Search Results (695)

The present study investigates the influence of geometric parameters on the vibro-acoustic performance of piezoelectric speakers, with the objective of establishing quantitative design guidelines for resonance tuning and sound pressure level (SPL) enhancement. Understanding the dimension-dependent behavior of such devices is essential for the development of compact and efficient acoustic transducers. To this end, a fully coupled electromechanical–acoustic finite element model is developed in the frequency domain, incorporating linear piezoelectric constitutive relations, structural dynamics, and an external acoustic air domain. The model systematically examines the effects of variations in piezoelectric disc thickness, brass diaphragm thickness, and diaphragm radius. The results demonstrate that increasing the piezoelectric disc thickness leads to a noticeable increase in resonance frequency and a measurable enhancement in SPL due to strengthened electromechanical coupling. In contrast, reducing the brass membrane thickness primarily shifts the resonance frequency to lower values, while producing negligible changes in SPL amplitude. Furthermore, enlarging the diaphragm radius significantly decreases the fundamental resonance frequency, confirming its dominant influence on stiffness-controlled vibration behavior. These findings quantitatively establish the relationship between geometric design parameters and acoustic response, providing a predictive framework for performance optimization. The proposed modeling approach offers an effective and reliable tool for the design and refinement of high-performance piezoelectric speaker systems. Full article

Electronic waste (e-waste) continues to increase globally, yet conventional bins cannot distinguish hazardous batteries and devices from recyclable metals. This article presents an AI- and IoT-enabled smart dustbin that automatically identifies and segregates general waste, metals, and electronic or battery-based hazards while providing real-time monitoring through a cloud-based dashboard. The system integrates inductive sensing, Time-of-Flight detection, an Espressif Systems Platform 32 (ESP32)-CAM module, and Google Gemini 1.5 Flash for image classification. The prototype achieved a waste segregation accuracy of 93.5% with a total cycle time of 4–6 s per item. The touch-free lid, swift mechanical actuation, and compact 59 × 59 × 100 cm footprint make the dustbin suitable for deployment in campuses, offices, and shopping malls. Dual ESP32 controllers, cloud connectivity through Message Queuing Telemetry Transport (MQTT), Firebase, and a Streamlit web interface enable automated alerts through Discord and email, demonstrating a scalable and energy-efficient approach to sustainable e-waste management. Full article

The deployment of heterogeneous Automated Guided Vehicles (AGVs) in smart manufacturing requires control strategies that can accommodate distinct actuation characteristics and constraints. This paper proposes a Multi-Factor Coupled Parameter-Adaptive Model Predictive Control (MFCP-AMPC) framework. Unlike conventional approaches requiring vehicle-specific tuning, this framework unifies differential-drive, dual-steer, and mecanum-wheel platforms under a single parameter-varying state-space model that respects the specific actuation limits of each topology. A key contribution is the multi-factor coupling mechanism that dynamically adjusts the prediction horizon and weighting matrices based on path curvature, vehicle speed, and tracking error. Experiments on industrial AGV prototypes demonstrate that the framework achieves robust tracking precision under varying payloads. Crucially, by acknowledging physical limits, the framework achieves strict millimeter-level accuracy (RMSE < 7 mm) in quasi-static low-speed complex maneuvers ($v\le 0.3$ m/s), and maintains highly competitive industrial precision (RMSE ≈ 15∼25 mm) under aggressive high-speed tracking ($v\ge 1.0$ m/s). Crucially, the proposed method significantly improves the control input smoothness (Smoothness Index > 0.75), thereby reducing mechanical wear and preventing actuator saturation. Real-time validation (12 ms average solve time on an Intel i7 IPC) confirms its suitability for resource-constrained industrial controllers. Full article

