The global energy transition is characterized by the simultaneous challenges of decarbonization, digitalization, and decentralization [...]

Monitoring landslide activity demands positioning systems that can operate continuously in difficult terrain while maintaining high accuracy. Traditional geodetic GNSS receivers provide excellent precision but are often too costly and delicate for large-scale deployments. Recent developments in affordable GNSS hardware have opened new opportunities for building dense monitoring networks at a fraction of the expense. This paper reviews the hardware components most critical to such systems, including receiver types, antennas, power solutions, and communication links. Low-cost single-frequency devices, such as u-blox modules, demonstrate promising results under favorable conditions, though they require longer convergence times. Dual-frequency receivers, such as the ZED-F9P, deliver faster initialization and more reliable precision, albeit with higher cost. Antenna configuration further influences performance, with geodetic-grade options ensuring stability and calibrated patch antennas offering practical compromises. Field deployments typically integrate solar panels with battery storage and rely on cellular or radio communication for real-time data transfer. With overall system costs ranging from €500 to €1500 per station, properly configured low-cost units have proven capable of tracking ground displacements with sufficient accuracy for landslide monitoring. The evidence suggests that careful hardware integration, balancing receiver choice, antenna performance, autonomous power supply, and connectivity is key to designing effective and resilient GNSS monitoring networks.

This paper explores the integration of control and monitoring systems within a graphical environment where factory (production line) simulations can be conducted. Gamification of simulators enables realistic testing of proposed solutions, allowing errors to be identified and resolved before equipment is installed in real factories. In this way, the 1-10-100 rule can be applied, where quality issues and costs grow rapidly depending on the stage at which they are detected. The most effective approach is to use simulators to verify design of factories before purchasing and installing components. For this reason, the new generation of gamified simulators can significantly reduce both costs and development time for new factories. When including components from different manufacturers and various types of equipment such as PLCs, robots, and CNC machines, gamified simulators can support rapid prototyping of industrial environments. The Simulator Factory I/O, in combination with TIA Portal, is recognized as an adequate environment for verifying the proposed design of an entire factory or a specific part of a production line

Landslides pose a serious hazard worldwide, and monitoring their slow displacements is crucial for early warning and risk mitigation [1]. Global Navigation Satellite System (GNSS) sensors provide continuous 3D positioning in all weather, but conventional geodetic-grade GNSS are expensive and fragile in harsh terrain. Recent years have seen the rise of low-cost GNSS units that offer centimeter-level accuracy at a fraction of the cost [2]. This paper reviews technical innovations that enable such performance, focusing on real field deployments. Key advances include high-precision positioning techniques (RTK and Precise Point Positioning, PPP) and hybrid PPP-RTK corrections that speed up convergence, data-driven approaches like “Virtual RINEX” (VRINEX) to emulate reference observations [3] [4], and integration of inexpensive MEMS inertial sensors to suppress GNSS noise [5]. Open-source processing (e.g. RTKLIB-based workflows) and community tools now make low-cost GNSS monitoring more accessible. Field tests confirm that properly deployed dual-frequency low-cost GNSS stations can track subcentimeter displacements. We summarize 14 representative studies, compare their setups and results (Tables 1&2), and conclude that multi-constellation dual-frequency receivers, short baselines or VRINEX references, and hybrid processing are recommended for cost-effective landslide monitoring. Future work should emphasize long-term autonomous networks and real-time PPP-RTK services to further democratize GNSS hazard monitoring.

This article presents the design of an IoT communication gateway architecture aimed at collecting data from wireless sensor nodes, processing it using edge computing techniques, and distributing it to higher-level systems, such as cloud technologies. The architecture is tailored for long-term outdoor installations and incorporates a backup communication channel to enhance system reliability. The design prioritizes energy efficiency and resilience to harsh climatic conditions. To validate the system’s functionality and reliability, in-situ testing was conducted, allowing for real-world testing and generating valuable data for further system optimization.

This study describes a method for measuring flexion and extension motions in the hand, which is crucial for healing and restoring a complete range of motion following accidents. Precise control is essential because of the human hand's intricate anatomy, which consists of various bones and joints. The method replicates the trajectories of the five fingers, emphasizing their position, speed, acceleration, and torque at four crucial structural points: four fingers that stretch to 90 degrees and then return to 0 degrees, with the thumb creating a 20-degree angle, a crucial initial position for successful recovery. The ability to simulate these trajectories is essential for correctly tracking patients' progress and customizing rehabilitation treatments, both of which improve patient care and rehabilitation outcomes.

