Optical Engineering最新文献_第6页

Low power digital signal processing-lite scheme for short-reach polarization demultiplexing-64 quadrature amplitude modulation coherent optical system 短距离偏振解复用的低功耗数字信号处理-64正交调幅相干光学系统

4区工程技术

Optical Engineering Pub Date : 2023-11-14 DOI: 10.1117/1.oe.62.11.118102

Qiang Li, Wei Ji, Penghui Li, Pengcheng Liu, Fengyu Liu, Yuqian Wang, Yanjun Zhu

{"title":"Low power digital signal processing-lite scheme for short-reach polarization demultiplexing-64 quadrature amplitude modulation coherent optical system","authors":"Qiang Li, Wei Ji, Penghui Li, Pengcheng Liu, Fengyu Liu, Yuqian Wang, Yanjun Zhu","doi":"10.1117/1.oe.62.11.118102","DOIUrl":"https://doi.org/10.1117/1.oe.62.11.118102","url":null,"abstract":"Coherent optical communication critically relies on efficient digital signal processing (DSP). We demonstrate a lite DSP scheme to reduce the transmission cost and power consumption, which is mainly realized by reducing the sampling rate and simplifying the DSP algorithm. On the one hand, the baud-rate sampling technique based on the integral circuit can reduce the amount of data processing in DSP. On the other hand, we proposed a simplified joint modified constant modulus algorithm (MCMA) and phase-dependent decision-directed least mean square (DD-LMS) algorithm for adaptive blind polarization demultiplexing and phase recovery in which the butterfly structure is no longer needed for DD-LMS. And we compare the proposed simplified algorithm with the joint traditional CMA and blind phase search and the MCMA, in terms of performance in the 80-Gbaud dual-polarization 64QAM homodyne detection system. The results show that the proposed lite DSP mechanism can effectively reduce the power consumption of DSP by sub-rate sampling and simplifying the corresponding algorithm, which provides an alternative scheme for low-power optical interconnection of data centers.","PeriodicalId":19561,"journal":{"name":"Optical Engineering","volume":"21 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134901233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exact formula for longitudinal chromatic aberration of a thick lens through design parameters 通过设计参数计算厚透镜纵向色差的精确公式

4区工程技术

Optical Engineering Pub Date : 2023-11-10 DOI: 10.1117/1.oe.62.11.115102

Roman Duplov

引用次数: 0

Small object detection for autonomous driving under hazy conditions on mountain motorways 山地高速公路雾霾条件下自动驾驶小目标检测

4区工程技术

Optical Engineering Pub Date : 2023-11-10 DOI: 10.1117/1.oe.62.11.113101

Yuantao Wang, Yongsheng Qiu, Haiyang Jiang, Yuanyao Lu

{"title":"Small object detection for autonomous driving under hazy conditions on mountain motorways","authors":"Yuantao Wang, Yongsheng Qiu, Haiyang Jiang, Yuanyao Lu","doi":"10.1117/1.oe.62.11.113101","DOIUrl":"https://doi.org/10.1117/1.oe.62.11.113101","url":null,"abstract":"To address the issue of high miss rates for distant small objects and the diminished system detection performance due to the influence of hazy when autonomous vehicles operate on mountain highways. We propose a framework for small object vehicle detection in hazy traffic environments (SHTDet). This framework aims to enhance small object detection for autonomous driving under hazy conditions on mountainous motorways. Specifically, to restore the clarity of hazy images, we designed an image enhancement (IE), and its parameters are predicted by a convolutional neural network [filter parameter estimation (FPE)]. In addition, to enhance the detection accuracy of small objects, we introduce a cascaded sparse query (CSQ) mechanism, which effectively utilizes high-resolution features while maintaining fast detection speed. We jointly optimize the IE and the detection network (CSQ-FCOS) in an end-to-end manner, ensuring that FPE module can learn a suitable IE. Our proposed SHTDet method is adept at adaptively handling sunny and hazy conditions. Extensive experiments demonstrate the efficacy of the SHTDet method in detecting small objects on hazy sections of mountain highways.","PeriodicalId":19561,"journal":{"name":"Optical Engineering","volume":"116 28","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135137950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ARODNet: adaptive rain image enhancement object detection network for autonomous driving in adverse weather conditions ARODNet:用于恶劣天气条件下自动驾驶的自适应雨图像增强目标检测网络

