Estimation of Pulmonary Signal Using a Digital Camera


  • (1)  Zainab Abd Alhussain Muhammd            Department of Medical Instrumentation Technical Engineering, Ministry of Higher Education and Scientific Research, Middle Technical University  
            Uzbekistan
    (*) Corresponding Author

Keywords:

Digital Camera

Abstract

This work aims to facilitate detection of IC information, including serial number and company by reading the IC printed numbers from the manufacture using an image processing program. This project proposes a low-cost system for extracting IC information using a digital camera. The experiments were conducted on a DIP ICs at a different distance of 2 m and MATLAB® system were then compared with the data sheet of it. The experimental results show a promising performance in comparison with the data sheet from the manufacture, with low error rate.

Downloads

Download data is not yet available.

References

How to Read an IC Part Number. Kevin Mason 2017. [Sciencing]

Strehle, E.M.; Gray, W.K.; Gopisetti, S.; Richardson, J.; McGuire, J.; Malone, S. Can home monitoring reduce mortality in infants at increased risk of sudden infant death syndrome? A systematic review. Acta Paediatr. 2012, 101, 8–13. [Google Scholar] [CrossRef] [PubMed]

Red Nose. National Scientific Advisory Group (NSAG). Information Statement: Home Monitoring; National SIDS Council of Australia: Melbourne, Australia, 2016; Available online: https://rednose.com.au/downloads/Home_Monitoring-Safe_Sleeping-Information_Statement.pdf (accessed on 25 December 2016).

Min, S.D.; Yoon, D.J.; Yoon, S.W.; Yun, Y.H.; Lee, M. A study on a non-contacting respiration signal monitoring system using Doppler ultrasound. Med. Biol. Eng. Comput. 2007, 45, 1113–1119. [Google Scholar] [CrossRef] [PubMed]

Cheng, H.-D.; Jiang, X. H.; Sun, Y.; Wang, J., Color image segmentation: advances and prospects. Pattern recognition 2001, 34, (12), 2259-2281.

Shih, F. Y.; Cheng, S., Automatic seeded region growing for color image segmentation. Image and vision computing 2005, 23, (10), 877-886.

Wu, X., YIQ vector quantization in a new color palette architecture. IEEE Transactions on Image Processing 1996, 5, (2), 321-329.

Fialka, O.; Cadik, M., FFT and convolution performance in image filtering on GPU. In Information Visualization, 2006. IV 2006. Tenth International Conference on, IEEE: 2006; pp 609-614.

Asiminoael, L.; Blaabjerg, F.; Hansen, S., Detection is key-Harmonic detection methods for active power filter applications. IEEE Industry Applications Magazine 2007, 13, (4), 22-33.

Banerjee, A.; Dhar, A. S.; Banerjee, S., FPGA realization of a CORDIC based FFT processor for biomedical signal processing. Microprocessors and Microsystems 2001, 25, (3), 131-142.

Rehman, A.; Mustafa, M.; Javaid, N.; Qasim, U.; Khan, Z., Analytical survey of wearable sensors. In Broadband, Wireless Computing, Communication and Applications (BWCCA), 2012 Seventh International Conference on, IEEE: 2012; pp 408-413.

Bland, J. M.; Altman, D. G., Statistical methods for assessing agreement between two methods of clinical measurement. International Journal of Nursing Studies 2010, 47, (8), 931-936.

K. Saladin, Anatomy & Physiology: The Unity of Form and Function, 4th ed., vol. 31, no. 1140. New York, NY: McGraw-Hill, 2007, p. 75.

R. Murthy and I. Pavlidis, “Noncontact measurement of breathing function,” IEEE Eng. Med. Biol. Mag., vol. 25, no. 3, pp. 57–67, 2006.

A. Alnaji, K. Gibson, S.-H. Lee, & J. Chahl, “Monitoring of cardiorespiratory signal: Principles of remote measurements and review of methods,” IEEE Access, vol. 5, no. 17124272, pp. 15776–15790, 2017.

A. Alonso, J. Sasin, and N. Bottini, “Signs of respiratory disease,” Apoptosis, vol. 85, no. 8, pp. 721–726, 2006.

P. Tripathy, S. Srivastava, and S. Singh, “A modified TLS-ESPRIT-based method for low-frequency mode identification in power systems utilizing synchrophasor measurements,” IEEE Trans. Power Syst., vol. 26, no. 2, pp. 719–727, 2011.

E. Billauer, Peakdet: peak detection using MATLAB, 2008, http://billauer.co.il/ peakdet.html.

O. Mazumder, A. S. Kundu, P. K. Lenka, and S. Bhaumik, “Multi-channel fusion based adaptive gait trajectory generation using wearable sensors,” J. Intell. Robotic Syst., vol. 86, no. 3–4, pp. 335–351, 2017.

J. Muraskas and K. Parsi, “The cost of saving the tiniest lives: NICUs versus prevention,” Virtual Mentor, vol. 10, no. 10, pp. 655–658, 2008.

J. M. Bland and D. G. Altman, “Statistical methods for assessing agreement between two methods of clinical measurement,” Int. J. Nurs. Stud., vol. 47, no. 8, pp. 931–936, 2010.

J. M. Bland and D. Altman, “Statistical methods for assessing agreement between two methods of clinical measurement,” Lancet, vol. 327, no. 8476, pp. 307–310, 1986.

C. Brüser, C. H. Antink, T. Wartzek, M. Walter, and S. Leonhardt, “Ambient and unobtrusive cardiorespiratory monitoring techniques,” IEEE Rev. Biomed. Eng., vol. 8, pp. 30–43, 2015.

Published

2024-01-01

How to Cite

Muhammd, Z. A. A. (2024). Estimation of Pulmonary Signal Using a Digital Camera. Middle European Scientific Bulletin, 44(1), 102-111. Retrieved from https://cejsr.academicjournal.io/index.php/journal/article/view/2066

Issue

Vol. 44 No. 1 (2024): JANUARY

Section

Medicine
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The work simultaneously licensed under a Creative Commons Attribution 4.0 International License

You are free to:

  • Adapt — remix, transform, and build upon the material for any purpose, even commercially.

The licensor cannot revoke these freedoms as long as you follow the license terms.

Under the following terms:

  • No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.