Fingerprinting Approach Research Articles

A Low Complexity Indoor Visible Light Positioning Method

The continuous positioning of a mobile user equipment (MU) in a prompt way is a fundamental requirement in several indoor applications related to ubiquitous computation, assisted living environments, and security/surveillance systems. Motivated by this fact, the current contribution reports a low-complexity visible light positioning (VLP) method suitable for indoor environments in the Big Data era. The proposed architecture consists of multiple light-emitting diodes (LEDs) as light sources and an MU equipped with a photodiode (PD). To guarantee higher spectral efficiency, the LEDs emit sinusoidal waveforms at slightly different predetermined frequencies. The light intensity received at the PD from every LED is continuously estimated after applying a short-time Fourier transformation. To this end, a grid-based numerical technique is proposed to recover the unknown MU position. Additionally, a closed-form solution is presented for the particular case of three LEDs that overrides the need for training points in the grid arrangement. Further, a method for handling noisy signals is proposed, based on averaging the calculated positions from densely overlapping received signals. Finally, a Kalman filter is employed as a post-processing precision improving tool. The proposed VLP efficiency is quantified through respective Monte Carlo simulations that allow the setting of different LED frequencies, received signal processing parameters, MU speed, and noise levels. The results reveal that the suggested approach is robust with significant tolerance in high ambient light levels, computationally efficient, and exhibits low positional error. Finally, to evaluate the performance improvements the new method introduces, we make comparisons against widely-used fingerprint approaches.

Tracking longitudinal language network reorganisation using functional MRI connectivity fingerprints

Large individual differences in how brain networks respond to treatment hinder efforts to personalise treatment in neurological conditions. We used a brain network fingerprinting approach to longitudinally track re-organisation of complementary phonological and semantic language networks in 19 patients before and after brain-tumour surgery. Patient task fingerprints were individually compared to normal networks established in 17 healthy controls. Additionally, pre- and post-operative patient fingerprints were directly compared to assess longitudinal network adaptations. We found that task networks remained stable over time in healthy controls, whereas treatment induced reorganisation in 47.4% of patient fluency networks and 15.8% of semantic networks. How networks adapted after surgery was highly unique; a subset of patients (10%) showed ‘normalisation’ while others (21%) developed newly atypical networks after treatment. The strongest predictor of adaptation of the fluency network was the presence of clinically reported language symptoms. Our findings indicate a tight coupling between processes disrupting performance and neural network adaptation, the patterns of which appear to be both task- and individually-unique. We propose that connectivity fingerprinting offers potential as a clinical marker to track adaptation of specific functional networks across treatment interventions over time.

To glove or not to glove? Investigations into the potential contamination from handling of paper-based cultural heritage through forensic fingerprinting approaches

The handling of cultural heritage objects has become a highly debated topic in the last decade. The work and outcomes described in this paper are aimed to provide objective data to assist in making appropriate decisions as to whether or not wearing gloves is appropriate in a given situation. The forensic fingermark development techniques of 1,2-indandione and single metal deposition II were used to investigate the efficacy of handwashing and glove use to improve the information available when deciding whether to use gloves when handling paper objects. It was found that fingermarks did not permeate through polymer glove types but could through cotton gloves. It was also shown that the amounts of observable fingermark residues were greater 5 min after handwashing than if handwashing had not occurred, undermining previous arguments for not wearing gloves if hands could be washed before object handling.

Hybrid Deep Learning Model Based Indoor Positioning Using Wi-Fi RSSI Heat Maps for Autonomous Applications

