2023

• Record 253 of
  
  Title:Nested multi-scale transform fusion model: The response evaluation of chemoradiotherapy for patients with lung tumors
  
  Author(s):Zhou, Tao(1,4); Liu, Shan(1,4); Lu, Huiling(2); Bai, Jing(1,4); Zhi, Lijia(1,4); Shi, Qiu(3)
  Source: Computer Methods and Programs in Biomedicine  Volume: 232  Issue: null  Article Number: 107445  DOI: 10.1016/j.cmpb.2023.107445  Published: April 2023  
  Abstract:Background and Objective: The response evaluation of chemoradiotherapy is an important method of precision treatment for patients with malignant lung tumors. In view of the existing evaluation criteria for chemoradiotherapy, it is difficult to synthesize the geometric and shape characteristics of lung tumors. In the present, the response evaluation of chemoradiotherapy is limited. Therefore, this paper constructs a response evaluation system of chemoradiotherapy based on PET/CT images. Methods: There are two parts in the system: a nested multi-scale fusion model and an attribute sets for the Response evaluation of chemoradiotherapy (AS-REC). In the first part, a new nested multi-scale transform method, i.e., latent low-rank representation (LATLRR) and non-subsampled contourlet transform (NSCT), is proposed. Then, the average gradient self-adaptive weighting is used for the low-frequency fusion rule, and the regional energy fusion rule is used for the high-frequency fusion rule. Further, the low-rank part fusion image is obtained by the inverse NSCT, and the fusion image is generated by adding the low-rank part fusion image and the significant part fusion image. In the second part, AS-REC is constructed to evaluate the growth direction of the tumor, the degree of tumor metabolic activity, and the tumor growth state. Results: the numerical results clearly show that the performance of our proposed method outperforms in comparison with several existing methods, among them, the value of Qabf increased by up to 69%. Conclusions: Through the experiment of three reexamination patients, the effectiveness of the evaluation system of radiotherapy and chemotherapy are proved. © 2023 Elsevier B.V.
  Accession Number: 20231013682084
• Record 254 of
  
  Title:A novel shape restoration algorithm for Ultra-fast morphology perception system based on multiplexing FBG array
  
  Author(s):Wang, Jindong(1,2,3); Li, Juan(1); Wang, Zhiyuan(1); Jin, Liyang(1); Huang, Jingsheng(1); Zhu, Tao(1)
  Source: Measurement: Journal of the International Measurement Confederation  Volume: 218  Issue: null  Article Number: 113130  DOI: 10.1016/j.measurement.2023.113130  Published: August 15, 2023  
  Abstract:Here, we demonstrate an FBG-array-based ultra-fast shape perception system where a novel shape restoration algorithm is proposed and a time-stretch technology is used to fast interrogate the wavelengths of FBGs. In the proposed algorithm, the curvatures on points are converted to the corresponding bending radius and direction, therefore reconstructed to curves and surfaces. On the premise of ensuring better restoration effect, our algorithm has shorter time and smaller amount of data, which is more conducive to instrumentation and practical implementation. The simulation results show that the reconstruction errors are better than 5% under typical conditions. The shape sensing system is applied to detect the shape of a wing under both static and dynamic conditions, with relative errors of 3.10% under twisted tension and 4.85% under uniform tension, which experimentally proves that the system can realize ultra-fast shape sensing with a sensing rate of more than 500 kHz. © 2023
  Accession Number: 20232414233261
• Record 255 of
  
