Search Results (235)

Data over sound (DoS) is an established technique that has experienced a resurgence in recent years, finding applications in areas such as contactless payments, device pairing, authentication, presence detection, toys, and offline data transfer. This study introduces CardiaWhisper, a system that extends the DoS concept to the medical domain by using a medical data-over-sound (MDoS) framework. CardiaWhisper integrates wearable biomedical sensors with home care systems, edge or IoT gateways, and telemedical networks or cloud platforms. Using a transmitter device, vital signs such as ECG (electrocardiogram) signals, PPG (photoplethysmogram) signals, RR (respiratory rate), and ACC (acceleration/movement) are sensed, conditioned, encoded, and acoustically transmitted to a nearby receiver—typically a smartphone, tablet, or other gadget—and can be further relayed to edge and cloud infrastructures. As a case study, this paper presents the real-time transmission and processing of ECG signals. The transmitter integrates an ECG sensing module, an encoder (either a PLL-based FM modulator chip or a microcontroller), and a sound emitter in the form of a standard piezoelectric speaker. The receiver, in the form of a mobile phone, tablet, or desktop computer, captures the acoustic signal via its built-in microphone and executes software routines to decode the data. It then enables a range of control and visualization functions for both local and remote users. Emphasis is placed on describing the system architecture and its key components, as well as the software methodologies used for signal decoding on the receiver side, where several algorithms are implemented using open-source, platform-independent technologies, such as JavaScript, HTML, and CSS. While the main focus is on the transmission of analog data, digital data transmission is also illustrated. The CardiaWhisper system is evaluated across several performance parameters, including functionality, complexity, speed, noise immunity, power consumption, range, and cost-efficiency. Quantitative measurements of the signal-to-noise ratio (SNR) were performed in various realistic indoor scenarios, including different distances, obstacles, and noise environments. Preliminary results are presented, along with a discussion of design challenges, limitations, and feasible applications. Our experience demonstrates that CardiaWhisper provides a low-power, eco-friendly alternative to traditional RF or Bluetooth-based medical wearables in various applications. Full article

In the context of increasing the complexity and intelligence of modern power systems, traditional maintenance approaches for circuit breakers have shown limitations in meeting both reliability and economic requirements. This paper proposes a multi-sensor data fusion fault detection method based on the RF-Adaboost algorithm for spring-operated circuit breakers. By integrating pressure, speed, coil current, and energy storage motor sensors into the mechanism, multi-source operational data are acquired and processed via denoising and feature extraction techniques. A fault detection model is then constructed using the RF-Adaboost classifier. The experimental results demonstrate that the proposed method achieves over 96% accuracy in identifying typical fault states such as coil voltage deviation, reset spring fatigue, and closing spring degradation, outperforming conventional approaches. These results validate the model’s effectiveness and robustness in diagnosing complex mechanical failures in circuit breakers. Full article

As data exchange demands increase also in widespread wearable technologies, transitioning to higher bandwidths and mmWave frequencies (30–300 GHz) is essential. This shift raises concerns about RF exposure. At such high frequencies, the most crucial human tissue for RF power absorption is the skin, since EMF penetration is superficial. It becomes thus very important to assess how the model used to represent the skin in numerical dosimetry studies affects the estimated level of absorbed power. The present study, for the first time, assesses the absorbed power density (APD) using FDTD simulations on two realistic human models in which: (i) the skin has a two-layer structure made of the stratum corneum and the viable epidermis and dermis layers, and (ii) the skin is modelled as a homogeneous dermis stratum. These results were compared with ones using flat phantom models, with and without the stratified skin. The exposure assessment study was performed with two sources (a wearable patch antenna and a plane wave) tuned to 28 GHz. For the wearable antenna, the results evidence that the exposure levels obtained when using the homogeneous version of the models are always lower than the levels in the stratified skin version with percentage differences from 16% to 30%. This trend is more noticeable with the female model. In the case of plane wave exposure, these differences were less pronounced and lower than 11%. Full article

