Fingerprinting Technique Research Articles

Identification of Sedimentary Environments through Dynamic Image Analysis of the Particle Morphology of Beach Sediments on the East and West Coasts of Hainan Island in South China

Particle morphology is an important feature of sediments that reflects their transport history and depositional environment. In this study, we used dynamic image analysis (DIA) to measure the size and shape of beach sediments on the east and west coasts of Hainan Island in South China Sea. DIA is a fast and accurate method that can capture and analyze a large number of sediment particles in real-time. We extracted morphological descriptors of each particle, such as equivalent diameter, sphericity, aspect ratio and symmetry, and their distributions based on volume and number. We performed multivariate analysis on the particle morphological data, including alpha diversity, statistical analysis and fingerprint techniques. We found that the Shannon index, calculated by the number distribution of sediment particle morphology, can effectively discriminate between the two sites, reflecting different sediment sources, transport processes and depositional conditions. We also established a composite fingerprint based on seven morphological parameters and diversity indices, which can accurately distinguish between aeolian and hydraulic sedimentary environments. Our study demonstrates the potential application of DIA in identifying sedimentary environments and establishing sediment fingerprints. This can help us understand the sediment transport processes and depositional mechanisms in coastal areas.

FL4IoT: IoT Device Fingerprinting and Identification Using Federated Learning

Unidentified devices in a network can result in devastating consequences. It is, therefore, necessary to fingerprint and identify IoT devices connected to private or critical networks. With the proliferation of massive but heterogeneous IoT devices, it is getting challenging to detect vulnerable devices connected to networks. Current machine learning-based techniques for fingerprinting and identifying devices necessitate a significant amount of data gathered from IoT networks that must be transmitted to a central cloud. Nevertheless, private IoT data cannot be shared with the central cloud in numerous sensitive scenarios. Federated learning (FL) has been regarded as a promising paradigm for decentralized learning and has been applied in many different use cases. It enables machine learning models to be trained in a privacy-preserving way. In this article, we propose a privacy-preserved IoT device fingerprinting and identification mechanisms using FL; we call it FL4IoT. FL4IoT is a two-phased system combining unsupervised-learning-based device fingerprinting and supervised-learning-based device identification. FL4IoT shows its practicality in different performance metrics in a federated and centralized setup. For instance, in the best cases, empirical results show that FL4IoT achieves ∼99% accuracy and F1-Score in identifying IoT devices using a federated setup without exposing any private data to a centralized cloud entity. In addition, FL4IoT can detect spoofed devices with over 99% accuracy .

Artificial Neural Network for Indoor Localization Based on Progressive Subdivided Quadrant Method

The exterior location of a user can be accurately determined using a global positioning system (GPS). However, accurately locating objects indoors poses challenges due to signal penetration limitations within buildings. In this study, an MLP with stochastic gradient descent (SGD) among artificial neural networks (ANNs) and signal strength indicator (RSSI) data received from a Zigbee sensor are used to estimate the indoor location of an object. Four fixed nodes (FNs) were placed at the corners of an unobstructed area measuring 3 m in both length and width. Within this designated space, mobile nodes (MNs) captured position data and received RSSI values from the nodes to establish a comprehensive database. To enhance the precision of our results, we used a data augmentation approach which effectively expanded the pool of selected cells. We also divided the area into sectors using an ANN to increase the estimation accuracy, focusing on selecting sectors that had measurements. To enhance both accuracy and computational speed in selecting coordinates, we used B-spline surface equations. This method, which is similar to using a lookup table, brought noticeable benefits: for indoor locations, the error margin decreased below the threshold of sensor hardware tolerance as the number of segmentation steps increased. By comparing our proposed deep learning methodology with the traditional fingerprinting technique that utilizes a progressive segmentation algorithm, we verified the accuracy and cost-effectiveness of our method. It is expected that this research will facilitate the development of practical indoor location-based services that can estimate accurate indoor locations with minimal data.

