Introducing and using the spider monkey optimization (SMO) as an optimization technique for the electromagnetics and antenna community is the main goal of this paper. The SMO is a new swarm intelligence technique whic...
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Introducing and using the spider monkey optimization (SMO) as an optimization technique for the electromagnetics and antenna community is the main goal of this paper. The SMO is a new swarm intelligence technique which models the foraging behavior of spider monkeys. To show the efficiency of the SMO, different examples are presented in this work. The optimization procedure is used to synthesize the array factor of a linear antenna array and to optimally design a coaxial feeding patch antenna for wireless applications. The obtained results show that SMO is efficient in reaching the optimum solutions with few number of experiments.
In this paper nonlinear time-varying and bilinear discrete-time systems with additive bounded disturbances are considered. First, conditions guaranteeing uniform ultimate boundedness for time-varying nonlinear systems...
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ISBN:
(纸本)9781612848006
In this paper nonlinear time-varying and bilinear discrete-time systems with additive bounded disturbances are considered. First, conditions guaranteeing uniform ultimate boundedness for time-varying nonlinear systems are established. Then, algebraic conditions ensuring the existence of polyhedral Lyapunov functions, uniform boundedness and positive invariance for closed-loop bilinear systems are obtained. Finally, these results are applied to various robust stabilization problems for bilinear systems subject to persistent additive disturbances. It is shown that these problems can be reduced to a single or a series of linear programming problems.
In computing sciences (e-Sciences) we need computer power, excellent algorithms and programming power in order to solve scientific problems leading to discoveries and development of innovative new products. So far, th...
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In this paper, we propose a cost-effective and low-power 64-point fast Fourier transform (FFT)/inverse FFT (IFFT) architecture and chip adopting the retrenched 8-point FFT/IFFT (R8-FFT) unit and an efficient data-swap...
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In this paper, we propose a cost-effective and low-power 64-point fast Fourier transform (FFT)/inverse FFT (IFFT) architecture and chip adopting the retrenched 8-point FFT/IFFT (R8-FFT) unit and an efficient data-swapping method based output buffer unit The whole chip systematic performance concerning about the area, power, latency and pending cycles for the application of IEEE 802.11a WLAN standard has been analyzed. The proposed R8-FFT unit utilizing the symmetry property of the matrix decomposition achieves half computation-complexity and less power consumption compared with the recently proposed FFT/IFFT designs. On the other hand, applying the proposed data-swapping method, a low-cost and low-power output buffer can be obtained. So as to further increase system performance, we propose one scheme: the multiplication-after-write (MAW) method. Applying MAW method with R8-FFT unit, the resulting FFT/IFFT design not only leads to the balancing pending cycle, but also abbreviating computation latency to 8 clock cycles. Consequently, adopting the above proposed two units and one scheme, the whole chip consumes 22.36mW under 1.2V@20 MHz in TSMC 0.13 1P8M CMOS process
In this paper we propose a new source localization method using underwater ambient noise modeling based on heteroscedasticity time series in array signal processing for a passive SONAR. In this application, measuremen...
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In this paper we propose a new source localization method using underwater ambient noise modeling based on heteroscedasticity time series in array signal processing for a passive SONAR. In this application, measurement of ambient noise in natural environment shows that noise can sometimes be significantly nonGaussian. Besides in many applications, such as those sensors having nonideal hardware, involving sparse hydrophones with prevailing external noise, the assumed noise model may be simplified by different sensors noise variances. Generalized Autoregressive Conditional Heteroscedasticity (GARCH) time series are feasible for heavy tailed probability density function (PDF) (as excess kurtosis) and time varying variances (a type of heteroscedasticity) of stochastic process. We use GARCH noise model in the Maximum Likelihood Approach for the estimation of Direction-Of-Arrivals (DOAs) of impinging sources. Through simulation, we show that the GARCH modeling is suitable for high-resolution source localization and noise suppression in an underwater environment.
Finding strongly connected components (SCCs) and the diameter of a directed network play a key role in a variety of discrete optimization problems, and subsequently, machine learning and control theory problems. On th...
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This paper presents a real-time vision system for assisting driver during nighttime driving. The proposed system provides the following features: 1) effectively detection and tracking of oncoming and preceding vehicle...
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This paper presents a real-time vision system for assisting driver during nighttime driving. The proposed system provides the following features: 1) effectively detection and tracking of oncoming and preceding vehicles based on image segmentation and pattern analysis techniques. 2) Robust and adaptive vehicle detection under various illuminated conditions at nighttime urban environments benefited by a novel automatic object segmentation scheme. 3) Providing beneficial information for assisting the driver to perceive surrounding traffic conditions outside the car during nighttime driving. 4) Providing a versatile control strategy for in-vehicle facilities of the autonomous vehicles. 5) Offering real-time traffic event-driven video surveillance machinery for recording evidences of possible traffic accidents. Experimental results demonstrate the feasibility and effectiveness of the proposed system on nighttime driver assistance issues.
In this paper, an adaptive neural network (NN) state-feedback controller for a class of nonlinear systems with mismatched uncertainties is presented. By using a radial basis (RBF) neural network, a bound of unknown no...
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In this paper, an adaptive neural network (NN) state-feedback controller for a class of nonlinear systems with mismatched uncertainties is presented. By using a radial basis (RBF) neural network, a bound of unknown nonlinear functions is approximated so that no information about the upper bound of mismatched uncertainties is required. The state-feedback is based on Lyapunov stability theory, and it is shown that the asymptotic convergence of the closed-loop system to zero is achieved while maintaining bounded states at the same time. The presented methods are more general than the previous approaches, handling systems with no restriction on the dimension of the system and the number of inputs. Simulation results on dynamic equations of vertical take-off and landing (VTOL) helicopter confirm the effectiveness of the proposed methods in the stabilization of mismatched nonlinear systems.
A smooth optimal control problem with mixed constraints is considered. Under the normality assumption, a proof of second-order necessary optimality conditions based on the Robinson stability theorem is provided. The m...
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A smooth optimal control problem with mixed constraints is considered. Under the normality assumption, a proof of second-order necessary optimality conditions based on the Robinson stability theorem is provided. The main feature of the obtained result is that the local regularity with respect to the mixed constraints is assumed, that is, a regularity in an ε-tube along the minimizer, but not the conventional global regularity hypothesis. This impacts the maximum condition. Therefore, the normal set of Lagrange multipliers fulfills the Legendre-Clebsch condition and the maximum principle. At the same time, the maximum condition is modified since, now, the maximum is taken over a reduced feasible set. Furthermore, the case of abnormal minimizers is considered. The same type of reduced maximum condition is obtained along with a refined Legendre-Clebsch condition which is meaningful in the abnormal case.
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