Filtreler

2013 - 20142
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Ayarlar

Yazar: Şavkay, Osman Levent × Konu: Cellular neural networks ×

Arama Sonuçları

Listeleniyor 1 - 2 / 2
  • Küçük Resim
    Yayın
    Realization of preprocessing blocks of CNN based CASA system on FPGA
    (2013) Şavkay, Osman Levent; Yıldız, Nerhun; Cesur, Evren; Yalçın Müştak, Erhan; Tavşanoğlu, Ahmet Vedat
    In this paper, hardware optimization of the preprocessing part of a computer aided semen analysis (CASA) system is proposed, which is also implemented on an FPGA device as a working prototype. A real-time cellular neural network (CNN) emulator (RTCNNP-v2) is used for the realization of the image processing algorithms, whose regular, flexible and reconfigurable infrastructure simplifies the prototyping process. For future work, the post-processing part of the CASA system is proposed to be implemented on the same FPGA device as software, using either a soft or hard processor core. By the integration of the pre- and post-processing parts, the designed CASA system will be capable of processing full-HD 1080p@60 (1080×1920) video images in real-time.
  • Küçük Resim
    Yayın
    Realization of processing blocks of CNN based CASA system on CPU and FPGA
    (IEEE, 2014) Şavkay, Osman Levent; Cesur, Evren; Yıldız, Nerhun; Yalçın, Mustak Erhan; Tavşanoğlu, Ahmet Vedat
    In this paper, hardware optimization of the preprocessing and software implementation of the processing blocks of a computer aided semen analysis (CASA) system are proposed, which is also implemented on an FPGA and ARM device as a working prototype. The software implementation of the track initialization, track maintenance, data validation and classification blocks of the processing part are implemented on a Zynq7000 ARM Cortex-A9 processor. In the preprocessing part, a real-time cellular neural network (CNN) emulator (RTCNNP-v2) is used for the realization of the image processing algorithms, whose regular, flexible and reconfigurable infrastructure simplifies the prototyping process. The CASA system introduced in this paper is capable of processing full-HD 1080p@60 (1080 x 1920) video images in real-time.