Positioning using Wi-Fi received signal strength indication (RSSI) signals is an effective method for identifying the user positions in an indoor scenario. Wi-Fi RSSI signals in an autonomous system can be easily used for vehicle tracking in underground parking. In Wi-Fi RSSI signal based positioning, the positioning system estimates the signal strength of the access points (APs) to the receiver and identifies the user’s indoor positions. The existing Wi-Fi RSSI based positioning systems use raw RSSI signals obtained from APs and estimate the user positions. These raw RSSI signals can easily fluctuate and be interfered with by the indoor channel conditions. This signal interference in the indoor channel condition reduces localization performance of these existing Wi-Fi RSSI signal based positioning systems. To enhance their performance and reduce the positioning error, we propose a hybrid deep learning model (HDLM) based indoor positioning system. The proposed HDLM based positioning system uses RSSI heat maps instead of raw RSSI signals from APs. This results in better localization performance for Wi-Fi RSSI signal based positioning systems. When compared to the existing Wi-Fi RSSI based positioning technologies such as fingerprint, trilateration, and Wi-Fi fusion approaches, the proposed approach achieves reasonably better positioning results for indoor localization. The experiment results show that a combination of convolutional neural network and long short-term memory network (CNN-LSTM) used in the proposed HDLM outperforms other deep learning models and gives a smaller localization error than conventional Wi-Fi RSSI signal based localization approaches. From the experiment result analysis, the proposed system can be easily implemented for autonomous applications.

Fi-score: a novel approach to characterise protein topology and aid in drug discovery studies

Target evaluation is at the centre of rational drug design and biologics development. In order to successfully engineer antibodies, T-cell receptors or small molecules it is necessary to identify and characterise potential binding or contact sites on therapeutically relevant target proteins. Currently, there are numerous challenges in achieving a better docking precision as well as characterising relevant sites. We devised a first-of-its-kind in silico protein fingerprinting approach based on the dihedral angle and B-factor distribution to probe binding sites and sites of structural importance. Our derived Fi-score can be used to classify protein regions or individual structural subsets of interest and the described scoring system could be integrated into other discovery pipelines, such as protein classification databases, or applied to investigate new targets. We further demonstrated how our method can be integrated into machine learning Gaussian mixture models to predict different structural elements. Fi-score, in combination with other biophysical analytical methods depending on the research goals, could help to classify and systematically analyse not only targets but also drug candidates that bind to specific sites. The described methodology could greatly improve pre-screening stage, target selection and drug repurposing efforts in finding other matching targets. HIGHLIGHTS Description and derivation of a first-of-its-kind in silico protein fingerprinting method using B-factors and dihedral angles. Derived Fi-score allows to characterise the whole protein or selected regions of interest. Demonstration how machine learning using Gaussian mixture models on Fi-scores captures and allows to predict functional protein topology elements. Fi-score is a novel method to help evaluate therapeutic targets and engineer effective biologics. Communicated by Ramaswamy H. Sarma

Genetic differentiation among populations of the threatened Bellevalia webbiana (Asparagaceae) and its consequence on conservation

The narrow central Italian endemic and threatened Webb’s hyacinth (Bellevalia webbiana), a perennial herb, is a clear example of a species that has disappeared from several localities due to the development of human settlements. We characterized population genetics of this species to infer possible threats to its viability. We used a dominant DNA fingerprinting approach to infer genetic relationships among the five richest populations known for this species (each with N > 50 individuals). We highlighted phenomena of genetic erosion, with values of intrapopulation-gene-diversity quite similar across all populations (mean value 0.113), but a mean F st value only slightly below the mean found in other plant species using similar approaches. Despite an overall genetic similarity among populations, a population from Faenza (Emilia-Romagna) is clearly separated from all the others on genetic grounds, and may be defined as an Evolutionarily Significant Unit, worth of special conservation attention. Interestingly, this latter population is also behaving differently from all the others in terms of both vegetative and reproductive functional strategies. Our results highlight the relevance of evolutionary approaches to conservation biology for preserving a genetic diversity linked to local adaptations.

Impact of Fe(III) (Oxyhydr)oxides Mineralogy on Iron Solubilization and Associated Microbial Communities.

