Emergency Response Application Research Articles

Enhancing Communication Security in Drones Using QRNG in Frequency Hopping Spread Spectrum

This paper presents a novel approach to enhancing the security and reliability of drone communications through the integration of Quantum Random Number Generators (QRNG) in Frequency Hopping Spread Spectrum (FHSS) systems. We propose a multi-drone framework that leverages QRNG technology to generate truly random frequency hopping sequences, significantly improving resistance against jamming and interception attempts. Our method introduces a concurrent access protocol for multiple drones to share a QRNG device efficiently, incorporating robust error handling and a shared memory system for random number distribution. The implementation includes secure communication protocols, ensuring data integrity and confidentiality through encryption and Hash-based Message Authentication Code (HMAC) verification. We demonstrate the system’s effectiveness through comprehensive simulations and statistical analyses, including spectral density, frequency distribution, and autocorrelation studies of the generated frequency sequences. The results show a significant enhancement in the unpredictability and uniformity of frequency distributions compared to traditional pseudo-random number generator-based approaches. Specifically, the frequency distributions of the drones exhibited a relatively uniform spread across the available spectrum, with minimal discernible patterns in the frequency sequences, indicating high unpredictability. Autocorrelation analyses revealed a sharp peak at zero lag and linear decrease to zero values for other lags, confirming a general absence of periodicity or predictability in the sequences, which enhances resistance to predictive attacks. Spectral analysis confirmed a relatively flat power spectral density across frequencies, characteristic of truly random sequences, thereby minimizing vulnerabilities to spectral-based jamming. Statistical tests, including Chi-squared and Kolmogorov-Smirnov, further confirm the unpredictability of the frequency sequences generated by QRNG, supporting enhanced security measures against predictive attacks. While some short-term correlations were observed, suggesting areas for improvement in QRNG technology, the overall findings confirm the potential of QRNG-based FHSS systems in significantly improving the security and reliability of drone communications. This work contributes to the growing field of quantum-enhanced wireless communications, offering substantial advancements in security and reliability for drone operations. The proposed system has potential applications in military, emergency response, and secure commercial drone operations, where enhanced communication security is paramount.

Amine-functionalized activated carbon for 2-chloroethyl phenyl sulphide (2-CEPS) sensing using a portable electrochemical circuit

In this study, activated carbon (AC) is synthesized from waste thermocol sheets and functionalized it with amine groups to enhance the surface properties and selectivity. The synthesized material is further characterized using X-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), and X-ray Photoelectron Spectroscopy (XPS). SEM images revealed excellent porosity, XRD confirmed AC synthesis, and FTIR and XPS verified amine functionalization. The size of particles of the synthesized material is determined to be 38.4 nm using Dynamic Light Scattering (DLS). The modified carbon is used as a working electrode in an innovative portable electrochemical sensing prototype for detecting 2-chloroethyl Phenyl Sulphide (2-CEPS). Electrochemical characterizations, including cyclic voltammetry (CV) and differential pulse voltammetry (DPV), were conducted to evaluate the electrode's performance. Studies on analyte concentration effects, calibration plots, reproducibility, and stability were also performed. The prototype demonstrated a limit of detection (LOD) of 82.89 µM and a limit of quantification (LOQ) of 251.18 µM, with a linear range of 50-300 µM. Stability tests using 150 µM of 2-CEPS at pH 6 showed consistent peak current values, indicating the device's reliability. The prototype is cost-efficient, portable, and suitable for onsite detection of 2-CEPS, with potential applications in environmental monitoring, war zones, emergency response, border security, and industrial safety.

Efficient CNN-based disaster events classification using UAV-aided images for emergency response application

Efficient CNN-based disaster events classification using UAV-aided images for emergency response application

Machine Learning technique for isotopic determination of radioisotopes using HPGe [formula omitted]-ray spectra

γ-ray spectroscopy is a quantitative, non-destructive technique that may be utilized for the identification and quantitative isotopic estimation of radionuclides. Traditional methods of isotopic determination have various challenges that contribute to statistical and systematic uncertainties in the estimated isotopics. Furthermore, these methods typically require numerous pre-processing steps, and have only been rigorously tested in laboratory settings with limited shielding. In this work, we examine the application of a number of machine learning based regression algorithms as alternatives to conventional approaches for analyzing γ-ray spectroscopy data in the Emergency Response arena. This approach not only eliminates many steps in the analysis procedure, and therefore offers potential to reduce this source of systematic uncertainty, but is also shown to offer comparable performance to conventional approaches in the Emergency Response Application.

