Search Results (135)

This study examines the hydrological response of the Klambu Dam Catchment in Central Java, Indonesia, to climatic and land cover changes from 2000–2023, with simulations extending to 2040. Utilizing CHIRPS satellite data calibrated with six ground stations, monthly precipitation and temperature datasets were analyzed and projected via linear regression aligned with IPCC scenarios, revealing a marginal temperature decline of 0.21 °C (from 28.25 °C in 2005 to 28.04 °C in 2023) and a 17% increase in rainfall variability. Land cover assessments from Landsat imagery highlighted drastic changes: a 73.8% reduction in forest area and a 467.8% increase in mixed farming areas, alongside moderate fluctuations in paddy fields and settlements. The Thornthwaite-Mather water balance method simulated monthly discharge, validated against observed data with Pearson correlations ranging from 0.5729 (2020) to 0.9439 (2015). Future projections using Cellular Automata-Markov modeling indicated stable volumetric flow but a temporal shift, including a 28.1% decrease in April rainfall from 2000 to 2040, contracting the wet season and extending dry spells. These shifts pose significant threats to agricultural and aquaculture activities, potentially exacerbating water scarcity and economic losses. The findings emphasize integrating dynamic land cover data, climate projections, and empirical runoff corrections for climate-resilient watershed management. Full article

To obtain radar images of a group of small space objects or to resolve individual elements of complex space objects in near-Earth orbit, a radar system must have high spatial resolution. High range resolution is achieved by using complex probing signals with a wide spectrum bandwidth. Achieving high angular resolution for small or complex space objects is based on the inverse synthetic aperture antenna effect. Among the various classes of complex signals, only two have found practical application in Inverse Synthetic Aperture Radar (ISAR) systems so far: the Linear Frequency-Modulated signal (chirp) and the Stepped-Frequency signal. Over the coherent integration interval of the echo signals, which corresponds to the ISAR aperture synthesis time, the combined correlation characteristics of the signal ensemble are analyzed. A high level of integral correlation noise in the ensemble of probing signals degrades the quality of the radar image. Therefore, a probing signal with a Zero Autocorrelation Zone (ZACZ) is highly relevant for ISAR applications. In this work, through simulation, radar images of a complex space object were obtained using both chirp and ZACZ probing signals. A comparative analysis of the correlation characteristics of the echo signals and the resulting radar images of the complex space object was performed. Full article

Although the elevated orbit and highly squinted observation geometry bring advantages for medium-earth-orbit (MEO) synthetic aperture radar (SAR) in applications, they also complicate signal processing. The severe spatial variability of Doppler parameters and large extended range distribution of echo make it challenging for the traditional imaging algorithms to get the expected results. To quantify the variation, a spatially variable two-dimensional (SV2D) spectrum is established in this paper. The sufficient order and spatially variable terms allow it to preserve the features of targets both in the scene center and at the edge. In addition, the huge data volume and incomplete azimuth signals of edge targets, caused by the large range walk when MEO SAR operates in squinted mode, are alleviated by the variable pulse repetition interval (VPRI) technique. Based on this, a novel data-focusing method for highly squinted MEO SAR is proposed. The azimuth resampling, achieved through the non-uniform fast Fourier transform (NUFFT), eliminates the impact of most Doppler parameter space variation. Then, a novel imaging kernel is applied to accomplish target focusing. The spatially variable range cell migration (RCM) is corrected by a similar idea, with Doppler parameter equalization, and an accurate high-order phase filter derived from the SV2D spectrum guarantees that the targets located in the center range gate and the center Doppler time are well focused. For other targets, inspired by the non-linear chirp scaling algorithm (NCSA), the residual spatially variable mismatch is eliminated by a cubic phase filter during the scaling process to achieve sufficient focusing depth. The simulation results are given at the end of this paper and these validate the effectiveness of the method. Full article

Reliable spatio-temporal rainfall prediction is a key element in disaster mitigation and water resource management in dynamic tropical regions such as the Bengawan Solo River Watershed. However, high climate variability and data limitations often pose significant challenges to the accuracy of conventional prediction models. This study introduces an innovative approach by applying ensemble stacking, which combines machine learning models such as Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Light Gradient-Boosting Machine (LGBM) and deep learning models like Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Temporal Convolutional Networks (TCN), Convolutional Neural Network (CNN), and Transformer architecture based on monthly Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data (1981–2024). The novelty of this research lies in the systematic exploration of various model combination scenarios—both classical and deep learning and the evaluation of their performance in projecting rainfall for 2025–2030. All base models were trained on the 1981–2019 period and validated with data from the 2020–2024 period, while ensemble stacking was developed using a linear regression meta-learner. The results show that the optimal ensemble scenario reduces the MAE to 53.735 mm, the RMSE to 69.242 mm, and increases the R² to 0.795826—better than all individual models. Spatial and temporal analyses also indicate consistent model performance at most locations and times. Annual rainfall projections for 2025–2030 were then interpolated using IDW to generate a spatio-temporal rainfall distribution map. The improved accuracy provides a strong scientific basis for disaster preparedness, flood and drought management, and sustainable water planning in the Bengawan Solo River Watershed. Beyond this case, the approach demonstrates significant transferability to other climate-sensitive and data-scarce regions. Full article

