Search Results (641)

Modern manufacturing is increasingly shaped by the paradigm of Industry 4.0 (Smart Manufacturing). As one of its nine pillars, additive manufacturing plays a crucial role, enabling high-quality final products with improved profitability in minimal time. Advances in this field have facilitated the emergence of diverse technologies—such as Fused Deposition Modelling (FDM), Stereolithography (SLA), and Selective Laser Sintering (SLS)—allowing the use of metallic, polymeric, and composite materials. Within this context, Klipper v.0.12, an open-source firmware for 3D printers, addresses the performance limitations of conventional consumer-grade systems. By offloading computationally intensive tasks to an external single-board computer (e.g., Raspberry Pi), Klipper enhances speed, precision, and flexibility while reducing prototyping time. The purpose of this study is twofold: first, to identify and analyze bottlenecks in low-cost 3D printers and second, to evaluate how these shortcomings can be mitigated through the integration of supplementary hardware and software (Klipper firmware, Raspberry Pi, additional sensors, and the Mainsail interface). The scientific contribution of this study lies in demonstrating that a consumer-grade FDM 3D printer can be significantly upgraded through this integration and systematic calibration, achieving up to a 50% reduction in printing time while maintaining dimensional accuracy and improving surface quality. Full article

This submission is focused on the implementation of a system that acquires data from various types of sensors and securely stores them after encryption on a chip with a reconfigurable architecture. The system has the unique capability of encrypting the input data with a single secret cryptographic key, which is stored only inside the hardware of the system itself, so the key remains unrecognizable upon completion of the system synthesis for any unauthorized user. Being stored as a part of the whole system architecture, the cryptographic key cannot be attained. It is not stored separately on the system RAM or any other supported memory, making the collected data fully protected. The reported work shows a data acquisition system which measures temperature with a high level of precision, transforms it to degrees Celsius, stores the collected data, and transfers them via serial interface when requested. Before storage, the data are encrypted with a 256-bit key, applying the AES algorithm. The data which are stored in the system memory and sent as UART packets towards the main computer do not include the cryptographic key in the data stream, so it is impossible for it to be retrieved from them. We show the flexibility of such kinds of data acquisition systems for sensing different types of signals, emphasizing secure storage and transferring, including data from meteorological sensors or highly confidential or biometrical data. Full article

Multi-camera systems (MCSs) are pivotal in aviation surveillance and autonomous navigation due to their wide coverage and high-resolution sensing. However, challenges such as complex setup, time-consuming data acquisition, and costly testing hinder research progress. To address these, we introduce MCS-Sim, a photo-realistic MCSsimulator for UAV visual perception research. MCS-Sim integrates vision sensor configurations, vehicle dynamics, and dynamic scenes, enabling rapid virtual prototyping and multi-task dataset generation. It supports dense flow estimation, 3D reconstruction, visual simultaneous localization and mapping, object detection, and tracking. With a hardware-in-loop interface, MCS-Sim facilitates closed-loop simulation for system validation. Experiments demonstrate its effectiveness in synthetic dataset generation, visual perception algorithm testing, and closed-loop simulation. Here we show that MCS-Sim significantly advances multi-camera UAV visual perception research, offering a versatile platform for future innovations. Full article

Reliable and affordable data acquisition is crucial in industrial applications and critical infrastructure monitoring. However, common low-cost sensors with an I²C interface have limited range and low resistance to interference, which limits their deployment in demanding conditions. This study aimed to design and verify a universal module that bridges the I²C communication interface with the robust RS-485 industrial bus. A hardware module was designed and constructed to serve as a gateway. The core of the system is an STM32F0x1 microcontroller, which controls communication between the local I²C bus, designed to connect a wide range of sensors, and the RS-485 industrial interface. The design emphasizes robustness, including multi-level protection of power and communication circuits. The functionality of the proposed solution was verified by testing the prototype in real conditions. The module, equipped with a combined SHT30 temperature and humidity sensor, was deployed on the premises of the University of Žilina, Slovakia near transport infrastructure. The data collected from two weeks of continuous operation, recorded at ten-minute intervals, confirmed its reliable and error-free functionality. The result of this work is a modular and scalable platform that enables the easy integration of inexpensive sensors into robust industrial networks. This solution significantly reduces the cost and complexity of building distributed monitoring systems in areas such as transportation, industrial automation, and environmental monitoring. Full article

