High power continuous-wave operation of self-organized In(Ga)As/GaAs quantum dot lasers

Quantum dot (QD) lasers are expected to have superior properties over conventional quantum well lasers due to a delta-function like density of states resulting from three dimensional quantum confinements. QD lasers can only be realized till significant improvements in uniformity of QDs with free of defects and increasing QD density as well in recent years. In this paper, we first briefly give a review on the techniques for preparing QDs, and emphasis on strain induced self-organized quantum dot growth. Secondly, self-organized In(Ga)As/GaAs, InAlAs/GaAlAs and InAs/InAlAs Qds grown on both GaAs and InP substrates with different orientations by using MBE and the Stranski-Krastanow (SK) growth mode at our labs are presented. Under optimizing the growth conditions such as growth temperature, V/III ratio, the amount of InAs, InxGa1-xAs, InxAl1-xAs coverage, the composition x etc., controlling the thickness of the strained layers, for example, just slightly larger than the critical thickness and choosing the substrate orientation or patterned substrates as well, the sheet density of ODs can reach as high as 10(11) cm(-2), and the dot size distribution is controlled to be less than 10% (see Fig. 1). Those are very important to obtain the lower threshold current density (J(th)) of the QD Laser. How to improve the dot lateral ordering and the dot vertical alignment for realizing lasing from the ground states of the QDs and further reducing the Jth Of the QD lasers are also described in detail. Thirdly based on the optimization of the band engineering design for QD laser and the structure geometry and growth conditions of QDs, a 1W continuous-wave (cw) laser operation of a single composite sheet or vertically coupled In(Ga)As quantum dots in a GaAs matrix (see Fig. 2) and a larger than 10W semiconductor laser module consisted nineteen QD laser diodes are demonstrated. The lifetime of the QD laser with an emitting wavelength around 960nm and 0.613W cw operation at room temperature is over than 3000 hrs, at this point the output power was only reduced to 0.83db. This is the best result as we know at moment. Finally the future trends and perspectives of the QD laser are also discussed.

Energy harvesting system design and optimization for wireless sensor networks

Wireless sensor networks (WSN) are becoming widely adopted for many applications including complicated tasks like building energy management. However, one major concern for WSN technologies is the short lifetime and high maintenance cost due to the limited battery energy. One of the solutions is to scavenge ambient energy, which is then rectified to power the WSN. The objective of this thesis was to investigate the feasibility of an ultra-low energy consumption power management system suitable for harvesting sub-mW photovoltaic and thermoelectric energy to power WSNs. To achieve this goal, energy harvesting system architectures have been analyzed. Detailed analysis of energy storage units (ESU) have led to an innovative ESU solution for the target applications. Battery-less, long-lifetime ESU and its associated power management circuitry, including fast-charge circuit, self-start circuit, output voltage regulation circuit and hybrid ESU, using a combination of super-capacitor and thin film battery, were developed to achieve continuous operation of energy harvester. Low start-up voltage DC/DC converters have been developed for 1mW level thermoelectric energy harvesting. The novel method of altering thermoelectric generator (TEG) configuration in order to match impedance has been verified in this work. Novel maximum power point tracking (MPPT) circuits, exploring the fractional open circuit voltage method, were particularly developed to suit the sub-1mW photovoltaic energy harvesting applications. The MPPT energy model has been developed and verified against both SPICE simulation and implemented prototypes. Both indoor light and thermoelectric energy harvesting methods proposed in this thesis have been implemented into prototype devices. The improved indoor light energy harvester prototype demonstrates 81% MPPT conversion efficiency with 0.5mW input power. This important improvement makes light energy harvesting from small energy sources (i.e. credit card size solar panel in 500lux indoor lighting conditions) a feasible approach. The 50mm × 54mm thermoelectric energy harvester prototype generates 0.95mW when placed on a 60oC heat source with 28% conversion efficiency. Both prototypes can be used to continuously power WSN for building energy management applications in typical office building environment. In addition to the hardware development, a comprehensive system energy model has been developed. This system energy model not only can be used to predict the available and consumed energy based on real-world ambient conditions, but also can be employed to optimize the system design and configuration. This energy model has been verified by indoor photovoltaic energy harvesting system prototypes in long-term deployed experiments.

A novel continuous forward algorithm for RBF neural modelling

A continuous forward algorithm (CFA) is proposed for nonlinear modelling and identification using radial basis function (RBF) neural networks. The problem considered here is simultaneous network construction and parameter optimization, well-known to be a mixed integer hard one. The proposed algorithm performs these two tasks within an integrated analytic framework, and offers two important advantages. First, the model performance can be significantly improved through continuous parameter optimization. Secondly, the neural representation can be built without generating and storing all candidate regressors, leading to significantly reduced memory usage and computational complexity. Computational complexity analysis and simulation results confirm the effectiveness.