Method and application of information sharing throughout the emergency rescue process based on 5G and AR wearable devices

The 2022 Winter Olympics were held in the three competition zones of Beijing, Yanqing and Zhangjiakou, China. The venues of this Winter Olympics were scattered and the terrain was complex. Moreover, the medical resources of Hebei and Beijing were relatively unbalanced. In the medical security of major events, the connection between first aid and in-hospital processes is of the utmost importance to rescue quality. 5th generation mobile network (5G) applications in medical scenarios are on the rise. It would be of great relevance to fully use 5G’s low-latency and high-speed features to share the process information of patients, ambulance personnel, and the destination hospital’s rescue team at emergency scenes and in transportation, improving rescue efficiency. This paper proposes a system scheme of cross-institutional emergency health information sharing based on 5G and augmented reality wearable devices. It also integrates the construction method of monitoring and other sign data sharing, in addition to testing the proposed scheme’s service quality in 5G environments. In the deployment area of the 5G emergency medical rescue information sharing scheme for the Beijing Winter Olympic Games, we selected two designated medical support institutions for testing. The test adopted a combination of fixed-point and driving tests to experiment on the service data, voice service, and streaming media indicators. The 5G signal's coverage rate was close to 100%, the standalone connection's success rate was 100%, and the drop rate was 0. The average downlink rate of multiple scenarios was 620mbps, and the average uplink rate of 5G was over 71.8mbps, which is higher than the average 5G level in China. The downlink rate was more than 20 times larger than the 4th generation mobile network (4G) rate. This study’s proposed scheme demonstrates the importance of 5G applications in emergency response and support, in addition to providing a suitable scheme for the integration of 5G networks in the medical scene.

Design and Development of ER MineTracer: A Mobile Emergency Response Application for Mining Industry

Most of the industries, especially mining companies, are facing nowadays an exciting future with growing demand as well as severe challenges. Several innovations in technology as well as in the organizational process and methods are developed in order to cope with the continuously advancing technologically-driven society and to address pressing issues that industries are facing, including emergency response-related problems. This study aimed to improve information access by developing ER MineTracer mobile emergency response application which combines web and mobile applications designed for reporters and rescuers in responding to employees’ emergency requests. It uses GPS to determine an employee’s current position and communicates the user’s name and present location straight to a web application installed in a command center for quick dispatching of emergency units. Descriptive-developmental research was employed in the study utilizing an object-oriented modeling tool in designing the ER MineTracer mobile application. Thirty (30) participants were purposefully chosen to assess the performance of the system and it was revealed that the developed ER MineTracer mobile application was efficient in terms of accessibility, accuracy, and usability. Hence, it is recommended to utilize ER MineTracer and optimize smartphones to assist individuals in saving lives during emergencies and accidents, especially in mining industries.

Design and Development of ER MineTracer: A Mobile Emergency Response Application for Mining Industry

Design and Development of ER MineTracer: A Mobile Emergency Response Application for Mining Industry

A Survey of Small, Low-Frequency Antennas: Recent designs, practical challenges, and research directions

Robust, secure, low-power wireless communications and networking are needed to support emerging applications in emergency and disaster response, robotics, autonomous vehicles, and the Internet of Things (IoT) in physically complex environments, such as indoor/outdoor, dense urban, and other challenging scenarios. These applications require a variety of wireless communications modalities, including high and very-high frequency (HF and VHF) bands. To effectively deploy compact wireless communications systems at these low frequencies, high-performance electrically small antennas (ESAs) are needed. In this article, we present a survey of state-of-the-art low-frequency ESAs, including passive resonant, tunable, multiband, and actively matched designs. While passive designs are subject to classic tradeoffs in size and efficiency, active and platform-integrated techniques offer potential performance-enhancing alternatives. And, though single ESAs tend to omnidirectional beam patterns, multiantenna arrays can achieve directionality, and designs include parasitic, biomimetic, and distributed approaches.

