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The significant number of road traffic accidents caused by fatigued drivers presents substantial risks to the public’s overall safety. In recent years, there has been a notable convergence of intelligent cameras and artificial intelligence (AI), leading to significant advancements in identifying driver drowsiness. Advances in computer vision technology allow for the identification of driver drowsiness by monitoring facial expressions such as yawning, eye movements, and head movements. These physical indications, together with assessments of the driver’s physiological condition and behavior, aid in assessing fatigue and lowering the likelihood of drowsy driving-related incidents. This study presents an extensive variety of meticulously designed algorithms that were thoroughly analyzed to assess their effectiveness in detecting drowsiness. At the core of this attempt lay the essential concept of feature extraction, an efficient technique for isolating facial and ocular regions from a particular set of input images. Following this, various deep learning models, such as a traditional CNN, VGG16, and MobileNet, facilitated detecting drowsiness. Among these approaches, the MobileNet model was a valuable choice for drowsiness detection in drivers due to its real-time processing capability and suitability for deployment in resource-constrained environments, with the highest achieved accuracy of 92.75%.

Given the complex spatiotemporal variability of aerosols, high-frequency satellite observations are essential for accurately mapping their distribution. However, optical remote sensing encounters difficulties in detecting Aerosol Optical Depth (AOD) over cloud-covered regions, creating data gaps that limit comprehensive environmental analysis. This study introduces a spatial gap-filling method for Himawari-8/Advanced Himawari Imager (AHI) hourly AOD data, using a Random Forest (RF) model that integrates meteorological variables and model-based AOD data. Developed and validated over South Korea from 1 January to 31 December 2019, the model effectively improved data coverage from 6% to 100%. The approach demonstrated high performance in blind tests, achieving a root mean square error (RMSE) of 0.064 and a correlation coefficient (CC) of 0.966. Meteorological analysis indicated optimal model performance under cold, dry conditions (RMSE: 0.047, CC: 0.956), compared to humid conditions (RMSE: 0.105, CC: 0.921). Validation against Aerosol Robotic Network (AERONET) ground observations showed that, while the original Himawari-8 data exhibited higher accuracy (RMSE: 0.189, CC: 0.815, n = 346), the gap-filled dataset maintained reasonable precision (RMSE: 0.208, CC: 0.711) and significantly increased the number of valid data points (n = 4149). Furthermore, the gap-filled dataset successfully captured seasonal AOD patterns, with values ranging from 0.245–0.300 in winter to 0.381–0.391 in summer, providing a comprehensive view of aerosol dynamics across South Korea.

The contamination of marine ecosystems by oil spills poses a significant threat to the marine environment, necessitating the prompt and effective implementation of measures to mitigate the associated damage. Satellites offer a spatial and temporal advantage over aircraft and unmanned aerial vehicles (UAVs) in oil spill detection due to their wide-area monitoring capabilities. While oil spill detection has traditionally relied on synthetic aperture radar (SAR) images, the combined use of optical satellite sensors alongside SAR can significantly enhance monitoring capabilities, providing improved spatial and temporal coverage. The advent of deep learning methodologies, particularly convolutional neural networks (CNNs) and Transformer models, has generated considerable interest in their potential for oil spill detection. In this study, we conducted a comprehensive and objective comparison to evaluate the suitability of CNN and Transformer models for marine oil spill detection. High-resolution optical satellite images were used to optimize DeepLabV3+, a widely utilized CNN model; Swin-UPerNet, a representative Transformer model; and Mask2Former, which employs a Transformer-based architecture for both encoding and decoding. The results of cross-validation demonstrate a mean Intersection over Union (mIoU) of 0.740, 0.840 and 0.804 for all the models, respectively, indicating their potential for detecting oil spills in the ocean. Additionally, we performed a histogram analysis on the predicted oil spill pixels, which allowed us to classify the types of oil. These findings highlight the considerable promise of the Swin Transformer models for oil spill detection in the context of future marine disaster monitoring.

This study provides a thorough examination of the important intersection of Wireless Sensor Networks (WSNs) with machine learning (ML) for improving security. WSNs play critical roles in a wide range of applications, but their inherent constraints create unique security challenges. To address these problems, numerous ML algorithms have been used to improve WSN security, with a special emphasis on their advantages and disadvantages. Notable difficulties include localisation, coverage, anomaly detection, congestion control, and Quality of Service (QoS), emphasising the need for innovation. This study provides insights into the beneficial potential of ML in bolstering WSN security through a comprehensive review of existing experiments. This study emphasises the need to use ML’s potential while expertly resolving subtle nuances to preserve the integrity and dependability of WSNs in the increasingly interconnected environment.

