Search Results (8,732)

Hand gesture recognition (HGR) is a convenient and natural form of human–computer interaction. It is suitable for various applications. Much research has already focused on wearable device-based HGR. By contrast, this paper gives an overview focused on device-free HGR. That means we evaluate HGR systems that do not require the user to wear something like a data glove or hold a device. HGR systems are explored regarding technology, hardware, and algorithms. The interconnectedness of timing and power requirements with hardware, pre-processing algorithm, classification, and technology and how they permit more or less granularity, accuracy, and number of gestures is clearly demonstrated. Sensor modalities evaluated are WIFI, vision, radar, mobile networks, and ultrasound. The pre-processing technologies stereo vision, multiple-input multiple-output (MIMO), spectrogram, phased array, range-doppler-map, range-angle-map, doppler-angle-map, and multilateration are explored. Classification approaches with and without ML are studied. Among those with ML, assessed algorithms range from simple tree structures to transformers. All applications are evaluated taking into account their level of integration. This encompasses determining whether the application presented is suitable for edge integration, their real-time capability, whether continuous learning is implemented, which robustness was achieved, whether ML is applied, and the accuracy level. Our survey aims to provide a thorough understanding of the current state of the art in device-free HGR on edge devices and in general. Finally, on the basis of present-day challenges and opportunities in this field, we outline which further research we suggest for HGR improvement. Our goal is to promote the development of efficient and accurate gesture recognition systems. Full article

This review paper explores the emerging field of conversion cathode materials, which hold significant promises for advancing the performance of lithium-ion (LIBs) and lithium–sulfur batteries (LSBs). Traditional cathode materials of LIBs, such as lithium cobalt oxide, have reached their limits in terms of energy density and capacity, driving the search for alternatives that can meet the increasing demands of modern technology, including electric vehicles and renewable energy systems. Conversion cathodes operate through a mechanism involving complete redox reactions, transforming into different phases, which enables the storage of more lithium ions and results in higher theoretical capacities compared to conventional intercalation materials. This study examines various conversion materials, including metal oxides, sulfides, and fluorides, highlighting their potential to significantly enhance energy density. Despite their advantages, conversion cathodes face numerous challenges, such as poor conductivity, significant volume changes during cycling, and issues with reversibility and stability. This review discusses current nanoengineering strategies employed to address these challenges, including nano structuring, composite formulation, and electrolyte optimization. By assessing recent research and developments in conversion cathode technology, this paper aims to provide a comprehensive overview of their potential to revolutionize lithium-ion batteries and contribute to the future of energy storage solutions. Full article

This paper presents a resonant wireless power transfer method that leverages a 90-degree voltage phase shift between the transmitting and receiving coils to enhance efficiency and maximize power transfer. When the resonant coupling is achieved, the secondary coil with an adjustable capacitor forms a tuned LC circuit. If the primary coil is driven at the resonant frequency of both the primary and secondary sides, the system can transmit 250W of power between the coils over a distance of 50 cm. Using a single power transmitting unit (PTU) board with multiple paralleled gallium nitride high-electron-mobility transistors (GaN HEMTs), the system achieves a maximum power transfer efficiency of 88%, highlighting the effectiveness of the design in high-efficiency, long-distance wireless power transmission. The key to the success of high-power, high-efficiency RWPT is in exhibiting the imaginary turn ratio presented on the air transformer. The imaginary turn ratio can realize the negative impedance conversion that converts the positive resistance on the power-receiving unit into a negative one, and thus, the damping of the resonance oscillation becomes negative and positively encourages more power to be delivered to the power-receiving unit (PRU) load. This paper derives the theory of the imaginary turn ratio and demonstrates the implementation of the RWPT system that exhibits the imaginary turn ratio effect. Full article