Iron-reducing bacteria (IRB) are strongly involved in Fe cycling in surface environments. Transformation of Fe and associated trace elements is strongly linked to the reactivity of various iron minerals. Mechanisms of Fe (oxyhydr)oxides bio-reduction have been mostly elucidated with pure bacterial strains belonging to Geobacter or Shewanella genera, whereas studies involving mixed IRB populations remain scarce. The present study aimed to evaluate the iron reducing rates of IRB enriched consortia originating from complex environmental samples, when grown in presence of Fe (oxyhydr)oxides of different mineralogy. The abundances of Geobacter and Shewanella were assessed in order to acquire knowledge about the abundance of these two genera in relation to the effects of mixed IRB populations on kinetic control of mineralogical Fe (oxyhydr)oxides reductive dissolution. Laboratory experiments were carried out with two freshly synthetized Fe (oxyhydr)oxides presenting contrasting specific surfaces, and two defined Fe-oxides, i.e., goethite and hematite. Three IRB consortia were enriched from environmental samples from a riverbank subjected to cyclic redox oscillations related to flooding periods (Decize, France): an unsaturated surface soil, a flooded surface soil and an aquatic sediment, with a mixture of organic compounds provided as electron donors. The consortia could all reduce iron-nitrilotriacetate acid (Fe(III)-NTA) in 1–2 days. When grown on Fe (oxyhydr)oxides, Fe solubilization rates decreased as follows: fresh Fe (oxyhydr)oxides > goethite > hematite. Based on a bacterial rrs gene fingerprinting approach (CE-SSCP), bacterial community structure in presence of Fe(III)-minerals was similar to those of the site sample communities from which they originated but differed from that of the Fe(III)-NTA enrichments. Shewanella was more abundant than Geobacter in all cultures. Its abundance was higher in presence of the most efficiently reduced Fe (oxyhydr)oxide than with other Fe(III)-minerals. Geobacter as a proportion of the total community was highest in the presence of the least easily solubilized Fe (oxyhydr)oxides. This study highlights the influence of Fe mineralogy on the abundance of Geobacter and Shewanella in relation to Fe bio-reduction kinetics in presence of a complex mixture of electron donors.

Extrapolation of drug induced liver injury responses from cancer cell lines using machine learning approaches

Currently, computational tools able to predict drug-induced liver injury (DILI) based on gene expression data from in vitro assays remain a challenge. Here we introduce a Qualitative Gene expression Activity Relationship (QGexAR) approach to assess the risk of DILI for any chemical. Using gene expression profiles of 276 chemicals tested on breast cancer cell line (MCF7) and prostate cancer cell line (PC3), we developed classification models based on Support Vector Machine (SVM) and Random Forest (RF) aiming at predicting the risk of DILI. With the combination of tissue expression filtering and a pathway enrichment fingerprint approach, a classification model with an accuracy reaching 0.72 in the training set and 0.95 in a validation set was obtained. Genes and pathways involved in liver metabolism contributed the most to the performance of the models. Notably, FoxO signalling pathway, alanine, aspartate and glutamate metabolism, drug metabolism and cytochrome P450 are highlighted, giving some indication to the mechanisms of action leading to DILI.

Detecting fraudulent additions in skimmed milk powder using a portable, hyphenated, optical multi-sensor approach in combination with one-class classification

The detection of fraudulent additions to milk powder is an ongoing research subject for governmental agencies, industry and academia. Current developments steer towards the application of so-called fingerprint approaches, describing authentic, reference samples with spectroscopy and using one-class classification (OCC) to identify “out-of-class”, or adulterated samples. Within this article we describe the application of a novel, portable device hyphenating ultraviolet–visible, fluorescence and near-infrared spectroscopy in combination with OCC modelling to discriminate authentic skimmed milk powders from adulterated ones. As adulterated samples we analyzed skimmed milk powder with the addition of plant protein powder, whey powder, starch, lactose, glucose, fructose as well as non-protein nitrogen like ammonium chloride, ammonium nitrate, melamine and urea in different concentrations. After fusion of the classification results from the three spectral techniques and several models two scenarios are presented. 100% (scenario 1) or 80% (scenario 2) of the authentic skimmed milk powders were correctly identified as “in-class”, whereas respectively 64% or 86% of the adulterated samples were correctly classified as “out-of-class”. In brief, this article provides insights in the application of novel, portable devices that may be applied in a non-invasive manner and gives an outlook on data handling and a new data fusion strategy.

