The Art of Designing Remote IoT Devices—Technologies and Strategies for a Long Battery Life

Callebaut, Gilles; Mulders, Jarne Van; Ottoy, Geoffrey; Strycker, Lieven De; Perre, Liesbet Van der

doi:10.3390/s21030913

Cited by 68 publications

(55 citation statements)

References 81 publications

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“…We divide the dataset into training and testing parts and check the results on testing part for validation [ 64 , 65 , 66 , 67 ]. For detection, Faster RCNN uses a combination of RPN and Fast RCNN with shared convolutional features.…”

Section: Resultsmentioning

confidence: 99%

Automatic Target Detection from Satellite Imagery Using Machine Learning

Tahir

Munawar

Akram

et al. 2022

Sensors

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Object detection is a vital step in satellite imagery-based computer vision applications such as precision agriculture, urban planning and defense applications. In satellite imagery, object detection is a very complicated task due to various reasons including low pixel resolution of objects and detection of small objects in the large scale (a single satellite image taken by Digital Globe comprises over 240 million pixels) satellite images. Object detection in satellite images has many challenges such as class variations, multiple objects pose, high variance in object size, illumination and a dense background. This study aims to compare the performance of existing deep learning algorithms for object detection in satellite imagery. We created the dataset of satellite imagery to perform object detection using convolutional neural network-based frameworks such as faster RCNN (faster region-based convolutional neural network), YOLO (you only look once), SSD (single-shot detector) and SIMRDWN (satellite imagery multiscale rapid detection with windowed networks). In addition to that, we also performed an analysis of these approaches in terms of accuracy and speed using the developed dataset of satellite imagery. The results showed that SIMRDWN has an accuracy of 97% on high-resolution images, while Faster RCNN has an accuracy of 95.31% on the standard resolution (1000 × 600). YOLOv3 has an accuracy of 94.20% on standard resolution (416 416) while on the other hand SSD has an accuracy of 84.61% on standard resolution (300 × 300). When it comes to speed and efficiency, YOLO is the obvious leader. In real-time surveillance, SIMRDWN fails. When YOLO takes 170 to 190 milliseconds to perform a task, SIMRDWN takes 5 to 103 milliseconds.

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Section: Resultsmentioning

confidence: 99%

Automatic Target Detection from Satellite Imagery Using Machine Learning

Tahir

Munawar

Akram

et al. 2022

Sensors

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“…However, this study provides a proof of concept for future mainstream AR glasses. Evidence suggests that AR glasses will become mainstream in the near future 26–36 . Additionally, big tech company investments 37 and patent records 38 point toward an AR revolution in the near future akin to the widespread adoption of smartphones in the last decade 39 .…”

Section: Discussionmentioning

confidence: 99%

A comparison of pre‐clinical instructional technologies: Natural teeth, 3D models, 3D printing, and augmented reality

Mahrous

Elgreatly

Qian

et al. 2021

Journal of Dental Education

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Purpose/Objectives To assess student perceptions on learning dental anatomy using natural extracted teeth, 3D printed models, 3D virtual models, and augmented reality (AR) technology. Methods Eighty first‐year dental students enrolled in the dental anatomy course were instructed to examine four stations. The stations included four versions of a mandibular first molar: an extracted natural tooth, a 3D printed model, a 3D virtual model, and a model displayed on a novel AR device. After examining all stations, the students were asked to complete an electronic survey. Bivariate analysis was performed to determine whether students’ perceptions are related to their demographic characteristics and technological experience with computer use, 3D modeling, and video games. Results Seventy students completed the survey (87.5% response rate). Students rated natural teeth to have the highest educational value, the 3D printed tooth to be the most accessible, and the AR application to be the most interesting modality. Students who played little to no video games were more likely to rate AR as high educational value (48.8% vs. 10.3%; p < 0.001), while students with little 3D modeling experience were more likely to rate high accessibility of the 3D Model (78.1% vs. 48.3%; p < 0.001). No statistically significant associations between the perceptions and students’ demographic characteristics or use of computer were noted. Conclusion The natural extracted tooth was rated highest in educational value, while the 3D printed model was rated easiest to use, and the AR model was the most interesting to the students.

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“…There is a description of how to handle the target coverage issue in [22][23][24][25][26]. As a result, there is a need to manage goals in order to get the greatest lifetime.…”

Section: Related Workmentioning

confidence: 99%

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

Akram

Munawar

Kouzani

et al. 2022

Sensors

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Innovation in wireless communications and microtechnology has progressed day by day, and this has resulted in the creation of wireless sensor networks. This technology is utilised in a variety of settings, including battlefield surveillance, home security, and healthcare monitoring, among others. However, since tiny batteries with very little power are used, this technology has power and target monitoring issues. With the development of various architectures and algorithms, considerable research has been done to address these problems. The adaptive learning automata algorithm (ALAA) is a scheduling machine learning method that is utilised in this study. It offers a time-saving scheduling method. As a result, each sensor node in the network has been outfitted with learning automata, allowing them to choose their appropriate state at any given moment. The sensor is in one of two states: active or sleep. Several experiments were conducted to get the findings of the suggested method. Different parameters are utilised in this experiment to verify the consistency of the method for scheduling the sensor node so that it can cover all of the targets while using less power. The experimental findings indicate that the proposed method is an effective approach to schedule sensor nodes to monitor all targets while using less electricity. Finally, we have benchmarked our technique against the LADSC scheduling algorithm. All of the experimental data collected thus far demonstrate that the suggested method has justified the problem description and achieved the project’s aim. Thus, while constructing an actual sensor network, our suggested algorithm may be utilised as a useful technique for scheduling sensor nodes.

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The Art of Designing Remote IoT Devices—Technologies and Strategies for a Long Battery Life

Cited by 68 publications

References 81 publications

Automatic Target Detection from Satellite Imagery Using Machine Learning

Automatic Target Detection from Satellite Imagery Using Machine Learning

A comparison of pre‐clinical instructional technologies: Natural teeth, 3D models, 3D printing, and augmented reality

Using Adaptive Sensors for Optimised Target Coverage in Wireless Sensor Networks

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