Barley is an ecologically adaptable crop widely used in agriculture and well suited for organic farming. Satellite imagery from Sentinel-2 can support crop monitoring and yield prediction, optimising production processes. This study compares two types of Sentinel-2 data—standard (S2) data with 10 m and 20 m resolution and Sentinel-2 Deep Resolution 3 (S2DR3) data with 1 m resolution—to assess their (i) relationship with yield in organically grown barley and (ii) utility for winter crop mapping. Vegetation indices were generated and analysed across different phenological phases to determine the most suitable predictors of yield. The results indicate that using 10 × 10 m data, the BBCH-41 phase is optimal for yield prediction, with the Green Chlorophyll Vegetation Index (GCVI; r = 0.80) showing the strongest correlation with yield. In contrast, S2DR3 data with a 1 × 1 m resolution demonstrated that Transformed the Chlorophyll Absorption in Reflectance Index (TCARI), TO, and Normalised Difference Red Edge Index (NDRE1) were consistently reliable across all phenological stages, except for BBCH-51, which showed weak correlations. These findings highlight the potential of remote sensing in organic barley farming and emphasise the importance of selecting appropriate data resolutions and vegetation indices for accurate yield prediction. With the use of three-date spectral band stacks, the Random Forest (RF) and Support Vector Classification (SVC) methods were used to differentiate between wheat, barley, and rapeseed. A five-fold cross-validation approach was applied, training data were stratified with 200 points per crop, and classification accuracy was assessed using the User’s and Producer’s accuracy metrics through pixel-by-pixel comparison with a reference raster. The results for S2 and S2DR3 were very similar to each other, confirming the significant potential of S2DR3 for high-resolution crop mapping. Full article

The rational design of ordered chromogenic supramolecular polymeric systems is critical for the advancement of next-generation stimuli-responsive, optical, and semiconducting materials. Previously, we reported the design of a stimuli-responsive, lamellar self-assembled platform composed of an imidazole-appended perylene diimide of varying methylene spacer length (n = 3, 4, and 6) and a commercially available diacid-functionalized diacetylene monomer, 10, 12 docosadiynedioic acid, in a 1:1 molar ratio. Herein, we expound on the importance of the composition of the imidazole-appended perylene diimide of varying methylene spacer length (n = 3, 4, and 6) and 10, 12 docosadiynedioic acid in the ratio of 2:1 to the supramolecular self-assembly, final morphology, and properties. Topochemical polymerization of the drop-cast films by UV radiation yielded blue-phase polydiacetylene formation, and subsequent thermal treatment of the films produced a thermoresponsive blue-to-red phase transformation. Differential scanning calorimetry (DSC) studies revealed a dual dependence of the methylene spacer length and stimuli treatment (UV and/or heat) on the thermal transitions of the films. Furthermore, small-angle X-ray scattering (SAXS) and wide-angle X-ray scattering (WAXS) showed well-defined hierarchical semiconducting nanostructures with interconnected “chessboard”-patterned lamellar stacking. Upon doping with an ionic liquid, the 2:1 platform showed higher ionic conductivity than the previous 1:1 one. The results presented here illustrate the importance of the composition and architecture to the ionic domain connectivity and ionic conductivity, which will have far-reaching implications for the rational design of semiconducting polymers for energy applications including fuel cells, batteries, ion-exchange membranes, and mixed ionic conductors. Full article

Predicting Signal Phase and Timing (SPaT) information and confidence levels is needed to enhance Green Light Optimal Speed Advisory (GLOSA) and/or Eco-Cooperative Adaptive Cruise Control (Eco-CACC) systems. This study proposes an architecture based on transformer encoders to improve prediction performance. This architecture is combined with different deep learning methods, including Multilayer Perceptrons (MLP), Long-Short-Term Memory neural networks (LSTM), and Convolutional Long-Short-Term Memory neural networks (CNNLSTM) to form an ensemble of predictors. The ensemble is used to make data-driven predictions of SPaT information obtained from traffic signal controllers for six different intersections along the Gallows Road corridor in Virginia. The study outlines three primary tasks. Task one is predicting whether a phase would change within 20 s. Task two is predicting the exact change time within 20 s. Task three is assigning a confidence level to that prediction. The experiments show that the proposed transformer-based architecture outperforms all the previously used deep learning methods for the first two prediction tasks. Specifically, for the first task, the transformer encoder model provides an average accuracy of 96%. For task two, the transformer encoder models provided an average mean absolute error (MAE) of 1.49 s, compared to 1.63 s for other models. Consensus between models is shown to be a good leading indicator of confidence in ensemble predictions. The ensemble predictions with the highest level of consensus are within one second of the true value for 90.2% of the time as opposed to those with the lowest confidence level, which are within one second for only 68.4% of the time. Full article