4区工程技术

Optical Engineering Pub Date : 2023-11-10 DOI: 10.1117/1.oe.62.11.118101

Yongsheng Qiu, Yuanyao Lu, Yuantao Wang, Haiyang Jiang

{"title":"ARODNet: adaptive rain image enhancement object detection network for autonomous driving in adverse weather conditions","authors":"Yongsheng Qiu, Yuanyao Lu, Yuantao Wang, Haiyang Jiang","doi":"10.1117/1.oe.62.11.118101","DOIUrl":"https://doi.org/10.1117/1.oe.62.11.118101","url":null,"abstract":"The current field of autonomous driving has achieved superior object detection performance in good weather conditions. However, the environment sensing capability of autonomous vehicles is severely affected in rainfall traffic environments. Although deep-learning-based image derain algorithms have made significant progress, integrating them with high-level vision tasks, such as object detection, remains challenging due to the significant differences between the derain and object detection algorithms. Additionally, the accuracy of object detection in real rain traffic environments is significantly reduced due to the domain transfer problem between the training dataset and the actual rain environment. To address this domain-shifting problem, we propose an adaptive rain image enhancement object detection network for autonomous driving in adverse weather conditions (ARODNet). This network architecture consists of an image adaptive enhancement module, an image derain module, and an object detection module. The baseline detection module (CBAM-YOLOv7) is built by incorporating the YOLOv7 object detection network into a feed-forward convolutional neural network, and it includes an attention module (CBAM). We propose a domain adaptive rain image enhancement module, DRIP, for low-quality images acquired under heavy rainfall conditions. DRIP enhances low-quality images on rainy days by adaptively learning multiple preprocessing weights. To remove the effects of rain patterns and fog clouds on image detection, we introduce DRIP-enhanced images into the depth estimation derain module (DeRain) to prevent rain and fog from obscuring the objects to be detected. Finally, the multistage joint training strategy is adopted to improve the training efficiency, and the object detection is performed while the image is derained. The efficacy of the ARODNet network for object detection in rainy weather traffic environments has been demonstrated through numerous quantitative and qualitative studies.","PeriodicalId":19561,"journal":{"name":"Optical Engineering","volume":"33 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135093260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Real-time compensation of projector lens distortion using a linear-grid model in fringe projection profilometry 条纹投影轮廓术中基于线性网格模型的投影透镜畸变实时补偿

4区工程技术

Optical Engineering Pub Date : 2023-11-10 DOI: 10.1117/1.oe.62.11.114103

Yi Bai, Geyou Zhang, Daniel L. Lau, Bo Zhang, Bin Xu, Kai Liu

{"title":"Real-time compensation of projector lens distortion using a linear-grid model in fringe projection profilometry","authors":"Yi Bai, Geyou Zhang, Daniel L. Lau, Bo Zhang, Bin Xu, Kai Liu","doi":"10.1117/1.oe.62.11.114103","DOIUrl":"https://doi.org/10.1117/1.oe.62.11.114103","url":null,"abstract":"In fringe projection profilometry, applying pre-distortion to fringe patterns reduces the errors caused by projector lens distortion. However, it is important to note that discontinuous fringe patterns, such as binary fringe patterns, introduce additional errors when using pre-distortion methods. While post-undistortion methods are applicable for discontinuous fringe patterns, the computation is typically time-consuming. We propose a linear-grid model for correcting lens distortion. First, we select multiple equidistant points within the grid to calculate the linear parameters and store them as look-up tables (LUTs). Second, by rounding down the captured distorted point to the nearest integer point, we obtain the index value for LUTs. Finally, we achieve real-time compensation for distortion error through linear expressions. The experimental results show that the proposed effectively mitigates the distortion by a factor of 6 × in terms of root mean squared error. Additionally, it exhibits a computational speed of 409.50 fps, which is an improvement compared to the traditional iterative model at 39.48 fps and the scale-offset model at 264.48 fps.","PeriodicalId":19561,"journal":{"name":"Optical Engineering","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135137567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rotary robotic gripper with LiDAR-tactile sensor fusion 旋转机器人抓手与激光雷达触觉传感器融合

