Hero Image

Our Work - Application of the latest technologies in practice

We use state-of-the-art technologies to solve our customers' problems as efficiently as possible and offer maximum benefit.

Case Studies

Commonwealth Bank

Research & Development

Mildura Cellular IoT Tracking System

Commonwealth Bank (CBA), Australia's largest bank, identified two major issues in the food industry's complex supply chains: a lack of traceability in the logistics chain and instances of food spoilage during transportation. To address these issues, CBA conducted a pilot project using an Ethereum-based blockchain for traceability and a hardware module (Particle B Series LTE CAT M1 development board) to monitor temperature and humidity. Collaborating with partners such as Olam Orchards Australia, Pacific National, Port of Melbourne, Patrick Terminals, and OOCL Limited, the project successfully demonstrated the integration of sensors, cloud technology, and power optimization to improve the efficiency and reliability of food transportation.

I believe in leveraging technology to address challenges in complex supply chains, and the trial with Commonwealth Bank illustrates the power of blockchain, IoT sensors, and cloud connectivity in enhancing traceability and monitoring conditions in the food industry. By collaborating with key players in the supply chain, we can achieve meaningful improvements and build more resilient systems.

Karthik Sukumar, Author

ARM Cortex-M0

Research & Development

Neural Network DC Motor Control

The text discusses the conventional use of PID controllers for DC motors and proposes the possibility of replacing them with neural networks. The idea is to train a neural network on a small microcontroller, such as an ARM Cortex-M0, to learn how to control the motor during a training phase. The trained neural network, designed to depict the inverse function of the motor, can then be applied in real-time as a controller. Experimental results demonstrate success with a neural network featuring a single hidden layer of seven neurons, showcasing its potential for real-time motor control and even continuous learning during normal operation.

Replacing traditional PID controllers in DC motors with neural networks is a revolutionary approach. By training a neural network on a lightweight microcontroller, like ARM Cortex-M0, we can achieve cost-effective and adaptive motor control without manual tuning. The key is a short training phase where the network learns the inverse function of the motor, ensuring efficient performance even during normal operation.

Karthik Sukumar, Author

We are here for you

Seize the opportunity for a free introductory meeting to find out find out more about our services and us. Contact us by e-mail or book an appointment easily and conveniently an appointment online.

Contact