  Title:Concentric ring optical traps for orbital rotation of particles
  
  Author(s):Li, Xing(3,4); Dan, Dan(3,4); Yu, Xianghua(3,4); Zhou, Yuan(3,4); Zhang, Yanan(3,4); Gao, Wenyu(3,4); Li, Manman(5); Xu, Xiaohao(5); Yan, Shaohui(3,4); Yao, Baoli(1,2,3,4)
  Source: Nanophotonics  Volume: 12  Issue: 24  Article Number: null  DOI: 10.1515/nanoph-2023-0600  Published: December 1, 2023  
  Abstract:Optical vortices (OVs), as eigenmodes of optical orbital angular momentum, have been widely used in particle micro-manipulation. Recently, perfect optical vortices (POVs), a subclass of OVs, are gaining increasing interest and becoming an indispensable tool in optical trapping due to their unique property of topological charge-independent vortex radius. Here, we expand the concept of POVs by proposing concentric ring optical traps (CROTs) and apply them to trapping and rotating particles. A CROT consists of a series of concentric rings, each being a vortex whose radius and topological charge can be controlled independently with respect to the other rings. Quantitative results show that the generated CROTs have weak sidelobes, good uniformity, and relatively high diffraction efficiency. In experiments, CROTs are observed to trap multiple dielectric particles simultaneously on different rings and rotate these particles with the direction and speed of rotation depending on the topological charge sign and value of each individual ring. In addition, gold particles are observed to be trapped and rotate in the dark region between two bright rings. As a novel tool, CROTs may find potential applications in fields like optical manipulation and microfluidic viscosity measurements. © 2023 De Gruyter. All rights reserved.
  Accession Number: 20234815140790
• Record 256 of
  
  Title:3D Printed Microlens Probe for Optical Coherence Tomography
  
  Author(s):Tai, Yalong(1,2); Li, Zhuorong(1,2); Liu, Dejun(1,2); Li, Bozhe(1,2); Zhu, Rui(3,4,5); Li, Jianan(4,5); Li, Qiang(4); Liu, Haiping(4); Liao, Changrui(1,2); Wang, Yiping(1,2)
  Source: 2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings, ACP/POEM 2023  Volume: null  Issue: null  Article Number: null  DOI: 10.1109/ACP/POEM59049.2023.10369650  Published: 2023  
  Abstract:Microsized endoscopy is increasingly demanded in medical clinical diagnosis for imaging in small blood vessels or delicate organs. In this paper, we propose a 3D printed side-viewing microlens on fiber tip by using the femtosecond laser two-photon polymerization (TPP) method for optical coherence tomography (OCT) imaging applications. The printed microlens is only 210 μm in width and 270 μm in height, its measured lateral resolution is around 9.4 μm in air. It is demonstrated that the proposed microlens can easily profile the depth resolved inner structure of a homemade plastic pipe. © 2023 IEEE.
  Accession Number: 20240515453366
• Record 257 of
  
  Title:The generation and observation of pulses with switchable single/dual wavelengths and tunable bandwidth from a SWNT mode-locked Er-doped fiber laser
  
  Author(s):Hu, Guoqing(1,2); Wang, Chen(1,2); Chen, Kai(3); Wen, Junyue(1,2); Guo, Guowen(1,2); Liu, Ya(4); Chen, Guangwei(1,2); Zhu, Lianqing(1,2)
  Source: Optics and Laser Technology  Volume: 158  Issue: null  Article Number: 108715  DOI: 10.1016/j.optlastec.2022.108715  Published: February 2023  
  Abstract:Mode-locked pulse outputs with switchable single/dual wavelengths and widely tunable spectral bandwidth are demonstrated by fully exploiting multiple soliton formation mechanisms combining single-wall carbon nanotube, intracavity loss tuning and nonlinear polarization rotation. An isolator with single-polarization fiber pigtails is additionally inserted in a ring fiber laser cavity to introduce polarization-dependent loss modulation for gain profile tuning and nonlinear polarization rotation. Mode-locked by a home-made single-wall carbon nanotube saturable absorber, switchable single/dual-wavelength pulses centered in the 1530- or/and 1550-nm gain regions of erbium-doped fiber are experimentally observed by tuning the intracavity loss to tilt the gain profile. Moreover, the spectral bandwidths of single-wavelength pulses centered in the 1530- and 1550 nm regions could be continuously tuned from 2.4 to 4.0 nm, and 2.7 to 9.0 nm, respectively. Such widely tunable spectral bandwidths are attributed to hybrid mode-locked mechanisms combining the nonlinear polarization rotation and single-wall carbon nanotube. Our results give a deep insight into the well-controlled gain profile tuning and provide a relatively simple setup and method to obtain bandwidth-tunable, wavelength-switchable pulse outputs, showing the potential to meet various requirements of ultrafast laser applications. © 2022
  Accession Number: 20224313003896
• Record 258 of
  
  Title:Femtosecond laser ablation in liquid synthesis of iron-oxidation nanoparticles with saturable absorption performance
  
