SPARTS: simulator for power aware and real-time systems

Real-time systems demand guaranteed and predictable run-time behaviour in order to ensure that no task has missed its deadline. Over the years we are witnessing an ever increasing demand for functionality enhancements in the embedded real-time systems. Along with the functionalities, the design itself grows more complex. Posed constraints, such as energy consumption, time, and space bounds, also require attention and proper handling. Additionally, efficient scheduling algorithms, as proven through analyses and simulations, often impose requirements that have significant run-time cost, specially in the context of multi-core systems. In order to further investigate the behaviour of such systems to quantify and compare these overheads involved, we have developed the SPARTS, a simulator of a generic embedded real- time device. The tasks in the simulator are described by externally visible parameters (e.g. minimum inter-arrival, sporadicity, WCET, BCET, etc.), rather than the code of the tasks. While our current implementation is primarily focused on our immediate needs in the area of power-aware scheduling, it is designed to be extensible to accommodate different task properties, scheduling algorithms and/or hardware models for the application in wide variety of simulations. The source code of the SPARTS is available for download at [1].

Multi-region routing

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do grau de Mestre em Engenharia Electrotécnica e de Computadores

Reconstructing the architectural evolution of volcanic islands from combined K/AR, morphologic, tectonic, and magnetic data. The Faial Island exemple (Azores)

The morpho-structural evolution of oceanic islands results from competition between volcano growth and partial destruction by mass-wasting processes. We present here a multi-disciplinary study of the successive stages of development of Faial (Azores) during the last 1 Myr. Using high-resolution digital elevation model (DEM), and new K/Ar, tectonic, and magnetic data, we reconstruct the rapidly evolving topography at successive stages, in response to complex interactions between volcanic construction and mass wasting, including the development of a graben. We show that: (1) sub-aerial evolution of the island first involved the rapid growth of a large elongated volcano at ca. 0.85 Ma, followed by its partial destruction over half a million years; (2) beginning about 360 ka a new small edifice grew on the NE of the island, and was subsequently cut by normal faults responsible for initiation of the graben; (3) after an apparent pause of ca. 250 kyr, the large Central Volcano (CV) developed on the western side of the island at ca 120 ka, accumulating a thick pile of lava flows in less than 20 kyr, which were partly channelized within the graben; (4) the period between 120 ka and 40 ka is marked by widespread deformation at the island scale, including westward propagation of faulting and associated erosion of the graben walls, which produced sedimentary deposits; subsequent growth of the CV at 40 ka was then constrained within the graben, with lava flowing onto the sediments up to the eastern shore; (5) the island evolution during the Holocene involves basaltic volcanic activity along the main southern faults and pyroclastic eruptions associated with the formation of a caldera volcano-tectonic depression. We conclude that the whole evolution of Faial Island has been characterized by successive short volcanic pulses probably controlled by brief episodes of regional deformation. Each pulse has been separated by considerable periods of volcanic inactivity during which the Faial graben gradually developed. We propose that the volume loss associated with sudden magma extraction from a shallow reservoir in different episodes triggered incremental downward graben movement, as observed historically, when immediate vertical collapse of up to 2 m was observed along the western segments of the graben at the end of the Capelinhos eruptive crises (1957-58).

Optimization of multi-stage amplifiers in deep-submicron CMOS using a distributed/parallel genetic algorithm

IEEE International Symposium on Circuits and Systems, pp. 724 – 727, Seattle, EUA

Road drivers performance appraisal using a MCDA method: a case study in the road transport company

Performance evaluation increasingly assumes a more important role in any organizational environment. In the transport area, the drivers are the company’s image and for this reason it is important to develop and increase their performance and commitment to the company goals. This evaluation can be used to motivate driver to improve their performance and to discover training needs. This work aims to create a performance appraisal evaluation model of the drivers based on the multi-criteria decision aid methodology. The MMASSI (Multicriteria Methodology to Support Selection of Information Systems) methodology was adapted by using a template supporting the evaluation according to the freight transportation company in study. The evaluation process involved all drivers (collaborators being evaluated), their supervisors and the company management. The final output is a ranking of the drivers, based on their performance, for each one of the scenarios used.

Scenarios Generation for Multi-Agent simulation of Electricity Markets based on Intelligent Data Analysis

This document presents a tool able to automatically gather data provided by real energy markets and to generate scenarios, capture and improve market players’ profiles and strategies by using knowledge discovery processes in databases supported by artificial intelligence techniques, data mining algorithms and machine learning methods. It provides the means for generating scenarios with different dimensions and characteristics, ensuring the representation of real and adapted markets, and their participating entities. The scenarios generator module enhances the MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) simulator, endowing a more effective tool for decision support. The achievements from the implementation of the proposed module enables researchers and electricity markets’ participating entities to analyze data, create real scenarios and make experiments with them. On the other hand, applying knowledge discovery techniques to real data also allows the improvement of MASCEM agents’ profiles and strategies resulting in a better representation of real market players’ behavior. This work aims to improve the comprehension of electricity markets and the interactions among the involved entities through adequate multi-agent simulation.

