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Abstract: In this paper we present a methodical evaluation of the performance of a new and two traditional approaches to automatic target recognition (ATR) based on silhouette representation of objects. Performance is evaluated under the simulated conditions of imperfect localization by a region of interest (ROI) algorithm (resulting in clipping and scale changes) as well as occlusions by other silhouettes, noise and out-of-plane rotations. The two traditional approaches are holistic in nature and are based on moment invariants and principal component analysis (PCA), while the proposed approach is based on local features (object parts) and is comprised of a block-by-block 2D Hadamard transform (HT) coupled with a Gaussian mixture model (GMM) classifier. Experiments show that the proposed approach has good robustness to clipping and, to a lesser extent, robustness to scale changes. The moment invariants based approach achieves poor performance in advantageous conditions and is easily affected by clipping and occlusions. The PCA based approach is highly affected by scale changes and clipping, while being relatively robust to occlusions and noise. Furthermore, we show that the performance of a silhouette recognition system subject to mismatches between training and test angles of silhouettes (caused by an out-of-plane rotation) can be considerably improved by extending the training set using only a few angles which are widely spaced apart. The improvement comes without affecting the performance at “side-on” views. [Copyright &y& Elsevier]

In this paper, we study a matricial version of a generalized moment problem with degree constraint. We introduce a new metric on multivariable spectral densities induced by the family of their spectral factors, which, in the scalar case, reduces to the Hellinger distance. We solve the corresponding constrained optimization problem via duality theory. A highly nontrivial existence theorem for the dual problem is established in the Byrnes-Lindquist spirit. A matricial Newton-type algorithm is finally provided for the numerical solution of the dual problem. Simulation indicates that the algorithm performs effectively and reliably. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a finite element (FE) computation model for estimating the equivalent mechanics parameters of a composite material with randomly distributed multi-scale grains. The model is based on statistical multi-scale analysis (SMSA). First, some definitions of the probability space and statistical two-scale analysis (STSA) of the mechanics parameters for a composite material with randomly distributed grains are briefly reviewed. Next, a computer simulation algorithm for composite materials with random grains is presented. Then an FE procedure based on STSA is proposed and some properties of the equivalent mechanics parameters are outlined. Finally, the FE procedure for a composite material with randomly distributed grains is described and two numerical examples are demonstrated. The numerical results show that the FE procedure based on SMSA is a valid and feasible method for predicting the mechanics parameters of a composite material with randomly distributed grains. [Copyright &y& Elsevier]

In this paper, a continuous feedback tracking controller is developed for a class of high-order multi-input multi-output (MIMO) nonlinear systems with an input gain matrix that has nonzero leading principal minors but can be nonsymmetric. Under the mild assumption that the signs of the leading minors of the control input gain matrix are known, the controller yields locally uniformly ultimately bounded (UUB) tracking while compensating for unstructured uncertainty in both the drift vector and the input matrix. First, a full-state feedback controller is designed based on limited assumptions on the structure of the system nonlinearities, and the singularity-free controller is proven to yield locally UUB tracking through a Lyapunov-based analysis. Then, it is shown that an output feedback control can be designed based on a high-gain observer. Simulation results are provided to illustrate the performance of the proposed control algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Consider the interval mapping method for quantitative trait loci (QTL) detection of an experimental organism in a backcross population. In a recent paper, Chen and Chen [2005. On some statistical aspects of the interval mapping for QTL detection. Statist. Sinica 15, 909–925] show that the limiting null distribution of the likelihood ratio test (LRT) statistic for testing existence of a QTL flanked by two markers is that of the supremum of a -process. In this note, an explicit representation of the supremum in terms of the recombination fraction between two markers of the -process is presented. Based on the explicit representation, a simulation algorithm for determining a limiting threshold of the LRT is given. [Copyright &y& Elsevier]