Wildfires pose significant environmental and societal threats, necessitating improved early detection methods. This study investigates the effectiveness of integrating real-time object detection deep learning models (YOLOv8 and RT-DETR) with advanced data augmentation techniques, including StyleGAN2-ADA, for wildfire smoke detection. We evaluated model performance on datasets enhanced with fundamental transformations and synthetic images, focusing on detection accuracy. YOLOv8X demonstrated superior overall performance with AP@0.33 of 0.962 and AP@0.5 of 0.900, while RT-DETR-X excelled in small object detection with a 0.983 detection rate. Data augmentation, particularly StyleGAN2-ADA, significantly enhanced model performance across various metrics. Our approach reduced average detection times to 1.52 min for YOLOv8X and 2.40 min for RT-DETR-X, outperforming previous methods. The models demonstrated robust performance under challenging conditions, like fog and camera noise, providing reassurance of their effectiveness. While false positives remain a challenge, these advancements contribute significantly to early wildfire smoke detection capabilities, potentially mitigating wildfire impacts through faster response times. This research establishes a foundation for more effective wildfire management strategies and underscores the potential of deep learning applications in environmental monitoring.

The increasing water temperature due to climate change has led to more frequent algae blooms and deteriorating water quality in coastal areas and rivers worldwide. To address this, we developed a deep learning-based model for identifying floating algae blooms using PlanetScope optical images and the Shifted Windows (Swin) Transformer architecture. We created 1,998 datasets from 105 scenes of PlanetScope imagery collected between 2018 and 2023, covering 14 water bodies known for frequent algae blooms. The methodology included data pre-processing, dataset generation, deep learning modeling, and inference result generation. The input images contained six bands, including vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI), enhancing the model’s responsiveness to algae blooms. Evaluations were conducted using both single-period and multi-period datasets. The single-period model achieved a mean Intersection over Union (mIoU) between 72.18% and 76.47%, while the multi-period model significantly improved performance, with an mIoU of 91.72%. This demonstrates the potential of our model and highlights the importance of change detection in multi-temporal images for algae bloom monitoring. Additionally, the padding technique proposed in this study resolved the border issue that arises when mosaicking inference results from individual patches, providing a seamless view of the satellite scene.

An adaptive Nesterov quasi-Newton acceleration (an-QNA)-optimized low-noise amplifier (LNA) is proposed in this paper. An optimized single-ended-to-differential two-stage LNA circuit is presented. It includes an improved post-linearization (IPL) technique to enhance the linearity. Traditional methods like conventional quasi-Newton (c-QN) often suffer from slow convergence and the tendency to get trapped in local minima. However, the proposed an-QNA method significantly accelerates the convergence speed. Furthermore, in this paper, modifications have been made to the an-QNA algorithm using a quadratic estimation to guarantee global convergence. The optimized an-QNA-based LNA, using standard 65 nm CMOS technology, achieves a simulated gain of 17.5 dB, a noise figure (NF) of 3.7 dB, and a 1 dB input compression point (IP1dB) of −13.1 dBm. It is also noted that the optimized LNA achieves a measured gain of 12.9 dB and an NF of 4.98 dB, and the IP1dB is −17.8 dB. The optimized LNA has a chip area of 0.67 mm2.

Abstract Past studies have focused on the importance of hydrological variables in analyzing landslide initiation condition. Even though precipitation is the main driver of shallow landslides and debris flows, use of only rainfall-based parameters has shown some limitations. Soil moisture has been used widely to improve threshold detection capabilities. Since soil moisture directly reflects the wetness status of the ground, it can be used to identify pore pressure fluctuations more effectively. This study used rainfall and soil moisture simultaneously to capture landslide initiation conditions in detail. Results showed that continued rainfall on the day of landslide leaded to a sudden increase in soil moisture, and that soil moisture increments (∆SM) were positive in 155 out of 170 landslide cases (91%). Two simple thresholds (daily precipitation over 40 mm, ∆SM over 0) and daily precipitation (P), Antecedent Precipitation Index (API), ∆SM-based three-dimensional threshold planes having 5%, 20% probability levels were applied and compared. With respect to false alarms (FA), P-based threshold was most effective among the single thresholds (FA ranging from 24 to 28 from September 2016 to December 2019 at five validation locations). Combining P- and ∆SM-based thresholds, FA reduced without compromising the detection accuracy (2 to 3 reduction in FA). Additionally combining three-dimensional threshold with 20% probability level, FA reduced significantly (ranging from 12 to 16), at the cost of two detection failures. These findings demonstrate the need for combining precipitation and soil moisture to determine landslide thresholds.

