Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization

Most applied indoor localization is based on distance and fingerprint techniques. The distance-based technique converts specific parameters to a distance, while the fingerprint technique stores parameters as the fingerprint database. The widely used Internet of Things (IoT) technologies, e.g., Wi-Fi and ZigBee, provide the localization parameters, i.e., received signal strength indicator (RSSI). The fingerprint technique advantages over the distance-based method as it straightforwardly uses the parameter and has better accuracy. However, the burden in database reconstruction in terms of complexity and cost is the disadvantage of this technique. Some solutions, i.e., interpolation, image-based method, machine learning (ML)-based, have been proposed to enhance the fingerprint methods. The limitations are complex and evaluated only in a single environment or simulation. This paper proposes applying classical interpolation and regression to create the synthetic fingerprint database using only a relatively sparse RSSI dataset. We use bilinear and polynomial interpolation and polynomial regression techniques to create the synthetic database and apply our methods to the 2D and 3D environments. We obtain an accuracy improvement of 0.2m for 2D and 0.13m for 3D by applying the synthetic database. Adding the synthetic database can tackle the sparsity issues, and the offline fingerprint database construction will be less burden. Doi: 10.28991/esj-2021-SP1-012 Full Text: PDF

Scattering Clustering Method for Terminal Fingerprint Positioning Based on MIMO Base Station

With the rapid development of wireless communication technology, location-based services are playing an increasingly important role in people’s lives. However, as the living environment becomes more and more complex, the existence of obstructions and various scatterers makes the accuracy of traditional positioning algorithms decrease, thus, fingerprint positioning has gradually become a research hotspot in the field of positioning. This paper researches the 5th Generation (5G) fingerprint location method based on machine learning. A massive multiple-in multiple-out (MIMO) channel is constructed on the MATLAB simulation platform, from which the fingerprint information is extracted to establish a fingerprint database. Considering the huge amount of data in the fingerprint database, and under the multipath effect, the channel characteristics are mainly affected by the scatterers near the point to be located. This paper proposes a scattering-based clustering method that combines the particularity of multipath propagation for clustering. Research shows that this method has excellent clustering effects, which can effectively improve algorithm efficiency and reduce data storage pressure on the base station side. (Foundation items: Social Development Projects of Jiangsu Science and Technology Department (No.BE2018704).)

The genetic diversity of wild grapes in Mexico

This is the first report evaluating the genetic diversity of Mexican grape species utilizing DNA-based markers to understand the distribution of grape species, and patterns of hybridization. The study utilized accessions maintained in three collections in Mexico, one in the USA and recently collected germplasm. Fifteen SSR markers were used to develop a fingerprint database to identify unique germplasm. Two different clustering analyses without prior population assignment, were used to identify groups that were verified by a Discriminant Analysis of Principal Components and a Principal Coordinate Analysis. Genetic diversity estimates were made across and within groups to validate the results obtained from the clustering analyses. Multiple clustering analyses and diversity parameters supported six genetic groups representing different geographic regions. The Northeastern group was the most diverse with a geographic range extending to Eastern and Central Mexico, while the Coahuila group was the least diverse. Vitis arizonica Engelm. and Vitis cinerea Engelm. ex Millardet were the most abundant species with many hybrid forms. We provide evidence that wild grape species in Mexico follow the physical barriers of mountain ranges like the Sierra Madre Oriental with an east–west divide and the Trans-Mexican Volcanic Belt as a corridor for gene flow among different grape species. Additional collections are required to fully understand the extent of hybridization and to clarify hybrid zones.

A High-Performance Database Management System for Managing and Analyzing Large-Scale SNP Data in Plant Genotyping and Breeding Applications

A DNA fingerprint database is an efficient, stable, and automated tool for plant molecular research that can provide comprehensive technical support for multiple fields of study, such as pan-genome analysis and crop breeding. However, constructing a DNA fingerprint database for plants requires significant resources for data output, storage, analysis, and quality control. Large amounts of heterogeneous data must be processed efficiently and accurately. Thus, we developed plant SNP database management system (PSNPdms) using an open-source web server and free software that is compatible with single nucleotide polymorphism (SNP), insertion–deletion (InDel) markers, Kompetitive Allele Specific PCR (KASP), SNP array platforms, and 23 species. It fully integrates with the KASP platform and allows for graphical presentation and modification of KASP data. The system has a simple, efficient, and versatile laboratory personnel management structure that adapts to complex and changing experimental needs with a simple workflow process. PSNPdms internally provides effective support for data quality control through multiple dimensions, such as the standardized experimental design, standard reference samples, fingerprint statistical selection algorithm, and raw data correlation queries. In addition, we developed a fingerprint-merging algorithm to solve the problem of merging fingerprints of mixed samples and single samples in plant detection, providing unique standard fingerprints of each plant species for construction of a standard DNA fingerprint database. Different laboratories can use the system to generate fingerprint packages for data interaction and sharing. In addition, we integrated genetic analysis into the system to enable drawing and downloading of dendrograms. PSNPdms has been widely used by 23 institutions and has proven to be a stable and effective system for sharing data and performing genetic analysis. Interested researchers are required to adapt and further develop the system.

