scholarly journals Machine learning assisted GaAsN circular polarimeter

2021 ◽  
Author(s):  
Alexander Aguirre-Perez ◽  
Rajagopal Shyamala Joshya ◽  
Helene Carrere ◽  
Xavier Marie ◽  
Thierry Amand ◽  
...  

Abstract We demonstrate the application of a two stage machine learning algorithm that enables to correlate the electrical signals from a GaAsxN1-x circular polarimeter with the intensity, degree of circular polarization and handedness of an incident light beam. Specifically, we employ a multimodal logistic regression to discriminate the handedness of light and a 6-layer neural network to establish the relationship between the input voltages, the intensity and degree of circular polarization. We have developed a particular neural network training strategy that substantially improves the accuracy of the device. The algorithm was trained and tested on theoretically generated photoconductivity and on photoluminescence experimental results. Even for a small training experimental dataset (70 instances), it is shown that the proposed algorithm correctly predicts linear, right and left circularly polarized light misclassifying less than 1.5% of the cases and attains an accuracy larger than 97% in the vast majority of the predictions (92%) for intensity and degree of circular polarization. These numbers are significantly improved for the larger theoretically generated datasets (4851 instances). The algorithm is versatile enough that it can be easily adjusted to other device configurations where a map needs to be established between the input parameters and the device response. Training and testing data files as well as the algorithm are provided as supplementary material.

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Peter M. Maloca ◽  
Philipp L. Müller ◽  
Aaron Y. Lee ◽  
Adnan Tufail ◽  
Konstantinos Balaskas ◽  
...  

AbstractMachine learning has greatly facilitated the analysis of medical data, while the internal operations usually remain intransparent. To better comprehend these opaque procedures, a convolutional neural network for optical coherence tomography image segmentation was enhanced with a Traceable Relevance Explainability (T-REX) technique. The proposed application was based on three components: ground truth generation by multiple graders, calculation of Hamming distances among graders and the machine learning algorithm, as well as a smart data visualization (‘neural recording’). An overall average variability of 1.75% between the human graders and the algorithm was found, slightly minor to 2.02% among human graders. The ambiguity in ground truth had noteworthy impact on machine learning results, which could be visualized. The convolutional neural network balanced between graders and allowed for modifiable predictions dependent on the compartment. Using the proposed T-REX setup, machine learning processes could be rendered more transparent and understandable, possibly leading to optimized applications.


2021 ◽  
Author(s):  
Aria Abubakar ◽  
Mandar Kulkarni ◽  
Anisha Kaul

Abstract In the process of deriving the reservoir petrophysical properties of a basin, identifying the pay capability of wells by interpreting various geological formations is key. Currently, this process is facilitated and preceded by well log correlation, which involves petrophysicists and geologists examining multiple raw log measurements for the well in question, indicating geological markers of formation changes and correlating them with those of neighboring wells. As it may seem, this activity of picking markers of a well is performed manually and the process of ‘examining’ may be highly subjective, thus, prone to inconsistencies. In our work, we propose to automate the well correlation workflow by using a Soft- Attention Convolutional Neural Network to predict well markers. The machine learning algorithm is supervised by examples of manual marker picks and their corresponding occurrence in logs such as gamma-ray, resistivity and density. Our experiments have shown that, specifically, the attention mechanism allows the Convolutional Neural Network to look at relevant features or patterns in the log measurements that suggest a change in formation, making the machine learning model highly precise.


2014 ◽  
Vol 10 (S306) ◽  
pp. 279-287 ◽  
Author(s):  
Michael Hobson ◽  
Philip Graff ◽  
Farhan Feroz ◽  
Anthony Lasenby

AbstractMachine-learning methods may be used to perform many tasks required in the analysis of astronomical data, including: data description and interpretation, pattern recognition, prediction, classification, compression, inference and many more. An intuitive and well-established approach to machine learning is the use of artificial neural networks (NNs), which consist of a group of interconnected nodes, each of which processes information that it receives and then passes this product on to other nodes via weighted connections. In particular, I discuss the first public release of the generic neural network training algorithm, calledSkyNet, and demonstrate its application to astronomical problems focusing on its use in the BAMBI package for accelerated Bayesian inference in cosmology, and the identification of gamma-ray bursters. TheSkyNetand BAMBI packages, which are fully parallelised using MPI, are available athttp://www.mrao.cam.ac.uk/software/.


2021 ◽  
Author(s):  
jorge cabrera Alvargonzalez ◽  
Ana Larranaga Janeiro ◽  
Sonia Perez ◽  
Javier Martinez Torres ◽  
Lucia martinez lamas ◽  
...  

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been and remains one of the major challenges humanity has faced thus far. Over the past few months, large amounts of information have been collected that are only now beginning to be assimilated. In the present work, the existence of residual information in the massive numbers of rRT-PCRs that tested positive out of the almost half a million tests that were performed during the pandemic is investigated. This residual information is believed to be highly related to a pattern in the number of cycles that are necessary to detect positive samples as such. Thus, a database of more than 20,000 positive samples was collected, and two supervised classification algorithms (a support vector machine and a neural network) were trained to temporally locate each sample based solely and exclusively on the number of cycles determined in the rRT-PCR of each individual. Finally, the results obtained from the classification show how the appearance of each wave is coincident with the surge of each of the variants present in the region of Galicia (Spain) during the development of the SARS-CoV-2 pandemic and clearly identified with the classification algorithm.


