonlogical portions of data paths, which are not sufficiently accurate to be used in high-performance circuits today. In our modeling, we assume that the delay of a wire is directly proportional to its length. This assumption is reasonable since the quadratic component of a wire delay is significantly smaller than its linear component when the more accurate Elmore delay model is used. A simple experiment was conducted to illustrate the validity of this assumption. We present two approaches to solve the retiming problem, both of which have polynomial time complexity. The first one can compute the optimal clock period, while the second one is an improvement over the first one in terms of practical applicability. The second approach gives solutions that are very close to the optimal (0.06% more than the optimal on average) but in a much shorter runtime. The optimally retimed circuit will then be realized physically by placing the registers and finding the net topologies. In contrast to many previous works [Proc. IEEE mt. Conf. Comput.-Aided Des., p. 136, 1998], [IEEE Trans. Comput.-Aided Design Integr. Circuits Syst., 22(7) Jul. 2003] that performed simple calculations to determine the register positions, our approach can preserve the optimal clock period that is obtained by the retiming step and utilize as few registers as possible. Minimization of register number saves both area and power in register and clock loading. Our topology optimization step is shown to be optimal for nets with four or fewer pins, and this type of nets constitutes over 90% of the nets in a sequential circuit on average. Using the ISCAS89 benchmark, we tested our algorithm with a 0.35-μsm complementary metal-oxide-semiconductor standard cell library. Silicon Ensemble was used to layout the design with a row utilization of 50%. Experimental results showed that our algorithm could find the best sharing of registers for a net in most of the cases, i.e., using the minimum number of registers while preserving the target clock period that is obtained by the retiming step, within a minute run on an Intel Pentium IV 1.5 GHz PC with 512 MB RAM. [ABSTRACT FROM AUTHOR]

Convex-optimization techniques are very popular in the very large-scale-integration design society due to guaranteed convergence to a global optimal point. The table data need to be fitted into convex forms to be used in the convex optimization problems. Fitting the tables into posynomials, which are analytically convex under logarithmic transformation, may suffer from the excessive fitting errors as the fitting problem is nonconvex. In this paper, we propose to directly adjust the lookup-table values into a numerically convex lookup table without any explicit analytical form. We show that numerically ‘convexifying’ the lookup-table data with minimum perturbation can be formulated as a convex semidefinite optimization problem, and hence, optimality can be reached in polynomial time. We also propose three algorithms to make the table data smooth to enable faster convergence of the convex optimizer. Results from extensive experiments on industrial cell libraries demonstrate 9.6x improvement in fitting error over a well- developed posynomial-fitting procedure. We illustrate the effectiveness of this model in a convex optimization problem by providing results for using our model in the optimal gate sizing of standard cells. We observe a 5.07% improvement in the delay of International Symposium on Circuits and Systems (ISCAS) benchmark circuits over the posynomial-fitting procedure. [ABSTRACT FROM AUTHOR]

Scheduling and binding are two tasks found in high-level synthesis of hardware as well as in compiling software. These tasks are realized on graphs that are models of the hardware or of the software to be compiled to run on a specific processor. Scheduling focuses on determining the start execution time of each node in the graph. Binding is the task of assigning each node in the graph to a specific computational element. Realize binding before or after scheduling can exclude generating high-quality designs (hardware or binary code). The latter statement is true in particular in the era of design for low power. Do not combine scheduling and binding can lead to designs with high switching activities and hence to high power consumption. To the best of our knowledge, there is no approach at this moment that addresses the problem of unifying scheduling and binding with an exact algorithm to produce designs with reduced power consumption. Known approaches to that problem are heuristics. That problem is NP-hard in general, since it is the composition of two NP-hard problems. Also, it has not yet been formulated in the literature. The problem becomes more complex when one has to deal with cyclic graphs and/or there are constraints to be met such as timings. For cyclic graphs, one has to integrate retiming in the unification of scheduling and binding. We propose a mathematical formulation to that problem. We extend this formulation to solve the problem of combining modulo scheduling, binding, and retiming under timings and resources constraints while reducing power consumption due to switching activities. The proposed approach is tested using known benchmarks. Based on obtained numerical results, this approach is able to reduce power consumption by 33.24% on average, with an average of 33.83 s as a run time. [ABSTRACT FROM AUTHOR]

