PG Diploma in Automotive Embedded Systems

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About the Course

Automotive Embedded Systems are real time systems pertaining to automotive field. As per the latest survey Electric vehicles design and ADAS are the recent trends in automotive sector. While still early in their development and adoption, autonomous and electric vehicles are rapidly advancing and appear poised to trigger the transportation industry’s largest shakeup in over a century.

This course content is designed keeping the industry requirements in mind .Each student will get the opportunity to learn product design from first principles and have adequate time to learn by designing using industry approved software and hardware development platform. An integral part of this program is to work in teams where each team designing a specific module, this will give the students valuable insights into how professionals work in the industry and the interdependencies across teams to achieve a commercially viable product.

Course duration

4 Months


Engineering graduates from ECE, E & I, ICE, Mechatronics, E & E, Computer Science Mechanical and Automobile Eng. Embedded Engineers working on C and who has tomove to Automotive domain.

Course Content

In this module, you will be introduced to Automotive Embedded Systems. In this session, we'll also introduce you to the program; help you discover the automotive workflow. We will also discuss on the market, job opportunities. We will also discuss on the services we provide over the course of the journey, and show you all the incredible projects that you can build.

In this module, you will be introduced to MATLAB and SIMULINK. You will be introduced to MATLAB technical computing environment. This Module is divided into two sections

In the first section, MATLAB Programming basics, MATLAB Programming basics, Numerical Computation, Data Analysis & visualization, Working with Functions, Concepts of data visualization, Data analysis, Visualization, Programming, Importing data, Processing data, Customizing visualizations, working with irregular data, relational-andlogical-functions,loop-statements-vectorising-codes,stringmanipulation, data-structures.

In the Second section, you will be introduced to Simulink and Model based design usingSimulink, Introduction to mathematical & physical modelling, Overview of Simulink blocklibrary, Introduction to solvers, Creating and modifying Simulink models and simulatingsystem dynamics, Modeling continuous-time, discrete-time, and hybrid systems,Modifying solver settings for simulation accuracy and speed Building hierarchy into aSimulink model, Creating reusable model components using subsystems, libraries, andmodel references. Concepts of SIL, HIL and MIL and FPGA in Loop will be covered.

Provide an overview of the control system design process and introduce how MATLAB and Simulink fit into that process. Control System Design Overview, Model Representation, System Identification, Parameter Estimation, System Analysis, Linearization, PID control in Simulink, Classic Control Theory, Response Estimation, Controller Implementation, Hardware Implementation, Preparing Simulink models for code generation, Generating code and test bench for a compatible Simulink model, Performing speed and area optimizations. System analysis functions. System Analyzer, DC motor analysis, Automation of analysis tasks, Open loop analysis.

In this module, you will be introduced to the concepts and latest trend in Internet of Things. You will demonstrated with exercises and case studies associated to IOT and its application. We will cover topics like Introduction to IoT Microcontrollers and Sensors, Hardware architecture of Microcontroller and ARM processor, developing real time IOT applications using Microcontroller and ARM, and deploying the data to cloud for data analysis. The topics in this module will be cover with hands on sessions on like Arduino microcontroller, Raspberry pi, Android Mobile Phones and various interfacing sensors so on. The real time data collected from this hardware will be loaded to cloud and corresponding data will be visualized.

In this module, you will be introduced to the concepts of Image and Computer Vision. You will be demonstrated with exercises and case studies associated to Basic and advanced topics associated to Computer Vision. Topics like Importing and exporting images, Removing noise , Aligning images and creating a panoramic scene, Detecting lines and circles in an image, Segmenting objects, Measuring and modifying object shape properties , Performing batch analysis over sets of images, Importing, displaying, and annotating images and videos, Detecting, extracting, and matching object features, Automatically aligning images using geometric transformations, Detecting objects in images and videos, Tracking objects and estimating their motion in a video, Removing lens distortion from images , Measuring planar objects. You’ll use a combination of cameras, software to perform these operations

