scholarly journals Deep Learning Approach for Raman Spectroscopy

2021 ◽  
Author(s):  
M.H. Wathsala N. Jinadasa ◽  
Amila C. Kahawalage ◽  
Maths Halstensen ◽  
Nils-Olav Skeie ◽  
Klaus-Joachim Jens
Keyword(s):  
Raman Spectroscopy ◽  
Deep Learning ◽  
Learning Strategy ◽  
Feature Learning ◽  
Medical Diagnostics ◽  
Vital Role ◽  
Raman Signal ◽  
Chemical Research ◽  
Spectral Interpretation ◽  
Complex Relationships

Raman spectroscopy is a widely used technique for organic and inorganic chemical material identification. Throughout the last century, major improvements in lasers, spectrometers, detectors, and holographic optical components have uplifted Raman spectroscopy as an effective device for a variety of different applications including fundamental chemical and material research, medical diagnostics, bio-science, in-situ process monitoring and planetary investigations. Undoubtedly, mathematical data analysis has been playing a vital role to speed up the migration of Raman spectroscopy to explore different applications. It supports researchers to customize spectral interpretation and overcome the limitations of the physical components in the Raman instrument. However, large, and complex datasets, interferences from instrumentation noise and sample properties which mask the true features of samples still make Raman spectroscopy as a challenging tool. Deep learning is a powerful machine learning strategy to build exploratory and predictive models from large raw datasets and has gained more attention in chemical research over recent years. This chapter demonstrates the application of deep learning techniques for Raman signal-extraction, feature-learning and modelling complex relationships as a support to researchers to overcome the challenges in Raman based chemical analysis.

scholarly journals REMOTE SENSING IMAGES CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK

2021 ◽  
pp. 1653-1655
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Language Processing ◽  
Data Extraction ◽  
Satellite Image ◽  
Field Evaluation ◽  
Medical Diagnostics ◽  
Vital Role ◽  
Neural Net ◽  
Remote Sensing Images

The Deep learning plays the vital role in day to day real time applications. Machine Learning helps in various fields, for example, Natural language Processing, Computer Vision, Medical diagnostics and remote sensing images classifications. The Convolutional Neural Net-works algorithms provide the higher exactness, solid capacity to data extraction. Remote satellite image classifications that are used for the examination of ecological and topographical fields are procured through remote sensing methods. The manual classification not suit-able for land field evaluation and definite report plan, .In this paper proposed the deep learning based remote satellite classification. In this system provides higher accuracy compared to the previous system

Real Time Gender and Age Prediction using Deep Learning Techniques

2020 ◽  
Vol 9 (6) ◽  
pp. 797-801
Keyword(s):  
Deep Learning ◽  
Face Recognition ◽  
Real Time ◽  
Vital Role ◽  
Learning Techniques ◽  
Gender And Age ◽  
The Face ◽  
And Gender ◽  
Gender Detection ◽  
Age Prediction

Face recognition plays a vital role in security purpose. In recent years, the researchers have focused on the pose illumination, face recognition, etc,. The traditional methods of face recognition focus on Open CV’s fisher faces which results in analyzing the face expressions and attributes. Deep learning method used in this proposed system is Convolutional Neural Network (CNN). Proposed work includes the following modules: [1] Face Detection [2] Gender Recognition [3] Age Prediction. Thus the results obtained from this work prove that real time age and gender detection using CNN provides better accuracy results compared to other existing approaches.

Hierarchical Quality-Relevant Feature Representation for Soft Sensor Modeling: A Novel Deep Learning Strategy

2020 ◽  
Vol 16 (6) ◽  
pp. 3721-3730 ◽  
Author(s):  
Xiaofeng Yuan ◽  
Jiao Zhou ◽  
Biao Huang ◽  
Yalin Wang ◽  
Chunhua Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Strategy ◽  
Soft Sensor ◽  
Feature Representation ◽  
Relevant Feature ◽  
Sensor Modeling

Rapid Detection of Bacteria Using Raman Spectroscopy and Deep Learning

2021 ◽  
Author(s):  
Kaitlyn Kukula ◽  
Denzel Farmer ◽  
Jesse Duran ◽  
Nishatul Majid ◽  
Christie Chatterley ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Deep Learning ◽  
Rapid Detection

scholarly journals Rapid uropathogen identification using surface enhanced Raman spectroscopy active filters

