Search Results (3,334)

This paper presents the design and fabrication of a triboelectric nanogenerator based on a Quadruple Parallel-cavity Helmholtz Resonator (4C–HR TENG) for the efficient harvesting of noise energy in marine engine room environments. The device utilizes sound waves to drive periodic contact and separation between polytetrafluoroethylene (PTFE) particles in the resonant cavity and the vibrating diaphragm as well as the upper electrode plate, thereby converting sound energy into mechanical energy and finally into electrical energy. The device consists of an acoustic waveguide with a length of 350 mm and both width and height of 60 mm, along with a Helmholtz Resonator with a diameter of 60 mm and a height of 40 mm. Experimental results indicate that under resonance conditions with a sound pressure level of 109.8 dB and a frequency of 110 Hz, the device demonstrates excellent output performance, achieving a peak output voltage of 250 V and a current of 4.85 μA. We analyzed and investigated the influence mechanism of key parameters (filling ratio, sound pressure level, the height between the electrode plates, and particle size) on the output performance. Through COMSOL Multiphysics simulation analysis, the sound pressure enhancement effect and the characteristic of concentrated diaphragm center displacement at the first-order resonance frequency were revealed, verifying the advantage of the four-cavity structure in terms of energy distribution uniformity. In practical applications, the minimum responsive sound pressure level corresponding to the operating frequency range of the 4C–HR TENG was determined. The output power reaches a maximum of 0.27 mW at a load resistance of 50 MΩ. At a sound pressure level of 115.1 dB, the device can charge a 1 μF capacitor to 4.73 V in just 32 s and simultaneously illuminate 180 LEDs in real-time, demonstrating its potential for environmental noise energy harvesting and micro-energy supply applications. This study provides new insights and experimental evidence for the efficient recovery of noise energy. Full article

This study, based on the specifications of implantable medical devices for wireless power transfer, presents a bootstrap-comparator rectifier circuit design characterized by high voltage conversion efficiency, high power conversion efficiency, and improved reliability. The design is implemented using a 0.18 µm process to achieve superior VCE and PCE performance. The input signal is a 2 MHz, 3.3 V sine wave, producing an output voltage of 2.94 V with a maximum operating current of 5 mA. At an output load of $R_L=8\mathrm{k}\Omega$, the maximum voltage conversion efficiency (VCE) reaches 89.02%, while the maximum power conversion efficiency (PCE) is 84.73% at $R_L=500\Omega$. The temperature rise ($\Delta T$) is 0.22–$0.45{}^{\circ }\mathrm{C}$. Full article

The paper deals with the design of a Digital Twin model of an energy harvesting cantilever beam for low frequency energy harvesting applications and specifically with a digital model matching simulations corresponding with Finite Element Method solutions in order to validate the model. The physical behavior is based on the main parameters to be investigated. The finite elements analysis is geometrically and parametrically carried out for a small PZT5A device of the orders of millimeters and is optimized to take into consideration the relationships between tip displacement, generated voltages and vibration gravitational forces for standard industrial applications in the acceleration range between 0.5 and 2 g. Then a procedure to integrate the Digital Twin into a design framework has been developed, including an artificial intelligence algorithm that supports the modelling of the real behavior of the device. The paper is devoted to help researchers involved in a Digital Twin adoption in the field of electronic design and of the physical characterization of low frequency energy harvesting devices exclusively using open-source tools. Full article

The rapid evolution of wireless communication toward Fifth Generation (5G) networks has enabled unprecedented performance improvement in terms of data rate, latency, reliability, sustainability, and connectivity. Recent years have witnessed an excessive deployment of new 5G networks worldwide. This deployment lead to an exponential growth in traffic flow and a massive number of connected devices requiring a new generation of energy-hungry base stations (BSs). This results in increased power consumption, higher operational costs, and greater environmental impact, making energy efficiency (EE) a critical research challenge. This paper presents a comprehensive survey of EE optimization strategies in 5G networks. It reviews the transition from traditional methods such as resources allocation, energy harvesting, BS sleep modes, and power control to modern artificial intelligence (AI)-driven solutions employing machine learning, deep reinforcement learning, and self-organizing networks (SON). Comparative analyses highlight the trade-offs between energy savings, network performance, and implementation complexity. Finally, the paper outlines key open issues and future directions toward sustainable 5G and beyond-5G (B5G/Sixth Generation (6G)) systems, emphasizing explainable AI, zero-energy communications, and holistic green network design. Full article

