Search Results (87)

Traditional specific emitter identification (SEI) systems often suffer significant performance degradation when confronted with previously unseen signal sources, underscoring the critical need for accurate detection and rejection of novel-class instances. To address this limitation, we propose an Integrated Deep Feature Representation and Isolation Forest (IDFIF) method for identifying novel-class radiation emitters. IDFIF begins by employing a convolutional neural network (CNN) to extract embedding features from raw In-phase/Quadrature (IQ) signals, enhancing inter-class separability while suppressing intra-class variability. These deep features are then used to construct an unsupervised iForest that learns the statistical distribution of known classes, enabling the effective detection of anomalies via a threshold-based scoring mechanism. Experiments conducted on a real-world ADS-B dataset demonstrate that the proposed method achieves a novel-class detection accuracy of over 94%, significantly outperforming comparative methods. Furthermore, the method exhibits low sensitivity to known-class samples, thereby ensuring robustness and generalization under open-set conditions. The proposed IDFIF method is promising for deployment in challenging electromagnetic environments. Full article

Accurate evapotranspiration (ET) monitoring is important for making scientific irrigation decisions. Unmanned aerial vehicle (UAV) remote sensing platforms allow for the flexible and efficient acquisition of field data, providing a valuable approach for large-scale ET monitoring. This study aims to enhance the accuracy and reliability of ET estimation in rice paddies through two synergistic approaches: (1) integrating the energy flux diurnal variations into the Two-Source Energy Balance (TSEB) model, which considers the canopy and soil temperature components separately, for physical estimation and (2) optimizing the flight altitudes and observation times for thermal infrared (TIR) data acquisition to enhance the data quality. The results indicated that the energy flux in rice paddies followed a single-peak diurnal pattern dominated by net radiation (Rn). The diurnal variation in the ratio of soil heat flux (G) to Rn could be well fitted by the cosine function with a max value and peak time (R² > 0.90). The optimal flight altitude and time (50 m and 11:00 am) for improved identification of temperature differentiation between treatments were further obtained through cross-comparison. These adaptations enabled the TSEB model to achieve a satisfactory accuracy in estimating energy flux compared to the single-source SEBAL model, with R² values of 0.8501 for Rn − G and 0.7503 for latent heat (LE), as well as reduced rRMSE values. In conclusion, this study presents a reliable method for paddy field scale ET estimation based on a calibrated TSEB model. Moreover, the integration of ground and UAV multimodal data highlights its potential for precise irrigation practices and sustainable water resource management. Full article

This research focuses on developing a sentiment-based system to analyze public opinion on electromagnetic radiation in online networks. Issues related to EMR, such as the NIMBY effect and negative public sentiment, can lead to health crises, social conflicts, and challenges in decision-making. This study addresses limitations in existing research, including inaccurate data collection and a lack of systematic analysis. By incorporating Jieba Chinese word segmentation technology, this study introduces an innovative data collection method based on topic similarity, significantly improving data accuracy. Additionally, this research establishes a comprehensive public opinion analysis framework that integrates user follower counts, geographical distribution, and interaction data. This framework facilitates the identification of sources of negative sentiment and the development of effective response strategies. As a case study, the dissemination patterns of EMR-related public opinion on Weibo are analyzed, focusing on group sentiment and social interaction. The proposed system achieves a 65.85% improvement in data collection accuracy, demonstrating its effectiveness. Furthermore, this study provides actionable recommendations for relevant departments and governments to monitor, analyze, and respond to EMR-related public opinion. By enhancing decision-making and protecting public interests, this study highlights the role of technology in improving social governance and substantial development. Full article

