bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automated classification technique for edge-on galaxies based on mathematical treatment of brightness data
Authors: Eassa Mohamed , Selim Ibrahim Mohamed , Dabour Walid , Elkafrawy Passent 
Published: December 2021

Abstract
Classification of edge-on galaxies is important to astronomical studies due to our Milky Way galaxy being an edge-on galaxy. Edge-on galaxies pose a problem to classification due to their less overall brightness levels and smaller numbers of pixels. In the current work, a novel technique for the classification of edge-on galaxies has been developed. This technique is based on the mathematical treatment of galaxy brightness data from their images. A special treatment for galaxies’ brightness data is developed to enhance faint galaxies and eliminate adverse effects of high brightness backgrounds as well as adverse effects of background bright stars. A novel slimness weighting factor is developed to classify edge-on galaxies based on their slimness. The technique has the capacity to be optimized for different catalogs with different brightness levels. In the current work, the developed technique is optimized for the EFIGI catalog and is trained using a set of 1800 galaxies from this catalog. Upon classification of the full set of 4458 galaxies from the EFIGI catalog, an accuracy of 97.5% has been achieved, with an average processing time of about 0.26 seconds per galaxy on an average laptop.
Solar observation with the Fourier transform spectrometer I : Preliminary results of the visible and near-infrared solar spectrum
Authors: Bai Xian-Yong , Zhang Zhi-Yong , Feng Zhi-Wei , Deng Yuan-Yong , Bao Xing-Ming , Yang Xiao , Song Yong-Liang , Tong Li-Yue , Jing Shuai 
Published: December 2021

Abstract
The Fourier transform spectrometer (FTS) is a core instrument for solar observation with high spectral resolution, especially in the infrared. The Infrared System for the Accurate Measurement of Solar Magnetic Field (AIMS), working at 10–13 μm, will use an FTS to observe the solar spectrum. The Bruker IFS-125HR, which meets the spectral resolution requirement of AIMS but simply equips with a point source detector, is employed to carry out preliminary experiment for AIMS. A sun-light feeding experimental system is further developed. Several experiments are taken with them during 2018 and 2019 to observe the solar spectrum in the visible and near infrared wavelength, respectively. We also proposed an inversion method to retrieve the solar spectrum from the observed interferogram and compared it with the standard solar spectrum atlas. Although there is a wavelength limitation due to the present sun-light feeding system, the results in the wavelength band from 0.45–1.0 μm and 1.0–2.2 μm show a good consistency with the solar spectrum atlas, indicating the validity of our observing configuration, the data analysis method and the potential to work in longer wavelength. The work provided valuable experience for the AIMS not only for the operation of an FTS but also for the development of its scientific data processing software.
Longevity and reliability of chronic unit recordings using the Utah, intracortical multi-electrode arrays
Authors: Sponheim Caleb , Papadourakis Vasileios , Collinger Jennifer L , Downey John , Weiss Jeffrey , Pentousi Lida , Elliott Kaisa , Hatsopoulos Nicholas G. 
Published: December 2021

Abstract

Objective. Microelectrode arrays are standard tools for conducting chronic electrophysiological experiments, allowing researchers to simultaneously record from large numbers of neurons. Specifically, Utah electrode arrays (UEAs) have been utilized by scientists in many species, including rodents, rhesus macaques, marmosets, and human participants. The field of clinical human brain-computer interfaces currently relies on the UEA as a number of research groups have clearance from the United States Federal Drug Administration (FDA) for this device through the investigational device exemption pathway. Despite its widespread usage in systems neuroscience, few studies have comprehensively evaluated the reliability and signal quality of the Utah array over long periods of time in a large dataset. Approach. We collected and analyzed over 6000 recorded datasets from various cortical areas spanning almost nine years of experiments, totaling 17 rhesus macaques (Macaca mulatta) and 2 human subjects, and 55 separate microelectrode Utah arrays. The scale of this dataset allowed us to evaluate the average life of these arrays, based primarily on the signal-to-noise ratio of each electrode over time. Main results. Using implants in primary motor, premotor, prefrontal, and somatosensory cortices, we found that the average lifespan of available recordings from UEAs was 622 days, although we provide several examples of these UEAs lasting over 1000 days and one up to 9 years; human implants were also shown to last longer than non-human primate implants. We also found that electrode length did not affect longevity and quality, but iridium oxide metallization on the electrode tip exhibited superior yield as compared to platinum metallization. Significance. Understanding longevity and reliability of microelectrode array recordings allows researchers to set expectations and plan experiments accordingly and maximize the amount of high-quality data gathered. Our results suggest that one can expect chronic unit recordings to last at least two years, with the possibility for arrays to last the better part of a decade.
The dependence of confinement on the isotope mass in the core and the edge of AUG and JET-ILW H-mode plasmas
Authors: Schneider P.A. , Angioni C. , Frassinetti L. , Horvath L. , Maslov M. , Auriemma F. , Cavedon M. , Challis C.D. , Delabie E. , Dunne M.G. , Fontdecaba J.M. , Hobirk J. , Kappatou A. , Keeling D.L. , Kurzan B. , Lennholm M. , Lomanowski B. , Maggi C.F. , McDermott R.M. , Pütterich T. , Thorman A. , Willensdorfer M. , the ASDEX Upgrade Team  , the EUROfusion MST1 Team  , JET Contributors  
Published: December 2021

