Search Results (425)

As more and more Internet of Things (IoT) devices are widely deployed, the issue of energy supply for these devices is becoming increasingly prominent. Considering not only the wireless information transfer (WIT) function of traditional IoT networks but also the characteristics of wireless power transfer (WPT), an RF energy harvesting–aided IoT network is proposed in this paper. In the new IoT network, a WPT transmitter and a WPT receiver are, respectively, introduced to the new gateway and the new end-device. A WPT transmitter is mainly composed of an antenna selection circuit, a power amplifier, and a directional antenna. A WPT receiver consists of a directional antenna, a matching network, a rectification circuit, and an energy management circuit. In order to coordinate WPT and WIT in an orderly manner and minimize WPT’s interference on WIT, a time-division scheme is adopted. The proposed new IoT network aims to offer a new IoT scheme combining both WIT and WPT technologies. In addition, IoT devices can obtain a new energy supply through RF energy harvesting. Both the effectiveness and efficiency of the proposed RF energy harvesting–aided IoT network have been validated through experimentation. Full article

The end-to-end efficiency of radio-frequency (RF)-powered wireless communication networks (WPCNs) in post-disaster underground mine environments can be enhanced through adaptive beamforming. The primary challenges in such scenarios include (i) identifying the most energy-constrained nodes, i.e., nodes with the lowest residual energy to prevent the loss of tracking and localization functionality; (ii) avoiding reliance on the computationally intensive channel state information (CSI) acquisition process; and (iii) ensuring long-range RF wireless power transfer (LoRa-RFWPT). To address these issues, this paper introduces an adaptive and safety-aware deep reinforcement learning (DRL) framework for energy beamforming in LoRa-enabled underground disaster networks. Specifically, we develop a Safe Adaptive Deep Q-Network (SADQN) that incorporates residual energy awareness to enhance energy harvesting under mobility, while also formulating a SADQN approach with dual-variable updates to mitigate constraint violations associated with fairness, minimum energy thresholds, duty cycle, and uplink utilization. A mathematical model is proposed to capture the dynamics of post-disaster underground mine environments, and the problem is formulated as a constrained Markov decision process (CMDP). To address the inherent NP hardness of this constrained reinforcement learning (CRL) formulation, we employ a Lagrangian relaxation technique to reduce complexity and derive near-optimal solutions. Comprehensive simulation results demonstrate that SADQN significantly outperforms all baseline algorithms: increasing cumulative harvested energy by approximately 11% versus DQN, 15% versus Safe-DQN, and 40% versus PSO, and achieving substantial gains over random beamforming and non-beamforming approaches. The proposed SADQN framework maintains fairness indices above 0.90, converges 27% faster than Safe-DQN and 43% faster than standard DQN in terms of episodes, and demonstrates superior stability, with 33% lower performance variance than Safe-DQN and 66% lower than DQN after convergence, making it particularly suitable for safety-critical underground mining disaster scenarios where reliable energy delivery and operational stability are paramount. Full article

This study aimed to evaluate the prediction potential of phenology metrics from two vegetation indices using Sentinel-2 images, the Normalized Difference Vegetation Index (NDVI) and Three Red-Edge Vegetation Index (NDVI3RE), for maize yield prediction. Ground truth maize yield samples were collected near Koška, Croatia, on 13 October 2023, using a Quantimeter yield mapping sensor on Claas Lexion 6900 combine harvester. The phenology analysis was performed based on a time-series of all available Sentinel-2 images during 2023, using the Beck logistic model for determining the start of season (SOS), peak of season (POS), end of season (EOS), greenup, maturity, senescence, and dormancy. A total of fourteen covariates, including vegetation indices at phenology metrics and their occurrence dates, were used for machine learning prediction of maize yield using Random Forest (RF) and Support Vector Machine (SVM) regression. The results suggested that the SVM method based on NDVI phenology metrics produced the highest accuracy for maize yield prediction (R² = 0.935, RMSE = 0.558 t ha⁻¹, MAE = 0.399 t ha⁻¹). Vegetation index values at greenup, dormancy and POS were the most important covariates for the prediction, while day of year (DOY) in which they occurred had only a minor effect on the prediction accuracy. This suggests that, despite its limitations regarding the saturation effect, NDVI outperformed NDVI3RE for maize yield prediction when combined with phenology metrics. Full article