The direction-of-arrival (DOA) estimation of multiple linear frequency modulation (LFM) signals typically requires the construction of a spatial time–frequency distribution (STFD) matrix via linear transforms or quadratic time–frequency distributions (QTFD) before joint spatial time–frequency estimation. Extensive research has been conducted on DOA estimation of LFM signals with overlapped instantaneous frequency (IF) trajectories and significantly different chirp rates. However, when LFM signals have the same chirp rate and slightly different initial frequencies with parallel and close IF trajectories, their linear transforms suffer from low resolution and quadratic distributions and are affected by cross-terms, both of which reduce accuracy. To address this problem, this study proposes a DOA estimation algorithm based on QTFD and the Hough transform. First, QTFD is used to improve the resolution and apply both spatial and directional smoothing to eliminate cross-terms. Second, the Hough transform is employed for IF estimation instead of threshold filtering to enhance accuracy. Finally, DOA results are obtained via time–frequency filtering and the multiple signal classification (MUSIC) algorithm. Experiments show that for two LFM signals at a −5 dB signal-to-noise ratio (SNR), the proposed algorithm improves accuracy by approximately 43.2% compared to similar algorithms and effectively estimates the DOA in underdetermined cases. Thus, the proposed algorithm enhances the DOA estimation accuracy for multiple LFM signals, is robust to noise, and expands the application scenarios of joint spatial time–frequency estimation. Full article

Heavy rainfall events, characterized by extreme downpours that exceed 100 mm per day, pose an intensifying hazard to the densely settled valleys of the western Himalaya; however, their coupling with expanding urban land cover remains under-quantified. This study mapped the spatiotemporal exposure of built-up areas to heavy-day rainfall (HDR) across Jammu, Kashmir, and Ladakh and the adjoining areas by integrating daily Climate Hazards Group InfraRed Precipitation with Stations product (CHIRPS) precipitation (0.05°) with Global Human Settlement Layer (GHSL) built-up fractions within the Google Earth Engine (GEE). Given the limited sub-hourly observations, a daily threshold of ≥100 mm was adopted as a proxy for HDR, with sensitivity evaluated at alternative thresholds. The results showed that HDR is strongly clustered along the Kashmir Valley and the Pir Panjal flank, as demonstrated by the mean annual count of threshold-exceeding pixels increasing from 12 yr⁻¹ (2000–2010) to 18 yr⁻¹ (2011–2020), with two pixel-scale hotspots recurring southwest of Srinagar and near Baramulla regions. The cumulative high-intensity areas covered 31,555.26 km², whereas 37,897.04 km² of adjacent terrain registered no HDR events. Within this hazard belt, the exposed built-up area increased from 45 km² in 2000 to 72 km² in 2020, totaling 828 km². The years with the most expansive rainfall footprints, 344 km² (2010), 520 km² (2012), and 650 km² (2014), coincided with heavy Western Disturbances (WDs) and locally vigorous convection, producing the largest exposure increments. We also performed a forecast using a univariate long short-term memory (LSTM), outperforming Autoregressive Integrated Moving Average (ARIMA) and linear baselines on a 2017–2020 holdout (Root Mean Square Error, RMSE 0.82 km²; measure of errors, MAE 0.65 km²; R² 0.89), projecting the annual built-up area intersecting HDR to increase from ~320 km² (2021) to ~420 km² (2030); 95% prediction intervals widened from ±6 to ±11 km² and remained above the historical median (~70 km²). In the absence of a long-term increase in total annual precipitation, the projected rise most likely reflects continued urban encroachment into recurrent high-intensity zones. The resulting spatial masks and exposure trajectories provide operational evidence to guide zoning, drainage design, and early warning protocols in the region. Full article

