Search Results (604)

Most existing cloud base height (CBH) retrieval algorithms are only applicable for daytime satellite observations due to their dependence on visible observations. This study presents a novel algorithm to retrieve day and night CBH using infrared observations of the geostationary Advanced Himawari Imager (AHI). The algorithm is featured by integrating deep learning techniques with a physical model. The algorithm first utilizes a convolutional neural network-based model to extract cloud top height (CTH) and cloud water path (CWP) from the AHI infrared observations. Then, a physical model is introduced to relate cloud geometric thickness (CGT) to CWP by constructing a look-up table of effective cloud water content (ECWC). Thus, the CBH can be obtained by subtracting CGT from CTH. The results demonstrate good agreement between our AHI CBH retrievals and the spaceborne active remote sensing measurements, with a mean bias of −0.14 ± 1.26 km for CloudSat-CALIPSO observations at daytime and −0.35 ± 1.84 km for EarthCARE measurements at nighttime. Additional validation against ground-based millimeter wave cloud radar (MMCR) measurements further confirms the effectiveness and reliability of the proposed algorithm across varying atmospheric conditions and temporal scales. Full article

When a CMOS (Complementary Metal–Oxide–Semiconductor) imaging system operates at a high frame rate or a high line rate, the exposure time of the imaging system is limited, and the acquired image data will be dark, with a low signal-to-noise ratio and unsatisfactory sharpness. Therefore, in order to improve the visibility and signal-to-noise ratio of remote sensing images based on CMOS imaging systems, this paper proposes a low-light remote sensing image enhancement method and a corresponding ZYNQ (Zynq-7000 All Programmable SoC) design scheme called the BGIR (Bilateral-Guided Image Restoration) algorithm, which uses an improved multi-scale Retinex algorithm in the HSV (hue–saturation–value) color space. First, the RGB image is used to separate the original image’s H, S, and V components. Then, the V component is processed using the improved algorithm based on bilateral filtering. The image is then adjusted using the gamma correction algorithm to make preliminary adjustments to the brightness and contrast of the whole image, and the S component is processed using segmented linear enhancement to obtain the base layer. The algorithm is also deployed to ZYNQ using ARM + FPGA software synergy, reasonably allocating each algorithm module and accelerating the algorithm by using a lookup table and constructing a pipeline. The experimental results show that the proposed method improves processing speed by nearly 30 times while maintaining the recovery effect, which has the advantages of fast processing speed, miniaturization, embeddability, and portability. Following the end-to-end deployment, the processing speeds for resolutions of 640 × 480 and 1280 × 720 are shown to reach 80 fps and 30 fps, respectively, thereby satisfying the performance requirements of the imaging system. Full article

This study presents an automatic PID control strategy for Tapping-Mode Atomic Force Microscopy (TM-AFM) that addresses the impacts of energy dissipation on tip–sample interactions. Our methodology integrates energy analysis to quantify the critical relationship between energy loss and phase lag dynamics in tapping mode. Additionally, systematic decomposition of interaction force is performed to enable the reconstruction of system transfer functions. The study in this work examines the fluctuations of PID gains during critical oscillations. A SIMULINK-based virtual TM-AFM is developed to simulate practical measurement conditions, based on which a lookup table for PID gains across various phase lags is generated. The efficacy of the proposed algorithm is experimentally validated through the experiments of a calibration nanogrid and two kinds of coated silicon samples, demonstrating the improved tracking accuracy and the improvement of surface height of 5.4% compared to regular control scheme. Full article

Background: The N-methyl-d-aspartate receptor (NMDA-R) is a glutamate ionotropic receptor in the brain that is crucial for synaptic plasticity, which underlies learning and memory formation. Dysfunction of NMDA receptors is implicated in various neurological diseases due to their roles in both normal cognition and excitotoxicity. However, their dynamics are challenging to capture accurately due to their high complexity and non-linear behavior. Methods: This article presents the elaboration and calibration of experimentally constrained computational models of GluN1/GluN2A NMDA-R dynamics: (1) a nine-state kinetic model optimized to replicate experimental data and (2) a computationally efficient look-up table model capable of replicating the dynamics of the nine-state kinetic model with a highly reduced footprint. Determination of the kinetic model’s parameter values was performed using the particle swarm optimization algorithm. The optimized kinetic model was then used to generate a rich input–output dataset to train the look-up table synapse model and estimate its coefficients. Results: Optimization produced a kinetic model capable of accurately reproducing experimentally found results such as frequency-dependent potentiation and the temporal response due to synaptic release of glutamate. Furthermore, the look-up table synapse model was able to closely mimic the dynamics of the optimized kinetic model. Conclusions: The results obtained with both models indicate that they constitute accurate alternatives for faithfully reproducing the dynamics of NMDA-Rs. High computational efficiency is also achieved with the use of the look-up table synapse model, making this implementation an ideal option for inclusion in large-scale neuronal models. Full article

