• Trity Course Scilab IoT

    Scilab for the Internet of Things

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  • Trity Course RPi IoT

    Raspberry Pi for the Internet of Things (with Pi-3)

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  • Trity Course Scilab AI

    Artificial Intelligence with Scilab

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  • Trity Course Scilab NCV

    Numerical Computation and Visualization with Scilab

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  • Trity Course Scilab IP

    Scilab for Image Processing and Computer Vision

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  • Trity Course Scilab BDA

    Big Data Training Series : Practical Guide to Big Data Analytics with Pig Latin, Hive and Scilab

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  • Trity Course Scilab DM

    Scilab for Data Mining

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  • Python Deep Learning

    Python for Machine and Deep Learning

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Scilab Courses

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Scilab is an open source, cross-platform numerical computational package and a high-level, numerically oriented programming language. It can be used for signal and image processing, statistical analysis, Internet of Things, data mining, etc. In Trity Technologies we have developed more than 20 courses based on Scilab since last few years.

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Raspberry Pi Courses

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The Raspberry Pi is a series of credit card–sized single-board computers developed in the United Kingdom by the Raspberry Pi Foundation with the intent to promote the teaching of basic computer science in schools and developing countries. Our very first Raspberry Pi Training is the aplication in IoT, and we are extending the training into other fields from time to time. 

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E4Coder - Automatic Code Generation

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E4Coder is a set of tools that can be used to simulate control algorithms and to generate code for embedded microcontrollers running with or without a realtime operating system. Our course focus on using the block diagram for algorithms development and the codes would be automatically generated and downloaded into the embedded boards such as Arduino Uno. A mobile robot application would be used for the training for practical hands-on. 

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Scilab for Digital Control

DCS and State Variable Methods for Conventional and Intelligent Control

Scilab provides standard algorithms and tools for control system study.

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Scilab brings you one step closer to the hardware reality world by analysing the control system in digital domain…”

Course Synopsisgraph

This course is conducted in a workshop-like manner, with a mixture of theory, hands-on coding and hardware in Scilab. Extensive exercises are provided throughout the course to cover every angle of algorithm design and implementation using Scilab. In additional, hardware implementation for digital control systems will also be conducted.

The course begins with an overview of signal processing in two parts namely (1) digital control: principles and design in transform-domain (covers digital control, models of digital control devices and systems, design of digital control algorithms); (2) state variable methods in automatic control (covers control system analysis using state variable methods, state variable analysis of digital control system;               pole-placement digital and state observers).

The derivation of the main algorithms are covered to enable better understanding and to provide insight on the conceptual ideas behind these algorithms. Application examples are provided at the end of each section to help reconcile theory with actual practice.

Course Objectivesrootlocus

This course is intended as a practical introduction to digital control techniques. As such, there will be a series of hands-on exercises which are generally aimed to help translate the theoretical models to practical applications.

Who Must Attend

Scientists, mathematicians, engineers and programmers at all levels who work with or need to learn about brightness and contrast techniques. No background in either of these topics is, however, assumed. The detailed course material and many source code listings will be invaluable for both learning and reference


A basic knowledge of control theory, signal processing and Scilab programming is an extra.


Course Outline


Overview of Digital Control: Principle and design in Transfer function

  • Introduction
  • Signal Sampling and Reconstruction
  • Multi Variable Control System
  • Sampled – Data Control System
  • Sample and hold operation
  • Z-Transforms for Analysis of Sampled-Data Systems

Discrete-time System Modelling

  • Transfer functions
  • Block Diagrams
  • Signal flow graphs

Time-domain and Z-domain Analysis

  • Stability
  • Stability test for discrete systems
  • Jury's Stability Test
  • Performance specifications of Discrete-time systems In time domain
Steady State Performance
  • Steady state error analysis of digital control systems
  • Root loci for digital control systems

Frequency-domain analysis

  • Nyquist plot
  • Bode plot

State space analysis of digital control systems

  • State equations and state transition equations for continuous-time systems
  • State Transition Matrix
  • State Equations for discrete-time system with S/H devices
  • Relationship between state equations and transfer functions
  • Eigenvalues and eigenvectors
  • State diagram
  • Controllability and Observability

Pole placement design and state observers

  • Pole Placement Design / State Observers
  • Transform Design of Digital Controllers compensator

Hardware demonstrations

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