Fusion positioning algorithm of indoor WiFi and bluetooth based on discrete mathematical model

Zhang, Fang

doi:10.1007/s12652-020-02635-2

Cited by 9 publications

(2 citation statements)

References 15 publications

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“…Two public domain databases Fingerprint verification competition 2002 (FVC2002) and fingerprint verification competition 2004 (FVC2004) have been used for the experimental results and to validate the suggested methods for fingerprint reconstruction and enhancement [12]. Fang Zhang [30] proposed a Fusion positioning algorithm of indoor WiFi and Bluetooth based on a discrete mathematical model. The position fingerprint method is used to realize WiFi indoor positioning through two stages of ''off-line/training'' and ''online positioning'' [30].…”

Section: Related Workmentioning

confidence: 99%

“…Fang Zhang [30] proposed a Fusion positioning algorithm of indoor WiFi and Bluetooth based on a discrete mathematical model. The position fingerprint method is used to realize WiFi indoor positioning through two stages of ''off-line/training'' and ''online positioning'' [30]. Pedro Martins [21] proposed Improving Bluetooth beacon-based indoor location and fingerprinting.…”

Section: Related Workmentioning

confidence: 99%

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A functional enhancement on scarred fingerprint using sigmoid filtering

Kolivand

Hamid

Asadianfam

et al. 2022

Neural Comput & Applic

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Fingerprint has been widely used in biometric applications. Numerous established researches on image enhancement techniques have been done to improve the quality of fingerprint images. However, the production of low-quality images due to the presence of scars remains a challenge in biometrics. The scars damage the fingerprint minutiae information due to broken ridges and they reduce the accuracy of identification. This research developed an image enhancement approach to improve the quality of scarred fingerprint images to generate accurate minutiae extraction. To achieve the aim, the scarred image was improved by removing noise using a new filter, Median Sigmoid (MS), and the corrected ridges were reconstructed using ridges structure enhancement algorithm. This was done to enhance the broken ridges structure. MS filter is a combination of median filter and modified sigmoid function that improves the image contrast and simultaneously removes noise in the fingerprint image. Following that, the filtered image was used in the ridges structure enhancement process. To identify true minutiae, the broken ridges structure in the filtered image needed to be accurately verified. In the ridges structure reconstruction process, an algorithm was enhanced to identify the best value of Sigma parameter (σ) used in the Gaussian Low-pass filter to generate a better orientation image. The image is important to reconstruct the corrupted fingerprint ridges structure. The evaluation for the proposed approach used the National Institute of Standards and Technology Special Database 14, and the results showed a 37% improvement of the quality index in comparison to approaches found in related research. The findings of the evaluation showed that the proposed enhancement approach produced a better minutiae extraction result and this is very significant in the field of fingerprint image enhancement.

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Section: Related Workmentioning

confidence: 99%