Fingerprint Positioning Method for Dual-Band Wi-Fi Based on Gaussian Process Regression and K-Nearest Neighbor

Since many Wi-Fi routers can currently transmit two-band signals, we aimed to study dual-band Wi-Fi to achieve better positioning results. Thus, this paper proposes a fingerprint positioning method for dual-band Wi-Fi based on Gaussian process regression (GPR) and the K-nearest neighbor (KNN) algorithm. In the offline stage, the received signal strength (RSS) measurements of the 2.4 GHz and 5 GHz signals at the reference points (RPs) are collected and normalized to generate the online dual-band fingerprint, a special fingerprint for dual-band Wi-Fi. Then, a dual-band fingerprint database, which is a dedicated fingerprint database for dual-band Wi-Fi, is built with the dual-band fingerprint and the corresponding RP coordinates. Each dual-band fingerprint constructs its positioning model with the GPR algorithm based on itself and its neighborhood fingerprints, and its corresponding RP coordinates are the label of this model. The neighborhood fingerprints are found by the spatial distances between RPs. In the online stage, the measured RSS values of dual-band Wi-Fi are used to generate the online dual-band fingerprint and the 5 GHz fingerprint. Due to the better stability of the 5 GHz signal, an initial position is solved with the 5 GHz fingerprint and the KNN algorithm. Then, the distances between the initial position and model labels are calculated to find a positioning model with the minimum distance, which is the optimal positioning model. Finally, the dual-band fingerprint is input into this model, and the output of this model is the final estimated position. To evaluate the proposed method, we selected two scenarios (A and B) as the test area. In scenario A, the mean error (ME) and root-mean-square error (RMSE) of the proposed method were 1.067 and 1.331 m, respectively. The ME and RMSE in scenario B were 1.432 and 1.712 m, respectively. The experimental results show that the proposed method can achieve a better positioning effect compared with the KNN, Rank, Coverage-area, and GPR algorithms.

Application of a WiFi/Geomagnetic Combined Positioning Method in a Single Access Point Environment

Because of the particularity of urban underground pipe corridor environment, the distribution of wireless access points is sparse. It causes great interference to a single WiFi positioning method or geomagnetic method. In order to meet the positioning needs of daily inspection staff, this paper proposes a WiFi/geomagnetic combined positioning method. In this combination method, firstly, the collected WiFi strength data was filtered by outlier detection method. Then, the filtered data set was used to construct the offline fingerprint database. In the following positioning operation, the classical k -nearest neighbor algorithm was firstly used for preliminary positioning. Then, a standard circle was constructed based on the points obtained by the algorithm and the actual coordinate points. The diameter of the standard circle was the error, and the geomagnetic data were used for more accurate positioning in this circle. The method reduced the WiFi mismatch rate caused by multipath effects and improved positioning accuracy. Finally, a positioning accuracy experiment was performed in a single AP distribution environment that simulates a pipe corridor environment. The results proves that the WiFi/geomagnetic combined positioning method proposed in this paper is superior to the traditional WiFi and geomagnetic positioning methods in terms of positioning accuracy.

Improved Indoor Fingerprinting Localization Method Using Clustering Algorithm and Dynamic Compensation

Many indoor fingerprinting localization methods are based on signal-domain distances with large localization error and low stability. An improved fingerprinting localization method using a clustering algorithm and dynamic compensation was proposed. In the offline stage, the fingerprint database was built and clustered based on offline hybrid distance and an affinity propagation clustering algorithm. Furthermore, clusters were adjusted using transition regions and a given radius, as well as updating the corresponding position and fingerprint of the cluster centroid. In the online stage, the lost received signal strength (RSS) in the reference fingerprint would be dynamically compensated by using a minimum RSS value, rather than a fixed one. Online signal-domain distance was calculated for cluster identification based on RSS readings and compensated reference fingerprint. Then, K reference points with minimum online signal-domain distances were selected, and affinity propagation clustering was reused by position-domain distances to choose the position-concentrated sub-cluster for location estimation. Experimental results show that the proposed method outperforms state-of-the-art fingerprinting methods, with the mean error of 2.328 m, the root mean square error of 1.865 m and the maximum error of 10.722 m in a testbed of 3200 square meters. The improvement rates, in terms of accuracy and stability, are more than 21% and 13%, respectively.

