Identification Techniques Research Articles

Overview of satellite nav spoofing and anti-spoofing techniques

In recent years, satellite navigation systems have witnessed widespread adoption across diverse fields, including military surveillance, precision agriculture, traffic monitoring, resource exploration, and disaster assessment. However, navigation signals are susceptible to interference, with deceptive interference posing the most significant threat to navigation systems. This paper provides a comprehensive overview of satellite navigation spoofing and anti-spoofing techniques. It reviews the current state of spoofing and anti-spoofing technologies, analyzing advancements in spoofing techniques and the evolution of countermeasures. Furthermore, the paper elaborates on the impact of spoofing interference on receiver performance, examining its effects on positioning, timing, and velocity estimations. A detailed analysis of various anti-spoofing methods is presented, categorizing them into detection, identification, suppression, and localization techniques. This review aims to provide a thorough understanding of the evolving landscape of satellite navigation spoofing and anti-spoofing technologies, fostering further research and development efforts to ensure the integrity and resilience of satellite navigation systems in the face of sophisticated threats.

Advancing Grapevine Variety Identification: A Systematic Review of Deep Learning and Machine Learning Approaches

The Eurasian grapevine (Vitis vinifera L.) is one of the most extensively cultivated horticultural crop worldwide, with significant economic relevance, particularly in wine production. Accurate grapevine variety identification is essential for ensuring product authenticity, quality control, and regulatory compliance. Traditional identification methods have inherent limitations limitations; ampelography is subjective and dependent on skilled experts, while molecular analysis is costly and time-consuming. To address these challenges, recent research has focused on applying deep learning (DL) and machine learning (ML) techniques for grapevine variety identification. This study systematically analyses 37 recent studies that employed DL and ML models for this purpose. The objective is to provide a detailed analysis of classification pipelines, highlighting the strengths and limitations of each approach. Most studies use DL models trained on leaf images captured in controlled environments at distances of up to 1.2 m. However, these studies often fail to address practical challenges, such as the inclusion of a broader range of grapevine varieties, using data directly acquired in the vineyards, and the evaluation of models under adverse conditions. This review also suggests potential directions for advancing research in this field.

Bayesian-based multi-class damage identification on prestressed reinforced concrete bridges

Model-driven structural system identification techniques are effective for comprehensive damage assessment but relying on a single model for structural identification (St-Id) can lead to unreliable results due to the ill-posed nature of the inverse calibration problem. To address this issue, this work explores the potential of multi-class digital twins, derived from sets of competing model classes, each one characterised by a distinct failure mechanism. To accelerate the calibration process, Kriging metamodels are employed, facilitating the fusion of diverse data types, such as modal displacements, frequencies, and static rotations. The Bayesian Information Criterion (BIC) is utilised to identify the most suitable model class. This research specifically targets the damage assessment of prestressed reinforced concrete girder bridges, which represent a significant portion of the global bridge stock. To this end, a numerical model of a representative span is developed, followed by a parametric analysis to evaluate the proposed approach’s effectiveness. Various damage scenarios including stiffness reduction and tendon prestress losses are considered, demonstrating the method’s capability for quasi-real-time multi-class damage identification. The study emphasizes the combined use of modal features and static rotations within a Bayesian framework, underscoring the importance of data fusion in achieving effective damage identification for these structures.

Genomic Characterizations of Klebsiella variicola: Emerging Pathogens Identified from Sepsis Patients in Ethiopian Referral Hospitals

