Discussions about FFT-based two-step phase-shifting algorithm

An FFT-based two-step phase-shifting (TPS) algorithm is described in detail and implemented by use of experimental interferograms. This algorithm has been proposed to solve the TPS problem with random phase shift except pi. By comparison with the visibility-function-based TPS algorithm, it proves that the FFT-based algorithm has obvious advantages in phase extracting. Meanwhile, we present a pi-phase-shift supplement to the TPS algorithm, which combines the two interferograms and demodulates the phase map by locating the extrema of the combined fringes after removing the respective backgrounds. So combining this method and FFT-based one, one could really implement the TPS with random phase shift. Whereafter, we systematically compare the TPS with single-interferogram analysis algorithm and conventional three-step phase-shifting one. The results demonstrate that the FFT-based TPS algorithm has a satisfactory accuracy. At last, based on the polarizing interferometry, a schematic setup of two-channel TPS interferometer with random phase shift is suggested to implement the simultaneous collection of interferograms. (c) 2007 Elsevier GrnbH. All rights reserved.

Neutronic optimization in high conversion Th-233U fuel assembly with simulated annealing

This paper reports on fuel design optimization of a PWR operating in a self sustainable Th-233U fuel cycle. Monte Carlo simulated annealing method was used in order to identify the fuel assembly configuration with the most attractive breeding performance. In previous studies, it was shown that breeding may be achieved by employing heterogeneous Seed-Blanket fuel geometry. The arrangement of seed and blanket pins within the assemblies may be determined by varying the designed parameters based on basic reactor physics phenomena which affect breeding. However, the amount of free parameters may still prove to be prohibitively large in order to systematically explore the design space for optimal solution. Therefore, the Monte Carlo annealing algorithm for neutronic optimization is applied in order to identify the most favorable design. The objective of simulated annealing optimization is to find a set of design parameters, which maximizes some given performance function (such as relative period of net breeding) under specified constraints (such as fuel cycle length). The first objective of the study was to demonstrate that the simulated annealing optimization algorithm will lead to the same fuel pins arrangement as was obtained in the previous studies which used only basic physics phenomena as guidance for optimization. In the second part of this work, the simulated annealing method was used to optimize fuel pins arrangement in much larger fuel assembly, where the basic physics intuition does not yield clearly optimal configuration. The simulated annealing method was found to be very efficient in selecting the optimal design in both cases. In the future, this method will be used for optimization of fuel assembly design with larger number of free parameters in order to determine the most favorable trade-off between the breeding performance and core average power density.

Multi-objective optimization of a fluid structure interaction benchmarking

The integration and application of a new multi-objective tabu search optimization algorithm for Fluid Structure Interaction (FSI) problems are presented. The aim is to enhance the computational design process for real world applications and to achieve higher performance of the whole system for the four considered objectives. The described system combines the optimizer with a well established FSI solver which is based on the fully implicit, monolithic formuFlation of the problem in the Arbitrary Lagrangian-Eulerian FEM approach. The proposed solver resolves the proposed uid-structure interaction benchmark which describes the self-induced elastic deformation of a beam attached to a cylinder in laminar channel ow. The optimized ow characteristics of the aforementioned geometrical arrangement illustrate the performance of the system in two dimensions. Special emphasis is given to the analysis of the simulation package, which is of high accuracy and is the core of application. The design process identifies the best combination of ow features for optimal system behavior and the most important objectives. In addition, the presented methodology has the potential to run in parallel, which will significantly speed-up the elapsed time. Finite Element Method (FEM), Fluid-Structure Interaction (FSI), Multi-Ojective Tabu search (MOTS2). Copyright © 2013 Tech Science Press.

Molecular Logic Gates and Luminescent Sensors Based on Photoinduced Electron Transfer

The competition between Photoinduced electron transfer (PET) and other de-excitation pathways such as fluorescence and phosphorescence can be controlled within designed molecular structures. Depending on the particular design, the resulting optical output is thus a function of various inputs such as ion concentration and excitation light dose. Once digitized into binary code, these input-output patterns can be interpreted according to Boolean logic. The single-input logic types of YES and NOT cover simple sensors and the double- (or higher-) input logic types represent other gates such as AND and OR. The logic-based arithmetic processors such as half-adders and half-subtractors are also featured. Naturally, a principal application of the more complex gates is in multi-sensing contexts.

