In 2020, over 10,000 bird strikes were reported in the USA, with average repair costs exceeding $200 million annually, rising to $1.2 billion worldwide. These collisions of avifauna with airplanes pose a significant threat to human safety and wildlife. This article presents a system dedicated to monitoring the space over an airport and is used to localize and identify moving objects. The solution is a stereovision based real-time bird protection system, which uses IoT and distributed computing concepts together with advanced HMI to provide the setup’s flexibility and usability. To create a high degree of customization, a modified stereovision system with freely oriented optical axes is proposed. To provide a market tailored solution affordable for small and medium size airports, a user-driven design methodology is used. The mathematical model is implemented and optimized in MATLAB. The implemented system prototype is verified in a real environment. The quantitative validation of the system performance is carried out using fixed-wing drones with GPS recorders. The results obtained prove the system’s high efficiency for detection and size classification in real-time, as well as a high degree of localization certainty. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Wind as a clean and renewable energy source has been used by humans for centuries. However, in recent years with the increase in the number and size of wind turbines, their impact on avifauna has become worrisome. Researchers estimated that in the U.S. up to 500,000 birds die annually due to collisions with wind turbines. This article proposes a system for mitigating bird mortality around wind farms. The solution is based on a stereo-vision system embedded in distributed computing and IoT paradigms. After a bird’s detection in a defined zone, the decision-making system activates a collision avoidance routine composed of light and sound deterrents and the turbine stopping procedure. The development process applies a User-Driven Design approach along with the process of component selection and heuristic adjustment. This proposal includes a bird detection method and localization procedure. The bird identification is carried out using artificial intelligence algorithms. Validation tests with a fixed-wing drone and verifying observations by ornithologists proved the system’s desired reliability of detecting a bird with wingspan over 1.5 m from at least 300 m. Moreover, the suitability of the system to classify the size of the detected bird into one of three wingspan categories, small, medium and large, was confirmed. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Robotization has become common in modern factories due to its efficiency and cost-effectiveness. Lots of robots and manipulators share their workspaces with humans what could lead to hazardous situations causing health damage or even death. This article presents a real-time safety system applying the distributed computing paradigm for a collaborative robot. The system consists of detection/sensing modules connected with a server working as decision-making system. Each configurable sensing module pre-processes vision information and then sends to the server the images cropped to new objects extracted from a background. After identifying persons from the images, the decision-making system sends a request to the robot to perform pre-defined action. In the proposed solution, there are indicated three safety zones defined by three different actions on a robot motion. As identification method, state-of-the-art of Machine Learning algorithms, the Histogram of Oriented Gradients (HOG), Viola-Jones, and You Only Look Once (YOLO), have been examined and presented. The industrial environment tests indicated that YOLOv3 algorithm outperformed other solutions in terms of identification capabilities, false positive rate and maximum latency.

The detection and classification of features or properties, which characterize people, things or even events can be done in reliable way due to the development of new technologies such as Internet of Things (IoT), and also due to advances in Artificial Intelligence (AI) and machine learning algorithms. Interconnection of users with sensors and actuators have become everyday reality and IoT, an advanced notation of a Multi-sensor System, has become an integral part of systems for assessment of people’s habits and skills as well as the evaluation of quality of things or events' performances. The assessment approach presented in this thesis could be understood as an evaluation of multidimensional fuzzy quantities, which lack standards or references.

The main objective of this thesis is systematical design of multi-sensor systems for industrial and behavioral applications. The systematization is based on User Oriented Design (UOD), the methodology where stakeholders and future users are actively involved in all steps of the development process. An impact of the application environment on design principles is quantitatively and qualitatively analyzed. It shows different design approaches, which can be used for developing systems monitoring human activities or industrial processes.

The features identification approach applied in this thesis involves the extraction of the necessary data, which could be used for behavior classification or skills assessment. The data used for these purposes are vision or radio-based localization and orientation combined with measurement data of speed, acceleration, execution time or the remaining energy level.

Background removal, colour segmentation, Canny filtering and Hough Transform are the algorithms used in vision applications presented in the thesis. In cases of radio-based solutions the methods of angle of arrival, time difference of arrival and pedestrian dead reckoning were utilized. The applied classification and assessment methods were based on AI with algorithms such as decision trees, support vector machines and k-nearest neighborhood.

The thesis proposes a graphical methodology for visualization and assessment of multidimensional fuzzy quantities, which facilitate assessor's conceptualization of strengths and weaknesses in a person's skills or abilities. Moreover, the proposed method can be concluded as a single number or score useful for the evaluation of skills improvement during of training.

The thesis is divided into two parts. The first part, Prolegomena, shows the technical background, an overview of applied theories along with research and design methods related to systems for identification and classification of people’s habits and skills as well as assessing the quality of things or performances. Moreover, this part shows relationships among the papers constituting the second part titled Papers, which includes six reformatted papers published in peer reviewed journals. All the papers concern the design of IoT systems for industrial and behavioral applications.

This paper presents the structure and operational principles of the Automatic Waterjet Positioning Vision System (PVS), which was implemented on the WaterJet (WJ) machine. Moreover, it presents an impact of calibration method on PVS performance. Two webcams mounted on the industrial WJ, form a basis of the system, and constitute its characteristics features. Together with the identification algorithm, the PVS was aimed for high accuracy positioning of the WJ machine. For this purpose, the two-step calibration procedure that uses a set of specific calibration color markers contrasted to the background, has been developed. The results analysis shows that, despite demanding environmental conditions, the proposed methodenables reliable high accuracy positioning of the WJ machine.