Pune Abstracts

How to Build an Autonomous Anything


Autonomous technology will touch nearly every part of our lives, changing the products we build and the way we do business. It’s not just in self-driving cars, robots, and drones; it’s in predictive engine maintenance, automated trading, medical image interpretation, and other applications. Autonomy—the ability of a system to learn to operate independently—requires three elements:

  • Massive amounts of data and computing power
  • A diverse set of algorithms, from communications and controls to vision and deep learning
  • The flexibility to leverage both cloud and embedded devices to deploy the autonomous technology

In this talk, Jim Tung shows you how engineers and scientists are combining these elements, using MATLAB® and Simulink®, to build autonomous technology into their products and services today—to build their autonomous anything.

Richard Rovner Jim Tung, MathWorks Fellow

What's New in MATLAB and Simulink


Engineers and scientists worldwide rely on MATLAB® and Simulink® to accelerate the pace of discovery, innovation, development, and learning. In this presentation, you will see how new capabilities in the latest releases will help your research, design, and development workflows become more efficient. Learn how you can accelerate Model-Based Design through enhanced simulation performance in Simulink and use machine learning and deep learning in MATLAB for data analytics and computer vision applications.

Prashant Rao Prashant Rao, Technical Manager, MathWorks India

Big Data and Machine Learning Using MATLAB


Predictive analytics is the engine of evidence-based decision-making. Today, big data and engineering techniques bring many opportunities to the world of analytics. However, using data to build accurate and robust models for prediction requires a combination of equipment, expertise, and statistical know-how.

In this session, Seth and Amit discuss strategies and techniques for handling large amounts of data. Attendees will learn about tools and algorithms used to create machine learning models that learn from data and scale those models up to big data problems.


  • Accessing data in large text files, databases, or from the Hadoop Distributed File System (HDFS)
  • Processing data that does not fit in memory
  • Applying machine learning techniques to explore and prepare data for modeling
Seth Deland Seth Deland, Product Manager, Data Analytics, MathWorks
Amit Doshi Amit Doshi, Senior Application Engineer, MathWorks India

Development of a Rule-Based Decision Support System for Connected Washing Machine to Select Appropriate Wash Program


The advent of connected appliances offers new opportunities for products to enhance the user experience with smartphone applications. Most new users of washing machines are not sure what the appropriate wash program is for their laundry. Contrast this with the designer of wash programs, who understands of the effect of each wash program.

IFB wants to leverage connectivity in order to solve this problem by developing a rule-based expert system that emulates suggestions created by the wash program designer. The challenges in developing a washing machine program selection system include processing the inputs (i.e., cloths, dirt, and color) and relating the inference to program parameters (i.e., wash rhythm, temperature, and water content), ensuring convenience to the end user. An iterative approach was necessary to fine-tune interaction between a decision support system for an end user with the algorithm development and final deployment.

This presentation explains how IFB used MATLAB® and Fuzzy Logic Toolbox™ for user-centric development of a rule-based decision support system for connected washing machines, which was later transferred to a mobile app.

N Prathap N Prathap, IFB Industries Ltd.

Integrating MATLAB Analytics into Enterprise Applications


MATLAB® applications and components can be deployed to a variety of platforms, providing you with the flexibility to determine the best solution for your organization. You can deploy any MATLAB program covering a range of industries and applications such as data analytics, semiconductor/electronics, manufacturing systems, image processing, aerospace and defense, and financial services. All applications and components are encrypted to protect your intellectual property and can be shared royalty free.

In this session, Pallavi presents various deployment options available in MATLAB to integrate your applications with today’s IT infrastructures without having to recode.


  • Royalty-free distribution of applications to users who do not need MATLAB
  • Integration of compiled MATLAB with C/Java/.NET/Python
  • Large-scale deployment to enterprise systems
  • Deployment of MATLAB code against Hadoop and Spark
Pallavi Kar Pallavi Kar, Application Engineer, MathWorks India

Developing and Deploying Analytics for IoT Systems


The combination of smart connected devices with data analytics and machine learning is enabling a wide range of applications, from home-grown traffic monitors to sophisticated predictive maintenance systems and futuristic consumer products. While the potential of the Internet of Things (IoT) is virtually limitless, designing IoT systems can seem daunting, requiring a complex web infrastructure and multinomial expertise.

In this session, Amit discusses how to prototype and deploy an IoT system with data analytics without developing custom web software or servers. The workflow is based on MATLAB® and ThingSpeak™, an analytic IoT platform that can run MATLAB code on demand in the cloud.