We propose a probabilistic shaping (PS) scheme based on a single-layer lookup table (LUT) that employs only one LUT for symbol mapping while achieving favorable system performance. This scheme reduces the average power of the signal by adjusting the symbol distribution using a specialized LUT architecture and a flexible shaping proportion. The simulation results indicate that the proposed PS scheme delivers performance comparable to that of the conventional constant-composition distribution-matching-based probabilistic shaping (CCDM-PS) algorithm. Specifically, it reduces the bit error rate (BER) from 1.2376 $\times {10}^{-4}$ to 6.3256 $\times {10}^{-5}$, corresponding to a 48.89% improvement. The radial basis function neural network (RBFNN) effectively compensates for nonlinear distortions and further enhances transmission performance due to its simple architecture and strong capacity for nonlinear learning. In this work, we combine lookup-table-based probabilistic shaping (LUT-PS) with RBFNN-based nonlinear equalization for the first time, completing the transmission of 16-QAM OFDM signals over a photonic terahertz-over-fiber system operating at 400 GHz. Simulation results show that the proposed approach reduces the BER by 81.45% and achieves a maximum Q-factor improvement of up to 23 dB. Full article

Wake steering has emerged as a promising strategy to mitigate turbine wake losses, with existing research largely focusing on the aerodynamic optimization of yaw angles. However, many prior approaches rely on static look-up tables (LUTs), offering limited adaptability to real-world wind variability and leading to non-optimal results. More importantly, these energy-focused strategies overlook the mechanical implications of frequent yaw activities in pursuit of the maximum power output, which may lead to premature exhaustion of the yaw system’s design life, thereby accelerating structural degradation. This study proposes a supervisory control framework that balances energy capture with structural reliability through three key innovations: (1) upstream-based inflow sensing for real-time capture of free-stream wind, (2) fatigue-responsive optimization constrained by a dynamic actuation quota system with adaptive yaw activation, and (3) a bidirectional threshold adjustment mechanism that redistributes unused actuation allowances and compensates for transient quota overruns. A case study at an offshore wind farm shows that the framework improves energy yield by 3.94%, which is only 0.29% below conventional optimization, while reducing yaw duration and activation frequency by 48.5% and 74.6%, respectively. These findings demonstrate the framework’s potential as a fatigue-aware control paradigm that balances energy efficiency with system longevity. Full article

Neural networks are increasingly attractive for digital predistortion applications due to their demonstrated superior performance. This is mainly attributed to their ability to capture the intrinsic traits of nonlinear systems. This paper presents a novel hybrid predistorter labeled as the look-up table assisted bidirectional long-short term memory (BiLSTM) neural network (LUT-A-BiNN) that combines a neural network cascaded with a look-up table in a manner that both sub-models complement each other. The main motivation in using this two-box arrangement is to eliminate the highly nonlinear static distortions of the PA with the look-up table, allowing the neural network to focus on the compensation of the dynamic distortions. The proposed predistorter is experimentally validated using 5G test signals. The results demonstrate the ability of the proposed predistorter to achieve a 5 dB enhancement in the adjacent channel leakage ratio when compared to its single-box counterpart (BiLSTM neural network predistorter) while maintaining the signal-agnostic performance of the BiLSTM predistorter. Full article

New Zealand’s Emissions Trading Scheme (ETS) enables growers to earn payments by accumulating carbon units as their forests increase in carbon stock. For forests of less than 100 hectares, growers use predefined lookup tables (LUTs) to estimate carbon stock changes based on forest age. Using a combination of growth models and productivity surfaces, underpinned by data from 1360 growth plots, the objective of this study was to provide draft updates for the Exotic Hardwoods LUTs. The updated LUTs were based on growth rates of three Eucalyptus species, E. fastigata, E. regnans, and E. nitens, which comprise a major proportion of the Exotic Hardwoods forest type in New Zealand. Carbon tables were first derived for each species. Then, a draft LUT was generated for New Zealand’s North Island, using a weighted average of the species-specific tables based on the relative importance of the species, while the E. nitens table was used for the South Island where this is the predominant Eucalyptus species. Carbon stock predictions at ages 30 and 50 years were 820 and 1340 tonnes CO₂ ha⁻¹ for the North Island, and slightly higher at 958 and 1609 tonnes CO₂ ha⁻¹ for the South Island. Regional variation was significant, with the highest predicted carbon in Southland (1691 tonnes CO₂ ha⁻¹ at age 50) and lowest in Hawke’s Bay/Southern North Island (1292 tonnes CO₂ ha⁻¹). Predictions closely matched the current Exotic Hardwood LUT to age 20 years but exceeded it by up to 45% at age 35. Growth and carbon sequestration rates were similar to other established Eucalyptus species and slightly higher than Acacia species, though further research is recommended. These findings suggest that the three Eucalyptus species studied here could serve as the default species for a revised Exotic Hardwoods LUT and that the current national tables could be regionalised. However, the government may consider factors other than the technical considerations outlined here when updating the LUTs. Full article