A Wireless Fingerprint Positioning Method Based on Wavelet Transform and Deep Learning

As the demand for location services increases, research on location technology has aroused great interest. In particular, signal-based fingerprint location positioning technology has become a research hotspot owing to its high positioning performance. In general, the received signal strength indicator (RSSI) will be used as a location feature to build a fingerprint database. However, at different locations, this feature distinction may not be obvious, resulting in low positioning accuracy. Considering the wavelet transform can get valuable features from the signals, the long-term evolution (LTE) signals were converted into wavelet feature images to construct the fingerprint database. To fully extract the signal features, a two-level hierarchical structure positioning system is proposed to achieve satisfactory positioning accuracy. A deep residual network (ResNet) rough locator is used to learn useful features from the wavelet feature fingerprint image database. Then, inspired by the transfer learning idea, a fine locator based on multilayer perceptron (MLP) is leveraged to further learn the features of the wavelet fingerprint image to obtain better localization performance. Additionally, multiple data enhancement techniques were adopted to increase the richness of the fingerprint dataset, thereby enhancing the robustness of the positioning system. Experimental results indicate that the proposed system leads to improved positioning performance in outdoor environments.

Screening of 200 Core SNPs and the Construction of a Systematic SNP-DNA Standard Fingerprint Database with More Than 20,000 Maize Varieties

To strengthen the management of maize varieties and the protection of intellectual property rights to new varieties, we constructed a comprehensive single nucleotide polymorphism (SNP)-DNA standard fingerprint database of 20,075 materials covering nationally and provincially approved maize hybrid lines, hybridized combinations, and inbred lines. The database was based on 200 core SNPs selected from 60 K SNPs distributed in intragenic regions, including 106 (53.0%) located in exons. Average minor allele frequencies (MAF) of the 200 SNPs in 6755 maize hybrids, 7837 hybridized combinations, and 3478 inbred lines were 0.385, 0.350, and 0.378, respectively, with corresponding average polymorphism information content (PIC) values of 0.354, 0.335, and 0.351. Heterozygous genotype frequencies of maize hybrids, hybridized combinations, and inbred lines averaged 0.48, 0.47, and 0.012, respectively. The number of different loci in the three different maize groups ranged from one up to 164, 160, and 140, respectively. The percentage of different SNPs within 5% (the number of difference SNPs is less than 10) accounted for 0.013%, 0.011%, and 0.030% among pairwise comparisons of samples within hybrid lines, hybridized combinations and inbred lines, respectively. Genetic distances between varieties based on the 200 core SNPs were highly correlated with those obtained using 60 K SNPs, with a correlation coefficient of 0.82 and 0.87 in in inbred and hybrid lines, respectively. The maize SNP-DNA fingerprint database established in this study can play an important role in variety authentication, purity determination and the protection of variety rights, thereby providing reliable, comprehensive data support for use in the seed industry.

Construction and Application of MALDI-TOF Mass Spectrometry for the Detection of Haemophilus parasuis

To construct a protein fingerprint database of Haemophilus parasuis (H. parasuis), thus improving its clinical diagnosis efficiency. A total of 15 H. parasuis standard strains were collected to establish a protein fingerprint database of H. parasuis using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS), and the effects of different culture media and culture time on the quality and identification results of the protein fingerprint were investigated. The results showed that tryptone soy agar (TSA) and tryptone soy broth (TSB) media and different incubation times had no significant effect on the characteristic peaks of the protein profiles. In addition, 18 clinical isolates were used to compare the identification results of the self-built protein fingerprint database, PCR detection, and basic database. Only one strain was identified in the original VITEK-MS system database, while the self-made protein fingerprint database of H. parasuis was 100% accurate for the detection of 18 clinical isolate strains. The protein fingerprint database of H. parasuis built by our laboratory is suitable for rapid clinical diagnosis of H. parasuis, due to its high accuracy, efficiency, and strong specificity.