Search Results (332)

This paper presents an engineering design framework for integrating Variational Quantum Circuits (VQCs) into industrial control systems via real-time quantum emulation on embedded hardware. In this work, we present a novel framework for fully embedded real-time quantum machine learning (QML), in which a four-qubit, four-layer VQC is both emulated and trained in situ on an FPGA-based embedded platform (dSPACE MicroLabBox 1202). The system achieves deterministic microsecond-scale response at a closed-loop frequency of 100 kHz, enabling its application in latency-critical control tasks. We demonstrate the feasibility of online VQC training within this architecture by approximating nonlinear functions in real time, thereby validating the potential of embedded QML for advanced signal processing and control applications. This approach provides a scalable and practical path toward real-time Quantum Reinforcement Learning (QRL) and other quantum-enhanced embedded controllers. The results validate the feasibility of real-time quantum emulation and establish a structured engineering design methodology for implementing trainable quantum machine learning (QML) models on embedded platforms, thereby enabling the development of deployable quantum-enhanced controllers. Full article

Agricultural research and propagation systems often suffer due to a lack of access to affordable, adaptable, and well-structured technological solutions. Traditional plant growth devices typically rely on ad hoc construction, which limits their scalability, reuse, and adaptability. This study employs a user-centered conceptual design methodology based on product platform development and modular architecture to design a growth chamber for plant cuttings. The approach followed three main phases: (i) identification and classification of user needs, (ii) functional modeling of the base system and its variants, and (iii) architectural modularization through heuristic principles. Interviews with researchers yielded 55 functional requirements, of which 26 were defined as essential. Functional models were developed for both a base system and two variant systems incorporating alternative irrigation and sensing technologies. Heuristic analysis identified independent modules, such as irrigation, lighting, environmental monitoring, and control. Subsequently, block diagrams were used to translate functional logic into spatially coherent conceptual designs. The resulting architecture supports modular integration, reconfiguration, and scalability for diverse experimental needs. This work demonstrates that structured design methodologies, which are commonly used in industrial contexts, can be effectively applied in agricultural research settings to produce solutions that are versatile, low-cost, and have enduring value, offering a pathway for innovation, reproducibility, and technology transfer in resource-limited environments. Full article

The integration of intelligent voice-control systems represents a critical pathway for enhancing driver comfort and reducing cognitive distraction in modern vehicles. Currently, voice assistants capable of accessing real-time vehicular data (e.g., engine parameters) or controlling actuators (e.g., door locks) remain exclusive to premium brands. While aftermarket solutions like Amazon’s Echo Auto provide multimedia functionality, they lack access to critical vehicle systems. To address this gap, we develop a novel architecture leveraging the OBD-II port to enable voice-controlled telematics and actuation in mass-production vehicles. Our system interfaces with a Toyota Hilux (2020) and Mazda CX-3 SUV (2021), utilizing an MCP2515 CAN controller for engine control unit (ECU) communication, an Arduino Nano for data processing, and an ESP01 Wi-Fi module for cloud transmission. The Blynk IoT platform orchestrates data flow and provides user interfaces, while a Voiceflow-programmed Alexa skill enables natural language commands (e.g., “unlock doors”) via Alexa Auto. Experimental validation confirms the successful real-time monitoring of engine variables (coolant temperature, air–fuel ratio, ignition timing) and secure door-lock control. This work demonstrates that high-end vehicle capabilities—previously restricted to luxury segments—can be effectively implemented in series-production automobiles through standardized OBD-II protocols and IoT integration, establishing a scalable framework for next-generation in-vehicle assistants. Full article

In recent years, advancements in semiconductor technologies have significantly transformed Radio Frequency Power Amplifiers (RFPAs), enhancing their efficiency, size, and performance. Despite these advancements, the design of RFPAs remains intrinsically linked to the specific applications for which they are intended. What proves effective in one context, such as communication technologies, may not be equally suitable in others, such as scientific instruments. This discrepancy highlights the lack of a systematic approach to RFPA design that can be applied across different applications. This paper delves into the fundamental concepts of RFPA design, adopting a comprehensive perspective. It further introduces an alternative categorization of RFPAs, thereby providing a generalized design approach. Full article