Natural complex substances: From molecules to the molecular complexes. Analytical and technological advances for their definition and differentiation from the corresponding synthetic substances

Natural complex substances (NCSs) are a heterogeneous family of substances that are notably used as ingredients in several products classified as food supplements, medical devices, cosmetics and traditional medicines, according to the correspondent regulatory framework. The compositions of NCSs vary widely and hundreds to thousands of compounds can be present at the same time. A key concept is that NCSs are much more than the simple sum of the compounds that constitute them, in fact some emerging phenomena are the result of the supramolecular interaction of the constituents of the system. Therefore, close attention should be paid to produce and characterize these systems.Today many natural compounds are produced by chemical synthesis and are intentionally added to NCSs, or to formulated natural products, to enhance their properties, lowering their production costs. Market analysis shows a tendency of people to use products made with NCSs and, currently, products made with ingredients of natural origin only are not conveniently distinguishable from those containing compounds of synthetic origin. Furthermore, the uncertainty of the current European regulatory framework does not allow consumers to correctly differentiate and identify products containing only ingredients of natural origin.The high demand for specific and effective NCSs and their high-cost offer on the market, create the conditions to economically motivated sophistications, characterized by the addition of a cheap material to a more expensive one, just to increase profit. This type of practice can concern both the addition of less valuable natural materials and the addition of pure artificial compounds with the same structure as those naturally present.In this scenario, it becomes essential for producers of natural products to have advanced analytical techniques to evaluate the effective naturalness of NCSs. In fact, synthetically obtained compounds are not identical to their naturally occurring counterparts, due to the isotopic composition or chirality, as well as the presence of different trace metabolites (since pure substances in nature do not exist).For this reason, in this review, the main analytical tests that can be performed to differentiate natural compounds from their synthetic counterparts will be highlighted and the main analytical technologies will be described. At the same time, the main fingerprint techniques useful for characterizing the complexity of the NCSs, also allowing their identification and quali-quantitative evaluation, will be described.Furthermore, NCSs can be produced through different manufacturing processes, not all of which are on the same level of quality. In this review the most suitable technologies for green processes that operate according to physical extraction principles will be presented, as according to the authors they are the ones that come closest to creating more life-cycle compatible NCSs and that are well suited to the European green deal, a strategy with the aim of transforming the EU into a sustainable and resource-efficient society by 2050.

Device Fingerprinting for Cyber-Physical Systems: A Survey

The continued growth of the cyber-physical system (CPS) and Internet of Things technologies raises device security and monitoring concerns. For device identification, authentication, conditioning, and security, device fingerprint/fingerprinting (DFP) is increasingly used. However, finding the correct DFP features and sources to establish a unique and stable fingerprint is challenging. We present a state-of-the-art survey of DFP techniques for CPS device applications. We investigate the numerous DFP features, their origins, characteristics, and applications. Additionally, we discuss the DFP characteristics and their sources in detail, taking into account the physical contexts of various entities (i.e., machines, sensors, networks, and computational devices), as well as their software and applications for the CPS. We believe that this article will provide researchers and developers with insights into the DFP and its applications, sources, aggregation methods, and factors affecting its use in CPS domains.

Carbon sequestration and climate change mitigation using macroalgae: a state of knowledge review.

The conservation, restoration, and improved management of terrestrial forests significantly contributes to mitigate climate change and its impacts, as well as providing numerous co-benefits. The pressing need to reduce emissions and increase carbon removal from the atmosphere is now also leading to the development of natural climate solutions in the ocean. Interest in the carbon sequestration potential of underwater macroalgal forests is growing rapidly among policy, conservation, and corporate sectors. Yet, our understanding of whether carbon sequestration from macroalgal forests can lead to tangible climate change mitigation remains severely limited, hampering their inclusion in international policy or carbon finance frameworks. Here, we examine the results of over 180 publications to synthesise evidence regarding macroalgal forest carbon sequestration potential. We show that research efforts on macroalgae carbon sequestration are heavily skewed towards particulate organic carbon (POC) pathways (77% of data publications), and that carbon fixation is the most studied flux (55%). Fluxes leading directly to carbon sequestration (e.g. carbon export or burial in marine sediments) remain poorly resolved, likely hindering regional or country-level assessments of carbon sequestration potential, which are only available from 17 of the 150 countries where macroalgal forests occur. To solve this issue, we present a framework to categorize coastlines according to their carbon sequestration potential. Finally, we review the multiple avenues through which this sequestration can translate into climate change mitigation capacity, which largely depends on whether management interventions can increase carbon removal above a natural baseline or avoid further carbon emissions. We find that conservation, restoration and afforestation interventions on macroalgal forests can potentially lead to carbon removal in the order of 10's of Tg C globally. Although this is lower than current estimates of natural sequestration value of all macroalgal habitats (61-268 Tg C year-1 ), it suggests that macroalgal forests could add to the total mitigation potential of coastal blue carbon ecosystems, and offer valuable mitigation opportunities in polar and temperate areas where blue carbon mitigation is currently low. Operationalizing that potential will necessitate the development of models that reliably estimate the proportion of production sequestered, improvements in macroalgae carbon fingerprinting techniques, and a rethinking of carbon accounting methodologies. The ocean provides major opportunities to mitigate and adapt to climate change, and the largest coastal vegetated habitat on Earth should not be ignored simply because it does not fit into existing frameworks.

