Geolocation Techniques Research Articles

Budget impact analysis of implementing trauma registries in Argentina

IntroductionIn high-income countries, quality improvement interventions and research are usually guided by trauma registries. In low- and middle-income countries, the implementation of trauma registries has been limited mainly for cost reasons. ObjectiveTo analyze the budgetary impact of the implementation of trauma registries in Argentina. MethodsWe estimated direct costs of implementing trauma registries in public hospitals located in cities with a population over 50,000 inhabitants. In large urban areas, we selected hospitals by estimating a minimum volume of 240 severe trauma admissions/year and using the NBATS-2 instrument with geolocation techniques. We estimated costs based on a micro-costing approach of a trauma registry developed by Fundación Trauma. Scenario analysis was carried out restricting the population to hospitals from bigger cities and/or with higher concentration of trauma patients’ care. For the high budget impact threshold, we used the total health spending estimation, and alternatively the health spending of the public sector. ResultsFor the base case, 139 hospitals from 104 cities were included, comprising 175,605 injury-related discharges and 13,707 severely injured patients/year. The average cost for the initial three years was USD 3,753,085 (21.4 USD/per patient), falling below the high budget impact thresholds. The scenarios analysis showed a significantly costs reduction. ConclusionsThe implementation of trauma registries in Argentina would be affordable, and in consequence, it would improve the coordination, management and quality of care for this great public health issue.

GIS-Based Spatial Analysis Model for Assessing Impact and Cumulative Risk in Road Traffic Accidents via Analytic Hierarchy Process (AHP)—Case Study: Romania

On a global scale, traffic incidents are a leading cause of mortality and material damage. Romania exhibits the highest rate of road traffic fatalities both in the European Union and worldwide, requiring a comprehensive examination of its overall influence on a national level. The current study uses an extensive approach centering on a spatial analysis model based on the Analytic Hierarchy Process (AHP). Employing a series of spatial databases, this model delineates the geographical distribution and characteristics of road accidents to establish both their cumulative national impact and the identification of high-risk areas. The spatial database, containing traffic incident data, is constructed using geolocation techniques and integrated through network analysis to evaluate the impact in relation to distance. The AHP framework is applied in analyzing the impact across five key dimensions: accident severity, occurrence mode, prevailing weather conditions, traffic restrictions, and road markings. This multi-level AHP analysis not only identifies high-risk hotspots but also confirms the effectiveness of the proposed spatial model.

Assessing the Performance of a Hybrid Geolocation Algorithm Integrating FP and TOA Techniques across Diverse Environmental Conditions

This paper presents a validation study of indoor geolocation accuracies using a hybrid approach that combines fingerprinting (FP) and time of arrival (TOA) techniques. The investigation focuses on three dense environments, examining the influence of furniture density and multipath components on geolocation accuracy, particularly in non-line-of-sight (NLOS) scenarios. The results indicate that geolocation performance improves in denser environments with higher furniture density due to increased multipath components. Additionally, optimizing the hybrid method with a polygon size of 50 cm and sampling rate of 80 GHz leads to further accuracy enhancements. These findings underscore the significance of furniture density and demonstrate the effectiveness of the hybrid method in addressing NLOS challenges. The research contributes to the advancement of indoor geolocation techniques and provides valuable insights for designing precise indoor positioning systems across various applications.