Healthcare in low- and middle-income countries is becoming problematic due to the emergence of multidrug-resistant bacteria causing serious morbidity and mortality. Klebsiella variicola carrying multiple antimicrobial resistance (AMR) genes was found significantly among sepsis patients in a study done between October 2019 and September 2020 at four Ethiopian hospitals located in the central (Tikur Anbessa and Yekatit 12), southern (Hawassa), and northern (Dessie) parts. Among 1416 sepsis patients, 74 K. variicola isolates were identified using MALDI-TOF, most of them at Dessie (n=44) and Hawassa (n=28) hospitals. Whole genome sequencing showed that K. variicola strains identified at Dessie Hospital displayed phylogenetic clonality, carried an IncM1 plasmid and the majority were ST3924. Many K. variicola identified at Hawassa Hospital were clonally clustered and the majority belonged to novel STs and carried IncFIB(K) and IncFII(K) plasmids concurrently. Fifty K. variicola carried ESBL genes while 2 isolates harbored AmpC. Other frequently found genes were aac(3)-lla, bla CTX-M-15, bla TEM-1B, bla LEN2, bla OXA-1, bla SCO-1, catB3, dfrA14, QnrB1, aac(6')-lb-cr and sul2. Virulence genes detected at both sites were mrk operons for biofilm formation and siderophore ABC transporter operons for iron uptake. Capsular alleles varied, with wzi 269 at Dessie and wzi 582 at Hawassa. The isolation of multidrug-resistant K. variicola as an emerging sepsis pathogen calls for strong infection prevention strategies and antimicrobial stewardship supported by advanced bacterial identification techniques.

Enhanced resolution of optical genome mapping utilizing telomere-to-telomere reference in genetic disorders.

Reference genomes serve as a baseline criterion for comparison of personal genomes to deduce clinical variants. The widely used reference genome, GRCh38, contains stretches of gaps and unresolved bases particularly in complex regions which could obscure variant discovery. In contrast, the gapless telomere-to-telomere CHM13 (T2T-CHM13) reference genome can be used to assess difficult regions of the genome. Optical genome mapping (OGM), an imaging technique for structural variation identification has improved resolution compared to traditional cytogenetic methods. Our study showcases the utility of the T2T-CHM13 reference genome for enhanced structural variant (SV) detection in complex regions. We illustrate this through two clinical cases, where improved alignment with T2T-CHM13 led to significantly higher confidence scores for critical SVs. We demonstrate improved clinical diagnostic outcomes with the updated T2T-CHM13 reference and advocate its adoption.

A Novel Frequency-Division Deep Learning Approach for Magnetotelluric Data Quality Enhancement

High signal-to-noise ratio magnetotelluric (MT) data are crucial for accurately interpreting subsurface structures. Recently, deep learning has become popular for MT denoising due to its ability to avoid parameter tuning and enable real-time processing. These methods typically fit or predict signals in noisy segments after identifying and segmenting signal and noise in the time domain. However, these methods struggle to preserve both low- and high-frequency signals effectively due to high noise levels in these segments. To address this issue, we propose a novel deep learning denoising method that separately recovers low- and high-frequency signals using distinct strategies. Low-frequency signals are fitted using an inverse autoencoder with a channel attention mechanism, effectively removing high-frequency components. High-frequency signals are then predicted using a bidirectional long short-term memory network (BiLSTM) combined with a squeeze-and-excitation (SE) mechanism, enhancing prediction by considering both global and local signal characteristics. Additionally, we introduce the multivariate state estimation technique (MSET) for real-time signal-noise identification. MSET analyzes residuals after separating low-frequency signals to identify noise. Denoising is performed only on segments with significant noise, preserving more effective signals. Finally, the fitted low-frequency dominant component and predicted high-frequency component are combined to form the denoised MT signals. This combined approach significantly improves the restoration quality of effective signals compared to existing methods. Experimental results demonstrate that our method exhibits superior denoising capabilities in both quantitative and qualitative evaluations, including apparent resistivity-phase curves and polarization direction analysis, offering enhanced performance over current deep learning methods.

