This project focuses on developing a plant growth monitoring system for space exploration missions using the ARM Cortex-M0 microcontroller core. The projects aim to develop a SOC based on ARM M0 core for interactive plant monitoring by interfacing AHB lite, GPIO, timers, and communication protocols such as UART, I2C, SPI, and co-processors.  This project also proposes two co-processors for interactive plant monitoring and control. One AI co-processor for classification and prediction of plant and environmental data.

This Project is to develop traffic light system that can reduce traffic congestion with the aid of counters for each lane and acts wisely with the intersection in real time based with a fixed time constrain, include both hardware and software requirements using SOC FPGA technology with fundamental specification for the Register Transfer Level (RTL).

Conventional healthcare is expensive and reliant on the physical presence of the patients. Continuous health monitoring tracks vital health parameters like heart rate, blood pressure, etc. While these work well in measuring the parameters, modern-day devices rely on the cloud to compute and interpret data. This results in an increase in data transfer between the device and the cloud, and if this connection breaks, there can be no interpretation of data. Hence, there is a need to shift the computation to the hardware, referred to as "Edge Computing".

The development of a Low-Cost and Low-Power Data Acquisition System(DAQs). The DAQs will be made up of end-terminal and a gateway. The end-terminal will be micro-controller-driven device built on a SoC FPGA technology with built-in capability for machine learning. The end-terminal will be able to transmit and receive data using the Low Power Wide Area Networking (LPWAN) communication protocol that functions on LoRA.LoRa is a wireless radio frequency technology that operates in a license-free radio frequency spectrum.

Advancements in electronics, wireless communications, and sensing technologies have made possible a multitude of smart sensing features in automotives. Integrating high-frequency sensors, digital signal processors and hardware accelerator engines on a single system on a chip (SoC) enhances sensing computation potential of radar sensors utilized in automotives.

A physical test environment is required for ASIC devices fabricated following tape out. The nanoSoC test board needs to provide a complete test environment for ASIC designs based on the nanoSoC reference design and enable showcase of any custom designs that utilise it. Reviewing the function of nanoSoC identifies a number of design criteria for the test board:

Based on these requirements, the following test board is under development:

The Arm PL022 provides an interface for synchronous serial communication with peripheral devices connected to the  SoC via the Advanced Peripheral Bus (APB). It supports a choice of interface operation, Motorola compatible Serial Peripheral Interface (SPI), National Semiconductor Microwire, or Texas Instruments synchronous serial interface. See the Techology page for details. 

We propose the A-Cube design methodology to create medical decision support on the edge. The design and implementation of an atrial fibrillation detector hardware core was selected as a proof-of-concept study. To facilitate the required atrial fibrillation functionality, we adopted an established AI model, based on Long Short-Term Memory (LSTM) technology for hardware implementation. The adaptation was done by varying design parameters such as data window and the number of LSTM units.

Battery storage systems are an important source for powering emerging clean energy applications. The Battery Management System (BMS) is a critical component of modern battery storage, essential for efficient system monitoring, reducing run-time failures, prolonging charge-discharge lifecycle, and preventing battery stress or catastrophic situations. The BMS performs functionalities such as data acquisition and monitoring, battery state estimation, cell equalization, and charge protection, making it computationally intensive to manage large scale battery storage.