Integration of Wearable Sensors Measurements for Indoor Pedestrian Tracking

Mahmoud, Ahmed; Sadruddin, Hamza; Coser, Pedro; Atia, Mohamed

doi:10.1109/mim.2022.9693454

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Known for its consistent observations, LiDAR is unaffected by external parameters such as lighting conditions or the time of day, and exhibits resilience under various weather conditions [1]. In light of the anticipated rise of wearable devices equipped with compact LiDAR scanners [2], industry specialists anticipate a growing dependence on LiDAR [3].…”

Section: Introductionmentioning

confidence: 99%

Refining the ONCE Benchmark With Hyperparameter Tuning

Golyadkin,

Gambashidze,

Nurgaliev

et al. 2024

IEEE Access

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In response to the growing demand for 3D object detection in applications such as autonomous driving, robotics, and augmented reality, this work focuses on the evaluation of semi-supervised learning approaches for point cloud data. The point cloud representation provides reliable and consistent observations regardless of lighting conditions, thanks to advances in LiDAR sensors. Data annotation is of paramount importance in the context of LiDAR applications, and automating 3D data annotation with semi-supervised methods is a pivotal challenge that promises to reduce the associated workload and facilitate the emergence of costeffective LiDAR solutions. Nevertheless, the task of semi-supervised learning in the context of unordered point cloud data remains formidable due to the inherent sparsity and incomplete shapes that hinder the generation of accurate pseudo-labels. In this study, we consider these challenges by posing the question: "To what extent does unlabelled data contribute to the enhancement of model performance?" We show that improvements from previous semi-supervised methods may not be as profound as previously thought. Our results suggest that simple grid search hyperparameter tuning applied to a supervised model can lead to state-of-the-art performance on the ONCE dataset, while the contribution of unlabelled data appears to be comparatively less exceptional.

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Section: Introductionmentioning

confidence: 99%

Refining the ONCE Benchmark With Hyperparameter Tuning

Golyadkin,

Gambashidze,

Nurgaliev

et al. 2024

IEEE Access

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“…The emergence of the internet of everything (IoE) as an evolution of the internet of things (IoT) promises the concept of smart management of the environment at homes, factories, and cities. The IoE provides the connection of people, sensors, and machines to the Internet, and so they can be considered as parts of the information network [1][2][3][4][5]. The implementation of the IoE requires extensive installation of sensors in the buildings to detect variations of the environment in terms of moisture, temperature, etc.…”

Section: Introductionmentioning

confidence: 99%

“…The implementation of the IoE requires extensive installation of sensors in the buildings to detect variations of the environment in terms of moisture, temperature, etc. [1][2][3]. Due to their number and proximity, the sensors should be small, affordable, and energy-efficient [5].…”

Section: Introductionmentioning

confidence: 99%

Printed Fractal Folded Coplanar-strips-fed Array Rectenna for IoE Applications

Badamchi¹,

Trinh²,

Bois³

et al. 2022

PIER C

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This paper presents a low-cost antenna integrable to a large set of indoor common building materials. Employing the printing technology on thin transparent polyethylene terephthalate material and using available building materials not only leads to a low-cost environmentally friendly solution for the expected massive sensor deployment but also eliminates the dispersive behavior of the materials that are interacting with them. A coplanar-strips fed fractal folded antenna element was designed and validated experimentally with four different materials including gypsum, plywood, and plexiglass. The aesthetically viable ground-free antenna achieves wideband performance and radiates in the broadside plane perpendicularly to the wall. The single antenna element covers the frequency band of 2.18-3.96 GHz with a gain of 1 dBi at 2.4 GHz. To take advantage of the large available surface, a high efficiency 2.4 GHz array rectenna for powering electronic devices intended for IoE technology is proposed. The proposed array rectenna has a dimension of 384 × 354 × 6.475 mm 3 and employs a single diode as the rectifier element. The measured results for the presented array rectenna reveal an AC-DC powerconversion-efficiency (PCE) of more than 20% for input powers as low as 0.025 µW/cm 2 with a peak PCE of 61.3% at 4.03 µW/cm 2 .

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