Selected microhabitat and surface temperatures of two sympatric lizard species

Many small lizards live in structurally and thermally heterogeneous environments where they can select preferred microhabitats and surface temperatures to optimize their activity. We examined microhabitat selection and analyzed surface temperatures selected by two ecologically similar lacertid species, Iberolacerta horvathi and Podarcis muralis, at three sites and in different ecological contexts: allotopic and syntopic conditions. We examined their microhabitat and spatial thermal selection by using high-precision geolocation techniques to locate the lizards and record surface temperatures with temperature data loggers. Microhabitat and thermal availability differed among sites: the I. horvathi allotopic site had more rock surfaces and was coldest; the P. muralis allotopic site had more ground surfaces and was warmer; and the syntopic site had the greatest surface and thermal heterogeneity. Our results highlight the differences in microhabitat selection between the two species, most notably in surface selection, but also in thermal characteristics. I. horvathi preferentially and almost exclusively selected rocks. P. muralis chose soil, but with a more general use of microhabitats. I. horvathi lizards chose colder temperatures than those available, while P. muralis chose higher temperatures than those available. Both species showed no differences in microhabitat selection between allotopic and syntopic sites, which could not be explained by differences between sites. Our results provided new insights into the difference in surface temperature selection between two lizard species that may be adapted to different environments.

Framework for Malware Triggering Using Steganography

Teaching offensive security (ethical hacking) is becoming a required component of information security curricula to develop better cybersecurity practitioners. Many academics and industry professionals believe that a good knowledge of the attacks a system can face is required to protect a system. The early detection of an attack is critical to effectively defending a system. We can’t wait for threats to be discovered in the wild to begin planning our defenses. For our study, we designed and developed an offensive model that aims to remain concealed in an image until it reaches the target location. Our attack approach exploits image steganography, which involves embedding malicious code and a geolocation code into a digital image. This study aimed to discover new ways to attack computer systems and stimulate awareness of such attacks among browser developers, thus encouraging them to handle images with more care. In our experiments, both stego-image analysis and geolocation techniques are tested. Our experience has confirmed that converting indiscriminate attacks into targeted attacks is possible.

Smart Management of Healthcare Professionals Involved in COVID-19 Contrast With SWAPS

The recent COVID-19 pandemic in Italy has highlighted several critical issues in the management process of infected people. At the health level, the management of the COVID-19 positive was mainly delegated to the regional authorities and centrally monitored by the State. Despite requested common activities (such as diagnosis of virus positivity, active surveillance of infected people and contact tracing), Regional Health Departments were able to issue specific directives in their territories and establish priority levels for each activity according to the specific needs related to the emergency in their area. The development of novel digital tools for the management of infected people become an urgent necessity to foster more organized and integrated solutions, able to quickly process large amounts of data. Mobile Crowdsensing methodologies could effectively facilitate needed lateral interviewing activities as well as the monitoring of crowds in environments with a high concentration of virus-positive subjects (such are hospital wards but also other locations), facilitating the tracing of possible outbreaks of contagion due to advanced geolocation techniques and big data analysis methods. This paper analyzes the functionality of SWAPS (Supporting Workflows for Healthcare Personnel management), a modular and scalable web platform which facilitate and reduces the management time of COVID positive health personnel within healthcare facilities. It also analyzes the possible integrations between SWAPS and ParticipACT, an advanced MCS platform developed by the University of Bologna that can help set up the alert notification in case of entry into a COVID risk area. This article surveys the current literature on software platforms to address COVID-19 and related tracing issues and presents the practical issues and on-the-field results obtained from the research developed by the University of Bologna by assisting the deployment of the proposed solution for a big Regional Health Department in the city of Bologna.

Deep-Learning-Based Automated Palm Tree Counting and Geolocation in Large Farms from Aerial Geotagged Images

In this paper, we propose an original deep learning framework for the automated counting and geolocation of palm trees from aerial images using convolutional neural networks. For this purpose, we collected aerial images from two different regions in Saudi Arabia, using two DJI drones, and we built a dataset of around 11,000 instances of palm trees. Then, we applied several recent convolutional neural network models (Faster R-CNN, YOLOv3, YOLOv4, and EfficientDet) to detect palms and other trees, and we conducted a complete comparative evaluation in terms of average precision and inference speed. YOLOv4 and EfficientDet-D5 yielded the best trade-off between accuracy and speed (up to 99% mean average precision and 7.4 FPS). Furthermore, using the geotagged metadata of aerial images, we used photogrammetry concepts and distance corrections to automatically detect the geographical location of detected palm trees. This geolocation technique was tested on two different types of drones (DJI Mavic Pro and Phantom 4 pro) and was assessed to provide an average geolocation accuracy that attains 1.6 m. This GPS tagging allows us to uniquely identify palm trees and count their number from a series of drone images, while correctly dealing with the issue of image overlapping. Moreover, this innovative combination between deep learning object detection and geolocalization can be generalized to any other objects in UAV images.

