Search Results (827)

Automotive millimeter-wave (MMW) synthetic aperture radar (SAR) systems can achieve high-resolution images of detection areas, providing environmental perceptions that facilitate intelligent driving. However, curved path is inevitable in complex urban road environments. Non-uniform spatial sampling, brought about by curved path, leads to cross-coupling and spatial variation deteriorates greatly, significantly impacting the imaging results. To deal with these issues, we developed an Extended Omega-K Algorithm (EOKA) for an automotive SAR with a curved path. First, an equivalent range model was constructed based on the relationship between the range history and Doppler frequency. Then, using azimuth time mapping, the echo data was reconstructed with a form similar to that of a uniform linear case. As a result, an analytical two-dimensional (2D) spectrum was easily derived without using of the method of series reversion (MSR) that could be exploited for EOKA. The results from the parking lot, open road, and obstacle experimental scenes demonstrate the performance and feasibility of an MMW SAR for environmental perception. Full article

Indoor positioning based on Wi-Fi signals has gained a lot of attention lately. There are many advantages related to the use of Wi-Fi signals for positioning, including the availability of Wi-Fi access points in indoor environments and the integration of Wi-Fi transceivers into consumer devices. However, since Wi-Fi uses an unlicensed spectrum, anyone can create their own access points. Therefore, it is possible to affect the function of the localization system by spoofing signals from access points and thus alter positioning accuracy. Previously published works focused mainly on the evaluation of spoofing on localization systems and the detection of anomalies when updating the radio map. Spoofing mitigation solutions were proposed; however, their application to systems that use off-the-shelf items is not straightforward. In this paper filtering algorithms are proposed to minimize the impact of access point spoofing. The filtering was applied with a combination of the widely used K-Nearest Neighbours (KNN) localization algorithm and their performance is evaluated using the UJIIndoorLoc dataset. During the evaluation, the spoofing of Access Points was performed in two different scenarios and the number of spoofed access points ranged from 1 to 10. Based on the achieved results proposed SFKNN provided good detection of the spoofing and helped to reduce the mean localization error by 2–5 m, especially when the number of spoofed access points was higher. Full article

This paper presents a novel approach to material classification using short-wave infrared (SWIR) imaging, aimed at applications where differentiating visually similar objects based on material properties is essential, such as in autonomous driving. Traditional vision systems, relying on visible spectrum imaging, struggle to distinguish between objects with similar appearances but different material compositions. Our method leverages SWIR’s distinct reflectance characteristics, particularly for materials containing moisture, and demonstrates a significant improvement in accuracy. Specifically, SWIR data achieved near-perfect classification results with an accuracy of 99% for distinguishing real from artificial objects, compared to 77% with visible spectrum data. In object detection tasks, our SWIR-based model achieved a mean average precision (mAP) of 0.98 for human detection and up to 1.00 for other objects, demonstrating its robustness in reducing false detections. This study underscores SWIR’s potential to enhance object recognition and reduce ambiguity in complex environments, offering a valuable contribution to material-based object recognition in autonomous driving, manufacturing, and beyond. Full article

Marine and freshwater plastic pollution is a worldwide problem affecting ecosystems and human health. Although remote sensing has been used to map large floating plastic rafts, there are research gaps in detecting submerged plastic due to the limited amount of in situ data. This study is the first to collect in situ data on submerged and floating plastics in a freshwater environment and analyse the effect of water submersion on the strength of the plastic signal. A large 10 × 10 m artificial polymer tarpaulin was deployed in a freshwater lake for a two-week period and was captured by a multi-sensor and multi-resolution unmanned aerial vehicle (UAV) and satellite. Spectral analysis was conducted to assess the attenuation of individual wavelengths of the submerged tarpaulin in UAV hyperspectral and Sentinel-2 multispectral data. A K-Means unsupervised clustering algorithm was used to classify the images into two clusters: plastic and water. Additionally, we estimated the optimal number of clusters present in the hyperspectral dataset and found that classifying the image into four classes (water, submerged plastic, near surface plastic and buoys) significantly improved the accuracy of the K-Means predictions. The submerged plastic tarpaulin was detectable to ~0.5 m below the water surface in near infrared (NIR) (~810 nm) and red edge (~730 nm) wavelengths. However, the red spectrum (~669 nm) performed the best with ~84% true plastic positives, classifying plastic pixels correctly even to ~1 m depth. These individual bands outperformed the dedicated Plastic Index (PI) derived from the UAV dataset. Additionally, this study showed that in neither Sentinel-2 bands, nor the derived indices (PI or Floating Debris Index (FDI), it is currently possible to determine if and how much of the tarpaulin was under the water surface, using a plastic tarpaulin object of 10 × 10 m. Overall, this paper showed that spatial resolution was more important than spectral resolution in detecting submerged tarpaulin. These findings directly contributed to Sustainable Development Goal 14.1 on mapping large marine plastic patches of 10 × 10 m and could be used to better define systems for monitoring submerged and floating plastic pollution. Full article