The use of analog radio-over-fiber (RoF) systems combined with power-over-fiber (PoF) systems has been proposed in recent years for applications involving remote sensors used in hazardous environments or where electrical wiring may be impractical. This article presents a hybrid architecture topology that combines PoF and RoF, using Raman amplification to obtain RF gain. The first emphasis is placed on the use of two types of high-power laser sources (HPLSs) for the PoF system: a 1480 nm Raman-based HPLS and a 1550 nm HPLS that is based on an erbium-doped fiber amplifier (EDFA). The second emphasis of this paper is on how these two HPLSs simulate Raman scattering (SRS) in the fiber, considering different lengths of SMF 28 for the link. Thus, a comparative analysis is proposed considering the effects induced on the RF signal, mainly focused on its RF power gain ($G_{RF}$), noise figure (NF), and spurious-free dynamic range (SFDR). The obtained results show that the architecture using a PoF system based on the 1550 nm HPLS benefits from a lower noise figure degradation, even when the noise generated by the optical amplification is considered. Full article

Under the background of “dual-carbon”, expanding renewable energy grid integration exacerbates grid net load volatility, and system climbing requirements escalate. In this paper, the problem of uncertain ramping demand prediction caused by net load prediction error in new power systems is investigated. First, the total system ramping demand calculation model is constructed, and the effects of deterministic and uncertain ramping demand on the total system ramping demand are analyzed. Secondly, a prediction model based on a CNN-LSTM hybrid neural network is proposed for the uncertain ramp-up demand, which extracts the spatial correlation features of the multi-source influencing factors through the convolutional layer, captures the dynamic evolution law in the time series by using the LSTM layer, and realizes the high-precision point prediction and reliable interval prediction by combining the quantile regression method. Finally, the actual operation data and forecast data of a provincial power grid are used for example verification, and the results show that the proposed model outperformed traditional models (SVM, RF, BPNN) and single deep learning models (CNN, LSTM) in point prediction performance, achieving higher prediction accuracy and validating the effectiveness of the spatio-temporal feature extraction module. In terms of interval prediction quality, compared with the histogram and QRF benchmark models, the proposed model achieves a significant reduction in the average width of the prediction interval, average upward ramp-up demand, and average downward ramp-down demand while maintaining 100% interval coverage. This demand realizes a better balance between prediction economic efficiency and safety, providing more reliable technical support for the precise assessment of uncertain ramp-up demand in new power systems. Full article

Aflatoxins are toxic organic substances that are synthesized on the surfaces of seeds, nuts, and similar products by some fungi under elevated humidity. They decompose at temperatures well above 130 °C, so standard heating or autoclaving is an obsolete technique for the degradation of toxins on surfaces without significant modification of the treated material. Non-equilibrium plasma was used to degrade aflatoxins at low temperatures and determine the efficiency of O atoms. A commercial mixture of aflatoxins was deposited on smooth substrates, and the solvent was evaporated so that about a 3 nm thick film of dry toxins remained on the substrates. The samples were exposed to low-pressure oxygen plasma sustained by an inductively coupled radiofrequency (RF) discharge in either the E or H mode. The gas pressure was 20 Pa, the forward RF power was between 50 and 700 W, and the O-atom flux was between 1.2 × 10²³ and 1.5 × 10²⁴ m⁻² s⁻¹. Plasma treatment caused the rapid degradation of aflatoxins, whose concentration was deduced from the fluorescence signal at 455 nm upon excitation with a monochromatic source at 365 nm. The degradation was faster at higher discharge powers, but the degradation curves fitted well when plotted against the dose of O atoms. The experiments showed that the aflatoxin concentration dropped below the detection limit of the fluorescence probe after receiving the O-atom dose of just above 10²⁵ m⁻². This dose was achieved within 10 s of treatment in plasma in the H mode, and approximately a minute when plasma was in the E mode. The method provides a low-temperature solution for the efficient detoxification of agricultural products. Full article