Abstract: In this paper, we propose two measurements-to-tracks data assignment algorithms for multi-target tracking. The first one is operated in the form of a matrix and is actually a modified version of the Simplex method. The second one is based upon the idea of a basic cell set and a rotational sort method, which not only inherits the advantages of the first one, but also reduces the computational burden. Numerical simulations show that the tracking performance of the two proposed data assignment algorithms is almost identical, and the second one is preferred when a large number of targets are tracked. [Copyright &y& Elsevier]

This paper investigates the applicability of the stencil-adaptive finite difference method for the simulation of two-dimensional unsteady incompressible viscous flows with curved boundary. The adaptive stencil refinement algorithm has been proven to be able to continuously adapt the stencil resolution according to the gradient of flow parameter of interest [Ding H, Shu C. A stencil adaptive algorithm for finite difference solution of incompressible viscous flows. J Comput Phys 2006;214:397–420], which facilitates the saving of the computational efforts. On the other hand, the capability of the domain-free discretization technique in dealing with the curved boundary provides a great flexibility for the finite difference scheme on the Cartesian grid. Here, we show that their combination makes it possible to simulate the unsteady incompressible flow with curved boundary on a dynamically changed grid. The methods are validated by simulating steady and unsteady incompressible viscous flows over a stationary circular cylinder. [Copyright &y& Elsevier]

Established system relationships for discrete systems, such as language inclusion, simulation, and bisimulation, require system observations to be identical. When interacting with the physical world, modeled by continuous or hybrid systems, exact relationships are restrictive and not robust. In this paper, we develop the first framework of system approximation that applies to both discrete and continuous systems by developing notions of approximate language inclusion, approximate simulation, and approximate bisimulation relations. We define a hierarchy of approximation pseudo-metrics between two systems that quantify the quality of the approximation, and capture the established exact relationships as zero sections. Our approximation framework is compositional for a synchronous composition operator. Algorithms are developed for computing the proposed pseudo-metrics, both exactly and approximately. The exact algorithms require the generalization of the fixed point algorithms for computing simulation and bisimulation relations, or dually, the solution of a static game whose cost is the so-called branching distance between the systems. Approximations for the pseudo-metrics can be obtained by considering Lyapunov-like functions called simulation and bisimulation functions. We illustrate our approximation framework in reducing the complexity of safety verification problems for both deterministic and nondeterministic continuous systems. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we first show that the BCJR algorithm (or Bahl algorithm) can be implemented via some matrix manipulations. As a direct result of this, we also show that this algorithm is equivalent to a feedforward neural network structure. We verified through computer simulations that this novel neural network implementation yields identical results with the BCJR algorithm. [Copyright &y& Elsevier]

Abstract: This paper describes a robust centroid method, which is a variant of principal component analysis. A genetic local search algorithm is presented to perform the calculations. Simulations are carried out to appraise the performance of the genetic local search algorithm. A real data set with missing data and multiple outliers is analyzed. [Copyright &y& Elsevier]

Abstract: This paper proposes an efficient method for the design of two-channel, quadrature mirror filter (QMF) bank. The design problem is formulated to minimize an objective function, which is a linear combination of pass-band error and stop-band residual energy of the low-pass analysis filter of the filter bank, and the square error of the overall transfer function of the QMF bank at the quadrature frequency. A new algorithm has been developed to minimize the objective function by optimizing the filter tap weights by using Marquardt optimization method. Simulation results of the algorithm show that the performance of the proposed method is better than that of the already existing methods. [Copyright &y& Elsevier]

This paper describes an approach to the control of continuous systems through the use of symbolic models describing the system behavior only at a finite number of points in the state space. These symbolic models can be seen as abstract representations of the continuous dynamics enabling the use of algorithmic controller design methods. We identify a class of linear control systems for which the loss of information incurred by working with symbolic subsystems can be compensated by feedback. We also show how to transform symbolic controllers designed for a symbolic subsystem mb controllers for the original system. The resulting controllers combine symbolic controller dynamics with continuous feedback control laws and can thus be seen as hybrid systems. Furthermore, if the symbolic controller already accounts for software/hardware requirements, the hybrid controller is guaranteed to enforce the desired specifications by construction thereby reducing the need for formal verification. [ABSTRACT FROM AUTHOR]

