Real-Time Three-Dimensional Pedestrian Localization System Using Smartphones.

Robust and accurate three-dimensional localization is essential for personal navigation, emergency rescue, and worker monitoring in indoor environments. For localization technology to be employed in various applications, it is necessary to reduce infrastructure dependence and limit the maximum error bound. This study aims to accurately estimate the location of various people using smartphones in a building with a cloud platform-based localization system. The proposed technology is modularized in a hierarchical structure to sequentially estimate the floor and location. This system comprises four localization modules: course level detection, fine level detection (FLD), fine location tracking (FLT), and level change detection (LCD). Each module operates organically according to the current user status. The position estimation range is defined as a total of three phases, and an appropriate location estimation module suitable for the corresponding phase operates to estimate the user's location gradually and precisely. When the user's floor is determined by an FLD, the two-dimensional position of the user is estimated by an FLT module that tracks the user's position by comparing the received signal strength indicator vector sequence and radio map. Also, LCD recognizes the user's floor change and converts the user's phase. To verify the proposed technology, various experiments were conducted in a six-story building, and an average accuracy of less than 2 m was obtained.

Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring.

A fully integrated sensor array assisted by pattern recognition algorithm has been a primary candidate for the assessment of complex vapor mixtures based on their chemical fingerprints. Diverse prototypes of electronic nose systems consisting of a multisensory device and a post processing engine have been developed. However, their precision and validity in recognizing chemical vapors are often limited by the collected database and applied classifiers. Here, we present a novel way of preparing the database and distinguishing chemical vapor mixtures with small data acquisition for chemical vapors and their mixtures of interest. The database for individual vapor analytes is expanded and the one for their mixtures is prepared in the first-order approximation. Recognition of individual target vapors of NO2, HCHO, and NH3 and their mixtures was evaluated by applying the support vector machine (SVM) classifier in different conditions of temperature and humidity. The suggested method demonstrated the recognition accuracy of 95.24%. The suggested method can pave a way to analyze gas mixtures in a variety of industrial and safety applications.

Underground Parking Lot Navigation System Using Long-Term Evolution Signal.

Some of the shopping malls, airports, hospitals, etc. have underground parking lots where hundreds of vehicles can be parked. However, first-time visitors find it difficult to determine their current location and need to keep moving the vehicle to find an empty parking space. Moreover, they need to remember the parked location, and find a nearby staircase or elevator to move toward the destination. In such a situation, if the user location can be estimated, a new navigation system can be offered, which can assist users. This study presents an underground parking lot navigation system using long-term evolution (LTE) signals. As the proposed system utilizes LTE network signals for which the infrastructure is already installed, no additional infrastructure is required. To estimate the location of the vehicle, the signal strength of the LTE signal is accumulated, and the location of the vehicle is estimated by comparing it with the previously stored database of the LTE received signal strength (RSS). In addition, the acceleration and gyroscope sensors of a smartphone are used to improve the vehicle position estimation performance. The effectiveness of the proposed system is verified by conducting an experiment in a large shopping-mall underground parking lot where approximately 500 vehicles can be parked. From the results of the experiment, an error of less than an average of 10 m was obtained, which shows that seamless navigation is possible using the proposed system even in an environment where GNSS does not function.

On-Chip Chemiresistive Sensor Array for On-Road NO x Monitoring with Quantification.

The adverse effects of air pollution on respiratory health make air quality monitoring with high spatial and temporal resolutions essential especially in cities. Despite considerable interest and efforts, the application of various types of sensors is considered immature owing to insufficient sensitivity and cross-interference under ambient conditions. Here, a fully integrated chemiresistive sensor array (CSA) with parts-per-trillion sensitivity is demonstrated with its application for on-road NO x monitoring. An analytical model is suggested to describe the kinetics of the sensor responses and quantify molecular binding affinities. Finally, the full characterization of the system is connected to implement on-road measurements on NO x vapor with quantification as its ultimate field application. The obtained results suggest that the CSA shows potential as an essential unit to realize an air-quality monitoring network with high spatial and temporal resolutions.

