This paper concerns the problem of on-line (real-time) computation of solutions to the optimal switching time problem in hybrid systems. The systems under consideration are autonomous, and the performance measure to b...
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ISBN:
(纸本)9781424414970;1424414970
This paper concerns the problem of on-line (real-time) computation of solutions to the optimal switching time problem in hybrid systems. The systems under consideration are autonomous, and the performance measure to be optimized has the form of a cost functional defined on the state trajectory. The state variable cannot, however, be measured directly and it has to be estimated by a suitable observer. In this paper, we propose an on-line optimization algorithm based on the state observer, and derive bounds on its convergence rate.
A printed UWB Vivaldi antenna is presented in this paper. Its geometry is based on a novel spline shape and optimised by an efficient global optimisation algorithm. A U-shaped slot is introduced into the geometry to n...
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ISBN:
(纸本)9781424420414
A printed UWB Vivaldi antenna is presented in this paper. Its geometry is based on a novel spline shape and optimised by an efficient global optimisation algorithm. A U-shaped slot is introduced into the geometry to notch out the 5.1 GHz to 5.8 GHz WLAN band. This can be used to mitigate interference between WLAN and UWB systems.
Cognitive radio is a technological concept pushing for the introduction of intelligent radio operation going beyond system adaptation and reconfiguration on the basis of simple criteria and rules. Insofar, a rather li...
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ISBN:
(纸本)9781424423019
Cognitive radio is a technological concept pushing for the introduction of intelligent radio operation going beyond system adaptation and reconfiguration on the basis of simple criteria and rules. Insofar, a rather limited amount of work has been published on the cognitive mechanisms that should be embedded into the communicating equipments to achieve such an intelligent behavior. Towards filling this gap, this paper presents an innovative optimization algorithm driving the decision making process supervising the cognitive radio reconfiguration. This cognitive algorithm, called RALFE for "Reason And Learn From Experience", presents interesting features since it allows to perform autonomous decision making with regard to multiple, possibly conflicting, operational objectives in the face of an uncertain environment. The proposed approach is illustrated for a case of cognitive waveform design.
As process, temperature and voltage variations become significant in deep submicron design, timing closure becomes a critical challenge using synchronous CAD flows. One attractive alternative is to use robust asynchro...
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ISBN:
(纸本)9781424406296
As process, temperature and voltage variations become significant in deep submicron design, timing closure becomes a critical challenge using synchronous CAD flows. One attractive alternative is to use robust asynchronous circuits which gracefully accommodate timing discrepancies. However, these asynchronous circuits typically suffer from high area and latency overhead. In this paper, an optimization algorithm is presented which reduces the area and delay of these circuits by relaxing their overly-restrictive style. The algorithm was implemented and experiments performed on a subset of MCNC circuits. On average, 49.2% of the gates could be implemented in a relaxed manner, 34.9% area improvement was achieved, and 16.1% delay improvement was achieved using a simple heuristic for targeting the critical path in the circuit. This is the first proposed approach that systematically optimizes asynchronous circuits based on the notion of local relaxation while still preserving the circuit's overall timing-robustness.
This paper presents an approach for constructing improved visual representations of high dimensional objective spaces using virtual reality. These spaces arise from the solution of multi-objective optimization problem...
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ISBN:
(纸本)9781424413393
This paper presents an approach for constructing improved visual representations of high dimensional objective spaces using virtual reality. These spaces arise from the solution of multi-objective optimization problems with more than objective functions which lead to high dimensional Pareto fronts. The 3-D representations of m-dimensional Pareto fronts, or their approximations, are constructed via similarity structure mappings between the original objective spaces and the 3-D space. Alpha shapes are introduced for the representation and compared with previous approaches based on convex hulls. In addition, the mappings minimizing a measure of the amount of dissimilarity loss are obtained via genetic programming. This approach is preliminarily investigated using both theoretically derived high dimensional Pareto fronts for a test problem (DTLZ2) and practically obtained objective spaces for the 4 dimensional knapsack problem via multi-objective evolutionary algorithms like HLGA, NSGA, and VEGA. The improved representation captures more accurately the real nature of the m-dimensional objective spaces and the quality of the mappings obtained with genetic programming is equivalent to those computed with classical optimization algorithms.