Design optimization of cold-formed steel portal frames taking into account the effect of building topology

Cold-formed steel portal frames are a popular form of construction for low-rise commercial, light industrial and agricultural buildings with spans of up to 20 m. In this article, a real-coded genetic algorithm is described that is used to minimize the cost of the main frame of such buildings. The key decision variables considered in this proposed algorithm consist of both the spacing and pitch of the frame as continuous variables, as well as the discrete section sizes.A routine taking the structural analysis and frame design for cold-formed steel sections is embedded into a genetic algorithm. The results show that the real-coded genetic algorithm handles effectively the mixture of design variables, with high robustness and consistency in achieving the optimum solution. All wind load combinations according to Australian code are considered in this research. Results for frames with knee braces are also included, for which the optimization achieved even larger savings in cost.

Advantages and limitations of navigation-based multicriteria optimization (MCO) for localized prostate cancer IMRT planning

Efficacy of inverse planning is becoming increasingly important for advanced radiotherapy techniques. This study's aims were to validate multicriteria optimization (MCO) in RayStation (v2.4, RaySearch Laboratories, Sweden) against standard intensity-modulated radiation therapy (IMRT) optimization in Oncentra (v4.1, Nucletron BV, the Netherlands) and characterize dose differences due to conversion of navigated MCO plans into deliverable multileaf collimator apertures. Step-and-shoot IMRT plans were created for 10 patients with localized prostate cancer using both standard optimization and MCO. Acceptable standard IMRT plans with minimal average rectal dose were chosen for comparison with deliverable MCO plans. The trade-off was, for the MCO plans, managed through a user interface that permits continuous navigation between fluence-based plans. Navigated MCO plans were made deliverable at incremental steps along a trajectory between maximal target homogeneity and maximal rectal sparing. Dosimetric differences between navigated and deliverable MCO plans were also quantified. MCO plans, chosen as acceptable under navigated and deliverable conditions resulted in similar rectal sparing compared with standard optimization (33.7 ± 1.8Gy vs 35.5 ± 4.2Gy, p = 0.117). The dose differences between navigated and deliverable MCO plans increased as higher priority was placed on rectal avoidance. If the best possible deliverable MCO was chosen, a significant reduction in rectal dose was observed in comparison with standard optimization (30.6 ± 1.4Gy vs 35.5 ± 4.2Gy, p = 0.047). Improvements were, however, to some extent, at the expense of less conformal dose distributions, which resulted in significantly higher doses to the bladder for 2 of the 3 tolerance levels. In conclusion, similar IMRT plans can be created for patients with prostate cancer using MCO compared with standard optimization. Limitations exist within MCO regarding conversion of navigated plans to deliverable apertures, particularly for plans that emphasize avoidance of critical structures. Minimizing these differences would result in better quality treatments for patients with prostate cancer who were treated with radiotherapy using MCO plans.

Parametric design optimization of hard turning of AISI 4340 steel (69 HRC)

Continuous research endeavors on hard turning (HT), both on machine tools and cutting tools, have made the previously reported daunting limits easily attainable in the modern scenario. This presents an opportunity for a systematic investigation on finding the current attainable limits of hard turning using a CNC turret lathe. Accordingly, this study aims to contribute to the existing literature by providing the latest experimental results of hard turning of AISI 4340 steel (69 HRC) using a CBN cutting tool. An orthogonal array was developed using a set of judiciously chosen cutting parameters. Subsequently, the longitudinal turning trials were carried out in accordance with a well-designed full factorial-based Taguchi matrix. The speculation indeed proved correct as a mirror finished optical quality machined surface (an average surface roughness value of 45 nm) was achieved by the conventional cutting method. Furthermore, Signal-to-noise (S/N) ratio analysis, Analysis of variance (ANOVA), and Multiple regression analysis were carried out on the experimental datasets to assert the dominance of each machining variable in dictating the machined surface roughness and to optimize the machining parameters. One of the key findings was that when feed rate during hard turning approaches very low (about 0.02mm/rev), it could alone be most significant (99.16%) parameter in influencing the machined surface roughness (Ra). This has, however also been shown that low feed rate results in high tool wear, so the selection of machining parameters for carrying out hard turning must be governed by a trade-off between the cost and quality considerations.