Surface Correlation-Based Fingerprinting Method Using LTE Signal for Localization in Urban Canyon.

The Global Satellite Navigation System (GNSS) used in various location-based services is accurate and stable in outdoor environments. However, it cannot be utilized in an indoor environment because of low signal availability and degradation of accuracy due to the multipath distortion of satellite signals in urban areas. On the contrary, LTE signals are available almost everywhere in urban areas and are quite stable without much variation throughout the year. This is because of the fixed location of base stations and the well-maintained policy of mobile communication service providers. Its varied stability and reliability make LTE signals a more viable method for localization. However, there are some complexities in utilizing LTE signals including signal interference distortion phenomena during propagation multipath fading, and various types of noise. In this paper, we propose a surface correlation-based fingerprinting method to utilize LTE signals for localization in urban areas. The surface correlation converts timely measured signal strength into spatial pattern using the walking distance from a Pedestrian Dead-Reckoning (PDR). The surface correlation is carried out by comparing the spatial signal strength pattern of a pedestrian`s movement trajectory with a fingerprinting database to estimate the location. A reference trajectory of the moving pedestrian is chosen to have a greater correlation among the multiple trajectory candidates generated from a link-based fingerprinting database. By comparing spatial signal strength patterns, the proposed method can improve robustness in localization overcoming the accuracy degradation problem due to RF multipath and noise that are dominant in the conventional RSS measurement-based LTE localization scheme. The test results in urban areas demonstrate that the proposed surface correlation-based fingerprinting method has improved performance compared to the other conventional methods, thus proving to be a useful complementary method to the GNSS in urban areas.

Spoofing Attack Results Determination in Code Domain Using a Spoofing Process Equation.

When a user receiver is tracking an authentic signal, a spoofing signal can be transmitted to the user antenna. The question is under what conditions does the tracking point of the receiver move from the authentic signal to the spoofing signal? In this study, we develop a spoofing process equation (SPE) that can be used to calculate the tracking point of the delay lock loop (DLL) at regular chip intervals for the entire spoofing process. The condition for a successful spoofing signal is analyzed using the SPE. To derive the SPE, parameters, such as the signal strength, sweep velocity, loop filter order, and DLL bandwidth are considered. The success or failure of a spoofing attack is determined for a specific spoofing signal using the SPE. In addition, a correlation between each parameter for a successful spoofing attack could be obtained through the SPE. The simulation results show that the SPE performance is largely consistent with that of general DLL methods, even though the computational load of SPE is very low.

Chemiresistive Electronic Nose toward Detection of Biomarkers in Exhaled Breath.

Detection of gas-phase chemicals finds a wide variety of applications, including food and beverages, fragrances, environmental monitoring, chemical and biochemical processing, medical diagnostics, and transportation. One approach for these tasks is to use arrays of highly sensitive and selective sensors as an electronic nose. Here, we present a high performance chemiresistive electronic nose (CEN) based on an array of metal oxide thin films, metal-catalyzed thin films, and nanostructured thin films. The gas sensing properties of the CEN show enhanced sensitive detection of H2S, NH3, and NO in an 80% relative humidity (RH) atmosphere similar to the composition of exhaled breath. The detection limits of the sensor elements we fabricated are in the following ranges: 534 ppt to 2.87 ppb for H2S, 4.45 to 42.29 ppb for NH3, and 206 ppt to 2.06 ppb for NO. The enhanced sensitivity is attributed to the spillover effect by Au nanoparticles and the high porosity of villi-like nanostructures, providing a large surface-to-volume ratio. The remarkable selectivity based on the collection of sensor responses manifests itself in the principal component analysis (PCA). The excellent sensing performance indicates that the CEN can detect the biomarkers of H2S, NH3, and NO in exhaled breath and even distinguish them clearly in the PCA. Our results show high potential of the CEN as an inexpensive and noninvasive diagnostic tool for halitosis, kidney disorder, and asthma.