FPAA and FPGA Based Universal Sensor Node Design for Environmental Research

Based on research by
Ayanga Kalupahana,
Nisal Hemadasa,
Nipun Wijerathne,
Anuranga Ranasinghe, and
Ajith Pasqual
from the Department of Electronic Engineering, University of Moratuwa, Sri Lanka.

Published: 06 December 2017

Keywords: FPAA, FPGA, universal sensor node, environmental monitoring, analog sensor interfacing, programmable analog hardware, remote sensing, IoT sensor platform, mixed-signal systems, configurable instrumentation, edge sensing, analog computing

Read the article below or download the formatted research presentation.

Environmental monitoring systems are becoming increasingly complex as researchers demand more flexibility, more sensor compatibility, and faster deployment cycles. Traditional sensor interface systems are often built around a single sensor type or a narrow set of applications, forcing researchers to redesign hardware every time measurement requirements change.

A research team from the University of Moratuwa proposed a different approach: a highly flexible universal sensor node architecture built around both Field Programmable Analog Arrays (FPAAs) and FPGAs. Their design demonstrates how programmable analog and digital hardware can work together to create scalable, reconfigurable sensor platforms capable of supporting a wide range of environmental sensing applications.

Why Universal Sensor Nodes Matter

Modern environmental research depends on gathering accurate real-world data across many different conditions and sensor types. Researchers commonly work with sensors that output:

Voltage signals
Current signals
Capacitive measurements
Resistive measurements

Most existing analog sensor interfaces are optimized for only one of these signal categories. This creates several problems:

Limited adaptability
Increased hardware complexity
Difficult future expansion
Higher development time
Increased deployment cost

The researchers identified the growing need for a universal analog sensor platform capable of dynamically adapting to different sensor types without redesigning the hardware architecture.

The Core Idea: Combining FPAA and FPGA Technologies

The proposed system combines two programmable hardware technologies:

FPAA (Field Programmable Analog Array) for analog signal conditioning and analog computation
FPGA (Field Programmable Gate Array) for digital control, protocol management, and system configuration

This combination creates a highly flexible mixed-signal architecture capable of supporting many sensor configurations simultaneously.

High-Level System Capabilities

The platform supports:

3 analog channels
2 digital channels
5V supply voltage
150mA supply current

Each analog channel can be dynamically configured to handle:

Signal Type	Supported Range
Voltage	0–3.3V single-ended, ±2.75V differential
Current	2.54µA–323.5µA
Capacitive	10pF–500pF
Resistive	80Ω–8.735kΩ

Meanwhile, the digital channels support:

SPI
I2C
UART

The FPGA dynamically assigns digital communication functions while also reconfiguring the FPAA architecture as needed.

Dynamic Reconfiguration as a Major Advantage

One of the most important aspects of the design is dynamic configurability.

Traditional discrete analog circuits require dedicated combinations of:

Resistors
Capacitors
Amplifiers
Filters

for every individual sensing application.

The FPAA replaces much of this fixed analog circuitry with programmable analog blocks that can be reconfigured in software.

This approach provides several important advantages:

Traditional Discrete Design	FPAA-Based Design
Fixed functionality	Dynamic reconfiguration
High design complexity	Simplified development
Larger physical footprint	Compact implementation
Difficult incremental upgrades	Rapid iteration
Component aging issues	Aging compensation
Limited adaptability	Environment-adjustable operation

Because a single FPAA can perform multiple analog processing operations simultaneously, the platform can interface with up to three analog sensors in parallel.

How the FPAA Processes Different Sensor Types

The research demonstrates several specialized FPAA-based analog processing methods.

Voltage Sensor Processing

For weak voltage signals, the FPAA:

Amplifies the signal
Applies a notch filter
Rectifies the output

This enables accurate measurement of low-level analog signals in noisy environments.

Current Sensor Processing

Current sensors are handled using:

Transimpedance amplifier CAMs (Configurable Analog Modules)

The FPAA converts current into voltage and dynamically adjusts transimpedance values to maintain accurate readings.

Resistive Sensor Processing

For resistive sensing:

The sensor is excited using a reference voltage
Signals are amplified and summed
Gain stages normalize the output

This configuration allows support for a wide range of resistive sensor types.

Capacitive Sensor Processing

The capacitive sensing implementation is particularly interesting.

The FPAA creates:

An oscillator
A Voltage Controlled Oscillator (VCO)
A phase-locked loop
A low-pass bilinear filter

Together, these components form a frequency-to-voltage converter where sensor capacitance changes alter oscillator behavior. The resulting control voltage becomes the measured output signal.

This enables support for capacitive sensors ranging from 10pF to 500pF.

Digital Processing and Cloud Connectivity

The digital side of the platform is equally important.

The FPGA handles:

FPAA dynamic configuration
Protocol conversion
Instruction decoding

The researchers used a Xilinx Spartan 3E FPGA implementation during testing.

For high-resolution data acquisition, the system integrates the AD7682 ADC from Analog Devices, supporting:

User-configurable sampling rates
Up to 200kSPS (200,000 samples per second)

Sensor data is then transmitted through:

A low-power BLE module
An Android smartphone gateway
A Node.js server using MQTT

The system achieved:

Less than one minute cloud update latency

This effectively transforms the architecture into an Internet-connected remote environmental sensing platform.

Mobile Configuration and Remote Monitoring

The researchers also implemented an Android application that allowed users to:

Configure channels remotely
Select sensor modes
Monitor readings
Control system behavior

This significantly improves usability for field deployments and remote experimentation.

Instead of manually reprogramming dedicated instrumentation hardware, researchers can dynamically reconfigure the sensor node directly from software interfaces.

Why This Research Still Matters

Even though the paper was published in 2017, the architectural ideas remain highly relevant today.

The combination of programmable analog hardware with programmable digital control aligns closely with several modern trends:

Edge computing
Adaptive sensing systems
Low-power embedded AI
Reconfigurable instrumentation
Remote scientific monitoring
Rapid prototyping platforms

Most importantly, the work demonstrates that analog programmability can dramatically reduce the rigidity traditionally associated with sensor interface design.

As sensor networks continue expanding across:

Environmental science
Industrial monitoring
Biomedical systems
Agriculture
Smart infrastructure

reconfigurable mixed-signal architectures like this become increasingly valuable.

The Broader Significance of FPAA Technology

FPAAs remain one of the more underexplored programmable hardware technologies compared to FPGAs, despite offering compelling advantages for analog signal processing workloads.

Applications continue to emerge in areas such as:

Ultra-low-power sensing
Biomedical instrumentation
Adaptive filters
Analog neural networks
Real-time signal conditioning
Secure analog communications

Research like this highlights how FPAA technology can move beyond niche laboratory experiments into practical, scalable sensing infrastructure.

For researchers building flexible instrumentation systems, universal sensor nodes based on programmable analog hardware offer a path toward faster experimentation, lower redesign overhead, and significantly more adaptable deployments.