Abstract: A method is proposed in this paper to assess the local influence of minor perturbations for a nonlinear structural equation model with missing data that are missing at random. The main idea is to apply Zhu and Lee''s (J. Roy. Statist. Soc. Ser. B 63 (2001) 111) approach to the conditional expectation of the complete-data log-likelihood function in the corresponding EM algorithm for deriving the conformal normal curvature. Building blocks for achieving the diagnostic measures are computed via latent variables that are generated by the Gibbs sampler and Metropolis–Hastings algorithm. It is shown that the proposed methodology is feasible for a wide variety of perturbation schemes. To illustrate the methodology, results that are obtained from analyses of some artificial examples, a simulation study, and a real example are presented. [Copyright &y& Elsevier]

We consider a setting where multiple vehicles form a team cooperating to visit multiple target points and collect rewards associated with them. The team objective is to maximize the total reward accumulated over a given time interval. Complicating factors include uncertainties regarding the locations of target points and the effectiveness of collecting rewards, differences among vehicle capabilities, and the fact that rewards are time-varying. We propose a receding horizon (RH) controller suitable for dynamic and uncertain environments, where combinatorially complex assignment algorithms are infeasible. The control scheme dynamically determines vehicle trajectories by solving a sequence of optimization problems over a planning horizon and executing them over a shorter action horizon. This centralized scheme can generate stationary trajectories in the sense that they guide vehicles to target points, even though the controller is not explicitly designed to perform any discrete point assignments. This paper establishes conditions under which this stationarity property holds in settings that are analytically tractable, quantifies the cooperative properties of the controller, and includes a number of illustrative simulation examples. [ABSTRACT FROM AUTHOR]

Abstract: In this paper it is shown that Winograd’s algorithm for computing convolutions and a fast, prime factor, discrete Fourier transform (DFT) algorithm can be modified to compute Fourier-like transforms of long sequences of 2 m −1 points over GF(2 m ), for 8⩽ m ⩽10. These new transform techniques can be used to decode Reed–Solomon (RS) codes of block length 2 m −1. The complexity of this new transform algorithm is reduced substantially from more conventional methods. A computer simulation verifies these new results. [Copyright &y& Elsevier]

Abstract-This paper studies state quantization schemes for feedback stabilization of control systems with limited information. The focus is on designing the least destabilizing quantizer subject to a given information constraint. We explore several ways of mea- suring the destabilizing effect of a quantizer on the closed-loop system, including (but not limited to) the worst-case quantization error. In each case, we show how quantizer design can be naturally reduced to a version of the so-called multicenter problem from locational optimization. Algorithms for obtaining solutions to such problems, all in terms of suitable Voronoi quantizers, are discussed. In particular, an iterative solver is developed for a novel weighted multicenter problem which most accurately represents the least destabilizing quantizer design. A simulation study is also presented. [ABSTRACT FROM AUTHOR]

Recently, Pareek et al. [Phys. Lett. A 309 (2003) 75] have developed a symmetric key block cipher algorithm using a one-dimensional chaotic map. In this paper, we propose a symmetric key block cipher algorithm in which multiple one-dimensional chaotic maps are used instead of a one-dimensional chaotic map. However, we also use an external secret key of variable length (maximum 128-bits) as used by Pareek et al. In the present cryptosystem, plaintext is divided into groups of variable length (i.e. number of blocks in each group is different) and these are encrypted sequentially by using randomly chosen chaotic map from a set of chaotic maps. For block-by-block encryption of variable length group, number of iterations and initial condition for the chaotic maps depend on the randomly chosen session key and encryption of previous block of plaintext, respectively. The whole process of encryption/decryption is governed by two dynamic tables, which are updated time to time during the encryption/decryption process. Simulation results show that the proposed cryptosystem requires less time to encrypt the plaintext as compared to the existing chaotic cryptosystems and further produces the ciphertext having flat distribution of same size as the plaintext. [Copyright &y& Elsevier]