A Survey for Recent Techniques and Algorithms of Geolocation and Target Tracking in Wireless and Satellite Systems

A single Radio-Frequency Interference (RFI) is a disturbance source of modern wireless systems depending on Global Navigation Satellite Systems (GNSS) and Satellite Communication (SatCom). In particular, significant applications such as aeronautics and satellite communication can be severely affected by intentional and unintentional interference, which are unmitigated. The matter requires finding a radical and effective solution to overcome this problem. The methods used for overcoming the RFI include interference detection, interference classification, interference geolocation, tracking and interference mitigation. RFI source geolocation and tracking methodology gained universal attention from numerous researchers, specialists, and scientists. In the last decade, various conventional techniques and algorithms have been adopted in geolocation and target tracking in civil and military operations. Previous conventional techniques did not address the challenges and demand for novel algorithms. Hence there is a necessity for focussing on the issues associated with this. This survey introduces a review of various conventional geolocation techniques, current orientations, and state-of-the-art techniques and highlights some approaches and algorithms employed in wireless and satellite systems for geolocation and target tracking that may be extremely beneficial. In addition, a comparison between different conventional geolocation techniques has been revealed, and the comparisons between various approaches and algorithms of geolocation and target tracking have been addressed, including H∞ and Kalman Filtering versions that have been implemented and investigated by authors.

Emitter Location with Azimuth and Elevation Measurements Using a Single Aerial Platform for Electronic Support Missions.

Passive ground emitter geolocation techniques are essential to electronic warfare systems, as they provide threat warnings in hostile environments, while ensuring the electronic silence of the mission platform. Geolocation of enemy emitters indicates the position of and type of adversary troops, and allows for the use of guided munition against enemy targets. Three-dimensional geolocation solutions based on least squares and particle filter estimation, using only azimuth and elevation measurements, were considered. Three batch-processing and one instantaneous solution algorithms, i.e., using a single pulse or a single observation point, were developed and investigated. The performance of the proposed solutions was demonstrated by simulations. Results showed that the batch-processing solutions achieved acceptable accuracies with a sufficient number of observation points. The performance degraded with fewer observation points. The instantaneous geolocation solution improved performance with increasing observation points, i.e., working in the sequential mode, and therefore could approach the accuracy of the batch-processing solutions.

Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning.

With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. What makes this frustrating is that private companies hold potentially useful data, but it is not accessible by the people who can use it to track poverty, reduce disease, or build urban infrastructure. This project set out to test whether we can transform an openly available dataset (Twitter) into a resource for urban planning and development. We test our hypothesis by creating road traffic crash location data, which is scarce in most resource-poor environments but essential for addressing the number one cause of mortality for children over five and young adults. The research project scraped 874,588 traffic related tweets in Nairobi, Kenya, applied a machine learning model to capture the occurrence of a crash, and developed an improved geoparsing algorithm to identify its location. We geolocate 32,991 crash reports in Twitter for 2012-2020 and cluster them into 22,872 unique crashes during this period. For a subset of crashes reported on Twitter, a motorcycle delivery service was dispatched in real-time to verify the crash and its location; the results show 92% accuracy. To our knowledge this is the first geolocated dataset of crashes for the city and allowed us to produce the first crash map for Nairobi. Using a spatial clustering algorithm, we are able to locate portions of the road network (<1%) where 50% of the crashes identified occurred. Even with limitations in the representativeness of the data, the results can provide urban planners with useful information that can be used to target road safety improvements where resources are limited. The work shows how twitter data might be used to create other types of essential data for urban planning in resource poor environments.

