- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Quantification of collagen fiber structure using second harmonic generation imaging and two-dimensional discrete Fourier transform analysis: Application to the human optic nerve head
摘要： Second Harmonic Generation (SHG) microscopy is widely used to image collagen fiber microarchitecture due to its high spatial resolution, optical sectioning capabilities and relatively non-destructive sample preparation. Quantification of SHG images requires sensitive methods to capture fiber alignment. This paper presents a 2D Discrete Fourier Transform (DFT) based method for collagen fiber structure analysis from SHG images. The method includes integrated Periodicity Plus Smooth Image Decomposition (PPSID) for correction of DFT edge discontinuity artefact, avoiding the loss of peripheral image data encountered with more commonly used windowing methods. Outputted parameters are: the collagen fiber orientation distribution, aligned collagen content and the degree of collagen fiber dispersion along the principal orientation. We demonstrate its application to determine collagen microstructure in the human optic nerve head, showing its capability to accurately capture characteristic structural features including radial fiber alignment in the innermost layers of the bounding sclera and a circumferential collagen ring in the mid-stromal tissue. Higher spatial resolution rendering of individual lamina cribrosa beams within the nerve head is also demonstrated. Validation of the method is provided in the form of correlative results from wide-angle X-ray scattering (WAXS) and application of the presented method to other fibrous tissues.
关键词： Second Harmonic Generation,edge effect artefact correction,Discrete Fourier Transform,optic nerve head,collagen fiber structure,Non-linear microscopy
更新于2025-09-23 15:23:52
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[IEEE 2018 41st International Conference on Telecommunications and Signal Processing (TSP) - Athens, Greece (2018.7.4-2018.7.6)] 2018 41st International Conference on Telecommunications and Signal Processing (TSP) - Improvement of Optic Disc Localization using Gabor Filters
摘要： The paper presents a supervised technique for the detection and localization of the optic disc (OD) in retinal images. The proposed processing technique is based on Discrete Fourier Transform (DFT) and Gabor filters (GFs). The algorithm of image patch processing and classification has two phases: the learning phase for the OD class definition and the testing phase for the patch processing and classification. Two features are used to check if a patch contains the OD: the magnitude and the phase values computed on the result of the convolution between the DFT of the patch and the bank of Gabor filters. Over 100 images from MESSIDOR database were tested and comparing with other similar works. The proposed algorithm gave better results in terms of accuracy of the OD localization for all types of OD.
关键词： Gabor filter,feature extraction,Discrete Fourier Transform,patch decomposition,optic disc localization
更新于2025-09-23 15:22:29
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Post Equalization Scheme Based on Deep Neural Network for a Probabilistic Shaping 128 QAM DFT-S OFDM Signal in Underwater Visible Light Communication System
摘要： We have presented a post equalization scheme based on Deep Neural Network (DNN) for DFT-S OFDM modulation using (PS) technique in underwater visible light communication (VLC) system. By this method, we successfully demonstrated a data rate of 1.74Gbit/s PS128QAM DFT-S OFDM modulation over 1.2meter underwater optical transmission with bit error rate (BER) below 7% FEC threshold of 3.8×10-3. Compared to the typical PS128QAM DFT-S OFDM modulation without DNN, the proposed method would lead to an improvement of system capacity of 5.4% by increasing the data rate by 90 Mbps. The experimental results validate that the proposed DNN-based post equalization scheme for odd order QAM PS technique can be a promising solution for future high speed underwater VLC system.
关键词： Probabilistic Shaping (PS),odd order QAM,Deep Neural Network (DNN),Underwater VLC,Discrete Fourier Transform-Spread (DFT-S) OFDM
更新于2025-09-16 10:30:52
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[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Machine Learning Scheme for Geometrically-shaped Constellation Classification utilizing Support Vector Machine in Multi-access Internet of Vehicle Lighting
摘要： In this paper, we propose a multi-access Internet of Vehicles (IoV) scheme based on multi-band DFTS-OFDM VLC system. The experimental results show that with the bandwidth of 62.5MHz, the dynamic range was enhanced 1.6 dBm employing SVM in hexagonal constellation Geometrically-shaped (GS) 16QAM and the overall capacity is 250Mbps.
关键词： discrete Fourier transform spread (DFT-S),Geometrically-shaped,optical orthogonal frequency division multiplexing (OFDM),Visible light communication,Internet of Vehicles,Support vector machine
更新于2025-09-16 10:30:52
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Solving method without integration of some differential equation systems for coherent dynamics of quantum media excited by laser radiation
摘要： A pragmatic mechanism to obtain exact analytical solutions of equations describing the coherent dynamics of quantum multilevel systems by laser radiation is implemented. The technique shows interesting features and significant connections between different mathematical structures for constructing exact solutions for a large family of quantum systems, and is based on discrete orthogonal polynomials which are used to express the Fourier spectra of the probability amplitudes of an excited quantum system.
关键词： Discrete orthogonal polynomials,Dynamical systems in quantum mechanics,Exact solutions,Discrete Fourier transform
更新于2025-09-12 10:27:22
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Convolution neural network-based time-domain equalizer for DFT-Spread OFDM VLC system
摘要： This paper presents a novel time-domain equalizer for visible light communication (VLC) system using machine learning (ML) method. In this work, we employ discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM) as modem scheme and convolution neural network (CNN) as kernel processing unit of equalizer. After estimating channel state information (CSI) from training sequence, the proposed equalizer recovers transmitted symbols according to the estimated CSI. Numerical simulations indicate that the equalizer can significantly enhance bit error rate (BER) performance. For example, when signal-to-noise ratio (SNR) is 20dB and 16/32/64-quadrature amplitude modulation (QAM) is exploited, original BER is about 0.5 while the BER after recovery achieves 10?5, which is much lower than forward error correction (FEC) limit 3.8×10?3. This work promotes the application of ML in VLC domain. To the best of our knowledge, this is the first time a CNN-based equalizer has been explored.
关键词： Machine learning (ML),Discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-S-OFDM),Visible light communication (VLC),Convolution neural network (CNN)
更新于2025-09-10 09:29:36