Accurate yaw alignment is critical for maximizing power capture in horizontal-axis wind turbines, as even moderate yaw misalignment leads to significant aerodynamic losses, increased actuator usage, and accelerated mechanical wear. This research paper proposes a hybrid smart yaw control system for small-scale wind turbines that combines real-time measurements with short-term wind direction prediction to improve alignment accuracy, operational reliability, and energy efficiency under realistic operating conditions. The system integrates four wind direction information sources, such as physical wind vane sensing, live online weather data, forecast data, and a data-driven prediction module within a structured priority framework (VANE → LIVE → FORECAST → AI), to ensure continuous yaw control during sensor or communication unavailability. The prediction module is based on a long short-term memory (LSTM) neural network trained in MATLAB using live data from an online platform, with sine–cosine encoding employed to address the circular nature of directional data. The yaw controller incorporates a ±15° deadband, dwell-time logic, shortest-path rotation, and cable-safe constraints to reduce unnecessary actuation while maintaining effective alignment. The proposed system is validated through MATLAB/Simulink simulations and real-time microcontroller-based experiments using a stepper motor-driven nacelle. Compared with conventional vane-based yaw control, the hybrid AI-assisted approach reduces the average yaw error by approximately 35–45%, maintains a yaw error within ±15° for more than 90% of the operating time, increases average electrical power output by 3–5%, and reduces yaw motor energy consumption by 10–15%, while decreasing corrective yaw actuation events by 30–40%. These results demonstrate that integrating an LSTM-based wind direction predictor with multi-source wind data provides a robust, low-cost, and practically deployable yaw control solution that enhances energy capture and mechanical durability in small-scale wind turbines. Full article

Ionic polymer–metal composites consist of an ion-conducting polymer–gel membrane sandwiched between two flexible electrodes, representing a class of soft electroactive materials capable of large deformation under low voltage. The gel membrane, swollen with solvent, facilitates ion migration under an electric field, enabling actuation. Tailoring the interfacial architecture between the electrode and the polymer–gel membrane is pivotal for advancing high-performance IPMC actuators. This study presents a comparative investigation of three core–shell nanocomposite electrodes, fabricated via in situ polymerization, for IPMC applications. Among these, the polyaniline-coated multi-walled carbon nanotube composite exhibits a deliberately designed hierarchical structure, with a specific surface area of 32.345 m²·g⁻¹ and a conductive doped polyaniline shell, as confirmed through XPS analysis. This optimized interface enables superior charge storage and transport, endowing the corresponding electrode with a specific capacitance of 40.28 mF·cm⁻² at 100 mV·s⁻¹—3.2 times greater than that of conventional silver-based electrodes—along with a reduced sheet resistance. When integrated with a Nafion ion–gel membrane, the PANI@MWCNT electrode achieves a 67% increase in force density and a larger displacement output compared to standard devices, directly correlated with its enhanced electrical and electrochemical properties. This work highlights the critical role of core–shell interfacial engineering in governing electromechanical performance at the electrode–gel interface and offers a practical design strategy for developing high-performance, cost-effective IPMC actuators for soft robotics, flexible electronics, and related applications. Full article

Segmented telescopes face significant challenges in achieving high segment positioning accuracy under complex disturbances, which directly impact observational sensitivity and resolution. Conventional rigid actuators with limited bandwidth (e.g., Keck ~20 Hz) struggle to maintain control stability. Novel dual-stage actuators combining coarse and fine adjustment (e.g., voice coil motors) now achieve <8 nm precision over millimeter-level strokes. Moreover, their higher closed-loop bandwidth (e.g., TMT ~60 Hz) can ensure rapid settling without overshoot and robust suppression of high-frequency disturbances (e.g., pulsating wind and mechanical vibration). In parallel, system-level control strategies have been updated accordingly. Ground-based systems focus on real-time multimodal decoupling, while space-based systems emphasize non-contact vibration isolation and nested multi-loop control to achieve sub-arcsecond pointing stability. This review surveys the design and control strategies of damping–positioning mechanisms for segmented telescopes and discusses the key trade-offs among critical performance metrics, including resolution, stroke, and load capacity. Particular attention is given to the disturbance-sensitivity analysis and active damping techniques (up to ~50% vibration reduction) implemented in the ELT “hard” actuator approach. Future directions include cross-scale collaborative control, smart material applications, and AI-based adaptive parameter optimization, which together provide a technical pathway toward high-precision imaging in next-generation highly segmented telescopes. Full article