A TOA-DOA Hybrid Factor Graph-Based Technique for Multi-Target Geolocation and Tracking

In this article, we propose a new distributed sensors-based multi-target geolocation and tracking technique. The proposed technique is a joint time-of-arrival (TOA) and direction-of-arrival (DOA) factor graph (FG) for multi-target geolocation (FG-GE), which is further combined with another FG for extend Kalman filtering (FG-GE-EKF) for tracking. Two-dimensional (2D) and three-dimensional (3D) scenarios are considered. In the FG-GE part, a new sensor association technique is proposed to solve the matching problem, which makes the right correspondence between the DOA/TOA information gathered by the distributed sensors and each target. With the proposed sensor association technique, the measured signals from targets can adequately be matched to their corresponding FGs. Thereby, the multi-target geolocation can be reduced to multiple independent single target geolocation. In addition, in the 3D scenario, each target is projected onto three orthogonal planes in the $(x,y,z)$ coordinate. With this operation, the 3D geolocation is decomposed into three 2D geolocation problems. In the FG-GE-EKF part, the whole tracking system can be divided into two steps: prediction step and update step. In the prediction step, the predicted state is obtained from the previous state. Then, we utilize the predicted state as a prior information, and also to update the message exchanged in FG-GE. In the update step, the estimates obtained by FG-GE are regarded as observation state which is used to refine the predicted state, and acquire the current state. With proposed the FG-GE-EKF, the position estimation accuracy and tracking performance can be improved dramatically, without requiring excessively high computational effort.

A New RSS-Based Wireless Geolocation Technique Utilizing Joint Voronoi and Factor Graph

A New RSS-Based Wireless Geolocation Technique Utilizing Joint Voronoi and Factor Graph

Interference Geolocation in Satellite Communications Systems: An Overview

Satellite interference geolocation plays a critical role in addressing radio interference problems related to satellite communications systems. Traditional interference geolocation approaches are mainly based on time-difference-of-arrival (TDOA) and frequency-difference-of-arrival (FDOA) measurements. However, since new technologies have appeared, such as the widespread use of high-frequency bands, spot beams, emerging large-scale low- Earth orbit satellite constellations, and onboard processing, traditional methods may be unsuitable for modern satellite communications. Currently, to overcome these challenges, new approaches, such as single-satellite geolocation, geolocation onboard, and geolocation using carrier identification (CID), have been investigated in the literature. In this article, we provide an overview of the principles of, and innovations involving, interference geolocation techniques in satellite communications. In addition, we present the fundamental constraints, key challenges, and future directions for performing geolocation.

RIPE IPmap active geolocation

RIPE IPmap is a multi-engine geolocation platform operated by the RIPE NCC. One of its engines, single-radius, uses active geolocation to infer the geographic coordinates of target IP addresses. In this paper, we first introduce the methodology of IPmap's single-radius engine, then we evaluate its accuracy, coverage, and consistency, and compare its results with commercial geolocation databases. We found that 79.5% of single-radius results have city-level accuracy for our ground truth dataset, and 87.0% have city-level consistency or geolocating different interfaces on the same routers. On our coverage evaluation dataset of 26,559 core infrastructure IP addresses, single-radius provided geolocation inferences for 78.5% of them.We offer recommendations to improve the single-radius engine and IPmap platform in general. The main contributions of this paper are to introduce and evaluate the IPmap single-radius engine and to provide a generalized evaluation workflow applicable to historical and future geolocation techniques.