Fixed automated (unmanned) above-water radiometric measurements are subject to unavoidable sky conditions and surface perturbations, leading to significant uncertainties in retrieved water surface remote sensing reflectances (R_rs(λ), sr⁻¹). This study evaluates various above-water R_rs(λ) glint correction methods using a comprehensive dataset collected at the Royal Netherlands Institute for Sea Research (NIOZ) Jetty Station located in the Marsdiep tidal inlet of the Dutch Wadden Sea, the Netherlands. The dataset includes in-situ water constituent concentrations (2006–2020), inherent optical properties (IOPs) (2006–2007), and above-water hyperspectral (ir)radiance observations collected every 10 min (2006–2023). The bio-optical models were validated using in-situ IOPs and utilized to generate glint-free remote sensing reflectances, R_rs,ref(λ), using a robust IOP-to-R_rs forward model. The R_rs,ref(λ) spectra were used as a benchmark to assess the accuracy of glint correction methods under various environmental conditions, including different sun positions, wind speeds, cloudiness, and aerosol loads. The results indicate that the three-component reflectance model (3C) outperforms other methods across all conditions, producing the highest percentage of high-quality R_rs(λ) spectra with minimal errors. Methods relying on fixed or lookup-table-based glint correction factors exhibited significant errors under overcast skies, high wind speeds, and varying aerosol optical thickness. The study highlights the critical importance of surface-reflected skylight corrections and wavelength-dependent glint estimations for accurate above-water R_rs(λ) retrievals. Two showcases on chlorophyll-a and total suspended matter retrieval further demonstrate the superiority of the 3C model in minimizing uncertainties. The findings highlight the importance of adaptable correction models that account for environmental variability to ensure accurate R_rs(λ) retrieval and reliable long-term water quality monitoring from hyperspectral radiometric measurements. Full article

Convolutional neural networks (CNNs) are widely used in geotechnical engineering. Real-time detection in complex geological environments, combined with the strict power constraints of embedded devices, makes Field-Programmable Gate Array (FPGA) platforms ideal for accelerating CNNs. Conventional parallelization strategies in FPGA-based accelerators often result in imbalanced resource utilization and computational inefficiency due to varying kernel sizes. To address this issue, we propose a customized heterogeneous hybrid parallel strategy and refine the bit-splitting approach for Digital Signal Processor (DSP) resources, improving timing performance and reducing Look-Up Table (LUT) consumption. Using this strategy, we deploy the lightweight YOLOv5n network on an FPGA platform, creating a high-speed, low-power subsurface geotechnical defect-detection system. A layer-wise quantization strategy reduces the model size with negligible mean average precision (mAP) loss. Operating at 300 MHz, the system reduces LUT usage by 33%, achieves a peak throughput of 328.25 GOPs in convolutional layers, and an overall throughput of 157.04 GOPs, with a power consumption of 9.4 W and energy efficiency of 16.7 GOPs/W. This implementation demonstrates more balanced performance improvements than existing solutions. Full article