This study uses annular circular rings to create multi-band applications using crescent-shaped patch antennas. It is designed to be made up of five circular, annular rings nested inside of each other. Three annular rings are positioned and merged on top of the larger rings, with two annular rings set along the bottom of the feed line. The factors that set them apart, such as bandwidths, radiation patterns, gain, impedance, and return loss (RL), are analysed. The outcomes show how compact the multi-band annular ring antenna is. The proposed circular annular ring antenna has return losses of −33 dB and operates at two frequencies: 3.1 GHz and 9.3 GHz. This design is modelled and simulated using ANSYS HFSS. The outcomes of the simulation and the tests agree quite well. The X band and WLAN resonant bands have bandwidth capacities of 500 and 4300 MHz, respectively. Additionally, the circular annular ring antenna design is advantageous for most services at these operating bands.

Forest fires are caused by various climatic and anthropogenic factors. In Republic of Korea, forest fires occur frequently during spring when the humidity is low. During the past decade, the number of forest fire incidents and the extent of the damaged area have increased. Satellite imagery can be applied to assess damage from these unpredictable forest fires. Despite the increasing threat, there is a lack of comprehensive analysis and effective strategies for addressing these forest fires, particularly considering the diverse topography of Republic of Korea. Herein, we present an approach for the automated detection of forest fire damage using Sentinel-2 images of 14 areas affected by forest fires in Republic of Korea during 2019–2023. The detection performance of deep learning (DL), machine learning, and spectral index methods was analyzed, and the optimal model for detecting forest fire damage was derived. To evaluate the independent performance of the models, two different burned areas exhibiting distinct characteristics were selected as test subjects. To increase the classification accuracy, tests were conducted on various combinations of input channels in DL. The combination of false-color RNG (B4, B8, and B3) images was optimal for detecting forest fire damage. Consequently, among the DL models, the HRNet model achieved excellent results for both test regions with intersection over union scores of 89.40 and 82.49, confirming that the proposed method is applicable for detecting forest fires in diverse Korean landscapes. Thus, suitable mitigation measures can be promptly designed based on the rapid analysis of damaged areas.

A reformed particle swarm optimization (RPSO)-based up-conversion mixer circuit is proposed for radar application in this paper. In practice, a non-optimized up-conversion mixer suffers from high power consumption, poor linearity, and conversion gain. Therefore, the RPSO algorithm is proposed to optimize the up-conversion mixer. The novelty of the proposed RPSO algorithm is it helps to solve the problem of local optima and premature convergence in traditional particle swarm optimization (TPSO). Furthermore, in the RPSO, a velocity position-based convergence (VPC) and wavelet mutation (WM) strategy are used to enhance RPSO’s swarm diversity. Moreover, this work also features novel circuit configurations based on the two-fold transconductance path (TTP), a technique used to improve linearity. A differential common source (DCS) amplifier is included in the primary transconductance path (PTP) of the TTP. As for the subsidiary transconductance path (STP), the enhanced cross-quad transconductor (ECQT) is implemented within the TTP. A benchmark function verification is conducted to demonstrate the effectiveness of the RPSO algorithm. The proposed RPSO has also been compared with other optimization algorithms such as the genetic algorithm (GA) and the non-dominated sorting genetic algorithm II (NSGA-II). By using RPSO, the proposed optimized mixer achieves a conversion gain (CG) of 2.5 dB (measured). In this study, the proposed mixer achieves a 1 dB compression point (OP1dB) of 4.2 dBm with a high linearity. In the proposed mixer, the noise figure (NF) is approximately 3.1 dB. While the power dissipation of the optimized mixer is 3.24 mW. Additionally, the average time for RPSO to design an up-conversion mixer is 4.535 s. Simulation and measured results demonstrate the excellent performance of the RPSO optimized up-conversion mixer.