The increasing penetration of electrified vehicles is accelerating the evolution of on-board and off-board charging systems, which must deliver higher efficiency, power density, safety, and bidirectionality under increasingly demanding constraints. This article presents a system-level review of state-of-the-art charging architectures, with a focus on galvanically isolated power conversion stages, wide-bandgap-based switching devices, battery pack design, and real-world implementation trends. The analysis spans the full energy path—from grid interface to battery terminals—highlighting key aspects such as AC/DC front-end topologies (Boost, Totem-Pole, Vienna, T-Type), high-frequency isolated DC/DC converters (LLC, PSFB, DAB), transformer modeling and optimization, and the functional integration of the Battery Management System (BMS). Attention is also given to electrochemical cell characteristics, pack architecture, and their impact on OBC design constraints, including voltage range, ripple sensitivity, and control bandwidth. Commercial solutions are examined across Tier 1–3 suppliers, illustrating how technical enablers such as SiC/GaN semiconductors, planar magnetics, and high-resolution BMS coordination are shaping production-grade OBCs. A system perspective is maintained throughout, emphasizing co-design approaches across hardware, firmware, and vehicle-level integration. The review concludes with a discussion of emerging trends in multi-functional power stages, V2G-enabled interfaces, predictive control, and platform-level convergence, positioning the on-board charger as a key node in the energy and information architecture of future electric vehicles. Full article

Electrolyte-gated transistors (EGTs) have emerged as a highly promising platform for neuromorphic computing and bioelectronics, offering potential solutions to overcome the limitations of the von Neumann architecture. This comprehensive review examines recent advancements in EGT technology, focusing on three critical dimensions: materials, device configurations, and external field regulation strategies. We systematically analyze the development and properties of diverse electrolyte materials, including liquid electrolyte, polymer-based electrolytes, and inorganic solid-state electrolytes, highlighting their influence on ionic conductivity, stability, specific capacitance, and operational characteristics. The fundamental operating mechanisms of EGTs and electric double layer transistors (EDLTs) based on electrostatic modulation and ECTs based on electrochemical doping are elucidated, along with prevalent device configurations. Furthermore, the review explores innovative strategies for regulating EGT performance through external stimuli, including electric fields, optical fields, and strain fields/piezopotentials. These multi-field regulation capabilities position EGTs as ideal candidates for building neuromorphic perception systems and energy-efficient intelligent hardware. Finally, we discuss the current challenges such as material stability, interfacial degradation, switching speed limitations, and integration density. Furthermore, we outline future research directions, emphasizing the need for novel hybrid electrolytes, advanced fabrication techniques, and holistic system-level integration to realize the full potential of EGTs in next-generation computing and bio-interfaced applications. Full article

Extended reality (XR) technologies are gaining traction in technical education due to their potential for creating immersive and interactive training environments. This study presents the development and empirical evaluation of X-RAPT, a collaborative VR-based platform designed to train students in industrial robotics programming. The system enables multi-user interaction, cross-platform compatibility (VR and PC), and real-time data logging through a modular simulation framework. A pilot evaluation was conducted in a vocational training institute with 15 students performing progressively complex tasks in alternating roles using both VR and PC interfaces. Performance metrics were captured automatically from system logs, while post-task questionnaires assessed usability, comfort, and interaction quality. The findings indicate high user engagement and a distinct learning curve, evidenced by progressively shorter task completion times across levels of increasing complexity. Role-based differences were observed, with main users showing greater interaction frequency but both roles contributing meaningfully. Although hardware demands and institutional constraints limited the scale of the pilot, the findings support the platform’s potential for enhancing robotics education. Full article

This paper presents a novel partial-power DC-DC converter architecture specifically designed for Photovoltaic (PV) energy systems. Unlike traditional full-power converters, the proposed topology processes only a fraction of the total power, resulting in improved overall efficiency, reduced component stress, and lower system cost. The converter is integrated into a PV-based energy system and regulated by a dual-loop control strategy consisting of a Proportional-Integral (PI) voltage controller and an inner Sliding-Mode Controller (SMC) for current regulation. This control scheme ensures robust tracking performance under dynamic variations in irradiance, load, and reference voltage. The paper provides a comprehensive mathematical model and control formulation, emphasizing the robustness and fast transient response offered by SMC. Simulation results obtained in MATLAB-Simulink, along with real-time implementation on the OPAL-RT hardware-in-the-loop (HIL) platform, confirm the effectiveness of the proposed design. The system achieves stable voltage regulation with low ripple and accurate current tracking. Compared to conventional boost configurations, the proposed converter demonstrates superior performance, particularly under moderate voltage conversion conditions. The system achieves high efficiency levels, validated through both analytical estimation and real-time hardware-in-the-loop (HIL) implementation. Its high efficiency, scalability, and real-time control feasibility make it a promising solution for next-generation PV systems, battery interfacing, and DC-microgrid applications. Full article

