Open-source library for deriving solar radiation and heat flux from standard environmental sensors. Based on published research, works on Arduino, ESP32, Raspberry Pi, and other embedded platforms.
Billions of sensors exist right now, measuring simple parameters only because that's how they were labeled. We are overlooking the true value of mathematical approximation.
As demonstrated in countless applications involving these sensors and sensing devices, a combination of such sensors can yield derived parameters—values we often cannot obtain directly with perfect precision.
But do we always need perfect precision?
Let's end this information crisis. Restricting sensor potential is like blocking our future generations from building upon what we create today. Old or new sensors, built for various purposes, combined together, can derive far more advanced real-world parameters in applications that don't demand absolute precision. We are wasting resources.
A single such waste can cause millions of organisms to suffer, fall behind, or be restricted.
Contribute to the next wave of planetary recovery—we can build frameworks that rely on frameworks. FiaPhy is a research initiative focused on maximizing the potential of sensors, the near-future carriers of full-human automation, to help sustain existing and upcoming innovations while enabling them to work with our frameworks for generations to come.
Contributing to the good of everywhere.
FiaPhy works across multiple platforms and development environments
Language support and syntax highlighting for FiaPhy library development
Install ExtensionProfessional embedded development with dependency management
Add to your platformio.ini configuration file:
Or install via command line:
FiaPhy implements the DTDSS (Differential Temporal Derivative Soft-Sensing) framework—a physics-based approach that transforms standard environmental sensors into capability-dense radiometers.
Derive GHI measurements without thermopile pyranometers. FiaPhy reconstructs solar radiation data from common sensor readings.
Obtain heat transfer measurements that traditional sensors cannot provide directly. Unlock energy exchange dynamics from your existing hardware.
Dual-node architecture separates environmental baseline from radiative flux detection. Common-mode noise rejection built into the hardware design.
Proprietary INR signal processing compensates for thermal inertia and quantization noise. Real-time derivative calculation without phase delay.
Dynamic air density calculation from pressure readings. Deploy from sea level to 5000m without firmware modification or recalibration.
Derived radiation values enable Penman-Monteith ET₀ calculations. Environmental monitoring without specialized instrument costs.
Configure your dual-sensor topology and let FiaPhy handle the physics. The library abstracts the differential processing internally.
A differential sensing architecture that derives atmospheric parameters from paired environmental sensors.
Ventilated sensor at equilibrium with ambient conditions
Reactive sensor responding to radiative and thermal fluxes
FiaPhy implements a dual-sensor differential topology using standard environmental sensors (BME280, etc.). By comparing a reference sensor at thermal equilibrium with a reactive flux sensor, the system extracts atmospheric parameters that would traditionally require expensive specialized instruments.
The built-in Inertial Noise Reduction (INR) filter ensures clean signals even in noisy environments, while altitude-agnostic calculations use real-time pressure readings to compute air density—eliminating the need for manual elevation calibration.
From Arduino sketches to industrial PLCs. FiaPhy is designed to run anywhere C/C++ compiles.
Native support for ESP32, ESP8266, Arduino (AVR, ARM Cortex), STM32, and Raspberry Pi Pico. Optimized for low memory and real-time operation. Install via PlatformIO or Arduino Library Manager.
Run FiaPhy on Raspberry Pi OS and Linux systems for research and data collection. Requires GCC 8.0+ and CMake 3.10+. Full source available on GitHub with example implementations.
From farms to factories, FiaPhy enables advanced environmental intelligence without specialized instruments.
Integrate environmental intelligence into home automation systems. Automate blinds, HVAC, and lighting based on real-time solar radiation and thermal conditions.
Monitor solar panel performance by comparing expected vs actual output. Detect panel degradation, shading, or soiling without expensive reference cells.
Optimize HVAC systems with real-time heat flux data. Understand thermal gains and losses through building envelopes for predictive climate control.
Upgrade DIY and citizen science weather stations with derived radiation parameters. Fill gaps in meteorological networks with low-cost instrumentation.
Yes. This is 100% free and open source. The library, research paper, and all source code are publicly available on GitHub.
This technology is based on the research paper "Temporal Derivative Soft-Sensing and Reconstructing Solar Radiation and Heat Flux from Common Environmental Sensors." The paper has been accepted by IEEE for peer review. We are currently in the review process and continuing to improve the technology. This is a pre-release version under active testing and development.
Yes. FiaPhy works across all major operating systems (Windows, macOS, Linux) and on embedded platforms (ESP32, Arduino, Raspberry Pi, etc.). The library integrates with your development environment (Arduino IDE, PlatformIO, VS Code) rather than directly with the operating system.
Look for the verified checkmark next to the publisher name on the VS Code Marketplace. For GitHub releases, verify the repository URL is github.com/fiaos-org/fiaphy. Official releases are signed by the FiaOS organization.
FiaPhy supports Arduino (AVR, ARM Cortex-M0/M3/M4), ESP32/ESP8266, Raspberry Pi (Linux ARM), STM32, and other embedded platforms. You can use Arduino IDE, PlatformIO, or compile directly with GCC on Linux systems.
Download the latest version and integrate into your project