  Author(s):Yang, Yong(1,2,3); Li, Guangying(1); Wang, Xi(1); Fan, Wenhui(1,4); Cheng, Guanghua(1); Si, Jinhai(2)
  Source: Optics Express  Volume: 31  Issue: 14  Article Number: null  DOI: 10.1364/OE.493436  Published: July 3, 2023  
  Abstract:"Naked" ferroferric-oxide nanoparticles (FONPs) synthesized by a femtosecond laser ablation on a bulk stainless steel in liquid were applied to the Nd: YVO4 laser to achieve passive Q-switched pulse laser output. Without the pollution of ligand, the inherent light characteristic of "naked" FONPs was unaffected. The analysis of the morphological characteristics, dominant chemical elements, and phase composition of the FONPs showed that they were mainly composed of Fe3O4, which was spherical with an average diameter of 40 nm. The electron transition and orbital splitting of the iron element’s octahedral center position under the laser-driven were considered the primary mechanisms of saturable absorption of Fe3O4 nanoparticles. © 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.
  Accession Number: 20233214502876
• Record 259 of
  
  Title:Spectral-spatial Attention Residual Networks for Hyperspectral Image Classification
  
  Author(s):Wang, Feifei(1,3); Zhao, Huijie(1,2,3); Li, Na(1,2,3); Li, Siyuan(4); Cai, Yu(5)
  Source: Guangzi Xuebao/Acta Photonica Sinica  Volume: 52  Issue: 12  Article Number: 1210002  DOI: 10.3788/gzxb20235212.1210002  Published: December 2023  
  Abstract:Hyperspectral image classification is a research hotspot in the field of hyperspectral image processing and application. Classification models predict the class of each pixel by analyzing the spectral and spatial information of each pixel and compare it to the actual features. In the hyperspectral classification task，the spatial context information of the data can be used to improve the classification accuracy，so this paper uses the powerful learning ability of 3D-CNN to extract effective spectral and spatial features into hyperspectral images，and then fuses the extracted spectra and spatial features to enhance the flow between different levels of the network，thereby improving the classification efficiency. Although CNN operations can mine deeper feature information as the network deepens，CNN is ineffective in modeling long-distance dependencies，so consider combining CNN with attention mechanisms. This combination can focus on the local position of the given information，assign corresponding weights to it，emphasize the key features in the feature map， adjust the global information of the attention statistics image through weight re-annotation，retain the features that are more conducive to the classification task，and improve the representation ability of extracted features. But the common attention mechanism is to calculate the average globally，that is，the pixel values of the entire image block，inevitably introducing information from different categories of pixels around it，which is not needed in classification tasks. Another spectral attention mechanism based on the center pixel provides weight values that ignore the effects of surrounding pixels in the same category. Therefore，a simple spectral attention mechanism in the central region is proposed，in which the central region is selected with the central pixel as the reference and the surrounding 3×3 range as the central region，on the one hand，the range contains certain spectral information of the same category，and on the other hand，the interference of different categories of pixels is reduced as much as possible. The spectral attention mechanism in the central region can minimize the influence of interfering pixels on spectral features while extracting as many effective spectral features as possible. Based on the spectral attention mechanism of the central region，this paper proposes a spectral spatial attention residual network for hyperspectral classification，which mainly includes spectral feature learning，spatial feature learning and classifier. The network first selects appropriately sized image blocks from hyperspectral images and then classifies them. Starting from balancing computing resources and overall accuracy，experimental comparison shows that the size of the image patch is uniformly 13×13. The spectral feature learning part includes 1 frequency spectral attention module and 1 spectral residual network module. The spectral attention module adopts the central spectral attention mechanism，which can effectively suppress redundant bands and increase the weight of important bands. The spectral features after the attention mechanism will be extracted by the spectral residual network module，and more spectral features can be extracted. Convolution kernels of 1×1×n do not affect the spatial structure when extracting spectral features while maintaining spatial correlation. The spatial feature learning component includes 1 spatial attention module and 2 spatial residual network modules. The spatial attention module can obtain the important spatial information of the pixels to be classified，and use the spatial residual network to extract its spatial information. Add a hop connection between each module in the network to connect the presentation layer of the hierarchical features into a continuous residual block to mitigate the loss of accuracy. Finally，these rich spectral and spatial features are sent to the classifier to obtain the final classification result. The proposed algorithm is compared with the latest algorithm on four public datasets. Indicators and visualization results verify the superiority of the proposed algorithm. © 2023 Chinese Optical Society. All rights reserved.
  Accession Number: 20240815570594
• Record 260 of
  