Radon is an inert gas produced by the radioactive decay of uranium-238, commonly found in the environment. Radon and its decay products are the main sources of human exposure to radiation from natural sources. When inhaled, radon’s alpha particles impact lung tissue, potentially causing lung cancer by damaging DNA and altering oxidative processes. This review article addresses the need for a deeper understanding of the genetic and molecular changes associated with radon-induced lung cancer, aiming to clarify key genetic mutations and protein markers linked to carcinogenesis. Particular attention in recent studies has been given to mutations in tumor suppressor genes (RASSF1, TP53), oncogenes (KRAS, EGFR), and changes in the expression levels of protein biomarkers associated with inflammation, stress, and apoptosis. Identifying these markers is critical for developing effective screening methods for radon-induced lung cancer, enabling timely identification of high-risk patients and supporting effective preventive strategies. Summarizing current genetic and protein biomarkers, this review highlights the importance of a comprehensive approach to studying radon-induced carcinogenesis. Understanding these molecular mechanisms could ultimately improve early diagnostic methods and enhance therapy for cancers associated with radon exposure. Full article

The spatial variation characteristics of near-fault velocity pulses lack in-depth understanding, and it is difficult to consider this feature in probabilistic seismic hazard analysis and the ground motion input for structural seismic analysis. Based on ground motion simulation, this study performs spatial characteristic analysis of velocity pulses. The Mw 6.58 strike-slip Imperial Valley and the Mw 6.8 dip-slip Northridge earthquakes are adopted as the cases, and the simulation method is validated by comparing synthetics with observations. The multi-component broadband ground motion fields are simulated, and the pulse parameters and the pulse area are extracted using the multi-component pulse identification method. The spatial characteristics of various pulse parameters are analyzed. The results show that for a single earthquake, the pulse period is a spatial variable related to source-to-site geometry, the pulse amplification factor has great spatial variation, and the orientation of the maximum pulse component is controlled by the radiation pattern. Finally, the influence of slip distribution on pulse is explored based on two earthquakes, in which the uniform slip, the random slip, and the hybrid slip are combined with different rupture directions. This study contributes to a more reasonable consideration of pulse-like ground motion in seismic risk assessment and earthquake response analysis. Full article

Aging is a natural process resulting in the progressive impairment of multiple functions in the human body, leading to a decline in cellular functionality and the development of aging-related diseases. External stress factors, such as ultraviolet (UV) radiation, pollution, and toxin exposure, increase oxidative stress, damage cellular repair mechanisms, and speed up aging processes. With the rise in the world’s aging population, there are enlarged demands for the use of sustainable natural products in food, nutrient supplements and cosmetics that can slow down aging and prolong healthy life and longevity. Algae, including both macroalgae and microalgae, have been recognised as a source of valuable proteins, amino acids, fatty acids, vitamins, and minerals useful for human consumption and medical applications. With increasing demands for nutraceutical and pharmaceutical bioproducts from environmentally friendly resources, the biotechnological industry, over recent decades, has had to provide new, advanced solutions using modern high-throughput omics technologies. The application of proteomics in the area of discoveries of natural products with anti-aging properties has become more popular for wide industry applications. New proteomics profiling provides a better understanding of changes occurring in protein and peptide content, their structure, function and interactions, as well as the regulatory processes and molecular pathways. Mass spectrometry-based proteomics has been used for a wide range of applications including protein identification, characterisation, as well as quantification of proteins within the proteome and sub-proteome. The application of chemical proteomics facilitated the identification of natural products approach and included the synthesis of probes and target fishing, allowing the advanced identification of proteins of interest. This review focuses on marine macro- and microalgal anti-aging compounds and novel proteomics approaches, providing recent experimental evidence of their involvement in anti-aging processes that should facilitate their use in innovative approaches and sustainable biotechnological applications. Full article