Author(s):  
Sercan Demirci ◽  
Durmuş Özkan Şahin ◽  
Ibrahim Halil Toprak

Skin cancer, which is one of the most common types of cancer in the world, is a malignant growth seen on the skin due to various reasons. There was an increase in the number of the cases of skin cancer nearly 200% between 2004-2009. Since the ozone layer is depleting, harmful rays reflected from the sun cannot be filtered. In this case, the likelihood of skin cancer will increase over the years and pose more risks for human beings. Early diagnosis is very significant as in all types of cancers. In this study, a mobile application is developed in order to detect whether the skin spots photographed by using the machine learning technique for early diagnosis have a suspicion of skin cancer. Thus, an auxiliary decision support system is developed that can be used both by the clinicians and individuals. For cases that are predicted to have a risk higher than a certain rate by the machine learning algorithm, early diagnosis could be initiated for the patients by consulting a physician when the case is considered to have a higher risk by machine learning algorithm.


2022 ◽  
pp. 350-374
Author(s):  
Mudassir Ismail ◽  
Ahmed Abdul Majeed ◽  
Yousif Abdullatif Albastaki

Machine odor detection has developed into an important aspect of our lives with various applications of it. From detecting food spoilage to diagnosis of diseases, it has been developed and tested in various fields and industries for specific purposes. This project, artificial-neural-network-based electronic nose (ANNeNose), is a machine-learning-based e-nose system that has been developed for detection of various types of odors for a general purpose. The system can be trained on any odor using various e-nose sensor types. It uses artificial neural network as its machine learning algorithm along with an OMX-GR semiconductor gas sensor for collecting odor data. The system was trained and tested with five different types of odors collected through a standard data collection method and then purified, which in turn had a result varying from 93% to 100% accuracy.


The iridescent cuticle of certain Rutelino scarab beetles, which is a form optically active and selectively reflects circularly polarized light, incorporates an NH 4 OH -extractable component The ultraviolet absorption spectrum of this component, together with its chromatographic and refractive properties, identify it as uric acid (2,6,8-trihydroxypurine). All species of Plusiotis examined have uric acid in their reflecting layers, as do several species of Anoplognathus. Plusiotis resplendens has a reflecting layer with a uric acid volume fraction of 0.7, P . optima a volume fraction of 0.6. The reflecting layer of P . resplenden s has an anticlockwise helicoidal architecture, the optical thickness of the helicoidal p itch being such that it constructively interferes with visible light wavelengths. An anticlockwise helicoid constructively interferes with only the left circularly polarized component of incident light, right circularly polarized light being transmitted without attenuation. P. resplendens has a 1.8 /xm thick unidirectional layer embedded within the helicoid which functions as a perfect halfwave retardation plate for wavelength 590 nm . This halfwave plate enables the helicoidal reflector in this species to reflect both left and right circularly polarized components of incident light. After passing through the halfwave plate, transmitted right circularly polarized light becomes left circularly polarized ; this light is now reflected and emerges from the cuticle right circularly polarized, after passing back through the halfwave plate. Consequently the total reflectivity of circularly polarized incident light is greater in P. resplendens than in any other species examined; the plate also reduces multiple internal reflexions. Interferometric analysis of the refractive properties of the helicoidal reflectors in species of Plusiotis showed that the ordered incorporation of uric acid increases the birefringence of the system by a factor of five times, e.g. the in tact birefringence of the unidirectional layer of P . resplendens is 0.166 at wavelength 560 nm ; after uric acid extraction the birefringence is reduced to 0.034. As the coefficient of reflexion of a helicoidal reflector is directly proportional to the birefringence of the individual planes comprising the helicoid, beetles incorporating uric acid into their reflecting surfaces reflect circularly polarized light far more efficiently than beetles lacking uric acid. Refractive index values for a single multicomponent plane of the helicoid have been summarized as a biaxial indicatrix, with the Z axis tilte dat 45° to the plane of the epicuticle. Beetle reflecting layers which incorporate uric acid have twenty times greater optical rotatory power compared with reflecting layers lacking this component. Mathematical treatments dealing with helicoidal reflectors predict the form optical rotatory power to be a function of the square of the birefringence, which is in agreement with the experimental observations. To enable uric acid to have the optical effects mentioned above, an epitaxial incorporation into the helicoidal framework is necessary. Although uric acid is a common cytoplasmic reflecting material in arthropods, this is the first record of its presence in an extracellular (cuticular) reflector.


1989 ◽  
Vol 28 (Part 1, No. 8) ◽  
pp. 1332-1336 ◽  
Author(s):  
Hiromichi Horinaka ◽  
Hiroshi Inada ◽  
Takashi Saijyo

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