Strategic Bidding in Electricity Markets: An agent-based simulator with game theory for scenario analysis

Electricity markets are complex environments, involving a large number of different entities, with specific characteristics and objectives, making their decisions and interacting in a dynamic scene. Game-theory has been widely used to support decisions in competitive environments; therefore its application in electricity markets can prove to be a high potential tool. This paper proposes a new scenario analysis algorithm, which includes the application of game-theory, to evaluate and preview different scenarios and provide players with the ability to strategically react in order to exhibit the behavior that better fits their objectives. This model includes forecasts of competitor players’ actions, to build models of their behavior, in order to define the most probable expected scenarios. Once the scenarios are defined, game theory is applied to support the choice of the action to be performed. Our use of game theory is intended for supporting one specific agent and not for achieving the equilibrium in the market. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. The scenario analysis algorithm has been tested within MASCEM and our experimental findings with a case study based on real data from the Iberian Electricity Market are presented and discussed.

Realistic Multi-Agent Simulation of Competitive Electricity Markets

The dynamism and ongoing changes that the electricity markets sector is constantly suffering, enhanced by the huge increase in competitiveness, create the need of using simulation platforms to support operators, regulators, and the involved players in understanding and dealing with this complex environment. This paper presents an enhanced electricity market simulator, based on multi-agent technology, which provides an advanced simulation framework for the study of real electricity markets operation, and the interactions between the involved players. MASCEM (Multi-Agent Simulator of Competitive Electricity Markets) uses real data for the creation of realistic simulation scenarios, which allow the study of the impacts and implications that electricity markets transformations bring to different countries. Also, the development of an upper-ontology to support the communication between participating agents, provides the means for the integration of this simulator with other frameworks, such as MAN-REM (Multi-Agent Negotiation and Risk Management in Electricity Markets). A case study using the enhanced simulation platform that results from the integration of several systems and different tools is presented, with a scenario based on real data, simulating the MIBEL electricity market environment, and comparing the simulation performance with the real electricity market results.

Data Mining Approach to support the Generation of Realistic Scenarios for Multi-Agent simulation of Electricity Markets

This paper presents the Realistic Scenarios Generator (RealScen), a tool that processes data from real electricity markets to generate realistic scenarios that enable the modeling of electricity market players’ characteristics and strategic behavior. The proposed tool provides significant advantages to the decision making process in an electricity market environment, especially when coupled with a multi-agent electricity markets simulator. The generation of realistic scenarios is performed using mechanisms for intelligent data analysis, which are based on artificial intelligence and data mining algorithms. These techniques allow the study of realistic scenarios, adapted to the existing markets, and improve the representation of market entities as software agents, enabling a detailed modeling of their profiles and strategies. This work contributes significantly to the understanding of the interactions between the entities acting in electricity markets by increasing the capability and realism of market simulations.

System Level Simulation and Radio Resource Management for Distributed Antenna Systems with Cognitive Radio and Multi-Cell Cooperation

4th International Conference on Future Generation Communication Technologies (FGCT 2015), Luton, United Kingdom.

The inhibitory activity of synthetic compounds and ions against transporters of multi-drug resistant bacteria