In this paper, our recently developed Self-adaptive Differential Evolution algorithm (SaDE) is extended to solve numerical optimization problems with multiple conflicting objectives. The performance of the proposed MO...
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ISBN:
(纸本)9781424413393
In this paper, our recently developed Self-adaptive Differential Evolution algorithm (SaDE) is extended to solve numerical optimization problems with multiple conflicting objectives. The performance of the proposed MOSaDE algorithm is evaluated on a suit of 19 benchmark problems provided for the CEC2007 special session (http://***/home/epnsugan/)on Performance Assessment of Multi-Objective optimization algorithms.
Based on reinforcement learning, an adaptive online optimization algorithm of time-out policy is presented for dynamic power management. First the time-out policy driven power-managed systems are formulated as semi-Ma...
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ISBN:
(纸本)9781424404421
Based on reinforcement learning, an adaptive online optimization algorithm of time-out policy is presented for dynamic power management. First the time-out policy driven power-managed systems are formulated as semi-Markov control processes. Under this analytic model, the equivalent effect on performance-power trade-off of time-out and stochastic policies is probed, and the equivalent relation between these two types policies is derived. Then an adaptive optimization algorithm that combines gradient estimation online and stochastic approximation is proposed. This algorithm doesn't depend on the prior knowledge of system parameters, and can achieve a global optimum with less computational cost. Simulation results demonstrate the analytic results and the effectiveness of the proposed algorithm.
An optimization algorithm for chemotherapy scheduling study is developed in this paper. We consider the density of host and cancer cells of a patient as states, and define the optimal chemotherapy scheduling as the sh...
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ISBN:
(纸本)9781424415281
An optimization algorithm for chemotherapy scheduling study is developed in this paper. We consider the density of host and cancer cells of a patient as states, and define the optimal chemotherapy scheduling as the shortest treatment path that will cure the patient, if possible. Given the fact that the treatment time is always interger-valued in our model, we used a modified version of the value iteration algorithm originally designed for Markov Decision Process. Simulation results and discussions are also given.
Recent algorithmic advances in Boolean satisfiability (SAT), along with highly efficient solver implementations, have enabled the successful deployment of SAT technology in a wide range of applications domains, and pa...
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Recent algorithmic advances in Boolean satisfiability (SAT), along with highly efficient solver implementations, have enabled the successful deployment of SAT technology in a wide range of applications domains, and particularly in electronic design automation (EDA). SAT is increasingly being used as the underlying model for a number of applications in EDA. This paper describes how to formulate two problems in power estimation of CMOS combinational circuits as SAT problems or 0-1 integer linear programming (ILP). In these circuits, it was proven that maximizing dissipation is equivalent to maximizing gate output activity, appropriately weighted to account for differing load capacitances. The first problem in this work deals with identifying an input vector pair that maximizes the weighted circuit activity. In the second application we attempt to find an estimate for the maximum power-up current in circuits where power cut-off or gating techniques are used to reduce leakage current. Both problems were successfully formulated as SAT problems. SAT-Based and generic Integer Linear Programming (ILP) solvers are then used to find a solution. The experimental results obtained on a large number of benchmark circuits provide promising evidence that the proposed complete approach is both viable and useful and outperforms the random approach. (C) 2007 Elsevier Ltd. All rights reserved.
The computation of the McMillan degree and structure at infinity of a transfer function model is considered for the family of early design models, referred to as Structured Transfer Function (STF) matrices. Such trans...
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The computation of the McMillan degree and structure at infinity of a transfer function model is considered for the family of early design models, referred to as Structured Transfer Function (STF) matrices. Such transfer functions have certain elements fixed to zero, some elements being constant and other elements expressing some identified dominant dynamics of the system. For the family of large dimension STF matrices the computation of the generic McMillan degree and structure at infinity are considered using genericity arguments which lead to optimization problems of integer matrices. A novel approach is introduced here that uses the notion of "irreducibility" of integer matrices, which is developed as the equivalent of irreducibility (properness) of polynomial matrices. This new notion provides the means for exploiting the structure of integer matrices and enables the termination of searching processes in a reduced number of steps, thus leading to an efficient new algorithm for the computation of the generic value of the McMillan degree and the structure at infinity of STFs. Links are made to standard optimization problems and to graph theory. The formulation of the optimization algorithm in terms of bipartite graphs offers better results and reduces the computational effort.
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