Sensitivity analysis of lightning stroke-to-flash grouping criteria

Lightning Location Systems provide detailed data on the lightning occurring within the area of the system's coverage. This kind of remote sensing equipment uses geolocation techniques to retrieve lightning cloud-to-ground return strokes. Prior to getting information regarding the number of lightning flashes, cloud-to-ground strokes need to be grouped into lightning flashes. The stroke-to-flash conversion is not straight forward and therefore, a stroke-to-flash grouping criteria (flash algorithm) is needed to obtain subsequent figures like the Ground Flash Density (NG). It is now more than twenty years since the most used stroke-to-flash grouping criteria was established: 1 s, 10 km radius and 500 ms inter-stroke interval.In the course of these years, new observational systems like the Lightning Mapping Array have been deployed to precisely depict the lightning flash extension and duration. The present work aims to introduce a new approach to the flash algorithm, taking advantage of the Lightning Mapping Array capabilities. Two methods based on this system have been used in this study as an alternative way for grouping cloud-to-ground strokes into flashes. These methods are compared with the classical flash algorithm. Results suggest shortening the total flash duration and enlarging the clustering radius. Besides, this work also analyses the sensitivity of the flash algorithm to the changes on the grouping criteria. Changes in these parameters will have a bearing on the retrieved annual number of lightning flashes, and thus will also influence derived lightning figures like the NG.

Mobile geolocation techniques for indoor environment monitoring

Advances in localization-based technologies and the increase in ubiquitous computing have led to a growing interest in location-based applications and services. High accuracy of the position of a wireless device is still a crucial requirement to be satisfied. Firstly, the rapid development of wireless communication technologies has affected the location accuracy of radio monitoring systems employed locally and globally. Secondly, the location is determined using standard complex computing methods and needs a relatively long execution time. In this paper, two geolocalization techniques, based on trigonometric and CORDIC computing processes, are proposed and implemented for Bluetooth-based indoor monitoring applications. Theoretical analysis and simulation results are investigated in terms of accuracy, scalability, and responsiveness. They show that the proposed techniques can locate a target wireless device accurately and are well suited for timing estimation.

Joint Scan Rate and Emitter Location Estimation Algorithm in SBR System Based on AOA and DTOST Measurements

A joint scan rate and emitter location estimation algorithm in scan-based radar (SBR) system based on the angle-of-arrival (AOA) and the difference time of scan time (DTOST) measurements is proposed in this paper. SBR localization is a kind of passive geolocation technique for stationary pulsed radars. To date, a huge number of passive localization techniques using AOA, DTOST or other measurements for SBR have been developed assuming prior knowledge of the scan rate. However, it is usually hard to obtain the accurate scan velocity in most of practical civilian and military applications. In this paper, by using the linear correlation between AOA and DTOST, the maximum likelihood (ML) estimation algorithm based on linear constraints is given to obtain the scan rate of SBR. And the new AOA information after fusion is acquired on this basis. Then the weighted least square (WLS) method is used for SBR localization and the Cramer-Rao lower bound (CRLB) is derived for SBR location estimation. A detailed analysis of scan rate and emitter location estimation process is given and the effectiveness of the proposed localization algorithm is illustrated with computer simulations.

Using museum pelt collections to generate pollen prints from high-risk regions: A new palynological forensic strategy for geolocation

The use of pollen as a forensic tool for geolocation is a well-established practice worldwide in cases ranging from the provenance of drugs and other illicit materials to tracking the travel of individuals in criminal investigations. Here we propose a novel approach to generation of pollen databases that uses pollen vacuumed from mammal pelts collected historically from international areas that are now deemed too high risk to visit. We present the results of a study we conducted using mammal pelts collected from Mexico. This new investigative technique is important because, although it would seem that the ubiquitous and geo-specific nature of pollen would make pollen analysis among the most promising forensic tools for law enforcement and intelligence agencies, it is not the case. The process is notoriously slow because pollen identification is a tedious task requiring trained specialists (palynologists) who are few in number worldwide, and the reference materials necessary for geolocation usually are rare or absent, especially from regions of the world that are no longer safe to visit because of war or threat of terrorism. Current forensic palynological work is carried out by a few highly trained palynologists who require accurate databases of pollen distribution, especially from sensitive areas, to do their jobs accurately and efficiently. Our project shows the suitability of using the untapped museum pelt resources to support homeland security programs. This first palynological study using museum pelts yielded 133 different pollen and spore types, including 8 moss or fern families, 12 gymnosperm genera and 112 angiosperm species. We show that the palynological print from each region is statistically different with some important clustering, demonstrating the potential to use this technique for geolocation.

