bluuebunny/crossref_metadata_2025 huggingface dataset:
The Cluster HEritage project withXMM-Newton: Mass Assembly and Thermodynamics at the Endpoint of structure formation
Authors:   The CHEX-MATE Collaboration, Arnaud M. , Ettori S. , Pratt G. W. , Rossetti M. , Eckert D. , Gastaldello F. , Gavazzi R. , Kay S.T. , Lovisari L. , Maughan B.J. , Pointecouteau E. , Sereno M. , Bartalucci I. , Bonafede A. , Bourdin H. , Cassano R. , Duffy R.T. , Iqbal A. , Maurogordato S. , Rasia E. , Sayers J. , Andrade-Santos F. , Aussel H. , Barnes D.J. , Barrena R. , Borgani S. , Burkutean S. , Clerc N. , Corasaniti P.-S. , Cuillandre J.-C. , De Grandi S. , De Petris M. , Dolag K. , Donahue M. , Ferragamo A. , Gaspari M. , Ghizzardi S. , Gitti M. , Haines C.P. , Jauzac M. , Johnston-Hollitt M. , Jones C. , Kéruzoré F. , LeBrun A.M.C. , Mayet F. , Mazzotta P. , Melin J.-B. , Molendi S. , Nonino M. , Okabe N. , Paltani S. , Perotto L. , Pires S. , Radovich M. , Rubino-Martin J.-A. , Salvati L. , Saro A. , Sartoris B. , Schellenberger G. , Streblyanska A. , Tarrío P. , Tozzi P. , Umetsu K. , van der Burg R.F.J. , Vazza F. , Venturi T. , Yepes G. , Zarattini S.
Published: March 2021

The Cluster HEritage project withXMM-Newton– Mass Assembly and Thermodynamics at the Endpoint of structure formation (CHEX-MATE) is a three-mega-second Multi-Year Heritage Programme to obtain X-ray observations of a minimally-biased, signal-to-noise-limited sample of 118 galaxy clusters detected byPlanckthrough the Sunyaev–Zeldovich effect. The programme, described in detail in this paper, aims to study the ultimate products of structure formation in time and mass. It is composed of a census of the most recent objects to have formed (Tier-1: 0.05 < z < 0.2; 2 × 1014 M⊙ < M500 < 9 × 1014 M⊙), together with a sample of the highest mass objects in the Universe (Tier-2:z < 0.6;M500 > 7.25 × 1014 M⊙). The programme will yield an accurate vision of the statistical properties of the underlying population, measure how the gas properties are shaped by collapse into the dark matter halo, uncover the provenance of non-gravitational heating, and resolve the major uncertainties in mass determination that limit the use of clusters for cosmological parameter estimation. We will acquire X-ray exposures of uniform depth, designed to obtain individual mass measurements accurate to 15 − 20% under the hydrostatic assumption. We present the project motivations, describe the programme definition, and detail the ongoing multi-wavelength observational (lensing, SZ, radio) and theoretical effort that is being deployed in support of the project.
Zur Beziehungsgestaltung mit Künstlicher emotionaler Intelligenz – vom „Hier und Jetzt“ zum „Dort und Dann“
Authors: Jordan Wolfgang
Published: March 2021

Die Beziehungsgestaltung in der Vergangenheit, der Gegenwart und der Zukunft wird von der Idee des Humanismus geprägt. Getragen von diesem Konstrukt beleuchtet der Beitrag verschiedene Aspekte und Ausgestaltungen des Humanismus auf einer Zeitachse vom „Hier und Jetzt“ zum „Dort und Dann“. Die gegenwärtige Versorgungsrealität geht mit einer emotionalen Entfremdung menschlicher Beziehungen einher. Eine voranschreitende Technologie und reduktionistische neurobiologische Vorstellungen können den Blick auf das psychische Kranksein eines Menschen in seiner Ganzheit erschweren. Jede (Kommunikations-)Technik, die in der Vergangenheit entwickelt wurde oder in der Zukunft noch zu entwickeln ist, wird früher oder später Einzug in die Psychiatrie und Psychotherapie halten und Beziehungen verändern. Der Transhumanismus birgt die Gefahr, dass die Menschen sich voneinander und ihrer Gattung entfremden. Neuronale Netzwerke sind Algorithmen, welche unabhängig von der verwendeten Hardware funktionieren, sei es, sie basieren auf organischen Kohlenstoffeinheiten wie der Mensch oder auf nichtorganischen Siliziumeinheiten wie der Computer/Cyborg. Es wird verschiedene Wege geben, eine Superintelligenz zu erlangen. Dabei ist Intelligenz ein „Muss“ und Bewusstsein ein „Kann“. Wenn es zu einem Wandel von einem homo- zu einem datenzentrischen Weltbild kommt und die Macht der Menschen auf die Algorithmen übergeht, könnte der Mensch sein Alleinstellungsmerkmal verlieren und die humanistischen Ziele von Gesundheit und Glück würden verloren gehen.
Der Einsatz von Künstlicher Intelligenz bei Alzheimer-Krankheit – Personalisierte Diagnostik und Therapie
Authors: Wiltfang Jens , Esselmann Hermann , Barnikol Utako B.
Published: March 2021