Tantalum diffusion layers were fabricated on 316L stainless steel substrates using the double glow plasma surface alloying technology (DGPSAT). The optimization rules of the Fe-Ta diffusion layer under varying alloying times were investigated, focusing on the effects of processing parameters on the phase structure and microstructure. The results indicate that, as the alloying time increases, the surface wrinkled structure in the Fe-Ta alloy layer gradually transforms into a nanoscale acicular α-Ta structure, improving surface roughness and water contact angle. The surface microstructure influenced by the alloying time enhanced mechanical properties significantly, increasing Vickers hardness from 152 HV_0.2 to 970 HV_0.2, improving bonding strength, and reducing the friction coefficient to 0.5. Electrochemical testing showed that the corrosion rate of the tantalum diffusion layer was significantly reduced from 1.04 × 10⁻² mm/a to 2.83 × 10⁻⁴ mm/a, demonstrating the excellent corrosion resistance. The island growth pattern during the formation of alloy layers was simulated by molecular dynamics. Replacing bulk materials with tantalum diffusion layers can economize rare metals, reduce costs, and be of great significant for the special equipment applications in harsh environments. Full article

The injection of CO₂ into coal reservoirs occurs in its supercritical state (ScCO₂), which significantly alters the pore structure and chemical composition of coal, thereby influencing the adsorption and diffusion behavior of methane (CH₄). Understanding these changes is crucial for optimizing CH₄ extraction and improving CO₂ sequestration efficiency. This study aims to investigate the effects of ScCO₂ on the pore structure, chemical bonds, and CH₄ diffusion mechanisms in bituminous coal to provide insights into coal reservoir stimulation and CO₂ storage. By utilizing high-pressure CO₂ injection adsorption, low-pressure CO₂ gas adsorption (LP-CO₂-GA), Fourier-transform infrared spectroscopy (FTIR), and reactive force field molecular dynamics (ReaxFF-MD) simulations, this study examines the multi-scale changes in coal at the nano- and molecular levels. The following results were found: Pore Structure Evolution: After ScCO₂ treatment, micropore volume increased by 19.1%, and specific surface area increased by 11.2%, while mesopore volume and specific surface area increased by 14.4% and 5.7%, respectively. Chemical Composition Changes: The content of aromatic structures, oxygen-containing functional groups, and hydroxyl groups decreased, while aliphatic structures increased. Specific molecular changes included an increase in (CH₂)_n, 2H, 1H, and secondary alcohol (-C-OH) and phenol (-C-O) groups, while Car-Car and Car-H bonds decreased. Mechanisms of Pore Volume Changes: The pore structure evolves through three distinct phases: Swelling Phase: Breakage of low-energy bonds generates new micropores. Aromatic structure expansion reduces intramolecular spacing but increases intermolecular spacing, causing a decrease in micropore volume and an increase in mesopore volume. Early Dissolution Phase: Continued bond breakage increases micropore volume, while released aliphatic and aromatic structures partially occupy these pores, converting some mesopores into micropores. Later Dissolution Phase: Minimal chemical bond alterations occur, but weakened π-π interactions and van der Waals forces between aromatic layers result in further mesopore volume expansion. Impact on CH₄ Diffusion: Changes in pore volume directly affect CH₄ migration. In the early stages of ScCO₂ interaction, pore shrinkage reduces the mean square displacement (MSD) and self-diffusion coefficient of CH₄. However, as the reaction progresses, pore expansion enhances CH₄ diffusion, ultimately improving gas extraction efficiency. This study provides a fundamental understanding of how ScCO₂ modifies coal structure and CH₄ transport properties, offering theoretical guidance for enhanced CH₄ recovery and CO₂ sequestration strategies. Full article

