Author Affiliations
Abstract
1 Peng Cheng Laboratory, Shenzhen, China
2 Shanghai Jiao Tong University, State Key Laboratory of Advanced Optical Communication Systems and Networks, Department of Electronic Engineering, Shanghai, China
3 University of L’Aquila, Department of Physical and Chemical Sciences, L’Aquila, Italy
We propose a joint look-up-table (LUT)-based nonlinear predistortion and digital resolution enhancement scheme to achieve high-speed and low-cost optical interconnects using low-resolution digital-to-analog converters (DACs). The LUT-based predistortion is employed to mitigate the pattern-dependent effect (PDE) of a semiconductor optical amplifier (SOA), while the digital resolution enhancer (DRE) is utilized to shape the quantization noise, lowering the requirement for the resolution of DAC. We experimentally demonstrate O-band intensity modulation and direct detection (IM/DD) transmission of 124-GBd 4 / 6-level pulse-amplitude modulation ( PAM ) -4 / 6 and 112-GBd PAM-8 signals over a 2-km standard single-mode fiber (SSMF) with 3 / 3.5 / 4-bit DACs. In the case of 40-km SSMF transmission with an SOA-based preamplifier, 124-GBd on-off-keying (OOK)/PAM-3/PAM-4 signals are successfully transmitted with 1.5 / 2 / 3-bit DACs. To the best of our knowledge, we have achieved the highest net data rates of 235.3-Gb / s PAM-4, 289.7-Gb / s PAM-6, and 294.7 Gb / s PAM-8 signals over 2-km SSMF, as well as 117.6-Gb / s OOK, 173.8-Gb / s PAM-3, and -231.8 Gb / s PAM-4 signals over 40-km SSMF, employing low-resolution DACs. The experimental results reveal that the joint LUT-based predistortion and DRE effectively mitigate the PDE and improve the signal-to-quantization noise ratio by shaping the noise. The proposed scheme can provide a powerful solution for low-cost IM/DD optical interconnects beyond 200 Gb / s.
look-up-table digital resolution enhancer quantization noise semiconductor optical amplifier pattern-dependent effect pulse-amplitude modulation Advanced Photonics Nexus
2024, 3(3): 036007
1 郑州大学郑州 450001
2 中国科学院高能物理研究所北京 100049
3 散裂中子源科学中心东莞 523803
微小角中子散射谱仪是中国散裂中子源(China spallation neutron source,CSNS)工程目前在建的谱仪之一,为了实现微小角散射模式下中子衍射的精确测量,要求中子探测器的位置分辨≤2 mm、探测效率≥60%@0.4 nm。在此物理精度需求下,研制了基于6LiF/ZnS(Ag)闪烁屏、波移光纤阵列和硅光电倍增管(Silicon Photomultiplier,SiPM)结构的位置灵敏型闪烁体探测器,以实现热中子的高效率和高分辨实时探测。探测效率测试以标准3He管的入射中子数归一化计算得到,位置分辨通过含有“CSNS”字样的含硼铝板验证。本文详细研究了0.5 mm直径波移光纤的光传输性能,对比了不同硅光电倍增管的增益和热噪声特性,并以此设计了有效面积为300 mm×300 mm的探测器工程样机。经测试,该探测器的位置分辨为1.2 mm×1.2 mm,探测效率为(61.8±0.2)%@0.4 nm,达到了工程设计指标,满足了CSNS工程微小角谱仪的中子衍射测量需求。
闪烁体探测器 硅光电倍增管 波移光纤 位置分辨 探测效率 Neutron scintillator detector Silicon photomultiplier Wavelength shift fiber Position resolution Detection efficiency
Author Affiliations
Abstract
1 Beijing Academy of Quantum Information Sciences, Beijing 100193, China
2 Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
3 College of Materials Science and Opto-Electronic Technology, University of Chinese Academy of Sciences, Beijing 100049, China
We demonstrate the photon-number resolution (PNR) capability of a 1.25 GHz gated InGaAs single-photon avalanche photodiode (APD) that is equipped with a simple, low-distortion ultra-narrowband interference circuit for the rejection of its background capacitive response. Through discriminating the avalanche current amplitude, we are able to resolve up to four detected photons in a single detection gate with a detection efficiency as high as 45%. The PNR capability is limited by the avalanche current saturation, and can be increased to five photons at a lower detection efficiency of 34%. The PNR capability, combined with high efficiency and low noise, will find applications in quantum information processing technique based on photonic qubits.