Identifying the source of movie piracy: cryptographic fingerprinting for tracking movie content

Background: The unauthorized distribution of movies through piracy has become a widespread issue affecting both movie studios and OTT streaming partners. This research introduces a novel method utilizing cryptographic fingerprinting to track movie content, with the goal of identifying the precise source of piracy. Methods: The developed specialized software that utilizes the cryptographic fingerprinting algorithm to analyze pirated movie content obtained from various online piracy platforms. The software employs computer vision techniques to identify the source of piracy, including the specific theatre, showtime, and recording location within the theatre. Results: Our experiments demonstrated the effectiveness of the proposed cryptographic fingerprinting approach. The software successfully detected and analyzed pirated movie content from a wide range of online piracy platforms. Conclusions: The cryptographic fingerprinting technique allows for the identification of the source of piracy, including the specific theatre, showtime, and recording location within the theatre. By swiftly reporting these details, our software aims to aid in the prevention of future piracy incidents, ultimately eliminating the source of piracy.

Palmprint enhancement network (PEN) for robust identification

Despite being reliable, palmprints have not received as much attention as other biometrics such as fingerprints, face or iris. Amount of information provided by high resolution palmprints and the fact that they have huge forensic value makes them a preferred biometric choice for large scale identification systems. In palmprints, extraction of reliable features for identification is still a challenging task especially because most of palmprints found in the real world, e.g., in crime scenes, are of poor quality. This makes palmprint enhancement a crucial pre cursor to identification. Errors during enhancement result in extraction of un-reliable features which deteriorate identification accuracy. Recent works in palmprints have focused more on matching algorithms and limited novelty has been introduced in enhancement. Enhancement techniques used on high resolution palmprints recently are either borrowed from fingerprint techniques or are built on the high-risk assumption that palm ridge pattern is stationary or smooth in a local area. Large size and abruptly changing ridge pattern of palmprints dictates the need for a more robust enhancement scheme. This paper proposes a novel deep learning based high resolution palmprint enhancement approach that is able to process large areas of palmprint without making the assumption that underlying ridge pattern is stationary. We have tested proposed enhancement approach on a renowned high resolution palmprint dataset which shows that proposed technique performs favourably in comparison to state of the art.

Dropout Autoencoder Fingerprint Augmentation for Enhanced Wi-Fi FTM-RSS Indoor Localization

In this letter, we propose a dropout autoencoder fingerprint augmentation approach for enhanced Wi-Fi fine time measurement and received signal strength signals-based indoor localization. Due to complex indoor environment, fingerprinting techniques suffers from unrecorded measurements at some reference points, leading to incomplete fingerprint datasets. The dropout autoencoder was employed to reconstruct clean signal features for the unrecorded fingerprint measurement which can significantly affect the localization accuracy of fingerprinting systems. The localization is accomplished by utilizing deep neural networks (DNN)-based regression. We collected two datasets from experiments conducted in two indoor offices using commercial off-the-shelf devices. The performance of our proposed method was compared to existing methods, and on the respective datasets, our proposal method showed better performance with a localization accuracy of 0.3m and 0.6m for the 1-σ percentile errors and 0.66m and 1.5m for the 2-σ percentile errors.

