Integrated Design and Implementation of Embedded Control Systems with Scilab

Embedded systems are playing an increasingly important role in control engineering. Despite their popularity, embedded systems are generally subject to resource constraints and it is therefore difficult to build complex control systems on embedded platforms. Traditionally, the design and implementation of control systems are often separated, which causes the development of embedded control systems to be highly time-consuming and costly. To address these problems, this paper presents a low-cost, reusable, reconfigurable platform that enables integrated design and implementation of embedded control systems. To minimize the cost, free and open source software packages such as Linux and Scilab are used. Scilab is ported to the embedded ARM-Linux system. The drivers for interfacing Scilab with several communication protocols including serial, Ethernet, and Modbus are developed. Experiments are conducted to test the developed embedded platform. The use of Scilab enables implementation of complex control algorithms on embedded platforms. With the developed platform, it is possible to perform all phases of the development cycle of embedded control systems in a unified environment, thus facilitating the reduction of development time and cost.Comment: 15 pages, 14 figures; Open Access at http://www.mdpi.org/sensors/papers/s8095501.pd

The Use of Scilab-Cloud for Teaching Digital Signal Processing Concepts in Electrical Engineering Curricula

The digital signal processing (DSP) is a relevant area in the electrical/computer engineering field, since several applications have been observed during the past decades. On the other hand, students have demonstrated difficulties to understand not only the eventual applications, but also its mathematical concepts and theory. Actually, open source packages are available and increasing, but the use of these tools are not very widespread in electrical engineering curriculum. This paper presents the use of Scilab-Cloud software platform for teaching some fundamentals of digital signal processing in undergraduate level, particularly for electrical engineering curriculum. Therefore, some experiments have carried out with undergraduate electrical engineering students and a questionnaire answered by them evidenced the potential of Scilab-Cloud as an interesting alternative tool to foster and motivate students for learning DSP skills

Performance Comparison of Open Source and Commercial Computing Tools in Educational and Other Use — Scilab vs. MATLAB

In this paper, the authors compare the features and the overall performance of the two high-level numerical computing and modeling software environments: the freeware Scilab and commercially available industry-standard MATLAB. The motivation for the work emanated from the educational use of these tools at the college and university level, but with a perspective to their professional and scientific use as well. Their performance is tested by measuring the execution times of several combined-task benchmarks implemented as test functions, built upon nine common numerical tasks that are often found in programs for solving standard engineering problems. They include basic algebra and matrix calculations, signal generation, signal analysis, and storing and retrieving data to and from the hard disk drive. Although MATLAB outperforms Scilab in all the benchmarks except the disk file manipulations, in the presumed vectorization versions of the benchmarks, it is not for much. The overall performance of the freeware rival is very satisfactory, making it a good choice not only for educational use but also for scientific and professional purposes, especially when funding is critical

A Review On The Comparative Roles Of Mathematical Softwares In Fostering Scientific And Mathematical Research

Mathematical software tools used in science, research and engineering have a developmental trend. Various subdivisions for mathematical software applications are available in the aforementioned areas but the research intent or problem under study, determines the choice of software required for mathematical analyses. Since these software applications have their limitations, the features present in one type are often augmented or complemented by revised versions of the original versions in order to increase their abilities to multi-task. For example, the dynamic mathematics software was designed with integrated advantages of different types of existing mathematics software as an improved version for understanding numerical related problems for advanced mathematical content (advanced simulation). In recent times, science institutions have adopted the use of computer codes in solving mathematics related problems. The treatment of complex numerical analysis with the aid of mathematical software is currently used in all branches of physical, biological and social sciences. However, the programming language for mathematics related software varies with their functionalities. Many invaluable researches have been compromised within the confines of unacceptable but expedient standards because of insufficient understanding of the valuable services the available variety of mathematical software could offer. In the developing countries, some mathematical software like Matlab and MathCAD are very common. A comparative review for some mathematical software was embarked upon in order to understand the advantages and limitations of some of the available mathematical software

Distributed Software Development Tools for Distributed Scientific Applications

This chapter provides a new methodology and two tools for user‐driven Wikinomics‐oriented scientific applications’ development. Service‐oriented architecture for such applications is used, where the entire research supporting computing or simulating process is broken down into a set of loosely coupled stages in the form of interoperating replaceable Web services that can be distributed over different clouds. Any piece of the code and any application component deployed on a system can be reused and transformed into a service. The combination of service‐oriented and cloud computing will indeed begin to challenge the way of research supporting computing development, the facilities of which are considered in this chapter

Cost-effective HPC clustering for computer vision applications

We will present a cost-effective and flexible realization of high performance computing (HPC) clustering and its potential in solving computationally intensive problems in computer vision. The featured software foundation to support the parallel programming is the GNU parallel Knoppix package with message passing interface (MPI) based Octave, Python and C interface capabilities. The implementation is especially of interest in applications where the main objective is to reuse the existing hardware infrastructure and to maintain the overall budget cost. We will present the benchmark results and compare and contrast the performances of Octave and MATLAB