Abstract
Experiments in ASDEX Upgrade (AUG) and JET with the ITER-like wall (JET-ILW) are performed to separate the pedestal and core contributions to confinement in H-modes with different main ion masses. A strong isotope mass dependence in the pedestal is found which is enhanced at high gas puffing. This is because the ELM type changes when going from D to H for matched engineering parameters, which is likely due to differences in the inter ELM transport with isotope mass. The pedestal can be matched in H and D plasmas by varying only the triangularity and keeping the engineering parameters relevant for core transport the same. With matched pedestals Astra/TGLF (Sat1geo) core transport simulations predict the experimental profiles equally well for H and D. These core transport simulations show a negligible mass dependence and no gyro-Bohm scaling is observed. However, to match the experimental observations at medium β it is required to take the fast-ion dilution and rotation into account. This is not enough for high β plasmas where for the first time a profile match between H and D plasmas was achieved experimentally. Under these conditions quasilinear modelling with TGLF over predicts the transport in the core of H and D plasmas alike.
Magnetic configuration scans during divertor operation of Wendelstein 7-X
Authors: Andreeva T. , Geiger J. , Dinklage A. , Wurden G. , Thomsen H. , Rahbarnia K. , Schmitt J.C. , Hirsch M. , Fuchert G. , Nührenberg C. , Alonso A. , Beidler C.D. , Beurskens M.N.A. , Bozhenkov S. , Brakel R. , Brandt C. , Bykov V. , Grahl M. , Grulke O. , Killer C. , Kocsis G. , Klinger T. , Krämer-Flecken A. , Lazerson S. , Otte M. , Pablant N. , Schilling J. , Windisch T. , the W7-X Team  
Published: December 2021

Abstract
Wendelstein 7-X (W7-X) (Greifswald, Germany) is an advanced stellarator, which uses the modular coil concept to realize a magnetic configuration optimized for fusion-relevant plasma properties. The magnet system of the machine allows a variation of the rotational transform (iota) at the boundary. In the latest W7-X operational phase a dedicated configuration scan has been performed varying the rotational transform between magnetic configurations with iota = 5/4 and iota = 5/5 at the boundary. This paper presents an overview of the experiments and of the main results with respect to confinement and stability. The main observation is an increase of the plasma energy in several intermediate configurations of the scan when the 5/5-islands are close to the plasma boundary but still inside the last-closed-flux-surface. In addition, these configurations showed marked MHD-activity with a crashing behavior related to the 5/5-islands. The corresponding mode amplitude was correlated with the size of the internal 5/5 islands.
Localization effects due to a random magnetic field on heat transport in a harmonic chain
Authors: Cane Gaëtan , Bhat Junaid Majeed , Dhar Abhishek , Bernardin Cédric 
Published: December 2021

Abstract
We consider a harmonic chain of N oscillators in the presence of a disordered magnetic field. The ends of the chain are connected to heat baths and we study the effects of the magnetic field randomness on heat transport. The disorder, in general, causes localization of the normal modes, due to which a system becomes insulating. However, for this system, the localization length diverges as the normal mode frequency approaches zero. Therefore, the low frequency modes contribute to the transmission,

T

N

(

ω

)

, and the heat current goes down as a power law with the system size, N. This power law is determined by the small frequency behaviour of some Lyapunov exponents, λ(ω), and the transmission in the thermodynamic limit,

T

∞

(

ω

)

. While it is known that in the presence of a constant magnetic field

T

∞

(

ω

)

∼

ω

3
/
2

,

ω

1
/
2

depending on the boundary conditions, we find that the Lyapunov exponent for the system behaves as λ(ω) ∼ ω for

B

≠
0

and λ(ω) ∼ ω
2/3 for

B

=
0

. Therefore, we obtain different power laws for current vs N depending on

B

and the boundary conditions.
Assessing Wear Out of Tyre using Opencv & Convolutional Neural Networks
Authors: Kishor Kumar Reddy C , Anisha P R , Madana Mohana R 
Published: December 2021

