Fusing Fixed and Hint Landmarks on Crowd Paths for Automatically Constructing Wi-Fi Fingerprint Database

In typical Wi-Fi based indoor positioning systems employing fingerprint model,plentiful fingerprints need to be trained by trained experts or technician,which extends labor costs and restricts their promotion.In this paper,a novel approach based on crowd paths to solve this problem is presented,which collects and constructs automatically fingerprints database for anonymous buildings through common crowd customers.However,the accuracy degradation problem may be introduced as crowd customers are not professional trained and equipped.Therefore,we define two concepts:fixed landmark and hint landmark,to rectify the fingerprint database in the practical system,in which common corridor crossing points serve as fixed landmark and cross point among different crowd paths serve as hint landmark.Machinelearning techniques are utilized for short range approximation around fixed landmarks and fuzzy logic decision technology is applied for searching hint landmarks in crowd traces space.Besides,the particle filter algorithm is also introduced to smooth the sample points in crowd paths.We implemented the approach on off-the-shelf smartphones and evaluate the performance.Experimental results indicate that the approach can availably construct WiFi fingerprint database without reduce the localization accuracy.

Fingerprint Database Updating Using Crowdsourcing in Indoor Bluetooth Positioning System

Fingerprint⁃based Bluetooth positioning is a popular indoor positioning technology.However,the change of indoor environment and Bluetooth anchor locations has significant impact on signal distribution,which will result in the decline of positioning accuracy.The widespread extension of Bluetooth positioning is limited by the need of manual effort to collect the fingerprints with position labels for fingerprint database construction and updating.To address this problem,this paper presents an adaptive fingerprint database updating approach.First,the crowdsourced data including the Bluetooth Received Signal Strength(RSS)sequences and the speed and heading of the pedestrian were recorded.Second,the recorded crowdsourced data were fused by the Kalman Filtering(KF),and then fed into the trajectory validity analysis model with the purpose of assigning the unlabeled RSS data with position labels to generate candidate fingerprints.Third,after enough candidate fingerprints were obtained at each Reference Point(RP),the Density⁃based Spatial Clustering of Applications with Noise(DBSCAN)approach was conducted on both the original and the candidate fingerprints to filter out the fingerprints which had been identified as the noise,and then the mean of fingerprints in the cluster with the largest data volume was selected as the updated fingerprint of the corresponding RP.Finally,the extensive experimental results show that with the increase of the number of candidate fingerprints and update iterations,the fingerprint⁃based Bluetooth positioning accuracy can be effectively improved.

Construction of Gliadin Fingerprints Database for the Main Wheat Cultivars Grown in North China

The standard gliadin fingerprints and their database of 68 major cultivars and a part of backbone parents, which have ever been extensively grown in North China since the 1950' s, were constructed by using CAWGES software and an improved method of pH 3.2 A-PAGE. In the meantime, investigations were made on the utilization of the database in the area of gliadin fingerprints analysis, variety identification and genetic relationship study. The results showed that it provided an effective method for building core collections and variety identification.

A Study on Automatic Latent Fingerprint Identification System

Latent fingerprints are the unintentional impressions found at the crime scenes and are considered crucial evidence in criminal identification.Law enforcement and forensic agencies have been using latent fingerprints as testimony in courts.However,since the latent fingerprints are accidentally leftover on different surfaces,the lifted prints look inferior.Therefore,a tremendous amount of research is being carried out in automatic latent fingerprint identification to improve the overall fingerprint recognition performance.As a result,there is an ever-growing demand to develop reliable and robust systems.In this regard,we present a comprehensive literature review of the existing methods utilized in latent fingerprint acquisition,segmentation,quality assessment,enhancement,feature extraction,and matching steps.Later,we provide insight into different benchmark latent datasets available to perform research in this area.Our study highlights various research challenges and gaps by performing detailed analysis on the existing state-of-the-art segmentation,enhancement,extraction,and matching approaches to strengthen the research.

Development and application of perfect SSR markers in cotton

Background:This study aimed to develop a set of perfect simple sequence repeat(SSR)markers with a single copy in the cotton genome,to construct a DNA fingerprint database suitable for authentication of cotton cultivars.We optimized the polymerase chain reaction(PCR)system for multi-platform compatibility and improving detection efficiency.Based on the reference genome of upland cotton and 10×resequencing data of 48 basic cotton germplasm lines,single-copy polymorphic SSR sites were identified and developed as diploidization SSR markers.The SSR markers were detected by denaturing polyacrylamide gel electrophoresis(PAGE)for initial screening,then fluorescence capillary electrophoresis for secondary screening.The final perfect SSR markers were evaluated and verified using 210 lines from different sources among Chinese cotton regional trials.Results:Using bioinformatics techniques,1246 SSR markers were designed from 26626 single-copy SSR loci.Adopting a stepwise(primary and secondary)screening strategy,a set of 60 perfect SSR markers was selected with high amplification efficiency and stability,easy interpretation of peak type,multiple allelic variations,high polymorphism information content(PIC)value,uniform chromosome distribution,and single-copy characteristics.A multiplex PCR system was established with ten SSR markers using capillary electrophoresis detection.Conclusions:A set of perfect SSR markers of cotton was developed and a high-throughput SSR marker detection system was established.This study lays a foundation for large-scale and standardized construction of a cotton DNA fingerprint database for authentication of cotton varieties.

Adaptive access points deployment for indoor bluetooth positioning accuracy enhancement

The indoor positioning system based on fingerprint receives more and more attention due to its high positioning accuracy and time efficiency.In the existing positioning approaches,much consideration is given to the positioning accuracy improvement by using the angle of signal,but the optimization of access points(APs)deployment is ignored.In this circumstance,an adaptive APs deployment approach is proposed.First of all,the criterion of reference points(RPs)effective coverage is proposed,and the number of deployed APs in target environment is obtained by using the region partition algorithm and full coverage algorithm.Secondly,the wireless signal propagation model is established for target environment,and meanwhile based on the initial APs deployment,the simulation fingerprint database is constructed for the sake of establishing the discrimination function with respect to fingerprint database.Thirdly,the greedy algorithm is applied to optimize APs deployment.Finally,the extensive experiments show that the proposed approach is capable of achieving adaptive APs deployment as well as improving positioning accuracy.