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    Wyświetlanie 1-4 z 4
    Tytuł:
    Noise quantization simulation analysis of optical convolutional networks
    Autorzy:
    Zhang, Ye
    Zhang, Saining
    Zhang, Danni
    Su, Yanmei
    Yi, Junkai
    Wang, Pengfei
    Wang, Ruiting
    Luo, Guangzhen
    Zhou, Xuliang
    Pan, Jiaoqing
    Powiązania:
    https://bibliotekanauki.pl/articles/27310111.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    optical neural network
    convolutional neural network
    noise
    quantization
    Opis:
    Optical neural network (ONN) has been regarded as one of the most prospective techniques in the future, due to its high-speed and low power cost. However, the realization of optical convolutional neural network (CNN) in non-ideal cases still remains a big challenge. In this paper, we propose an optical convolutional networks system for classification problems by applying general matrix multiply (GEMM) technology. The results show that under the influence of noise, this system still has good performance with low TOP-1 and TOP-5 error rates of 44.26% and 14.51% for ImageNet. We also propose a quantization model of CNN. The noise quantization model reaches a sufficient prediction accuracy of about 96% for MNIST handwritten dataset.
    Źródło:
    Optica Applicata; 2023, 53, 3; 483--493
    0078-5466
    1899-7015
    Pojawia się w:
    Optica Applicata
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Fringe pattern inpainting based on dual-exposure fused fringe guiding CNN denoiser prior
    Autorzy:
    Peng, Guangze
    Chen, Wenjing
    Powiązania:
    https://bibliotekanauki.pl/articles/2086757.pdf
    Data publikacji:
    2022
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    fringe projection profilometry
    phase calculation
    convolutional neural network
    denoiser prior
    Opis:
    The intensity of some pixels of the captured fringe will be saturated when fringe projection profilometry is used to measure objects with high reflectivity, which will significantly affect the reconstruction of the measured object. In this paper, we propose a fringe pattern inpainting method based on the convolutional neural network (CNN) denoiser prior guided by additional information from a fringe captured in short exposure time. First, a binary mask obtained by Otsu algorithm from the modulation information of the short exposure fringe is used to detect the high-saturation region in the normal exposure fringe. Then, the corrected gray-scales of the region of the short exposure fringe selected by the mask are inserted in the saturated region of the normal fringe to form an initial fringe for iteration. At last, fringe pattern inpainting is achieved by using a CNN denoiser prior. The correct phase can be reconstructed from the inpainted fringes. The computer simulation and experiments verify the effectiveness of the proposed method.
    Źródło:
    Optica Applicata; 2022, 52, 2; 179--193
    0078-5466
    1899-7015
    Pojawia się w:
    Optica Applicata
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Simultaneous monitoring of chromatic dispersion and optical signal to noise ratio in optical links using convolutional neural network and asynchronous delay-tap sampling
    Autorzy:
    Mrozek, Tomasz
    Perlicki, Krzysztof
    Jakubiak, Andrzej
    Powiązania:
    https://bibliotekanauki.pl/articles/1835803.pdf
    Data publikacji:
    2020
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    deep learning
    convolutional neural network
    chromatic dispersion
    OSNR
    asynchronous delay-tap sampling
    Opis:
    The article presents a method for image analysis using asynchronous delay-tap sampling (ADTS) technique and convolutional neural networks (CNNs), allowing simultaneous monitoring of many phenomena occurring in the physical layer of the optical network. The ADTS method makes it possible to visualize the course of the optical signal in the form of characteristics (so-called phase portraits), which change their shape under the influence of phenomena (including chromatic dispersion, amplified spontaneous emission noise and other). Using the VPI photonics software,a simulation model of the ADTS technique was built. After the simulation tests, 10000 images were obtained, which after proper preparation were subjected to further analysis using CNN algorithms. The main goal of the study was to train a CNN to recognize the selected impairment (distortion); then to test its accuracy and estimate the impairment for the selected set of test images. The input data consisted of processed binary images in the form of two-dimensional matrices, with the position of the pixel. This article focuses on the analysis of images containing simultaneously the phenomena of chromatic dispersion and optical signal to noise ratio.
    Źródło:
    Optica Applicata; 2020, 50, 3; 331-341
    0078-5466
    1899-7015
    Pojawia się w:
    Optica Applicata
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Identification of advanced optical modulation format and estimation of signal-to-noise-ratio based on parallel-twin convolutional neural network
    Autorzy:
    Dong, Xiaowei
    Yu, Zhihui
    Powiązania:
    https://bibliotekanauki.pl/articles/27310103.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    deep learning
    PT-CNN
    parallel-twin convolutional neural network
    constellation diagram
    modulation format identification
    SNR estimation
    Opis:
    In this paper, we design a parallel-twin convolutional neural network (PT-CNN) deep learning model and use the signal constellation diagram to realize the identification of six advanced optical modulation formats (QPSK, 4QAM, 8PSK, 8QAM, 16PSK, 16QAM) and signal-to-noise-ratio (SNR) estimation. The influence of PT-CNN with different layers and kernel sizes is investigated and the optimal network model is chosen. Simulation results demonstrate that the proposed method has the advantages of not requiring manual feature extraction, having the ability to clearly distinguish the six modulation formats with 100% accuracy when SNR of the received signal sequences is higher than 12 dB. In addition, the high-accurate SNR estimation is realized simultaneously without increasing additional system complexity.
    Źródło:
    Optica Applicata; 2023, 53, 2; 281--289
    0078-5466
    1899-7015
    Pojawia się w:
    Optica Applicata
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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