Infrared cameras form images by capturing the thermal radiation emitted by objects in the infrared spectrum, making them complex sensors widely used in maritime surveillance. However, the broad spectral range of the infrared band makes it susceptible to environmental interference, which can reduce the contrast between the target and the background. As a result, detecting infrared targets in complex marine environments remains challenging. This paper presents a novel and enhanced detection model developed from the real-time detection transformer (RT-DETR), which is designated as MAFF-DETR. The model incorporates a novel backbone by integrating CSP and parallelized patch-aware attention to enhance sensitivity to infrared imagery. Additionally, a channel attention module is employed during feature selection, leveraging high-level features to filter low-level information and enabling efficient multi-level fusion. The model’s target detection performance on resource-constrained devices is further enhanced by incorporating advanced techniques such as group convolution and ShuffleNetV2. The experimental results show that, although the enhanced RT-DETR algorithm still experiences missed detections under severe object occlusion, it has significantly improved overall performance, including a 1.7% increase in mAP, a reduction in 4.3 M parameters, and a 5.8 GFLOPs decrease in computational complexity. It can be widely applied to tasks such as coastline monitoring and maritime search and rescue. Full article

In recent years, Golden Hard Anodizing (G.H.A.^®) has been developed as a variant of the traditional hard anodizing process with the addition of Ag⁺ ions in the nanoporous structure. The tribological properties of this innovative surface treatment are still not well understood. In this study, ball-on-disk tests were conducted in dry sliding conditions using 100Cr6 (AISI 52100) bearing steel balls as a counterbody and GHA^®-anodized EN AW-6082 aluminum alloy disks. The novelty of this work lies in the mapping of the wear properties of the tribocouple under different test conditions for a better comparison of the results. Three different normal loads (equal to 5, 10, and 15 N) and three different reciprocating frequencies (equal to 2, 3, and 4 Hz) were selected to investigate a spectrum of operating conditions for polished and unpolished G.H.A.^®-anodized EN AW-6082 aluminum alloy. Quantitative wear maps were built based on the resulting wear rate values to define the critical operating limits of the considered tribocouple. The results suggest that the coefficient of friction (COF) was independent of test conditions, while different wear maps were found for polished and non-polished surfaces. Polishing before anodizing permitted the acquisition of lower wear for the anodized disks and the steel balls. Full article

This study explores the novel dynamics of the (3+1)-dimensional generalized Korteweg–de Vries–Zakharov–Kuznetsov (KdV-ZK) equation. A Galilean transformation is employed to derive the associated system of equations. Perturbing this system allows us to investigate the presence and characteristics of chaotic behavior, including return maps, fractal dimension, power spectrum, recurrence plots, and strange attractors, supported by 2D and time-dependent phase portraits. A sensitivity analysis is demonstrated to show how the system behaves when there are small changes in initial values. Finally, the planar dynamical system method is used to derive anti-kink, dark soliton, and kink soliton solutions, advancing our understanding of the range of solutions admitted by the model. Full article

In the realm of mobile robotics, the capability to navigate and map uncharted territories is paramount, and Simultaneous Localization and Mapping (SLAM) stands as a cornerstone technology enabling this capability. While traditional SLAM methods like Extended Kalman Filter (EKF) and FastSLAM have made strides, they often struggle with the complexities of non-linear dynamics and non-Gaussian noise, particularly in dynamic settings. Moreover, these methods can be computationally intensive, limiting their applicability in real-world scenarios. This paper introduces an innovative enhancement to the FastSLAM framework by integrating Multi-Objective Particle Swarm Optimization (MO-PSO), aiming to bolster the robustness and accuracy of SLAM in mobile robots. We outline the theoretical underpinnings of FastSLAM and underscore its significance in robotic autonomy for mapping and exploration. Our approach innovates by crafting a specialized fitness function within the MO-PSO paradigm, which is instrumental in optimizing the particle distribution and addressing the challenges inherent in traditional particle filtering methods. This strategic fusion of MO-PSO with FastSLAM not only circumvents the pitfalls of particle degeneration, but also enhances the overall robustness and precision of the SLAM process across a spectrum of operational environments. Our empirical evaluation involves testing the proposed method on three distinct simulation benchmarks, comparing its performance against four other algorithms. The results indicate that our MO-PSO-enhanced FastSLAM method outperforms the traditional particle filtering approach by significantly reducing particle degeneration and ensuring more reliable and precise SLAM performance in challenging environments. This research demonstrates that the integration of MO-PSO with FastSLAM is a promising direction for improving SLAM in mobile robots, providing a robust solution for accurate mapping and localization even in complex and unknown settings. Full article