Stained glass has played important roles in heritage building construction, however, conventional fabrication techniques have become economically prohibitive due to both capital costs and energy inefficiency, as well as high-level artistic and craft skills. To overcome these challenges, this study provides a new design methodology for customized 3D-printed polycarbonate (PC)-based stained-glass window alternatives using a fully open-source toolchain and methodology based on digital fabrication and hybrid crafts. Based on design thinking and open design principles, this procedure involves fabricating an additional insert made of (i) a PC substrate and (ii) custom geometries directly 3D printed on the substrate with PC-based 3D printing feedstock (iii) to be painted after the 3D printing process. This alternative is intended for customizable stained-glass design patterns to be used instead of traditional stained glass or in addition to conventional windows, making stained glass accessible and customizable according to users’ needs. Three approaches are developed and demonstrated to generate customized painted stained-glass geometries according to the different users’ skills and needs using (i) online-retrieved 3D and 2D patterns; (ii) custom patterns, i.e., hand-drawn and digital-drawn images; and (iii) AI-generated patterns. The proposed methodology shows potential for distributed applications in the building and heritage sectors, demonstrating its practical feasibility. Its use makes stained-glass-based products accessible to a broader range of end-users, especially for repairing and replicating existing conventional stained glass and designing new customizable products. The developed custom patterns are 50 times less expensive than traditional stained glass and can potentially improve thermal insulation, paving the way to energy efficiency and economic savings. Full article

Barium stannate (BaSnO₃) has emerged as a promising alternative electron transport material owing to its superior electron mobility, resistance to UV degradation, and energy bandgap tunability, yet BaSnO₃-based perovskite solar cells have not reached the efficiency levels of TiO₂-based designs. This theoretical study presents a design-driven evaluation of BaSnO₃-based perovskite solar cell architectures, incorporating MAPbI₃ or FAMAPbI₃ perovskite materials, Spiro-OMeTAD, or Cu₂O hole transport materials as well as hole-free configurations, under varying light intensity. Using a systematic device modelling approach, we explore the influence of key design variables—such as layer thickness, donor density, and interface defect concentration—of BaSnO₃ and operating temperature on the power conversion efficiency (PCE). Among the proposed designs, the FTO/BaSnO₃/FAMAPbI₃/Cu₂O/Au heterostructure exhibits an exceptionally effective arrangement with PCE of 38.2% under concentrated light (10,000 W/m², or 10 Sun). The structure also demonstrates strong thermal robustness up to 400 K, with a low temperature coefficient of −0.078% K⁻¹. These results underscore the importance of material and structural optimisation in PSC design and highlight the role of high-mobility, thermally stable inorganic transport layers—BaSnO₃ as the electron transport material (ETM) and Cu₂O as the hole transport material (HTM)—in enabling efficient and stable photovoltaic performance under high irradiance. The study contributes valuable insights into the rational design of high-performance PSCs for emerging solar technologies. Full article

This article presents the development of a compact, high-precision, and energy-efficient temperature monitoring system designed for tracking applications where continuous and accurate thermal monitoring is essential. Built around the HY0020 System-on-Chip (SoC), the system integrates two bandgap-based temperature sensors—one internal to the SoC and one external (Si7020-A20)—mounted on a custom PCB and powered by a coin cell battery. A distinctive feature of the system is its support for real-time parameterization of the internal sensor, which enables advanced capabilities such as thermal profiling, cross-validation, and onboard diagnostics. The system was evaluated under both room temperature and refrigeration conditions, demonstrating high accuracy with the internal sensor showing an average error of 0.041 °C and −0.36 °C, respectively, and absolute errors below ±0.5 °C. With an average current draw of just 0.01727 mA, the system achieves an estimated autonomy of 6.6 years on a 1000 mAh battery. Data are transmitted via Bluetooth Low Energy (BLE) to a Raspberry Pi 4 gateway and forwarded to an IoT cloud platform for remote access and analysis. With a total cost of approximately EUR 20 and built entirely from commercially available components, this system offers a scalable and cost-effective solution for a wide range of temperature-sensitive applications. Its combination of precision, long-term autonomy, and advanced diagnostic capabilities make it suitable for deployment in diverse fields such as supply chain monitoring, environmental sensing, biomedical storage, and smart infrastructure—where reliable, low-maintenance thermal tracking is essential. Full article