This paper proposes a hybrid energy-harvesting chip that utilizes both radio-frequency (RF) energy and solar energy for low-power applications and extended service life. The key contributions include a wide input power range, a compact chip area, and a high maximum power conversion efficiency (PCE). Solar energy is a clean and readily available source. The hybrid energy harvesting system has gained popularity by combining RF and solar energy to improve overall energy availability and efficiency. The proposed chip comprises a matching network, rectifier, charge pump, DC combiner, overvoltage protection circuit, and low-dropout voltage regulator (LDO). The matching network ensures maximum power delivery from the antenna to the rectifier. The rectifier circuit utilizes a cross-coupled differential drive rectifier to convert radio frequency energy into DC voltage, incorporating boosting functionality. In addition, a solar harvester is employed to provide an additional energy source to extend service time and stabilize the output by combining it with the radio-frequency source using a DC combiner. The overvoltage protection circuit safeguards against high voltage passing from the DC combiner to the LDO. Finally, the LDO facilitates the production of a stable output voltage. The entire circuit is simulated using the Taiwan Semiconductor Manufacturing Company 0.18 µm 1P6M complementary metal–oxide–semiconductor standard process developed by the Taiwan Semiconductor Research Institute. The simulation results indicated a rectifier conversion efficiency of approximately 41.6% for the proposed radio-frequency-energy-harvesting system. It can operate with power levels ranging from −1 to 20 dBm, and the rectifier circuit’s output voltage is within the range of 1.7–1.8 V. A 0.2 W monocrystalline silicon solar panel (70 × 30 mm²) was used to generate a supplied voltage of 1 V. The overvoltage protection circuit limited the output voltage to 3.6 V. Finally, the LDO yielded a stable output voltage of 3.3 V. Full article

The optical, electrical, and material properties of In–Zn–O (IZO) films were optimized by adjusting the deposition power and annealing temperature. Films deposited at 125 W and annealed at 300 °C exhibited the best performance, with the lowest resistivity (1.43 × 10⁻³ Ω·cm), highest mobility (11.12 cm²/V·s), and highest carrier concentration (4.61 × 10²⁰ cm⁻³). The average transmittance and optical energy gap were 82.57% and 3.372 eV, respectively. The electrical characteristics of amorphous In-Ga-Zn-O (a-IGZO) thin-film transistors (TFTs) using IZO source-drain (S–D) electrodes with various sputtering powers and annealing temperatures were investigated. The optimal sputtering power of 125 W and annealing temperature of 300 °C for the IZO S–D electrodes resulted in the highest field-effect mobility (~12.31 cm²/V·s) and on current (~2.09 × 10⁻⁶ A). This improvement is attributed to enhanced carrier concentration and mobility, which result from the high In/Zn ratio, the larger grain size, and low RMS roughness in the IZO films. The parasitic contact resistance (R_SD) and channel resistance (R_CH) were analyzed using the total resistance method. R_SD decreased with increasing IZO S–D sputtering power, while R_CH reached a minimum at 125 W. Both resistances decreased significantly as the annealing temperature increased from 200 °C to 300 °C. Full article

Point clouds continue to be the main data source in 3D modeling studies with unmanned aerial vehicle (UAV) images. Structure-from-Motion (SfM) and MultiView Stereo (MVS) have high time costs for point cloud generation, especially in large data sets. For this reason, state-of-the-art methods such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have emerged as powerful alternatives for point cloud generation. This paper explores the performance of NeRF and 3DGS methods in generating point clouds from UAV images. For this purpose, the Nerfacto, Instant-NGP, and Splatfacto methods developed in the Nerfstudio framework were used. The obtained point clouds were evaluated by taking the point cloud produced with the photogrammetric method as reference. In this study, the effects of image size and iteration number on the performance of the algorithms were investigated in two different study areas. According to the results, Splatfacto demonstrates promising capabilities in addressing challenges related to scene complexity, rendering efficiency, and accuracy in UAV imagery. Full article

