Prashant Rao is the Head of Application Engineering at MathWorks India and leads a team of customer-facing engineers encompassing the application engineering and pilot engineering roles. By applying industry and application expertise across numerous domains, Prashant and his team work with customers to enable the adoption of MATLAB and Simulink products for technical computing and Model-Based Design. Prashant has over 18 years of experience in application engineering and hardware design engineering roles. In this interview, Prashant talks about next gen model design approach, Data-driven predictive models, deep learning and autonomous driving.
Why is Model Based design in important in embedded systems? Can you share your views on next generation Model Based Design approach?
Model-Based Design is an efficient and cost-effective way to develop complex embedded systems in aerospace, automotive, communications, and other industries. Rather than relying on physical prototypes and textual specifications, Model-Based Design utilizes a system model as an executable specification throughout development. It supports system-level and component-level design and simulation, automatic code generation, and continuous test and verification. These capabilities combined with improved communications using graphical models, powerful analytical tools, and traceability from requirements to models to code, have made Model-Based Design the de facto design methodology for embedded systems.
Model-Based Design with MATLAB and Simulink enables engineering teams to work at higher levels of abstraction, simplify their work flows, improve the quality of their products, and reduce overall development time.
Next generation Model-Based Design
- Agile and Model-Based Design for engineering software development
- Most teams developing software for engineering applications today recognize the drawbacks of traditional (waterfall) methodologies. These include the discovery of defects and design problems in the later stages of a project, the inability to accommodate changes in requirements, and the risk of delivering a system that does not meet customer needs. To overcome these drawbacks, many teams have adopted an approach that combines agile methods with Model-Based Design. An examination of Model-Based Design and agile methods shows that Model-Based Design complements and even enables agile for engineering applications. Like Agile, Model-Based Design originated to support fast iterations.
- Frontloading of software verification using Model-Based Design
- As industry experts have long recognized, safety is more about getting the system and its requirements right than about the software and how it was coded. With the rising demand for industries to comply with standards for developing safety critical systems including ISO 26262for automotive software,DO-178B/C for airborne systems, and IEC 61508 for industrial systems, there’s a need to frontload the software development and verification process to reduce the development and testing time. Verification and validation using Model-Based Design enables detection of design errors and incorrect requirements early in the development process saving valuable time and improving product quality.
How Predictive Analytics and Deep learning playing a key role in industry? Why it matters and how does your products improve the performance?
With increased competition, businesses seek an edge in bringing products and services to crowded markets. Data-driven predictive models can help companies solve long-standing problems in new ways. Equipment manufacturers, for example, can find it hard to innovate in hardware alone. Product developers can add predictive capabilities to existing solutions to increase value to the customer. Using predictive analytics for equipment maintenance, or predictive maintenance, can anticipate equipment failures, forecast energy needs, and reduce operating costs. For example, sensors that measure vibrations in automotive parts can signal the need for maintenance before the vehicle fails on the road. Companies also use predictive analytics to create more accurate forecasts, such as forecasting the demand for electricity on the electrical grid. These forecasts enable resource planning to be done more effectively across industries like automotive – with autonomous vehicles, aerospace – with aircraft engine health monitoring, financial services – with credit risk model development, etc.
To extract value from big data, businesses apply algorithms to large data sets using tools such as Hadoop and Spark. The data sources might consist of transactional databases, equipment log files, images, video, audio, sensor, or other types of data. Innovation often comes from combining data from several sources. With all this data, tools are necessary to extract insights and trends. Machine learning techniques, such as Deep Learning are used to find patterns in data and to build models that predict future outcomes. A variety of machine learning algorithms are available, including linear and nonlinear regression, neural networks, support vector machines, decision trees, and other algorithms.
Engineering teams use MATLAB for predictive analytics for four key reasons:
- MATLAB analytics work with both business and engineering data: MATLAB has native support for sensor, image, video, telemetry, binary, and other real-time formats. Explore this data using MATLAB Tall arrays for Hadoop and Spark, and by connecting interfaces to ODBC/JDBC databases.
- MATLAB lets engineers do data science themselves: Helps domain experts to do data science, with powerful tools to help them do machine learning, deep learning, statistics, optimization, signal analysis, and image processing.
- MATLAB analytics run in embedded systems: Develop analytics to run on embedded platforms, by creating portable C and C++ code from MATLAB code.