The proliferation of radar technology has given rise to a growing demand for advanced, high-performance transmitter front-ends operating in the 24 GHz frequency band. This paper presents a design analysis of a radio frequency (RF) transmitter (TX) front-end operated at a 24 GHz frequency and designed using 65 nm complementary metal-oxide-semiconductor (CMOS) technology for radar applications. The proposed TX front-end design includes the integration of an up-conversion mixer and power amplifier (PA). The up-conversion mixer is a Gilbert cell-based design that translates the 2.4 GHz intermediate frequency (IF) signal and 21.6 GHz local oscillator (LO) signal to the 24 GHz RF output signal. The mixer is designed with a novel technique that includes a duplex transconductance path (DTP) for enhancing the mixer’s linearity. The DTP of the mixer includes a primary transconductance path (PTP) and a secondary transconductance path (STP). The PTP incorporates a common source (CS) amplifier, while the STP incorporates an improved cross-quad transconductor (ICQT). The integrated PA in the TX front-end is a class AB tunable two-stage PA that can be tuned with the help of varactors as a synchronous mode to increase the PA bandwidth or stagger mode to obtain a high gain. The PA is tuned to 24 GHz as a synchronous mode PA for the TX front-end operation. The proposed TX front-end showed an excellent output power of 11.7 dBm and dissipated 7.5 mW from a 1.2 V supply. In addition, the TX front-end achieved a power-added efficiency (PAE) of 47% and 1 dB compression point (OP1dB) of 10.5 dBm. In this case, the output power is 10.5 dBm higher than the linear portion of the response. The methodologies presented herein have the potential to advance the state of the art in 24 GHz radar technology, fostering innovations in fields such as autonomous vehicles, industrial automation, and remote sensing.

Soil moisture (SM) is a crucial hydrologic factor that affects the global cycle of energy, carbon, and water, as well as plant growth and crop yield; therefore, an accurate estimate of SM is important for both the global environment and agriculture. Satellite-based SM data have been provided by the National Aeronautics and Space Administration (NASA)’s Soil Moisture Active Passive (SMAP) and the European Space Agency (ESA)’s Soil Moisture and Ocean Salinity (SMOS) satellite missions, but these data are based on passive microwave sensors, which have limited spatial resolution. Thus, detailed observations and analyses of the local distribution of SM are limited. The recent emergence of deep learning techniques, such as rectified linear unit (ReLU) and dropout, has produced effective solutions to complex problems. Deep neural networks (DNNs) have been used to accurately estimate hydrologic factors, such as SM and evapotranspiration, but studies of SM estimates derived from the joint use of DNN and high-resolution satellite data, such as Sentinel-1 and Sentinel-2, are lacking. In this study, we aim to estimate high-resolution SM at 30 m resolution, which is important for local-scale SM monitoring in croplands. We used a variety of input data, such as radar factors, optical factors, and vegetation indices, which can be extracted from Sentinel-1 and -2, terrain information (e.g., elevation), and crop information (e.g., cover type and month), and developed an integrated SM model across various crop surfaces by using these input data and DNN (which can learn the complexity and nonlinearity of the various data). The study was performed in the agricultural areas of Manitoba and Saskatchewan, Canada, and the in situ SM data for these areas were obtained from the Agriculture and Agri-Food Canada (AAFC) Real-time In Situ Soil Monitoring for Agriculture (RISMA) network. We conducted various experiments with several hyperparameters that affected the performance of the DNN-based model and ultimately obtained a high-performing SM model. The optimal SM model had a root-mean-square error (RMSE) of 0.0416 m3/m3 and a correlation coefficient (CC) of 0.9226. This model’s estimates showed better agreement with in situ SM than the SMAP 9 km SM. The accuracy of the model was high when the daily precipitation was zero or very low and also during the vegetation growth stage. However, its accuracy decreased when precipitation or the vitality of the vegetation were high. This suggests that precipitation affects surface erosion and water layer formation, and vegetation adds complexity to the SM estimate. Nevertheless, the distribution of SM estimated by our model generally reflected the local soil characteristics. This work will aid in drought and flood prevention and mitigation, and serve as a tool for assessing the potential growth of crops according to SM conditions.