The integration of embedded software in multi-sensor wearable devices has revolutionized real-time monitoring across health, fitness, industrial, and environmental applications. This paper presents a comprehensive approach to designing and implementing embedded software architectures that enable efficient, low-power, and high-accuracy data acquisition and processing from heterogeneous sensor arrays. We explore key challenges such as synchronization of sensor data streams, real-time operating system (RTOS) integration, power management strategies, and wireless communication protocols. The reviewed framework supports modular scalability, allowing for seamless incorporation of additional sensors or features without significant system overhead. Future research directions of the embedded software include Hardware-in-the-Loop and real-world validation, on-device machine learning and edge intelligence, adaptive sensor fusion, energy harvesting and power autonomy, enhanced wireless communications and security, standardization and interoperability, as well as user-centered design and personalization. By adopting this focus, we can highlight the potential of the embedded software to support proactive decision-making and user feedback through edge-level intelligence, paving the way for next-generation wearable monitoring systems. Full article

Photovoltaic generation systems (PVGSs) face significant efficiency challenges under partial shading conditions and rapidly changing irradiance due to the limitations of conventional maximum power point tracking (MPPT) methods. To address these challenges, this paper proposes a Transfer Learning-based Fractional-Order Recurrent Neural Network (TL-FRNN) for robust global maximum power point (GMPP) tracking across diverse operating conditions. The incorporation of fractional-order dynamics introduces long-term memory and non-local behavior, enabling smoother state evolution and improved discrimination between local and global maxima, particularly under weak and partially shaded conditions. The proposed approach leverages Caputo fractional derivatives with Grünwald–Letnikov approximation to capture the history-dependent behavior of PVGSs while implementing a parameter-partitioning strategy that separates shared features from task-specific parameters. The architecture employs a multi-head design with GMPP regression and partial shading classification capabilities, trained through a two-stage process of pretraining on general PV data followed by efficient fine-tuning on target systems with limited site-specific data. The TL-FRNN achieved 99.2% tracking efficiency with 98.7% GMPP detection accuracy, reducing convergence time by 53% compared to state-of-the-art alternatives while requiring 72% less retraining time through transfer learning. This approach represents a significant advancement in adaptive, intelligent MPPT control for real-world photovoltaic energy-harvesting systems. Full article

This investigation explored how seasonal variation affects the phytochemical composition and biological potential of Rosmarinus officinalis L., a widely used aromatic and medicinal plant. Aerial parts collected during spring, summer, autumn, and winter were extracted with ethanol and analyzed using LC-ESI-MS/MS, while total phenolic (TPC) and flavonoid (TFC) contents were determined spectrophotometrically. The extracts were evaluated for antioxidant, anti-inflammatory, enzyme inhibitory, analgesic, antimicrobial, cytotoxic, and photoprotective properties. Major constituents identified in all seasons included luteolin, kaempferol, rutin, and biochanin A. The autumn extract contained the highest phenolic (353.21 ± 4.05 µg GAE/mg) and flavonoid (190.11 ± 5.65 µg QE/mg) levels. Antioxidant assays revealed that the autumn extract had the strongest DPPH radical scavenging activity (IC₅₀ = 24.72 ± 0.16 µg/mL), while the spring extract exhibited the greatest reducing power (A_0.5 = 7.62 ± 0.30 µg/mL). The winter extract demonstrated superior anti-inflammatory activity (IC₅₀ = 28.60 ± 2.84 µg/mL), exceeding the reference drug diclofenac. Only the spring extract inhibited urease (IC₅₀ = 62.26 ± 0.58 µg/mL) and moderately inhibited α-amylase. All seasonal extracts showed notable photoprotective potential, with SPF values between 25.18 and 32.46, well above the recommended minimum. The spring extract also presented strong analgesic activity and no acute toxicity up to 2000 mg/kg. Antimicrobial effects were weak, limited to slight inhibition of Staphylococcus aureus, while moderate cytotoxicity was observed against MCF-7 and MDA-MB-231 breast cancer cells. Overall, seasonal variation significantly influenced the chemical profile and bioactivities of R. officinalis, with autumn and spring identified as the most suitable harvesting periods for pharmaceutical and cosmetic applications. Full article

