MACHINE LEARNING & AI
Applications
Harnessing the power of machine learning alongside sensor technology opens up a realm of possibilities in our customers various industries, revolutionizing how they can collect, analyse, and interpret data. At the heart of this synergy lies our sophisticated machine learning algorithms that enable sensors to not just detect signals but to understand and respond to them intelligently. These algorithms serve as the backbone of our sensor-driven systems, empowering them with the ability to learn from data patterns, adapt to changing environments, and make informed decisions in real-time.
One of the hallmarks of our machine learning algorithms is their adaptability and scalability, making them suitable for a wide range of sensor applications across diverse domains. Whether they are monitoring environmental conditions, detecting intrusions, or optimizing industrial processes our algorithms seamlessly integrate with sensor networks to deliver to our customers unparalleled performance and reliability. By continuously learning from new data inputs, our algorithms evolve over time, enhancing their accuracy and robustness to meet evolving needs and challenges.
In the dynamic landscape of technology, the concept of processing “at the edge” has gained prominence. Edge computing involves performing computations as close as possible to the data source or point of collection. When applied to sensor technology, edge computing revolutionizes how we perceive, analyse, and act upon real-world data. In the following, we explore examples of different sensor types used by our customers, and highlight the pivotal role of including machine learning (ML) algorithms at the edge.
Radio Signal Detection
Edge-Deployed RF Sensors:
Multi-purpose RF sensors, equipped with embedded hardware and ML capabilities, operate at the edge—close to the signal source. These sensors vigilantly scan the RF spectrum, detecting signals such as Wi-Fi, Bluetooth, and cellular transmissions.
Unlike conventional techniques, which rely on centralized processing, edge-based sensors exploit an order-of-magnitude speed advantage.
The result is fast detection and identification of RF signals, even in complex and congested environments.
Unique Radio Identification:
Imagine a scenario where we need to distinguish one radio device from a sea of similar devices. Perhaps it’s an intruder’s unauthorized transmitter or a specific mobile phone. By combining software-defined radio (SDR) sensing capabilities with ML techniques, we achieve unique radio identification. These algorithms learn from patterns, spectral features, and modulation characteristics.
Whether it’s a rogue transmitter in a secure facility or a legitimate device emitting suspicious signals, ML models at the edge can pinpoint the source.
The marriage of SDR and ML enables us to unmask the invisible, alerting us to the presence of specific radios be the friend or foe.
Environmental Sensors:
Vision Sensors:
Gas and Chemical Sensors:
IoT Sensors at the Edge:
