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"Land mines"
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A Deep Learning Approach for Landmines Detection Based on Airborne Magnetometry Imaging and Edge Computing
Landmines continue to pose an ongoing threat in various regions around the world, with countless buried landmines affecting numerous human lives. The detonation of these landmines results in thousands of casualties reported worldwide annually. Therefore, there is a pressing need to employ diverse landmine detection techniques for their removal. One effective approach for landmine detection is UAV (Unmanned Aerial Vehicle) based Airborne Magnetometry, which identifies magnetic anomalies in the local terrestrial magnetic field. It can generate a contour plot or heat map that visually represents the magnetic field strength. Despite the effectiveness of this approach, landmine removal remains a challenging and resource-intensive task, fraught with risks. Edge computing, on the other hand, can play a crucial role in critical drone monitoring applications like landmine detection. By processing data locally on a nearby edge server, edge computing can reduce communication latency and bandwidth requirements, allowing real-time analysis of magnetic field data. It enables faster decision-making and more efficient landmine detection, potentially saving lives and minimizing the risks involved in the process. Furthermore, edge computing can provide enhanced security and privacy by keeping sensitive data close to the source, reducing the chances of data exposure during transmission. This paper introduces the MAGnetometry Imaging based Classification System (MAGICS), a fully automated UAV-based system designed for landmine and buried object detection and localization. We have developed an efficient deep learning-based strategy for automatic image classification using magnetometry dataset traces. By simulating the proposal in various network scenarios, we have successfully detected landmine signatures present in the magnetometry images. The trained models exhibit significant performance improvements, achieving a maximum mean average precision value of 97.8%.
Journal Article
White Crocodile
\"Tess Hardy thought she had put Luke, her violent ex-husband, firmly in her past. Then he calls from Cambodia, where he is working as a mine-clearer, and there's something in his voice she hasn't heard before: fear. Two weeks later, he's dead. Against her better judgment, Tess is drawn to Cambodia and to the killing fields. Keeping her relationship to Luke a closely guarded secret, Tess joins his team of mine-clearers, who are shaken to the core by Luke's sudden death\" -- Provided by publisher.
Book
Escherichia coli bioreporters for the detection of 2,4-dinitrotoluene and 2,4,6-trinitrotoluene
The primary explosive found in most land mines, 2,4,6-trinitrotoluene (2,4,6-TNT), is often accompanied by 2,4-dinitrotoluene (2,4-DNT) and 1,3-dinitrobenzene (1,3-DNB) impurities. The latter two compounds, being more volatile, have been reported to slowly leak through land mine covers and permeate the soil under which they are located, thus serving as potential indicators for buried land mines. We report on the construction of genetically engineered
Escherichia coli
bioreporter strains for the detection of these compounds, based on a genetic fusion between two gene promoters,
yqjF
and
ybiJ
, to either the green fluorescent protein gene GFPmut2 or to
Photorhabdus luminescens
bioluminescence
luxCDABE
genes. These two gene promoters were identified by exposing to 2,4-DNT a comprehensive library of about 2,000
E. coli
reporter strains, each harboring a different
E. coli
gene promoter controlling a fluorescent protein reporter gene. Both reporter strains detected 2,4-DNT in an aqueous solution as well as in vapor form or when buried in soil. Performance of the
yqjF
-based sensor was significantly improved in terms of detection threshold, response time, and signal intensity, following two rounds of random mutagenesis in the promoter region. Both
yqjF
-based and
ybiJ
-based reporters were also induced by 2,4,6-TNT and 1,3-DNB. It was further demonstrated that both 2,4,6-TNT and 2,4-DNT are metabolized by
E. coli
and that the actual induction of both
yqjF
and
ybiJ
is caused by yet unidentified degradation products. This is the first demonstration of an
E. coli
whole-cell sensor strain for 2,4-DNT and 2,4,6-TNT, constructed using its own endogenous sensing elements.