Rice yield prediction at high spatial resolution is essential to support precision management and sustainable intensification in irrigated systems. While many remote sensing studies provide yield estimates at the field scale, pixel-level predictions are required to characterize within-field variability. This study assesses the potential of multitemporal Sentinel-2 imagery and machine learning to estimate rice yield at pixel level in the Albufera rice area (Valencia, Spain). Yield data from combine harvester maps were collected for ‘JSendra’ and ‘Bomba’ Japonica varieties over five growing seasons (2020–2024) and linked to 10 m Sentinel-2 bands in the visible, near-infrared (NIR) and short-wave infrared (SWIR) regions. Random Forest (RF) and XGBoost (XGB) models were trained with 2020–2023 data and independently validated in 2024. XGB systematically outperformed RF, achieving at 110 and 130 DAS (days after showing), R² values of 0.74 and 0.85 and RMSE values of 0.63 and 0.28 t·ha⁻¹ for ‘JSendra’ and ‘Bomba’. Prediction accuracy increased as the season progressed, and models using all spectral bands clearly outperformed configurations based only on spectral indices, confirming the dominant contribution of NIR reflectance. Spatial error analysis revealed errors at field edges and headlands, while central pixels were more accurately predicted. Overall, the proposed approach provides accurate, spatially explicit rice yield maps that capture within-field variability and support both end-of-season yield estimation and early season forecasting, enabling the identification of potentially low-yield zones to support targeted management decisions. Full article

The cotton boll opening rate (BOR) is an important indicator for evaluating the physiological maturation process of cotton and the critical stage of yield formation, and it provides essential guidance for subsequent defoliant application and mechanical harvesting. The investigation of cotton BOR usually relies on manual field surveys, which are time-consuming and destructive, making it difficult to achieve large-scale and efficient monitoring. UAV remote sensing technology has been widely used in crop growth monitoring due to its operational flexibility and high image resolution. However, because of the dense growth of the cotton canopy in UAV remote sensing imagery, the boll opening condition in the lower parts of the canopy cannot be completely observed. In contrast, UAV imagery can effectively monitor cotton leaf chlorophyll content (SPAD) and leaf area index (LAI), both of which undergo continuous changes during the boll opening process. Therefore, this study proposes using SPAD and LAI retrieved from UAV multispectral imagery as physiological intermediary variables to construct an empirical statistical equation and compare it with end-to-end machine learning baselines. Multispectral and ground synchronous data (n = 360) were collected in Baibi Town, Anyang, Henan Province, across four dates (8/28, 9/6, 9/13, 9/24). Twenty-eight commonly used vegetation indices were calculated from multispectral imagery, and Pearson’s correlation analysis was conducted to select indices sensitive to cotton SPAD, LAI, and BOR. Prediction models were constructed using the Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Support Vector Machine (SVM), and Partial Least Squares (PLS) models. The results showed that GBDT achieved the best prediction performance for SPAD (R² = 0.86, RMSE = 1.19), while SVM performed best for LAI (R² = 0.77, RMSE = 0.38). The quadratic polynomial equation constructed using SPAD and LAI achieved R² = 0.807 and RMSE = 0.109 in BOR testing, which was significantly better than the baseline model using vegetation indices to directly regress BOR. The method demonstrated stable performance in spatial mapping of BOR during the boll opening period and showed promising potential for guiding defoliant application and harvest timing. Full article

Battery-Free Sensor Nodes (BFSNs) used in Simultaneous Wireless Information and Power Transfer (SWIPT) systems often rely on lightweight communication protocols with minimal security overhead due to strict energy constraints. As a result, conventional protocol-dependent security mechanisms cannot be employed, leaving BFSNs vulnerable to replay, spoofing, and other security threats. This paper explores a protocol-independent security mechanism that enhances BFSN security by exploiting the power wave for controlled backscattering. The method introduces a Manchester-encoded digital private key generated by the BFSN’s low-power microcontroller and backscattered through a polarization-shifting module enabled by a fail-safe RF switch, thereby avoiding the need for a dedicated backscattering rectifier. A LoRaWAN-based BFSN integrating this add-on module was implemented to experimentally validate the approach. Results show successful extraction of the backscattered key with minimal energy overhead (approximately 95 µJ for a 3 ms identification sequence), while the original high-efficiency RF rectifier used for harvesting remains unmodified. The orthogonal polarization between the incoming and backscattered waves additionally reduces clutter and cross-jamming effects. These findings demonstrate that secure identification can be seamlessly incorporated into existing BFSNs without altering their core architecture, offering an easy-to-integrate and energy-efficient solution for improving security in SWIPT-based sensing systems. Full article