  • Conceptual overview of ThingSpeak
  • Collecting and storing sensor data
  • Integrating online analysis and visualization using MATLAB
  • Scaling IoT solutions with analytics
Amit Doshi Amit Doshi, Senior Application Engineer, MathWorks India

Parallel Computing with MATLAB and Simulink


Large-scale simulations and data processing tasks take an unreasonably long time to complete or require a lot of computer memory. Users can expedite these tasks by taking advantage of high-performance computing resources, such as multicore computers, GPUs, computer clusters, and cloud computing services.

In this session, Alka discusses how to boost the execution speed of computationally and data-intensive problems using MATLAB® and parallel computing products. Alka demonstrates several high-level programming constructs that allow you to easily create parallel MATLAB applications without low-level programming.


  • Learn high-level programming constructs as well as built-in parallel algorithms to solve computationally and data-intensive problems using multicore processors and GPUs
  • Scale up to clusters, grids, and clouds using MATLAB Distributed Computing Server™ with minimum programming efforts
  • Run multiple simulations of a model in parallel
Alka Nair Alka Nair, Application Engineer, MathWorks India

Simulink as Your Enterprise Simulation Platform


Engineers are increasing their adoption of system simulation to develop the complex integrated systems needed in today’s market. Gone are the days when embedded software can be written and tested directly on physical prototypes. An environment that can model both the algorithmic and physical components of a system is needed to fully understand and develop the systems of tomorrow.

Simulink® is an enterprise simulation platform that meets those needs. With its scalable multidomain modeling and simulation capabilities, you can author components using both textual and graphical elements—including MATLAB® functions and objects, block diagrams, state machines, and flow charts—and simulate discrete, continuous, discrete-event, and physical systems. An enterprise simulation platform also needs to scale to make teams more efficient in working together and in simulating large systems that consume and produce massive amounts of data. Finally, an enterprise simulation platform also needs to be able to integrate third-party IP to address specific component modeling needs.

Join this session to discover how to use Simulink as an enterprise simulation platform.

Naga Pemmaraju Naga Pemmaraju, Senior Application Engineer, MathWorks India
Prasanna Deshpande Prasanna Deshpande, Senior Application Engineer, MathWorks India

Powertrain Control Feature Development Using Model-Based Design


Model-Based Design is widely used by OEMs and developers to formulate software strategies and verify in the virtual environment. This leads to innovation and both time and cost reduction prior to physical prototyping. Powertrain control software is complex with lots of interdependencies, including fault management, and is expected to perform reliably under all operating scenarios. Resolving conflicting scenarios and optimization calls for a step-by-step approach to fine tune the control algorithm and strategies. Model-Based Design provides platform for function development, verification, calibration checking, and safety management. This approach also helps build knowledge for better understanding of strategies in order to experiment with various options, leading to innovation.

This presentation discusses how TATA Motors is developing the Auto Clutch Control Module (ACCM) using MATLAB® and Simulink®. Designing the ACCM is highly complicated as only the clutch function is automated, balancing the need to be in sync with manual gear shifting. To meet this complex requirement, TATA Motors deployed a model-based methodology, following these steps:

  • Requirement capturing and review
    • High-level and low-level requirements
    • Review and finalization
  • Development of control model using MATLAB®, Stateflow®, and Simulink®
  • Model verification using Model Advisor
  • Model coverage and design verification using Simulink Design Verifier™
  • Software-in-the-loop checking
  • Code generation

Proto testing with reference board is in progress and in this session, we present issues faced during development and results achieved so far.

V.A. Ashoka Kumary V.A. Ashoka Kumar, TATA Motors

Generating Optimized Code for Embedded Microcontroller Algorithms


Embedded code generation is fundamentally changing the way engineers work. Instead of writing thousands of lines of code by hand, engineers are automatically generating their production code to increase productivity, improve quality, and foster innovation. Because code generation is a proven approach for dealing with the ever-increasing complexity of embedded software algorithms, the need to automatically generate code is increasing daily. One challenge every embedded code generation or C coding engineer faces is reducing the effort required for implementing optimized code into their resource-constrained, mass-production microcontrollers.

Join this session to learn about:

  • The latest features in Simulink®, Stateflow®, and Embedded Coder® for generating highly optimized code
  • Automating the process for generating algorithm code that plugs into and integrates with device drivers, schedulers, and build processes
  • Tuning and monitoring controller parameters and variables in real time
Gaurav Dubey Gaurav Dubey, Senior Team Lead, Pilot Engineering, MathWorks India

Modeling Mechanical and Hydraulic Systems in Simscape


Do you still rely on hardware prototypes for designing your mechanical or hydraulic systems? Do you also face the challenge of unifying multiple domains to simulate the performance of the entire system? Join this session to learn how Simscape™ helps engineers “reach for the run button” and enables them to use simulation to save time and money. You will also learn how Simscape and its add-on libraries enable engineers to model and simulate a wide range of systems, including multibody systems and fluid power systems.

