At Falconers we digitize the world through remote sensing technologies.
Our vision is to make a positive impact on the environment by decreasing the computational cost required for sensor dataprocessing, machine learning and AI.
The key to ensuring sensing system reliability in Edge systems and RF-denied environments is optimized hardware. We have experience developing hardware for ground as well as space payload carriers, from high resolution cameras to novel optical sensors and radars.
Optical sensing has made a decisive leap forward, giving machines with novel sensors and intelligence the capacity to detect and assess what would be left unseen for the human – subtle or fast movements, complex patterns, traces left in the environment or camouflaged objects.
Our AI and machine vision toolset can be tuned to autonomously observe, detect and track situational developments of areas/objects of interest. This gathered data can be then visualized or observed as historical data or in real-time.
When building autonomous devices or ISTAR systems, which require sensor dataprocessing, machine learning and AI, there is the challenge of computational cost. Depending on the use-case this can be power consumption limits in Edge devices, hardware constraints for device geometry/mass or physical limits of off-the-shelf computational components.
Analyzing sensor data on the Edge is becoming the norm as data volumes increase exponentially. Eliminating energy waste can go a long way for longer battery cycles, preventing unwanted heat in electronics and system reliance longevity.
Higher efficiency sensor-AI combinations can reduce the mass, volume and cost of devices with potentially having a positive “snowball effect” on the entire system. This translates to more cost-efficient and lighter bill-of-materials.
Optimising the real-time data processing can help reach higher goals: beit faster reaction speeds, increase in range or accuracy. Former bottlenecks can translate to competitive advantages
