Pedro Pereira

In this paper, three metaheuristics are investigated to optimize RF Integrated Inductors; namely Differential Evolution (DE) from the field of evolutionary computation, Gravitational Search Algorithm (GSA) based on the law of gravity and mass interactions and Particle Swarm Optimization (PSO) inspired by swarm behaviors in nature. A particular interest is given to the optimization of RF Integrated Inductors. Performances in terms of optimum quality and computing time of the metaheuristics are checked via three test functions and one application that consist of optimizing performances of characterize integrated inductors based on the double$π$-model.

Tourism is an industry that has been growing rapidly in the last few years and it is expected that it will continue to grow. Due to the evolution of technology, mobile applications are being increasingly used in all kinds of industries, being one of them tourism. Presently there are already a few mobile applications used to increase the experience of the user when visiting a place, but these mobile applications lack some important features. This paper describes the development of a mobile application with integrated routing algorithms used to increase the experience of the tourists when visiting the city of Avila, Spain. The tourist will have at their disposal real time information about all the monuments available for visit, a full set of predefined circuits with different visit times and degrees of difficulty and also the possibility to create an optimized or personalized circuit combining the user preferences such as visiting time and number of monuments to visit.

In this paper we deal with analog active filter design using discrete components taking into consideration tolerance effects. Sensitivity analysis is performed to determine the most influential components in the considered circuit, thus relative higher precision is offered to those parameters. Further, an in-loop optimization technique is considered, thus actual IC models are handled. An application example is presented. HSpice simulation results, supported by Monte Carlo analysis, are given to highlight efficiency of the proposed selection technique.

The key step of the analog active filter design is the optimal selection of component values due to manufactured series (E12, E24, E48, E96 and E192). In this paper, four simulation-based metaheuristics are applied to optimize four active filters using commercials available ICs as building blocks. The emphasis of this work is applying Richardson extrapolation-based sensitivity analysis in the optimization process of analog active filters. Indeed, Richardson extrapolation technique facilitates the calculation of the partial derivatives for the sensitivity using the simulation-based evaluation, without an explicit mathematical expression. Viability and benefits of the sensitivity analysis are highlighted. Monte Carlo analysis is performed in order to investigate robustness of the proposed sensitivity analysis of the active filters in case of component value variations due to specified tolerances of manufactured series.

The complex data exchange between architectural design and building energy simulation constitutes the main challenge in the use of energy performance analyses in the early design stage. The enhancement of BIM model data with additional specific energy-related information and the subsequent mapping to the input of an energy analysis or simulation tool is yet an open issue. This paper examines three approaches for the data transfer from 3D CAD applications to building performance simulations using BIM as central data repository and points out their current and envisaged use in practice. The first approach addresses design scenarios. It focuses on the supporting tools needed to achieve interoperability given a 74 wide-spread commercial BIM model (Autodesk Revit) and a dedicated pre-processing tool (DesignBuilder) for EnergyPlus. The second approach is similar but addresses retrofitting scenarios. In both workflows gbXML is used as the transformation format. In the third approach a standard BIM model, IFC is used as basis for the transfer process for any relevant lifecycle phase.

This work deals with the multi-objective optimization of analog circuits by generating the Pareto front where elements are low sensitive to parameters' variations. NSGA-II is used for obtaining the non-dominated solutions. Richardson extrapolation technique is used for the in-loop optimization approach for computing partial derivatives and, thus, the solutions' sensitivity. NSGA-II Pareto fronts' intrinsic ranking is exploited for the generation of the new ‘low-sensitive’ Pareto front. The case of the optimal sizing of a CMOS voltage follower is considered to exemplify the proposed approach.

Sand pile and Bean models have already been applied to describe single grain HTS bulks. An extension to that approach was used to model multiseed bulks, needed for several practical applications as electric motors or flywheels with superconducting bearings. The use of genetic algorithms was then proposed to determine intra- and intergrain current densities, and application to two and three seeds samples using trapped flux experimental measurements was exemplified. However, this model assumed some simplifications, as equal properties in grain boundaries between neighboring grains. In this paper an extension to this methodology is proposed and evaluated by analyzing measurements performed in plans at different distances from surfaces of samples with three seeds. Discussion of its influence on a practical application is also explored.