Background: Catheter ablation is a first-line treatment for rhythm control strategies in patients with atrial fibrillation (AF), with different energy sources available, including pulsed-field ablation (PFA), high-power short-duration radiofrequency (HPSD RF), conventional radiofrequency (RF), and cryoballoon ablation. Limited evidence exists on how different ablation techniques affect patient-reported outcomes, such as patients’ quality of life (QoL) and perceived symptoms. This study aims to assess the impact of ablation energy sources on reported QoL and symptom perception after AF ablation. Methods: The study included 148 patients who underwent catheter ablation in different centers. Patients were divided into four groups according to the energy source used. Follow-up was conducted during the 6 months post-procedure. Patients were asked to complete a 20-item questionnaire evaluating quality of life, activity resumption, recovery process, perceived symptoms, and satisfaction. Comparative analyses were performed across energy groups, anesthesia types, and anesthetic drugs. Results: PFA patients reported the highest improvement in QoL scores compared to RF, HPSD RF, and cryoablation (p < 0.001). Activity resumption and symptom relief were significantly better in the PFA group compared to others (p < 0.001). Anesthesia type and anesthetic drug influenced QoL outcomes, with patients under general anesthesia showing higher QoL scores compared to deep sedation (p < 0.001). The energy source and anesthetic drug resulted in independent predictors of QoL improvement. Conclusions: Ablation energy source could impact patients’ perceived QoL and symptom relief after AF ablation. PFA demonstrated superior performance scores in QoL and symptom perception compared to other techniques. Anesthetic drugs also play a role in patient-reported outcomes and activity resumption. Full article

This study applies machine learning (ML) techniques to classify fertile [for porphyry Cu and (or) Au systems] and barren adakites using geochemical data from New Brunswick, Canada. It emphasizes that not all intrusive units, including adakites, are inherently fertile and should not be directly used as the heat source evidence layer in mineral prospectivity mapping without prior analysis. Adakites play a crucial role in mineral exploration by helping distinguish between fertile and barren intrusive units, which significantly influence ore-forming processes. A dataset of 99 fertile and 66 barren adakites was analyzed using seven ML models: support vector machine (SVM), neural network, random forest (RF), decision tree, AdaBoost, gradient boosting, and logistic regression. These models were applied to classify 829 adakite samples from around the world into fertile and barren categories, with performance evaluated using area under the curve (AUC), classification accuracy, F1 score, precision, recall, and Matthews correlation coefficient (MCC). SVM achieved the highest performance (AUC = 0.91), followed by gradient boosting (0.90) and RF (0.89). For model validation, 160 globally recognized fertile adakites were selected from the dataset based on well-documented fertility characteristics. Among the tested models, SVM demonstrated the highest classification accuracy (93.75%), underscoring its effectiveness in distinguishing fertile from barren adakites for mineral prospectivity mapping. Statistical analysis and feature selection identified middle rare earth elements (REEs), including Gd and Dy, with Hf, as key indicators of fertility. A comprehensive analysis of 1596 scatter plots, generated from 57 geochemical variables, was conducted using linear discriminant analysis (LDA) to determine the most effective variable pairs for distinguishing fertile and barren adakites. The most informative scatter plots featured element vs. element combinations (e.g., Ga vs. Dy, Ga vs. Gd, and Pr vs. Gd), followed by element vs. major oxide (e.g., Fe₂O₃T vs. Gd and Al₂O₃ vs. Hf) and ratio vs. element (e.g., La/Sm vs. Gd, Rb/Sr vs. Hf) plots, whereas major oxide vs. major oxide, ratio vs. ratio, and major oxide vs. ratio plots had limited discriminatory power. Full article

Solar radiation is one of the most abundant energy sources in the world and is a crucial parameter that must be researched and developed for the sustainable projects of future generations. This study evaluates the performance of different machine learning methods for solar radiation prediction in Konya, Turkey, a region with high solar energy potential. The analysis is based on hydro-meteorological data collected from NASA/POWER, covering the period from 1 January 1984 to 31 December 2022. The study compares the performance of Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), Bidirectional GRU (Bi-GRU), LSBoost, XGBoost, Bagging, Random Forest (RF), General Regression Neural Network (GRNN), Support Vector Machines (SVM), and Artificial Neural Networks (MLANN, RBANN). The hydro-meteorological variables used include temperature, relative humidity, precipitation, and wind speed, while the target variable is solar radiation. The dataset was divided into 75% for training and 25% for testing. Performance evaluations were conducted using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R²). The results indicate that LSTM and Bi-LSTM models performed best in the test phase, demonstrating the superiority of deep learning-based approaches for solar radiation prediction. Full article

