Fábio Favarim

Active power filters compensator implementation using parallel structures in FPGA devices

This paper presents a parallel implementation approach of selective harmonic compensator for active power filters. This approach uses field programmable gate array (FPGAs) in order to reduce the compensator computational time. To compensate for even a small number of harmonics digital filters require multiple calculation instructions involving multiplications and additions. Thus, to improve the performance of the computer system it is proposed the digital compensator implementation using parallel structures in FPGA devices. Experimental results are presented to compare the speedup of the proposed parallel approach with the DSP sequential execution time conventionally used in active power filters applications.

Development of a low cost weather station using free hardware and software

Weather stations are a valuable tool to many different areas, like agriculture, aviation, construction, sports and recreation, specially because they collect weather data, which can be stored and processed to gather specific information to correlate events to the weather action. This work shows a development model of a low cost weather station, using only free hardware and software, connected to the internet, giving real time data taken from the station, as well as history of the stored data. This station, even though using low cost or handcrafted sensors, like those that measure wind speed, wind direction and rainfall index, showed consistent results costing only 10% of the price, in comparison with some professional weather stations, allowing its popularization in agriculture, homes and institutions.

Resources Planning in Database Infrastructures

Anticipating resources consumption is essential to project robust database infrastructures able to support transactions to be processed with certain quality levels. In Database-as-a-Service (DBaaS), for example, it could help to construct Service Level Agreements (SLA) to intermediate service customers and providers. A proper database resources assessment can avoid mistakes when choosing technology, hardware, network, client profiles, etc. However, to be properly evaluated, a database transaction usually requires the physical system to be measured, which can be expensive an time consuming. As most information about resource consumption are useful at design time, before developing the whole system, is essential to have mechanisms that partially open the black box hiding the in-operation system. This motivates the adoption of predictive evaluation models. In this paper, we propose a simulation model that can be used to estimate performance and availability of database transactions at design time, when the system is still being conceived. By not requiring real time inputs to be simulated, the model can provide useful information for resources planning. The accuracy of the model is checked in the context of a SLA composition process, in which database operations are simulated and model estimations are compared to measurements collected from a real database system.

Combining Learning Algorithms: An Approach to Markov Decision Processes

In this paper we present a technique for estimating policies which combines instance-based learning and reinforcement learning algorithms in Markovian environments. This approach has been developed for speeding up the convergence of adaptive intelligent agents that using reinforcement learning algorithms. Speeding up the learning of an intelligent agent is a complex task since the choice of inadequate updating techniques may cause delays in the learning process or even induce an unexpected acceleration that causes the agent to converge to a non-satisfactory policy. Experimental results in real-world scenarios have shown that the proposed technique is able to speed up the convergence of the agents while achieving optimal policies, overcoming problems of classical reinforcement learning approaches.