Abstract This paper proposes a hybrid methodology for the evaluation of integrated inductors sensitivity against technological/geometrical parameters variation. The obtained results are used in an optimization-based design environment for integrated inductors, as a way of guaranteeing that obtained solutions are robust against parameter variation. For the inductor characterization, a lumped element model is used, where each element value is evaluated through physics based equations. The sensitivity of the inductor characterization to parameter variations is evaluated at two levels. At the physical level, the sensitivity of the model element values to technological/geometrical parameters variations is computed through an equation-based strategy. Then, the sensitivity of the inductor characterization to the model parameter variations is obtained through a simulation-based approach, where the Richardson extrapolation technique is used for the calculation of the partial derivatives. Several examples considering the evaluation of sensitivity of both inductance and quality factor of two inductors in \{UMC130\} technology are presented. Obtained results are compared against Monte-Carlo simulations.

The continuing size reduction of electronic devices imposes design challenges to optimize the performances of modern electronic systems, such as: wireless services, telecom and mobile computing. Fortunately, those design challenges can be overcome thanks to the development of Electronic Design Automation (EDA) tools. In the analog, mixed signal and radio-frequency (AMS/RF) domains, circuit optimization tools have demonstrated their usefulness in addressing design problems taking into account downscaling technological aspects. Recent advances in EDA have shown that the simulation-based sizing technique is a very interesting solution to the ‘complex’ modelling task in the circuit design optimization problem. In this paper we propose a multi-objective simulation-based optimization tool. A CMOS LC-VCO circuit is presented to show the viability of this tool. The tool is used to generate the Pareto front linking two conflicting objectives, namely the VCO Phase Noise and Power Consumption. The accuracy of the results is checked against HSPICE/RF simulations.

This Chapter presents the optimal design of radio-frequency integrated spiral inductors. The basic idea is to generate an analytical model to characterize integrated inductors based on the double {\ensuremathπ}-model, and offer to the designer an approach to determine the inductor layout parameters. Particle Swarm Optimization technique is used to generate optimal values of parameters of the developed models. Viability of the proposed models is highlighted via comparison with ASITIC simulation results.

Inductive superconducting fault current limiters have already demonstrated their technical viability in electrical networks. Its architecture and robustness make them potentially adequate for distribution networks, and this type of devices is considered as an enabling technology for the advent of embedded generation with renewable energy sources. In order to promote the growth and maturity of these superconducting technologies, fast design tools must be developed, allowing simulating devices with different materials in grids with diverse characteristics. This work presents advances in the development of such tool, which, at present stage, is an effective alternative to software simulations by finite elements methods, reducing dramatically computation time. The algorithms are now compared with experimental results from a laboratory scale prototype, showing the need to refine them.

This paper introduces a variability-aware methodology for the design of LC-VCOs in Nano-CMOS technologies. The complexity of the design as well as the necessity for having an environment offering the possibility for exploring design trade-offs has led to the development of design methodologies based multi-objective optimization procedures yielding the generation of Pareto-optimal surfaces. The efficiency of the process is granted by using analytical models for both passive and active devices. Although physics-based analytical expressions have been proposed for the evaluation of the lumped elements, the variability of the process parameters is usually ignored due to the difficulty to formalize it into an optimization performance index. The usually adopted methodology of considering only optimum solutions for the Pareto surface, may lead to pruning quasi-optimal solutions that may prove to be better, should their sensitivity to process parameter variation be accounted for. In this work we propose starting by generating an extended Pareto surface where both optimum and quasi-optimum solutions are considered. Finally information on the sensitivity to process parameter variations, is used for electing the best design solution.

This paper addresses an optimization-based approach for the design of RF integrated inductors. The methodology presented deals with the complexity of the design problem by formulating it as a multi-objective optimization. The multi-modal nature of the underlying functions combined with the need to be able to explore design trade-offs leads to the use of niching methods. This allows exploring not only the best trade-off solutions lying on the Pareto-optimum surface but also the quasi-optimum solutions that would be otherwise discarded. In this paper we take advantage of the niching properties of lbest PSO algorithm using ring topology to devise a simple optimizer able to find the local-optima. For the efficiency of the process analytical models are used for the passive/active devices. In spite the use of physics-based analytical expressions for the evaluation of the lumped elements, the variability of the process parameters is ignored in the optimization stage due to the significant computational burden it involves. Thus in the final stage both the Pareto-optimum solutions and the quasi-optimum solutions are evaluated with respect to the sensitivity to process parameter variations.