Deep learning-based image transmission through a multi-mode fiber

2020 ◽  
Author(s):  
Sahin Kurekci ◽  
Abdussamet Tarik Temur ◽  
Muhammed Ekrem Odabas ◽  
Gilda Afshari ◽  
Emre Yüce
Keyword(s):  
Deep Learning ◽  
Image Transmission ◽  
Multi Mode

scholarly journals Deep-learning-based high-resolution recognition of fractional-spatial-mode-encoded data for free-space optical communications

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Youngbin Na ◽  
Do-Kyeong Ko
Keyword(s):  
Deep Learning ◽  
Communication Systems ◽  
Degrees Of Freedom ◽  
Image Transmission ◽  
Data Traffic ◽  
Free Space Optical ◽  
Learning Classifier ◽  
Communication Capacity ◽  
Data Rates ◽  
Mode Space

AbstractStructured light with spatial degrees of freedom (DoF) is considered a potential solution to address the unprecedented demand for data traffic, but there is a limit to effectively improving the communication capacity by its integer quantization. We propose a data transmission system using fractional mode encoding and deep-learning decoding. Spatial modes of Bessel-Gaussian beams separated by fractional intervals are employed to represent 8-bit symbols. Data encoded by switching phase holograms is efficiently decoded by a deep-learning classifier that only requires the intensity profile of transmitted modes. Our results show that the trained model can simultaneously recognize two independent DoF without any mode sorter and precisely detect small differences between fractional modes. Moreover, the proposed scheme successfully achieves image transmission despite its densely packed mode space. This research will present a new approach to realizing higher data rates for advanced optical communication systems.

Deep Learning Imaging through Specialty Multi-mode Fibers

2020 ◽  
Author(s):  
Jian Zhao ◽  
Shengli Fan ◽  
Jose Enrique Antonio-Lopez ◽  
Axel Schülzgen
Keyword(s):  
Deep Learning ◽  
Multi Mode

scholarly journals Classification of Chaotic Squeak And Rattle Vibrations By CNN Using Recurrence Pattern

2021 ◽  
Author(s):  
Jaehyeon Nam ◽  
Jaeyoung Kang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Lyapunov Exponent ◽  
Numerical Experiment ◽  
Vibration Signal ◽  
Single Mode ◽  
Recurrence Plot ◽  
Recurrence Pattern ◽  
Multi Mode

Abstract The chaotic squeak and rattle (S&R) vibrations in mechanical systems were classified by deep learning. The rattle, single-mode, and multi-mode squeak models were constructed to generate chaotic S&R signals. The repetition of nonlinear signals generated by them was visualized using an unthresholded recurrence plot and learned using a convolutional neural network (CNN). The results showed that even if the signal of the S&R model is chaos, it could be classified. The accuracy of the classification was verified by calculating the Lyapunov exponent of the vibration signal. The numerical experiment confirmed that the CNN classification using nonlinear vibration images as the proposed procedure has more than 90% accuracy. The chaotic status and each model can be classified into six classes.

scholarly journals Progressive Transmission of Medical Images via a Bank of Generative Adversarial Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ching-Chun Chang ◽  
Xu Wang ◽  
Ji-Hwei Horng ◽  
Isao Echizen
Keyword(s):  
Deep Learning ◽  
Medical Images ◽  
Image Transmission ◽  
Compression Algorithm ◽  
Healthcare Sector ◽  
Generative Adversarial Networks ◽  
Progressive Image Transmission ◽  
Adversarial Networks ◽  
Bit Plane ◽  
Bit Planes