In this study, a new semi-analytical model was developed that can calculate the output voltage of low-power microwave rectifiers as a function of frequency and input power. The model integrates diode rectification characteristics and frequency-dependent impedance mismatches within the same mathematical structure. Defined by second-order polynomial expressions for input power and frequency, the model directly incorporates reflection coefficient (S₁₁) data into the equations to account for frequency-dependent power losses caused by impedance mismatch, thereby improving calculation accuracy under wide-band conditions. To validate the model, a wide-band rectifier prototype with an FR4-based T-type matching network and a voltage doubler structure was designed and manufactured. Model calculations showed over 95% agreement with simulation results and closely followed the measured output voltage trends over the 0.5–3 GHz frequency range and input power levels from −12 dBm to 0 dBm. The proposed model provides a design-oriented and computationally efficient tool for wide-band, low-power RF energy harvesting and wireless power transfer applications, enabling rapid evaluation of impedance matching strategies with reduced reliance on electromagnetic simulations. Full article

In rectifier design, the key parameters are the voltage–conversion ratio and the power conversion efficiency. A new circuit design approach is presented in which a capacitor-based, cross-coupled, differential-driven topology is used to boost the voltage–conversion ratio. The scheme also integrates an auxiliary current path to raise the power conversion efficiency. To demonstrate its practicality, two three-stage rectifiers were designed and fabricated using standard 65 nm CMOS technology. The designs were tested under various conditions to assess their performance. The first rectifier targets indoor light energy harvesting applications. It achieves a peak voltage conversion ratio of 3.94 and a maximum power conversion efficiency of 58.7% when driving a 600 Ω load, while supplying over 2 mA of output current. The second rectifier is optimized for RF energy harvesting at 2.4 GHz. Experimental results indicate that it can deliver 70 µA to a 50 kΩ load, with a peak voltage conversion ratio of 5 and a power conversion efficiency of 17.5%. Full article

BU–MIT whistler wave injection experiments, which were conducted at Arecibo Observatory, started with the joint US–USSR Active Space Plasma Program Experiment on 24 December 1989. In this experiment, a satellite-borne VLF transmitter injected radio waves at the frequency and power of 10 kHz and 10 kW. A series of controlled whistler wave experiments with the Arecibo HF heater were subsequently carried out during 1990–1998 until the HF heater was damaged by Hurricane Georges in 1998. In these ionospheric HF heating experiments, 28.5 kHz whistler waves were launched from the nearby naval transmitter (code-named NAU) located at Aguadilla, Puerto Rico. HF heater waves were used to create ionospheric ducts (in the form of parallel-plate waveguides) to facilitate the entry of NAU whistler waves from the neutral atmosphere into the ionosphere. Conjugate whistler wave propagation experiments were conducted between Arecibo, Puerto Rico and Trelew, Argentina in 1997. After 1999, whistler wave experiments in the absence of an HF heater had been conducted. Naturally-occurring large-scale ionospheric irregularities due to spread F or Traveling Ionospheric Disturbances (TIDs) were relied on to guide NAU launched 40.75 kHz whistler waves to propagate from the ionosphere further into the radiation belts, to cause 390 keV charged-particle precipitation. A train of TIDs, resulting from the 9.2 Mw earthquake off the west coast of Sumatra, Indonesia, was observed in our 26 December 2004 Arecibo experiments, about a day after the earthquake-launched tsunami waves traveled across the Indian Ocean, then into remote parts of the Atlantic Ocean. The author’s recent research efforts, motivated by Arecibo experiments, focus on Solar Powered Microwave Transmitting Systems, to simulate Solar Energy Harvesting via Solar Power Satellite (SPS) (also known as Space Based Solar Power (SBSP)) These experiments involved a large number of the author’s BU and MIT students working on theses and participating in the Undergraduate Research Opportunities Program (UROP), in collaboration with other colleagues at several universities and national laboratories. Full article

Buried, battery-free sensor nodes offer a promising solution for structural health monitoring, reducing maintenance and improving infrastructure sustainability by monitoring slow-varying parameters such as temperature and humidity, which do not require high sampling frequencies. This study shows the practical implementation of an autonomous LoRa node powered solely by RF energy harvested from a gateway using an 868 MHz rectenna and a custom energy management circuit charging a supercapacitor. Experimental characterization revealed that, with a single rectenna placed 40 cm from the gateway, communication intervals ranged from 58 min (+14 dBm) to 10 min (+20 dBm), clearly linking available RF power and energy management to achievable monitoring frequency. To further illustrate this, deploying a multi-element rectenna array enabled reliable node operation at distances greater than 10 m, demonstrating that the number of rectenna elements is the dominant factor governing harvested energy and the achievable operating range. Configuring the gateway as both a communication hub and an energy source further simplified deployment. These results highlight strategies for overcoming power delivery constraints in deeply embedded wireless sensing applications for civil structures. Full article