Spatially transferable dwelling extraction from Multi-Sensor imagery in IDP/Refugee Settlements: A meta-Learning approach

Dwelling information is very important for various applications in humanitarian emergency response. For this, Earth observation is crucial to have spatially explicit and temporally frequent observations. Coupled with advances in computer vision, especially with the proliferation of state-of-the-art deep learning models, are providing a new opportunity for automatic information retrieval from remotely sensed imagery. Despite their proven performance, they have two known limitations, viz, the requirement of intensive data for training and lack of universal generalization under changing scene characteristics and respective data distributions. To tackle this problem, the current study has investigated the relevance of a meta-learning approach for the creation of a spatially transferable optimal model for dwelling extraction in IDP/refugee settlements. The study followed a Model Agnostic meta-Learning (MAML) strategy with newly designed and tested variates with weighted loss gradient update plus self-supervision in the adaptation phase to the target locations using a few samples. The approach is tested using multi-sensor, multi-temporal satellite imagery from eight IDP settlements. Furthermore, a thorough investigation is undertaken on task-specific transfers and their association with deep-embedded feature space and image structural similarity. Results indicate that for some target sites, task-specific transfers perform better than MAML approaches. When MAML is trained with a weighted loss gradient update, it yielded better performance. The best performance (MIoU 0.623 and an F-1 score of 76.7 %) was achieved when MAML is aided with self-supervision using pseudo-labels from unlabelled target data. In all experimental setups, though increasing adaptation samples contribute to positive transfer, the marginal contribution from additional samples is decreasing and stagnates when the adaptation sample size reaches ∼ 35 % of the target dataset.

Multi-mitigation strategies in medical supplies for epidemic outbreaks

The outbreak of Coronavirus disease 2019 (COVID-19) highlights the importance of sufficient medical supplies stockpiling at the pre-event stage. In contrast, the potential disadvantages of maintaining adequate items at strategic locations (i.e., reserves) are considerable inventory-related costs. Unpredicted demand leads to a high degree of uncertainty. Efforts to mitigate the uncertainty should rely not only on prepositioning supplies at reserves but also on integrating various channels of medical materials. This paper proposes multi-mitigation strategies in medical supplies to ensure uninterrupted supply for hospitals and significant savings by introducing two-type suppliers, reserving and manufacturing suppliers. Thus, each hospital with uncertain demand is enabled to be served by various channels during pandemics: prepositioning in reserves, backups served by reserving suppliers, and medical commodities produced by manufacturing suppliers. Stochasticity is also incorporated into the raw materials available to produce. This research aims to develop an emergency response application that integrates preparedness action (reserve location, inventory level, and contract supplier's selection) with post-event operations (allocating medical materials from various channels). We formulate a two-stage stochastic mixed integer program to determine prepositioning strategy, including two-type suppliers' selection, and post-event allocation of multiple sources. A branch-and-Benders-cut method is developed for this problem and significantly outperforms both the classical Benders decomposition and Gurobi in the solution time. Different-sized test instances also verify the robustness of the proposed method. Based on a realistic and typical case study (inspired by the COVID-19 pandemic in Wuhan, China), significant savings, an increase in inventory utilization and an increase in demand fulfilment are obtained by our approach.

방사선비상 대응을 위한 인공지능 적용 현황 분석과 향후 연구방향 제언

Increasing concerns for global environments has emphasized the need for low-carbon energy. Nuclear energy stands out as a promising energy source in many countries and thereupon, has been extensively investigated. However, as possibility of radiation disasters exists, research for radiation emergency response also have been demanded. In recent years, artificial intelligence (AI) is actively applied as an effective approach for the management of radiation emergencies. This paper presents a systematic review on the successful AI applications in radiation emergency response. Specifically, this study provides the integration of AI into different disaster management phases: Mitigation, Preparedness, Response, Recovery. Based on the comprehensive summary, current limitations and future research directions that significantly advance the radiation emergency response system are also discussed.

