Predicting Joint Effects on CubeSats to Enhance Internet of Things in GCC Region Using Artificial Neural Network

Almalki, Faris A.; Soufiene, Ben Othman

doi:10.1155/2021/1827155

Cited by 7 publications

(3 citation statements)

References 53 publications

(43 reference statements)

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“…The proposed AI framework trained with dataset contains 2000 different images that been sourced from [44][45][46]. The dataset was divided as dataset 70% training, 15% testing, 15% validation.…”

Section: Ai Accuracy Predictionsmentioning

confidence: 99%

Developing CubeSat and AI Framework for Crowd Management Case of Short-Term Large-Scale Events

Almalki¹,

Mutawi²,

Turkistani³

et al. 2022

Adv. sci. technol. eng. syst. j.

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Many consequences can be resulted in mismanagement of crowd, which might get people injured or even lose their lives. Thus, crowd management helps in controlling overcrowded areas during events, and allowing authorities to monitor, manage and reduce incidents. Space science and technology have made huge leap in crowd management, let alone when this technology integrated with Artificial intelligent. Hence, space-based systems like CubeSats are seen as the best approach to help monitoring and providing various reasons. For instance, wide coverage footprint, collect specific area data simultaneously, assist in aerial photography, as well as providing wide range of wireless communications services that can integrate appropriately with AI and wearable devices. This work aims to design a CubeSat vehicle to manage crowds during short-term, large-scale events. The proposed system is, also, coupled with AI framework along with the camera and wearable devices to monitor the crowds' performance continuously by relying on two aspects: Firstly, aerial imaging (e.g., photos and videos). Secondly, using wearable devices that can be worn to monitor vital signs of crowds. Moreover, the proposed system can independently relieve congestion, as well as notify the ground controller of all problems to take further actions. The obtained results confirm that the proposed innovative solution is for crowd management with accuracy reached 95%, and MSE equal to 0.049.

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Section: Ai Accuracy Predictionsmentioning

confidence: 99%

Developing CubeSat and AI Framework for Crowd Management Case of Short-Term Large-Scale Events

Almalki¹,

Mutawi²,

Turkistani³

et al. 2022

Adv. sci. technol. eng. syst. j.

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“…Their small weight considerably lowers launch costs, making the idea of multiple satellites constellation in orbit possible. Several CubeSats have been proposed in the last couple of years, such as the RainCube precipitation radar, Internet of Space Things (IoST), space exploration, rural communication, remote sensing, and other contemporary CubeSat constellation projects [ 1 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ].…”

Section: Introductionmentioning

confidence: 99%

Low-Profile Antenna System for Cognitive Radio in IoST CubeSat Applications

Aljaloud

Sultan

Ikram

et al. 2023

Sensors

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Since the CubeSats have become inherently used for the Internet of space things (IoST) applications, the limited spectral band at the ultra-high frequency (UHF) and very high frequency should be efficiently utilized to be sufficient for different applications of CubeSats. Therefore, cognitive radio (CR) has been used as an enabling technology for efficient, dynamic, and flexible spectrum utilization. So, this paper proposes a low-profile antenna for cognitive radio in IoST CubeSat applications at the UHF band. The proposed antenna comprises a circularly polarized wideband (WB) semi-hexagonal slot and two narrowband (NB) frequency reconfigurable loop slots integrated into a single-layer substrate. The semi-hexagonal-shaped slot antenna is excited by two orthogonal +/−45° tapered feed lines and loaded by a capacitor in order to achieve left/right-handed circular polarization in wide bandwidth from 0.57 GHz to 0.95 GHz. In addition, two NB frequency reconfigurable slot loop-based antennas are tuned over a wide frequency band from 0.6 GHz to 1.05 GH. The antenna tuning is achieved based on a varactor diode integrated into the slot loop antenna. The two NB antennas are designed as meander loops to miniaturize the physical length and point in different directions to achieve pattern diversity. The antenna design is fabricated on FR-4 substrate, and measured results have verified the simulated results.

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“…Using AI techniques and UAVs is extraordinarily priceless since it leads to pairing the real-time machine learning ability with the exploratory abilities of unmanned drones offering ground-level operators a human-like eye in the sky. This integration not only becomes help in computer vision and image processing from aerial imaging aspects but also becomes an enabler in wireless communications via drone to fulfil the increasing and diverse requirements across a large range of application scenarios [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Utilizing Artificial Intelligence and Lotus Effect in an Emerging Intelligent Drone for Persevering Solar Panel Efficiency

Almalki

Albraikan

Soufiene

et al. 2022

Wireless Communications and Mobile Computing

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Solar energy has been receiving increasing attention due to the wide range of functionality it now supports and the promise of reduced environmental pollution. However, there are a few ongoing challenges, one of which relates to regular maintenance, especially for those high roof-mounted solar panels whose efficiency might have been degraded because of dust and dirt particle deposits. This paper is aimed at developing an autonomous drone which is equipped with a camera, a GPS sensor, a transceiver, and a pump conical tank to hold water with lotus effects, for the purpose of maintenance cleaning of solar panels which have been mounted on the roof top of buildings. The proposed system considers the combination of MobileNet and VGG-16 CNN techniques together for object detection and recognition that will enable assessment of solar panels, alongside the lotus effect physical technique that will enable maintenance of the solar panels. This would also put out of danger those human lives who have previously risked carrying out such high installation inspections and maintenance. A pump tank is designed and fabricated using a 3D printer to hold water with the lotus effect and is fitted on the drone. The proposed intelligent drone has been prototyped and deployed on-site at Taif University. The experimental results confirm the ability to detect dust and dirt particle deposits on solar panels while flying autonomously before proceeding to maintain them. The cleaning process with the use of the lotus effect has led to an increase in the power efficiency of the solar panels by approximately 31%, while the accuracy of the MobileNet framework for detecting particle deposits on the solar panels and thereafter making maintenance decisions rose to approximately 99%.

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Predicting Joint Effects on CubeSats to Enhance Internet of Things in GCC Region Using Artificial Neural Network

Cited by 7 publications

References 53 publications

Developing CubeSat and AI Framework for Crowd Management Case of Short-Term Large-Scale Events

Developing CubeSat and AI Framework for Crowd Management Case of Short-Term Large-Scale Events

Low-Profile Antenna System for Cognitive Radio in IoST CubeSat Applications

Utilizing Artificial Intelligence and Lotus Effect in an Emerging Intelligent Drone for Persevering Solar Panel Efficiency

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