Abstract: We describe an algorithm and a C++ implementation that we have written and made available for calculating the fully nonlinear evolution of 5D braneworld models with scalar fields. Bulk fields allow for the stabilization of the extra dimension. However, they complicate the dynamics of the system, so that analytic calculations (performed within an effective 4D theory) are usually only reliable for static bulk configurations or when the evolution of the extra dimension is negligible. In the general case, the nonlinear 5D dynamics can be studied numerically, and the algorithm and code we describe are the first ones of that type designed for this task. The program and its full documentation are available on the Web at http://www.cita.utoronto.ca/~jmartin/BRANECODE/. 1 [1] We also maintain a mirror of the BRANECODE website at http://www.cita.utoronto.ca/~kofman/BRANECODE/. In this paper we provide a brief overview of what the program does and how to use it. Program summary: Title of program: BRANECODE Catalogue identifier: ADVX Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADVX Program obtainable from: CPC Program Library, Queen''s University of Belfast, N. Ireland Licensing provisions: none Operating systems under which the program has been tested: Linux Programming language used: C++ Memory required to execute with typical data: less than 1 MB Has the code been vectorized?: no Peripherals used: none No. of lines in distributed program, including test data, etc.: 8277 No. of bytes in distributed program, including test data, etc.: 74 939 CPC Program Library subprograms used: none Nature of physical problem: Dynamics of two co-dimension one branes in a five-dimensional spacetime with a bulk scalar field and arbitrary potentials. The dynamics is governed by the five dimensional Einstein equations of gravity and the junction conditions at the position of the branes. Method of solution: Leapfrog algorithm to solve system of -dimensional partial differential equations; Initial and boundary value problem. Restrictions on the complexity of the problem: Assumption of homogeneity along three spatial dimensions parallel to the branes. Typical running time: Depending on the grid size and length of the time evolution: from ∼1 s to ∼1 h or longer. Unusual features of the program:none [Copyright &y& Elsevier]

Abstract: Simple linear independent component analysis (ICA) algorithms work efficiently only in linear mixing environments. Whereas, a nonlinear ICA model, which is more complicated, would be more practical for general applications as it can work with both linear and nonlinear mixtures. In this paper, we introduce a novel method for nonlinear ICA problem. The proposed method follows the post nonlinear approach to model the mixtures, and exploits the difference between a linear mixture and a nonlinear one from their nature of distributions in a multidimensional space to develop a separation scheme. The nonlinear mixture is represented by a nonlinear surface while the linear mixture is represented by a plane. A geometric learning algorithm named as post nonlinear geometric ICA (pnGICA) is developed by geometrically transforming the nonlinear surface to a plane, i.e., to a linear mixture. Computer simulations of the algorithm provide promising performance on different data sets. [Copyright &y& Elsevier]

Three-dimensional (3D) bulk forming simulation by reproducing kernel particle method is presented in this paper. For increasing computational efficiency a lower order integration scheme is proposed and a general benchmark test for explicit non-linear program is developed, to verify the correctness and the accuracy of the present scheme. A particle to segment contact algorithm for treatment of the contact between deformable and rigid bodies is also presented. The algorithm is found to be very effective and suitable for 3D bulk forming analysis. [Copyright &y& Elsevier]