Soil moisture (SM) is an indicator of the moisture status of the land surface, which is useful for monitoring extreme weather events. Representative global SM datasets include the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP), the Global Land Data Assimilation System (GLDAS), and the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5 (ERA5), but due to their low spatial resolutions, none of these datasets well describe SM changes in local areas, and they tend to have a low accuracy. Machine learning (ML)-based SM predictions have demonstrated high accuracy, but obtaining semi-real-time SM information remains challenging, and the dependence of the validation accuracy on the data sampling method used, such as random or yearly sampling, has led to uncertainties. In this study, we aimed to develop an ML-based model for real-time SM estimation that can capture local-scale variabilities in SM and have reliable accuracy, regardless of the sampling method. This study was conducted in South Korea, and satellite image data, numerical weather prediction (NWP) data, and topographic data provided within one day were used as the input data. For SM modeling, 13 input variables affecting the surface SM status were selected: 10- and 20-day cumulative standardized precipitation indexes (SPI10 and SPI20), a normalized difference vegetation index (NDVI), downward shortwave radiation (DSR), air temperature (Tair), land surface temperature (LST), soil temperature (Tsoil), relative humidity (RH), latent heat flux (LE), slope, elevation, topographic ruggedness index (TRI), and aspect. Then, SM models based on random forest (RF) and automated machine learning (AutoML) were constructed, trained, and validated using random sampling and leave-one-year-out (LOYO) cross-validation. The RF- and AutoML-based SM models had significantly high accuracy rates based on comparisons with in situ SM (mean absolute error (MAE) = 2.212–4.132%; mean bias error (MBE) = −0.110–0.136%; root mean square error (RMSE) = 3.186–5.384%; correlation coefficient (CC) = 0.732–0.913), while the AutoML-based SM model tended to have a higher accuracy than the RF-based SM model, regardless of the data sampling method used. In addition, when compared to in situ SM data, the SM models demonstrated the highest accuracy, outperforming both GLDAS and ERA5 SM data and well representing changes in the dryness/wetness of the land surface according to meteorological events (heatwave, drought, and rainfall). The SM models proposed in this study can, thus, offer semi-real-time SM data, aiding in the monitoring of moisture changes in the land surface, as well as short-term meteorological disasters, like flash droughts or floods.

Satellite remote sensing can measure large ocean surface areas, but the infrared-based sea surface temperature (SST) might not be correctly calculated for the pixels under clouds, resulting in missing values in satellite images. Early studies for the gap-free raster maps of satellite SST were based on spatial interpolation using in situ measurements. In this paper, however, an alternative spatial gap-filling method using regression residual kriging (RRK) for the Geostationary Korea Multi-Purpose Satellite-2A (GK2A) daily SST was examined for the seas around the Korean Peninsula. Extreme outliers were first removed from the in situ measurements and the GK2A daily SST images using multi-step statistical procedures. For the pixels on the in situ measurements after the quality control, a multiple linear regression (MLR) model was built using the selected meteorological variables such as daily SST climatology value, specific humidity, and maximum wind speed. The irregular point residuals from the MLR model were transformed into a residual grid by optimized kriging for the residual compensation for the MLR estimation of the null pixels. The RRK residual compensation method improved accuracy considerably compared with the in situ measurements. The gap-filled 18,876 pixels showed the mean bias error (MBE) of −0.001 °C, the mean absolute error (MAE) of 0.315 °C, the root mean square error (RMSE) of 0.550 °C, and the correlation coefficient (CC) of 0.994. The case studies made sure that the gap-filled SST with RRK had very similar values to the in situ measurements to those of the MLR-only method. This was more apparent in the typhoon case: our RRK result was also stable under the influence of typhoons because it can cope with the abrupt changes in marine meteorology.

Vegetation indices based on selected wavelength reflectance measurements are used to represent crop growth and physiological conditions. However, it has been determined that the anisotropic properties of the crop canopy surface can govern both the spectral reflectance and vegetation indices. In this study, in order to investigate how crop surface reflectance and vegetation indices varied according to the direction of the light source and sensor viewing, a hyper-spectrometer of visible to nearinfrared wavelengths mounted on a field goniometer was used at vegetative and reproductive growth stages in rice paddy. It was found that most of the wavelength reflectance measurements produced by the sparse vegetation cover fraction were not sensitive to solar-illumination and sensor-viewing angles. In addition, the reflectance of visible wavelengths was found to be less sensitive to the solar and sensor angles than the red-edge and near-infrared wavelengths. The lowest normalized difference vegetation index value in a day occurred at the nadir sensor-viewing angle before rice heading, but after heading, when ripened grains began to bow, the lowest value was recorded at the sensor zenith angle of 25°. Enhanced vegetation index measurements were found to be more sensitive to the direction of sensor viewing and less affected by sun glint than normalized difference vegetation index measurements. Additional field observation measurements should increase our level of understanding of how vegetation indices change on anisotropic crop surfaces. [ABSTRACT FROM AUTHOR]