This paper presents methods of calculation of parameters of single-phase permanent-magnet (SPPM) motor, in terms of motor dimensions and material properties, which are utilized in the dynamic model of the motor. The intention of the study is to develop means of SPPM performance calculations, which lend themselves to be employed within a mathematical design optimization approach. The calculated parameters are compared with measured values and are shown to be accurate for the purpose of the study. [ABSTRACT FROM AUTHOR]

We minimize average transmit power with finite-rate feedback for coherent communications in a wireless sensor network (WSN), where sensors communicate with a fusion center using adaptive modulation and coding over a wireless fading channel. By viewing the coherent WSN setup as a distributed space-time multiple-input single-output (MISO) system, we present optimal distributed beamforming and resource allocation strategies when the full (F-) channel state information at the transmitters (CSIT) is available through a feedback channel. We also develop optimal adaptive transmission policies and design optimal quantizers for the finite-rate feedback case where the sensors only have quantized (Q-) CSIT, or, each sensor has F-CSIT of its own link with the FC but only Q-CSIT of other sensors. Numerical results confirm that our novel finite-rate feedback-based strategies achieve near-optimal power savings based on even a small number of feedback bits. [ABSTRACT FROM AUTHOR]

We present a new method for recovering the 3D shape of a featureless smooth surface from three or more calibrated images illuminated by different light sources (three of them are independent). This method is unique in its ability to handle images taken from unconstrained perspective viewpoints and unconstrained illumination directions. The correspondence between such images is hard to compute and no other known method can handle this problem locally from a small number of images. Our method combines geometric and photometric information in order to recover dense correspondence between the images and accurately computes the 3D shape. Only a single pass starting at one point and local computation are used. This is in contrast to methods that use the occluding contours recovered from many images to initialize and constrain an optimization process. The output of our method can be used to initialize such processes. In the special case of fixed viewpoint, the proposed method becomes a new perspective photometric stereo algorithm. Nevertheless, the introduction of the multiview setup, self-occlusions, and regions close to the occluding boundaries are better handled, and the method is more robust to noise than photometric stereo. Experimental results are presented for simulated and real images. [ABSTRACT FROM AUTHOR]

In continuation to an earlier work, we further develop bounds on the mean-squared error (MSE) when estimating a deterministic parameter vector ϴ0 in a given estimation problem, as well as estimators that achieve the optimal performance. The traditional Cramér-Rao (CR) type bounds provide benchmarks on the variance of any estimator of ϴ0 under suitable regularity conditions, while requiring a priori specification of a desired bias gradient. To circumvent the need to choose the bias, which is imprac- tical in many applications, it was suggested in our earlier work to directly treat the MSE, which is the sum of the variance and the squared-norm of the bias. While previously we developed MSE bounds assuming a linear bias vector, here we study, in the same spirit, affine bias vectors. We demonstrate through several examples that allowing for an affine transformation can often improve the performance significantly over a linear approach. Using convex optimization tools we show that in many cases we can choose an affine bias that results in an MSE bound that is smaller than the unbiased CR bound for all values of ϴ0. Furthermore, we explicitly construct estimators that achieve these bounds in cases where an efficient estimator exists, by performing an affine transformation of the standard maximum likelihood (ML) estimator. This leads to estimators that result in a smaller MSE than ML for all possible values of ϴ0. [ABSTRACT FROM AUTHOR]