Heuristics for optimizing 3D mapping missions over swarm-powered ad-hoc clouds

Drones have been getting more and more popular in many economy sectors. Both scientific and industrial communities aim at making the impact of drones even more disruptive by empowering collaborative autonomous behaviors—also known as swarming behaviors—within fleets of multiple drones. In swarming-powered 3D mapping missions, unmanned aerial vehicles typically collect the aerial pictures of the target area whereas the 3D reconstruction process is performed in a centralized manner. However, such approaches do not leverage computational and storage resources from the swarm members. We address the optimization of a swarm-powered distributed 3D mapping mission for a real-life humanitarian emergency response application through the exploitation of a swarm-powered ad hoc cloud. Producing the relevant 3D maps in a timely manner, even when the cloud connectivity is not available, is crucial to increase the chances of success of the operation. In this work, we present a mathematical programming heuristic based on decomposition and a variable neighborhood search heuristic to minimize the completion time of the 3D reconstruction process necessary in such missions. Our computational results reveal that the proposed heuristics either quickly reach optimality or improve the best known solutions for almost all tested realistic instances comprising up to 1000 images and fifteen drones.

BIM generation from 3D point clouds by combining 3D deep learning and improved morphological approach

The demand for the models of BIM has increased due to the numerous applications in emergency response and location-based services. Creating a semantically rich 3D interior model has traditionally been a very manual process. Although the latest LiDAR scanning and photogrammetry techniques efficiently capture the existing building, the automatic reconstruction of complete volumetric and functional interior models is still an intensive and challenging research topic. This paper presents a novel parametric modeling method for reconstructing semantic volumetric building interiors from the unstructured point cloud of a building. Unlike existing partitioning-based methods, our proposed method overcomes the limitations by providing a flexible framework for combining 3D Deep Learning and an improved morphological approach for inverse BIM modeling. By employing a flexible and robust three-step mechanism, the proposed method first classifies the point cloud into thirteen types using a 3D Deep Learning method, then extracts the surfaces and generates the initial feature space. In regularizing the space and modeling BIM, the space boundaries are optimized by formulating the subordination between the grid cells generated by walls and the segmented boundary shapes generated by the gridded floor plan through energy minimization. Then, a grammar-enhanced point-line polygon and parametric description are designed to generate the final BIM model. By using a robust space partitioning and optimization method, three main goals are achieved: recovery of geometric and semantic information, robustness in different data sources (especially in non-Manhattan scenes), parametric BIM modeling in our proposed method. The paper demonstrates the potential of our algorithms for both RGB-D and LiDAR point clouds acquired from scenes in Manhattan and outside Manhattan. Experimental results show that this method is capable of automatically generating a geometrically and semantically consistent BIM model that is competitive with the existing method in terms of geometric accuracy and model completeness.

Line‐Source Model Based Rapid Inversion for Deriving Large Earthquake Rupture Characteristics Using High‐Rate GNSS Observations

AbstractWe present a new method using Global Navigation Satellite System‐derived peak ground velocities to rapidly determine rupture characteristics for large earthquakes (Mw > 6). A three‐step strategy is adopted to sequentially estimate the fault rupture length, direction, and pattern (unilateral or bilateral). It does not require any a priori constraint on the fault plane, and can avoid drift and clipping problems in seismic data based inversion methods. Through retrospective analysis of six recent large earthquakes, the line‐source rupture characteristics can be determined within 30 s. Comparison with the United States Geological Survey released products demonstrates that the line‐source parameters can be estimated with considerable accuracy. We also analyze the contribution of the earthquake rupture characteristics to the prediction of ground shaking intensity. It is shown that the method has the potential to improve ground shaking predictions with applications in disaster assessment and emergency response.

Enhancing the luminescence performance of Ca2−xSrxBO3Cl:Eu2+,Dy3+ by substitution of Sr2+ for Ca2+

As reusable materials, persistent luminescence (PersL) materials have a wide range of applications in emergency response, information encryption and other fields.

APLIKASI TANGGAP DARURAT MASYARAKAT DI KECAMATAN TELANAIPURA KOTA JAMBI

In improving services regarding emergency response to the community, it is necessary to build a Community Emergency response application that can assist the Telanaipura sub-district, Jambi city in obtaining information on emergency reports or complaints from the community, in order to help speed up and simplify the emergency response process. Telanaipura sub-district office, Jambi city, which is located at Jl. Piere tended to RT. 12 Ex. Telanaipura Kec. Telanaipura Jambi City. The purpose of this research is to create a Mobile-Based Community Emergency Response Application in Telanaipura District, Jambi City. Making this application is done by means of data collection, system planning, system analysis, system and database design. Making application programs developed with PHP. The database used is MySQL. The input needs of the application program are user data, community data, complaint data, category data, contact data and news data. Process requirements needed are: login process, master data process, complaint process and report process. Expenditure data generated is in the form of information that will be displayed, among others: complaint data information, community data information, complaint status data information, contact data information and news data information. With the existence of a new system that has been designed and created by researchers, it can help facilitate the parties involved in improving emergency response services to the community

