scholarly journals Increasing the Reliability of Flood Embankments with Neural Imaging Method

2018 ◽  
Vol 8 (9) ◽  
pp. 1457 ◽  
Author(s):  
Grzegorz Kłosowski ◽  
Tomasz Rymarczyk ◽  
Arkadiusz Gola
Keyword(s):  
Neural Networks ◽  
Internal Structure ◽  
Single Point ◽  
Tomographic Reconstruction ◽  
Imaging Method ◽  
Neural Imaging ◽  
Non Invasive ◽  
Innovative System ◽  
High Resolution Images ◽  
Resolution Problem

This paper presents an innovative system of many artificial neural networks that enables the tomographic reconstruction of the internal structure of a flood embankment. An advantage of the proposed method is that it allows us to obtain high-resolution images, which essentially contributes to early, precise and reliable prediction of operational hazards. The method consists in training a cluster of separate neural networks, each of which generates a single point of the output image. The simultaneous and parallel application of the set of neural networks led to effective reconstruction of the internal structure of a deposition site for floatation tailings. Results obtained from the study allow us to solve the low resolution problem that usually occurs with non-invasive imaging methods. This effect was possible thanks to the design of a new intelligent image reconstruction system.

Rehabilitating and (Re-)Training the Injured Brain

2019 ◽  
pp. 179-218
Author(s):  
Amir Raz ◽  
Sheida Rabipour
Keyword(s):  
Neural Networks ◽  
Brain Injury ◽  
Scientific Research ◽  
Acquired Brain Injury ◽  
Brain Training ◽  
Cultural Contexts ◽  
Neural Imaging ◽  
Non Invasive ◽  
Injured Brain ◽  
Type Of Injury

“Rehabilitating and (Re-)Training the Injured Brain” features approaches to brain training that aim to rehabilitate or remediate cognitive functions in acquired brain injury. It looks at the different approaches based on the type of injury and any additional benefits that may occur. The chapter sketches out methods to restore typical function by activating or stimulating relevant neural networks. Such techniques may help to overcome challenges related to rehabilitation using tools such as neural imaging and non-invasive stimulation. The focus of this chapter is on scientific research and empirical findings, but historical and cultural contexts that shroud these technological and clinical developments are also highlighted.

X-ray microtomography

1988 ◽  
Vol 46 ◽  
pp. 998-999
Author(s):  
H.W. Deckman ◽  
B.F. Flannery ◽  
J.H. Dunsmuir ◽  
K.D' Amico
Keyword(s):  
Computed Tomography ◽  
Internal Structure ◽  
Imaging Technique ◽  
Three Dimensional ◽  
Tissue Structure ◽  
Individual Element ◽  
X Ray ◽  
Non Invasive ◽  
Panoramic Imaging ◽  
Three Dimensional Images

We have developed a new X-ray microscope which produces complete three dimensional images of samples. The microscope operates by performing X-ray tomography with unprecedented resolution. Tomography is a non-invasive imaging technique that creates maps of the internal structure of samples from measurement of the attenuation of penetrating radiation. As conventionally practiced in medical Computed Tomography (CT), radiologists produce maps of bone and tissue structure in several planar sections that reveal features with 1mm resolution and 1% contrast. Microtomography extends the capability of CT in several ways. First, the resolution which approaches one micron, is one thousand times higher than that of the medical CT. Second, our approach acquires and analyses the data in a panoramic imaging format that directly produces three-dimensional maps in a series of contiguous stacked planes. Typical maps available today consist of three hundred planar sections each containing 512x512 pixels. Finally, and perhaps of most import scientifically, microtomography using a synchrotron X-ray source, allows us to generate maps of individual element.

Employment of artificial neural networks for non-invasive estimation of leaf water status using color features: a case study in Spathiphyllum wallisii

2021 ◽  
Vol 43 (5) ◽  
Author(s):  
Amin Taheri-Garavand ◽  
Abdolhossein Rezaei Nejad ◽  
Dimitrios Fanourakis ◽  
Soodabeh Fatahi ◽  
Masoumeh Ahmadi Majd
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Status ◽  
Leaf Water ◽  
Leaf Water Status ◽  
Non Invasive ◽  
Color Features ◽  
Artificial Neural

scholarly journals POS1388 ULTRASOUND FOR PANNICULITIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 977.1-977
Author(s):  
A. Potapova ◽  
O. Egorova ◽  
O. Alekseeva ◽  
A. Volkov ◽  
S. Radenska-Lopovok
Keyword(s):  
Dynamic Range ◽  
Subcutaneous Fat ◽  
Diagnostic Value ◽  
High Energy ◽  
Contralateral Side ◽  
Imaging Method ◽  
Non Invasive ◽  
M 12