We consider single-carrier and single-user transmissions over a frequency-selective channel. We address the problem of joint estimation of the channel and the carrier frequency offset using a known training sequence (TS). Since in general the TS that is optimum for CFO estimation is not optimum for channel estimation, here we design the TS to minimize the mean-square error on the soft estimates of the data symbols obtained using a Wiener equalizer after CFO compensation. Our design results apply to the general scenarios of correlated and/or Ricean channel taps. Simulations measuring the bit error rate show an interesting gain when using the proposed training schemes in lieu of standard schemes. [ABSTRACT FROM AUTHOR]

We propose a multilevel full-chip routing algorithm that improves testability and diagnosability, manufacturability, and signal integrity for yield enhancement. Two major issues are addressed. 1) The oscillation ring test (ORT) and its diagnosis scheme for interconnects based on the popular IEEE Standard 1500 are integrated into the multilevel routing framework to achieve testability enhancement. We augment the traditional multilevel framework of coarsening and uncoarsening by introducing a preprocessing stage that analyzes the interconnect structure for better resource estimation before the coarsening stage, and a final stage after uncoarsening that improves testability to achieve 100% interconnect fault coverage and maximal diagnosability. 2) We present a heuristic to reduce routing congestion to optimize the multiple-fault probability, chemical-mechanical polishing- and optical proximity correction-induced manufacturability, and crosstalk effects, for yield improvement. Experimental results on the Microelectronics Center for North Carolina benchmark circuits show that the proposed ORT method achieves 100% fault coverage and the optimal diagnosis resolution for interconnects. Further, the multilevel routing algorithm effectively balances the routing density to achieve 100% routing completion. [ABSTRACT FROM AUTHOR]

Taking inspiration from the interacting process among organizations in human societies, this correspondence designs a kind of structured population and corresponding evolutionary operators to form a novel algorithm, Organizational Evolutionary Algorithm (OEA), for solving both unconstrained and constrained optimization problems. In OEA, a population consists of organizations, and an organization consists of individuals. All evolutionary operators are designed to simulate the interaction among organizations. In experiments, 15 unconstrained functions, 13 constrained functions, and 4 engineering design problems are used to validate the performance of OEA, and thorough comparisons are made between the OEA and the existing approaches. The results show that the OEA obtains good performances in both the solution quality and the computational cost. Moreover, for the constrained problems, the good performances are obtained by only incorporating two simple constraints handling techniques into the OEA. Furthermore, systematic analyses have been made on all parameters of the OEA. The results show that the OEA is quite robust and easy to use. [ABSTRACT FROM AUTHOR]

The computational overhead of genetic programming (GP) may be directly addressed without recourse to hardware solutions using active learning algorithms based on the random or dynamic subset selection heuristics (RSS or DSS). This correspondence begins by presenting a family of hierarchical DSS algorithms: RSS-DSS, cascaded RSS-DSS, and the balanced block DSS algorithm, where the latter has not been previously introduced. Extensive benchmarking over four unbalanced real-world binary classification problems with 30 000-500 000 training exemplars demonstrates that both the cascade and balanced block algorithms are able to reduce the likelihood of degenerates while providing a significant improvement in classification accuracy relative to the original RSS-DSS algorithm. Moreover, comparison with GP trained without an active learning algorithm indicates that classification performance is not compromised, while training is completed in minutes as opposed to half a day. [ABSTRACT FROM AUTHOR]

This correspondence studies the problem of observer-based H∞ control for time-delay Takagi-Sugeno (T-S) fuzzy systems. It provides a delay-dependent linear matrix inequality (LMI)-based method for the control design. It is known that the key important problem in the literature, even for delay-independent case, lies in the difficulty of decoupling matrix variables in corresponding matrix inequalities. This correspondence suggests a decoupling technique for solving matrix in- equalities with coupled variables, and provides an LMI-based algorithm by adopting the idea of the cone complementarity problem. The derivation relies on the appropriate choice of Lyaponuv-Krasovskii functionals which incorporate the intersections among local systems. Illustrative examples are given to show the effectiveness of the present delay-dependent result. [ABSTRACT FROM AUTHOR]