Confidence interval estimation for fingerprint-based indoor localization

Fingerprint-based localization methods provide high accuracy location estimation, which use machine learning algorithms to recognize the statistical patterns of collected data. In these methods, the users’ locations can be estimated based on the received signal strength vectors from some transmitters. However, the data collection is a labor-intensive phase, and the collected data should be updated periodically. Many researchers have contributed to reducing this cost. The easiest way to remove the data collection cost is to use fingerprints generated by the model-based approaches, in which the trained machine learning algorithm can be updated based on the environment changes. Probabilistic-based localization algorithms, in addition to the user location, can estimate a region of interest called 2σ confidence interval in which the probability of user presence is 95%. Gaussian process regression (GPR) is a probabilistic method that can be used to achieve this goal. However, conventional GPR (CGPR) cannot accurately estimate the confidence interval when noise-free fingerprints generated by the model-based approaches are used in the training phase. In this paper, we propose a novel GPR-based localization algorithm, named enhanced GPR (EGPR), which improves the accuracy level of confidence interval estimation compared to the existing methods while fixing the level of computational complexity in the online phase. We also theoretically prove that GPR-based algorithms are minimum variance unbiased and efficient estimators. Experiments under line-of-sight and non-line-of-sight conditions demonstrate the superiority of our proposed method over counterparts in terms of accuracy as well as applicability in real-time localization systems

Joint Coordinate Optimization in Fingerprint-Based Indoor Positioning

Fingerprint-based indoor positioning uses pattern recognition algorithms (PRAs) to estimate the users’ locations in wireless local area network environments, where satellite-based positioning methods cannot work properly. Traditionally, the training phase of PRA is separately conducted for \u1d465 and \u1d466 coordinates. However, the received signal strength from access points is a unique fingerprint for each measured point, not for \u1d465 and \u1d466 coordinates separately. In this letter, we propose a method to jointly employ the \u1d465 and \u1d466 coordinates during the training phase using a novel PRA-based Gaussian process regression (GPR), named 2D-GPR. Experimental results show that the proposed 2D-GPR improves the accuracy of positioning more than 40\u1d450\u1d45a in limited data samples and has a lower calculation cost compared with conventional GPR

CSI-Based Human Activity Recognition using Convolutional Neural Networks

Human activity recognition (HAR) as an emerging technology can have undeniable impacts on several applications such as health monitoring, context-aware systems, transportation, robotics, and smart cities. Among the main research methods in HAR (sensor, image, and WiFi-based), the WiFi-based method has attracted considerable attention due to the ubiquity of WiFi devices. WiFi devices can be utilized to distinguish daily activities such as “walk”, “run”, and “sleep”. These activities affect WiFi signal propagation and can be further used to recognize activities. This paper proposes a Deep Learning method for HAR tasks using channel state information (CSI). A new model is developed in which CSI data are converted to grayscale images. These images are then fed into a 2D-Convolutional Neural Network (CNN) for activity classification. We take advantage of CNN's high accuracy on image classification along with WiFi-based ubiquity. The experimental results demonstrate that our proposed approach achieves acceptable performance in HAR tasks

A CSI-Based Human Activity Recognition Using Deep Learning

The Internet of Things (IoT) has become quite popular due to advancements in Information and Communications technologies and has revolutionized the entire research area in Human Activity Recognition (HAR). For the HAR task, vision-based and sensor-based methods can present better data but at the cost of users’ inconvenience and social constraints such as privacy issues. Due to the ubiquity of WiFi devices, the use of WiFi in intelligent daily activity monitoring for elderly persons has gained popularity in modern healthcare applications. Channel State Information (CSI) as one of the characteristics ofWiFi signals, can be utilized to recognize different human activities. We have employed a Raspberry Pi 4 to collect CSI data for seven different human daily activities, and converted CSI data to images and then used these images as inputs of a 2D Convolutional Neural Network (CNN) classifier. Our experiments have shown that the proposed CSI-based HAR outperforms other competitor methods including 1D-CNN, Long Short-Term Memory (LSTM), and Bi-directional LSTM, and achieves an accuracy of around 95% for seven activities

Using Synthetic Data to Enhance the Accuracy of Fingerprint-Based Localization: A Deep Learning Approach

Human-centered data collection is typically costly and implicates issues of privacy. Various solutions have been proposed in the literature to reduce this cost, such as crowd-sourced data collection, or the use of semisupervised algorithms. However, semisupervised algorithms require a source of unlabeled data, and crowd-sourcing methods require numbers of active participants. An alternative passive data collection modality is fingerprint-based localization. Such methods use received signal strength or channel state information in wireless sensor networks to localize users in indoor/outdoor environments. In this letter, we introduce a novel approach to reduce training data collection costs in fingerprint-based localization by using synthetic data. Generative adversarial networks (GANs) are used to learn the distribution of a limited sample of collected data and, following this, to produce synthetic data that can be used to augment the real collected data in order to increase overall positioning accuracy. Experimental results on a benchmark dataset show that by applying the proposed method and using a combination of 10% collected data and 90% synthetic data, we can obtain essentially similar positioning accuracy to that which would be obtained by using the full set of collected data. This means that by employing GAN-generated synthetic data, we can use 90% less real data, thereby reducing data-collection costs while achieving acceptable accuracy