This work presents a comparative design of the sensing elements for the Lunar Dust GRID System (LD GRIDS), a dust analyser conceived to measure charged particles on future lunar missions. LD GRIDS replaces traditional electrodes with continuous conductive grids, i.e., the sensing elements of the instrument, which are able to collect induced charge when charged particles pass through them. The investigation focuses on evaluating the influence of various grid geometrical parameters (size, thickness, and patterns) on the sensor’s performance, either from an electrical or a mechanical perspective. All simulations were carried out using off-the-shelf numerical modelling software, where electrostatic simulation (i.e., induction performance), modal analysis, and quasi-static structural responses under a high acceleration quasi-static load were examined. The results indicate that while grids with round patterns tend to produce a higher induced charge, they also experience higher localised stresses compared to square pattern ones. Moreover, grid size does not significantly affect the instrument sensitivity, whereas increasing the grid thickness significantly reduces peak stresses, with only minor effects on electrostatic performance. Overall, the findings provided valuable insights for optimising the LD GRIDS design, aimed at balancing either electrostatic sensitivity or mechanical resistance, facing the harsh lunar environment. Full article

Heating, ventilation, and air conditioning (HVAC) systems account for 60% of the energy consumption in commercial buildings. Each year, millions of dollars are spent on electricity bills by commercial building operators. To address this energy consumption challenge, a predictive model named Bayesian optimisation Convolution Neural Network Multivariate Long Short-term Memory (BO CNN-M-LSTM) is introduced in this research. The proposed model is designed to perform load forecasting, optimizing energy usage in commercial buildings. The CNN block extracts local features, whereas the M-LSTM captures temporal dependencies. The hyperparameter fine tuning framework applied Bayesian optimization to enhance output prediction by modifying model properties with data characteristics. Moreover, to improve occupant well-being in commercial buildings, the thermal comfort adaptive model developed by de Dear and Brager was applied to ambient temperature in the preprocessing stage. As a result, across all four datasets, the BO CNN-M-LSTM consistently outperformed other models, achieving an 8% improvement in mean percentage absolute error (MAPE), 2% in normalized root mean square error (NRMSE), and 2% in R² score.This indicates the consistent performance of BO CNN-M-LSTM under varying environmental factors, highlight the model robustness and adaptability. Hence, the BO CNN-M-LSTM model is a highly effective predictive load forecasting tool for commercial building HVAC systems. Full article

This paper presents a novel algorithm, Variable Spacing and Extrusion (VaSE), designed to optimize the infill pattern of material extrusion (ME) 3D-printed parts for specified mechanical performance while ensuring manufacturability. The algorithm adjusts deposition spacing and width across layers to achieve functionally graded infill distributions derived from input density maps. First, the variable line spacing algorithm is implemented by normalizing the weighted density distribution. Errors in between the desired density and the density from the line spacing are corrected with a varying extrusion width algorithm. Two application scenarios are demonstrated with the proposed VaSE algorithm. First, beam samples are optimized for flexural stiffness and tested under three-point bending, showing a 10.8–19.2% stiffness increase compared to homogeneous infill, except at low (25%) volume fractions, where local buckling dominated failure. The second scenario involves maximizing the frequency of the first three modes of beams under an induced vibration. The optimized beams, taken straight from a topology optimization algorithm performed in the ANSYS 2023 finite element software, were compared to the beams that were instead put through the VaSE algorithm after the topology optimization. While all manufactured beams underperform relative to simulation, the VaSE-optimized beams show substantial frequency gains (34–63% for the first mode, 0.82–65% for the second mode) over purely geometry-based designs, with the exception of high-mass-fraction beams. These findings highlight the significance of the VaSE algorithm in enhancing mechanical performance and extending the design space of ME additive manufacturing beyond conventional homogeneous infill strategies. Full article

X5CrNi18-10 is becoming highly popular due to its excellent properties like corrosion resistance and high toughness. These properties make it difficult to machine and reach a surface finish with high precision and class accuracy, which the automotive and marine industries require. Manufacturers can achieve a high-quality surface finish by analyzing surface integrity. This research aims to understand the effect of cutting parameters on surface integrity and highlight the parameters that provide good results. This study uses Taguchi’s L₁₈ orthogonal array (OA) to examine the effects of cutting parameters and their impact on surface integrity. To completely understand the results, correlation analysis, confocal microscopy, light optical microscopy, and microhardness analysis were performed to analyze the effects of cutting parameters on the surface integrity. Results suggest that depth of cut (DOC) and cutting speed (v_c) have minimal effect, while feed rate (f) has more effects on the surface quality. In correlation analysis, it was found that feed rate influences arithmetic average surface roughness (R_a) and mean surface roughness depth (R_z). It can be concluded that v_c—300 m/min, DOC (a_p)—0.5 mm, and f—0.08 mm/rev provide good results in turning X5CrNi18-10. Full article