  Title:A pupil detection method based on Unet with attention module and shape-prior loss
  
  Author(s):Song, Wenhui(1,2,3); Wang, Hui(1,2,3); Gui, Yawei(1,2,3); Dang, Ruochen(1,2,3); Hu, Bingliang(1,3); Wang, Quan(1,3)
  Source: Proceedings of SPIE - The International Society for Optical Engineering  Volume: 12558  Issue: null  Article Number: 125580L  DOI: 10.1117/12.2651952  Published: 2023  
  Abstract:In recent years, pupil detection in human eye images or videos has played a key role in many fields. In the field of eye tracking, the position of the center of the pupil is a basic problem, and the error of pupil detection will be magnified in subsequent calculations, which will seriously affect the performance of eye tracking. In this paper, we propose to use the currently popular semantic segmentation network for pupil detection task. We first train the Unet architecture as a benchmark, then introduce two different attention modules into Unet, and compare with the benchmark network. The results show that our method has a higher detection rate within 1-15 pixel errors. We also added an ellipse fitting error term to the loss function of the network to further improve the network performance. The training of the model is done on the LPW dataset. Finally, we also investigate the effect of data augmentation on generalization performance, with the model trained on the LPW dataset and tested on the I-SOCIALDB dataset. Although data enhancement slightly reduces the detection rate of the model in the original data set, it can improve the generalization performance of the model. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
  Accession Number: 20230713574407
• Record 261 of
  
  Title:VG-Swarm: A Vision-Based Gene Regulation Network for UAVs Swarm Behavior Emergence
  
  Author(s):Li, Huanlin(1); Cai, Yuwei(1); Hong, Juncao(1); Xu, Peng(1); Cheng, Hui(2); Zhu, Xiaomin(3); Hu, Bingliang(4); Hao, Zhifeng(1); Fan, Zhun(1)
  Source: IEEE Robotics and Automation Letters  Volume: 8  Issue: 3  Article Number: null  DOI: 10.1109/LRA.2023.3236565  Published: March 1, 2023  
  Abstract:We present VG-Swarm, a practical and effective method for aerial robots dynamic encirclement, which consists of a vision-based gene regulatory network (V-GRN) and a visual perception module. For each flying robot deployed with the proposed method, the relative spatial positions of the surrounding robots, targets, and obstacles are first obtained by omnidirectional monocular vision. Then the proposed method is used to generate the concentration field within its own perception range according to the obtained position information. The agent individually calculates and selects an optimal moving direction in its concentration field, and finally stays on its selected encirclement pattern (a closed concentration contour around the target). As a result, a swarm of flying robots can emerge adaptive pattern formations to entrap the targets even without any communication and global information. We verify the effectiveness and robustness of the proposed method in various simulations and real-world experiments. © 2016 IEEE.
  Accession Number: 20230613542994
• Record 262 of
  
