Fingerprint Distance Research Articles

A WKNN Indoor Fingerprint Localization Technique Based on Improved Discrimination Capability of RSS Similarity.

There are various indoor fingerprint localization techniques utilizing the similarity of received signal strength (RSS) to discriminate the similarity of positions. However, due to the varied states of different wireless access points (APs), each AP's contribution to RSS similarity varies, which affects the accuracy of localization. In our study, we analyzed several critical causes that affect APs' contribution, including APs' health states and APs' positions. Inspired by these insights, for a large-scale indoor space with ubiquitous APs, a threshold was set for all sample RSS to eliminate the abnormal APs dynamically, a correction quantity for each RSS was provided by the distance between the AP and the sample position to emphasize closer APs, and a priority weight was designed by RSS differences (RSSD) to further optimize the capability of fingerprint distances (FDs, the Euclidean distance of RSS) to discriminate physical distance (PDs, the Euclidean distance of positions). Integrating the above policies for the classical WKNN algorithm, a new indoor fingerprint localization technique is redefined, referred to as FDs' discrimination capability improvement WKNN (FDDC-WKNN). Our simulation results showed that the correlation and consistency between FDs and PDs are well improved, with the strong correlation increasing from 0 to 76% and the high consistency increasing from 26% to 99%, which confirms that the proposed policies can greatly enhance the discrimination capabilities of RSS similarity. We also found that abnormal APs can cause significant impact on FDs discrimination capability. Further, by implementing the FDDC-WKNN algorithm in experiments, we obtained the optimal K value in both the simulation scene and real library scene, under which the mean errors have been reduced from 2.2732 m to 1.2290 m and from 4.0489 m to 2.4320 m, respectively. In addition, compared to not using the FDDC-WKNN, the cumulative distribution function (CDF) of the localization errors curve converged faster and the error fluctuation was smaller, which demonstrates the FDDC-WKNN having stronger robustness and more stable localization performance.

Access Point Selection for Indoor Positioning Using Fingerprint Distance Maximization

This paper addresses the issue of access point (AP) selection for indoor positioning using wireless networks. A fingerprint distance maximization (FDM) method is proposed. The novelty of the method is the treatment of the distance between fingerprints, where the APs are evaluated based on how frequently they contribute to the largest difference term between fingerprints measured at different locations, instead of the overall distance between fingerprints which is conventionally applied. This strategy is effective because it increases the number of distinctive fingerprints in the database by preserving distinguishing features, even if these features are dominant in only a small number of fingerprints. Performance analysis is carried out comparing the FDM method with several competing techniques using experimental data from a well-known dataset. It is shown that the FDM method leads to the highest accuracy and lowest uncertainty. Additionally, the FDM method can be combined with principal component analysis (PCA) to further reduce the dimensionality of the problem. Results show that for a fixed accuracy, the FDM-PCA technique reduces the data storage requirement by 30% and the online execution time by 5.5% compared to its closest competitor. The FDM-PCA technique is thus suitable for applications where mobile devices with limited storage and capacity are used as measuring instruments.

Cell region fingerprints enable highly precise single-cell tracking and lineage reconstruction.

Experimental studies of cell growth, inheritance and their associated processes by microscopy require accurate single-cell observations of sufficient duration to reconstruct the genealogy. However, cell tracking-assigning identical cells on consecutive images to a track-is often challenging, resulting in laborious manual verification. Here, we propose fingerprints to identify problematic assignments rapidly. A fingerprint distance compares the structural information contained in the low frequencies of a Fourier transform to measure the similarity between cells in two consecutive images. We show that fingerprints are broadly applicable across cell types and image modalities, provided the image has sufficient structural information. Our tracker (TracX) uses fingerprints to reject unlikely assignments, thereby increasing tracking performance on published and newly generated long-term data sets. For Saccharomyces cerevisiae, we propose a comprehensive model for cell size control at the single-cell and population level centered on the Whi5 regulator, demonstrating how precise tracking can help uncover previously undescribed single-cell biology.