Effective grinding of residual agricultural materials helps to improve yield in the production of chemical compounds through hydrothermal technology. Milling pretreatment has different types of pre-treatment where ball mills, roller mills, and finally, the knife mill stand out. The knife mill being a mill with continuous processing, its multiple benefits and contributions highlight the knife milling process; however, it is a process that is generally carried out with dry biomass that generates extra processing of the biomass before grinding, implying longer times and wear than other equipment. This work presents the design of a knife mill with an adaptation of free convection drying as a joint process of knife milling and drying. The design is based on lignocellulosic biomass, and the knife milling results are presented for two biomasses: peapods and coffee cherries. The knife mill is designed with a motor, a housing with an integrated drive system, followed by a knife system and a feeding system with a housing and finally the free convection drying system achieving particle sizes in these biomasses smaller than 30 mm, depending on the time processed. The data demonstrate the significant impact of particle size on the yields of various platform chemicals obtained from coffee cherry and peapod waste biomass. For coffee cherry biomass, smaller particle sizes, especially 0.5 mm, result in higher total yields compared to larger sizes while for peapod biomass at the smallest particle size of 0.5 mm, the total yield is the highest, at 45.13%, with notable contributions from sugar (15.63%) and formic acid (19.14%). Full article

This paper proposes a fault-tolerant flexible manufacturing system (FMS) that features a dual-level fault tolerance mechanism at both the workcell and system levels to enhance reliability. The workcell controller was implemented on a Field Programmable Gate Array (FPGA). Reconfigurable duplication was used as the first level of fault tolerance at the workcell level. It was shown how to detect and recover from FPGA faults such as Single Event Upsets (SEUs), hard faults, and Single Event Functional Interrupts (SEFIs). The prototype of the workcell controller was successfully implemented using two Zybo Z7-20 AMD boards and an Arduino DUE. Petri Nets were used to prove that controller reliability increased by 346% after 1440 operational hours. The second level of fault tolerance was at the FMS level; the Supervisor (SUP) took over the responsibilities of any malfunctioning workcell controller. Riverbed software was used to prove that the system successfully met the end-to-end delay requirements. Finally, Matlab showed that there is a further increase in performability. Full article

Smart lawnmowers are becoming increasingly integrated into daily life as their performance continues to improve. To ensure consistent advancement, it is important to conduct a comprehensive analysis of the performance of various modern smart lawnmowers. However, there appears to be a lack of thorough performance evaluation and analysis of their broader impact. This review explores the key performance indicators influencing smart lawnmower performance, particularly in navigation and obstacle avoidance, operational efficiency, and human–machine interaction (HMI). Key performance indicators identified for evaluation include operating time, Effective Field Capacity (FCe), and field efficiency (%). Additionally, it examines the theoretical and practical implications of smart lawnmower development. Smart lawnmowers have been found to contribute to advancements in machine learning algorithms and possibly swarm robotics. Environmental benefits, such as reduced emissions and noise pollution, were also highlighted in this review. Future research directions are discussed, both in the short and long term, to further optimize smart lawnmower performance. This review serves as a foundation for future studies and experimental investigations aimed at enhancing the real-world applicability of smart lawnmowers. Full article

The practice of operating hydraulic machines and equipment shows that failures can occur earlier than the specified lifespan. At the same time, at the stage of carrying out strength calculations of the designed machines and equipment, significant safety margins are incorporated into parts and units. That is, calculated machine lifespans often exceed actual values. Accurate data require full-scale lifespan testing or observations of operation. However, resource tests are economically expensive, since they require a significant amount of energy, and, as a result, lead to a negative impact on the environment. It is possible to level out the listed shortcomings during resource tests by using energy-efficient and energy-saving technologies, such as energy recovery. This study enhances energy efficiency and assesses engineering systems during equipment design. In particular, we present a hydromechanical drive design for testing reciprocating hydraulic machines. The study analyzes energy-saving and energy recovery methods during operation. On the basis of the analysis and previously conducted studies, we developed a mathematical model for hydraulic equipment testing. The developed model is based on the volumetric stiffness theory, enabling analysis of the design and functional characteristics of test stand components on their dynamic behavior and energy efficiency. Full article