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        An Algorithm to Obtain Boat Engine RPM from Passive Sonar Signals Based on DEMON Processing and Wavelets Packets Transform

        Guillermo Kemper,David Ponce,Joel Telles,Christian del Carpio 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.6

        The detection of the engine rotational speed in revolutions per minute (RPM) is of great importance to estimate the speed of boats. This value can be obtained from the fundamental frequency component of acquired sonar signals. However, detection can often be seriously afected by noise and distortion introduced by the underwater environment. Several methods have been proposed for fundamental component detection, but they do not specifcally take advantage of the passive sonar signal characteristics to improve the performance of the process. In this context, the proposed algorithm uses DEMON processing applied to wavelets packets subbands to exploit the characterization of the sonar signal in the time and frequency domains. The algorithm involves signal segmentation, wavelet packet decomposition, subband envelope cross-correlation and fundamental component detection from the power spectrum. The method was applied in passive sonar signals acquired in navigation and also obtained by simulation. The performance of the proposed algorithm was evaluated with signals of diferent SNR values that were also corrupted by a simulated multipath underwater channel. The signals were evaluated by both the experienced sonar operators and the proposed algorithm. The results obtained were very satisfactory for RPM detection and are detailed at the end of this document.

      • A Detection Method of Ectocervical Cell Nuclei for Pap test Images, Based on Adaptive Thresholds and Local Derivatives

        Julio Oscanoa,Marcelo Mena,Guillermo Kemper 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.2

        Cervical cancer is one of the main causes of death by disease worldwide. In Peru, it holds the first place in frequency and represents 8% of deaths caused by sickness. To detect the disease in the early stages, one of the most used screening tests is the cervix Papanicolaou test. Currently, digital images are increasingly being used to improve Pap test efficiency. This work develops an algorithm based on adaptive thresholds, which will be used in Pap smear assisted quality control software. The first stage of the method is a pre-processing step, in which noise and background removal is done. Next, a block is segmented for each one of the points selected as not background, and a local threshold per block is calculated to search for cell nuclei. If a nucleus is detected, an artifact rejection follows, where only cell nuclei and inflammatory cells are left for the doctors to interpret. The method was validated with a set of 55 images containing 2317 cells. The algorithm successfully recognized 92.3% of the total nuclei in all images collected.

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