Among other uses, DC-DC converters are employed in the auxiliary power modules (APMs) of electric vehicles (EVs), connecting the high-voltage traction battery to the low-voltage auxiliary system (AS). Traditionally, the APM is an isolated two-port, two-level (2L) DC-DC converter, and the auxiliary loads are fed at a fixed voltage level, e.g., 12 V in passenger cars. Dual-active-bridge (DAB) converters are commonly used for this application, as they provide galvanic isolation, high power density and efficiency, and bidirectional power flow capability. However, the auxiliary loads do not present a uniform optimum supply voltage, hindering overall efficiency. Thus, a more flexible approach, providing multiple supply voltages, would be more suitable for this application. Multiport DC-DC converters capable of feeding auxiliary loads at different voltage levels are a promising alternative. Multilevel neutral-point-clamped (NPC) DAB converters offer several advantages compared to conventional two-level (2L) ones, such as greater efficiency, reduced voltage stress, and enhanced scalability. The series connection of the NPC DC-link capacitors enables a multiport configuration without additional conversion stages. Moreover, the modular nature of the ML NPC DAB converter enables scalability while using semiconductors with the same voltage rating and without requiring additional passive components, thereby enhancing the converter’s power density and efficiency. This paper proposes a modulation strategy and decoupled closed-loop control strategy for the generalized multiport 2L-NL NPC DAB converter interfacing the EV traction battery with the AS, and its performance is validated through hardware-in-the-loop testing and simulations. The proposed modulation strategy minimizes conduction losses in the converter, and the control strategy effectively regulates the LV battery modules’ states of charge (SoC) by varying the required SoC and the power sunk by the LV loads, with the system stabilizing in less than 0.5 s in both scenarios. Full article

The digital revolution has profoundly influenced the automotive industry, shifting the paradigm from conventional vehicles to smart cars (SCs). The SCs rely on in-vehicle communication among electronic control units (ECUs) enabled by assorted protocols. The Controller Area Network (CAN) serves as the de facto standard for interconnecting these units, enabling critical functionalities. However, inherited non-delineation in SCs— transmits messages without explicit destination addressing—poses significant security risks, necessitating the evolution of an astute and resilient self-defense mechanism (SDM) to neutralize cyber threats. To this end, this study introduces a lightweight intrusion mitigation mechanism based on an adaptive momentum-based deep denoising autoencoder (AM-DDAE). Employing real-time CAN bus data from renowned smart vehicles, the proposed framework effectively reconstructs original data compromised by adversarial activities. Simulation results illustrate the efficacy of the AM-DDAE-based SDM, achieving a reconstruction error (RE) of less than 1% and an average execution time of 0.145532 s for data recovery. When validated on a new unseen attack, and on an Adversarial Machine Learning attack, the proposed model demonstrated equally strong performance with RE < 1%. Furthermore, the model’s decision-making capabilities were analysed using Explainable AI techinques such as SHAP and LIME. Additionally, the scheme offers applicable deployment flexibility: it can either be (a) embedded directly into individual ECU firmware or (b) implemented as a centralized hardware component interfacing between the CAN bus and ECUs, preloaded with the proposed mitigation algorithm. Full article

As critical infrastructure, hydropower dams require efficient and accurate detection of underwater structural surface defects to ensure their safety. This paper presents the design and implementation of a robotic control system specifically developed for underwater dam inspection in hydropower stations, aiming to enhance the robot’s operational capability under harsh hydraulic conditions. The study includes the hardware design of the control system and the development of a surface human–machine interface unit. At the software level, a modular architecture is adopted to ensure real-time performance and reliability. The solution employs a hierarchical architecture comprising hardware sensing, real-time interaction protocols, and an adaptive controller, and the integrated algorithm combining a fixed-time disturbance observer with adaptive super-twisting controller compensates for complex hydrodynamic forces. To validate the system’s effectiveness, field tests were conducted at the Baihetan Hydropower Station. Experimental results demonstrate that the proposed control system enables stable and precise dam inspection, with standard deviations of multi-degree-of-freedom automatic control below 0.5 and hovering control below 0.1. These findings confirm the system’s feasibility and superiority in performing high-precision, high-stability inspection tasks in complex underwater environments of real hydropower dams. The developed system provides reliable technical support for intelligent underwater dam inspection and holds significant practical value for improving the safety and maintenance of major hydraulic infrastructure. Full article