The global shift toward sustainable energy systems has led to an increased adoption of PV systems, driven by their enhanced performance and environmental benefits, including reduced carbon emissions. Improving the efficiency of Grid-Tied Photovoltaic Systems (GTPVS) is essential for guaranteeing reliable and sustainable renewable power integration. This research paper presents advanced hybrid Maximum Power Point Tracking (MPPT) designed for GTPVS to maximize PV energy harvesting and support grid sustainability. The proposed technique combines Advanced Variable Step Size Incremental Conductance (AVIC) for reference voltage generation and an Integral Backstepping Control (IBC) to regulate the control of the step-up converter. This hybrid technique enables rapid convergence speed, reduces power losses, and enhances stability under fast-changing environmental conditions, Partial Shading Conditions (PSCs), and grid disturbances conditions. This MPPT is evaluated via the MATLAB/Simulink environment, version 2020b, and validated in real time using a Processor-in-the-Loop (PIL) setup on the eZdsp TMS320F28335 platform. Comparative analysis with benchmark methods confirms its superiority, with an average tracking performance of 99.57%, a response time of 0.02 s, and a Total Harmonic Distortion (THD) of 0.69%, accompanied by negligible steady-state oscillations. These findings indicate the validity and sustainability of the AVIC-IBC MPPT for real-time GTPVS operating under realistic climatic conditions. Full article

The widespread adoption of low-power devices and microelectronic systems has intensified the need for efficient energy harvesting solutions. While cantilever-beam piezoelectric energy harvesters (PEHs) are popular for their simplicity, their performance is often limited by conventional mass block designs. This study addresses this by proposing a comprehensive structural optimization framework for a triangular cantilever PEH to significantly enhance its electromechanical conversion efficiency. The methodology involved a multi-stage approach: first, an embedded coupling design was introduced to connect the mass block and cantilever beam, improving space utilization and strain distribution. Subsequently, the mass block’s shape was optimized. Furthermore, a negative Poisson’s ratio (NPR) honeycomb structure was integrated into the cantilever beam substrate to induce biaxial strain in the piezoelectric layer. Finally, a variable-density mass block was implemented. The synergistic combination of all optimizations—embedded coupling, NPR substrate, and variable-density mass block—culminated in a total performance enhancement of 69.07% (17.76 V) in voltage output and a 44.34% (28.01 Hz) reduction in resonant frequency. Through experimental testing, the output performance of the prototype machine showed good consistency with the simulation results, successfully verifying the effectiveness of the structural optimization method proposed in this study. These findings conclusively show that strategic morphological reconfiguration of key components is highly effective in developing high-performance, low-frequency adaptive piezoelectric energy harvesting systems. Full article

We numerically investigate vibrational resonance (VR) and vibrational energy harvesting (VEH) in a mechanical system driven by a low-frequency periodic force, using time-periodic phase modulation of the potential function. We focus on how the characteristics of high-frequency excitations influence frequency response, power output, and harvesting efficiency. We uncover two modulation-induced phenomena—resonant induction and resonant amplification—that together produce a double VR effect. We demonstrate that in the weak low-frequency regime ($\omega \le 0.3$), the power output can exceed that of the moderate regime ($\omega \approx 1$). Among the modulating waveforms, square waveform (SQW) demonstrated superior efficiency over other waveforms, which corresponds to higher response amplitude. In addition, the frequency ratio $K=6.7$ yielded optimal performance compared to other frequency ratios, thereby providing both maximum power output and efficiency. These findings suggest a new design strategy for energy harvesters, leveraging both primary and induced VR to enhance performance. Full article

Industrial wireless sensor and actuator networks (IWSANs) are central to Industry 4.0, supporting distributed sensing, actuation, and communication in cyber-physical production systems. Unlike previous studies, which focus on isolated constraints, this review synthesises recent work across eight coupled dimensions. These span reliability and fault tolerance, security and trust, time synchronisation, energy harvesting and power management, media access control (MAC) and scheduling, interoperability, routing and topology control, and real-world validation, within a unified comparative framework. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a Scopus search identified 60 primary publications published between 2022 and 2025. The analysis shows a clear shift from reactive designs to predictive approaches that incorporate learning methods and energy considerations. Fault detection now relies on deep learning (DL) and statistical modelling, security incorporates trust and intrusion detection, and new synchronisation and MAC schemes approach wired levels of determinism. Regarding applied contributions, the analysis notes that routing and energy harvesting advances extend network lifetime. However, gaps remain in mobility support, interoperability across protocol layers, and field validation. The present work outlines these open issues and highlights research directions needed to mature IWSANs into robust infrastructure for Industry 4.0 and the emerging Industry 5.0 vision. Full article