A polymer-dispersed liquid crystal (PDLC) film is a device that can transition from opaque to transparent when electrically charged. These films can be used as actuators to control light levels in response to changing natural light. However, the current state of the art for controlling PDLC films is limited to on/off functionality, and few works in the current body of literature have explored continuous control. This study develops a novel nonlinear model for PDLCs in the context of the feedback control of light. This study also demonstrates the model’s utility by comparing experimental data of a PDLC in feedback with a proportional–integral (PI) controller for disturbance rejection and tracking of a desired light setpoint. This development is motivated by the need for a smart greenhouse that can provide programmable optimized light levels for plant growth. Specifically, a light sensor is composed of a circuit with photodiodes and calibrated for the photosynthetically active radiation range. The light sensor is placed under the film, separate from an exogenous light source, allowing for feedback control to be applied. A proportional–integral type control law is selected for stiffness and the ability to eliminate steady-state error, and it is implemented using a microcontroller. An equivalent analog control effort is applied to the PDLC via a PWM voltage signal and an H-bridge type driver. Details necessary for the driving of the PDLC are presented. Open-loop identification of the nonlinear quasi-static and dynamic step-response transients of the PDLC at different control levels are presented and modeled. Finally, closed-loop experimental and simulated results are presented for both light disturbance rejection and setpoint tracking. This shows that the proposed control framework allows for continuous control of light. Full article

Linear antenna arrays exhibit radiation patterns that are restricted to a half-space and feature axial radiation, which can be a significant drawback for applications that require omnidirectional coverage. To address this limitation, the synthesis method utilizing the Taguchi approach, originally designed for linear arrays, can be effectively extended to two-dimensional or planar antenna arrays. In the context of a linear array, the synthesis process primarily involves determining the feeding law and/or the spatial distribution of the elements along a single axis. Conversely, for a planar array, the synthesis becomes more complex, as it requires the identification of the complex weighting of the feed and/or the spatial distribution of sources across a two-dimensional plane. This adaptation to planar arrays is facilitated by substituting the direction $\theta$ with the pair of directions $(\theta ,\varphi )$, allowing for a more comprehensive coverage of the angular domain. This article focuses on exploring various configurations of planar arrays, aiming to enhance their performance. The primary objective of these configurations is often to minimize the levels of secondary lobes and/or array lobes while enabling a full sweep of the angular space. Secondary lobes can significantly impede system performance, particularly in multibeam applications, where they restrict the minimum distance for frequency channel reuse. This restriction is critical, as it affects the overall efficiency and effectiveness of communication systems that rely on precise beamforming and frequency allocation. By investigating alternative planar array designs and their synthesis methods, this research seeks to provide solutions that improve coverage, reduce interference from secondary lobes, and ultimately enhance the functionality of antennas in diverse applications, including telecommunications, radar systems, and wireless communication. Full article

Communication radiation source individual identification technology is an essential technique in electronic reconnaissance and a crucial link in electronic warfare support measures. Nevertheless, when the sample set encounters complex circumstances, such as class imbalance or a small sample, the classification network model, driven by big data, disrupts the symmetry between the recognition effect and the quantity of the datasets, leading to suboptimal recognition performance. Thus, it is requisite to optimize the existing models and algorithms to better propose more representative fingerprint features. This paper references the speech signal recognition model multivariate long short-term memory–fully convolutional network (MLSTM-FCN), and ameliorates the recognition algorithm and training strategy for the two scenarios of class imbalance and a small sample. It puts forward a communication radiation source individual identification method based on MLSTM-FCN incremental random feature concatenation and a communication radiation source individual identification method based on meta-learning. Proceeding from improving the class imbalance issue among features and small-sample learning, the experimental results under various signal-to-noise ratios demonstrate that the proposed methods have superior recognition effects and higher accuracy. Full article