Towards the Acoustic Monitoring of Birds Migrating at Night

Every year billions of birds migrate between their breeding and wintering areas. As birds are an important indicator in nature conservation, migratory bird studies have been conducted for many decades, mostly by bird-ringing programmes and direct observation. However, most birds migrate at night, and therefore much information about their migration is lost. Novel methods have been developed to overcome this difficulty; including thermal imaging, radar, geolocation techniques, and acoustic recognition of bird calls. Many bird species are detected by their characteristic sounds. This method of identification occurs more often than by direct observation, and therefore recordings are widely used in avian research. The commonly used approach is to record the birds automatically, and to manually study the bird sounds in the recordings afterwards (Furnas and Callas 2015, Frommolt 2017). However, the tagging of recordings is a tedious and time-consuming process that requires expert knowledge, and, as a result, automatic detection of flight calls is in high demand. The first experiments towards this used energy thresholds or template matching (Bardeli et al. 2010, Towsey et al. 2012), and later on the machine and deep learning methods were applied (Stowell et al. 2018). Nevertheless, not many studies have focused specifically on night flight calls (Salamon et al. 2016, Lostanlen et al. 2018). Such acoustic monitoring could complement daytime avian research, especially when the field recording station is close to the bird-ringing station, as it is in our project. In this study, we present the initial results of a long-term bird audio monitoring project using automatic methods for bird detection. Passive acoustic recorders were deployed at a narrow spit between a lake and the Baltic sea in Dąbkowice, West Pomeranian Voivodeship, Poland . We recorded bird calls nightly from sunset till sunrise during the passerine autumn migration for 3 seasons. As a result, we collected over 3000 hours of recordings each season. We annotated a subset of over 50 hours, from different nights with various weather conditions. As avian flight calls are sporadic and short, we created a balanced set for training - recordings were divided into partially overlapping 500-ms clips, and we retained all clips containing calls and created about the same number of clips without bird sounds. Different signal representations were then examined (e.g. mel-spectrograms and multitaper). Afterwards, various convolutional neural networks were checked and their performance was compared using the area under the receiver operating characteristic curve (AUC) measure. Moreover, an initial attempt was made to take advantage of the transfer learning from image classification models. The results obtained by the deep learning methods are promising (AUC exceeding 80%), but higher bird detection accuracy is still needed. For a chosen bird species – Song thrush (Turdus philomelos) – we observed a correlation between calls recorded at night and birds caught in the nets during the day. This fact, as well as the promising results from the detection of calls from long-term recordings, indicate that acoustic monitoring of nocturnal birds has great potential and could be used to supplement the research of the phenomenon of seasonal bird migration.

Secure distributed data geolocation scheme against location forgery attack

The emergence of the Internet of Things (IoT) has been greatly applied for improving people’s lives. Through the combination with cloud computing resources, the IoT is no longer constrained by computing and storage resources. For the storage service of IoT/cloud, it is crucial to keep the data within some specific geolocation borders for improving storage service quality. At the same time, it can protect the security of data against natural or man-made disasters. However, the outsourcing model results in the data owners losing their physical control over their data, which makes it difficult for the data owners to check the geolocation of their outsourced data with the potentially semi-honest cloud service providers (CSPs). To provide a secure cloud data geolocation against semi-honest CSPs, we propose an efficient data geolocation scheme, and it can be applied into distributed cloud data geolocation. It combines both the proof of retrievability technique and the delay-based geolocation technique for cloud users, the purpose is to check both the retrievability and geolocation of their outsourced data stored in CSPs. By using a novel server-centric broadcast measurement, the scheme can provide more effective detection of the location forgery attacks from potentially semi-honest CSPs. Finally, the theoretical analysis and experimental evaluation show that the proposed scheme is secure and effective.