A genetic programming approach to WiFi fingerprint meta-distance learning

Driven by the continuous growth in the number of mobile smart devices, location-based services are becoming a fundamental aspect in the ubiquitous computing domain. In this work, we focus on indoor scenarios, where positioning supports tasks such as navigation, logistics, and access management and control. Most indoor positioning solutions are based on WiFi fingerprinting, thanks to its ease of deployment. Such a technique often requires the adoption of a suitable distance metric to compare the currently observed WiFi access points with those pertaining to fingerprints contained in a database, and whose position is already known. Results from the literature make it evident that classical distance functions among WiFi fingerprints do not preserve spatial information in its entirety. Here, we explore the possibility of addressing such a shortcoming by combining a selection of fingerprint distance functions into a meta-distance, using a genetic programming approach to solve a symbolic regression problem. The outcomes of the investigation, based on 16 publicly available datasets, show that a small, but statistically relevant, improvement can be achieved in preserving spatial information, and that the developed meta-distance has a generalization capability no worse than top-performing classical fingerprint distance functions when trained on a dataset and tested on the others. In addition, when used within a k-nearest-neighbor positioning framework, the meta-distance outperforms all the contenders, despite not being expressly designed to support position estimation. This sheds a light on a significant relationship between preservation of spatial information and localization performance. The achieved results pave the way for the development of more advanced metric learning solutions, that go beyond the limitations of currently-employed fingerprint distance functions.

Investigating spatial heterogeneity within fracture networks using hierarchical clustering and graph distance metrics

Abstract. Rock fractures organize as networks, exhibiting natural variation in their spatial arrangements. Therefore, identifying, quantifying, and comparing variations in spatial arrangements within network geometries are of interest when explicit fracture representations or discrete fracture network models are chosen to capture the influence of fractures on bulk rock behaviour. Treating fracture networks as spatial graphs, we introduce a novel approach to quantify spatial variation. The method combines graph similarity measures with hierarchical clustering and is applied to investigate the spatial variation within large-scale 2-D fracture networks digitized from the well-known Lilstock limestone pavements, Bristol Channel, UK. We consider three large, fractured regions, comprising nearly 300 000 fractures spread over 14 200 m2 from the Lilstock pavements. Using a moving-window sampling approach, we first subsample the large networks into subgraphs. Four graph similarity measures – fingerprint distance, D-measure, Network Laplacian spectral descriptor (NetLSD), and portrait divergence – that encapsulate topological relationships and geometry of fracture networks are then used to compute pair-wise subgraph distances serving as input for the statistical hierarchical clustering technique. In the form of hierarchical dendrograms and derived spatial variation maps, the results indicate spatial autocorrelation with localized spatial clusters that gradually vary over distances of tens of metres with visually discernable and quantifiable boundaries. Fractures within the identified clusters exhibit differences in fracture orientations and topology. The comparison of graph similarity-derived clusters with fracture persistence measures indicates an intra-network spatial variation that is not immediately obvious from the ubiquitous fracture intensity and density maps. The proposed method provides a quantitative way to identify spatial variations in fracture networks, guiding stochastic and geostatistical approaches to fracture network modelling.

Maximum volume simplex method for automatic selection and classification of atomic environments and environment descriptor compression

Fingerprint distances, which measure the similarity of atomic environments, are commonly calculated from atomic environment fingerprint vectors. In this work, we present the simplex method that can perform the inverse operation, i.e., calculating fingerprint vectors from fingerprint distances. The fingerprint vectors found in this way point to the corners of a simplex. For a large dataset of fingerprints, we can find a particular largest simplex, whose dimension gives the effective dimension of the fingerprint vector space. We show that the corners of this simplex correspond to landmark environments that can be used in a fully automatic way to analyze structures. In this way, we can, for instance, detect atoms in grain boundaries or on edges of carbon flakes without any human input about the expected environment. By projecting fingerprints on the largest simplex, we can also obtain fingerprint vectors that are considerably shorter than the original ones but whose information content is not significantly reduced.

Online Collaborative Localization

Can fingerprinting-based localization benefit from simultaneous observations from different users? This article provides an affirmative answer to this question. Existing fingerprinting schemes work in a kind of stand-alone mode, as they localize a user merely based on her own observations about surrounding environments. However, we argue the existence of scenarios where multiple users request for localization services simultaneously. If they are not far apart, their observations are actually distributed versions of the same environment, which can be exploited to calibrate underlying localization systems. In this article, we propose an online collaborative localization (OCLoc) scheme to boost received signal strength (RSS) fingerprinting in scenarios with multiple requesting users. The basic idea of OCLoc is based on the fact of distance attenuated radio propagations. For two users, their received signals from the same access point (AP) can be used to infer who is closer to this AP. We exploit such information for online collaboration and propose a series of operations to calibrate reference points (RPs) from distributed versions of radio attenuation observations. By integrating user calibration and fingerprint distance, we define a new ranking measure for reselecting and weighting candidate RPs to output the final location. Experiments on field measurements validate the effectiveness of such online collaborations in terms of greatly reduced localization errors.