This paper presents a comprehensive comparative analysis of Golden Section Search (GSS), Artificial Neural Network (ANN), and Adaptive Neuro Fuzzy Inference System (ANFIS), alongside well-known Perturb and Observe and Incremental Conductance (INC) Maximum Power Point Tracking (MPPT) algorithms for Photovoltaic (PV) system. The methodology involves theoretical development, simulation, and real-time experimentation using Matlab/Simulink and the Humusoft MF 634 data-acquisition card. Real-time experiments validate algorithm effectiveness under real-world conditions, facilitated by precise control mechanisms using Taraz's power electronics converter modules. The results contribute to ongoing efforts in optimizing MPPT technology and advancing the efficiency of PV systems for renewable energy generation.

As the future electric power grid will be driven by distributed renewable energy sources, the deployment of grid-connected power converters will also grow to enable seamless grid and energy source interaction. To provide the reliable operation of these converters, the estimation of fundamental grid parameters is important. The most common estimation techniques are a phase-locked loops (PLL) and a frequency-locked loops (FLL). However, those techniques encounter challenges in conducting parameter estimation when the input signal is unbalanced due to DC-offset, harmonics, signal sags, and frequency and phase variations. This paper presents an enhanced FLL loop enriched with an additional loop for estimation and rejection of the DC-offset. Active and reactive power calculations in grid-connected microgrids by using the modified FLL loops with DC-offset rejection is a novel application introduced in this paper. Experimental verification has demonstrated that the enhanced FLL loop provides fast and reliable parameter estimation as well as stable and robust power calculations, even in the presence of a DC-offset.

For many years now MATLAB has been considered the academia standard when it comes to technical computing and simulation. Many university and college courses rely on multiple tool-boxes and ad-dons that MATLAB provides. With its relatively simple syntax, and large user community it has been, for so many years, a logical choice for academia. However, more often than not, students fresh out of university have been facing a new software that has very quickly become an industry standard in many areas of electrical engineering. On a simple example of DC motor control, this paper aims to showcase advantages of early adoption and using LabViewfor programming and simulation purposes in academia.

This paper presents the implementation of the Binary Search Algorithm (BSA) to determine the Maximum Power Point (MPP) of a photovoltaic (PV) system under variable weather conditions. Additionally, the conventional well-known Perturb and Observe (P&O) algorithm is also implemented to be compared with the binary search based Maximum Power Point Tracking (MPPT) algorithm. Both algorithms are implemented in real time in MATLAB/Simulink environment. The experimental study is performed using the two 260 W series connected PV modules, the buck converter, and Humusoft MF 634 card to enable real-time operation. The value of the duty cycle for the buck converter is being updated in each step moving the operation point closer to MPP. The obtained experimental results demonstrate that the binary search based MPPT algorithm is more efficient and accurate when compared to the P&O MPPT algorithm.

Gesture recognition is a field of study that involves recognizing human movements and gestures through sensors. In this paper, a basic gesture recognition system is proposed that uses three Time-of-Flight (TOF) VL53L0X distance sensors positioned in an L-shape able to recognize gesture through cover glass and up to 40 cm of distance. The system is capable of recognizing four basic gestures: swipe right, swipe left, swipe up and swipe down. This system can be applied in various fields such as Human-Computer Interaction (HCI), Gaming, Virtual Reality (VR) and Robotics, this paper will focus on the implementation and evaluation of the proposed system. The inspiration for the system is to simplify interaction with medical panel PCs and monitors while improving the hygienic aspect of the same, while taking into consideration data privacy.

Age estimation has become inordinately significant for human beings for many reasons, such as detecting legal and criminal responsibility and other social events like a marriage license, birth certificate, etc. This paper aims to decide on the most desirable machine learning algorithm (from conventional machine learning algorithms to deep learning) for dental age estimation based on buccal bone level. The database consisted of 150 CBCT images (73 males and 77 females) from an existing base of the Faculty of Dental Medicine with Clinics, University of Sarajevo, aged 20-69. Results were obtained using the Waikato Environment for Knowledge Analysis (Weka), machine learning software in Java. Left and Right Buccal Alveolar Bone Levels are increasing with age, so they showed to be the most important attributes, especially the latter. Random Forest classifier provided the greatest result with the correlation coefficient of 0.803 and the mean absolute error of 6.022. We have also shown that considering sinus-related features can be a significant addition to the databases. Our paper is probably one of the first studies where regression algorithms based on the Support Vector Machines and Random Forest were utilized.