You will learn how to:

  • Easily build Simscape models of the physical systems
  • Import CAD models for reusing it in Simscape platform
  • Perform simulation modes for analyzing motion and calculating forces
  • Model hydraulic systems with components such as valves, cylinders, and pipelines
  • Leverage MATLAB® capabilities for finding optimal designs
Dhirendra Singh Dhirendra Singh, Application Engineer, MathWorks India

Verification, Validation, and Test in Model-Based Design


Applying verification and validation techniques throughout the development process enables you to find design errors before they can derail your project. Most system design errors are introduced in the original specification but are not found until the test phase. When engineering teams use models to perform virtual testing early in a project, they eliminate problems and reduce development time.

In this session, Manohar discusses how you can apply early verification and validation activities at every stage of the development process in Model-Based Design.

Highlights include:

  • Detecting design errors in models using formal verification methods
  • Systematic simulation testing of design by using an automation framework
  • Using model slicing to analyze and debug problematic behavior in a model
  • Automatically generating reusable tests that satisfy model and code coverage
Manohar Reddy Manohar Reddy, Senior Application Engineer, MathWorks India

Designing and Implementing Real-Time Signal Processing Systems


Signal processing is essential for a wide range of applications, from data science to real-time embedded systems. Some of the challenges in developing signal processing system are the acquiring and processing raw data from sensors to derive meaningful information and designing algorithms for real-time processing. MATLAB® and Simulink® provide a platform for exploring and analyzing time-series data and a unified workflow for the development of embedded DSP software and hardware by providing a complete workflow for fixed-point design and C and HDL code generation.

During this session, you will learn about:

  • Acquiring, measuring, and analyzing signals from various sources
  • Designing streaming algorithms to analyze patterns in signals and extract meaningful information
  • Implementing, prototyping, and testing DSP algorithms on embedded processors, SoCs, and FPGAs
Vidya Viswanathan Vidya Viswanathan, Application Engineer, MathWorks India

Automated Product Quality Inspection


Visual quality inspection of manufactured or processed products and commodities before packaging at the end of the processing line is a critical step for many industries. Shape defects, dimensional variations, and surface defects are some of the types of defects that should not pass a quality check. Consequences industries face in case of compromised quality inspection include:

  • Negative impact on the brand value, which leads to loss of business
  • High incurred costs when product must be recalled from the market

Currently, there are two solutions employed by the manufacturing and processing industries: manual and automated inspection. Automated inspection systems employ customized and specialized image processing techniques to perform quality check.

Manual inspection, subjective in nature, may compromise consistency. Manual inspection is error-prone due to fatigue and boredom. In case of dimensional checks, manual inspection is time consuming and cannot be performed for every product if the production rate is very high.

For automated systems reliant on customized image processing techniques, adaptability to a new variant or product is challenging. In some cases, depending on the extent of changes, automated systems may need to be developed again from scratch, which is both time consuming and expensive.

To ensure that only good products are delivered to customers, a side effect of automated systems may be that some good products may be rejected. This adds losses to the industry.

Utkarsh Siddu Utkarsh Siddu, Robert Bosch
Prabhakaran Sengodan Prabhakaran Sengodan, Robert Bosch

Developing Autonomous Systems with MATLAB and Simulink


Image and vision, radar, EOIR, IMU, and a combination of sensor technologies are all used to automate aspects of autonomous systems. Critical functions such as object and collision detection, path and motion planning, spatial localization and mapping are designed using advanced concepts including sensor fusion and machine learning to drive guidance, navigation, and control (GNC) algorithms.

To develop these complex multidomain autonomous systems, engineers must analyze the behavior of mechanical and electrical subsystems, sensor and perception algorithms, and controls as an integrated platform, and deploy to the actual hardware. In this session, you will learn how using Model-Based Design with MATLAB® and Simulink® can help you address these challenges.

Using a quadrotor, Vivek shows how to:

  • Model environmental effects and 6DOF aircraft simulations
  • Develop and implement flight controls algorithms
  • Design and test vision, radar, and IR perception algorithms
  • Perform sensor fusion and controls development
  • Connect MATLAB and Simulink to ROS environment
Vivek Raju Vivek Raju, Application Engineer, MathWorks India

Developing and Prototyping Next-Generation Communications Systems


Wireless communication has seen a proliferation of standards addressing many traditional applications, such as mobile telephone and wireless broadband internet access, and emerging areas, such as Internet of Things and vehicle-to-vehicle communication. Developing radios for next-generation communications systems requires expertise in antenna and RF design, DSP and digital logic implementation, embedded software development, and system architecture modeling and simulation. MATLAB® and Simulink® provide a platform that encompasses algorithm design, system simulation, over-the-air testing, prototyping, and implementation.