The governance of energy systems is undergoing a transformative shift, vital to advancing the energy transition. Understanding the dynamics of energy citizenship and the factors that influence citizen engagement in energy matters is critical for driving social and institutional change. This paper informs on the key results of a comprehensive analysis of 54 energy citizenship cases in the Baltic states (Latvia, Estonia, and Lithuania). The study explores the role of citizens in the energy transition and characterizes the socio-economic and geopolitical factors shaping energy citizenship activities in the region. The governance of energy systems represents a significant transformational shift that is essential for energy transition. A more comprehensive understanding of the current state of energy citizenship and the factors influencing the energy transition process could inform the social and institutional changes necessary for the involvement of citizens in energy matters. This desk study represents a crucial element of the EU Horizon 2000 EnergyPROSPECTS project, which aims to map the landscape of energy citizenship in Europe. This paper presents an in-depth analysis of 54 cases from the Baltic states. The findings provide insight into the role of citizens in the transition process and the underlying factors and conditions that shape energy citizenship activities within the specific socio-economic and geopolitical context of the region. In general, energy citizenship in the Baltic states can be seen to exist on a spectrum between reformative and transformative practices. Overall, progress is being made toward systemic changes in the energy sector, with a focus on the democratization of processes. Nevertheless, additional measures to enhance and reinforce energy citizenship, coupled with the advancement of enabling conditions, are imperative at all levels of governance and across all energy transition scenarios. Full article

The solution of the Helmholtz equation describing the propagation of light in free space from a plane to another can be described by the angular spectrum operator, which acts in the frequency domain. Many applications require this operator to be generalized to handle tilted source and target planes, which has led to research investigating the implications of these adaptations. However, the frequency domain representation intrinsically limits the understanding the way the signal is transformed through propagation. Instead, studying how the operator maps the space–frequency components of the wavefield provides essential information that is not available in the frequency domain. In this work, we highlight and exploit the deep relation between wave optics and quantum mechanics to explicitly describe the symplectic action of the tilted angular spectrum in phase space, using mathematical tools that have proven their efficiency for quantum particle physics. These derivations lead to new algorithms that open unprecedented perspectives in various domains involving the propagation of coherent light. Full article

Congenital anemias include a broad range of disorders marked by inherent abnormalities in red blood cells. These abnormalities include enzymatic, membrane, and congenital defects in erythropoiesis, as well as hemoglobinopathies such as sickle cell disease and thalassemia. These conditions range in presentation from asymptomatic cases to those requiring frequent blood transfusions, exhibiting phenotypic heterogeneity and different degrees of severity. Despite understanding their different etiologies, all of them have a common pathophysiological origin with congenital defects of erythropoiesis. We can find different types, from congenital sideroblastic anemia (CSA), which is a bone marrow failure anemia, to hemoglobinopathies as sickle cell disease and thalassemia, with a higher prevalence and clinical impact. Recent efforts have focused on understanding erythropoiesis dysfunction in these anemias but, so far, deep gene sequencing analysis comparing all of them has not been performed. Our study used Quant 3′ mRNA-Sequencing to compare transcriptomic profiles of four sickle cell disease patients, ten thalassemia patients, and one rare case of SLC25A38 CSA. Our results showed clear differentiated gene map expressions in all of them with respect to healthy controls. Our study reveals that genes related to metabolic processes, membrane genes, and erythropoiesis are upregulated with respect to healthy controls in all pathologies studied except in the SLC25A38 CSA patient, who shows a unique gene expression pattern compared to the rest of the congenital anemias studied. Our analysis is the first that compares gene expression patterns across different congenital anemias to provide a broad spectrum of genes that could have clinical relevance in these pathologies. Full article

Background: There is only limited research on the coronal cementum of a tooth, and the mechanisms of its forming process are not well-defined. This report presents a coronal cementum on the occlusal surfaces of enamel in an impacted wisdom tooth in a human, which is not nearly the cervical portion. Materials and Methods: The tooth (Tooth #1) was derived from a 46-year-old female. Histological analysis, including hematoxylin and eosin (HE) and toluidine blue (TB) staining, and Scanning Electron Microscopy and Energy Dispersive X-ray Spectrometer (SEM-EDS) analysis of the extracted tooth were conducted. Radiographic examination showed that Tooth #1 was horizontally impacted in the maxilla and had the apex of a single root placed between the buccal and palatal roots of Tooth #2. Results: Coronal cementum was distributed widely on the enamel, and reduced enamel epithelium was also found with enamel matrix proteins histologically. The formation of acellular cementum was observed to be more predominant than that of the cellular cementum in Tooth #1. SEM showed that the occlusal cementum connected directly with enamel. Calcium mapping revealed an almost similar occlusal cementum and enamel. In addition, the spectrum of elements in coronal cementum resembled the primary cementum according to SEM-EDS. Discussion: Thus, coronal cementogenesis in impacted human teeth might be related to the existence of reduced enamel epithelium. Full article