For fast 3D electric simulation, the multi-zone collocation boundary element analysis (BEA) with iterative solver like GMRES algorithm is required. In this paper, we present a scheme of equation assembly with ordering unknowns and collocation points, and a matrix storing structure. A group of easily computed preconditioners based on the mesh neighbor method are then proposed to remarkably quicken the convergence of GMRES iteration, and demonstrate at least 30% time reduction than using the diagonal preconditioner. Compared with the equation assembly in Merkel et al. [Engng Anal Bound Elem 22 (1998) 183], where two unknowns on same interfacial node are arranged subsequently, and two storing schemes in Li et al. and Araujo et al. [Sys Engng Electron 21 (1999) 10; J Chin Inst Engrs 23 (2000) 269], the proposed method generates fewest non-zero blocks and facilitates the matrix–vector multiplication remarkably. Numerical experiments verify the analysis and show a fast iterative multi-zone BEA simulator for actual very large-scale integration interconnects with a large amount of zones. [Copyright &y& Elsevier]

This paper introduces blind separation methods that are based on minimization of mutual information. Direct minimization of mutual information leads to estimating functions that change on every iteration of separating algorithm unlike in the maximum likelihood approach employing fixed non-linearity. We propose objective functions for source separation that are comprised of a symmetric and an asymmetric part. This allows for separating signals that may have skewed distributions. The optimal weighting between the symmetric and the asymmetric part is determined from the data based on an efficacy measure. The performance of the proposed objective functions is studied in cases where some source signals may be asymmetrically distributed. The capability of adapting to different type of source distributions is demonstrated in simulations. [Copyright &y& Elsevier]

Multivariate survival data arises when subjects in the same group are related to each other or when there are multiple recurrences of the disease in the same subject. A common goal of survival analysis is to relate the outcome (time to event) to a set of covariates. In this paper, we focus on prognostic classification for multivariate survival data where identifying subgroups of patients with similar prognosis is of interest. We propose a computationally feasible method to identify prognostic groups with the widely used Classification and Regression Trees (CART) algorithm. The proposed method extends CART algorithm to multivariate survival data by introducing a gamma frailty to account for dependence among correlated events. The method is applied to a catheter infection data, and the performance of the method is also investigated by several simulation studies. [Copyright &y& Elsevier]

Due to model complexity, spatial statistics often relies on simulation methods. Probably the most common such method is Markov chain Monte Carlo (MCMC) which draws approximate samples of the target distribution as the equilibrium distribution of a Markov chain. Perfect simulation methods are MCMC algorithms which ensure that the exact target distribution is sampled. This paper describes perfect simulation methods of locally stable point processes based on coupling from the past algorithms and provides an intensive simulation study analyzing the behaviour of these techniques under a large variety of practical situations. [Copyright &y& Elsevier]

After a brief review of recent developments in the pricing and hedging of American options, this paper modifies the basis function approach to adaptive control and neuro-dynamic programming, and applies it to develop: 1) nonparametric pricing formulas for actively traded American options and 2) simulation-based optimization strategies for complex over-the-counter options, whose optimal stopping problems are prohibitively difficult to solve numerically by standard backward induction algorithms because of the curse of dimensionality. An important issue in this approach is the choice of basis functions, for which some guidelines and their underlying theory are provided. [ABSTRACT FROM AUTHOR]

Under the general framework of a previous paper, a unified approach via filtering is developed to estimate stochastic volatility for micromovement models. The key feature of the models is that they can be transformed as filtering problems with counting process. observations. In order to obtain trade-by-trade, real-time Bayes estimates of stochastic volatility, the Markov chain approximation method is applied to the filtering equation to construct a consistent recursive algorithm, which computes the joint posterior. To illustrate the approach, a recursive algorithm is constructed in detail for a jumping stochastic volatility micromovement model. Simulation results show that the Bayes estimates for stochastic volatilities capture the movement of volatility. Trade-by-trade stochastic volatility estimates for a Microsoft transaction data set are obtained and they provide strong affirmative evidence that volatility changes even more dramatically at trade-by-trade level. [ABSTRACT FROM AUTHOR]