Background:Ultrasound (US) is a non-invasive and safe imaging method that allows in vivo differentiation of the morphological structures of subcutaneous fat (SCF) tissue in in normal and pathology.Objectives:Reveal features of ultrasound changes in SCF in panniculitis (Pn).Methods:57 patients (f – 45, m - 12) aged 18 - 67 years with an initial diagnosis of erythema nodosum and a disease duration of 3.6 ± 1.4 years were examined. In addition to the general clinical examination, a computed tomography of the chest organs and a pathomorphological examination of a skin biopsy from the site of the node were performed. Ultrasound was performed on a MyLabTwice apparatus (ESAOTE, Italy) using a multi-frequency linear transducer (10-18 MHz) with the PD technique, the parameters of which were adapted for recording low-speed flows (PRF 300-600 Hz, low filter, dynamic range - 20-40 dB), the presence of vascularization was assessed not only in the affected area, but also on the contralateral side using high-energy Doppler.Results:33 patients were diagnosed with septal Pn (SPn), 24 - lobular Pn (LPn). In all cases, the diagnosis was verified by histological examination. Ultrasound made it possible to assess the thickness, echoicity and vascularization of the SCF. In 35 patients, significant thickening of the SCF was revealed (as compared to the contralateral side), of which in 14 cases with SPn, in 21 - with LPn. Significant diffuse thickening of the SCF with the contralateral side was observed in 18 patients, incl. in 12 (66%) patients with LPn. Limited thickening was more typical for SPn (73%). A significant increase in the echoicity of the SCF was noted in all forms of Pn. A “lobular” echo pattern with an anechogenic environment was observed in 25 patients, of which 18 (72%) had LPn. An increase in vascularization compared to the contralateral side was recorded in 30 cases (SPn-17, LPn-13).Conclusion:The obtained preliminary results indicate the important role of ultrasound in assessing the depth and prevalence of the inflammatory process at Pn. To clarify the diagnostic value of this method, further studies are needed on a larger sample of patients.Disclosure of Interests:None declared

scholarly journals Towards Amazon Forest Restoration: Automatic Detection of Species from UAV Imagery

2021 ◽  
Vol 13 (13) ◽  
pp. 2627
Author(s):  
Marks Melo Moura ◽  
Luiz Eduardo Soares de Oliveira ◽  
Carlos Roberto Sanquetta ◽  
Alexis Bastos ◽  
Midhun Mohan ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Forest Restoration ◽  
Amazon Forest ◽  
Forest Species ◽  
Accuracy And Precision ◽  
Identification Of Species ◽  
Aerial Vehicle ◽  
High Resolution Images ◽  
Uav Images

Precise assessments of forest species’ composition help analyze biodiversity patterns, estimate wood stocks, and improve carbon stock estimates. Therefore, the objective of this work was to evaluate the use of high-resolution images obtained from Unmanned Aerial Vehicle (UAV) for the identification of forest species in areas of forest regeneration in the Amazon. For this purpose, convolutional neural networks (CNN) were trained using the Keras–Tensorflow package with the faster_rcnn_inception_v2_pets model. Samples of six forest species were used to train CNN. From these, attempts were made with the number of thresholds, which is the cutoff value of the function; any value below this output is considered 0, and values above are treated as an output 1; that is, values above the value stipulated in the Threshold are considered as identified species. The results showed that the reduction in the threshold decreases the accuracy of identification, as well as the overlap of the polygons of species identification. However, in comparison with the data collected in the field, it was observed that there exists a high correlation between the trees identified by the CNN and those observed in the plots. The statistical metrics used to validate the classification results showed that CNN are able to identify species with accuracy above 90%. Based on our results, which demonstrate good accuracy and precision in the identification of species, we conclude that convolutional neural networks are an effective tool in classifying objects from UAV images.

scholarly journals Convolutional Neural Networks Model in Premature Detection of Melanoma

2019 ◽  
Vol 8 (12S2) ◽  
pp. 6-11 ◽  
Keyword(s):  
Neural Networks ◽  
Skin Cancer ◽  
Convolutional Neural Networks ◽  
Additional Data ◽  
Data Set ◽  
Non Invasive ◽  
Convolution Filter ◽  
The University ◽  
Improved Accuracy