The growing need for self-powered Internet of Things networks has raised interest in converting abundant waste into reliable energy harvesters despite long-standing material and technology challenges. As demand for environmentally friendly self-powered IoT devices continues to rise, attention toward green waste as an eco-friendly energy source has strengthened. However, its direct utilisation in high-performance energy harvesters remains a significant challenge. Driven by the growing need for renewable sources, the triboelectric nanogenerator has emerged as an innovative technology for converting mechanical energy into electricity. In this work, the design, fabrication, and characterisation of a rotating triboelectric energy harvester as a prototype device employing date seed waste as the tribopositive layer are presented. The date seeds particles, measuring 1.2 to 2 mm, were pulverised using a grinder, mixed with epoxy resin, and subsequently applied to the grating-disc structure. The coated surface was machined on a lathe to provide a smooth surface facing. The performance of the prototype was evaluated through a series of experiments to examine the effects of rotational speed, the number of grating-disc structures, the epoxy mixing process, and the prototype’s influence on the primary system, as well as to determine the optimal power output. An increase in rotational speed (RPM) enhanced power generation. Furthermore, increasing the number of gratings and pre-mixing of epoxy with the biomaterial resulted in enhanced output power. Additionally, with 10 gratings, operating at 1500 rpm, and a 24 h pre-mixing method, the harvester achieved maximum voltage and power outputs of 129 volts and 1183 μW at 7 MΩ. Full article

This study presents the design optimization and semi-active resonance control of a small-scale vortex-induced vibration (VIV) bladeless wind turbine (BWT) equipped with a power efficient binary resonance controller. The proposed system integrates a smart-material-based stiffness-tuning module that adaptively adjusts the structure frequency of the BWT to match varying wind speeds. A coupled mechanical–electromagnetic model for BWT was formulated to quantify the relationships among key design parameters, including mast geometry, pivot length, and rod dimensions, and the resulting induced voltage. Multi-parameter optimization was performed to maximize energy-harvesting efficiency under mass and geometric constraints. Experimental evaluation verified an 88.9 % resonance shift capability, broadening the operational lock-in wind speed range from 1.7 to 3.2 m/s. The results confirm the potential of the semi-active BWT control concept for compact, low-noise, and adaptive wind-energy harvesters. Full article

This study addresses the lack of transparent methods for calculating the energy requirements of pulsed electric field (PEF) pretreatments in biogas research. Two detailed approaches are proposed and evaluated to quantify the energy consumed during the pretreatment of lignocellulosic harvest residues (corn, soybean, and sunflower) using a low-frequency electric field. The first approach is based on previously measured capacitor parameters, including resistance (R_s, R_p), inductance (L_s), capacitance (C_p), and loss factor (D), which were interpolated to 50 Hz from measurements performed over the frequency range of 100 Hz to 10 kHz. The second approach relies on direct measurements of the effective voltage and current waveforms across the capacitor, followed by calculation of the power factor (cos φ). Both methods enable reliable estimation of energy consumption and differ primarily in the type of input data required: Method 1 is based on capacitor characteristics determined before and after pretreatment, while Method 2 uses real-time treatment data. Despite these differences, the two approaches yielded highly consistent results, confirming their robustness and applicability. The calculated energy values were subsequently incorporated into a net energy balance by comparing the energy consumed during pretreatment with the methane energy output from anaerobic digestion. For all three investigated lignocellulosic substrates, PEF pretreatment resulted in a positive energy balance under the applied process conditions. Full article

Energy efficiency is a key requirement for Internet of Things (IoT) nodes, particularly in applications powered by energy harvesting that operate without batteries. In this work, we present a parametric power model of a LoRa transceiver (Semtech SX1276) aimed at ultra-low power remote sensing scenarios. The transceiver was characterized in all relevant states (startup, transmission, reception, and sleep), and the results were used to build a state-based model that predicts average power consumption as a function of transmission power, sleep strategy, packetization, and input data rate. Experimental validation confirmed that the cubic fit for transmission peaks achieves a determination coefficient of 0.99, while reception is added as a constant consumption. The model was implemented in a Python simulator that provides mean, best-case, and worst-case estimates of system power consumption, and it was validated in an ASIC-based sensor node demonstration, with predictions within 10% of measured values. The framework highlights the trade-offs between energy efficiency and robustness (e.g., minimal SF and no CRC vs. higher spreading factors and error-control) and supports the design of custom controllers for ultra-low power IoT nodes as well as more energy-permissive applications. Full article