Study on electrochemical and X-ray diffraction fingerprint of Tibetan medicine “zuotai”

This study investigates the chemical composition and electroactive compounds in ten samples of Zuotai, a traditional Tibetan medicinal material, utilizing electrochemical fingerprinting techniques. A commonality in the oscillatory behavior of the induction curves across samples is noted, suggesting identical or similar types of chemical or electroactive components. Nonetheless, observable differences in oscillation reactions and periods hint at variations in the concentration and activity of these components. The disparities in the fingerprint spectra of Zuotai from different manufacturers are likely due to differing quantities of electroactive compounds and choices of raw materials in the production process. A cluster analysis of the electrochemical fingerprint spectra further corroborates these findings, showing a significant similarity to the X-ray diffraction (XRD) results. This study underscores the importance of standardized manufacturing practices in ensuring consistent quality and efficacy of Zuotai.

Greatly Enhanced Raman Scattering of Graphene on Metals by a Boron Nitride Film Covering.

Raman spectroscopy, a nondestructive fingerprinting technique, is mainly utilized to identify molecular species and phonon modes of materials. However, direct Raman characterization of two-dimensional materials typically synthesized on catalytic metal substrates is extremely challenging because of the significant electric screening and interfacial electronic couplings. Here, we demonstrate that by covering as-grown graphene with boron nitride (BN) films, the Raman intensity of graphene can be enhanced by two orders of magnitude and is also several times stronger than that of suspended graphene. This great Raman enhancement originates from the optical field amplification by Fabry-Pérot cavity in BN films and the local field plasmon near copper steps. We further demonstrate the direct characterization of the local strain and doping level of as-grown graphene and in situ monitoring of the molecule reaction process by enhanced Raman spectroscopy. Our results will broaden the optical investigations of interfacial sciences on metals, including photoinduced charge transfer dynamics and photocatalysis at metal surfaces.

Comparative Analysis of Different Biometric Techniques for Security Systems

Biometrics is the automated process of identifying a person based on biological and behavioral characteristics. It can be used to determine your identity and strengthen your ability to use accurate, secure, reliable, and less expensive authentication for a large number of applications. Biometry has been successfully implemented in numerous disciplines, including criminology, medicine, security, identity, and authorization. This article is all about Comparison Analysis of five biometric identification technologies i.e., iris recognition, fingerprint, voice recognition, face recognition, and signature recognition. It also discusses the mode of operation and advantages. And disadvantages. of each above technology, application, limitation, acceptance, uniqueness, security, and performance. The author has concluded that the fingerprint technique is the fastest and most accurate biometric technique for a more dependable and secure system based on performance and fast communication. Due to the unique characteristics of the iris (the iris approach delivers the most secure performance, accuracy, uniqueness, and acceptability of all biometric procedures). It can be also used forever as a password. Finally, Iris is the only part of a human that cannot be changed and provides the finest answer overall.

Detectable Anthropogenic Intensification of the Summer Compound Hot and Dry Events Over Global Land Areas

AbstractCompound hot and dry events (CHDEs) pose an increasing threat to global warming and have received growing attention in recent decades. We conducted a detection and attribution analysis (D&A) using the optimal fingerprint technique to analyze the changes in observed CHDEs at continental and sub‐continental scales from 1961 to 2014. The responses of CHDEs changes to anthropogenic (ANT), greenhouse gases (GHG), aerosols (AER), and natural (NAT) forcings were obtained from the Coupled Model Intercomparison Project phase 6 forcing experiment. In a two‐signal D&A analysis that considered ANT and NAT simultaneously, ANT signals were robustly detected and separated from NAT in all continental domains and more than 65% of subcontinental domains (29 out of 44 domains). The trend attributed to the ANT forcing is comparable to the observed trend over domains where the ANT signal is detectable. We also conducted a three‐signal D&A analysis that regressed observation onto GHG, AER, and NAT simultaneously. The GHG signals were detectable in all continents except Oceania, and it contributed dominantly to the observed trend over 22 out of 29 regions where ANT signals had emerged. Multivariate biases correction can largely reduce the models' biases in simulating the marginal distribution and the dependence structure, which can provide a more accurate future risk of CDHEs. We found that limiting global warming to 1.5℃ rather than 2.0℃ can significantly mitigate the impact of compound events in most areas of global land, particularly in South America, Africa, and Oceania.