The healthcare sector is currently undergoing a major transformation due to the recent advances in deep learning and artificial intelligence. Despite a significant breakthrough in medical imaging and diagnosis, there are still many open issues and undeveloped applications in the healthcare domain. In particular, transmission of a large volume of medical images proves to be a challenging and time-consuming problem, and yet no prior studies have investigated the use of deep neural networks towards this task. The purpose of this paper is to introduce and develop a deep-learning approach for the efficient transmission of medical images, with a particular interest in the progressive coding of bit-planes. We establish a connection between bit-plane synthesis and image-to-image translation and propose a two-step pipeline for progressive image transmission. First, a bank of generative adversarial networks is trained for predicting bit-planes in a top-down manner, and then prediction residuals are encoded with a tailored adaptive lossless compression algorithm. Experimental results validate the effectiveness of the network bank for generating an accurate low-order bit-plane from high-order bit-planes and demonstrate an advantage of the tailored compression algorithm over conventional arithmetic coding for this special type of prediction residuals in terms of compression ratio.

Deep learning assisted image transmission in multimode fibers

2019 ◽  
Author(s):  
Babak Rahmani ◽  
Damien Loterie ◽  
Georgia Konstantinou ◽  
Demetri Psaltis ◽  
Christophe Moser
Keyword(s):  
Deep Learning ◽  
Image Transmission ◽  
Multimode Fibers

scholarly journals Wireless Image Transmission Interference Signal Recognition System Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhijun Guo ◽  
Shuai Liu
Keyword(s):  
Deep Learning ◽  
Feature Vector ◽  
Recognition Performance ◽  
Image Transmission ◽  
Recognition System ◽  
Interference Signal ◽  
Signal Recognition ◽  
Interference Signals ◽  
The Time Domain ◽  
Wireless Image Transmission

In the process of wireless image transmission, there are a large number of interference signals, but the traditional interference signal recognition system is limited by various modulation modes, it is difficult to accurately identify the target signal, and the reliability of the system needs to be further improved. In order to solve this problem, a wireless image transmission interference signal recognition system based on deep learning is designed in this paper. In the hardware part, STM32F107VT and SI4463 are used to form a wireless controller to control the execution of each instruction. In the software part, aiming at the time-domain characteristics of the interference signal, the feature vector of the interference signal is extracted. With the support of GAP-CNN model, the interference signal is recognized through the training and learning of feature vector. The experimental results show that the packet loss rate of the designed system is less than 0.5%, the recognition performance is good, and the reliability of the system is improved.

Direct image transmission through a multi-mode square optical fiber

1998 ◽  
Vol 157 (1-6) ◽  
pp. 17-22 ◽  
Author(s):  
C.Y. Wu ◽  
A.R.D. Somervell ◽  
T.H. Barnes
Keyword(s):  
Optical Fiber ◽  
Image Transmission ◽  
Direct Image ◽  
Multi Mode

scholarly journals CubeSat cloud detection based on JPEG2000 compression and deep learning

2018 ◽  
Vol 10 (10) ◽  
pp. 168781401880817 ◽  
Author(s):  
Zhaoxiang Zhang ◽  
Guodong Xu ◽  
Jianing Song
Keyword(s):  
Deep Learning ◽  
Imaging System ◽  
Image Transmission ◽  
Landsat 8 ◽  
Cloud Detection ◽  
Data Set ◽  
Optical Imaging System ◽  
Deep Learning Network ◽  
Good Possibility ◽  
New Framework

In order to enhance the efficiency of the image transmission system and the robustness of the optical imaging system of the Association of Sino-Russian Technical Universities satellite, a new framework of on-board cloud detection by utilizing a lightweight U-Net and JPEG compression strategy is described. In this method, a careful compression strategy is introduced and evaluated to acquire a balanced result between the efficiency and power consuming. A deep-learning network combined with lightweight U-Net and Mobilenet is trained and verified with a public Landsat-8 data set Spatial Procedures for Automated Removal of Cloud and Shadow. Experiment results indicate that by utilizing image-compression strategy and depthwise separable convolutions, the maximum memory cost and inference speed are dramatically reduced into 0.7133 Mb and 0.0378 s per million pixels while the overall accuracy achieves around 93.1%. A good possibility of the on-board cloud detection based on deep learning is explored by the proposed method.

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