Internet of Things technology in mineral remote sensing monitoring

Liu, Yanbing; Dhakal, Sanjev

doi:10.1002/cta.2890

Cited by 7 publications

(3 citation statements)

References 22 publications

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“…Further, the surface maps can be combined with AI-based spatial estimation models [72] applied to subsurface geochemical and geophysical data to develop 3D geological models of potential mineralized zones. The approaches will enable the mineral exploration and mining industry to achieve Industry 4.0 [73] through IoT [74][75][76] and blockchain [77] solutions for secure data sharing within the mining industry.…”

Section: Discussionmentioning

confidence: 99%

Lithological Mapping of Kohat Basin in Pakistan Using Multispectral Remote Sensing Data: A Comparison of Support Vector Machine (SVM) and Artificial Neural Network (ANN)

et al. 2022

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Artificial intelligence (AI)-based multispectral remote sensing has been the best supporting tool using limited resources to enhance the lithological mapping abilities with accuracy, supported by ground truthing through traditional mapping techniques. The availability of the dataset, choice of algorithm, cost, accuracy, computational time, data labeling, and terrain features are some crucial considerations that researchers continue to explore. In this research, support vector machine (SVM) and artificial neural network (ANN) were applied to the Sentinel-2 MSI dataset for classifying lithologies having subtle compositional differences in the Kohat Basin’s remote, inaccessible regions within Pakistan. First, we used principal component analysis (PCA), minimum noise fraction (MNF), and available maps for reliable data annotation for training SVM and (ANN) models for mapping ten classes (nine lithological units + water). The ANN and SVM results were compared with the previously conducted studies in the area and ground truth survey to evaluate their accuracy. SVM mapped ten classes with an overall accuracy (OA) of 95.78% and kappa coefficient of 0.95, compared to 95.73% and 0.95 by ANN classification. The SVM algorithm was more efficient concerning computational efficiency, accuracy, and ease due to available features within Google Earth Engine (GEE). Contrarily, ANN required time-consuming data transformation from GEE to Google Cloud before application in Google Colab.

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Section: Discussionmentioning

confidence: 99%

Lithological Mapping of Kohat Basin in Pakistan Using Multispectral Remote Sensing Data: A Comparison of Support Vector Machine (SVM) and Artificial Neural Network (ANN)

et al. 2022

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“…The absence of standardized approaches in telemetry results in prolonged development times and delays in bringing products to market, ultimately driving up costs. By embracing standardization in IoT, organizations can benefit from reduced development costs, shorter time to market, and improved interoperability between different IoT devices and systems [ 123 ].…”

Section: Iot Devices and Cloud Processingmentioning

confidence: 99%

IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey

Prauzek,

Kucova,

Konecny

et al. 2023

Sensors

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Geothermal energy installations are becoming increasingly common in new city developments and renovations. With a broad range of technological applications and improvements in this field, the demand for suitable monitoring technologies and control processes for geothermal energy installations is also growing. This article identifies opportunities for the future development and deployment of IoT sensors applied to geothermal energy installations. The first part of the survey describes the technologies and applications of various sensor types. Sensors that monitor temperature, flow rate and other mechanical parameters are presented with a technological background and their potential applications. The second part of the article surveys Internet-of-Things (IoT), communication technology and cloud solutions applicable to geothermal energy monitoring, with a focus on IoT node designs, data transmission technologies and cloud services. Energy harvesting technologies and edge computing methods are also reviewed. The survey concludes with a discussion of research challenges and an outline of new areas of application for monitoring geothermal installations and innovating technologies to produce IoT sensor solutions.

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“…The absolute value detector is an important part of different sensors. Detector helps extract useful information from complex signals for control, monitoring, diagnosis, and processing of various real-world situations [2][3][4]. For example, it can realize the rectification of the waveform [5].…”

Section: Introductionmentioning

confidence: 99%

A 6-bit absolute value detector based on MOSFET and PTL structure

2024

ACE

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As science and technology develop fast, people nowadays can have an easy approach to various advanced products. The use of Internet of Things has become a reality. To ensure its reliable implementation in daily life, stable and efficient hardware equipment like sensor is essential, where absolute value detector is widely used. A detector is created on the basis of this. The circuit can compare a signed 6-bit number with the threshold of a 5-bit number. This design uses MOS and pass-transistor logic (PLT) structure as the basic parts to form the transcoding logic and the subtraction logic. This paper also discusses the optimization of its minimum latency and energy usage based on logic effort parameters. In order to match a sequence of minimum delays, the article gives various power supply voltages and calculates each schemes lowest energy consumption for each voltage. The reduction of the energy cost is significant, which means the circuit has application value in reality. Besides, the different results are listed in a scatterplot to find out the trend of optimization, which may be helpful for selecting a proper scheme in the real program.

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Internet of Things technology in mineral remote sensing monitoring

Cited by 7 publications

References 22 publications

Lithological Mapping of Kohat Basin in Pakistan Using Multispectral Remote Sensing Data: A Comparison of Support Vector Machine (SVM) and Artificial Neural Network (ANN)

Lithological Mapping of Kohat Basin in Pakistan Using Multispectral Remote Sensing Data: A Comparison of Support Vector Machine (SVM) and Artificial Neural Network (ANN)

IoT Sensor Challenges for Geothermal Energy Installations Monitoring: A Survey

A 6-bit absolute value detector based on MOSFET and PTL structure

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