Specific emitter identification (SEI) is a promising physical-layer authentication technique that serves as a crucial complement to upper-layer authentication mechanisms. SEI capitalizes on the inherent radio frequency fingerprints stemming from circuit discrepancies, which are intrinsic hardware properties and challenging to counterfeit. Recently, various deep learning (DL)-based SEI methods have been proposed, achieving outstanding performance. However, collecting and annotating substantial data for novel or unknown radiation sources is not only time-consuming but also cost-intensive. To address this issue, this paper proposes a few-shot (FS) metric learning-based time-frequency fusion network. To enhance the discriminative capability for radiation source signals, the model employs a convolutional block attention module (CBAM) and feature transformation to effectively fuse the raw signal’s time domain and time-frequency domain representations. Furthermore, to improve the extraction of discriminative features under FS scenarios, the proxy-anchor loss and center loss are introduced to reinforce intra-class compactness and inter-class separability. Experiments on the ADS-B and Wi-Fi datasets demonstrate that the proposed TFAF-Net consistently outperforms existing models in FS-SEI tasks. On the ADS-B dataset, TFAF-Net achieves a 9.59% higher accuracy in 30-way 1-shot classification compared to the second-best model, and reaches an accuracy of 85.02% in 10-way classification. On the Wi-Fi dataset, TFAF-Net attains 90.39% accuracy in 5-way 1-shot classification, outperforming the next best model by 6.28%, and shows a 13.18% improvement in 6-way classification. Full article

In this article, we present a software tool developed in Python, named T-WorkFlow. It has been devised to meet some of the design needs of Tatuus Racing S.p.a., a leading company in the design and production of racing cars for the FIA Formula 3 Regional and Formula 4 categories. The software leverages the open-source tools OpenFOAM and FreeCAD to fully automate the fluid dynamics simulation process within car radiators. The goal of T-WorkFlow is to provide designers with precise and easily interpretable results that facilitate the identification of the geometry, ensuring optimal flow distribution in the radiator channels. T-WorkFlow requires the radiator’s geometry files in .stp and .stl formats, along with additional user inputs provided through a graphical interface. For mesh generation, the software leverages the OpenFOAM tools blockMesh and snappyHexMesh. To ensure uniform mesh quality across different configurations, and thus, comparable numerical results, various pre-processing operations on the specific geometry files are needed. After generating the mesh, T-WorkFlow automatically defines a control surface for each radiator channel to monitor the volumetric flow rate distribution. This is achieved by combining the OpenFOAM command topoSet with specific geometric information directly obtained from the radiator’s CAD through FreeCAD. During the simulation, the software provides various outputs that automate the main post-processing operations, enabling quick and easy identification of the configuration that ensures the desired performance. Full article

Radar radiation source recognition is critical for the reliable operation of radar communication systems. However, in increasingly complex electromagnetic environments, traditional identification methods face significant limitations. These methods often struggle with high noise levels and diverse modulation types, making it difficult to maintain accuracy, especially when the Signal-to-Noise Ratio (SNR) is low or the available training data are limited. These difficulties are further intensified by the necessity to generalize in environments characterized by a substantial quantity of noisy, low-quality signal samples while being constrained by a limited number of desirable high-quality training samples. To more effectively address these issues, this paper proposes a novel approach utilizing Model-Agnostic Meta-Learning (MAML) to enhance model adaptability in few-shot learning scenarios, allowing the model to quickly learn with limited data and optimize parameters effectively. Furthermore, a multimodal fusion neural network, DCFANet, is designed, incorporating residual blocks, squeeze and excitation blocks, and a multi-scale CNN, to fuse I/Q waveform data and time–frequency image data for more comprehensive feature extraction. Our model enables more robust signal recognition, even when the signal quality is severely degraded by noise or when only a few examples of a signal type are available. Testing on 13 intra-pulse modulated signals in an Additive White Gaussian Noise (AWGN) environment across SNRs ranging from −20 to 10 dB demonstrated the approach’s effectiveness. Particularly, under a $5-way5-shot$ setting, the model achieves high classification accuracy even at −10dB SNR. Our research underscores the model’s ability to address the key challenges of radar emitter signal recognition in low-SNR and data-scarce conditions, demonstrating its strong adaptability and effectiveness in complex, real-world electromagnetic environments. Full article

