Author: IRNAS & Nordic Semiconductor

Introducing RAM-1

For Izoelektro, a Slovenian company providing products intended for maintenance, renewal and set up of electro-energy systems, we are developing RAM-1, a cellular IoT- and BLE-powered remote monitoring device, allowing power grid operators to remotely monitor smart power grids and thus take immediate action in cases of failures.

To provide RAM-1 with wireless connectivity, we have selected Nordic Semiconductor’s nRF9160 low power SiP with integrated LTE-M/NB-IoT modem and GPS, in combination with the nRF52811 Bluetooth® Low Energy (Bluetooth LE, BLE) multiprotocol System-on-Chip (SoC).

As the nRF Connect SDK offers support for the Zephyr RTOS ecosystem…


We are happy to announce, our wireless optical communication system for urban environments KORUZA has been granted funding from the European Union’s Horizon 2020 research and innovation programme within the framework of the NGI Pointer project.

Thanks to NGI Pointer, who we have been recognized as Next Generation Internet (NGI) Architects by, we will be able to develop KORUZA for the next 12 months. We are looking forward to the mentorship!

KORUZA wireless optical communication system

KORUZA is a wireless optical communication system for urban environments, based on FSO (Free Space Optical) technology, designed for last-mile, 5G and IoT applications. It uses an eye-safe collimated…

Author: Marko Sagadin, Embedded Machine Learning Engineer at IRNAS


I recently wrote a Master’s thesis on detecting elephants from thermal cameras, with the help of Machine Learning. Thesis is publicly available on my GitHub repository.

Classifying images has come a long way in the last decade; at this point, it is quite a norm in the field of web, mobile and desktop applications, however, it is not yet widely used in embedded systems.
Recent developments in the field of embedded Machine Learning, also known as TinyML, have pushed boundaries of what is possible. …

Power consumption optimization is at the heart of IRNAS’ development of advanced applied IoT solutions. As such, we are always working actively to optimize our workflows and automate testing with continuous integration. Following our guiding principles and the active development of solutions, based on Nordic Semiconductor products, we were very excited to learn about the newly introduced Power Profiler Kit II (PPK2).

This new generation of a very useful tool enables our developers to have a power consumption analysis tool handy at all times. While the graphical interface through nRF Connect application is very nice, we also like to use…

For the past two months, IRNAS and Smart Parks, in partnerships with, have been working on building the next generation OpenCollar ElephantEdge tracker, closely looking at requirements and features that make this a success. Two key concepts are driving it, rock-solid field performance and an intuitive user experience. In the previous blog and webinar, we did a deep-dive into the technical choices made in the design process and the experiences shared by Smart Parks, we hereby look deeper into the firmware and workflow aspects.

Together, we make sure to deliver sustainable, future-proof solutions. This requires a skillful integration of…

This decade started with a rapid growth of connectivity options such as BLE5, LoRaWAN, NB-IoT, satellite technologies and many other exciting options. In particular, the power consumption of the devices has decreased an order of magnitude on the processing and communication front, leading to the capability of acquiring a large amount of sensor data at very low power while being power and cost limited to only send a handful of measurements.

At IRNAS, we work with a global range of customers from industrial sensing in critical infrastructure to remote monitoring of animals all over the globe. …

IRNAS and Smart Parks have been working on designing the next generation of open-source tracking solutions for national park management and wildlife protection for the past two years and the deployment of these solutions in the field has proven very valuable.


Event info

  • First OpenCollar for Elephant successfully deployed in Liwonde National Park — Park Rangers in Liwonde National Park in Malawi are for the first time able to see elephant locations every fifteen minutes. This is possible due to new Smart Parks technology providing Rangers with GPS-locations four times an hour. With this information, elephants can be better protected…

At IRNAS we have set up a unique combination of development and manufacturing capabilities, empowering our rapid innovation and taking customers from ideas to batches of devices in minimal time. Having our own infrastructure gives us a high level of control over our in-house manufacturing and assembly processes. With that, it comes also making sure that design decisions are based upon manufacturing and assembly capabilities.

In this blog post, we will talk about using adhesives and glues, which we’ve been avoiding to some extent in the past for two main reasons.

First, glueing is a messy, time-consuming process. Of course…

Author: Vid Rajtmajer, student intern at IRNAS


Fault-tolerant solutions result in happy users, which remain oblivious to certain system failures if they are recovered in time. At IRNAS we rapidly develop advanced applied solutions and often design complex systems with rapid innovation. In experimental phases of the projects, these get deployed to various locations where they are stress tested and also user stories are yet to be found and defined. IRNAS team is usually the first observer of a user actually using the device for the first time and this is where we often learn the most. With innovation, it…

Author: Marko Sagadin, student intern at IRNAS

Welcome to the second article about running machine learning algorithms on microcontrollers.
In the previous article, we have created and trained a simple Keras model that was able to classify 3 different classes from CIFAR-10 dataset.
We looked at how to prepare that model for running on a microcontroller, quantized it and saved it to disk as a C file.

It is now time to look at how to setup the development environment and how to run inference on an actual microcontroller.

For this we will need two things to follow along. First, we will need to clone MicroMl project…

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