Efficient diagnostic model for iron deficiency anaemia detection: a comparison of CNN and object detection algorithms in peripheral blood smear images

ABSTRACT Iron Deficiency Anaemia (IDA) is the most prevalent form of anaemia, affecting 24.8% of the global population. An examination of the complete blood count (CBC) is performed to determine general health and the presence of illnesses. Accurate and timely diagnosis of IDA is essential for proper treatment, yet traditional methods can be time-consuming and costly. This study uses machine learning and computer vision techniques for the automatic identification of hypochromic microcytes from Peripheral Blood Smear (PBS) images to improve IDA diagnosis. Two approaches were implemented: first, a ResNet50 model was used to classify PBS images as Normal or IDA; second, the YOLOv7 object detection model was employed to localize hypochromic microcytes within the images. The YOLOv7 model was tested on 17 images containing 425 instances of hypochromic microcytes and demonstrated superior performance, achieving a test mean Average Precision (mAP) of 89% with faster inference times than ResNet50. By providing localized detection of hypochromic microcytes, YOLOv7 enhances diagnostic accuracy and speed compared to image-level classification. This study highlights the potential of object detection models for improving automated anaemia diagnosis, with implications for faster and more cost-effective healthcare solutions.

유류화재 증거물의 검지 가능 기간 및 감식기법에 관한 연구

In the event of an oil fire, oil may not be found at the site because it was initially ignited, completely burned, easily flushed, or evaporated until the fire investigator arrived at the site after the occurrence of the fire. In investigating the causes of a fire, collecting evidence from oil fragments is very important for measuring and analyzing the oil composition. Therefore, an experimental study was conducted on how long the oil components can be measured with a symptom detector from the evidence of the debris collected from an oil fire, Based on an analysis of fire cases and investigations, the results were derived through a reproduction experiment. The oil detection period differed according to the fire extinguishing method and increased with natural, extinguishing, suffocation extinguishing, and using drinking water. In the case of suffocation extinguishing using a cover, the oil content could be measured for up to 12 days. The applicable identification technique was presented to strengthen the oil fire detection ability of fire investigators.

Probabilistic-based identification of gunshot residues (GSR) using Laser-Induced Breakdown Spectroscopy (LIBS) and Support Vector Machine (SVM) algorithm

Firearm violence results in thousands of victims annually in Brazil, which is ascribed, among other factors, to the low homicide resolution rates. One method for generating forensic evidence in firearm discharges is the identification of gunshot residues (GSR), typically composed of inorganic particles containing primarily lead (Pb), barium (Ba), and antimony (Sb). The “gold standard” technique for GSR identification is Scanning Electron Microscopy with Energy-Dispersive X-ray Spectroscopy (SEM-EDS) which is highly accurate but demands significant time and labor for each sample analysis. Other tests to detect GSR include the colorimetric rhodizonate test and spectroscopic techniques such as Laser-Induced Breakdown Spectroscopy (LIBS), which are quicker, but exhibit lower reliability and a higher potential for false positives and potentially compromise the investigation. This study introduces a novel GSR analytical protocol as a potential alternative to SEM-EDS and colorimetric tests, which is based on LIBS analysis combined with dimensionality reduction performed by PCA and probabilistic Support Vector Machine (SVM) algorithm. The developed protocol accurately predicts both positive and negative samples, and establishes the probability of each prediction, allowing for the exclusion of questionable samples from the analysis. The initial tests show the protocol achieved 100 % accuracy in both training and external validation. As a stress test, the protocol is able to exclude questionable samples from the analysis and accurately predict true samples with 100 % correct classifications from a set of artificially contaminated samples containing elements characteristic of GSR, highlighting the protocol’s significant reduction in the likelihood of false positives.

Research and application of key techniques for identification of mainstream line of the yellow river mainstream

Research and application of key techniques for identification of mainstream line of the yellow river mainstream

Fiber distribution in UHPC under different influencing factors evaluated with a novel method based on deep learning