The delivery of energy and water meter data, management and control information on separate networks is expensive and defeats the gains of the Advanced Metering Infrastructure (AMI) Smart Grid (SG). In most cases, energy, gas and water services are offered by the same organizational entity, hence the use of different infrastructure for data, service delivery, control and management is expensive and highly illogical. There is a need for a combined energy and water infrastructure to reap the benefits of the AMI SG. Furthermore, combined metering will result in accurate billing, potential cost savings, and improved resource management. This work therefore develops and investigates a combined energy and water AMI smart metering framework. This is possible through a thorough understanding of the AMI technological standards. The implementation of such a system is not trivial, as it depends on many factors: environmental, geographical, technological, economical, regulatory and the existing legacy infrastructure. Optimal technological implementation choices are developed towards an integrated AMI infrastructure. An experimental test bed is developed for delivering energy and water metering data to the utility. The optimal placement results favor the system of separating energy and water actuators at the home area network of the SG while using an integrated communication system. Such a system is feasible, given the different evolution of electricity and water meters and their placement at the home area network, and enables water metering to benefit from the more advanced electrical metering infrastructure. Full article

The pervasive adoption of AI and AIoT applications at the network edge presents both opportunities and challenges for smart cities. With a focus on the energy efficiency of AI in urban environments, this paper provides a systematic comparative analysis of representative edge hardware platforms, i.e., embedded GPUs, FPGAs, and ultra-low-power microcontroller-/sensor-class devices, assessing their suitability for AI workloads in IoT-driven smart city infrastructures. The evaluation, based on direct characterization of diverse neural networks and relevant datasets, quantifies computational performance and energy behavior through inference latency, throughput, and energy/per inference measurements. Across the evaluated network–board pairs, the measured inference power spans several orders of magnitude, ranging from 0.1–10 mW for ultra-low-power Intelligent Sensor Processing Units (ISPUs) up to 1–10 W for embedded GPUs, highlighting the wide design space between the least and most power-demanding configurations. Results indicate that embedded GPUs provide a favorable performance-to-power ratio for computationally intensive workloads, while MCU/ISPU-class solutions, despite throughput limitations, offer compelling advantages in ultra-low-power scenarios when combined with quantization and pruning, making them well-suited for distributed sensing and actuation typical of smart city deployments. Overall, this comparative analysis guides hardware selection for heterogeneous, sustainable AI-enabled urban services. Full article

Advancement of precision agriculture increasingly relies on cost-effective and scalable technologies for real-time environmental management, particularly in greenhouse environments where vertical and spatial microclimate heterogeneity influences crop performance. This study presents the design, implementation, and experimental validation of an Android-based smartphone application edge supervisory monitoring system integrated with multi-layer wireless sensing and control nodes for real-time monitoring in a smart greenhouse. The system combined multi-layer wireless sensor nodes, wireless control nodes, a Long-Range Wide Area Network (LoRaWAN) gateway, Message Queuing Telemetry Transport (MQTT) communication, and a cloud-synchronized smartphone-based supervisory interface for visualizing environmental data, detecting defined abnormal events, and controlling actuators remotely. For feasibility tests, 54 sensing nodes and 12 actuator nodes were deployed across three vertical layers in two sections, measuring temperature, humidity, CO₂ concentration, and light intensity. Abnormality was defined as environmental threshold violations, statistical signal deviations, actuator power inconsistencies, and communication timeout events. Experimental results revealed vertical and spatial environmental variability across greenhouse sections, while real-time time-series and 3D spatial maps enabled the rapid detection of abnormal conditions. The rule-based abnormality detection engine identified out-of-range environmental values and sensor-related inconsistencies and generated immediate notifications. Smartphone profiling revealed that display and system-level processes accounted for energy consumption, with battery power reaching a peak of 3.5 W and application CPU utilization ranging from 40% to 70% during active monitoring. The results demonstrate system-level feasibility, responsiveness, and scalability under commercial greenhouse workloads, supporting future integration of predictive control and energy-efficient operation. Full article