In this session, Amod discusses:

  • Modeling and simulating LTE and WLAN standards-compliant PHY
  • Developing 5G-candidate technologies such as new modulation schemes and massive MIMO
  • Multilayer modelling and simulation for MAC-PHY codesign
  • Over-the-air testing and prototyping on software-defined radio platforms
Amod Anandkumar Dr. Amod Anandkumar, Team Lead – Signal Processing and Communications, Application Engineering Group, MathWorks India

Simplifying Image Processing and Computer Vision Application Development


Image processing and computer vision is an enabling technology that is driving the development of several of the smart systems today including self-driving cars, augmented reality, hyperspectral imaging, and medical imaging. Developers of modern image processing and computer vision applications face many challenges regarding handling large data sets and working with new computing paradigms, such GPU computing. You can use MATLAB® to simplify your image processing and computer vision application development workflow.

Join this session to gain insight into:

  • Object detection and recognition using machine learning and deep learning
  • Image processing on 3D data sets, including pixel operations, local filtering, and morphology
Elza John Elza John, Training Engineer, MathWorks India

Automated Driving: Design and Verify Perception Systems


Automated driving systems perceive the environment using sensors, including vision, radar, and lidar, and dynamically control driving tasks such as steering, braking, and acceleration. These automated driving systems range from advanced driver assistance systems (ADAS) to full autonomy. Join this session to learn how MATLAB® can help you:

  • Automate ground truth labelling tasks for deep learning
  • Design sensor fusion and tracking algorithms based on logged sensor data
  • Verify algorithms by synthesizing sensor data and generating traffic scenarios
Mark Corless Mark Corless, Principal Application Engineer, MathWorks
Amod Anandkumar Dr. Amod Anandkumar, Team Lead – Signal Processing and Communications, Application Engineering Group, MathWorks India

Development of a Numerical Simulink Model to Predict Tail Pipe Emissions of a Vehicle with Lean NOx Trap in Real Drive Cycles


Lean NOx Trap (LNT) is one of the leading technologies that can address the stringent BS6/EU6 NOX emission norms. Barium oxide in the LNT adsorbs NOx with the presence of platinum (Pt) or palladium (Pd), when the engine runs in lean condition. The adsorbed NOx is regenerated in rich condition in the presence of rhodium (Rh). The rich mode operation of the engine, called Regeneration/Purge, is deliberate and is run consecutive to lean mode operation based on the LNT filling status. Steady-state LNT conversion efficiencies and storage capacities can be obtained in the synthetic gas bench or engine dynamometer. Understanding the LNT behavior and determining the tail pipe emissions in Real Driving Emission (RDE) cycles is challenging until the total vehicle-level prototype with final calibration is available.

A numerical model is developed in Simulink® for the LNT technology, which provides an estimate of LNT NOx conversion efficiency in various driving cycles. The developed numerical model uses efficiency maps, storage capacity maps, prerequisite engine operating conditions for regeneration, and transient temperature effects, which can be generated at engine dyno or SGB. The resulting Tail Pipe NOx and efficiency for a given drive cycle can be estimated using the model. The regeneration control logic can also be developed using the model. Comparison of simulation results with test results in NEDC cycle can show correlation. This numerical simulation can reduce product development time considerably by providing an estimate of crucial parameters of engine and LNT. Parameters include feasibility of LNT for the engine conforming to the emission norms, size of LNT required, and engine out emissions required.

M V Harish Babu, Mahindra & Mahindra
R Padmavathi, Mahindra & Mahindra

Building Fast and Accurate Powertrain Models for System and Control Development


A good system simulation model is essential for activities such as architecture studies, component sizing, calibration, and controller testing. However, building a fast and accurate system model is time consuming and often requires a specialist.

In this session Prasanna shows you how you can use Simulink products to accelerate the development of your powertrain system models and controllers. Through case studies, he demonstrates how to:

  • Use these models for systematic design optimization
  • Model detailed components using Simscape add-on libraries
Prasanna Deshpanden Prasanna Deshpande, Senior Application Engineer, MathWorks India

From Simulink to AUTOSAR: Enabling AUTOSAR Code Generation with Model-Based Design


Model-Based Design affords many advantages over traditional development by offering high-level design abstractions and automatic generation of production code. Modeling and code generation for AUTOSAR software components lets you automate the process of specifying and synchronizing lengthy identifiers in designs, code, and description files.