180 Disparities in Distribution of PulsePoint Responders and Potential Impacts on Pandemic Response in Underserved Communities of Allegheny County

Due to their ubiquity, smartphone applications are becoming increasingly important for emergency response, including providing a means of mobilizing volunteer responders. Data from these applications may be useful for identifying potential disparities in emergency response by revealing geographic gaps and racial and income-based inequity in the availability of volunteers. This could in turn be used to create targeted interventions to increase equitable emergency response coverage. The purpose of our study was to examine associations between race, SES factors, and access to emergency resources using data from PulsePoint (PP), a smartphone-based emergency response application for public cardiac arrest.

Toponym usage in social media in emergencies

Emergency responders require accurate geographic information to locate and assess incidents reported on social media. Unfortunately, however, social media often lacks geographic metadata or includes geotags that inaccurately reflect the locations of reported events. Toponyms, place names in social media content, provide another source of geographic information yet toponym usage in emergencies remains poorly understood.To address this gap, this study examines toponym usage across 22,343 tweets posted during a severe storm in the Northeastern United States. Tweets were qualitatively coded for nineteen types of storm-related information as well as toponym usage, location reference, and granularity. Findings show that users i) include more geographic information, including toponyms and geotags, in tweets about emergencies than about other topics; ii) report geographically distributed events but tend to include toponyms rather than geotags when reporting local information; and iii) include mostly municipal and regional toponyms in tweets about emergencies but tend to include hyperlocal toponyms and additional municipal or regional markers in reports of infrastructure damage and service disruption.Together these findings offer implications for social sensing applications in emergency response and management. These include more opportunities for course-grained damage assessment than fine-grained situational awareness, and the need for internal and external sources of geographic information to distinguish information about local and remote events. Lastly, findings suggest opportunities for passive and active sensing approaches responsive to the recipient design of social media: users provide additional geographic context that help others distinguish local and remote risks within noisy information spaces that emerge during geographically distributed crises.

Impact of land surface physics in WRF on the simulation of sea breeze circulation over southeast coast of India

In this work, the impact of land surface physics in simulating the sea and land breeze circulation along the south east coast of India near Kalpakkam is studied using the Weather Research and Forecasting (WRF) model for emergency response application. Simulations were conducted with three different land surface models (LSMs)—5-Layer soil thermal diffusion scheme (5-Layer), Noah land surface model (Noah) and Noah Multi-Physics land surface model (Noah-MP) for different days in summer and southwest monsoon. Observations from meteorological towers, automated weather stations, sonic anemometers and GPS Radiosonde profiles were used for comparison. Results indicate that the Noah-MP followed by Noah schemes produce more realistic simulations of the sea breeze circulation in terms of intensity, onset time, duration, horizontal and vertical extents in most of the studied cases compared to the 5-Layer scheme. Results indicate that the 5-Layer model grossly overestimated air temperature, heat flux and boundary layer height relative to Noah and Noah-MP schemes. The Noah-MP scheme better represented the land-surface feedback in terms of soil temperature, skin temperature and surface energy fluxes leading to more realistic simulations of sea breeze and boundary layer characteristics.

Design and implementation of aerial communication using directional antennas: learning control in unknown communication environments

To support an increasing number of commercial multi-unmanned aerial vehicle (UAV) applications, robust UAV-to-UAV communication is critical. A promising solution is aerial communication using directional antennas (ACDA), with features like long communication distance, low power consumption, broad bandwidth, and interference rejection. Nevertheless, ACDA requires the automatic alignment of directional antennas, which is not easy to achieve considering the imperfect communication environment unknown in advance and the limited sensing devices onboard due to the constrained payloads and power sources. In this study, the authors design and implement a new ACDA system, including the platform, communication, computing, control, middleware, and interface components. Practical implementation issues for the emergency response application are also considered. The ACDA system features a communication and control co-design, where the communication quality indicator, received signal strength indicator, serves as the goal function for antenna alignment. The solution also features a reinforcement learning-based directional antenna control algorithm that learns the unknown communication environment models. The performance of the ACDA system is verified using simulation studies, field tests, and disaster drills.