The objective of this research is provide to the specialists in skin cancer, a premature, rapid and non-invasive diagnosis of melanoma identification, using an image of the lesion, to apply to the treatment of a patient, the method used is the architecture contrast of Convolutional neural networks proposed by Laura Kocobinski of the University of Boston, against our architecture, which reduce the depth of the convolution filter of the last two convolutional layers to obtain maps of more significant characteristics. The performance of the model was reflected in the accuracy during the validation, considering the best result obtained, which is confirmed with the additional data set. The findings found with the application of this base architecture were improved accuracy from 0.79 to 0.83, with 30 epochs, compared to Kocobinski's AlexNet architecture, it was not possible to improve the accuracy of 0.90, however, the complexity of the network played an important role in the results we obtained, which was able to balance and obtain better results without increasing the epochs, the application of our research is very helpful for doctors, since it will allow them to quickly identify if an injury is melanoma or not and consequently treat it efficiently.

scholarly journals A Green Prospective for Learned Post-processing in Sparse-view Tomographic Reconstruction

2021 ◽  
Author(s):  
Elena Morotti ◽  
Davide Evangelista ◽  
Elena Loli Piccolomini
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Tomographic Reconstruction ◽  
Research Field ◽  
Filtered Backprojection ◽  
Post Processing ◽  
Training Set ◽  
Imaging Reconstruction ◽  
Active Research

Deep Learning is developing interesting tools which are of great interest for inverse imaging applications. In this work, we consider a medical imaging reconstruction task from subsampled measurements, which is an active research field where Convolutional Neural Networks have already revealed their great potential. However, the commonly used architectures are very deep and, hence, prone to overfitting and unfeasible for clinical usages. Inspired by the ideas of the green-AI literature, we here propose a shallow neural network to perform an efficient Learned Post-Processing on images roughly reconstructed by the filtered backprojection algorithm. The results obtained on images from the training set and on unseen images, using both the non-expensive network and the widely used very deep ResUNet show that the proposed network computes images of comparable or higher quality in about one fourth of time.

scholarly journals Full-Pulse Tomographic Reconstruction with Deep Neural Networks

2018 ◽  
Vol 74 (1-2) ◽  
pp. 47-56 ◽  
Author(s):  
Diogo R. Ferreira ◽  
Pedro J. Carvalho ◽  
Horácio Fernandes ◽  
JET Contributors
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Tomographic Reconstruction

Measurement Tools for Non-Invasive Monitoring of the Plants Growth Conditions by Using Hyperspectral Imaging Methods: a Review

2021 ◽  
Vol 2 (43) ◽  
pp. 54-61
Author(s):  
Dmitriy A. Burynin ◽  
◽  
Aleksandr A. Smirnov
Keyword(s):  
Hyperspectral Imaging ◽  
Physiological State ◽  
Effective Means ◽  
Research Problem ◽  
High Spectral Resolution ◽  
Research Purpose ◽  
Imaging Method ◽  
Invasive Monitoring ◽  
Imaging Methods ◽  
Non Invasive

Portable spectroradiometers and hyperspectral cameras are increasingly being used to quickly assess the physiological state of plants. The operation of these devices is based on the registration of reflection or reflection and transmission spectra. (Research purpose) The research purpose is in analyzing the technical means and methods of non-invasive monitoring of the plant state based on the registration of the reflection spectra of leaves. (Materials and methods) The article presents a review of the work on the application of hyperspectral imaging methods. Authors classified and analyzed materials on spectroscopic radiometers and hyperspectral cameras, and outlined the prospects for implementation. Authors applied the methods of a systematic approach to the research problem. (Results and discussion) Hyperspectral imaging methods serve as an effective means of monitoring plants. It is possible to determine the pigment composition of plants, lack of nutrition, and detect biotic stress through hyperspectral imaging. The article presents methods of application of portable spectroradiometers and hyperspectral cameras. With the help of these devices it is possible to carry out measurements with high spectral resolution. The difficulty of accurately detecting the content of pigments in the leaves lies in the mutual overlap of the areas of light absorption by them. The main drawback of spectroradiometers is that they measure only at one point on a single sheet. The article presents the difficulties encountered in interpreting the results obtained by the hyperspectral camera. The background reflectivity of the soil, the geometry of the vegetation cover, and the uneven lighting can make errors in the measurements. (Conclusions) The article presents the disadvantages of the hyperspectral imaging method when using only the reflection spectrum. In order to increase the accuracy of the determination of pigments and stresses of various origins, it is necessary to develop a portable device that combines the methods of recording reflection and fluorescence.

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