Journal Article
A comprehensive review on landmine detection using deep learning techniques in 5G environment: open issues and challenges
Detection of Landmines, especially anti-tank mines, bombs, and unexploded substances, is one of the major challenges facing humanity. The devastation and human tragedy associated with undetected explosives are self-evident in war-torn communities. To deal with this problem, we are only left with proactive measures that such substances must be detected and dealt with before the fallout. Most available solutions have major shortcomings, such as cost, efficiency, and accuracy, where the trade-offs among them are inversely related. On the other hand, advances in deep learning, unmanned aerial vehicle, and sensing are making their way as potential technologies to revolutionize the detection and removal of landmines. In this paper, we go through the literature reviewing the most recent work featuring computerized technologies to detect landmines. To our knowledge, no such study has taken place in this respect. Our aim is to find out how deep learning can be integrated with landmine detection. We identify open challenges toward viable automated solutions that enable deep learning to optimize performance effectively.
Journal Article
The political economy of the American frontier
\"This book offers an analytical explanation for the origins of and change in property institutions on the American frontier during the nineteenth century. Its scope is interdisciplinary, integrating insights from political science, economics, law, and history. This book shows how claim clubs - informal governments established by squatters in each of the major frontier sectors of agriculture, mining, logging, and ranching - substituted for the state as a source of private property institutions and how they changed the course of who received a legal title, and for what price, throughout the nineteenth century. Unlike existing analytical studies of the frontier that emphasize one or two sectors, this book considers all major sectors, as well as the relationship between informal and formal property institutions, while also proposing a novel theory of emergence and change in property institutions that provides a framework to interpret the complicated history of land laws in the United States\"-- Provided by publisher.
Book
Optimization: Drone-Operated Metal Detection Based on Machine Learning and PID Controller
This paper proposes a methodology to detect metals using a drone equipped with a metal detector and programmed by machine learning (ML) models. Our proposed research process could be considered a safe and efficient unmanned mine detection technology for the eventual removal of landmines. Users of this methodology can remotely control the drones without entering the minefield to detect the metal buried and to distinguish whether the metal is mine or not. To realize this idea, we have first stabilized and improved the attitude control of a drone with an attached metal detector by using the micro genetic algorithm-based optimization of proportional–integral–differential control gains. Next, for metal detection, ML models such as a support vector machine and a back-propagation neural network were trained using the annotated dataset. Finally, we have built a controlled drone equipped with a metal detector and trained ML models and experimentally validated our methodology. According to the experimental results, the present study secured the flight stability of the unmanned metal detection drones and the high detection success rate.
Journal Article
Land, chiefs, mining : South Africa's North West Province since 1840
\"Land, Chiefs, Mining, explores aspects of the experience of the Batswana in the thornveld and bushveld regions of the North-West Province, shedding light on defining issues, moments and individuals in this lesser known region of South Africa. Some of the focuses are: an important Tswana kgosi (chief) , Moiloa 11 of the Bahurutshe ; responses to and participation in the South African War and its aftermath, 1899-1907; land acquisition ; economic and political conditions in the reserves ; resistance to Mangope's Bophuthatswana; the impact of game parks and the Sun City resort; rural resistance and the liberation struggle; and African reaction to the platinum mining revolution.\" -- Publisher: http://witspress.co.za/catalogue/land-chiefs-mining/
Book
An Energy-Efficient T-Based Routing Topology for Target Tracking in Battery Operated Mobile Wireless Sensor Networks
Real-time smart applications are now possible because to developments in communication and sensor technology. Wireless sensor networks (WSNs) are used to collect data from specific disaster sites, such as fire events, gas leaks, land mines, earthquake, landslides, etc., where it is necessary to know the exact location of the detected information to safely rescue the people. For instance, the detection and disposal of explosive materials is a difficult task because land mines consistently threaten human life. Here, the T-based Routing Topology (TRT) is suggested to gather data from sensors (metal detectors, Ground Penetrating Radars (GPR), Infra-Red sensors, etc.), Global Positioning System (GPS), and cameras in land mine-affected areas. Buried explosive materials can be found and located with high accuracy. Additionally, it will be simpler to eliminate bombs and reduce threats to humans. The efficiency of the suggested data collection method is evaluated using Network Simulator-2 (NS-2). Also, the proposed T-based routing topology requires a minimal number of nodes to cover the entire searching area and establish effective communication. In contrast, the number of nodes participating in the sensing area grows, as the depth of the tree increases in the existing tree topology-based data gathering. And for cluster topology, the number of nodes deployment depends on the transmission range of the sensor nodes.
Journal Article