Novel critical phenomena in optimization driven processes on networks

The work presented in this Ph.D thesis was developed in the context of complex network theory, from a statistical physics standpoint. We examine two distinct problems in this research field, taking a special interest in their respective critical properties. In both cases, the emergence of criticality is driven by a local optimization dynamics. Firstly, a recently introduced class of percolation problems that attracted a significant amount of attention from the scientific community, and was quickly followed up by an abundance of other works. Percolation transitions were believed to be continuous, until, recently, an 'explosive' percolation problem was reported to undergo a discontinuous transition, in [93]. The system's evolution is driven by a metropolis-like algorithm, apparently producing a discontinuous jump on the giant component's size at the percolation threshold. This finding was subsequently supported by number of other experimental studies [96, 97, 98, 99, 100, 101]. However, in [1] we have proved that the explosive percolation transition is actually continuous. The discontinuity which was observed in the evolution of the giant component's relative size is explained by the unusual smallness of the corresponding critical exponent, combined with the finiteness of the systems considered in experiments. Therefore, the size of the jump vanishes as the system's size goes to infinity. Additionally, we provide the complete theoretical description of the critical properties for a generalized version of the explosive percolation model [2], as well as a method [3] for a precise calculation of percolation's critical properties from numerical data (useful when exact results are not available). Secondly, we study a network flow optimization model, where the dynamics consists of consecutive mergings and splittings of currents flowing in the network. The current conservation constraint does not impose any particular criterion for the split of current among channels outgoing nodes, allowing us to introduce an asymmetrical rule, observed in several real systems. We solved analytically the dynamic equations describing this model in the high and low current regimes. The solutions found are compared with numerical results, for the two regimes, showing an excellent agreement. Surprisingly, in the low current regime, this model exhibits some features usually associated with continuous phase transitions.

Optimization models for a short sea fuel oil distribution problem

O transporte marítimo e o principal meio de transporte de mercadorias em todo o mundo. Combustíveis e produtos petrolíferos representam grande parte das mercadorias transportadas por via marítima. Sendo Cabo Verde um arquipelago o transporte por mar desempenha um papel de grande relevância na economia do país. Consideramos o problema da distribuicao de combustíveis em Cabo Verde, onde uma companhia e responsavel por coordenar a distribuicao de produtos petrolíferos com a gestão dos respetivos níveis armazenados em cada porto, de modo a satisfazer a procura dos varios produtos. O objetivo consiste em determinar políticas de distribuicão de combustíveis que minimizam o custo total de distribuiçao (transporte e operacões) enquanto os n íveis de armazenamento sao mantidos nos n íveis desejados. Por conveniencia, de acordo com o planeamento temporal, o prob¬lema e divido em dois sub-problemas interligados. Um de curto prazo e outro de medio prazo. Para o problema de curto prazo sao discutidos modelos matemáticos de programacao inteira mista, que consideram simultaneamente uma medicao temporal cont ínua e uma discreta de modo a modelar multiplas janelas temporais e taxas de consumo que variam diariamente. Os modelos sao fortalecidos com a inclusão de desigualdades validas. O problema e então resolvido usando um "software" comercial. Para o problema de medio prazo sao inicialmente discutidos e comparados varios modelos de programacao inteira mista para um horizonte temporal curto assumindo agora uma taxa de consumo constante, e sao introduzidas novas desigualdades validas. Com base no modelo escolhido sao compara¬das estrategias heurísticas que combinam três heur ísticas bem conhecidas: "Rolling Horizon", "Feasibility Pump" e "Local Branching", de modo a gerar boas soluçoes admissíveis para planeamentos com horizontes temporais de varios meses. Finalmente, de modo a lidar com situaçoes imprevistas, mas impor¬tantes no transporte marítimo, como as mas condicões meteorológicas e congestionamento dos portos, apresentamos um modelo estocastico para um problema de curto prazo, onde os tempos de viagens e os tempos de espera nos portos sao aleatórios. O problema e formulado como um modelo em duas etapas, onde na primeira etapa sao tomadas as decisões relativas as rotas do navio e quantidades a carregar e descarregar e na segunda etapa (designada por sub-problema) sao consideradas as decisoes (com recurso) relativas ao escalonamento das operacões. O problema e resolvido por um metodo de decomposto que usa um algoritmo eficiente para separar as desigualdades violadas no sub-problema.

Q-enhancement with on-chip inductor optimization for reconfigurable Δ-Σ radio-frequency ADC

The paper details on-chip inductor optimization for a reconfigurable continuous-time delta-sigma (Δ-Σ) modulator based radio-frequency analog-to-digital converter. Inductor optimisation enables the Δ-Σ modulator with Q enhanced LC tank circuits employing a single high Q-factor on-chip inductor and lesser quantizer levels thereby reducing the circuit complexity for excess loop delay, power dissipation and dynamic element matching. System level simulations indicate at a Q-factor of 75 Δ- Σ modulator with a 3-level quantizer achieves dynamic ranges of 106, 82 dB and 84 dB for RFID, TETRA, and Galileo over bandwidths of 200 kHz, 10 MHz and 40 MHz respectively.