RESUMO: Sessenta e três derivados de hidantoína foram utilizados para avaliar possíveis efeitos de modulação na actividade das bombas de efluxo (BE) na Salmonella NCTC 13349 utilizando um método fluorimétrico semi-automático. Nenhum dos compostos apresentaram actividade anti-bacteriana até concentrações de 240 mg/L. Entre todos os compostos, SZ-7 demonstrou possuir propriedades de modulação de effluxo na presença de glucose. Para testar esta actividade, estirpes de Salmonella resistentes à ciprofloxacina, induzidas a elevados níveis de resistência com sobre-expressão de BE, foram expostas ao SZ-7. Este derivado afectou a susceptibilidade das estirpes à ciprofloxacina. Uma vez que os 63 compostos estudados apresentaram pouco efeito inibitório /cumulativo, apesar de serem conhecidos pelos seus efeitos moduladores de BE-dependentes de iões em eucariotas, foi questionado o papel dos iões na regulação de BE bacterianas, que poderão influenciar a eficácia de novos compostos. Para este estudo, utilizamos a Escherichia coli AG100 como modelo, devido ao extenso conhecimento no que respeita a estrutura e actividade das BE. Devido à importância de iões de cálcio (Ca2+) nos canais de transporte membranar e na actividade de ATPases, a sua actividade na modulação do efluxo foi investigada. De resultados anteriormente obtidos concluiu-se que a pH 5 o efluxo é independente de energia metabólica; contudo, a pH 8 é absolutamente dependente, sendo que o Ca2+ é indispensável para manter a actividade das ATPases bacterianas. A acumulação/effluxo de EtBr pela E. coli AG100 foi determinada na presença/ausência de Ca2+, clorpromazina (inibidor de ligação de Ca2+ a proteínas), e ácido etilenodiamino tetra-acético (quelante de Ca2+). Acumulação/effluxo aumentou a pH 8, contudo o Ca2+ reverte estes efeitos evidenciando a sua importância no funcionamento das BE bacterianas. Em resumo este trabalho colocou em evidência que muitos aspectos bioquímicos e bioenergéticos devem ser tomados em consideração no estudo da resistência bacteriana mediada por BE.------- ABSTRACT: Sixty-three hydantoin derivatives were evaluated for their modulating effects on efflux pump (EP) activity of Salmonella NCTC 13349 utilizing a semi-automatic fluorometric method. None of the compounds presented antibacterial activities at concentrations as high as 240 mg/L. Among all compounds, SZ-7 showed possible efflux modulating activity in the presence of glucose, indicative of a potential EP inhibitor. To verify its potential effects, ciprofloxacin-resistant Salmonella strains, induced to high level resistance with over-expressing EPs, were exposed to SZ-7. This derivative affected the susceptibility of the ciprofloxacin-resistant strains. Since the 63 compounds studied had very low inhibitory/accumulative effects, even though their known for being efficient in modulating ion-driven eukaryotic EPs, we questioned whether ions had a leading role in regulating bacterial EPs, influencing the effectiveness of new compounds. For this study we used Escherichia coli AG100 as a model, due to the extensive knowledge on its EPs structure and activity. Owing the importance of calcium ions (Ca2+) for membrane transport channels and activity of ATPases, the role of Ca2+ was investigated. From previous results we concluded that at pH 5 efflux is independent of metabolic energy; however, at pH 8 it is entirely dependent of metabolic energy and the Ca2+ ions are essential to maintain the activity of bacterial ATPases. Accumulation and efflux of ethidium bromide (EtBr) by E. coli AG100 was determined in the presence and absence of Ca2+, chlorpromazine (inhibitor of Ca2+-binding to proteins), and ethylenediaminetetraacetic acid (Ca2+ chelator). Accumulation of EtBr increased at pH 8; however Ca2+ reversed these effects providing information as to the importance of this ion in the regulation of bacterial EP systems. Overall this work puts in evidence that many biochemical and bioenergetic aspects related to the strains physiology need to be taken into consideration in bacterial drug resistance mediated by EPs.

Serviços pós-4G em redes de satélite com recepção multi-pacote de acesso com agendamento

Dissertação para obtenção do Grau de Mestre em Engenharia Eletrotécnica e Computadores

A multiagent based shop floor transportation system simulator

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

An agent-based simulator to estimate domestic energy use

Throughout recent years, there has been an increase in the population size, as well as a fast economic growth, which has led to an increase of the energy demand that comes mainly from fossil fuels. In order to reduce the ecological footprint, governments have implemented sustainable measures and it is expected that by 2035 the energy produced from renewable energy sources, such as wind and solar would be responsible for one-third of the energy produced globally. However, since the energy produced from renewable sources is governed by the availability of the respective primary energy source there is often a mismatch between production and demand, which could be solved by adding flexibility on the demand side through demand response (DR). DR programs influence the end-user electricity usage by changing its cost along the time. Under this scenario the user needs to estimate the energy demand and on-site production in advance to plan its energy demand according to the energy price. This work focuses on the development of an agent-based electrical simulator, capable of: (a) estimating the energy demand and on-site generation with a 1-min time resolution for a 24-h period, (b) calculating the energy price for a given scenario, (c) making suggestions on how to maximize the usage of renewable energy produced on-site and to lower the electricity costs by rescheduling the use of certain appliances. The results show that this simulator allows reducing the energy bill by 11% and almost doubling the use of renewable energy produced on-site.

A public transport bus assignment problem: parallel metaheuristics assessment

Combinatorial Optimization Problems occur in a wide variety of contexts and generally are NP-hard problems. At a corporate level solving this problems is of great importance since they contribute to the optimization of operational costs. In this thesis we propose to solve the Public Transport Bus Assignment problem considering an heterogeneous fleet and line exchanges, a variant of the Multi-Depot Vehicle Scheduling Problem in which additional constraints are enforced to model a real life scenario. The number of constraints involved and the large number of variables makes impracticable solving to optimality using complete search techniques. Therefore, we explore metaheuristics, that sacrifice optimality to produce solutions in feasible time. More concretely, we focus on the development of algorithms based on a sophisticated metaheuristic, Ant-Colony Optimization (ACO), which is based on a stochastic learning mechanism. For complex problems with a considerable number of constraints, sophisticated metaheuristics may fail to produce quality solutions in a reasonable amount of time. Thus, we developed parallel shared-memory (SM) synchronous ACO algorithms, however, synchronism originates the straggler problem. Therefore, we proposed three SM asynchronous algorithms that break the original algorithm semantics and differ on the degree of concurrency allowed while manipulating the learned information. Our results show that our sequential ACO algorithms produced better solutions than a Restarts metaheuristic, the ACO algorithms were able to learn and better solutions were achieved by increasing the amount of cooperation (number of search agents). Regarding parallel algorithms, our asynchronous ACO algorithms outperformed synchronous ones in terms of speedup and solution quality, achieving speedups of 17.6x. The cooperation scheme imposed by asynchronism also achieved a better learning rate than the original one.