Behavioral changes are critical for addressing sustainability challenges, which have become increasingly urgent due to the growing impact of global greenhouse gas emissions on ecosystems and human livelihoods. However, translating awareness into meaningful action requires practical tools to bridge this gap. Mobile applications, utilizing strategies from human–computer interaction (HCI) such as gamification, nudging, and persuasive technologies, have proven to be powerful in promoting sustainable behaviors. To support designers in developing effective apps of this kind, theory-based design guidelines were created, drawing on established theories and design approaches aimed at shaping and encouraging virtuous user behaviors fostering sustainability. To make these guidelines more accessible and enhance their usability during the design phase, this study presents their transformation into the SBAM card deck, a deck of 11 design cards. The SBAM cards aim to simplify theoretical concepts, stimulate creativity, and provide structured support for design discussions, helping designers generate solutions tailored to specific project contexts. This study also evaluates the effectiveness of the SBAM cards in the design process through two workshops with design students. Results show that the cards enhance ideation, foster creativity, and improve designers’ perceived self-efficacy compared to the exploitation of the same design guidelines information presented in traditional textual formats. This paper discusses the SBAM cards design and evaluation methodology, findings, and implications, offering insights into how the SBAM design cards can bridge the gap between theory and practice in sustainability-focused mobile app development. To ensure broader accessibility, the SBAM cards have been made available to the public through a dedicated website. Full article

The production of silicon steel involves complex metallurgical processes, where the kind, composition, size, and quantity of the inclusions generated affect the silicon steel properties. This article is based on the smelting process for W350 non-oriented silicon steel produced by a certain factory. By systematically sampling, at key nodes of the converter–RH refining–tundish smelting process, the change in cleanliness of molten steel in the whole smelting process, the evolution of typical inclusions, and the transformation rules for the precipitated phase were analyzed by means of SEM-EDS, ASPEX, and Thermal-Calc. The results indicate that the total oxygen mass fraction in the steel decreases by more than 95% after deoxidation alloying, and the average oxygen mass fraction in the RH outbound steel is 0.0012%. While the nitrogen mass fraction shows a rising trend as a whole, the average nitrogen mass fraction in the tundish steel reaches approximately 0.0014%. Before RH refining, large Al₂O₃–CaO–SiO₂ and Al₂O₃–CaO–SiO₂–MgO composite inclusions are the main inclusions. MnO and Al₂O₃–SiO₂–MnO inclusions are the main inclusions after RH inlet and RH decarburization. After RH deoxidation with aluminum, the inclusions were almost entirely transformed into Al₂O₃ inclusions. After RH alloying, with the content of Si and Mn increased, the inclusions transformed into Al₂O₃–SiO₂–MnO inclusions. The number of inclusions from RH desulfurization to the RH outbound stage declined significantly, and composite inclusions containing CaS and precipitates such as AlN and MnS began to appear. The inclusions’ main types were Al₂O₃–MgO–CaS, AlN–MnS, AlN, and Al₂O₃–MgO. The inclusions inside the tundish were the same, but the numbers were slightly increased due to the secondary oxidation of molten steel. More than 80% of the oxide inclusions in the whole process were between 1 μm and 5 μm in size. The average size and the number of inclusions per unit area reached 5.45 μm and 63.1 per mm², respectively, after RH deoxidation, and respectively decreased to 3.71 μm and 1.9 per mm² during the RH outbound stage, but both increased slightly in the tundish. Thermodynamic calculation shows that Al₂O₃–MgO inclusions are formed when w([Mg]) > 0.0033% in molten steel at 1873 K. Under the actual temperature of 1828K and w([Al]s) = 0.6515%, the range of w([Mg]) corresponding to the stable existence of Al₂O₃–MgO is between 0.0053% and 0.1676%. The liquidus temperature of W350 non-oriented silicon steel is 1489 °C. MnS and AlN inclusions are precipitated successively with the solidification of molten steel, and the precipitation temperatures are 1460.7 °C and 1422.2 °C, respectively. As the temperature decreases, the sequence of inclusion precipitation calculated in liquid was as follows: Al₂O₃–CaO → 2Al₂O₃–CaO + MnS → 6Al₂O₃–CaO → Al₂O₃ + AlN + MnS + CaS. Full article

Modern radar systems commonly utilize monopulse angle estimation techniques for target angle estimation, with the phase comparison method being one of the most widely adopted approaches. While the phase comparison method achieves high estimation precision, it is highly susceptible to noise and exhibits a suboptimal performance under low Signal-to-Noise Ratio (SNR) conditions, leading to a high SNR threshold. Moreover, conventional monopulse angle estimation methods provide limited target information, as a single measurement cannot reveal the target’s motion direction. To address these shortcomings, a novel approach based on the phase comparison method is proposed in this study, with the variation in the wave path difference modeled as a first-order motion model. By accumulating the conjugate-multiplied signals over multiple time steps, the Wave Path Difference Rate of Change Ambiguity Function (WPD-ROC AF) is constructed. A fast algorithm employing the 2D Chirp-Z Transform (2D-CZT) is proposed, enabling multi-pulse angle estimation through the identification of frequency and phase values corresponding to spectral peaks. Simulation results validate that the proposed method outperforms traditional monopulse angle estimation techniques under low-SNR conditions and effectively suppresses static clutter interference. Furthermore, the sign of the WPD-ROC AF is shown to be correlated with the target’s motion direction, providing practical utility for determining the direction of movement in remote sensing scenarios. Full article