Filters method in direct search optimization, new measures to admissibility

Constrained nonlinear optimization problems are usually solved using penalty or barrier methods combined with unconstrained optimization methods. Another alternative used to solve constrained nonlinear optimization problems is the lters method. Filters method, introduced by Fletcher and Ley er in 2002, have been widely used in several areas of constrained nonlinear optimization. These methods treat optimization problem as bi-objective attempts to minimize the objective function and a continuous function that aggregates the constraint violation functions. Audet and Dennis have presented the rst lters method for derivative-free nonlinear programming, based on pattern search methods. Motivated by this work we have de- veloped a new direct search method, based on simplex methods, for general constrained optimization, that combines the features of the simplex method and lters method. This work presents a new variant of these methods which combines the lters method with other direct search methods and are proposed some alternatives to aggregate the constraint violation functions.

Optimization of bioplastics production from cheese whey

Dissertation presented in partial fulfillment of the requirements for the degree of Master in Biotechnology

Optimization of the Pultrusion process

Manufacturing processes need permanently to innovate and optimize because any can be susceptible to continuous improvement. Innovation and commitment to the development of these new solutions resulting from existing expertise and the continuing need to increase productivity, flexibility and ensuring the necessary quality of the manufactured products. To increase flexibility, it is necessary to significantly reduce set-up times and lead time in order to ensure the delivery of products ever faster. This objective can be achieved through a normalization of the pultrusion line elements. Implicitly, there is an increase of productivity by this way. This work is intended to optimize the pultrusion process of structural profiles. We consider all elements of the system from the storehouse of the fibers (rack) to the pultrusion die. Particular attention was devoted to (a) the guidance system of the fibers and webs, (b) the resin container where the fibers are impregnated, (c) standard plates positioning of the fibers towards the entrance to the spinneret and also (d) reviewed the whole process of assembling and fixing the die as well as its the heating system. With the implementation of these new systems was achieved a significant saving of time set-up and were clearly reduced the unit costs of production. Quality assurance was also increased.

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.

Visible-light-mediated synthesis of helicenes in batch and continuous flow systems

Le présent mémoire décrit le développement d’une méthode de synthèse des hélicènes catalysée par la lumière visible. Les conditions pour la formation de [5]hélicène ont été établies par une optimisation du photocatalyseur, du solvant, du système d’oxydation et du temps réactionnel. Suite aux études mécanistiques préliminaires, un mécanisme oxydatif est proposé. Les conditions optimisées ont été appliquées à la synthèse de [6]hélicènes pour laquelle la régiosélectivité a été améliorée en ajoutant des substituants sur la colonne hélicale. La synthèse de thiohélicènes a aussi été testée en utilisant les mêmes conditions sous irradiation par la lumière visible. La méthode a été inefficace pour la formation de benzodithiophènes et de naphtothiophènes, par contre elle permet la formation du phenanthro[3,4-b]thiophène avec un rendement acceptable. En prolongeant la surface-π de la colonne hélicale, le pyrène a été fusionné aux motifs de [4]- et [5]hélicène. Trois dérivés de pyrène-hélicène ont été synthétisés en utilisant les conditions optimisées pour la photocyclisation et leurs caractéristiques physiques ont été étudiées. La méthode de cyclisation sous l’action de la lumière visible a aussi été étudiée en flux continu. Une optimisation du montage expérimental ainsi que de la source lumineuse a été effectuée et les meilleures conditions ont été appliquées à la formation de [5]hélicène et des trois dérivés du pyrène-hélicène. Une amélioration ou conservation des rendements a été observée pour la plupart des produits formés en flux continu comparativement à la synthèse en batch. La concentration de la réaction a aussi été conservée et le temps réactionnel a été réduit par un facteur de dix toujours en comparaison avec la synthèse en batch.

Triangulation by Continuous Embedding

When triangulating a belief network we aim to obtain a junction tree of minimum state space. Searching for the optimal triangulation can be cast as a search over all the permutations of the network's vaeriables. Our approach is to embed the discrete set of permutations in a convex continuous domain D. By suitably extending the cost function over D and solving the continous nonlinear optimization task we hope to obtain a good triangulation with respect to the aformentioned cost. In this paper we introduce an upper bound to the total junction tree weight as the cost function. The appropriatedness of this choice is discussed and explored by simulations. Then we present two ways of embedding the new objective function into continuous domains and show that they perform well compared to the best known heuristic.