Abnormal Crowd Traffic Detection for Crowdsourced Indoor Positioning in Heterogeneous Communications Networks

WiFi fingerprint-based indoor positioning system emerges to provide fundamental location-related service in heterogeneous communications networks. It relies on crowdsourcing technology in the collection of received signal strength (RSS) to dynamically update fingerprint database. However, this crowdsourced indoor positioning system is vulnerable to the intrusion of dishonest users (i.e., attackers). Attackers may manipulate the number of users who submit RSS fingerprints, and finally mislead the evaluation of crowd traffic evaluation. In this paper, we propose an abnormal crowd traffic detection (ACTD) scheme to identify attackers according to their abnormal RSS sensing behaviors. Specifically, a fog server is explored to serve as the crowdsourcing platform to perform data storage and detection. We first categorize attackers into three different levels according to their real geographical locations and collusion. Then, through the analysis of pseudonym changing behavior in RSS submission, we propose a rarity-based outlier detection to classify attackers of level-1. Furthermore, we propose a variable-length Markov model, i.e., probabilistic suffix tree (PST), to detect the colluded users who are not at the target point of interest (POI). In addition, a metric learning algorithm is developed to detect the collusion of AP organizer based on RSS fingerprint distance difference. The extensive simulation results show that the ACTD scheme can effectively resist attackers with high accuracy and appropriately deal with traffic evaluation from RSS fingerprint information.

A Soft Range Limited K-Nearest Neighbors Algorithm for Indoor Localization Enhancement

This paper proposes a soft range limited K nearest neighbours (SRL-KNN) localization fingerprinting algorithm. The conventional KNN determines the neighbours of a user by calculating and ranking the fingerprint distance measured at the unknown user location and the reference locations in the database. Different from that method, SRL-KNN scales the fingerprint distance by a range factor related to the physical distance between the user's previous position and the reference location in the database to reduce the spatial ambiguity in localization. Although utilizing the prior locations, SRL-KNN does not require knowledge of the exact moving speed and direction of the user. Moreover, to take into account of the temporal fluctuations of the received signal strength indicator (RSSI), RSSI histogram is incorporated into the distance calculation. Actual on-site experiments demonstrate that the new algorithm achieves an average localization error of $0.66$ m with $80\%$ of the errors under $0.89$ m, which outperforms conventional KNN algorithms by $45\%$ under the same test environment.

Robust QSAR prediction models for volume of distribution at steady state in humans using relative distance measurements

ABSTRACTThe building of quantitative structure–activity relationship (QSAR) models for the in silico prediction of volume distribution for drugs at steady-state levels is vital for the selection of potential drugs at the synthesis stage. Using molecular descriptor matrixes, some regression models presenting low accuracy have been proposed, mainly due to the difficulty of compiling an appropriate dataset and the lack of information on dataset representation. In this paper, we use a benchmark dataset of very diverse drugs for the development of predictive models for volume distribution based on the use of relative distance matrixes as the input data to QSAR algorithms. Support vector machine, complex tree, bagged tree and Gaussian process regression algorithms were tested for fingerprint, similarity and relative distance matrixes used as input data, and the results of the built models were compared. Relative distance matrixes generated robust regression models in the training and external validation stages performed using cross-validation, obtaining values for correlation coefficient, bias, slope and root-mean-square error close to the ideal. Relative distance matrixes were also used for the design of classification models, obtaining excellent results with values of accuracy and area under receiver operating characteristic (AUC) close to 100%.

Computationally efficient characterization of potential energy surfaces based on fingerprint distances.

An analysis of the network defined by the potential energy minima of multi-atomic systems and their connectivity via reaction pathways that go through transition states allows us to understand important characteristics like thermodynamic, dynamic, and structural properties. Unfortunately computing the transition states and reaction pathways in addition to the significant energetically low-lying local minima is a computationally demanding task. We here introduce a computationally efficient method that is based on a combination of the minima hopping global optimization method and the insight that uphill barriers tend to increase with increasing structural distances of the educt and product states. This method allows us to replace the exact connectivity information and transition state energies with alternative and approximate concepts. Without adding any significant additional cost to the minima hopping global optimization approach, this method allows us to generate an approximate network of the minima, their connectivity, and a rough measure for the energy needed for their interconversion. This can be used to obtain a first qualitative idea on important physical and chemical properties by means of a disconnectivity graph analysis. Besides the physical insight obtained by such an analysis, the gained knowledge can be used to make a decision if it is worthwhile or not to invest computational resources for an exact computation of the transition states and the reaction pathways. Furthermore it is demonstrated that the here presented method can be used for finding physically reasonable interconversion pathways that are promising input pathways for methods like transition path sampling or discrete path sampling.