Functional safety in a four-wheel steering system is critical for smart vehicles, especially under steer-by-wire configurations where conventional methods fail to achieve 100% safety. In this study, we developed a hybrid AI-based fail-safe control framework that combines long short-term memory and convolution neural network classifiers for real-time fault detection, prediction, and safe state decision-making. The developed system was trained using steering sensor data, incorporating accurate response modeling and fault induction mechanisms. An LSTM model predicts deviations in steering behavior, while a random forest (RF) classifier identifies fault types and initiates safe state transitions in accordance with ISO 26262 functional safety guidelines. The system includes a Texas Instruments TMS320F28377D microcontroller and a rear steering actuator with a permanent magnet synchronous motor, utilizing controller area network communication for diagnostics and actuation. The system presents the potential of machine learning techniques to enhance the fail-safe capabilities of next-generation steer-by-wire systems. Full article

This systematic review synthesizes passive and passive-first cooling strategies for greenhouses in hot–arid climates, organizing evidence across four domains: Airflow & Ventilation, Shading & Radiative Control, Thermal Storage & Ground Coupling, and Structural Design & Geometry. Drawing on the project corpus, we analyze 10–13 distinct techniques including ridge and side natural ventilation, windcatchers and solar chimneys, external shade nets, NIR-selective and transparent radiative-cooling films, and dynamic PV shading; earth-to-air heat exchangers (EAHE/GAHT), rock-bed sensible storage, phase-change materials (PCMs), and sunken or buried envelopes; as well as roof slope and shape, span number, and orientation. Across studies, cooling outcomes are reported as peak or daytime indoor air temperature reductions, defined relative either to outdoor conditions or to a control greenhouse, with the reference frame and temporal aggregation specified in the synthesis. Typical outcomes include ≈3–7 °C daytime reduction for optimized ventilation, ≈2–4 °C for shading and spectral covers while preserving PAR, ≈5–7 °C intake cooling for EAHE with winter pre-heating, and up to ≈14 °C peak attenuation for rock-bed storage under favorable conditions. Structural choices consistently amplify these effects by sustaining pressure head and limiting thermal heterogeneity. Performance is strongly context-dependent—governed by wind regime, diurnal amplitude, dust and UV exposure, and crop-specific light and temperature thresholds—and the most robust results arise from stacked, site-specific designs that combine skin-level radiative rejection, buoyancy-supportive geometry, and ground or latent buffering with minimal active backup. Smart controllers that modulate vents, shading, and targeted fogging or fans based on VPD or temperature differentials improve stability and reduce water and energy use by engaging actuation only when passive capacity is exceeded. We recommend standardized composite metrics encompassing temperature moderation, humidity stability, PAR availability, and water and energy use per unit yield to enable fair cross-study comparison, multi-season validation, and policy adoption. Collectively, the synthesized techniques provide a practical palette for improved greenhouse climate management under hot and arid conditions. Full article

Stimuli-responsive hydrogels are an emerging class of smart materials with immense potential across biomedical engineering, soft robotics, environmental systems, and advanced manufacturing. In this review, we present an in-depth exploration of their material design, classification, fabrication strategies, and real-world applications. We examine how a wide range of external stimuli—such as temperature, pH, moisture, ions, electricity, magnetism, redox conditions, and light—interact with polymer composition and crosslinking chemistry to shape the responsive behavior of hydrogels. Special attention is given to the growing field of 4D printing, where time-dependent shape and property changes enable dynamic, programmable systems. Unlike existing reviews that often treat materials, stimuli, or applications in isolation, this work introduces a multidimensional comparative framework that connects stimulus-response behavior with fabrication techniques and end-use domains. We also highlight key challenges that limit practical deployment—including mechanical fragility, slow actuation, and scale-up difficulties—and outline engineering solutions such as hybrid material design, anisotropic structuring, and multi-stimuli integration. Our aim is to offer a forward-looking perspective that bridges material innovation with functional design, serving as a resource for researchers and engineers working to develop next-generation adaptive systems. Full article

