A Randomness Detection Method of ZigBee Protocol in a Wireless Sensor Network

Tang, Yongli; Lian, Huanhuan; Li, Lixiang; Wang, Xiaojun; Yan, Xixi

doi:10.3390/s18113962

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…It is a high-level communication protocol designed to generate personal area networks for low-bandwidth needs, such as home automation and medical device data collection [20]. Features of Zigbee include the following [21]:…”

Section: Zigbee Technologymentioning

confidence: 99%

Intelligent Mobile Coronavirus Recognition Centre Based on IEEE 802.15.4

Alzahrani

Alheeti

Thabit

et al. 2021

Int. J. Interact. Mob. Technol.

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<p class="0abstract">The novel coronavirus (COVID-19) has become widespread around the world. It started in Wuhan, China, and has since spread rapidly among people living in other countries. Hence, the World Health Organization has considered COVID-19 as a pandemic that threatens millions of people’s lives. Due to the high number of infected people, many hospitals have been facing critical issues in providing the required medical services. For instance, some clinical centers have been unable to provide one of the most important medical services, namely blood tests to determine whether an individual is infected with COVID-19. Therefore, it is important to present an alternative diagnosis option to prevent the further spread of COVID-19. In this paper, a proposed intelligent detection communication system (IDCS) is configured for distributed mobile clinical centers to control the pandemic. In addition, the intelligent system is integrated with the Zigbee communication protocol to build a mobile COVID-19 detection system. The proposed system was trained on X-ray COVID-19 lung images used to identify infected people. The Zigbee protocol and decision tree algorithm were used to design the IDCS. The results of the proposed system show high accuracy 94.69% and accept results according to the performance measurements.</p>

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Section: Zigbee Technologymentioning

confidence: 99%

Intelligent Mobile Coronavirus Recognition Centre Based on IEEE 802.15.4

Alzahrani

Alheeti

Thabit

et al. 2021

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

show abstract

“…Digital images contain a lot of important information, which can be extracted in di erent ways in di erent elds. For example, it can be used in the eld of encryption technology [1,2], information security [3][4][5][6], in the eld of industrial engineering [7], in the eld of agriculture [8], and so on. Digital image processing technology includes many categories [9,10], and image segmentation is one of them.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Identification of Coal Crack in CT Images Based on Deep Learning

et al. 2022

Computational Intelligence and Neuroscience

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Automatic segmentation of coal crack in CT images is of great significance for the establishment of digital cores. In addition, segmentation in this field remains challenging due to some properties of coal crack CT images: high noise, small targets, unbalanced positive and negative samples, and complex, diverse backgrounds. In this paper, a segmentation method of coal crack CT images is proposed and a dataset of coal crack CT images is established. Based on the semantic segmentation model DeepLabV3+ of deep learning, the OS of the backbone has been modified to 8, and the ASPP module rate has also been modified. A new loss function is defined by combining CE loss and Dice loss. This deep learning method avoids the problem of manually setting thresholds in traditional threshold segmentation and can automatically and intelligently extract cracks. Besides, the proposed model has 0.1%, 1.2%, 2.9%, and 0.5% increase in Acc, mAcc, MioU, and FWIoU compared with other techniques and has 0.1%, 0.8%, 2%, and 0.4% increase compared with the original DeepLabV3+ on the dataset of coal CT images. The obtained results denote that the proposed segmentation method outperforms existing crack detection techniques and have practical application value in safety engineering.

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A Randomness Detection Method of ZigBee Protocol in a Wireless Sensor Network

Cited by 2 publications

References 15 publications

Intelligent Mobile Coronavirus Recognition Centre Based on IEEE 802.15.4

Intelligent Mobile Coronavirus Recognition Centre Based on IEEE 802.15.4

Intelligent Identification of Coal Crack in CT Images Based on Deep Learning

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