This session is intended for systems and software engineers who wish to understand the basic concepts, best approaches, and advanced features for using Simulink® for AUTOSAR design and Embedded Coder® for software implementation.

Durvesh will provide a brief overview of AUTOSAR standards and provide product demonstrations showing how you can use Simulink products to design, simulate, verify, and generate code for AUTOSAR application software components.

Session highlights include:

  • Simulink approach to AUTOSAR
  • Modeling styles
  • AUTOSAR design workflows using third-party authoring tools while showing advanced AUTOSAR features
Durvesh Kulkarni Durvesh Kulkarni, Senior Application Engineer, MathWorks India

Leveraging Formal Methods-Based Software Verification to Prove Code Quality and Achieve MISRA Compliance


In this session, Prashant presents the use of Polyspace® products to verify critical embedded software. Polyspace products use formal methods–based static analysis to find run-time errors and prove that the software is safe. They provide comprehensive software verification capability for early stage development use, spanning bug finding, coding rules checking, and proof of the absence of run-time errors.

Polyspace Code Prover™ proves the absence of overflow, divide-by-zero, out-of-bounds array access, and other critical run-time errors in the source code. Using a unique formal methods approach called abstract interpretation, Polyspace Code Prover finds critical errors that other verification techniques can miss. Using Polyspace Bug Finder™, you can identify coding rule violations (MISRA®), programming errors, data flow problems, and other defects enabling you to triage and fix bugs early in the development process.

Through demonstrations and examples, Prashant shows how Polyspace products help detect critical run-time errors and prove that your software does not contain those errors. You will also learn how to use these verification results to certify your code to standards such as DO-178, ISO 26262, IEC 61508, and derivatives.

Prashant Mathapati Prashant Mathapati, Senior Application Engineer, MathWorks India

Effective Teaching Techniques Using MATLAB and Simulink, Part 1


Faculty introducing computational tools into curriculum must answer questions such as, is this a good time to introduce the students to this tool? After introducing students to the theoretical concepts, how do I facilitate their understanding of the behavior a system, be it a PID controller, an FIR filter, or a simple pendulum?

In this session, Dr. Nataraj covers methodologies for teaching engineering concepts with MATLAB® and Simulink® to facilitate the student learning process. He discusses various engaging ways to introduce the tools and emphasizes the transition from theory to practice.

Dr. P.S.V. Nataraj Dr. P.S.V. Nataraj, Professor, Systems and Control Engg Group, IIT Bombay

Effective Teaching Techniques Using MATLAB and Simulink, Part 2


Challenges faced by faculty while introducing computational tools into curriculum involves answering questions such as – is this a good time to introduce the students to this tool? How do I facilitate the students’ understanding of the behavior a system, be it a PID Controller, an FIR filter, or a simple pendulum after introducing them to the theoretical concepts?

In this session, Dr. Nataraj will cover methodologies to teach engineering concepts with MATLAB® and Simulink® thereby facilitating the student learning process. He will discuss various engaging ways to introduce the tools and emphasize the transition from theory to practice.

Dr. P.S.V. Nataraj Dr. P.S.V. Nataraj, Professor, Systems and Control Engg Group, IIT Bombay

Leveraging MATLAB and Simulink for Higher Education: An Overview of MathWorks Resources for Academia


Engineering education is undergoing significant changes in the way students learn, educators teach, and the disciplines. Whereas some of the trends are disruptive, some are also evolutionary, such as:

  • A shift toward online learning including anywhere-anytime access to coursework and computational tools that are self-paced and provide instant feedback
  • A “seeing is believing” trend toward learning engineering concepts
  • Learning via maker communities and student competition participation

Modeling and simulation tools such as MATLAB® and Simulink® can help educators and students embrace these changes, enabling them to be more effective and more productive in their careers. Join this session to learn about the MathWorks resources available for academia and on how educators can work toward building an effective curriculum using these resources.

Lakshminarayan Viju Ravichandran Dr. Lakshminarayan Viju Ravichandran, Ph.D., Educational Technical Evangelist, MathWorks India

Building a Course Implementation Plan


Join this session to learn how courses incorporating MATLAB® and Simulink® have been developed and delivered. Using actual engagements as case studies, the presenters share their experiences in developing courses, challenges faced, solutions, and results.

Lakshminarayan Viju Ravichandran Dr. Lakshminarayan Viju Ravichandran, Ph.D., Educational Technical Evangelist, MathWorks India
Anuja Apte Anuja Apte, Senior Partner Manager, MathWorks India