Abstract
This work proposes a process to detect the wear and tear of car tires. Tire is the only part of the road that does not interact with the road. The condition of the wheel should therefore be monitored in a timely manner for safe driving. Tired fatigue occurs due to limitations such as that the tread limit is less than 1.6 cm, the damage to the rubber, where there are pipes around 4 to 5, the affected tire. We look at some of the above limitations of tire wear testing using computer viewing techniques such as opencv and convolutional neural networks. Opencv and convolutional neural networks are widely used for object detection and image classification. We used these methods and obtained 90.90% accuracy, with which we can predict tire wear to avoid dangerous accidents..
Predict Diabetes Mellitus Using Machine Learning Algorithms
Authors: Farhana Bano , Munidhanalakshmi K , Madana Mohana Dr R. 
Published: December 2021

Abstract
Diabetes mellitus has become a very frequent disease that affects totally different organs of human body. Diabetes cause diverge depending on genetic, family history, health and environmental factors. Diabetes mellitus refers to a gaggle of diseases that affect how your body uses blood glucose. The underlying reason behind diabetes varies by type. But, despite what kind of diabetes you’ve got, it will cause excess sugar in your blood. Diabetes will be of two types, they are Type1 Diabetes and Type2 Diabetes. Early prediction will help in society a lot. It will provides the humanlife in safe way. The aim of this analysis is to develop a system that predicts the diabetes with a better accuracy. Parameters used to predict the type of Diabetes Mellitus are Glucose, Pregnancies, skin thickness, Blood pressure, Insulin, BMI, Diabetes pedigree function, age and upshot. In this we are with different machine learning algorithms, namely SVM, ANN, Decision tree, Logistic regression and Farthest first to predict the accuracy. Our experimental results show that farthest first attain superior correctness compare to dissimilar machine learning techniques.
Pole Like Object Detection using PCA inTerrestrial LiDAR System
Authors: Agarwal P , Husain A , Ranjan RK 
Published: December 2021

Abstract
Many vertical objects like trees, poles, and pole like structures play a crucial role in the inspection of road safety and planning for road development. The detection of such objects further proves to be helpful in averting roadside accidents and other problems. Light Detection and Ranging technology can be used in identifying these objects. In this paper, we have proposed to detect pole like structures from the dataset generated using a Light Detection and Ranging system. Our proposed pole like object detection approach first segments data into multiple small clusters. The clusters are further analyzed to compute the covariance to identify the linear relationship among the variables. Then eigenvectors and eigenvalues are computed to identify the directions and strength of the data points of clusters. Finally, the Principal Component Analysis approach is used to detect the pole-like structures. The approach is used to identify the target object which uses a threshold value for the angle of the object greater than 70° with respect to the surface. It also uses a normalized eigenvalue equals to 0.7. The efficiency of the proposed is recorded as 93.7%, and the time taken to process the data and detection of the pole-like structures from the dataset is 15 min and 30 sec.
MAC Based Security Integration using Face Recognition in Cloud Environment
Authors: Yash Sharma , Neeraj Kumar Pandey 
Published: December 2021

AbstractThe major challenges, which come across face recognition system, are to find the age and gender in 2D/3D image of the person specifically in cloud environment. This paperis centered on face detection with MAC (Media Access Control) and biometric technology. Face scanning along with machine’s MAC address and biometric technologies has been shown to improve security controls. Face recognition can be used to search and label users and their assigned machines for sensitive purposes. Following that, it was stored in a specific database with their unique ID. In addition, the verification process has begun by comparing the models in the database. Face scanning along with speech and biometric technologies is used to improve security controls. Face recognition system may also be set up in high security machines to improve protection by allowing only registered individuals or others users. Related strategies for determining the age and gender and 2D/3D image from a specific picture are explored, as well as several modern methods for preserving protection. In this paper, the full model is explored independently with security implemented in cloud environment. The proposed model of the paper provides the integrated security features using MAC address of machine and face recognition of the machine user.
Secure Cloud Risk Architecture analysis for Mobile Banking system and its performance analysis based on Machine learning approaches
Authors: Rajarajeswari P. , Sreevani M. , Lalitha Suryakumari P. 
Published: December 2021

Abstract
With advances in the mobile communications, many service-related tasks can be made quickly and easily. Mobile banking is one such service that has eliminated the need for a consumer to go to a branch to carry out many common transactions. In a country like India, where the last mile reach through brick-and-mortar banking facilities, mobile phones can complement the reach. This paper describes how mobile cloud architecture can be employed for banking and services to customers to enhance their banking experience as well as ensuring information security. This paper focuses on cloud-based risk architecture for banking solutions to address various issues related to mobile banking such as processing speed and storage capacity. Improved random forecast algorithm is used for the evaluation of the system. This proposed system achieves 99% of the system.
Indian Currency Denomination Recognition and Fake Currency Identification
Authors: Padmaja B , Naga Shyam Bhargav P , Ganga Sagar H , Diwakar Nayak B , Bhushan Rao M 
Published: December 2021