The paper investigates blind finite-impulse-response (FIR) equalization schemes for quadrature amplitude modulation signalling. We compare a bootstrap maximum a posteriori probability (MAP) equalizer with a recently introduced concurrent constant modulus algorithm (CMA) and decision directed (DD) equalizer (CMA+DD). Both equalizers are known to outperform the CMA considerably in the situations where carrier recovery is performed and the signal constellation is known. The concurrent CMA+DD equalizer has a complexity that is slightly more than twice of the CMA, and the bootstrap MAP equalizer has computational requirements that are only slightly more complex than the CMA. Simulation results indicate that the bootstrap MAP blind FIR equalizer has a faster convergence rate and better steady-state performance than the concurrent CMA+DD blind FIR equalizer, but tuning of the former is more complicated than the latter. [Copyright &y& Elsevier]

In this paper a reconfigurable adaptive control (RAC) algorithm is presented. The algorithm relies on lattice filtering and is suitable for linear discrete jump systems. The utilized lattice filter identifies recursively the system’s dynamics as an autoregressive model through the transfer function’s reflection coefficients. In addition, the system’s order is monitored through the magnitude of the estimated reflection coefficients. An internal model control (IMC) structure is employed, where the stable-inverse of the filter is utilized in the forward path in cascade with a gain compensator and a lowpass detuning filter. Upon sudden variations of the estimated system’s order the RA controller reconfigures its structure and calculates the command signal based on the IMC principle. Simulation studies are used to investigate the efficacy of the suggested scheme. [Copyright &y& Elsevier]

The matrix inversion lemma gives an explicit formula of the inverse of a positive definite matrix added to a block of dyads (represented as BBH) as follows: (A + BBH)-1=A-1 - A-1 B(I+BH A-1B)-1 BH A-1. It is well known in the literature that this formula is very useful to develop a block-based recursive least squares algorithm for the block-based recursive identifi- cation of linear systems or the design of adaptive filters.We extend this result to the case when the matrix A is singular and present a matrix pseudoinversion lemma along with some illustrative examples. Based on this result, we propose a block-based adaptive multichannel superexponential algorithm. We present simulation results for the performance of the block-based algorithm in order to show the usefulness of the matrix pseudoinversion lemma. [ABSTRACT FROM AUTHOR]

An analysis of a near-end crosstalk (NEXT) cancelation system that uses adaptive digital filters is described. The analysis is based on two well-known models for the NEXT coupling factor, the Bradley and Lin models, and yields the minimum number of adaptive filters required to reduce the NEXT below a prescribed level to within a defined confidence factor. With the minimum number of adaptive filters known, the required computational resources for the application at hand can be estimated. The analysis is further extended to practical situations where the largest NEXT signals chosen for elimination are incorrectly detected, and estimates of the minimum and maximum increase in the uncanceled NEXT due to incorrect detection are then deduced. Simulations show that the estimated minimum number of adaptive filters required and the maximum and minimum increase in uncanceled NEXT due to incorrect detection are fairly close to corresponding estimates obtained on the basis of measurements for both the Bradley and the Lin models. Therefore, by using the proposed analysis the minimum number of adaptive filters can be deduced without the need for time-consuming and expensive experiments. [ABSTRACT FROM AUTHOR]

An extension of blind source extraction (BSE) of one or a group of sources to the case of ill-conditioned and post-non-linear (PNL) mixing is introduced. This is achieved by a "mixed objective" type of cost function which jointly maximizes the kurtosis of a recovered source and estimates a measure of nonlinearity within the mixing system. This helps to circumvent problems with existing BSE methods, which are limited to noiseless and linear mixing models. Simulations illustrate the performance of the proposed algorithm and its usefulness, especially in the presence of very ill-conditioned mixing systems. [ABSTRACT FROM AUTHOR]