Wheat (Triticum aestivum L.) is a key crop in Spain, especially in Castilla and León Region. However, there are few studies evaluating predictive models based on spectral indices and multivariate analysis to estimate yield in direct seeding (DS) and conventional seeding (CS) systems. This study addresses this need by implementing a split-plot experimental design in the city of Palencia, Spain, analyzing crop physiological data and nine spectral indices derived from multispectral aerial images captured by drones. The analysis included multivariate techniques such as Principal Component Analysis (PCA) and Random Forest (RF), supplemented with statistical tests, ROC curves, and prediction analysis. The results showed that the RF model successfully classified treatments with 93.75% accuracy and a Kappa index of 0.875, highlighting performance, nitrogen, and protein as key variables. Among the vegetation indices, the Soil-Adjusted Vegetation Index (SAVI) and the Advanced Vegetation Index (AVI) were the most relevant in the flowering stage, with ROC curve values of 0.7778 and 0.8025, respectively. Spearman’s correlations confirmed a significant relationship between these indices and key physiological variables, allowing to distinguish between DS and CS systems. The RF-based prediction model for performance showed R² values above 91% in the indices with the highest correlation. However, predictive capacity was higher in DS, suggesting that conditions inherent in non-mechanized handling significantly influence model performance. This highlights the importance of using non-destructive procedures to estimate production, enabling the development of adaptive and sustainable strategies that contribute to efficient agricultural production, since it is possible to anticipate crop yields before harvest, optimizing resources such as fertilizers and water. Full article

Underground mine emergencies destroy communication infrastructure when situational awareness is most critical. Current systems rely on centralized network infrastructure, which fails during emergencies when miners are trapped and require rescue coordination. This paper proposes an energy-harvesting LoRa mesh network that addresses self-powered operation, interference management, and adaptive physical layer optimization under severe underground propagation conditions. A dual-antenna architecture separates RF energy harvesting (860 MHz) from LoRa communication (915 MHz), enabling continuous operation with supercapacitor storage. The core contribution is a decentralized scheduler that derives optimal timing offsets by modeling concurrent transmissions as a Poisson collision process, exploiting LoRa’s capture effect while maintaining network coherence. A SINR-aware physical layer adapts spreading factor, bandwidth, and coding rate with hysteresis, controls recomputing timing parameters after each change. Experimental validation in Missouri S&T’s operational mine demonstrates far-field wireless power transfer (WPT) reaching 35 m. Simulations across 2000 independent trials show a 2.2× throughput improvement over ALOHA (49% vs. 22% delivery ratio at 10 nodes/hop), 64% collision reduction, and 67% energy efficiency gains, demonstrating resilient emergency communications for underground environments. Full article

The widespread integration of low-power electronic devices in IoT, biomedical, and sensing applications has intensified the demand for energy-autonomous solutions. Radio Frequency Energy Harvesting (RFEH) offers a promising alternative by leveraging ambient RF signals available in both indoor and outdoor environments. Despite its conceptual appeal, practical deployment still faces major challenges. This systematic literature review (SLR) examines 25 recent studies, following the PRISMA methodology, to provide a comprehensive overview of current RFEH architectures. It focuses on three critical components: receiving antennas, impedance matching circuits (IMCs), and RF-to-DC rectifiers. Design strategies are reviewed and compared across antenna types, matching techniques, and rectifier configurations. The review also highlights persistent challenges and outlines directions for the development of compact, efficient, and robust energy-harvesting systems for next-generation wireless technologies. Full article

RF Energy Harvesting (RFEH) circuits have been extensively researched in recent years. Researchers have proposed dozens of RFEH models with various architectures and topologies. Due to the small amount of energy available to be harvested, RFEH circuits must be as efficient as possible, both in terms of receiving energy and its further processing. Recent research has identified that in resonant circuits, some of the received energy is retransmitted and therefore lost. Full article

This paper presents an advanced radio frequency (RF)–direct current (DC) power conversion architecture based on a multistage Cockcroft–Walton topology. The proposed design achieves an enhanced voltage conversion ratio while maintaining superior RF-DC conversion efficiency under low input power conditions. To address the inherent limitations of cascading Cockcroft–Walton topologies with class-F load networks, a novel ground plane isolation technique was developed, which utilizes the reverse-side metallization of the circuit board. A 5.8 GHz two-stage Cockcroft–Walton voltage multiplier rectifier was fabricated and characterized. Measurement results demonstrate that the circuit achieves a maximum output voltage of 7.4 V and a peak conversion efficiency of 70.5% with an input power of only 30 mW, while maintaining stable performance across varying load conditions. A comparison with a two-stage Dickson rectifier reveals that the Cockcroft–Walton rectifier exhibits superior output voltage and conversion efficiency. The proposed architecture delivers significant improvements in power conversion efficiency and voltage multiplication capability compared to conventional designs, establishing a new benchmark for low-power wireless energy harvesting applications. Full article