Variance-based fingerprint distance adjustment algorithm for indoor localization

The multipath effect and movements of people in indoor environments lead to inaccurate localization. Through the test, calculation and analysis on the received signal strength indication (RSSI) and the variance of RSSI, we propose a novel variance-based fingerprint distance adjustment algorithm (VFDA). Based on the rule that variance decreases with the increase of RSSI mean, VFDA calculates RSSI variance with the mean value of received RSSIs. Then, we can get the correction weight. VFDA adjusts the fingerprint distances with the correction weight based on the variance of RSSI, which is used to correct the fingerprint distance. Besides, a threshold value is applied to VFDA to improve its performance further. VFDA and VFDA with the threshold value are applied in two kinds of real typical indoor environments deployed with several Wi-Fi access points. One is a quadrate lab room, and the other is a long and narrow corridor of a building. Experimental results and performance analysis show that in indoor environments, both VFDA and VFDA with the threshold have better positioning accuracy and environmental adaptability than the current typical positioning methods based on the k-nearest neighbor algorithm and the weighted k-nearest neighbor algorithm with similar computational costs.

Improving Wi-Fi Indoor Positioning via AP Sets Similarity and Semi-Supervised Affinity Propagation Clustering

Indoor localization techniques using Wi-Fi fingerprints have become prevalent in recent years because of their cost-effectiveness and high accuracy. The most common algorithm adopted for Wi-Fi fingerprinting is weighted K-nearest neighbors (WKNN), which calculates K-nearest neighboring points to a mobile user. However, existing WKNN cannot effectively address the problems that there is a difference in observed AP sets during offline and online stages and also not all the K neighbors are physically close to the user. In this paper, similarity coefficient is used to measure the similarity of AP sets, which is then combined with radio signal strength values to calculate the fingerprint distance. In addition, isolated points are identified and removed before clustering based on semi-supervised affinity propagation. Real-world experiments are conducted on a university campus and results show the proposed approach does outperform existing approaches.

Моделювання орієнтаційних полів відбитків пальців за допомогою раціональних комплексних функцій

This article researches the stability of fingerprints identification algorithms based on minutiae methods used with local alignment functions of fingerprints source images. Robust increasing algorithms that uses local alignment of input images of fingerprint and minutiae pair distance normalization is analyzed.

Structure and interactions of calcite spherulites with α-chitin in the brown shrimp ( Penaeus aztecus) shell

White spots form in the brown shrimp ( Penaeus aztecus, Decapoda) shell during frozen storage. The mineral formed consists of calcite incorporated into an amorphous α-chitin matrix. We studied mechanisms of interaction of amorphous α-chitin macromolecules with hkl crystal planes to form highly ordered structures, as well as the role of specific sites in the biopolymer, which can be related to nucleation and spheroidal crystal growth. We used low vacuum scanning electron microscopy (LVSEM), X-ray powder diffraction (XRD), atomic force microscopy (AFM), Fourier-transform infrared spectroscopy (FT-IR), and molecular mechanics modeling (MM+ method). AFM images showed fingerprint distances in the biopolymer and a highly layered structure in the crystalline material. The presence of α-chitin, with a specific spatial distribution of radicals, is thought to be responsible for nucleation and to thermodynamically stabilize ions to form the spherulite crystalline phase, which are usually oval to spherical (0.10 to 200 μm in diameter). Our models of crystal–biopolymer interaction found high affinity of CO 3 2− anions in the (104) crystalline plane (the main plane in calcite monocrystals) to NH– groups of the biopolymer, as well as of the C O in the biopolymer to Ca 2+ cations in the crystalline structure. These interactions explain the spherical growth and inhibition in some planes. The specific physicochemical interactions (docking of groups depending on their geometrical distribution) suggest that the biomineral structure is controlled by the biopolymer on a local scale. This information is useful for further design and improvement of (hybrid) materials for versatile application, from nanotechnology to biomedicine and engineering.