Mindaugas Zilys

Urban mobility worldwide is seeing an increasing use of micromobility vehicles such as bicycles and e-scooters. However, accurate methods for measuring their traffic parameters remain limited, which could result in outdated infrastructure planning practices. Conventional sensing technologies such as pneumatic tubes, computer vision, and inductive loops struggle with the small dimensions, low metal content, and high maneuverability of micromobility vehicles. This work presents an experimental classification method based on magnetic signatures, which is designed for micromobility traffic monitoring in smart city applications. A sensor array consisting of 11 magnetic field sensors installed across a bicycle path was used to collect 236 real-world vehicle signatures. Fourteen temporal, spatial, and frequency-domain features were extracted from each signature and evaluated using multiple machine learning models. Gradient Boosting model achieved the highest performance with a cross-validation accuracy of 82%, demonstrating that magnetic field signatures contain sufficient information to differentiate bicycles, e-scooters, and heavy micromobility vehicles. Even with a limited dataset, results demonstrate the feasibility of micromobility traffic monitoring based on magnetic signatures. Expanding the dataset and implementing additional signal processing algorithms is expected to further enhance classification accuracy and support safe and accessible mobility in smart cities.

The Internet of Things (IoT) plays an important role in the development of smart cities. IoT forms a large network, and optimal controller placement plays a crucial role in ensuring network performance and resilience. This paper proposes a hybrid optimization approach that combines Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to strategically place controllers. Kaunas (Lithuania) was selected as a real-world smart city model. A large-scale Narrowband Internet of Things (NB-IoT) network with 2000 nodes was simulated, and 10 controllers were optimally placed in the network to minimize latency, balance load, enhance energy efficiency, and redundancy. The performance of the proposed hybrid GA-PSO algorithm was compared with random and K-Means clustering placements under three scenarios: normal operation, node failures, and traffic spikes. Simulation results demonstrate that the hybrid approach outperforms the other two methods in terms of load balancing, packet loss, energy efficiency, scalability, and redundancy. These findings highlight the robustness and effectiveness of the proposed hybrid algorithm in optimizing controller placement for smart city environments.

The ventilation system is one of the most important elements of a building for the appropriate insurance of indoor climate parameters. Nowadays, textile ventilation systems are increasingly being used as a solution for low-energy buildings. Greater air movement and distribution in ventilation systems often leads to one of the most noticeable issues for people—increased noise in the indoor environment. One of the solutions is to use noise reducing diffusers. The aim of this research was to design and test a diffuser that fulfills noise regulations, would be light (weight less than 3 kg), be able to flexibly change geometry and have a design that harmonizes with the interior design, could be easily installed into a suspended ceiling, have a simple connection to the ventilation duct and be able to be effortlessly removed for maintenance, and be sustainable (usage of recycled materials). Three types of diffusers were created according to set characteristics and tested. The test results showed that the aim of the research was achieved—the emitted noise levels are below the regulation’s required level of less than 45 dBA. Also, it is light—the weight is 1.7 kg and 2.8 kg, respectively, for square and rectangular diffusers; has a flexible construction and design; is made from recycled materials.

This paper proposes a steady-state thermal model for the passive cooling of photovoltaic (PV) modules integrated into a vertical building façade by means of a solar chimney, including an empirical correlation for turbulent free convection from a vertical isothermal plate. The proposed analytical model estimates the air velocities at the inlet and at the outlet of the ventilation channel of such a cooling system and the average temperature of the façade-integrated PV modules. A configuration composed of a maximum of six vertically installed PV modules and one solar chimney is considered. The air velocities at the inlet and at the outlet of the ventilation channel obtained for the case of installing PV modules on the building façade are compared with those calculated for the case where the PV modules are integrated into the roof with a slope of 37°. By comparing each of the solutions with one PV module to the corresponding one with six PV modules, it was found that the increase in the air velocity due to the effects of the solar irradiance and the height difference between the two openings of the ventilation channel ranges between 41.05% in the case of “Roof” and 141.14% in the case of “Façade”. In addition, it was obtained that an increase in the solar chimney height of 1 m leads to a decrease in the average PV section temperature by 1.95–7.21% and 0.65–2.92% in the cases of “Roof” and “Façade”, respectively. Finally, the obtained results confirmed that the use of solar chimneys for passive cooling of façade-integrated PV modules is technically justified.

