Among the top 10 leading causes of mortality, tuberculosis (TB) is a chronic lung illness caused by a bacterial infection. Due to its efficiency and performance, using deep learning technology with FPGA as an accelerator has become a standard application in this work. However, considering the vast amount of data collected for medical diagnosis, the average inference speed is inadequate. In this scenario, the FPGA speeds the deep learning inference process enabling the real-time deployment of TB classification with low latency. This paper summarizes the findings of model deployment across various computing devices in inferencing deep learning technology with FPGA. The study includes model performance evaluation, throughput, and latency comparison with different batch sizes to the extent of expected delay for real-world deployment. The result concludes that FPGA is the most suitable to act as a deep learning inference accelerator with a high throughput-to-latency ratio and fast parallel inference. The FPGA inferencing demonstrated an increment of 21.8% in throughput while maintaining a 31% lower latency than GPU inferencing and 6x more energy efficiency. The proposed inferencing also delivered over 90% accuracy and selectivity to detect and localize the TB.

The heart rate of a person is able to tell whether they are healthy. A heart-rate monitoring device is able to measure or record the heart rate of a person in real time, whether it is an electrocardiogram (ECG) or a photoplethysmogram (PPG). In this work, a microprocessor system loaded with a heart-rate monitoring algorithm is implemented. The microprocessor system is the Nios II processor system, which interfaces with an analogue-to-digital converter (ADC) and a pulse sensor. A beat-finding algorithm is used in the microprocessor system for heart rate measurement. An experiment is carried out to analyse the functionality of the microprocessor system loaded with the algorithm. The results show that the detected heart rate is in the range of the average human being’s heart rate. The signal flow within the microprocessor system is observed and analysed using SignalTap II from Quartus’ software. Based on a power analysis report, the proposed microprocessor system has a total power dissipation of around 218.26 mW.

Wireless microcontrollers have become widely used in domestic and industrial applications, where Bluetooth is one of the most popular wireless communication med