  Title:Constraining the atmospheric elements in hot Jupiters with Ariel
  
  Author(s):Wang, Fang(1,2,3); Changeat, Quentin(3,4); Tinetti, Giovanna(3); Turrini, Diego(5,6); Wright, Sam O. M.(3)
  Source: Monthly Notices of the Royal Astronomical Society  Volume: 523  Issue: 3  Article Number: null  DOI: 10.1093/mnras/stad1721  Published: August 1, 2023  
  Abstract:One of the main objectives of the European Space Agency's Ariel telescope (launch 2029) is to understand the formation and evolution processes of a large sample of planets in our Galaxy. Important indicators of such processes in giant planets are the elemental compositions of their atmospheres. Here we investigate the capability of Ariel to constrain four key atmospheric markers: metallicity, C/O, S/O, and N/O, for three well-known, representative hot-Jupiter atmospheres observed with transit spectroscopy, i.e. HD 209458b, HD 189733b, and WASP-121b. We have performed retrieval simulations for these targets to verify how the planetary formation markers listed above would be recovered by Ariel when observed as part of the Ariel Tier 3 survey. We have considered eight simplified different atmospheric scenarios with a cloud-free isothermal atmosphere. Additionally, extra cases were tested to illustrate the effect of C/O and metallicity in recovering the N/O. From our retrieval results, we conclude that Ariel is able to recover the majority of planetary formation markers. The contributions from CO and CO2 are dominant for the C/O in the solar scenario. In a C-rich case, C2H2, HCN, and CH4 may provide additional spectral signatures that can be captured by Ariel. In our simulations, H2S is the main tracer for the S/O in hot-Jupiter atmospheres. In the super-solar metallicity cases and the cases with C/O > 1, the increased abundance of HCN is easily detectable and the main contributor to N/O, while other N-bearing species contribute little to the N/O in the investigated atmospheres. © 2023 The Author(s).
  Accession Number: 20232714332595
• Record 263 of
  
  Title:Simulation study of an x-ray sub-picosecond resolution detection system based on time-domain amplification
  
  Author(s):Wang, Gang(1,2); Liu, Yiheng(1,2); Yan, Xin(1); Gao, Guilong(1); Wang, Tao(1); Li, Lili(1); Zhao, Yuetong(1,2); Zhao, Jinbo(3); He, Kai(1); Tian, Jinshou(1,2,4)
  Source: Applied Optics  Volume: 62  Issue: 20  Article Number: null  DOI: 10.1364/AO.492458  Published: July 2023  
  Abstract:This study proposes what we believe to be a novel x-ray detection system that achieves a temporal resolution of 930 fs with photorefractive and four-wave mixing effects. The system comprises two parts: a signal-conversion system and signal-acquisition system. The signal-conversion system is based on the photorefractive effect, which converts x-ray evolution into the variation of infrared interference intensity. The signal-conversion sensor consists of ultra-fast response LT-GaAs and a high-resolution interference cavity, achieving a resolution of 767 fs. The signal-acquisition system consists of a time-domain amplification system based on four-wave mixing and a high-resolution signal-recording system with a resolution of 21 ps, providing a temporal resolution of 525 fs. © 2023 Optica Publishing Group.
  Accession Number: 20233214488066
• Record 264 of
  
  Title:One-Stage Cascade Refinement Networks for Infrared Small Target Detection
  
  Author(s):Dai, Yimian(1); Li, Xiang(2); Zhou, Fei(3); Qian, Yulei(4); Chen, Yaohong(5); Yang, Jian(1)
  Source: IEEE Transactions on Geoscience and Remote Sensing  Volume: 61  Issue: null  Article Number: 5000917  DOI: 10.1109/TGRS.2023.3243062  Published: 2023  
  Abstract:Single-frame infrared small target (SIRST) detection has been a challenging task due to a lack of inherent characteristics, imprecise bounding box regression, a scarcity of real-world datasets, and sensitive localization evaluation. In this article, we propose a comprehensive solution to these challenges. First, we find that the existing anchor-free label assignment method is prone to mislabeling small targets as background, leading to their omission by detectors. To overcome this issue, we propose an all-scale pseudobox-based label assignment scheme that relaxes the constraints on the scale and decouples the spatial assignment from the size of the ground-truth target. Second, motivated by the structured prior of feature pyramids, we introduce the one-stage cascade refinement network (OSCAR), which uses the high-level head as soft proposal for the low-level refinement head. This allows OSCAR to process the same target in a cascade coarse-to-fine manner. Finally, we present a new research benchmark for infrared small target detection, consisting of the SIRST-V2 dataset of real-world, high-resolution single-frame targets, the normalized contrast evaluation metric, and the DeepInfrared toolkit for detection. We conduct extensive ablation studies to evaluate the components of OSCAR and compare its performance to state-of-the-art model- and data-driven methods on the SIRST-V2 benchmark. Our results demonstrate that a top-down cascade refinement framework can improve the accuracy of infrared small target detection without sacrificing efficiency. The DeepInfrared toolkit, dataset, and trained models are available at https://github.com/YimianDai/open-deepinfrared. © 1980-2012 IEEE.
  Accession Number: 20230813626345