Recent progress in autonomous vehicle technology has led to the development of accurate and efficient tools for ensuring safety, which is crucial for verifying the reliability and security of vehicles. These vehicles operate under diverse conditions, necessitating the analysis of varying initial conditions and parameter values. Ensuring the safe operation of the vehicle under all these varying conditions is essential. Reachability analysis is an important tool to certify the safety and stability of the vehicle dynamics. We propose a reachability analysis approach for evaluating the response of the vehicle dynamics, specifically addressing uncertainties in the initial states and model parameters. Reachable sets illustrate all the possible states of a dynamical system that can be obtained from a given set of uncertain initial conditions. The analysis is crucial for understanding how variations in initial conditions or system parameters can lead to outcomes such as vehicle collisions or deviations from desired paths. By mapping out these reachable states, it is possible to design systems that maintain safety and reliability despite uncertainties. These insights help to ensure the stability and reliability of the vehicles, even in unpredictable conditions, by reducing accidents and optimizing performance. The nonlinearity of the model complicates the computation of reachable sets in vehicle dynamics. This paper proposes a Koopman theory-based approach that utilizes the Koopman principal eigenfunctions and the Koopman spectrum. By leveraging the Koopman principal eigenfunction, our method simplifies the computational process and offers a formal approximation for backward and forward reachable sets. First, our method effectively computes backward and forward reachable sets for a nonlinear quarter-car model with fixed parameter values. Furthermore, we applied our approach to analyze the uncertainty response for cases with uncertain parameters of the vehicle model. When compared to time-domain simulations, our proposed Koopman approach provided accurate results and also reduced the computational time by half in most cases. This demonstrates the efficiency and reliability of our proposed approach in dynamic systems uncertainty analysis using the reachable sets. Full article

The rotational mechanism, which plays a critical role in energy supply, payload antenna pointing, and attitude stabilization in satellites is essential for the overall functionality and performance stability of the satellite. This paper takes the space turntable of a specific satellite model as an example, utilizing high-frequency and high-dimensional telemetry data. An improved informer model is used to predict and diagnose features related to the turntable’s operational health, including temperature, rotational speed, and current. In this paper, we present a forecasting method for turntable temperature data using a hybrid model that combines singular spectrum analysis with an enhanced informer model (SSA-Informer), comparing the results with threshold limits to determine if faults occur in the satellite’s rotational mechanism. First, during telemetry data processing, singular spectrum analysis (SSA) is proposed to retain the long-term and oscillatory trends in the original data while filtering out noise from interference. Next, the improved informer model predicts the turntable temperature based on the mapping relationship between the turntable subsystem’s motor current and temperature, with multiple experiments conducted to obtain optimal parameters. Finally, temperature thresholds generated from the prediction results are used to forecast faults in the rotational mechanism over different time periods. The proposed method is compared with current popular time-series prediction models. The experimental results show that the model achieves high prediction accuracy, with reductions of at least 10% in both the MAE and MSE than CNN-LSTM, DA-RNN, TCN-SE and informer, demonstrating the outstanding advantages of the SSA and improved informer-based method in predicting temperature faults in satellite rotational mechanisms. Full article

Depth map super-resolution (DSR) is a technique aimed at restoring high-resolution (HR) depth maps from low-resolution (LR) depth maps. In this process, color images are commonly used as guidance to enhance the restoration procedure. However, the intricate degradation of LR depth poses a challenge, and previous image-guided DSR approaches, which implicitly model the degradation in the spatial domain, often fall short of producing satisfactory results. To address this challenge, we propose a novel approach called the Degradation-Guided Multi-modal Fusion Network (DMFNet). DMFNet explicitly characterizes the degradation and incorporates multi-modal fusion in both spatial and frequency domains to improve the depth quality. Specifically, we first introduce the deep degradation regularization loss function, which enables the model to learn the explicit degradation from the LR depth maps. Simultaneously, DMFNet converts the color images and depth maps into spectrum representations to provide comprehensive multi-domain guidance. Consequently, we present the multi-modal fusion block to restore the depth maps by leveraging both the RGB-D spectrum representations and the depth degradation. Extensive experiments demonstrate that DMFNet achieves state-of-the-art (SoTA) performance on four benchmarks, namely the NYU-v2, Middlebury, Lu, and RGB-D-D datasets. Full article