Improving the quantification of sediment source contributions using different mathematical models and spectral preprocessing techniques for individual or combined spectra of ultraviolet–visible, near- and middle-infrared spectroscopy

In recent years, several sediment fingerprinting studies have used ultraviolet–visible (UV–Vis), near-infrared (NIR) and middle-infrared (MIR) spectroscopy as a low cost, non-destructive and fast alternative to obtain tracer properties to estimate sediment source contributions. For this purpose, partial last square regression (PLSR) has often been used to build predictive parametric models. However, spectra preprocessing and more robust and non-parametric models such as support vector machines (SVM) has gained little attention in these studies. Accordingly, the objectives of the current research were to evaluate (i) the accuracy of two multivariate methods (PLSR and SVM), (ii) the effect of eight spectra preprocessing techniques, and (iii) the effect of using the information contained in the UV–Vis, NIR and MIR regions considered either separately or in combination on sediment source apportionment. The estimated source contribution was then compared with contributions obtained by the conventional fingerprinting approach based on geochemical tracers. This study was carried out in the Arvorezinha catchment (1.23 km2) located in southern Brazil. Forty soil samples were collected in three main potential source (cropland surface, unpaved roads and stream channels) and twenty-nine suspended sediment samples collected at the catchment outlet during nine rainfall-runoff events were used in this study. Both PLSR and SVM models showed a higher accuracy when calibrated and validated with the spectra submitted to spectral processing when compared to the direct use of the raw spectra. The best model results were obtained with PLSR and SVM mathematical models associated with the spectral preprocessing techniques 1st derivative Savitzky-Golay (SGD1), normalization (NOR) and combining NOR + SGD1 in the UV–Vis + NIR + MIR. The lowest errors were observed when the UV–Vis + NIR + MIR bands were combined due to the gain in information and, consequently, the increase in discriminant power achieved by the models. Despite the good accuracy of the models calibrated and validated with the artificial mixtures, significant errors remain when results of source contributions are compared to those obtained with the conventional sediment fingerprinting technique based on geochemical tracers. Nevertheless, the magnitude of the contributions calculated by the spectroscopy and geochemical approaches remains very similar for all sources, especially when using the SVM-UV–Vis + NIR + MIR model. Therefore, spectroscopy proved to be a fast, cheap and accurate technique, offering an alternative to the conventional geochemical approach for discriminating sediment source contributions in agricultural catchments located in subtropical regions.

Efficacy based ginger fingerprinting reveals potential antiproliferative analytes for triple negative breast cancer

Ginger (Zingiber officinale) is one of the most widely consumed dietary supplements worldwide. Its anticancer potential has been demonstrated in various studies. However, ginger roots obtained from different geographical locations showed extensive variability in their activities, mainly due to differences in the levels of bioactive compounds. Here we evaluated the effect of these differences on the anticancer activity of ginger by performing efficacy-based fingerprinting. We characterized the fingerprint profiles of 22 ginger samples using liquid chromatography-mass spectroscopy, followed by a principal component analysis (PCA) and pearson correlation analysis. We also evaluated the anti-proliferative effects (IC50) of these samples on triple-negative breast cancer cells using the MTT assays. The supervised PCA identified a subset of analytes whose abundance strongly associated with the IC50 values of the ginger extracts, providing a link between ginger extract composition and in vitro anticancer efficacy. This study demonstrated that variation in the ginger fingerprint profiles resulting from differences in their chemical composition could have a significant impact on efficacy and bioactivity of ginger extracts. Also, this first-of-a-kind efficacy-based fingerprinting approach proposed here can identify potent anticancer candidates from the ginger fingerprint without the need for isolating individual components from the extracts.