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Simon D. Dryden ◽  
Salzitsa Anastasova ◽  
Giovanni Satta ◽  
Alex J. Thompson ◽  
Daniel R. Leff ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Bacterial Infections ◽  
Surface Enhanced Raman Spectroscopy ◽  
Active Filters ◽  
Raman Signal ◽  
Rapid Diagnostics ◽  
Bacterial Classification ◽  
Surface Enhanced ◽  
Surface Enhanced Raman

AbstractUrinary tract infection is one of the most common bacterial infections leading to increased morbidity, mortality and societal costs. Current diagnostics exacerbate this problem due to an inability to provide timely pathogen identification. Surface enhanced Raman spectroscopy (SERS) has the potential to overcome these issues by providing immediate bacterial classification. To date, achieving accurate classification has required technically complicated processes to capture pathogens, which has precluded the integration of SERS into rapid diagnostics. This work demonstrates that gold-coated membrane filters capture and aggregate bacteria, separating them from urine, while also providing Raman signal enhancement. An optimal gold coating thickness of 50 nm was demonstrated, and the diagnostic performance of the SERS-active filters was assessed using phantom urine infection samples at clinically relevant concentrations (105 CFU/ml). Infected and uninfected (control) samples were identified with an accuracy of 91.1%. Amongst infected samples only, classification of three bacteria (Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae) was achieved at a rate of 91.6%.

Deep Learning based Thermal Crack Detection on Structural Concrete Exposed to Elevated Temperature

2021 ◽  
pp. 136943322098663
Author(s):  
Diana Andrushia A ◽  
Anand N ◽  
Eva Lubloy ◽  
Prince Arulraj G
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Detection Method ◽  
Concrete Structures ◽  
Vital Role ◽  
Subjective Knowledge ◽  
Thermal Cracks ◽  
Reinforced Cement ◽  
Novel Method ◽  
Human Inspection

Health monitoring of concrete including, detecting defects such as cracking, spalling on fire affected concrete structures plays a vital role in the maintenance of reinforced cement concrete structures. However, this process mostly uses human inspection and relies on subjective knowledge of the inspectors. To overcome this limitation, a deep learning based automatic crack detection method is proposed. Deep learning is a vibrant strategy under computer vision field. The proposed method consists of U-Net architecture with an encoder and decoder framework. It performs pixel wise classification to detect the thermal cracks accurately. Binary Cross Entropy (BCA) based loss function is selected as the evaluation function. Trained U-Net is capable of detecting major thermal cracks and minor thermal cracks under various heating durations. The proposed, U-Net crack detection is a novel method which can be used to detect the thermal cracks developed on fire exposed concrete structures. The proposed method is compared with the other state-of-the-art methods and found to be accurate with 78.12% Intersection over Union (IoU).

scholarly journals Part-Aware Mask-Guided Attention for Thorax Disease Classification

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 653
Author(s):  
Ruihua Zhang ◽  
Fan Yang ◽  
Yan Luo ◽  
Jianyi Liu ◽  
Jinbin Li ◽  
...  
Keyword(s):  
Visual Cues ◽  
Learning Strategy ◽  
Feature Learning ◽  
Classification Performance ◽  
Disease Classification ◽  
Feature Representations ◽  
Chest X Ray ◽  
Global And Local ◽  
Aided Diagnosis ◽  
Single Organ

Thorax disease classification is a challenging task due to complex pathologies and subtle texture changes, etc. It has been extensively studied for years largely because of its wide application in computer-aided diagnosis. Most existing methods directly learn global feature representations from whole Chest X-ray (CXR) images, without considering in depth the richer visual cues lying around informative local regions. Thus, these methods often produce sub-optimal thorax disease classification performance because they ignore the very informative pathological changes around organs. In this paper, we propose a novel Part-Aware Mask-Guided Attention Network (PMGAN) that learns complementary global and local feature representations from all-organ region and multiple single-organ regions simultaneously for thorax disease classification. Specifically, multiple innovative soft attention modules are designed to progressively guide feature learning toward the global informative regions of whole CXR image. A mask-guided attention module is designed to further search for informative regions and visual cues within the all-organ or single-organ images, where attention is elegantly regularized by automatically generated organ masks and without introducing computation during the inference stage. In addition, a multi-task learning strategy is designed, which effectively maximizes the learning of complementary local and global representations. The proposed PMGAN has been evaluated on the ChestX-ray14 dataset and the experimental results demonstrate its superior thorax disease classification performance against the state-of-the-art methods.