Lead isotope fingerprinting techniques help identify and quantify 3000 years of atmospheric lead pollution from Laguna Roya, northwestern Iberia

Legacy pollution research has established that over the past 3000 years, mining and metallurgical activities have resulted in widespread deposition of lead (Pb) pollution. However, there is still a limited understanding of how humans have impacted the long-term cycling of Pb in the environment. We present a 4,000-year lake sediment Pb isotope record from Laguna Roya, northwestern Iberia, that identifies and quantifies the predominant sources of atmospheric Pb pollution. For the first time, Pb isotopic compositions of ancient slag samples dated (∼600 BCE–200 CE) from a mining district in the southwest of the Iberian Peninsula are compared to Pb isotope ratios of Pb pollution deposited contemporaneously in lake sediments. In addition, literature Pb isotope ratios of ores from mining regions throughout Iberia are compared with those of leaded gasoline and coal to identify additional sources of anthropogenic Pb. Deposition of atmospheric Pb pollution begins after 950 BCE, and until 1750 CE, the Pb isotopic composition most resembles the southwestern slag deposits, containing a mixture of Pb ores from southeast Iberia (up to 36%) and southwest Iberia (∼74%). Between 1750 and 1960 CE, Pb pollution is attributed to Pb mining in southcentral Iberia. After 1960 CE, the dominant Pb pollution source (∼85%) is again metal refining in southwestern Iberia, and only ∼15% is from leaded gasoline. Provenance and reconstruction of the temporal and spatial distribution of legacy Pb pollution further our understanding of how humans have affected the biogeochemical cycle of this toxic element in the environment over time.

Static and dynamic source identification of trace elements in river and soil environments under anthropogenic activities in the Haraz plain, Northern Iran

Unsustainable human activities have disrupted the natural cycle of trace elements, causing the accumulation of chemical pollutants and making it challenging to determine their sources due to interwoven natural and human-induced processes. A novel approach was introduced for identifying the sources and for quantifying the contribution of trace elements discharge from rivers to soils. We integrated fingerprinting techniques, soil and sediment geochemical data, geographically weighted regression model (GWR) and soil quality indices. The FingerPro package and the state-of-the-art tracer selection techniques including the conservative index (CI) and consensus ranking (CR) were used to quantify the relative contribution of different upland sub-watersheds in trace element discharge soil. Our analysis revealed that off-site sources (upland watersheds) and in-site sources (land use) both play an important role in transferring trace elements to the Haraz plain (northern Iran). The unmixing model's results suggest that the Haraz sub-watersheds exhibit a higher contribution to trace elements transfer in the Haraz plain, and therefore, require greater attention in terms of implementing soil and water conservation strategies. However, it is noteworthy that the Babolroud (adjacent to Haraz) exhibited a better performance of the model. A spatial correlation between certain heavy metals, such as As and Cu, and rice cultivation existed. Additionally, we found a significant spatial correlation between Pb and residential areas, particularly in the Amol region. Our result highlights the importance of using advanced spatial statistical techniques, such as GWR, to identify subtle but critical associations between environmental variables and sources of pollution. The methodology used comprehensively identifies dynamic trace element sourcing at the watershed scale, allowing for pollutant source identification and practical strategies for soil and water quality control. Tracer selection techniques (CI and CR) based on conservatives and consensus improve unmixing model accuracy and flexibility for precise fingerprinting.

Evaluating the quality consistency of antiviral oral liquid by high-performance liquid chromatography five-wavelength matched average fusion fingerprint combined with electrochemical fingerprint and ultraviolet spectral quantum fingerprint