The objective of the present study is to detect chirped optical solitons of the perturbed Chen–Lee–Liu equation with full nonlinearity. By virtue of the traveling wave hypothesis, the discussed model is reduced to a simple form known as an elliptic equation. The latter equation, which is a second-order ordinary differential equation, is handled by the undetermined coefficient method of two forms expressed in terms of the hyperbolic secant and tangent functions. Additionally, the auxiliary equation method is applied to derive several miscellaneous solutions. Various types of chirped solitons are revealed such as W-shaped, bright, dark, gray, kink and anti-kink waves. Taking into consideration the existence conditions, the dynamical behaviors of optical solitons and their corresponding chirp are illustrated. The modulation instability of the perturbed CLL equation is examined by means of the linear stability analysis. It is found that all solutions are stable against small perturbations. These entirely new results, compared to previous works, can be employed to understand pulse propagation in optical fiber mediums and dynamic characteristics of waves in plasma. Full article

Light detection and ranging (LiDAR) sensors are essential for applications where high-resolution distance and velocity measurements are required. In particular, frequency-modulated continuous wave (FMCW) LiDAR, compared with other LiDAR implementations, provides superior receiver sensitivity, enhanced range resolution, and the capability to measure velocity. Integrating LiDARs into electronic and photonic semiconductor chips can lower their cost, size, and power consumption, making them affordable for cost-sensitive applications. Additionally, simple designs are required, such as FMCW signal generation by the direct modulation of the current of a semiconductor laser. However, semiconductor lasers are inherently nonlinear, and the driving waveform needs to be optimized to generate linear FMCW signals. In this paper, we employ pre-distortion techniques to compensate for chirp nonlinearity, achieving frequency nonlinearities of 0.0029% for the down-ramp and the up-ramp at 55 kHz. Experimental results demonstrate a highly accurate LiDAR system with a resolution of under 5 cm, operating over a 210-m range through single-mode fiber, which corresponds to approximately 308 m in free space, towards meeting the requirements for long-range autonomous driving. Full article

Accurate rainfall data with good spatial–temporal distribution remain a challenge worldwide, particularly in arid regions such as western Saudi Arabia, where variability critically influences water resource management and flood mitigation. This study evaluates five satellite-based rainfall products—GPM, GPCP, CHIRPS, PERSIANN-CDR and PERSIANN—against observed monthly rainfall at 28-gauge stations, using the correlation coefficient (CC), root mean square error (RMSE), relative bias (RB) and mean absolute error (MAE). Among uncorrected products, GPM achieved the highest mean CC (0.52), and lowest RMSE (17.0 mm) and MAE (9.18 mm) compared with CC = 0.39 (RMSE 19.9 mm) for GPCP, CC = 0.20 (RMSE 21.6 mm) for CHIRPS, CC = 0.43 (RMSE 19.2 mm) for PERSIANN-CDR and CC = 0.26 (RMSE 57.3 mm) for PERSIANN. Four bias correction methods—linear scaling, nonlinear adjustment, quantile mapping and artificial neural networks (ANN)—were applied. The ANN reduced GPM’s RMSE by 19% to 13.8 mm, increased CC to 0.59, lowered RB to 2.5% and achieved an MAE of 6.89 mm. These results demonstrate that GPM, particularly when bias-corrected via ANN, provides a dependable rainfall dataset for hydrological modeling and flood risk assessment in arid environments. Full article

A linear frequency modulation (LFM) signal and its corresponding de-chirp operation are one of the basic methods for wideband radar signal processing, which can reduce the burden of the radar system sampling rate and is more suitable for large-bandwidth signal processing. More importantly, most existing methods against interrupted sampling repeater jamming (ISRJ) are based on time–frequency (TF) or frequency domain analysis of the de-chirped signal. However, the above anti-ISRJ methods cannot be directly applied to multiple-input multiple-output (MIMO) radar with multiple beams, because the angular waveform (AW) in mainlobe directions does not possess the TF properties of the LFM signal. Consequently, this work focuses on the co-optimization of transmit beampattern and AW similarity in wideband MIMO radar systems. Different from the existing works, which only concern the space–frequency pattern of the transmit waveform, we recast the transmit beampattern and AW expressions for wideband MIMO radar in a more compact form. Based on the compact expressions, a co-optimization model of the transmit beampattern and AWs is formulated where the similarity constraint is added to force the AW to share the TF properties of the LFM signal. An algorithm based on the alternating direction method of multipliers (ADMM) framework is proposed to address the aforementioned problem. Numerical simulations show that the optimized waveform can form the desired transmit beampattern and its AWs have similar TF properties and de-chirp results to the LFM signal. Full article

In this paper, we obtain uncertainty principles associated with the linear canonical transform (LCT) of hypercomplex functions. First, we derive the uncertainty principle for hypercomplex functions in the time and LCT domains. Moreover, we exploit the uncertainty principle in two LCT domains. The lower bounds are related to the LCT parameters and the covariance, and the uncertainty principle presented herein is sharper than what has been presented in the existing literature.These tighter bounds can be obtained using hypercomplex chirp functions for a Gaussian envelope. Finally, we verify the validity of the uncertainty principles through some examples and discuss several potential applications of the new results in signal processing. Full article