This study develops a finite element solution to analyze the vibration response of multi-layer shape memory alloy (SMA) composite beams. Using Euler–Bernoulli beam motion equations with tension–compression asymmetry, based on Poorasadion’s model, the Newmark method and Newton–Raphson technique are employed. Validating the model against ABAQUS/Standard results for a homogeneous SMA beam shows good agreement. This research explores the dynamic characteristics of bi-layer and tri-layer SMA beams, presenting deflection–time, stress–strain, and velocity–deflection profiles. SMAs’ hysteresis property effectively reduces early-stage vibration amplitudes, and their energy-dissipating feature during phase transformations makes them promising for controlling dynamic performance in engineering applications. Full article

In this study, we examine the bifurcations and dynamics of a piecewise linear van der Pol equation—a model that captures self-sustained oscillations and is applied in various scientific disciplines, including electronics, neuroscience, biology, and economics. The van der Pol equation is transformed into a piecewise linear system to simplify the analysis of stability and controllability, which is particularly beneficial in engineering applications. This work explores the impact of increasing the number of linear segments on the system’s dynamics, focusing on the stability of the equilibria, phase portraits, and bifurcations. The findings reveal that while the bifurcation structure at critical values of the bifurcation parameter is complex, the topology of the piecewise linear model remains unaffected by an increase in the number of linear segments from three to four. This research contributes to our understanding of the dynamics of nonlinear systems with piecewise linear characteristics and has implications for the analysis and design of real-world systems exhibiting such behavior. Full article

A single (Ba_0.85Ca_0.15)(Zr_0.10Ti_0.90)O₃ phase material with a tetragonal structure is processed and synthesized with a modified Pechini method using ethylenediaminetetraacetic acid and glycerol as chelating and esterifying agents, respectively. The complete chemical transformation to the desired phase is achieved at 900 °C, which is 300 °C lower than conventional synthesis methods. Its consolidation, reaching up to 91% relative density, is carried out at 1400 °C. It is clearly demonstrated that the use of ethylenediaminetetraacetic acid and glycerol reagents is particularly beneficial for inducing a homogeneous grain size distribution (10 μm), which leads to very promising electromechanical properties (d₃₃ = 451 pC/N; d₃₁ = 160 pC/N; kp = 0.40; $\epsilon {\prime }_{33}^T$ = 4790 and Q_m = 358) of the densified system. Full article

A high-sensitivity magnetic field sensor based on an optoelectronic oscillator (OEO) with a Mach–Zehnder interferometer (MZI) is proposed and experimentally demonstrated. The magnetic field sensor consists of a fiber Mach–Zehnder interferometer, with the lower arm of the interferometer wound around a magnetostrictive transducer. Due to the magnetostrictive effect, an optical phase shift induced by magnetic field variation is generated between two orthogonal light waves transmitted in the upper and lower arms of the MZI. The polarization-dependent property of a Mach–Zehnder modulator (MZM) is utilized to transform the magnetostrictive phase shift into the phase difference between the sidebands and optical carrier, which is mapped to the oscillating frequency upon the completion of an OEO loop. High-sensitivity magnetic field sensing is achieved by observing the frequency shift of the radio frequency (RF) signal. Temperature-induced cross-sensitivity is mitigated through precise length matching of the MZI arms. In the experiment, the high magnetic field sensitivity of 6.824 MHz/mT with a range of 25 mT to 25.3 mT is achieved and the sensing accuracy measured by an electrical spectrum analyzer (ESA) at “maxhold” mode is 0.002 mT. The proposed sensing structure has excellent magnetic field detection performance and provides a solution for temperature-insensitive magnetic field detection, which would have broad application prospects. Full article