For tropical forests characterized by tall and densely packed trees, even long-wavelength SAR signals may fail to achieve full penetration, posing a significant challenge for retrieving sub-canopy terrain using polarimetric interferometric SAR (InSAR)(PolInSAR) techniques. This paper proposes a single-baseline PolInSAR-based correction method for sub-canopy terrain estimation based on a one-dimensional lookup table (LUT) that links forest height to unpenetrated depth. The approach begins by applying an optimal normal matrix approximation to constrain the complex coherence measurements. Subsequently, the difference between the PolInSAR Digital Terrain Model (DTM) derived from the Random Volume over Ground (RVoG) model and the LiDAR DTM is defined as the unpenetrated depth. A nonlinear iterative optimization algorithm is then employed to estimate forest height, from which a fundamental mapping between forest height and unpenetrated depth is established. This mapping can be used to correct the bias in sub-canopy terrain estimation based on the PolInSAR RVoG model, even with only a small amount of sparse LiDAR DTM data. To validate the effectiveness of the method, experiments were conducted using fully polarimetric P-band airborne SAR data acquired by the European Space Agency (ESA) during the AfriSAR campaign over the Mabounie region in Gabon, Africa, in 2016. The experimental results demonstrate that the proposed method effectively mitigates terrain estimation errors caused by insufficient signal penetration or the limitation of single-interferometric geometry. Further analysis reveals that the availability of sufficient and precise forest height data significantly improves sub-canopy terrain accuracy. Compared with LiDAR-derived DTM, the proposed method achieves an average root mean square error (RMSE) of 5.90 m, representing an accuracy improvement of approximately 38.3% over traditional RVoG-derived InSAR DTM retrieval. These findings further confirm that there exist unpenetrated phenomena in single-baseline low-frequency PolInSAR-derived DTMs of tropical forested areas. Nevertheless, when sparse LiDAR topographic data is available, the integration of fully PolInSAR data with LUT-based compensation enables improved sub-canopy terrain retrieval. This provides a promising technical pathway with single-baseline configuration for spaceborne missions, such as ESA’s BIOMASS mission, to estimate sub-canopy terrain in tropical-rainforest regions. Full article

In this work, we develop a method for rational approximation of the Fourier transform (FT) based on the real and imaginary parts of the complex error function $w\left(z\right)=e^{-z^2}(1-\mathrm{erf}(-iz))=K(x,y)+iL(x,y)$, $z=x+iy$, where $K(x,y)$ and $L(x,y)$ are known as the Voigt and imaginary Voigt functions, respectively. In contrast to our previous rational approximation of the FT, the expansion coefficients in this method are not dependent on the values of a sampled function. As the values of the Voigt functions remain the same, this approach can be used for rapid computation with help of look-up tables. Mathematica codes with some examples are presented. Full article

Dual-fuel engines are a way of transitioning the marine sector to carbon-neutral fuels like hydrogen and methanol. For the development of these engines, accurate simulation of the combustion process is needed, for which calculating the pilot’s ignition delay is essential. The present work investigates novel methodologies for calculating this. This involves the use of chemical kinetic schemes to compute the ignition delay for various operating conditions. Machine learning techniques are used to train models on these data sets. A neural network model is then implemented in a dual-fuel combustion model to calculate the ignition delay time and is compared using a lookup table or a correlation. The numerical results are compared with experimental data from a dual-fuel medium-speed marine engine operating with hydrogen or methanol, from which the method with best accuracy and fastest calculation is selected. Full article

Kolmogorov GAM (K-GAM) networks have been shown to be an efficient architecture for both training and inference. They are additive models with embeddings that are independent of the target function of interest. They provide an alternative to Transformer architectures. They are the machine learning version of Kolmogorov’s superposition theorem (KST), which provides an efficient representation of multivariate functions. Such representations are useful in machine learning for encoding dictionaries (a.k.a. “look-up” tables). KST theory also provides a representation based on translates of the Köppen function. The goal of our paper is to interpret this representation in a machine learning context for applications in artificial intelligence (AI). Our architecture is equivalent to a topological embedding, which is independent of the function, together with an additive layer that uses a generalized additive model (GAM). This provides a class of learning procedures with far fewer parameters than current deep learning algorithms. Implementation can be parallelizable, which makes our algorithms computationally attractive. To illustrate our methodology, we use the iris data from statistical learning. We also show that our additive model with non-linear embedding provides an alternative to Transformer architectures, which, from a statistical viewpoint, are kernel smoothers. Additive KAN models, therefore, provide a natural alternative to Transformers. Finally, we conclude with directions for future research. Full article

Permanent magnet flux harmonics in Permanent Magnet Synchronous Motors (PMSMs) can cause torque ripple. Traditional torque ripple suppression methods based on analytical models are highly dependent on the accuracy of motor parameters, while existing flux harmonic identification techniques often suffer from limited precision, compromising the effectiveness of ripple suppression. This paper proposes an online flux harmonic identification method that considers the dead-time effect of inverters. A dead-time compensation algorithm is introduced to effectively mitigate current harmonics induced by inverter dead-time. The current harmonic signals are extracted using a multi-synchronous rotating coordinate system. A harmonic controller is employed to suppress current harmonics, and its output voltage is used to identify the permanent magnet flux harmonics, from which a flux harmonic lookup table is constructed. Based on the identified flux harmonics, the torque ripple suppression strategy using analytical methods is further optimized. Experimental results validate the effectiveness of the proposed method in improving flux harmonic identification accuracy and reducing torque ripple. Full article