Optical Lensless-Camera Communications Aided by Neural Network
| Autor: | Zhong Suhua, Hanyang Shi, Shuang Wang, Xuefen Chi, Yuhong Zhu, Hong-liang Sun |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION 02 engineering and technology BP neural network 01 natural sciences lcsh:Technology 010309 optics lcsh:Chemistry lensless-camera 0103 physical sciences 0202 electrical engineering electronic engineering information engineering General Materials Science Computer vision optical camera communications Instrumentation lcsh:QH301-705.5 Fluid Flow and Transfer Processes Artificial neural network business.industry lcsh:T Process Chemistry and Technology General Engineering 020206 networking & telecommunications lcsh:QC1-999 Computer Science Applications lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 light-emitting diode Artificial intelligence business lcsh:Engineering (General). Civil engineering (General) lcsh:Physics |
| Zdroj: | Applied Sciences, Vol 9, Iss 16, p 3238 (2019) Applied Sciences Volume 9 Issue 16 |
| ISSN: | 2076-3417 |
| Popis: | Currently, the optical components of a camera embedded in the device constrain its overall thickness. Moreover, if the camera is strongly shaken, the lens and sensor may be misaligned, resulting in a defocusing effect. In this paper, we propose a novel lensless-camera communication model, which removes the lens of camera, therefore decreasing the overall thickness of the device without affecting communications. To decode the images captured by the lensless camera, a decoding algorithm aided by back propagation (BP) neural network was designed, which recognizes the blurred image patterns efficiently. To adapt to time-varying environments, an adaptive training sequence adjustment mechanism was designed. Simulation results show that the proposed image decoding algorithm presents a good bit-error-rate (BER) performance. The proposed system has robust movements and provides resilience to interference, benefiting from the neural network and the designed algorithm. |
| Databáze: | OpenAIRE |
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