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- Modelo matemático + Sistema físico = Teoria dos SistemasPublication . Lima, JoãoAutomatic Control Systems are far and wide used in all modern and industrialized societies. Devices designed to control automatized tasks are each time more present from small plants to large industrial buildings. The development of mathematical models is a compulsory task for whom aim at analyzing or design any control systems. These mathematical models should reproduce some performance measures as accurate as possible. So, no matter the physical nature of the process we aim at control, an accurate mathematical model should be evaluate. So, the development of mathematical models can be considered an hi-level step over the physical nature of the system that we aim at analyze or design. For this reason the study of Systems Theory and Control Systems are considered transversal areas of the knowledge and them studies are compulsory in many branches of sciences and technologies in many universities all over the world. In spite of normal systems are non-linear the linearization procedure simplify the analysis and design of control systems and, depending on the accuracy of the model can give us good results.
- Exploiting Kant and Kimura's Matrix Inversion Algorithm on FPGAPublication . Perina, Andre Bannwart; Matias, Paulo; Marques, Eduardo; Bonato, Vanderlei; Lima, João; Kubatova, H.; Novotny, M.; Skavhaug, A.Matrix inversion for real-time applications can be a challenge for the designers since its computational complexity is typically cubic. Parallelism has been widely exploited to reduce such complexity, however most traditional methods do not scale well with the matrix size leading to communication bottlenecks. In this paper we exploit a decentralised parallel hardware architecture based on a strongly non-singular matrix inversion algorithm proposed by Kant and Kimura in 1978, which is a parallel-orientated method with communication mode independent of the matrix size, mitigating the problem of matrix scalability. The hardware architecture is implemented in two different approaches using fixed-point arithmetic: dedicated and shared. In the first approach a matrix can be inverted in linear time while the latter, for the best case, has a square complexity. Experimental results are demonstrated using a Stratix V GX FPGA. For instance, in dedicated approach an 8x8 matrix is inverted in 1.27us, while in shared approach a 64x64 matrix is inverted in 153.40us using 64 pipelined processing elements.