Surrogate-based analysis and optimization for the design of heat sinks with jet impingement

Heat sinks are widely used for cooling electronic devices and systems. Their thermal performance is usually determined by the material, shape, and size of the heat sink. With the assistance of computational fluid dynamics (CFD) and surrogate-based optimization, heat sinks can be designed and optimized to achieve a high level of performance. In this paper, the design and optimization of a plate-fin-type heat sink cooled by impingement jet is presented. The flow and thermal fields are simulated using the CFD simulation; the thermal resistance of the heat sink is then estimated. A Kriging surrogate model is developed to approximate the objective function (thermal resistance) as a function of design variables. Surrogate-based optimization is implemented by adaptively adding infill points based on an integrated strategy of the minimum value, the maximum mean square error approach, and the expected improvement approaches. The results show the influence of design variables on the thermal resistance and give the optimal heat sink with lowest thermal resistance for given jet impingement conditions. 

Building pH Sensors into Paper-based Small-molecular Logic Sys-tems for Very Simple Detection of Edges of Objects

Genetically-engineered bacteria and reactive DNA networks detect edges of objects, as done in our retinas and as also found within computer vision. We now demonstrate that simple molecular logic systems (a combination of a pH sensor, a photo acid generator and a pH buffer spread on paper) without any organization can achieve this relatively complex computational goal with good-fidelity. This causes a jump in the complexity achievable by molecular logic-based computation and extends its applicability. The molecular species involved in light dose-driven 'off-on-off' fluorescence is diverted in the ‘on’ state by proton diffusion from irradiated to unirradiated regions where it escapes a strong quencher, thus visualizing the edge of a mask.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.

Cell Coverage Optimization for the Multicell Massive MIMO Uplink

We investigate the cell coverage optimization problem for the massive multiple-input multiple-output (MIMO) uplink. By deploying tilt-adjustable antenna arrays at the base stations, cell coverage optimization can become a promising technique which is able to strike a compromise between covering cell-edge users and pilot contamination suppression. We formulate a detailed description of this optimization problem by maximizing the cell throughput, which is shown to be mainly determined by the user distribution within several key geometrical regions. Then, the formulated problem is applied to different example scenarios: for a network with hexagonal shaped cells and uniformly distributed users, we derive an analytical lower bound of the ergodic throughput in the objective cell, based on which, it is shown that the optimal choice for the cell coverage should ensure that the coverage of different cells does not overlap; for a more generic network with sectoral shaped cells and non-uniformly distributed users, we propose an analytical approximation of the ergodic throughput. After that, a practical coverage optimization algorithm is proposed, where the optimal solution can be easily obtained through a simple one-dimensional line searching within a confined searching region. Our numerical results show that the proposed coverage optimization method is able to greatly increase the system throughput in macrocells for the massive MIMO uplink transmission, compared with the traditional schemes where the cell coverage is fixed.

A Constrained Optimization Approach to Dynamic State Estimation for Power Systems Including PMU and Missing Measurements.

In this brief, a hybrid filter algorithm is developed to deal with the state estimation (SE) problem for power systems by taking into account the impact from the phasor measurement units (PMUs). Our aim is to include PMU measurements when designing the dynamic state estimators for power systems with traditional measurements. Also, as data dropouts inevitably occur in the transmission channels of traditional measurements from the meters to the control center, the missing measurement phenomenon is also tackled in the state estimator design. In the framework of extended Kalman filter (EKF) algorithm, the PMU measurements are treated as inequality constraints on the states with the aid of the statistical criterion, and then the addressed SE problem becomes a constrained optimization one based on the probability-maximization method. The resulting constrained optimization problem is then solved using the particle swarm optimization algorithm together with the penalty function approach. The proposed algorithm is applied to estimate the states of the power systems with both traditional and PMU measurements in the presence of probabilistic data missing phenomenon. Extensive simulations are carried out on the IEEE 14-bus test system and it is shown that the proposed algorithm gives much improved estimation performances over the traditional EKF method.

A Clinical Support System Based on Quality of Life Estimation

Quality of life is a concept influenced by social, economic, psychological, spiritual or medical state factors. More specifically, the perceived quality of an individual's daily life is an assessment of their well-being or lack of it. In this context, information technologies may help on the management of services for healthcare of chronic patients such as estimating the patient quality of life and helping the medical staff to take appropriate measures to increase each patient quality of life. This paper describes a Quality of Life estimation system developed using information technologies and the application of data mining algorithms to access the information of clinical data of patients with cancer from Otorhinolaryngology and Head and Neck services of an oncology institution. The system was evaluated with a sample composed of 3013 patients. The results achieved show that there are variables that may be significant predictors for the Quality of Life of the patient: years of smoking (p value 0.049) and size of the tumor (p value < 0.001). In order to assign the variables to the classification of the quality of life the best accuracy was obtained by applying the John Platt's sequential minimal optimization algorithm for training a support vector classifier. In conclusion data mining techniques allow having access to patients additional information helping the physicians to be able to know the quality of life and produce a well-informed clinical decision.

