InPhase: Phase-based Ranging and Localization

Ranging and subsequent localization have become more and more critical in today’s factories and logistics. Tracking goods precisely enables just-in-time manufacturing processes. We present the InPhase system for ranging and localization applications. It employs narrowband 2.4 GHz IEEE 802.15.4 radio transceivers to acquire the radio channel’s phase response. In comparison, most other systems employ time-of-flight schemes with Ultra Wideband transceivers. Our software can be used with existing wireless sensor network hardware, providing ranging and localization for existing devices at no extra cost. The introduced Complex-valued Distance Estimation algorithm evaluates the phase response to compute the distance between two radio devices. We achieve high ranging accuracy and precision with a mean absolute error of 0.149 m and a standard deviation of 0.104 m. We show that our algorithm is resilient against noise and burst errors from the phase-data acquisition. Further, we present a localization algorithm based on a particle filter implementation. It achieves a mean absolute error of 0.95 m in a realistic 3D live tracking scenario.

A Social Distance Estimation and Crowd Monitoring System for Surveillance Cameras

Social distancing is crucial to restrain the spread of diseases such as COVID-19, but complete adherence to safety guidelines is not guaranteed. Monitoring social distancing through mass surveillance is paramount to develop appropriate mitigation plans and exit strategies. Nevertheless, it is a labor-intensive task that is prone to human error and tainted with plausible breaches of privacy. This paper presents a privacy-preserving adaptive social distance estimation and crowd monitoring solution for camera surveillance systems. We develop a novel person localization strategy through pose estimation, build a privacy-preserving adaptive smoothing and tracking model to mitigate occlusions and noisy/missing measurements, compute inter-personal distances in the real-world coordinates, detect social distance infractions, and identify overcrowded regions in a scene. Performance evaluation is carried out by testing the system’s ability in person detection, localization, density estimation, anomaly recognition, and high-risk areas identification. We compare the proposed system to the latest techniques and examine the performance gain delivered by the localization and smoothing/tracking algorithms. Experimental results indicate a considerable improvement, across different metrics, when utilizing the developed system. In addition, they show its potential and functionality for applications other than social distancing.

Machine Learning Techniques for IoT-Based Indoor Tracking and Localization

The GPS signals are not available inside the buildings; hence, indoor localization systems rely on indoor technologies such as Bluetooth, WiFi, and RFID. These signals are used for estimating the distance between a target and available reference points. By combining the estimated distances, the location of the target nodes is determined. The wide spreading of the internet and the exponential increase in small hardware diversity allow the creation of the internet of things (IoT)-based indoor localization systems. This chapter reviews the traditional and machine learning-based methods for IoT-based positioning systems. The traditional methods include various distance estimation and localization approaches; however, these approaches have some limitations. Because of the high prediction performance, machine learning algorithms are used for indoor localization problems in recent years. The chapter focuses on presenting an overview of the application of machine learning algorithms in indoor localization problems where the traditional methods remain incapable.

A simple faulted phase-based fault distance estimation algorithm for a loop distribution system

This paper presents a single ended faulted phase-based traveling wave fault localization algorithm for loop distribution grids which is that the sensor can get many reflected signals from the fault point to face the complexity of localization. This localization algorithm uses a band pass filter to remove noise from the corrupted signal. The arriving times of the faulted phase-based filtered signals can be obtained by using phase-modal and discrete wavelet transformations. The estimated fault distance can be calculated using the traveling wave method. The proposed algorithm presents detail level analysis using three detail levels coefficients. The proposed algorithm is tested with MATLAB simulation single line to ground fault in a 10 kV grounded loop distribution system. The simulation result shows that the faulted phase time delay can give better accuracy than using conventional time delays. The proposed algorithm can give fault distance estimation accuracy up to 99.7% with 30 dB contaminated signal-to-noise ratio (SNR) for the nearest lines from the measured terminal.

Drone to Obstacle Distance Estimation Using YOLO V3 Network and Mathematical Principles

Now a days, drones are very commonly used in various real time applications. Moving towards autonomy, these drones rely on obstacle detection sensors and various collision avoidance algorithms programmed into it. Development of fully autonomous drones provide the fundamental benefits of being able to operate in hazardous environments without a human pilot. Among the various sensors, monocular cameras provide a rich source of information and are one of the main sensing mechanisms in low flying drones. These drones can be used for rescue and search operations, traffic monitoring, infrastructure, and pipeline inspection, and in construction sites. In this paper, we propose an onboard obstacle detection model using deep learning techniques, combined with a mathematical approach to calculate the distance between the detected obstacle and the drone. This when implemented does not need any additional sensor or Global Positioning Systems (GPS) other than the vision sensor.

Changes of Locomotion Speed Affect Distance Estimations in Virtual Reality

Modern Virtual Reality (VR) applications often use artificial locomotion to allow users to travel distances within VR space that exceed the available space used to transfer real-world and real-time motion into the virtual environment. The locomotion speed is usually not fixed and can be selected dynamically by the user. Due to motion adaptation effects, variations of locomotion speed could affect how distances in VR are perceived. In the context of cartographic VR applications aimed to experience and communicate spatial information, such effects on distance perception could be problematic, because they might lead to distortions in cognitive representations of space acquired via interaction with VR environments. By conducting a VR-based distance estimation study, we demonstrate how changes of artificial locomotion speed affect distance estimations in VR. Increasing locomotion speeds after letting users adapt to a lower locomotion speed led to lower distance estimations and decreasing locomotion speeds led to higher distance estimations. These findings should sensitize VR developers to consider the choice of applied locomotion techniques when a developed VR application is supposed to communicate distance information or to support the acquisition of a cognitive representation of geographic space.

Isochrone fitting of ACS survey globular clusters using the PAdova and TRieste Stellar Evolution Code (PARSEC)

We present new isochrone fits to color magnitude diagrams (CMDs) of five globular clusters (GCs) including NGC 1261, NGC 1851, NGC 2298, NGC 3201, and NGC 4590. We used archival data obtained from the Advanced Camera for Survey (ACS) on board the Hubble Space Telescope (HST). The data of these five GCs were collected in F606W (V) and F814W (I) filters. In this study, the isochrone fitting to GC CMDs was analyzed using the PAdova and TRieste Stellar Evolution Code (PARSEC), which is the fundamental tool for age and distance estimation and modelling the evolution of stellar clusters and other galaxies. The main purpose is to estimate the fundamental physical properties of the GC samples using the PARSEC code and compare with results from published articles. The fundamental physical parameters determined in the study are age, metallicity, reddening, and distance modulus. The theoretical isochrone fits properly with the shape of CMD at the turn-off point that can be used to estimate the age and metallicity of clusters. We found that the age of these five GCs; NGC 1261, NGC 1851, NGC 2298, NGC 3201, and NGC 4590 are 12.6±1.0 Gyr, 12.0±1.0 Gyr, 12.7±1.0 Gyr, 12.0±1.0 Gyr, and 13.0±1.0 Gyr, respectively. Among the analyzed clusters, the results show that NGC 4590 is the oldest GC and has lowest metallicity value compare with other cluster samples. Studies of the properties and distribution of GCs play an important role to understand formation and evolution of the Milky Way.