Microelectronics
Hands-on experience with embedded systems, environmental sensing, and electronic circuit design.
Platforms & Hardware
Arduino
Prototyping sensor-driven systems with Arduino boards. Experience with analog/digital I/O, serial communication, and integration with environmental monitoring circuits.
Raspberry Pi
Building data collection and processing nodes using Raspberry Pi. Working with GPIO, Python scripting, headless deployments, and networked sensor arrays.
ESP32
Wireless IoT solutions with ESP32 microcontrollers. Wi-Fi and Bluetooth enabled environmental monitoring, low-power sensor deployments, and remote data transmission.
Environmental Sensors
Professional and academic experience working with environmental sensing technologies, understanding their operating principles, measurement epistemology, and practical deployment considerations.
Atmospheric Sensors
Temperature, humidity, and barometric pressure measurement. Understanding of thermistor-based sensing, capacitive humidity detection, and piezoresistive pressure transducers. Calibration methodology and measurement uncertainty quantification.
Air Quality Monitoring
Particulate matter (PM2.5/PM10), VOC, CO2, and gas concentration sensing. Knowledge of optical scattering methods, electrochemical cells, and NDIR spectroscopy. Sensor cross-sensitivity considerations and data quality assurance.
Water Quality Sensors
pH, dissolved oxygen, turbidity, and conductivity measurement. Electrode-based and optical sensing principles. Understanding of buffer calibration, sensor drift compensation, and field deployment constraints.
Radiation & Light Sensors
UV index, solar irradiance, and lux measurement. Photodiode and spectroradiometer principles. Understanding of spectral response curves, cosine correction, and radiometric vs. photometric quantities.
Measurement Epistemology & Circuit Translation
Beyond building circuits, I study how sensors generate knowledge—the relationship between physical phenomena, transduction, signal conditioning, and the interpretive frameworks that give measurement data its meaning.
Transduction Principles
How physical quantities (temperature, pressure, light intensity) are converted into electrical signals through resistive, capacitive, inductive, and piezoelectric mechanisms. Understanding what is directly measured versus what is inferred.
Signal Conditioning Circuits
Amplification, filtering, and analog-to-digital conversion paths. Wheatstone bridge configurations, instrumentation amplifiers, anti-aliasing filters, and the impact of ADC resolution on measurement fidelity.
Electronic Circuit Translation
Translating between schematic representations, breadboard layouts, and PCB designs. Reading datasheets to extract operating parameters, connecting sensor outputs to microcontroller inputs, and designing appropriate power regulation and protection circuits.
Uncertainty & Calibration
Quantifying measurement uncertainty through calibration against reference standards. Understanding systematic and random error sources, drift compensation strategies, and the epistemological limits of indirect measurement.