The dispersion of fibers in UHPC significantly affects its mechanical properties. Currently, conventional image analysis methods are employed to evaluate fiber dispersion. However, due to the settling of steel fibers in UHPC and the subjectivity of image processing, the evaluation of fiber dispersion is neither sufficiently thorough nor accurate. In this study, removal method was used to optimize a deep learning-based U-net model for fiber identification. And sinkage coefficient was proposed as a complementary index for evaluating fiber dispersion. Subsequently, the accuracy of both the identification technique and evaluation method was separately validated. Then, the image processing techniques and evaluation method proposed in this study are applied to investigate the impact of various factors on fiber distribution in UHPC. The results indicate that the optimized deep learning-based network accomplished batch image semantic segmentation and image denoising. This approach mitigated the subjectivity during threshold selection and minimized the influence of low-quality images on fiber identification. The proposed sinkage coefficient can reflect the impact of fiber sinking on its performance. This coefficient associate with distribution and orientation coefficients can evaluate fiber dispersion comprehensively. Experimental investigations revealed that during vibration, fibers in UHPC exhibit sinking or floating phenomena. The consistency of the slurry emerges as a pivotal factor affecting fiber dispersion. Lower consistency results in pronounced fiber sinking, whereas higher consistency induce fiber floating. The incorporation of coarse aggregate facilitates the formation of a network structure with steel fibers, thereby ameliorating fiber sinking or floating tendencies, further affecting the fiber dispersion. Additionally, the increase in vibration time is also detrimental to the uniform dispersion of fibers.

Trends in Machine and Deep Learning Techniques for Plant Disease Identification: A Systematic Review

This review explores the use of machine learning (ML) techniques for detecting pests and diseases in crops, which is a significant challenge in agriculture, leading to substantial yield losses worldwide. This study focuses on the integration of ML models, particularly Convolutional Neural Networks (CNNs), which have shown promise in accurately identifying and classifying plant diseases from images. By analyzing studies published from 2019 to 2024, this work summarizes the common methodologies involving stages of data acquisition, preprocessing, segmentation, feature extraction, and prediction to develop robust ML models. The findings indicate that the incorporation of advanced image processing and ML algorithms significantly enhances disease detection capabilities, leading to the early and precise diagnosis of crop ailments. This can not only improve crop yield and quality but also reduce the dependency on chemical pesticides, contributing to more sustainable agricultural practices. Future research should focus on enhancing the robustness of these models to varying environmental conditions and expanding the datasets to include a wider variety of crops and diseases. CNN-based models, particularly specialized architectures like ResNet, are the most widely used in the studies reviewed, making up 42.36% of all models, with ResNet alone contributing 7.65%. This highlights ResNet’s appeal for tasks that demand deep architectures and sophisticated feature extraction. Additionally, SVM models account for 9.41% of the models examined. The prominence of both ResNet and MobileNet reflects a trend toward architectures with residual connections for deeper networks, alongside efficiency-focused designs like MobileNet, which are well-suited for mobile and edge applications.

Assessment of heavy metal concentrations, environmental risks and human health implications in marine sediments along the coastal ecosystems of the Republic of Congo.

The aim of this study is to assess the level of heavy metals (HMs) in surface sediments along the coastal estuaries of the Republic of Congo. A variety of approaches were employed, integrating elemental analysis (Atomique Absorption Spectroscopy), environmental, ecological, health risk assessment, and source identification techniques to evaluate the distribution, origin, environmental, ecological and health risks of potentially toxic element (PTE) contamination in sediments. The ascending mean concentration (mg/kg) of Cd (0.4) < Cu (18.6) < Pb (28.8) < Zn (62.1) < Mn (107) < Mg (569.6) complied with the local background, except for Cd and Pb. However, notable variations across sampling points and sites were observed, with a strong relationship between metals and organic matter. The comparing of PTEs with USEPA international standards and sediment toxicity guidelines showed compliance. The assessment of environmental risk parameters, based on the calculation of the geo-accumulation index, concentration factor and pollutant load indices showed low pollution of sediments. The ecological risk assessment, on the other hand, revealed a low level of pollution of biota and a low toxicity risk of ecosystems related to Cd and Pb and then to Cu in some sites. Regarding human health risk assessment, related to the PTEs, the cancerogenic effect indices were calculated for Cd and Pb through ingestion and inhalation pathways, and the non-cancerogenic effect indices were calculated for all HMs through ingestion, inhalation, and cutaneous contact routes, for children and adults, and showed that the results comply with the international standards set by USEPA, ranging from 10-4 to 10-6 and less than 1, respectively, where children were more sensitive and vulnerable than adults to the effects of the PTEs. Ultimately, this study revealed sediment lithology, anthropogenic activities, and mixed origins of HMs, with a large contribution from industrial activities. However, no significant impacts are discernible in the study area. In addition to the importance of this study, it forms part of the region database and could serve as a reference for future monitoring studies, since no data are available in th Congolese coastal environment.