The rapid advancement of terahertz (THz) communication systems has positioned this technology as a key enabler for next-generation telecommunication networks, including 6G, secure communications, and hybrid wireless-optical systems. This review comprehensively analyzes THz communication, emphasizing its integration with free-space optical (FSO) systems to overcome conventional bandwidth limitations. While THz-FSO technology promises ultra-high data rates, it is significantly affected by atmospheric absorption, particularly absorption beyond 500 GHz, where the attenuation exceeds 100 dB/km, which severely limits its transmission range. However, the presence of a lower-loss transmission window at 680 GHz provides an opportunity for optimized THz-FSO communication. This paper explores recent developments in high-power THz sources, such as quantum cascade lasers, photonic mixers, and free-electron lasers, which facilitate the attainment of ultra-high data rates. Additionally, adaptive optics, machine learning-based beam alignment, and low-loss materials are examined as potential solutions to mitigating signal degradation due to atmospheric absorption. The integration of THz-FSO systems with optical and radio frequency (RF) technologies is assessed within the framework of software-defined networking (SDN) and multi-band adaptive communication, enhancing their reliability and range. Furthermore, this review discusses emerging applications such as self-driving systems in 6G networks, ultra-low latency communication, holographic telepresence, and inter-satellite links. Future research directions include the use of artificial intelligence for network optimization, creating energy-efficient system designs, and quantum encryption to obtain secure THz communications. Despite the severe constraints imposed by atmospheric attenuation, the technology’s power efficiency, and the materials that are used, THz-FSO technology is promising for the field of ultra-fast and secure next-generation networks. Addressing these limitations through hybrid optical-THz architectures, AI-driven adaptation, and advanced waveguides will be critical for the full realization of THz-FSO communication in modern telecommunication infrastructures. Full article

Energy harvesting technology allows Internet of Things (IoT) devices to be powered continuously without needing battery charging or replacement. In addressing existing and emerging massive IoT energy supply challenges, this paper presents the design of multi-sourced multiple input and multiple output (MIMO) multiband hybrid wireless RF-perovskite photovoltaic energy harvesting subsystems for IoT application. The research findings evaluate the efficiency and power output of different RF configurations (1 to 16 antennas) within MIMO RF subsystems. A Delon quadruple rectifier in the RF energy harvesting system demonstrates a system-level power conversion efficiency of 51%. The research also explores the I-V and P-V characteristics of the adopted perovskite tandem cell. This results in an impressive array capable of producing 6.4 V and generating a maximum power of 650 mW. For the first time, the combined mathematical modelling of the system architecture is presented. The achieved efficiency of the combined system is 90% (for 8 MIMO) and 98% (for 16 MIMO) at 0 dBm input RF power. This novel study holds great promise for next-generation 5G/6G smart IoT passive electronics. Additionally, it establishes the hybrid RF-perovskite energy harvester as a promising, compact, and eco-friendly solution for efficiently powering IoT devices in smart cities. This work contributes to the development of sustainable, scalable, and smart energy solutions for IoT integration into smart city infrastructures. Full article

Wind energy is a crucial renewable resource for sustainable power generation; however, challenges such as high initial investment costs and difficulties in identifying efficient locations hinder its widespread adoption. Accurate wind energy forecasting is essential for energy planning, trading, and grid optimization. This study presents short-term, medium-term, and long-term –wind power forecasts for the Söke–Çatalbük Wind Power Plant in Aydın, Turkey, using meteorological data and production records from 2018 to 2022. Five machine learning algorithms were employed—Artificial Neural Network (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), K-Nearest Neighbors Regression (KNN), and Multi-Layer Perceptron (MLP ANN)—utilizing both MinMax and Standard Scaling methods. Prediction performance was evaluated using Mean Absolute Error (MAE), Coefficient of Determination (R²), and Root Mean Square Error (RMSE) metrics. The results indicate that Min-Max Scaling improved short-term predictions with KNN, while XGBoost and Random Forest provided more stable and accurate forecasts in medium- and long-term predictions. Additionally, Standard Scaling significantly enhanced MLP ANN’s performance in medium-term forecasting. These findings provide practical insights for optimizing wind energy forecasting models, which can improve energy trading strategies, enhance grid stability, and support informed decision making in renewable energy investments. The results are particularly valuable for energy planners and policymakers seeking to maximize the efficiency of wind power plants and facilitate the integration of renewable energy sources into national grids more effectively. Full article