Essential Oil Variability of Superior Myrtle (Myrtus communis L.) Accessions Grown under Same Conditions

Myrtle (Myrtus communis L., Myrtaceae) has numerous applications in pharmacology, food technology, and cosmetic industry. The current research aimed at measuring variations in the leaf essential oil (EO) compositions of 14 superior myrtle accessions originating in natural habitats of south Iran. The plants were grown under greenhouse conditions. Fresh leaf samples were harvested in June 2021. Based on dry matter, the extractable amount of EO in the accessions ranged from 0.42% (BN2) to 2.6% (BN5). According to GC/MS analysis, the major compounds in the EO were α-pinene (2.35–53.09%), linalyl acetate (0–45.3%), caryophyllene oxide (0.97–21.8%), germacrene D (0–19.19%), α-humulene (0–18.97%), 1,8-cineole (0–18.0%), limonene (0–17.4%), and p-cymene (0–13.2%). These myrtle accessions were classified into four groups, including I: caryophyllene oxide/germacrene D/α-humulene/methyl eugenol chemotype; II: α-pinene/p-cymene/α-humulene and (E)-β-caryophyllene; III: α-pinene/1,8-cineole, and linalool; IV: linalyl acetate/γ-terpinene/1,8, cineole/limonene. These classifications were established by considering the main EO components using hierarchical cluster analysis (HCA) and principal component analysis (PCA). In summary, this study provided new insights into available opportunities of selecting suitable genotypes for commercial cultivation purposes and planning breeding programs in the future

Ultra-High Energy Cosmic Rays and Stable H-dibaryon

It is shown that an instanton induced interaction between quarks produces a very deeply bound H-dibaryon with mass below 2M_N, M_H=1718 MeV. Therefore the H-dibaryon is predicted to be a stable particle. The reaction of photodisintegration of H-dibaryon to $2\Lambda$ in during of its penetration into cosmic microwave background will result in a new possible cut-off in the cosmic-ray spectrum. This provides an explanation of ultra-high energy cosmic ray events observed above the GZK cut-off as a result of the strong interaction of high energy H-dibaryons from cosmic rays with nuclei in Earth's atmosphere.Comment: 5 pages, Late

Evaluation of mTOR-regulated mRNA translation.

mTOR, the mammalian target of rapamycin, regulates protein synthesis (mRNA translation) by affecting the phosphorylation or activity of several translation factors. Here, we describe methods for studying the impact of mTOR signalling on protein synthesis, using inhibitors of mTOR such as rapamycin (which impairs some of its functions) or mTOR kinase inhibitors (which probably block all functions).To assess effects of mTOR inhibition on general protein synthesis in cells, the incorporation of radiolabelled amino acids into protein is measured. This does not yield information on the effects of mTOR on the synthesis of specific proteins. To do this, two methods are described. In one, stable-isotope labelled amino acids are used, and their incorporation into new proteins is determined using mass spectrometric methods. The proportions of labelled vs. unlabeled versions of each peptide from a given protein provide quantitative information about the rate of that protein's synthesis under different conditions. Actively translated mRNAs are associated with ribosomes in polyribosomes (polysomes); thus, examining which mRNAs are found in polysomes under different conditions provides information on the translation of specific mRNAs under different conditions. A method for the separation of polysomes from non-polysomal mRNAs is describe

Dihyperon in Chiral Colour Dielectric Model

The mass of dihyperon with spin, parity $J^{\pi}=0^{+}$ and isospin $I = 0$ is calculated in the framework of Chiral colour dielectric model. The wave function of the dihyperon is expressed as a product of two colour-singlet baryon clusters. Thus the quark wave functions within the cluster are antisymmetric. Appropriate operators are then used to antisymmetrize inter-cluster quark wave functions. The radial part of the quark wavefunctions are obtained by solving the the quark and dielectric field equations of motion obtained in the Colour dielectric model. The mass of the dihyperon is computed by including the colour magnetic energy as well as the energy due to meson interaction. The recoil correction to the dihyperon mass is incorporated by Peierls-Yoccoz technique. We find that the mass of the dihyperon is smaller than the $\Lambda-\Lambda$ threshold by over 100 MeV. The implications of our results on the present day relativistic heavy ion experiments is discussed.Comment: LaTeX, 13 page