The antiviral oral liquid (AOL) was an antiviral drug currently in clinical trials against coronavirus disease 2019. This study aimed to improve its quality consistency evaluation method using fingerprint techniques from several aspects. First, the five-wavelength matched average fusion fingerprint (FMAFFP) for HPLC, electrochemical fingerprint (ECFP), and ultraviolet spectral quantum fingerprint (UVFP) was established for 22 samples, respectively. Their quality was then assessed using the average linear quantitative fingerprint method, and 22 samples were classified into eight quality grades. OPLS and PCA were then used further to explore the characteristic parameters of these three fingerprints. Five compounds were quantified simultaneously for the first time, and then the relationship between the average linear quantitative similarity (PL) and the sum of the five quantitative components (P5c) was investigated. A linear correlation (r ≥ 0.9735) between PL and P5c suggested that PL may be used to predict chemical content. Finally, to investigate the antioxidant potential of the AOL, correlation analyses were performed for FMAFFP peaks-PEC and UVFP peaks-PEC, respectively, where the PEC value was defined as the quantitative similarity of ECFP. The Pearson correlation coefficient and gray correlation analysis were consistent, allowing us to initially explore the antioxidant capacity of the unidentified components of the samples. This study researched AOL using multidimensional fingerprints to provide a comprehensive and reliable method for quality consistency control of herbal compound preparations.

MolFPG: Multi-level fingerprint-based Graph Transformer for accurate and robust drug toxicity prediction

Drug toxicity prediction is essential to drug development, which can help screen compounds with potential toxicity and reduce the cost and risk of animal experiments and clinical trials. However, traditional handcrafted feature-based and molecular-graph-based approaches are insufficient for molecular representation learning. To address the problem, we developed an innovative molecular fingerprint Graph Transformer framework (MolFPG) with a global-aware module for interpretable toxicity prediction. Our approach encodes compounds using multiple molecular fingerprinting techniques and integrates Graph Transformer-based molecular representation for feature learning and toxic prediction. Experimental results show that our proposed approach has high accuracy and reliability in predicting drug toxicity. In addition, we explored the relationship between drug features and toxicity through an interpretive analysis approach, which improved the interpretability of the approach. Our results highlight the potential of Graph Transformers and multi-level fingerprints for accelerating the drug discovery process by reliably, effectively alarming drug safety. We believe that our study will provide vital support and reference for further development in the field of drug development and toxicity assessment.

Identification of geographical origins of Astragalus membranaceus in China using electrochemical fingerprinting

Astragalus membranaceus quality differs across different geographical locations. The electrochemical fingerprint technique is a detection technology that captures the electrochemical activity of a sample. The electrochemical fingerprint feature represents each sample, and can collect information about the electrochemical active components in traditional Chinese medicine, which can be used to distinguish between different medicinal materials and verify their authenticity. In this study, electrochemical fingerprinting was used to collect data from three different geographical locations of Astragalus membranaceus. We employed differential pulse voltammetry (DPV) to establish an electrochemical fingerprint of Astragalus membranaceus, reflecting the proportional content of various components. Ten potential markers were selected from the collected data for similarity calculations. Additionally, the original fingerprint data was used for cluster analysis and heatmap construction. The results demonstrate the potential of electrochemical fingerprinting as a technique for identifying the origin of traditional Chinese medicinal materials. This study explored the feasibility of using electrochemical fingerprint sensors for identifying the origin of the same medicinal material from different geographical locations.

An Integrated Approach to Securing In-Vehicle CAN Bus Network Using ECU Fingerprinting and Image Classification Techniques

An Integrated Approach to Securing In-Vehicle CAN Bus Network Using ECU Fingerprinting and Image Classification Techniques

Difficulties in Kinship Analysis for Victims’ Identification in Armed Conflicts

Regarding human identification in armed conflicts, various complications can be observed. Usually, such difficulties can be social-related, which can include the lack of access to the relative’s genetic material, or the unwillingness of administrative and judicial authorities to participate in the process of identification. In the case of genetics, the analysis allows identifying the individual from a blood sample, a part of an organ, or from skeletal remains, which is why it is considered a much more extensive and effective method when compared with fingerprint techniques or odontology. However, several factors can prevent this identification, such as considerably degraded genetic material. For successful identification, it is mandatory to have access to antemortem biological samples unequivocally attributed to the individual in question, using recombinant nuclear markers, as well as using biological samples from close relatives, whether parents or sons. Nevertheless, the problems associated with armed conflicts make this type of study very difficult. In this article, we focus on the main difficulties encountered when identifying an individual victim of an armed conflict, as well as on the possibilities that exist and on viable measures that could be required to improve the identification of these victims.