Clinical fetal monitoring devices can only be operated by medical professionals and are overly costly, prone to detrimental false positives, and emit radiation. Thus, highly accurate, easily accessible, simplified, and cost-effective fetal monitoring devices have gained an enormous interest in obstetrics. In this study, a cost-effective and user-friendly wearable home fetal movement and distress detection device is developed and assessed for early-stage design progression by facilitating continuous, comfortable, and non-invasive monitoring of the fetus during the final trimester. The functionality of the developed prototype is mainly based on a microcontroller, a single accelerometer, and a specialized fetal phonocardiography (fPCG) acquisition board with a low-cost microphone. The developed system is capable of identifying fetal movement and monitors fetal heart rhythm owing to its considerable sensitivity. Further, the device includes a Global System for Mobile Communication (GSM)-based alert system for instant distress notifications to the mother, proxy, and emergency services. By incorporating digital signal processing, the system achieves zero false negatives in detecting fetal movements, which was validated against an open-source database. The acquired results clearly substantiated the efficacy of the fPCG acquisition board and alarm system, ensuring the prompt identification of fetal distress. Full article

Computed tomography (CT) is one of the fundamental imaging modalities used in medicine, allowing for the acquisition of accurate cross-sectional images of internal body tissues. However, during the acquisition and reconstruction process, various artifacts can arise, and one of them is ring artifacts. These artifacts result from the inherent limitations of CT scanner components and the properties of the scanned material, such as detector defects, non-uniform distribution of radiation from the source, or the presence of metallic elements within the scanning region. The purpose of this study was to identify and reduce ring artifacts in tomographic images using image decomposition and average filtering methods. In this study, tests were conducted on the effectiveness of identifying ring artifacts using wavelet decomposition methods for images. The test was performed on a Shepp–Logan phantom with implemented artifacts of different intensity levels. The analysis was performed using different wavelet families, and linear approximation methods were used to filter the image in the identified areas. Additional filtering was performed using moving average methods and empirical mode decomposition (EMD) techniques. Image comparison methods, i.e., RMSE, SSIM and MS-SSIM, were used to evaluate performance. The results of this study showed a significant improvement in the quality of tomographic phantom images. The authors obtained more than 50% improvement in image quality with reference to the image without any filtration. The different wavelet families had different efficiencies with relation to the identification of the induction regions of ring artifacts. The Haar wavelet and Coiflet 1 showed the best performance in identifying artifact induction regions, with comparative RMSE values for these wavelets of 0.1477 for Haar and 0.1469 for Coiflet 1. The applied additional moving average filtering and EMD permitted us to improve image quality, which is confirmed by the results of the image comparison. The obtained results allow us to assess how the used methods affect the reduction in ring artifacts in phantom images with induced artifacts. Full article

The global, near-real-time monitoring of volcano thermal activity has become feasible through thermal infrared sensors on various satellite platforms, which enable accurate estimations of volcanic emissions. Specifically, these sensors facilitate reliable estimation of Volcanic Radiative Power (VRP), representing the heat radiated during volcanic activity. A critical factor influencing VRP estimates is the identification of hotspots in satellite imagery, typically based on intensity. Different satellite sensors employ unique algorithms due to their distinct characteristics. Integrating data from multiple satellite sources, each with different spatial and spectral resolutions, offers a more comprehensive analysis than using individual data sources alone. We introduce an innovative Remote Sensing Data Fusion (RSDF) algorithm, developed within a Cloud Computing environment that provides scalable, on-demand computing resources and services via the internet, to monitor VRP locally using data from various multispectral satellite sensors: the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS), the Sea and Land Surface Temperature Radiometer (SLSTR), and the Visible Infrared Imaging Radiometer Suite (VIIRS), along with the geostationary Spinning Enhanced Visible and InfraRed Imager (SEVIRI). We describe and demonstrate the operation of this algorithm through the analysis of recent eruptive activities at the Etna and Stromboli volcanoes. The RSDF algorithm, leveraging both spatial and intensity features, demonstrates heightened sensitivity in detecting high-temperature volcanic features, thereby improving VRP monitoring compared to conventional pre-processed products available online. The overall accuracy increased significantly, with the omission rate dropping from 75.5% to 3.7% and the false detection rate decreasing from 11.0% to 4.3%. The proposed multi-sensor approach markedly enhances the ability to monitor and analyze volcanic activity. Full article