Soil Moisture Retrieval Using UWB Echoes via Fuzzy Logic and Machine Learning

Liang, Jing; Liu, Xiaoxu; Liao, Kuo

doi:10.1109/jiot.2017.2760338

Cited by 31 publications

(14 citation statements)

References 17 publications

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“…The new attributes formed are arranged according to their explained variance. Examples of IoT use-cases that utilized PCA include include soil moisture retrieval [102], face recognition [103], intrusion detection [104], structural health monitoring [105], network…”

Section: Dimensionality Reductionmentioning

confidence: 99%

Data Analytics in the Internet of Things: A Survey

Saleem

Chishti

2019

SCPE

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The plethora of sensors deployed in Internet of Things (IoT) environments generate unprecedented volumes of data, thereby creating a data deluge. Data collected from these sensors can be used to comprehend, examine and control intricate environments around us, facilitating greater intelligence, smarter decision-making, and better performance. The key challenge here is how to mine out proﬁcient information from such immense data. Copious solutions have been put forth to obtain valuable inferences and insights, however, these solutions are still in their developing stages. Moreover, conventional procedures do not address the surging analytical demands of IoT systems. Motivated to resolve this concern, this work investigates the key enablers for performing desired data analytics in IoT applications. A comprehensive survey on the identiﬁed key enablers including their role in IoT data analytics, use cases in which they have been applied and the corresponding IoT applications for the use cases is presented. Furthermore, open research challenges and future research opportunities are also discussed. This article can be used as a basis to foster advanced research in the arena of IoT data analytics.

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Section: Dimensionality Reductionmentioning

confidence: 99%

Data Analytics in the Internet of Things: A Survey

Saleem

Chishti

2019

SCPE

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“…At present, threshold segmentation method [6][7][8][9][10], algorithm based on image processing [11], Gaussian mixture model [12], cluster analysis [13], and machine learning [14][15][16][17][18][19][20][21][22] have been used for convective target identification. Especially, the method based on machine learning is widely used in radar meteorology and hydrology research and in recent years, such as fuzzy logic, decision tree and neural network, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Especially, the method based on machine learning is widely used in radar meteorology and hydrology research and in recent years, such as fuzzy logic, decision tree and neural network, etc. Through the identification of hydraulic condensate particles (e.g., Dolan B 2008 andLiu Y 2014) [18][19][20] and the inversion of soil moisture (e.g., Jing Liang et al 2017 and 2018) [21,22], the law of solid-liquidgas three-state transformation in hydrological process is explored, which provides hydrological basis for engineering construction management and human activities. But the results of the machine learning algorithm depend on the accuracy of the input data, so it is not suitable for the simulation data in this paper for the time being.…”

Section: Introductionmentioning

confidence: 99%

Thunderstorm identification algorithm research based on simulated airborne weather radar reflectivity data

Wang

Liao

Li³

et al. 2020

J Wireless Com Network

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In the past few decades, radar reflectivity data have been widely used in thunderstorm identification research. Many thunderstorm identification algorithms for ground-based weather radar have been developed. But for airborne weather radar, due to the relative scarcity of data, the thunderstorm identification research is insufficient and there are still few effective identification methods. Airborne weather radar has the realization capability of close-range detection, but most existing airborne weather radars do not have scanning capability. This paper proposes an airborne weather radar volume scan mode, under which there is a total of 31 sector scans at 31 elevations in a volume scan. And a reflectivity data simulation model of the airborne weather radar is established based on this scan strategy, then the ground-based X-band radar reflectivity data are used as input to obtain the simulated X-band airborne radar reflectivity data. Moreover, this paper studies a thunderstorm identification algorithm for the X-band airborne radar with the proposed scan mode. An improved SCI (storm cell identification) algorithm is proposed on the basis of the traditional SCI algorithm which is applicable to S-band ground-based weather radar. The results of thunderstorm identification carried out on the simulated airborne radar data show that the algorithm can effectively identify the thunderstorm cells in the mature stage and the developing stage.

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“…"Radar devices" involve a wide variety of highly specialized applications for which UWB technology and UWB antennas are widely used even if many of the preferred UWB antennas radiate on different frequencies than the FCC designated 3.1-10.6 GHz band. Ground-penetrating radars (GPR) is one such application for which radars are used either to detect objects buried in the ground [9], to estimate soil characteristics (i.e., moisture) [10], or even to detect living beings trapped in ruins, after a physical disaster such as an earthquake or a hurricane. For this latter case, a more sophisticated radar-through-wall imaging radar-can also be used.…”

Section: Introductionmentioning

confidence: 99%

Antennas for UWB Applications

Nikolaou¹,

Quddious²

2020

UWB Technology - Circuits and Systems

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Antennas for UWB Applications" chapter deals with an overview of ultrawideband (UWB) antennas used for different applications. Some fundamental and widely used radiators, such as fat monopole, microstrip-fed and coplanar waveguide (CPW)-fed slot antennas, and tapered end-fire antennas are presented. Selected antenna designs are presented in relation to the UWB applications and their dictating radiation and operation principles. The demonstrated UWB antennas include antennas for handheld devices used for personal area network (PAN) communications; antennas for localization and positioning; UWB antennas for radio-frequency identifications (RFIDs); radar antennas for through-wall imaging, for ground-penetrating radar (GPR), and for breast tumor detection; and more generally, UWB antennas used for sensing. For some of the aforementioned applications, UWB antennas with special characteristics are needed, and they are presented and associated with the relevant applications. These include reconfigurable UWB antennas, metamaterial-loaded UWB antennas, and conformal UWB antennas. The usefulness of these special characteristics in comparison with the claimed advantages is critically evaluated. For the UWB applications presented in the chapter, one type or UWB antenna is recommended.

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Soil Moisture Retrieval Using UWB Echoes via Fuzzy Logic and Machine Learning

Cited by 31 publications

References 17 publications

Data Analytics in the Internet of Things: A Survey

Data Analytics in the Internet of Things: A Survey

Thunderstorm identification algorithm research based on simulated airborne weather radar reflectivity data

Antennas for UWB Applications

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