White Paper: MATLAB and Simulink in AI, IoT, Power Electronics, and DSP — Global Applications and India–USA Use Cases

Abstract

MATLAB and Simulink have emerged as cornerstone tools in engineering, enabling rapid prototyping, modeling, simulation, and deployment across diverse domains such as Artificial Intelligence (AI), Internet of Things (IoT), Power Electronics, and Digital Signal Processing (DSP). This white paper presents a comprehensive exploration of their applications, training resources, and practical case studies from India and the United States. It emphasizes integrated system design, cross-domain innovation, and the strategic adoption of MathWorks toolchains to address the needs of academia, research institutions, and industry.

1. Introduction

MATLAB and Simulink are widely used in both academic and industrial environments due to their high-level programming interface, rich library of toolboxes, and model-based design capabilities. Their applications range from algorithm development and embedded code generation to real-time hardware integration.
In India, demand for MATLAB expertise is driven by the growth of renewable energy, electric vehicles, telecommunications, and defense R&D. In the USA, MATLAB is deeply embedded in aerospace, automotive, biomedical, and semiconductor industries.

2. Methodology

This research synthesizes:

  • Books on MATLAB, Simulink, AI, IoT, DSP, and power electronics
  • MathWorks official documentation and toolbox references
  • MOOC & NPTEL course content relevant to Indian academia
  • Industry project case studies from India and the USA
  • Cross-domain integration analysis

3. Domain Applications

3.1 Artificial Intelligence (AI)

Applications:

  • Training and deploying deep learning models for object detection and speech recognition using MATLAB Deep Learning Toolbox
  • Automated feature extraction for healthcare diagnostics
  • Reinforcement learning for control systems optimization

India Use Case:

  • AI-enabled solar forecasting system using MATLAB’s Machine Learning Toolbox for Indian renewable energy operators.
    USA Use Case:
  • Autonomous vehicle vision systems for lane detection using MATLAB Computer Vision Toolbox.

3.2 Internet of Things (IoT)

Applications:

  • IoT data acquisition via MATLAB ThingSpeak
  • Predictive maintenance systems for industrial equipment
  • Edge AI deployments with MATLAB Coder for embedded hardware

India Use Case:

  • IoT-based smart agriculture monitoring using MATLAB and Simulink to connect sensors with GSM/WiFi modules.
    USA Use Case:
  • Smart HVAC systems integrating MATLAB analytics for commercial building energy optimization.

3.3 Power Electronics

Applications:

  • Simulation of DC-DC converters, inverters, and HVDC systems
  • Control algorithm development for electric vehicle chargers
  • Grid-tied renewable energy systems analysis

India Use Case:

  • Simulink-based model for three-phase grid-connected solar inverters in rural electrification projects.
    USA Use Case:
  • Simulink model of battery energy storage systems integrated with smart grids for frequency regulation.

3.4 Digital Signal Processing (DSP)

Applications:

  • Speech recognition algorithms
  • Image enhancement for medical imaging
  • Real-time filtering for communications

India Use Case:

  • MATLAB-based noise reduction for low-cost mobile communication devices.
    USA Use Case:
  • DSP algorithms for real-time audio enhancement in hearing aids.

4. Cross-Domain Integration

MATLAB and Simulink enable integration between domains, such as:

  • AI-enhanced DSP for biomedical signal analysis
  • IoT-enabled power electronics for smart grid management
  • DSP-based AI in autonomous drones

5. Challenges

  • High licensing costs in emerging markets like India
  • Need for skilled manpower
  • Integration with open-source alternatives (Python, Scilab) in hybrid workflows

6. Future Directions

  • Cloud-native MATLAB for collaborative global R&D
  • Integration with open-source ML frameworks like PyTorch and TensorFlow
  • Real-time hardware-in-the-loop (HIL) testing for EV and aerospace applications

7. Conclusion

MATLAB and Simulink provide a unified environment that accelerates innovation in AI, IoT, power electronics, and DSP. Leveraging these tools with structured learning resources and domain-specific toolboxes can help India and the USA address their respective industrial and academic challenges while fostering global collaboration.

References

  1. Attaway, S. (2018). MATLAB: A Practical Introduction to Programming and Problem Solving. Academic Press.
  2. MathWorks. (2024). Simulink Documentation. Retrieved from https://www.mathworks.com/help/simulink
  3. Beale, M., Hagan, M., & Demuth, H. (2023). Neural Network Toolbox User's Guide. MathWorks.
  4. Pal, B., & Chaudhuri, B. (2005). Robust Control in Power Systems. Springer.
  5. Proakis, J. G., & Manolakis, D. G. (2006). Digital Signal Processing: Principles, Algorithms, and Applications. Pearson.
  6. Singh, B., & Chandra, A. (2019). Power Quality: Problems and Mitigation Techniques. Wiley.
  7. NPTEL. (2023). Digital Signal Processing by IIT Kharagpur. Retrieved from https://nptel.ac.in
  8. MathWorks. (2024). ThingSpeak Documentation. Retrieved from https://thingspeak.com
  9. Mohan, N., Undeland, T., & Robbins, W. (2012). Power Electronics: Converters, Applications, and Design. Wiley.