This paper presents a case study on the integration of embedded IoT hardware with a modern Android application, demonstrated through the development of a compact autoclave system for small-scale food sterilization. The device is controlled by an ESP8266-based module and communicates securely with a Kotlin-based Android app via MQTT using HiveMQ. The app incorporates advanced Android design patterns such as coroutines, LiveData, Navigation UI, and DataStore. Each device is uniquely addressable and fully configurable from the mobile interface. The work highlights Android’s role as a powerful interface for managing embedded IoT systems. Full article

This study introduces a promising authentication framework utilizing brain–computer interface (BCI) technology to enhance both security protocols and user experience. A key strength of this approach lies in its reliance on objective, physiological signals—specifically, brainwave patterns—which are inherently difficult to replicate or forge, thereby providing a robust foundation for secure authentication. The authentication system was developed and implemented in four sequential stages: signal acquisition, preprocessing, feature extraction, and classification. Objective feature extraction methods, including Fisher’s Linear Discriminant (FLD) and Discrete Wavelet Transform (DWT), were employed to isolate meaningful brainwave features. These features were then classified using advanced machine learning techniques, with Quadratic Discriminant Analysis (QDA) and Convolutional Neural Networks (CNN) achieving accuracy rates exceeding 99%. These results highlight the effectiveness of the proposed BCI-based system and underscore the value of objective, data-driven methodologies in developing secure and user-friendly authentication solutions. To further address usability and efficiency, the number of BCI channels was systematically reduced from 64 to 32, and then to 16, resulting in accuracy rates of 92.64% and 80.18%, respectively. This reduction streamlined the authentication process, demonstrating that objective methods can maintain high performance even with simplified hardware and pointing to future directions for practical, real-world implementation. Additionally, we developed a real-time application using our custom dataset, reaching 99.75% accuracy with a CNN model. Full article

This study presents a low-cost wearable system for monitoring and classifying paddle strokes in open-water environments. Building upon our previous work in controlled aquatic and dryland settings, the proposed system consists of ESP32-based embedded nodes equipped with MPU6050 accelerometer–gyroscope sensors. These nodes communicate via the ESP-NOW protocol in a master–slave architecture. With minimal hardware modifications, the system implements gesture classification using Dynamic Time Warping (DTW) to distinguish between left and right paddle strokes. The collected data, including stroke type, count, and motion similarity, are transmitted in real time to a local interface for visualization. Field experiments were conducted on a calm lake using a paddleboard, where users performed a series of alternating strokes. In addition to gesture recognition, the study includes empirical testing of ESP-NOW communication range in the open lake environment. The results demonstrate reliable wireless communication over distances exceeding 100 m with minimal packet loss, confirming the suitability of ESP-NOW for low-latency data transfer in open-water conditions. The system achieved over 80% accuracy in stroke classification and sustained more than 3 h of operational battery life. This approach demonstrates the feasibility of real-time, wearable-based motion tracking for water sports in natural environments, with potential applications in kayaking, rowing, and aquatic training systems. Full article

This paper introduces the ProVANT Simulator, a comprehensive environment for developing and validating control algorithms for Unmanned Aerial Vehicles (UAVs). Built on the Gazebo physics engine and integrated with the Robot Operating System (ROS), it enables reliable Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL) testing. Addressing key challenges such as modeling complex multi-body dynamics, simulating disturbances, and supporting real-time implementation, the framework features a modular architecture, an intuitive graphical interface, and versatile capabilities for modeling, control, and hardware validation. Case studies demonstrate its effectiveness across various UAV configurations, including quadrotors, tilt-rotors, and unmanned aerial manipulators, highlighting its applications in aggressive maneuvers, load transportation, and trajectory tracking under disturbances. Serving both academic research and industrial development, the ProVANT Simulator reduces prototyping costs, development time, and associated risks. Full article