- MATLAB analytics deploy to enterprise IT systems: MATLAB integrates into enterprise systems, clusters, and clouds, with a royalty-free deployable runtime.
How Artificial Intelligence is making wave in industries? How MathWorks is making AI Accessible?
AI is everywhere. It’s not just powering applications like smart assistants, machine translation, and automated driving, it’s also giving engineers and scientists a set of techniques for tackling common tasks in new ways. As Artificial Intelligence (AI), and areas within it like machine learning, deep learning and data science, become an integral part of design and development workflows, MATLAB makes it easy for teams to get started. In fact, earlier in April, LinkedIn announced its top 50 companies people want to work for in the US, within which MATLAB was listed as one of the fastest-growing skills based on the LinkedIn employee profiles of five companies on the list: Alphabet, Apple, Johnson & Johnson, Intel and Pinterest.
Top engineering companies wouldn’t be surprised to see MATLAB on the list, and MathWorks isn’t either. Some of the reasons why organizations get ready for AI with MATLAB include:
- Simplifying adoption of AI for engineering system design: MATLAB offers a complete AI development workflow from data preparation, AI modeling, and system design to deployment and integration
- Supporting AI-driven hiring trend: MATLAB keeps pace with the evolution of job roles from skills in one specific domain e.g., physics or computer science, to skills with applied experience skills. This matches AI requirements of cultivating mindset and engineering systems-based training alongside domain experience
How do you look at Autonomous Vehicles trends? And How MathWorks products help in designing Autonomous Systems?
The automobile went through its first digital transformation with the addition of electronic controls in virtually every system. With automated driving and predictive maintenance, the automotive industry is experiencing another digital transformation in which data-driven algorithms for implementing artificial intelligence are playing a key role.
One trend is where the advanced levels of perception, enabled by deep learning, is contributing to the success of automated driving, from advanced driver assistance systems (ADAS) to fully autonomous driving. Another trend is to develop a virtual test ground for ADAS and automated driving features using the reference applications and the 3D environment. The third trend is creating a cooperative communication technical that enables continuous, high-speed, authenticable interactions between moving vehicles – known as vehicles to everything (V2X), this has the potential to significantly change our roads for the better.
In our continued efforts to support engineers and scientists with the challenges and demands from their evolving job roles, we have introduced products like Automated Driving Toolbox, Vehicle Dynamics Blockset which helps to design, simulate and test ADAS and autonomous driving systems . In addition to this, automotive engineers use MATLAB and Simulink for a broader range of activities such as designing automated driving system functionality including sensing, path planning, and sensor fusion and controls. With MATLAB and Simulink, these engineering teams can:
- Develop perception systems using prebuilt algorithms, sensor models, and apps for computer vision, lidar and radar processing, and sensor fusion.
- Design control systems and model vehicle dynamics in a 3D environment using fully assembled reference applications.
- Test and verify systems by authoring driving scenarios using synthetic sensor models.
- Plan driving paths by designing and using vehicle costmaps, and motion-planning algorithms.
- Automatically generate C code for rapid prototyping and HIL testing and reduce the engineering effort needed to comply with ISO 26262.
Tell us about your Engineering Development Group Program and how it helps engineers?
The Engineering Development Group (EDG) is a rotational leadership development program in MathWorks that offers successful candidates from a variety of technical backgrounds multiple opportunities to develop and demonstrate their skills for a rewarding career. Hiring recent graduates with degrees in engineering and computer science, the team aims to help customers solve technical problems using MATLAB and Simulink. Team members in the EDG program develop leadership and technical skills, identify and explore areas of interest, and contribute to collaborative work. They also receive mentoring and coaching that allows them to transition into other technical teams at MathWorks.
Within EDG, there are many different focus areas in combination with several different roles. Below are a few of the focus areas across the India EDG teams.:
- Bangalore: Software Development/Engineering, Machine Learning, Algorithm Development, IOT, Code Coverage, Solvers, Embedded Targets, Automatic Code Generation, Unmanned Ariel Vehicles(UAVs)/Drones, Communication Protocols, Motor Control, Controls for Power Conversion, Hardware Platforms, Code Infrastructure/Architecture, System Software
- Hyderabad: ADAS, Robotics/UAV, HDL, Deep Learning, Communications, and Sensors/Hardware.
The popularity of this program has led to a number of current and upcoming EDG Program job openings across our Bangalore and Hyderabad offices, which have large EDG teams.