Multiple wavelengths maximization fusion fingerprint profiling for quality evaluation of compound liquorice tablets and related antioxidant activity analysis

With natural plant entering a worldwide new arena in clinical medicine and pharmaceutical, herbal preparation are becoming popular all over the world. Compound liquorice tablets (CLQTs) have been widely applied as effective and typical antitussive, expectorant and antiasthma. It is worth noting that quality determines effectiveness and safety, which make it essential for quality control of CLQTs. Considering the different UV absorption characteristics of various components, high-performance liquid chromatography fingerprint of 156 batches of CLQTs were determined at four wavelengths. Additionally, multi-wavelength maximization fusion profiling was developed to digest and synthesize the valuable fingerprint information of each sample for its holistic quality evaluation. Quantitative determination of five major ingredients was performed and no significant differences were found in five-compound content among 156 samples (p > 0.05). All samples were clarified into six quality grades using systematic quantified fingerprint method from qualitative and quantitative perspective and showed significant difference among nine manufactures (p < 0.05). A well established 2,2-azino-bis (3-ethylbenzthiazoline-6-sulfonic acid) (ABTS) and diphenyl-1-picrylhydrazyl (DPPH) scavenging assay was applied to study the in vitro antioxidant activity of CLQTs samples using L-ascorbic acid as positive control. Furthermore, OPLS was established for the fingerprint-efficacy relationship analysis and demonstrated the significant contribution of peaks 15-liquiritin and 30-glycyrrhizic acid (VIP > 0.5). The study suggested that this quantitative fingerprint approach is reliable and beneficial for quality control of complex herbal medicine or herbal preparation.

Genetic analyses reveal complex introduction histories for the invasive tree Acacia dealbata Link around the world

AbstractAimTo compare genetic diversity and structure between Acacia dealbata populations sampled across the species’ native range in Australia and from its non‐native ranges in Chile, Madagascar, New Zealand, Portugal, La Réunion island, South Africa and the United States, and to investigate the most likely introduction scenarios to non‐native ranges.LocationGlobal.TaxonAcacia dealbata, Fabaceae.MethodsOur dataset comprised 1615 samples representing 92 populations sampled in the species’ native and non‐native ranges. We employed a combination of genetic fingerprinting (microsatellite markers) and genetic modelling approaches. We calculated genetic diversity for each population and tested for genetic isolation by distance within each range. A combination of Bayesian assignment tests and multivariate ordination was applied to identify genetic structure among populations. Approximate Bayesian Computation (ABC) analyses were conducted to test different competing introduction scenarios for each non‐native range.ResultsThe majority of the species’ non‐native ranges was characterized by high genetic diversity and low levels of genetic structure. With regard to introduction histories, however, our results supported different introduction scenarios for different non‐native ranges. We did not find strong support for any of tested introduction scenarios for populations in Chile and Madagascar, but these likely originated from multiple introductions followed by admixture. Populations in New Zealand and La Réunion most likely originated directly from Tasmania, possibly through multiple introductions. Similar to previous findings for South African populations, no clear introduction history could be identified for populations in Portugal and the United States.Main conclusionsOur study shows that global introductions of A. dealbata were complex and one scenario does not fit the invasion history of the species in different regions. We discuss how this complexity needs to be considered when formulating strategies for the effective management of the species. Future research needs to help bridge persisting knowledge gaps are discussed.

Facial Biometric for Securing Hardware Accelerators

This article presents a novel facial biometrics-based hardware security methodology to secure hardware accelerators [such as digital signal processing (DSP) and multimedia intellectual property (IP) cores] against ownership threats/IP piracy. In this approach, an IP vendor's facial biometrics is first converted into a corresponding facial signature representing digital template, followed by embedding facial signature's digital template into the design in the form of secret biometric constraints, thereby generating a secured hardware accelerator design. The results report the following qualitative and quantitative analysis of the proposed biometric fingerprint approach: 1) impact of five different facial biometrics constraints on probability of coincidence (Pc) metric (indicating strength of digital evidence). The proposed approach achieves a very low Pc value in the range of 1.54E-5 to 2.01E-5; 2) impact of different facial feature set of a facial biometric image on total number of generated secret constraints and Pc. As evident, for all facial feature sets implemented, Pc ranges between 3.31E-4 and 2.01E-5; and 3) comparative analysis of proposed approach with recent work, for different DSP applications and five different facial biometric images, in terms of Pc. As evident, the proposed approach achieves significantly lower Pc, compared with recent work.