scholarly journals Fabrication-friendly polarization-sensitive plasmonic grating for optimal surface-enhanced Raman spectroscopy

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Arpan Dutta ◽  
Tarmo Nuutinen ◽  
Khairul Alam ◽  
Antti Matikainen ◽  
Peng Li ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Fill Factor ◽  
Surface Enhanced Raman Spectroscopy ◽  
Transverse Magnetic ◽  
Raman Signal ◽  
Optical Resonance ◽  
Excitation Light ◽  
Surface Enhanced ◽  
Surface Enhanced Raman ◽  
Plasmonic Gratings

Abstract Plasmonic nanostructures are widely utilized in surface-enhanced Raman spectroscopy (SERS) from ultraviolet to near-infrared applications. Periodic nanoplasmonic systems such as plasmonic gratings are of great interest as SERS-active substrates due to their strong polarization dependence and ease of fabrication. In this work, we modelled a silver grating that manifests a subradiant plasmonic resonance as a dip in its reflectivity with significant near-field enhancement only for transverse-magnetic (TM) polarization of light. We investigated the role of its fill factor, commonly defined as a ratio between the width of the grating groove and the grating period, on the SERS enhancement. We designed multiple gratings having different fill factors using finite-difference time-domain (FDTD) simulations to incorporate different degrees of spectral detunings in their reflection dips from our Raman excitation (488 nm). Our numerical studies suggested that by tuning the spectral position of the optical resonance of the grating, via modifying their fill factor, we could optimize the achievable SERS enhancement. Moreover, by changing the polarization of the excitation light from transverse-magnetic to transverse-electric, we can disable the optical resonance of the gratings resulting in negligible SERS performance. To verify this, we fabricated and optically characterized the modelled gratings and ensured the presence of the desired detunings in their optical responses. Our Raman analysis on riboflavin confirmed that the higher overlap between the grating resonance and the intended Raman excitation yields stronger Raman enhancement only for TM polarized light. Our findings provide insight on the development of fabrication-friendly plasmonic gratings for optimal intensification of the Raman signal with an extra degree of control through the polarization of the excitation light. This feature enables studying Raman signal of exactly the same molecules with and without electromagnetic SERS enhancements, just by changing the polarization of the excitation, and thereby permits detailed studies on the selection rules and the chemical enhancements possibly involved in SERS.

scholarly journals Laser Raman Spectroscopy with Different Excitation Sources and Extension to Surface Enhanced Raman Spectroscopy

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Md. Wahadoszamen ◽  
Arifur Rahaman ◽  
Nabil Md. Rakinul Hoque ◽  
Aminul I Talukder ◽  
Kazi Monowar Abedin ◽  
...  
Keyword(s):  
Raman Spectroscopy ◽  
Silver Nanoparticles ◽  
Rhodamine 6G ◽  
Detection Efficiency ◽  
Colloidal Suspension ◽  
Surface Enhanced Raman Spectroscopy ◽  
Laser Raman Spectroscopy ◽  
Raman Signal ◽  
Fluorescence Signal ◽  
Present System

A dispersive Raman spectrometer was used with three different excitation sources (Argon-ion, He-Ne, and Diode lasers operating at 514.5 nm, 633 nm, and 782 nm, resp.). The system was employed to a variety of Raman active compounds. Many of the compounds exhibit very strong fluorescence while being excited with a laser emitting at UV-VIS region, hereby imposing severe limitation to the detection efficiency of the particular Raman system. The Raman system with variable excitation laser sources provided us with a desired flexibility toward the suppression of unwanted fluorescence signal. With this Raman system, we could detect and specify the different vibrational modes of various hazardous organic compounds and some typical dyes (both fluorescent and nonfluorescent). We then compared those results with the ones reported in literature and found the deviation within the range of ±2 cm−1, which indicates reasonable accuracy and usability of the Raman system. Then, the surface enhancement technique of Raman spectrum was employed to the present system. To this end, we used chemically prepared colloidal suspension of silver nanoparticles as substrate and Rhodamine 6G as probe. We could observe significant enhancement of Raman signal from Rhodamine 6G using the colloidal solution of silver nanoparticles the average magnitude of which is estimated to be 103.

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