4区工程技术

Optical Engineering Pub Date : 2023-11-09 DOI: 10.1117/1.oe.62.11.114102

Qi Wang, Dagong Jia, Bing Yan, Fei Teng, Caiming Sun, Xuejin Li

引用次数: 0

Low-loss silica-based 90 deg optical hybrid in c band based on 4 × 4 multimode interference coupler for coherent receiver 基于4 × 4多模干涉耦合器的c波段低损耗硅基90度光混合

4区工程技术

Optical Engineering Pub Date : 2023-11-08 DOI: 10.1117/1.oe.62.11.115101

Pengwei Cui, Junchi Ma, Yue Wang, Jiashun Zhang, Jin You, Junming An, Yuanda Wu

引用次数: 0

Optical fiber sensors for water and air quality monitoring: a review 用于水和空气质量监测的光纤传感器:综述

4区工程技术

Optical Engineering Pub Date : 2023-11-08 DOI: 10.1117/1.oe.63.3.031004

Dajuan Lyu, Qing Huang, Xiaokun Wu, Yanbo Nie, Minghong Yang

引用次数: 0

Influence of airborne LiDAR wavelength on the detection distance of clear air turbulence 机载激光雷达波长对晴空湍流探测距离的影响

4区工程技术

Optical Engineering Pub Date : 2023-11-08 DOI: 10.1117/1.oe.62.11.114101

Jing Zhao, Xiujuan Luo, Zhaohui Liu, Hui Liu, Minglai Chen, Yu Zhang, Zelin Yue

引用次数: 0

Vanadium dioxide-assisted multifunctional terahertz devices platform: modulator, polarization converter, and biosensor 二氧化钒辅助的多功能太赫兹器件平台:调制器、偏振变换器和生物传感器

4区工程技术

Optical Engineering Pub Date : 2023-11-07 DOI: 10.1117/1.oe.62.11.117101

Yonggang Zhang, Xin Cao, Haiyun Yao, Zhongjun Tian, Lanju Liang, Xin Yan, ChengCheng Huang, Fu Qiu, Rui Zhang, Wei Liu, Xiaofei Hu, Zhenhua Li, Ziqun Wang

{"title":"Vanadium dioxide-assisted multifunctional terahertz devices platform: modulator, polarization converter, and biosensor","authors":"Yonggang Zhang, Xin Cao, Haiyun Yao, Zhongjun Tian, Lanju Liang, Xin Yan, ChengCheng Huang, Fu Qiu, Rui Zhang, Wei Liu, Xiaofei Hu, Zhenhua Li, Ziqun Wang","doi":"10.1117/1.oe.62.11.117101","DOIUrl":"https://doi.org/10.1117/1.oe.62.11.117101","url":null,"abstract":"Abstract. Exploiting the phase transition of vanadium dioxide (VO2), a switchable terahertz multifunctional device integrating narrowband absorption and broadband polarization switching was proposed in our study. The transformation of VO2 from insulating state to metallic state can realize different functions. When VO2 was in metal state, the metamaterial device exhibited an absorption effect exceeding 90% in the range of 1.96 to 4.06 THz, and its amplitude was tunable via changing the conductivity of VO2. Further, the modulation depth reached 171%. This device was also used as a broadband cross-polarization converter, achieving more than 97% cross-polarization conversion in the range of 2.03 to 3.93 THz. When VO2 was in insulating state, a narrow-band absorption peak with an absorption rate exceeding 94% was obtained, and the device can be used as a biosensor with a sensitivity of 363 GHz/RIU. The designed THz metamaterials have broad application prospects in modulation, sensing, radar communication, and other fields.","PeriodicalId":19561,"journal":{"name":"Optical Engineering","volume":"93 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135476138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0