Particle swarm optimization for two-connected networks with bounded rings

The Two-Connected Network with Bounded Ring (2CNBR) problem is a network design problem addressing the connection of servers to create a survivable network with limited redirections in the event of failures. Particle Swarm Optimization (PSO) is a stochastic population-based optimization technique modeled on the social behaviour of flocking birds or schooling fish. This thesis applies PSO to the 2CNBR problem. As PSO is originally designed to handle a continuous solution space, modification of the algorithm was necessary in order to adapt it for such a highly constrained discrete combinatorial optimization problem. Presented are an indirect transcription scheme for applying PSO to such discrete optimization problems and an oscillating mechanism for averting stagnation.

Generating Aesthetically Pleasing Images in a Virtual Environment using Particle Swarm Optimization

This research focuses on generating aesthetically pleasing images in virtual environments using the particle swarm optimization (PSO) algorithm. The PSO is a stochastic population based search algorithm that is inspired by the flocking behavior of birds. In this research, we implement swarms of cameras flying through a virtual world in search of an image that is aesthetically pleasing. Virtual world exploration using particle swarm optimization is considered to be a new research area and is of interest to both the scientific and artistic communities. Aesthetic rules such as rule of thirds, subject matter, colour similarity and horizon line are all analyzed together as a multi-objective problem to analyze and solve with rendered images. A new multi-objective PSO algorithm, the sum of ranks PSO, is introduced. It is empirically compared to other single-objective and multi-objective swarm algorithms. An advantage of the sum of ranks PSO is that it is useful for solving high-dimensional problems within the context of this research. Throughout many experiments, we show that our approach is capable of automatically producing images satisfying a variety of supplied aesthetic criteria.

Design Techniques for Sigmadelta based ADC for Wireless Applications

Analog-to digital Converters (ADC) have an important impact on the overall performance of signal processing system. This research is to explore efficient techniques for the design of sigma-delta ADC,specially for multi-standard wireless tranceivers. In particular, the aim is to develop novel models and algorithms to address this problem and to implement software tools which are avle to assist the designer's decisions in the system-level exploration phase. To this end, this thesis presents a framework of techniques to design sigma-delta analog to digital converters.A2-2-2 reconfigurable sigma-delta modulator is proposed which can meet the design specifications of the three wireless communication standards namely GSM,WCDMA and WLAN. A sigma-delta modulator design tool is developed using the Graphical User Interface Development Environment (GUIDE) In MATLAB.Genetic Algorithm(GA) based search method is introduced to find the optimum value of the scaling coefficients and to maximize the dynamic range in a sigma-delta modulator.

A hybridised evolutionary algorithm for multi-criterion minimum spanning tree problems

A hybridised and Knowledge-based Evolutionary Algorithm (KEA) is applied to the multi-criterion minimum spanning tree problems. Hybridisation is used across its three phases. In the first phase a deterministic single objective optimization algorithm finds the extreme points of the Pareto front. In the second phase a K-best approach finds the first neighbours of the extreme points, which serve as an elitist parent population to an evolutionary algorithm in the third phase. A knowledge-based mutation operator is applied in each generation to reproduce individuals that are at least as good as the unique parent. The advantages of KEA over previous algorithms include its speed (making it applicable to large real-world problems), its scalability to more than two criteria, and its ability to find both the supported and unsupported optimal solutions.

Sparse model construction using coordinate descent optimization

We propose a new sparse model construction method aimed at maximizing a model’s generalisation capability for a large class of linear-in-the-parameters models. The coordinate descent optimization algorithm is employed with a modified l1- penalized least squares cost function in order to estimate a single parameter and its regularization parameter simultaneously based on the leave one out mean square error (LOOMSE). Our original contribution is to derive a closed form of optimal LOOMSE regularization parameter for a single term model, for which we show that the LOOMSE can be analytically computed without actually splitting the data set leading to a very simple parameter estimation method. We then integrate the new results within the coordinate descent optimization algorithm to update model parameters one at the time for linear-in-the-parameters models. Consequently a fully automated procedure is achieved without resort to any other validation data set for iterative model evaluation. Illustrative examples are included to demonstrate the effectiveness of the new approaches.