Scientific advances regarding the effect of carbonated alkaline waste materials on pozzolanic reactivity

Using alkaline waste materials as biomass ash (BA), Recycled Concrete Fines (RCF) from constructions and demolition waste (CDW) and ladle furnace slags (LFS), as CO2 massive sinks worldwide through the generation of new potential SCM by Accelerated Carbonation Technologies to reduce the cement industry's carbon footprint is increasingly attracting attention for its environmental benefits and the materials' improved performance in blended cement matrices. This paper employs characterization and identification techniques (XRF, XRD–Rietveld, SEM/EDX, BET, TG/DTG, FTIR, NMR) to analyse the effect of accelerated carbonation of white ladle furnace slag, siliceous construction and demolition waste and biomass ash on those materials' physical and mineralogical properties, their chemical reactivity and their pozzolan/lime systems' mineralogical phases. The results show that although behaviour differs depending on the carbonated waste materials' alkalinity, all three present notable increases in BET surface area (7–17.5 m2/g) and substantially altered potentially carbonatable mineralogical phases (e.g. portlandite, Ca-olivine, periclase, hydrated cement phases), which mostly transition towards calcite, due to the CO2 uptake. Thermodynamic modelling of the pozzolanic reaction indicates that CSH/C(A)SH) gels are the most stable phases, followed by ettringite, C4AH13 and C4AcH12.

External electric grid impact on mode parameters at a load node

Relevance. A passive experiment is one of the advanced techniques for a static load voltage characteristic identification. External electric grid impact on mode parameters at a load node – a grid response – sophisticates the statistical processing of passive experiment results significantly. For this reason, the analysis of a grid response impact and detection of its reduction are relevant problems. Aim. Quantitative evaluation of a grid response impact is produced by grid response active and reactive power coefficients; therefore the aim of this paper is to analyze the variance of grid response coefficients in relation on scheme and mode parameters. This allows finding techniques of reducing a grid response impact. Methods. Transformations of the Jacobi matrix, when scheme and mode parameters are formed as complex numbers. Results. The authors have proposed the analytic expressions, reproducing dependences of grid response coefficients on scheme and mode parameters in general variants. Conclusions. Based on the analytic and schematic dependencies the authors determined the measures for reducing a grid response impact. It is demonstrated that with increase in the impedance among power and load nodes, a grid response impact grows as well by active and reactive power. The authors proposed the practical technique for calculating a grid response impact. The perspective trends for improving accuracy in identifying a grid response impact using current mode and scheme parameters are defined.

Wohlfahrtiimonas chitiniclastica: current insights and complementary review from Chinese cases.

Wohlfahrtiimonas chitiniclastica is an emerging zoonotic pathogen associated with bacteremia, myiasis, and soft tissue infections. It is insufficiently identified and underestimated due to reasons, such as shortcomings of the traditional identification techniques and language barriers in local case reports from different regions. In this review, we summarize the currently available literature. In particular, we added previously overlooked cases from Chinese and other medical communities. The clinical characteristics, identification, and treatment of W. chitiniclastica are discussed. This work provides a complete review of the previous work including cases from human, animal, and other sources.

Comparative Analysis of the Morphological Traits of Cucumis melo Seed Remains Discovered in the Graveyard of the Haihun Marquis in Nanchang and Modern C. melo Seeds Using Scanning Electron Microscopy.

