Projects

Hands-on hardware and software builds — from analog amplifier design to live IoT dashboards.

Audio-tracking vehicle project

Audio-Tracking Vehicle

Raspberry Pi 4PythonMOSFET AmplifiersFlash ADCGit
  • Led project management efforts using Git and Crystal methodologies, ensuring timely deliverables, effective debugging, and clear technical documentation under strict constraints to implement an autonomous audio-tracking vehicle using a Raspberry Pi 4
  • Designed a push-pull amplifier for ±12V regulation using MOSFETs to power operational amplifiers required for the project
  • Developed signal processing and amplification systems, including a frequency-tracking filter, and a 2-bit Flash ADC for audio signal conversion
  • Contributed to the software development for the audio-tracking vehicle, with a focus on multi-threading processes using Python's threading library
Electric farm fence project

Electric Farm Fence

OpenCVCaffeMQTT555 TimerRaspberry Pi
  • Designed and implemented a conceptual electric farm fence powered by a voltage multiplier, integrated with facial detection using OpenCV and a Caffe deep learning model
  • Utilized a 555 timer-based waveform generator to drive the voltage multiplier circuit
  • Developed logic where the fence only activates via MQTT signal if no human presence is detected by the laptop camera
  • Built a transistor-based switching mechanism to interface with the Raspberry Pi for circuit activation
Physical system for the AeroPress coffee monitor

AeroPress Coffee System

ESP32MQTTKalman FilterLoad CellThermistor
  • Developed a portable IoT-enabled AeroPress monitoring system using an ESP32, integrating a 10kOhm NTC thermistor, 5kg load cell, and MEMS pressure sensor to measure real-time brew temperature and water volume
  • Implemented analog signal processing and digital Kalman filtering to stabilize temperature measurements, achieving thermistor accuracy within approximately ±1-2°C across a brewing range of 60-100°C with low noise
  • Calibrated a 5kg load cell using linear regression and amplified Wheatstone bridge sensing to estimate water volume from mass, achieving repeatable volume measurements within ±5-10 mL of the true volume
  • Hosted JSON sensor data over MQTT to a web dashboard for live visualization — now embedded directly in this site
Open Live Dashboard →