A novel photonics-assisted technique for generating reconfigurable frequency hopping (FH) signals is proposed and demonstrated through optical comb filtering (OCF). The arithmetic progression of frequency difference between OCF passbands and optical frequency comb lines is exploited to enable wavelength selection controlled by an intermediate frequency signal, with ultra-wideband FH signals subsequently being generated through optical heterodyning. Comprehensive theoretical and numerical investigations are conducted, demonstrating the successful generation of diverse FH waveforms including 5-, 10-, and 25-level stepped frequency signals, Costas-coded patterns, as well as complex wideband signals such as 30 GHz linear frequency modulated and 24 GHz sinusoidal chirped waveforms. Critical system considerations including laser frequency stability, FH speed, and parameter optimization are examined. Wide tunable bandwidth exceeding 30 GHz, good stability, and inherent compatibility with photonic integration is achieved, showing significant potential for advanced applications in cognitive radio and modern radar systems where high-performance frequency-agile signal generation is required. Full article

This research addresses the challenge of bias in Remotely Sensed Rainfall (RSR) datasets used for hydrological planning in Uganda’s data-scarce, ungauged catchments. Four bias correction methods, Quantile Mapping (QM), Linear Transformation (LT), Delta Multiplicative (DM), and Polynomial Regression (PR), were evaluated using daily rainfall data from four gauged stations (Gulu, Soroti, Jinja, Mbarara). QM consistently outperformed other methods based on statistical metrics, e.g., for National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA_CPC) RSR data at Gulu, Root-Mean-Square Error (RMSE) was reduced from 29.20 mm to 19.00 mm, Mean Absolute Error (MAE) reduced from 22.44 mm to 12.84 mm, and Percent Bias (PBIAS) reduced from −19.23% to 1.05%, and improved performance goodness-of-fit tests (KS = 0.03, p = 1.00), while PR, though statistically strong, failed due to overfitting. A bias correction framework was developed for ungauged catchments, using predetermined bias factors derived from observed station data. Validation at Arua (tropical savannah) and Fort Portal (tropical monsoon) demonstrated significant improvements in RSR data when the bias correction framework was applied. At Arua, bias correction of Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data reduced RMSE from 49.14 mm to 21.41 mm, MAE from 45.74 mm to 17.38 mm, and PBIAS from −59.83% to −8.18%, while at Fort Portal, bias correction of the CHIRPS dataset reduced RMSE from 28.35 mm to 15.02 mm, MAE from 25.28 mm to 11.35 mm, and PBIAS from −46.2% to 4.74%. Our research concludes that QM is the most effective method, and that the framework is a tool for improving RSR data in ungauged catchments. Recommendations for future work includes machine learning integration and broader regional validation. Full article

In this paper, we present a general methodology suitable for analyzing any IBG (Integrated Bragg Grating) as a linear time-invariant (LTI) system using the effective refractive index (ERI) and transfer matrix method (TMM). This approach is based on the translation of the IBG’s physical structure into a matrix of effective refractive indexes, n_eff, which is wavelength-dependent and describes the behavior of light in the IBG while avoiding the use of approximations like Coupled Mode Theory does. This procedure allows to obtain very accurate reflection and transmission spectra, regardless of the perturbation complexity of the grating. Using this methodology, different apodization and chirp methods are revised and compared. Its generality is considered by analyzing two distinct technological platforms, silicon-on-insulator (SOI) and aluminum oxide (Al₂O₃). Full article

To achieve precise detection of target geometric features, Ka/W/sub-terahertz band imaging radar systems with ultra-wide instantaneous bandwidth have been developed. Although dechirp-based receiver architectures allow for low-sampling-rate signal acquisition, they require precise linearity in chirp signals, often necessitating precompensation for nonlinear errors. While most research addresses polynomial-based error correction, periodic errors remain underexplored, despite their potential to obscure weak targets and introduce spurious ones. This paper proposes a novel software-based correction method that integrates neural networks and joint optimization strategies to correct periodic phase errors. The method first employs neural networks for frequency estimation, followed by phase-matching techniques to extract amplitude and phase data. Parameter estimation is refined using the Adaptive Moment Estimation (ADAM) algorithm and Limited-Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS) optimization. Nonlinear errors are corrected via matched Fourier transforms. Simulations and experiments demonstrate that the proposed method effectively suppresses spurious targets and enhances the detection of weak targets, demonstrating strong robustness and practical applicability, thereby significantly enhancing the target detection performance of the ultra-wideband radar system. Full article