This work presents a novel carrier frequency offset (CFO) estimation algorithm, based on pseudo-CFO (P-CFO), to estimate the CFO value under the conditions of I/Q mismatch for direct conversion structures with 2-dB gain error and 20-deg. phase error in frequency selective fading channels. To circumvent CFO with I/Q mismatch, the proposed P-CFO algorithm rotates three training symbols by adding extra frequency offset into the received sequence to improve CFO estimation. Simulation results indicate that the estimation error of the proposed method is about 0.3 ppm, which is lower than those of two-repeat preamble-based methods. Additionally, the proposed P-CFO algorithm is compatible with the conventional method, and is appropriate for SoC implementation. The proposed scheme is implemented as part of an orthogonal frequency-division multiplexing wireless receiver fabricated in a 0.13-μm CMOS process with 3.3 × 0.4 mm² core area and 10-mW power consumption at 54-Mbits/s data rate. [ABSTRACT FROM AUTHOR]

Differential flatness of buck, buck-boost, and boost converter models is shown. Its benefits if used for controlling the output voltage of these converters are revealed by comparing the flatness-based control with passivity-based and linear control. Two observers for the boost converter are suggested one of which requires only the measurement of the converters output voltage. Both observers can be used with minor changes for the buck-boost converter. Two flatness-based online trajectory planning algorithms are suggested. They exploit the parametrization of the trajectories in the energy. One of them is designed to achieve fast setpoint transitions during converter start-up or despite sudden load steps while simultaneously respecting the converters physical constraints. The other one is considered for applications in power factor correction. Different stabilization strategies are compared. The viability of the observers, the algorithm, and the stabilization strategies are verified by simulations of switched nonideal converter models. [ABSTRACT FROM AUTHOR]

A methodology for the optimal parasitic-aware design of RF power amplifiers toward maximum power efficiency is presented. It is based on a template-driven simulation-based optimization approach, including the effect of all device parasitics (transistors, passives) during the sizing. The combination of expert knowledge (in the template) with a state-of-the-art evolutionary algorithm results in a highly flexible and optimal sizing methodology tailored to RF circuits. Parasitic information is obtained through interaction with device profilers. The methodology is implemented in a fully featured software tool called M-DESIGN and is applied to the optimal sizing of a two-stage Class E power amplifier for maximum efficiency. The complete sizing was obtained in less than one hour of CPU time. Moreover, the constraint templates that were used are presented and discussed. An amplifier manufactured in a commercial 0.35-μm SM2P CMOS process and sized using the proposed methodology shows a maximum value of 67% for the drain efficiency (DE) versus 66% simulated. Measurement results show that it works at 850 MHz and has a maximum output power of 30 dBm at 23 V. The power-added efficiency (PAE) is always greater than 60% for an output power above 160 mW and a maximum PAE of 66% is achieved. [ABSTRACT FROM AUTHOR]

A new algorithm based on a SPICE-like simulator that searches for multiple operating points automatically, with no user intervention required, is presented. This algorithm, which exploits the asymmetrical properties of nonlinear mappings that describe multistable circuits, has been implemented into a program which automatically finds multiple (in most cases, all) operating points of a circuit. In addition to finding multiple operating points, this method offers another feature: it is capable of detecting the stability of a particular operating point. Another useful feature of this method is that it allows the user to gauge how close a particular circuit is to possessing multiple operating points. For circuits known to possess multiple operating points, this method allows the user to specify which operating point is encountered first. Unlike other continuation methods, circuit element models are not modified; only augmenting resistors are required. Hence, this approach lends itself well as an "add-on" to existing circuit simulators. A number of circuit examples are given. [ABSTRACT FROM AUTHOR]

The small signal behavior of a frequency controlled resonant converter is modeled as an equivalent pulsewidth modulation (PWM) converter. By this model, a recently developed simulation algorithm for PWM convener Is modified to simulate both the open loop and closed loop small signal behaviour. Experimental results show that this approach is accurate and efficient. Resonant converters have many advantages over conventional PWM converters. There are many research works on the analysis and design of resonant converters, covering both the steady states and theft small-signal responses.