Multimodal Signal Processing Library for Remote Monitoring Stations

Challenge

A hardware development company specializing in remote monitoring stations needed a solution for processing data from dozens of sensors — measuring parameters such as temperature, humidity, pressure, seismic activity, and magnetic field variations.
Because the stations were deployed in remote locations, stable internet connectivity and power supply were often unavailable. Transmitting raw sensor data to a central server was impractical and costly.

The main challenge was to develop a local signal processing library capable of:

• simultaneously handling signals from all sensor types,

• classifying user-defined events in real time,

• operating efficiently on low-power embedded devices.

Solution

The Hidden Core team developed a custom multimodal signal processing library capable of fusing and analyzing data from over 20 types of sensors within a unified computational pipeline.

The solution combined:

• classical digital signal processing (DSP) techniques for signal filtering, frequency transformation, and normalization;

• deep learning architectures optimized for multimodal data fusion and real-time event classification.

The model was fine-tuned and optimized for deployment on a low-power computing module embedded within each monitoring station.
The resulting SDK was delivered to the client and successfully integrated into their core software platform.

Impact

With the new library, the client gained a fully autonomous signal processing system that no longer required high-speed network connectivity.
This innovation allowed the company to:

• eliminate the need for expensive communication infrastructure, reducing operational costs;

• increase event classification accuracy by processing raw data locally, without transmission noise or signal distortion;

• simplify station maintenance and reduce overall product costs.

Hidden Core Contribution

Our team delivered a complete end-to-end solution — from multimodal architecture design and data pipeline development to model training and on-device optimization.
The resulting library became the analytical core of the client’s next-generation remote monitoring platform.