The graveyard of the Haihun Marquis in Nanchang is the largest and best-preserved Han Dynasty mausoleum discovered in China. Within the tomb's beam bank (M1), archaeologists have uncovered a variety of plant remains, such as melon seeds, the traits and origins of which warrant timely investigation. Cucumis melo L. has a long history of being cultivated and consumed in China. In this study, macroscopic identification techniques, scanning electron microscopy, and cluster analyses of the seeds of ancient C. melo and 16 varieties of modern melons (9 with thick skin, 6 with thin skin, and 1 thick/thin-skinned hybrid) were performed to compare their morphological trait indexes and seed coat ultrastructures. Through the measurement of various morphological parameters (length, width, upper one-third width, lower one-third width, thickness, and surface area) of each type of melon seed and subsequent cluster analysis of the morphological data, the ancient melon was determined to belong to the same cluster as the C. melo varieties "Lvbao", "Huangpi", "Boyang No. 9", "Malianzhuang", "Yangjiaomi", and "Zhimami", exhibiting the closest affinity to the thin-skinned types, particularly "Huangpi", "Malianzhuang", and "Zhimami". Scanning electron microscopy analysis of the epidermal ultrastructure revealed that the reticulated morphology of the seed coat of the ancient melon closely resembled that of the thin-skinned melon. Consequently, we hypothesize that the melon seeds excavated from the graveyard of the Haihun Marquis belong to the thin-skinned type of melon.

Identification method of cyclic top defects in track geometry measurements in its early stage

Track irregularities in the long wavelengths are often overlooked in the development of new track identification and detection techniques, yet they can have serious implications for the safety of freight wagons and the comfort of passengers. This study is aimed to extend the current published literature, by proposing a methodology for the identification and classification of evolving cyclic top (CT) irregularities in the mid and long wavelength range between 25 and 70 m. The methodology required a dataset of historical vertical track geometry measurements to identify and classify CT irregularities, by analysing the growing wavelength contents. The classification of detected CT into early and advanced stage of evolution allows maintenance interventions to be timely planned, improving transportation efficiency. This study contributes to enhancing monitoring methodologies for track geometry and highlights the importance of considering mid and long wavelength irregularities.

Ascertainment of community exposure sites to Ross River virus during the 2020 outbreak in Brisbane, Australia.

This study investigated potential Ross River virus (RRV) exposure sites in Greater Brisbane during the Queensland COVID-19 lockdown (January-July 2020). Using RRV notifications, cluster identification techniques, and mobile phone data for movement network analysis, the study examined 993 RRV cases and 9 million movement trajectories from residential RRV cluster areas (hot-spots). The findings revealed that population movement was a key risk factor to RRV incidence within hotspots whereby highly interconnected areas had more RRV cases during lockdown. While environmental conditions within RRV hot-spot were less significant compared to their connectivity, areas with higher vegetation density had fewer RRV cases. The study also noted that individuals from RRV hot-spots spent less time in green areas pre-lockdown than during and after lockdown. The results suggest that population movement significantly influenced the 2020 RRV outbreak. These insights can help adapt current vector control and surveillance protocols to target areas identified in this study.

Dynamics Modelling and Adaptive Identification: Towards Improved Human-Robot Interaction in Collaborative Systems

This article presents a novel approach to enhancing human-robot collaboration and safety through advanced dynamic modelling and adaptive identification techniques. We introduce a comprehensive methodology that integrates motion trajectory design with real-time torque detection, addressing the critical limitations of conventional systems that rely on costly joint torque sensors. By simultaneously identifying friction forces in an integrated joint and a simplified two-bar mechanism, our approach leverages existing kinematic and dynamic models to achieve precise dynamic parameter identification. The proposed method significantly advances the fields of drag-teaching and collision detection by eliminating the need for force sensors, thus making it more feasible for mass-produced robotic systems. Our findings demonstrate that accurate dynamic modelling is essential for effective zero-force control, particularly in high-speed drag-teaching scenarios, where inertia and friction present substantial challenges. Experimental validation confirms the efficacy of our dynamic feed-forward controller design and the adaptability of drag-teaching parameters, leading to improved operational flexibility and safety in collaborative environments. This research contributes a critical framework for future developments in intelligent robotic systems, providing a robust basis for integrating advanced human-robot interactions in industrial applications.