Solitary pulmonary nodule imaging approaches and the role of optical fibre-based technologies.

Solitary pulmonary nodules (SPNs) are a clinical challenge, given there is no single clinical sign or radiological feature that definitively identifies a benign from a malignant SPN. The early detection of lung cancer has a huge impact on survival outcome. Consequently, there is great interest in the prompt diagnosis, and treatment of malignant SPNs. Current diagnostic pathways involve endobronchial/transthoracic tissue biopsies or radiological surveillance, which can be associated with suboptimal diagnostic yield, healthcare costs and patient anxiety. Cutting-edge technologies are needed to disrupt and improve, existing care pathways. Optical fibre-based techniques, which can be delivered via the working channel of a bronchoscope or via transthoracic needle, may deliver advanced diagnostic capabilities in patients with SPNs. Optical endomicroscopy, an autofluorescence-based imaging technique, demonstrates abnormal alveolar structure in SPNs in vivo. Alternative optical fingerprinting approaches, such as time-resolved fluorescence spectroscopy and fluorescence-lifetime imaging microscopy, have shown promise in discriminating lung cancer from surrounding healthy tissue. Whilst fibre-based Raman spectroscopy has enabled real-time characterisation of SPNs in vivo. Fibre-based technologies have the potential to enable in situ characterisation and real-time microscopic imaging of SPNs, which could aid immediate treatment decisions in patients with SPNs. This review discusses advances in current imaging modalities for evaluating SPNs, including computed tomography (CT) and positron emission tomography-CT. It explores the emergence of optical fibre-based technologies, and discusses their potential role in patients with SPNs and suspected lung cancer.

GC-MS-based untargeted metabolomics of plasma and urine to evaluate metabolic changes in prostate cancer

Prostate cancer (CaP) is a common cancer in men. Its late detection and inefficient diagnosis are a challenge for researchers who are currently searching for new cancer-related indicators that would facilitate better detectability of CaP and explain its pathogenesis. In the present preliminary study, endogenous volatile metabolites were detected in plasma and urine samples by using the metabolic fingerprinting approach. The analyses were performed using the GC-QqQ/MS technique in the scan mode. The detected and putatively identified metabolites were statistically analyzed using advanced univariate and multivariate statistical methods. Eleven urinary and three plasma metabolites were selected as statistically significant in patients with CaP as compared to those in healthy controls. Supervised methods such as logistic regression and quadratic support vector machine were applied to obtain the classification models. The accuracy, sensitivity, and specificity of the models were above 83%, 85%, and 81%, respectively. The putatively identified metabolites were associated with biochemical pathways such as tricarboxylic acid cycle, glycolysis, carbohydrate conversion, and steroidal lipid metabolism that are mainly involved in energy production for cell growth and proliferation.

Cross-mixing study of a poisonous Cestrum species, Cestrum diurnum in herbal raw material by chemical fingerprinting using LC-ESI-QTOF-MS/MS

Poisonous plants are widely distributed and may have risk of phytotoxicity upon mixing with medicinal plants. Several species of Cestrum genus are poisonous and linked with many serious health issues. In the present study, cross-mixing of a toxic plant, Cestrum diurnum with morphologically resembling medicinal plant, Adhatoda vasica was studied using chemical fingerprinting approach. LC-ESI-MS/MS tool was used to develop the chemical fingerprints of three toxic species of Cestrum, including, C. diurnum, C. nocturnum and C. parqui. Total forty-three compounds were identified using high-resolution LC-ESI-MS/MS data comparison. Chemometric analyses were done to compare the distribution of identified compounds present in these Cestrum species. One of the identified compounds, nornicotine (a toxic compound) was also quantified using LC-IT-MS/MS. Adulteration study was conducted by mixing toxic C. diurnum in A. vasica with various ratios (w/w) and five differentiable compounds were identified to detect the adulteration. The method was able to detect up to the limit of 5% mixing of toxic C. diurnum. Moreover, cytotoxicity of the methanolic extracts of these three species were also studied on normal human PBMC (peripheral blood mononuclear cells) and all found to be toxic, while the C. nocturnum showed the highest level of toxicity with the IC50 12.5 μg/mL.

