Bionic System Solutions BSS Aps. is a new and innovative research and development company founded in 2017, with the purpose of commercialization of research done at the University of Southern Denmark SDU, Maersk Institute, and Biological Institute. Our core competences are within advanced signaling, sensor infrastructure and artificial intelligence where we focus our activities in time-series analysis and sensor control. The company is building sensor-technology and IoRT infrastructure, where we combine Internet Of Robotic Things with artificial intelligence, machine learning and biologically inspired algorithms for data processing and clustering of big data. Currently we are focusing our activities in two segments, machine maintenance and drone safety -and control. In regards to machine maintenance, our objective is to provide an innovative and highly advanced cloud platform for surveillance and monitoring of industrial machinery and robots

Drone surveillance & control

Research and development of innovative solutions for passive and active sound source localization. In close collaboration with the Danish Defense and the University of Southern Denmark, we are developing a new and innovative sensor for localization and control of unmanned aerial vehicles (UAVs), e.g., drones.

Machine maintenance

Research and development in sensor technology and cloud platforms for robot systems surveillance. Currently we are developing a new and innovative cloud-platform for internet of robotic things, the AutomationLab platform. The platform offers remote monitoring and surveillance of robots and industrial machinery.

Consumer market

Research & development in sensor technology enabling communications to and from voice-activated devices allowing for human interactions.

The AutomationLab Platfrom

AutomationLab is a unique and innovative platform for advanced monitoring and surveillance of robots and industrial machinery. The platform is based on scientific disciplines within Artificial Intelligence AI, Internet of Robotic Things IoRT, Industrial Internet of Things IIoT, machine learning, and biologically inspired algorithms.

The platform combines methods within frequency analysis, machine learning, and biology-inspired algorithms to perform error detection and fault localization on discrete-time signals. For error detection, the platform uses pattern recognition based on frequency analysis combined with machine learning and neural networks, while defect localization is performed using the Lizard Ear model, which is a biologically inspired algorithm for a time difference of arrival estimation.

Defect detection is performed using a unique algorithm for pattern recognition, where we train a neural network to separate healthy patterns from patterns indicating faults. The algorithm combines frequency analysis with an artificial neural network, where we use the fast Fourier transform to translate the collected time series into the frequency domain and analyze the frequency pattern using a multilayer perceptron neural network. Training of the neural network is based on supervised learning where we will use the backpropagation algorithm to perform this task. Backpropagation is a gradient descent training algorithm that recursively traverses all weights in the neural network and tries to optimize the network so that it can recognize all the patterns for which it has been trained for.

Defect localization will be carried out using the Lizard Ear Model, which is a biologically inspired algorithm for time difference of arrival estimation based on the peripheral sound system of lizards. The peripheral sound system of lizards, such as mabuya, macularia and geckos are incredibly directional but also relatively simple, making this system extremely responsive to changes in the sound image. Due to the simple design, the lizard is able to calculate the direction of its prey and attack it, solely by using its hearing. Similar sound systems are seen in frogs, dolphins, and bats when hunting for food.

Predictive maintenance for maritime use

AutomationLab is a unique and innovative IoT platform for advanced surveillance and monitoring of industrial and vessels machinery such as motors, pumps and gears. The platform is based on scientific disciplines in artificia intelligence AI, Internet of Robotic Things IoRT, Industrial Internet of Things IIoT, machine learning and biologically inspired algorithms. The platform is generic and can be used for various applications in the maritime industry, e.g. condition monitoring of compressors, gears, propellers, shaft systems main and auxiliary motors, etc. The system is developed with a focus on analysis of time series and vibration measurement, but are also compatible with other types of sensors such as. flow meter, temperature sensors, acoustic sensors, pressure sensors, etc.

Directional sound source localization for Unmanned Aerial Vehicle and Drones

In collaboration with the Danish Defense, University of Southern Denmark and Bionic System Solution a new research project has been established, for detection of drones and unmanned aerial vehicles detection.

The aim of the project is to ensure the outstanding research and development, which can lead to a number of use scenarios for solutions based on the identification of sound and the location of the sound source.

The idea is based on the study of the Lizard Ear Model used for sound source localization, performed at University of Southern Denmark in collaboration with Bionic System Solutions.

The research has led to a news study and now patent application. The patent application relates to sound and vibration and how to estimate and locate a point based on a sound or vibration source. The knowledge of sound filtering and sound detection has been further investigated and researched in order to function in relation to a number of possible solutions.

The solution could be a stationary setting e.g. for detecting vehicles traveling towards a target which ensures a reaction e.g. an alarm, detecting unmanned aerial vehicles (UAVs) e.g. drones or for robotic navigation that identify and respond to a voice or other audio sources and underwater solutions for identifying unwanted objects

The Lizard Ear Model

The directionality of the lizard hearing system is generated by acoustic coupling of the two eardrums created by the highly efficient transmission of sound through internal pathways in the head. This transmission is stronglyinfluenced by the properties of these internal paths as well as by the main size and is thereforefrequency-dependent.

The peripheral sound system of the lizard uses the phase shift between the sound waves arriving from each side of the lizard's head. Using specially designed filters that emerge through the transmission channel through the lizard's head, the lizard is able to locate its prey with very high precision. The transmission channel through the lizard's head is collected from both inputs so that the sound can pass from one input to the other. This is of great importance for a directional determination as the amplitude of the signal from one side of the head offsets the amplitude on the other.

The theoretical implantation of "The Lizard Ear Model" is based on 4 IIR (Infinite Impulse Response) filters, which are coupled in 2 pairs to mimic the transmission channel in the lizard's head. The order of the filters can be scaled to match the system in which the model is implemented. Similarly, the filter-characteristics of the individual filters can be modified so that the filter is optimized for the frequency spectrum being listened to..

The lizard ear model has currently been evaluated for sound source localization, where University of Southern Denmark has been contributing with research in this area over a period of two decades. In addition to this, the lizard ear model has been further investigated in an attempt to commercialize the algorithm I collaboration with Lizard-Technology Aps and the LEGO group.


A Braitenberg Lizard, Continuous Phonotaxis with a Lizard Ear Model
Danish Shaikh, John Hallam, Jakob Christensen-Dalsgaard, Lei Zhang

An Adaptive Neural Mechanism with a Lizard Ear Model for Binaural Acoustic Tracking
Danish Shaikh, Poramate Manoonpong

Predictive Acoustic Tracking with an Adaptive Neural Mechanism
Danish Shaikh, Poramate Manoonpong

Directionality of the lizard ear.
Jakob Christensen-Dalsgaard


Gert Nielsen

Mads Helle


Jens Kristian Damsgaard
Chairman of the board

Gert Nielsen
Member of the board

Mads Helle
Member of the board

Frank Waller
Member of the board

Erik Thaysen
Member of the board

George Bradford Beach
Member of the board


BSS Aps.
Østre Stationsvej 41P
5000 Odense C

CVR: 38378007
Phone: +45 51 665 030
Email: gn@bionicsystemsolutions.com