Pantograph-based electrical current transmission systems are used in electric traction vehicles. The contact surface between the pantograph and the catenary wire experiences mechanical and thermal effects during the train’s movement. Typically, this contact surface on the pantograph is covered by a segmented carbon or copper rod, attached to an aluminum base. Railways implement organizational measures for pantograph condition monitoring, based on scheduled inspections. Constitutionally, the option to replace contact elements or individual segments of the pantograph exists if wear is detected. Many scientific publications describe ideas for pantograph visualization and automated condition monitoring. These ideas are based on analyzing mechanical vibrations generated by the pantograph, acoustic vibration signal analysis, 3D geometric data of the pantograph surface captured by laser scanning, and combinations of several methods. However, in these publications, mechanical vibration analysis is limited to signal shape and spectral analysis. The possibility of treating the vibration signal as a random process using statistical methods has not been utilized. This study describes the possibility of evaluating classified mechanical pantograph vibrations using the signal’s autocorrelation transformation. A laboratory experiment confirmed the proposed method for evaluating informative signal classification features. The proposed method can distinguish between signals generated by a defective pantograph surface and identify different types of defects.

In Intelligent Transportation Systems the identification and tracking of vehicles play an important role in enhancing traffic management, security, and overall road safety. Traditional means for vehicle re-identification rely solely on video-based systems which are not resilient to harsh environment conditions, suffer from visual obstructions, and are facing other challenges. To address these shortcomings and provide a more robust solution, alternative methods can be employed. This study addresses the gap in vehicle re-identification accuracy under harsh environmental conditions and visual obstructions faced by traditional video-based systems by integrating magnetic sensors into the road surface. The essence of this study revolves around a comprehensive comparison of various algorithms employed for feature extraction from registered magnetic field distortions. These distortions are treated as transient time series and various distance metrics are applied to calculate their similarity. Useful features are extracted and their classification performance is compared using a single neighbor classifier also taking into account calculation time. The validation experiments demonstrate the efficacy of presented approach in extracting critical features that hold the potential for successfully re-identifying same vehicles. For tested subset up to 90 % re-identification accuracy can be reached. The main contribution of this work involves determining which magnetic sensor axis to use—whether single or in combination—and identifying the most effective methods for feature extraction from the registered magnetic field distortions.

Intelligent transportation systems represent innovative solutions for traffic congestion minimization, mobility improvements and safety enhancement. These systems require various inputs about vehicles and traffic state. Vehicle re-identification systems based on video cameras are most popular; however, more strict privacy policy necessitates depersonalized vehicle re-identification systems. Promising research for depersonalized vehicle re-identification systems involves leveraging the captured unique distortions induced in the Earth’s magnetic field by passing vehicles. Employing anisotropic magneto-resistive sensors embedded in the road surface system captures vehicle magnetic signatures for similarity evaluation. A novel vehicle re-identification algorithm utilizing Euclidean distances and Pearson correlation coefficients is analyzed, and performance is evaluated. Initial processing is applied on registered magnetic signatures, useful features for decision making are extracted, different classification algorithms are applied and prediction accuracy is checked. The results demonstrate the effectiveness of our approach, achieving 97% accuracy in vehicle re-identification for a subset of 300 different vehicles passing the sensor a few times.

The growing population and lack of change in resources of cultivated land have led to the search for more efficient farming solutions. The recovery of soil is facilitated by using chemicals designed for the enrichment of cultivated soil. Fertilisers are made of a combination of various substances that determine not only the chemical but also the shape characteristics of the fertiliser pellets. The effect of the quality of fertilisation on yield size is related to even distribution. Shape and size are closely related to the quality of the fertilisation process. The intense control of the production process would not be possible without automatised and quick measurements within the production line. Constant control is necessary to ensure that the products meet quality standards. The contactless assessment of pellet sizes allows a quick reaction to changes in production quality and reduces the costs arising from the reprocessing of defective pellets. The results of the assessment of pellet volume using their two-dimensional image are presented in this publication. Pellets must be analysed according to their most characteristic position, which can provide valuable information about their properties. The aim is to determine the placement positions of the equipment based on calculations and to compare the results with those of gold-standard equipment. Correctly calibrated equipment ensures that the measurement results match the results of the control equipment of fertiliser producers. Reliable non-contact measurements can reduce the reaction time to production changes.

Geothermal energy installations are becoming increasingly common in new city developments and renovations. With a broad range of technological applications and improvements in this field, the demand for suitable monitoring technologies and control processes for geothermal energy installations is also growing. This article identifies opportunities for the future development and deployment of IoT sensors applied to geothermal energy installations. The first part of the survey describes the technologies and applications of various sensor types. Sensors that monitor temperature, flow rate and other mechanical parameters are presented with a technological background and their potential applications. The second part of the article surveys Internet-of-Things (IoT), communication technology and cloud solutions applicable to geothermal energy monitoring, with a focus on IoT node designs, data transmission technologies and cloud services. Energy harvesting technologies and edge computing methods are also reviewed. The survey concludes with a discussion of research challenges and an outline of new areas of application for monitoring geothermal installations and innovating technologies to produce IoT sensor solutions.