Sediment source fingerprinting: benchmarking recent outputs, remaining challenges and emerging themes

PurposeThis review of sediment source fingerprinting assesses the current state-of-the-art, remaining challenges and emerging themes. It combines inputs from international scientists either with track records in the approach or with expertise relevant to progressing the science.MethodsWeb of Science and Google Scholar were used to review published papers spanning the period 2013–2019, inclusive, to confirm publication trends in quantities of papers by study area country and the types of tracers used. The most recent (2018–2019, inclusive) papers were also benchmarked using a methodological decision-tree published in 2017.ScopeAreas requiring further research and international consensus on methodological detail are reviewed, and these comprise spatial variability in tracers and corresponding sampling implications for end-members, temporal variability in tracers and sampling implications for end-members and target sediment, tracer conservation and knowledge-based pre-selection, the physico-chemical basis for source discrimination and dissemination of fingerprinting results to stakeholders. Emerging themes are also discussed: novel tracers, concentration-dependence for biomarkers, combining sediment fingerprinting and age-dating, applications to sediment-bound pollutants, incorporation of supportive spatial information to augment discrimination and modelling, aeolian sediment source fingerprinting, integration with process-based models and development of open-access software tools for data processing.ConclusionsThe popularity of sediment source fingerprinting continues on an upward trend globally, but with this growth comes issues surrounding lack of standardisation and procedural diversity. Nonetheless, the last 2 years have also evidenced growing uptake of critical requirements for robust applications and this review is intended to signpost investigators, both old and new, towards these benchmarks and remaining research challenges for, and emerging options for different applications of, the fingerprinting approach.

Probing the heterogeneous structure of eumelanin using ultrafast vibrational fingerprinting

Eumelanin is a brown-black biological pigment with sunscreen and radical scavenging functions important to numerous organisms. Eumelanin is also a promising redox-active material for energy conversion and storage, but the chemical structures present in this heterogeneous pigment remain unknown, limiting understanding of the properties of its light-responsive subunits. Here, we introduce an ultrafast vibrational fingerprinting approach for probing the structure and interactions of chromophores in heterogeneous materials like eumelanin. Specifically, transient vibrational spectra in the double-bond stretching region are recorded for subsets of electronic chromophores photoselected by an ultrafast excitation pulse tuned through the UV-visible spectrum. All subsets show a common vibrational fingerprint, indicating that the diverse electronic absorbers in eumelanin, regardless of transition energy, contain the same distribution of IR-active functional groups. Aggregation of chromophores diverse in oxidation state is the key structural property underlying the universal, ultrafast deactivation behavior of eumelanin in response to photoexcitation with any wavelength.

Device-Free Localization and Identification Using Sub-GHz Passive Radio Mapping

The research domain of device-free localization (DFL) is centered on the study of localization techniques which do not require targets to wear any kind of device. Passive radio mapping or passive fingerprinting is an example of a training-based DFL technique which uses the impact of a human target on radio frequency (RF) communication between stationary nodes to perform localization. We describe a set of experiments performed in a 42 m2 empty office environment in which we installed a RF network with nodes communicating on the 433 MHz and 868 MHz bands. We attempted to locate a single stationary human target based solely on signal strength measurements and did so for six different participants using two different fingerprinting methods. One method was based on Euclidean distance minimization while the other made use of a naive Bayesian classifier. We investigated the impact of frequency band, number of nodes and target body type on localization accuracy. Results indicated that a root mean square error of 48 cm could be obtained with only four nodes, provided that the data from both frequency bands was combined. Additionally, we investigated the potential of these fingerprinting approaches to distinguish between targets based on body type and perform a rudimentary form of passive identification. Accuracy rates for identification could vary significantly depending on target location, with results ranging from 0.07 to 0.75 in the exact same environment. However, the experiment participant with the lowest height and weight could be distinguished from the other participants in over 90% of cases.