Fiber actuators underpin soft robots, artificial muscles, and smart textiles. A persistent bottleneck is the fabrication of monodomain liquid crystal elastomer (LCE) microfibers with narrow size distributions while preserving axial alignment. This work establishes a melt-centrifugal spinning (MCS) route with two-step UV fixation that separates flow-induced alignment from network crosslinking. High-speed rotation creates a long extensional jet; an obliquely incident, on-the-fly UV dose at touchdown locks the director, and a post-cure consolidates the network. The obtained LCE microfiber can achieve large reversible contraction (L/L₀ = 0.56), lift a weight, and trigger the tweezers. The method produces a new approach for the fabrication of device-ready LCE actuators, establishes a general design principle for diameter control via curing sequence, and opens a practical path toward artificial muscles and flexible micro robotics. Full article

This paper presents the design and implementation of an IoT-based automation system for indoor hydroponic plant cultivation using the Nutrient Film Technique. The system employs an ESP32-based controller with multiple sensors and actuators. These enable real-time monitoring and control of pH, TDS, temperature, humidity, light, tank level, and flow conditions. A modular five-layer architecture was developed. It combines the MING stack, which includes MQTT communication, InfluxDB time-series storage, Node-RED flow processing, and Grafana visualization. The system also includes a Flutter-based mobile app for remote access. Key features include temperature-compensated calibration, hysteresis-based control algorithms, dual-mode operation, TLS/ACL security, and automated alarm mechanisms. These features enhance reliability and safety. Experimental results showed stable pH/TDS regulation, dependable actuator and alarm responses, and secure long-term data logging. The proposed open-source and low-cost platform is scalable. It provides a solution for small-scale producers and urban farming, bridging the gap between academic prototypes and production-grade smart agriculture systems. In comparison to related works that mainly focus on monitoring, this study advances the state of the art. It combines continuous time-series logging, secure communication, flow verification, and integrated safety mechanisms to provide a reproducible testbed for future smart agriculture research. Full article

Shape-memory polymers (SMPs) are a class of smart materials capable of recovering their original shape from a programmed temporary shape in response to external stimuli such as heat, light, or magnetic fields. SMPs have attracted significant interest for biomedical devices and soft robotics due to their large recoverable strains, programmable mechanical and thermal properties, tunable activation temperatures, responsiveness to various stimuli, low density, and ease of processing via additive manufacturing techniques, as well as demonstrated biocompatibility and potential bioresorbability. This review summarises recent progress in the fundamentals, classification, activation mechanisms, and fabrication strategies of SMPs, focusing particularly on design principles that influence performance relevant to specific applications. Both thermally and non-thermally activated SMP systems are discussed, alongside methods for controlling activation temperatures, including plasticisation, copolymerisation, and modulation of cross-linking density. The use of functional nanofillers to enhance thermal and electrical conductivity, mechanical strength, and actuation efficiency is also considered. Current manufacturing techniques are critically evaluated in terms of resolution, material compatibility, scalability, and integration potential. Biodegradable SMPs are highlighted, with discussion of degradation behaviour, biocompatibility, and demonstrations in devices such as haemostatic foams, embolic implants, and bone scaffolds. However, despite their promising potential, the widespread application of SMPs faces several challenges, including non-uniform activation, the need to balance mechanical strength with shape recovery, and limited standardisation. Addressing these issues is critical for advancing SMPs from laboratory research to clinical and industrial applications. Full article