single photon avalanche diode (APD) photon number resolution (PNR) detection efficiency Journal of Semiconductors
2024, 45(3): 032702
精准高效地从高分辨率遥感影像中提取建筑物信息对国土规划和地图制图意义重大,近年来基于卷积神经网络进行建筑物信息提取已经取得了很大的进展,然而在处理高分辨率遥感影像时仍存在影像的高级语义特征利用不够充分,难以获得细节丰富高精度分割影像的问题。文章针对以上问题提出了一种用于建筑物自动提取的深度学习网络结构空洞空间与通道感知网络(Atrous Space and Channel Perception Network,ASCP-Net)。该模型将空洞空间金子塔池化(Atrous Spatial Pyramid Pooling, ASPP)和空间与通道注意力 (Spatial and Channel Attention, SCA)模块融入到编码器-解码器结构中,通过ASPP模块来捕获和聚合多尺度上下文信息,采用SCA模块选择性增强特定位置和通道中更有用的信息,并将高低层特征信息输入解码网络完成建筑物信息的高效提取。在WHU建筑数据集(WHU Building Dataset)上进行实验,结果表明:文章提出的方法总体精度和F1评分分别达到了97.4%和94.6%,相比其他模型能够获得更清晰的建筑物边界,尤其对图像边缘不完整建筑的提取效果较好,有效提升了建筑物提取的精度和完整性。
高分辨率遥感影像 双注意力机制 空洞卷积 建筑物提取 high-resolution remote sensing images dual attention mechanism atrous convolution building extraction
北京航空航天大学 电子信息工程学院 电磁兼容技术研究所,北京 100191
现有的反射面电磁成像系统体积庞大,无法满足机载、车载、无人机等应用平台要求。针对此类问题,研究了龙伯透镜的结构特性和成像特性,设计了大视场龙伯透镜电磁成像系统,利用空不变成像特性进行超分辨图像处理,实现了快速、大视场、宽频带、高分辨电磁辐射源分布成像。计算了口径300 mm带球核分层龙伯透镜参数,仿真了4~18 GHz龙伯透镜焦弧面场强分布,验证了龙伯透镜空不变的成像特性及其超分辨算法的有效性。实验对比了抛物反射面电磁成像系统和本文龙伯透镜电磁成像系统的体积、成像范围、源数目和分辨率,结果证明了本文系统的优越性,同样分辨率下,达到了方位角及俯仰角均为40°的大视场范围。
大视场电磁成像 龙伯透镜 空不变 图像超分辨 large field of view Luneburg lens space invariant super-resolution 强激光与粒子束
2024, 36(4): 043017
Author Affiliations
Abstract
1 School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui 230026, P. R. China
2 Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, P. R. China
3 School of Physical Science and Technology, Suzhou University of Science and Technology, Suzhou, Jiangsu 215009, P. R. China
Structured illumination microscopy (SIM) is a popular and powerful super-resolution (SR) technique in biomedical research. However, the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio (SNR) of raw images. To obtain high-quality SR images, several raw images need to be captured under high fluorescence level, which further restricts SIM’s temporal resolution and its applications. Deep learning (DL) is a data-driven technology that has been used to expand the limits of optical microscopy. In this study, we propose a deep neural network based on multi-level wavelet and attention mechanism (MWAM) for SIM. Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image, resulting in superior SR images compared to those generated using wide-field images as input data. We also demonstrate that the number of SIM raw images can be reduced to three, with one image in each illumination orientation, to achieve the optimal tradeoff between temporal and spatial resolution. Furthermore, our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms. We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.
Super-resolution reconstruction multi-level wavelet packet transform residual channel attention selective kernel attention Journal of Innovative Optical Health Sciences
2024, 17(2): 2350015