AbstractVisually impaired and senior citizens find it difficult to identify different banknotes, driving the need for an automated system to recognize currency notes. This study proposes recognizing Indian currency notes of various denominations using Deep Learning through the CNN model. While not recognizing currency notes is one issue, identifying fake notes is another major issue. Currency counterfeiting is the illegal imitation of currency to deceive its recipient. The current existing methodologies for identifying a phony note rely on hardware. A method completely devoid of hardware that relies on specific security features to help distinguish a legitimate currency note from an illegitimate one is much needed. These features are extracted using the boundary box region of interest (ROI) and Canny Edge detection in OpenCV implemented in Python, and the multi scale template matching algorithm is applied to match the security features and differentiate fake notes from legitimate notes.
A Survey on Techniques used in Medical Imaging Processing
Authors: Chatterjee Priyadarshini , Sushama Rani Dutta 
Published: December 2021

AbstractAutomated diagnosis of diseases in the recent years have gain lots of advantages and potential. Specially automated screening of cancers has helped the clinicians over the time. Sometimes it is seen that the diagnosis of the clinicians is biased but automated detection can help them to come to a proper conclusion. Automated screening is implemented using either artificial inter connected system or convolutional inter connected system. As Artificial neural network is slow in computation, so Convolutional Neural Network has achieved lots of importance in the recent years. It is also seen that Convolutional Neural Network architecture requires a smaller number of datasets. This also provides them an edge over Artificial Neural Networks. Convolutional Neural Networks is used for both segmentation and classification. Image dissection is one of the important steps in the model used for any kind of image analysis. This paper surveys various such Convolutional Neural Networks that are used for medical image analysis.
She-Pwm Low Cost Multi Level Inverter for Pv Based Water Pumping Applications
Authors: Garapati Durga Prasad , Nalli Praveen Kumar , Swaroop K P , Vijay Kumar Y 
Published: December 2021

Abstract
In this article different multi-level inverter (MLI) configuration is introduced by a decreased quantity of power elements. At the output side the MLI topology generates the seven level voltage waveform with minimum number of components. The said topology configuration requires less dc voltage and power semiconductor switches. It also reduces the voltage block on switches, which reduces the inverter topology complexity and costs. Such capabilities were discovered by contrasting the topology to traditional topologies from the above perspectives. Testing were carried out to demonstrate the efficacy of the generalized MLI topology in both simulation and hardware, and the findings are presented for better understanding.
Design of Exponentially Weighted Median Filter Cascaded With Adaptive Median Filter
Authors: Nalli Praveen Kumar , Kadali Kalyan Sagar , Bhukya Ramu , Palleswari Y.T.R. , Siva Asapu , Pragaspathy S 
Published: December 2021

Abstract
The objective of this paper is to design an II phase algorithm employing median filters for enlightening the performance in removing impulse noise during the processing of the image. The cascaded filter section employs an Adaptive median filter in the first phase followed by a Recursive weighted median filter (RWM) in the second phase. The RWM filter weight is selected with the Median Controlled Algorithm. As a design parameter, the exponential weights of RWM filters are used in the feedback path. The projected algorithm can achieve suggestively improved quality of image when compared to fixed weight or the Center Weighted Median filters.
Insights into Artificial Neural Network techniques, and its Application in Steganography
Authors: Gurunath R , Samanta Debabrata 
Published: December 2021

Abstract
Deep Steganography is a data concealment technology that uses artificial intelligence (AI) to automate the process of hiding and extracting information through layers of training. It enables for the automated generation of a cover depending on the concealed message. Previously, the technique depended on the existing cover to hide data, which limited the number of Steganographic characteristics available. Artificial intelligence and deep learning techniques have been used to steganography recently and the results are satisfactory. Although neural networks have demonstrated their ability to imitate human talents, it is still too early to draw comparisons between people and them. To improve their capabilities, neural networks are being employed in a number of disciplines, including steganography. Recurrent Neural Networks (RNN) is a widely used technology that automatically creates Stego-text regardless of payload volume. The features are extracted using a convolution neural network (CNN) based on the image. Perceptron, Multi-Layer Perceptron (MLP), Feed Forward Neural Network, Long Short Term Memory (LSTM) networks, and others are examples of this. In this research, we looked at all of the neural network approaches for Steganographic purposes in depth. This article also discusses the problems that each technology faces, as well as potential solutions.
Face Recognition System with Machine Learning
Authors: Padmakar Vuppu , Ramana Murthy B V 
Published: December 2021

Abstract
This venture plans to give improved security by enabling a client to realize who is actually getting to the framework utilizing facial acknowledgment. The framework enables just approved clients to get entrance. Python is a programming language utilized alongside Machine learning methods and an open source library which is utilized to configuration, construct and train Machine learning models. Interface component is additionally accommodated unapproved clients to enroll to obtain entrance with the earlier authorization from the Admin.