Machine Learning

The module will demonstrates the use of unsupervised learning to discover features in large data sets and supervised learning to build predictive models. Examples and exercises highlight techniques for visualization and evaluation of results. Topics include: Importing and organizing data, Finding natural patterns in data, Building predictive models, Evaluating and improving the model, Introduction to Machine Learning for Computer Vision Applications Hands-on exercises: Image pre-processing Train and compare classifiers using Classification Learner App Feature extraction and machine learning for image recognition. Why use MATLAB for Machine Learning, Data types that we may Encounter, Importing data into MATLAB, Understanding the table data type, KNearest Neighbor Building a model with subset of classes, missing values and instances weights, K-Nearest Neighbor- Dealing with scaling issue and copying a learned model, KNearest Neighbor-Learning KNN model with features subset and with non numeric data, K-Nearest Neighbor- Nearest Neighbor Intuition, K-Nearest Neighbor- Properties of KNN(K-Nearest Neighbor ).

Deep Learning

Deep Learning overview, CNN Architecture Modifying Network Layers, Setting Training Options, Training the Network, Hands on: Real time Image classification using Deep Learning. Hands on: Real time face detection, Object Classification and Image and Video Capturing. learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist. Classifying Images with Convolutional Networks, Get an overview of the course. Perform image classification using pretrained networks. Use transfer learning to train customized classification networks, Interpreting Network Behavior, Gain insight into how a network is operating by visualizing image data as it passes through the network. Apply this technique to different kinds of images, Extracting and Visualizing Activations, Representing Signal Data as Images, Feature Extraction for Machine Learning, Creating Networks, Build Convolutional networks from scratch. Understand how information is passed between network layers and how different types of layers work. Training from Scratch, Landcover Classification, Creating Network Architectures, Understanding Neural Networks Convolutional Layers, Creating Networks, Training Networks Understand how training algorithms work.

Introduction to ADAS,. Label ground truth data in a video or sequence of images interactively. Automate the labeling with detection and tracking algorithms. Overview of the Ground Truth Labeler app. Label regions of interest (ROIs) and scenes. Automate labelling. View and export ground truth results. Segment and model parabolic lane boundaries. Use pretrained object detectors to detect vehicles. Perform a bird’s-eye view transform, Detect lane features, Compute lane model, Validate lane detection with ground truth, Detect vehicles with pretrained object detectors.

AUTOSAR (Automotive Open System Architecture) a worldwide consortium of OEMs, suppliers and other companies, Fusion founded in 2003 have been working on the development and introduction of an open and standardized software architecture for the automotive industry. AUTOSAR concept is based on modular components with defined interfaces. AUTOSAR is a layered architecture. In AUTOSAR, the ECU software is abstracted and sub-classified as software (BSW) layer, runtime environment (RTE) and application layer. AUTOSAR Architectures, Layers usage, Protocols: CAN, LIN, I2C, SPI and UART. Diagnostics: UDS services, CANoe, CANalyser, CAPL Programming, Panel creation using CANoe, Communication Stack - CAN and AUTOSAR, Testing Life Cycle, Test Methodologies, V & Agile Model, Defect Life Cycle, SVN Tools & Industry Work Flow Functional Safety-ISO 26262.

The course focuses on how to employ flow charts, state machines, truth tables, and state transition tables in Simulink® designs. Topics include: Implement decision flows with flow charts, Junctions and transitions, Flow chart behaviour, Stateflow interface, Conditions and condition actions, Chart data, Common patterns, Flow charts, State machines, Hierarchical state machines, Parallel state machines, Events in state machines, Functions in state machines, Truth tables, State transition tables, Componentbased modelling.

This course discusses the use of Polyspace® Code Prover™ to prove code correctness, improve software quality metrics, and ensure product integrity. This hands-on course is intended for engineers who develop software or models targeting embedded systems. Creating a verification project, Reviewing and understanding verification results, Emulating target execution environments, Handling missing functions and data, Managing unproven code (color-coded in orange by Polyspace products), Applying MISRA-C® rules, Reporting analysis results