Fast identification of fluorescent components in three-dimensional excitation-emission matrix fluorescence spectra via deep learning

2021 ◽  
pp. 132893
Author(s):  
Run-Ze Xu ◽  
Jia-Shun Cao ◽  
Ganyu Feng ◽  
Jing-Yang Luo ◽  
Qian Feng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fluorescence Spectra ◽  
Three Dimensional ◽  
Excitation Emission Matrix ◽  
Fast Identification

3DEEM spectroscopy analysis to assess the EPS composition in different carriers in HMBR systems

2016 ◽  
Vol 74 (11) ◽  
pp. 2708-2716 ◽  
Author(s):  
Meixiang Sun ◽  
Man Wu ◽  
Wen Liu ◽  
Huiying Liu ◽  
Yezhong Zhang ◽  
...  
Keyword(s):  
Humic Acid ◽  
Fulvic Acid ◽  
Membrane Fouling ◽  
Fluorescence Spectra ◽  
Membrane Bioreactor ◽  
Extracellular Polymeric Substances ◽  
Three Dimensional ◽  
Spectroscopy Analysis ◽  
Excitation Emission Matrix ◽  
Parallel Factor

A hybrid membrane bioreactor (HMBR) with biological band carriers (Reactor A) and an HMBR with suspended honeycomb carriers (Reactor B) were conducted in parallel to investigate the effects of different carriers on extracellular polymeric substances (EPS). Composition and concentration of EPS were examined by three-dimensional excitation-emission matrix (3DEEM) fluorescence spectra and parallel factor analysis (PARAFAC). 3DEEM spectra demonstrated that the main organic substances of the EPS in two reactors were protein-like, humic acid-like and fulvic acid-like substances. The fluorescence intensity (FI) indicated that the protein-like composition was dominant in EPS, and its intensity in reactor B was stronger than that in A (392.94 > 250.25). Results of the FI identified from the 3DEEM by PARAFAC showed that the EPS in two reactors included two humic acid-like compositions C1 (230, 320/406 nm), C2 (250, 360/440 nm) and one protein-like C4 (230, 280/340 nm), while C3 was fulvic acid-like (220/429 nm) and protein-like (230/357 nm) in reactor A and B, respectively. The proportion and FI of protein-like substances in reactor B were higher than that in A. Consequently, it was concluded that reactor A could control the membrane fouling effectively, compared with reactor B.

scholarly journals Deep learning‐based carotid media‐adventitia and lumen‐intima boundary segmentation from three‐dimensional ultrasound images

2019 ◽  
Vol 46 (7) ◽  
pp. 3180-3193 ◽  
Author(s):  
Ran Zhou ◽  
Aaron Fenster ◽  
Yujiao Xia ◽  
J. David Spence ◽  
Mingyue Ding
Keyword(s):  
Deep Learning ◽  
Three Dimensional ◽  
Ultrasound Images

scholarly journals Automated Segmentation of Infarct Lesions in T1-Weighted MRI Scans Using Variational Mode Decomposition and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1952
Author(s):  
May Phu Paing ◽  
Supan Tungjitkusolmun ◽  
Toan Huy Bui ◽  
Sarinporn Visitsattapongse ◽  
Chuchart Pintavirooj
Keyword(s):  
Deep Learning ◽  
Brain Infarction ◽  
Three Dimensional ◽  
Dice Similarity Coefficient ◽  
Automated Segmentation ◽  
Variational Mode Decomposition ◽  
Brain Scans ◽  
Automated Method ◽  
Mode Decomposition ◽  
Segmentation Task

Automated segmentation methods are critical for early detection, prompt actions, and immediate treatments in reducing disability and death risks of brain infarction. This paper aims to develop a fully automated method to segment the infarct lesions from T1-weighted brain scans. As a key novelty, the proposed method combines variational mode decomposition and deep learning-based segmentation to take advantages of both methods and provide better results. There are three main technical contributions in this paper. First, variational mode decomposition is applied as a pre-processing to discriminate the infarct lesions from unwanted non-infarct tissues. Second, overlapped patches strategy is proposed to reduce the workload of the deep-learning-based segmentation task. Finally, a three-dimensional U-Net model is developed to perform patch-wise segmentation of infarct lesions. A total of 239 brain scans from a public dataset is utilized to develop and evaluate the proposed method. Empirical results reveal that the proposed automated segmentation can provide promising performances with an average dice similarity coefficient (DSC) of 0.6684, intersection over union (IoU) of 0.5022, and average symmetric surface distance (ASSD) of 0.3932, respectively.

scholarly journals Multi-Dimensional Underwater Point Cloud Detection Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 884
Author(s):  
Chia-Ming Tsai ◽  
Yi-Horng Lai ◽  
Yung-Da Sun ◽  
Yu-Jen Chung ◽  
Jau-Woei Perng
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Three Dimensional ◽  
Point Clouds ◽  
Training Data ◽  
Network Architectures ◽  
Point Cloud Data ◽  
Data Types ◽  
Raw Data ◽  
Cloud Data

Numerous sensors can obtain images or point cloud data on land, however, the rapid attenuation of electromagnetic signals and the lack of light in water have been observed to restrict sensing functions. This study expands the utilization of two- and three-dimensional detection technologies in underwater applications to detect abandoned tires. A three-dimensional acoustic sensor, the BV5000, is used in this study to collect underwater point cloud data. Some pre-processing steps are proposed to remove noise and the seabed from raw data. Point clouds are then processed to obtain two data types: a 2D image and a 3D point cloud. Deep learning methods with different dimensions are used to train the models. In the two-dimensional method, the point cloud is transferred into a bird’s eye view image. The Faster R-CNN and YOLOv3 network architectures are used to detect tires. Meanwhile, in the three-dimensional method, the point cloud associated with a tire is cut out from the raw data and is used as training data. The PointNet and PointConv network architectures are then used for tire classification. The results show that both approaches provide good accuracy.

scholarly journals An algorithm for using deep learning convolutional neural networks with three dimensional depth sensor imaging in scoliosis detection

2021 ◽  
Author(s):  
Terufumi Kokabu ◽  
Satoshi Kanai ◽  
Noriaki Kawakami ◽  
Koki Uno ◽  
Toshiaki Kotani ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Three Dimensional ◽  
Depth Sensor

Enhanced Performance of Ultrafiltration Membrane with Powdered Zeolite Addition for Secondary Effluent Treatment

2013 ◽  
Vol 838-841 ◽  
pp. 2712-2716
Author(s):  
Yong Tu ◽  
Yong Gang Bai ◽  
Yong Chen ◽  
Wei Jing Liu ◽  
Jun Xu ◽  
...  
Keyword(s):  
Membrane Fouling ◽  
Fluorescence Spectra ◽  
Municipal Wastewater ◽  
Three Dimensional ◽  
Treatment Plant ◽  
Ultrafiltration Membrane ◽  
Effluent Treatment ◽  
Secondary Effluent ◽  
Municipal Wastewater Treatment ◽  
Sem Images

The research on ultrafiltration membrane assisted by powdered zeolite for the treatment of secondary effluent from a municipal wastewater treatment plant was studied. The results show that membrane fouling rate is reduced by pre-coating the ultrafiltration membrane with powdered zeolite, and the treatment performance of secondary effluent is enhanced. UV-vis, three-dimensional excitation emission matrix (3D-EEM) fluorescence spectra and scanning electron microscopy (SEM) images for ultrafiltration were also discussed.

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