Am Beispiel der Demenz bei Alzheimer-Krankheit wird aufgezeigt, welche Chancen, aber auch Risiken neuere methodische Ansätze der Künstlichen Intelligenz (KI) (Artificial Intelligence, AI) für die Diagnostik und Therapie der Alzheimer-Demenz (AD) bieten. Daneben wird KI im Kontext einer ethisch-philosophischen Technologiekritik beleuchtet.
Pole-piece position distance identification of cylindrical lithium-ion battery through x-ray testing technology
Authors: Wu Yapeng , Yang Min , Wang Yishuai , Li Honggang , Gui ZhiGuo , Liu Jing
Published: March 2021

In the preparation process of cylindrical lithium-ion batteries, a rigorous manufacturing process demands that the position distances between positive and negative pole-pieces must be kept within a reasonable range of variation. Otherwise, a too small position distance may cause safety problems, such as short circuits and thermal runaway. To inspect the position distances between positive and negative pole-pieces automatically, and to decrease the risk of safety and economic losses during the subsequent use, this paper proposes a method to identify the position distance defects of a cylindrical lithium-ion battery on the base of x-ray digital radiography (DR) images. According to this method, the DR image is firstly enhanced by the GPU-accelerated homomorphic filtering algorithm to intensify its contrast and detailed information. Then through the Shi-Tomasi corner detection algorithm, corners of all the positive and negative pole-pieces are preliminarily detected in a region of interest which is defined in advance. To delete the false corners and find the lost corners, one-dimension region growing and curve-fitting methods are adopted. Finally, the minimum position distance, repeated accuracy and alignment metrics are calculated at the base of the detected corners. The experimental results show that the corner positions of five 26 650 cylindrical lithium-ion batteries with different pole-piece structural characteristics can be effectively identified by the proposed method, which provides a useful approach to filtrate unqualified batteries during the process of manufacture.
Characterization of Ce:CSSO, Pr:CSSO, and co-doped Ce,Pr:CSSO phosphors for aerosol phosphor thermometry
Authors: Herzog Joshua M , Witkowski Dustin , Rothamer David A
Published: March 2021

The phosphor Ce:Ca3Sc2Si3O12(Ce:CSSO) was recently investigated for aerosol phosphor thermometry (APT) and shown to be capable of temperature imaging up to at least 1400 K. To date, no thorough characterization of the temperature dependent emission properties of the phosphor has been performed up to 1400 K. Additionally, due to limited sensitivity over certain temperature ranges continuous temperature imaging from 300 to 1400 K was not possible. Here, cerium and praseodymium doped into CSSO are investigated to address these limitations. Singly-doped Ce:CSSO and Pr:CSSO, and co-doped Ce,Pr:CSSO phosphor samples were studied. Emission lifetimes and quenching behavior were characterized in a tube furnace for each phosphor. Results from the singly-doped phosphors were used to interpret the co-doped phosphor results. The phosphor characterization data was used to estimate thermometry performance at temperatures relevant to low-temperature ignition studies in engines. The proposed diagnostic uses a combination of co-doped APT, host-referenced APT, and scattering-referenced APT approaches to increase the range over which high-precision temperature measurements can be made. Characterization results include the first reported measurements of luminescence lifetimes for Ce:CSSO and Pr:CSSO at temperatures up to 1400 K, and the highest reported quenching temperature for a phosphor using 4f5d emission (1230 K for Ce:CSSO). Furnace measurements showed a normalized temperature-sensitivity of

>

0.25

% K−1 from room temperature to 1400 K for at least one diagnostic approach using the co-doped Ce,Pr:CSSO phosphor. Estimates suggest that the co-doped Ce,Pr:CSSO phosphor is feasible for APT at temperatures ranging from 300 to 925 K and 1030 to at least 1400 K, with better than 40-K single-shot precision. The performance of Ce:CSSO and Ce,Pr:CSSO are similar throughout the investigated temperature range.
Fast super-resolution ultrasound microvessel imaging using spatiotemporal data with deep fully convolutional neural network
Authors: Lok U-Wai , Huang Chengwu , Gong Ping , Tang Shanshan , Yang Lulu , Zhang Wei , Kim Yohan , Korfiatis Panagiotis , Blezek Daniel J , Lucien Fabrice , Zheng Rongqin , Trzasko Joshua D , Chen Shigao
Published: March 2021

Ultrasound localization microscopy (ULM) has been proposed to image microvasculature beyond the ultrasound diffraction limit. Although ULM can attain microvascular images with a sub-diffraction resolution, long data acquisition time and processing time are the critical limitations. Deep learning-based ULM (deep-ULM) has been proposed to mitigate these limitations. However, microbubble (MB) localization used in deep-ULMs is currently based on spatial information without the use of temporal information. The highly spatiotemporally coherent MB signals provide a strong feature that can be used to differentiate MB signals from background artifacts. In this study, a deep neural network was employed and trained with spatiotemporal ultrasound datasets to better identify the MB signals by leveraging both the spatial and temporal information of the MB signals. Training, validation and testing datasets were acquired from MB suspension to mimic the realistic intensity-varying and moving MB signals. The performance of the proposed network was first demonstrated in the chicken embryo chorioallantoic membrane dataset with an optical microscopic image as the reference standard. Substantial improvement in spatial resolution was shown for the reconstructed super-resolved images compared with power Doppler images. The full-width-half-maximum (FWHM) of a microvessel was improved from 133 μm to 35 μm, which is smaller than the ultrasound wavelength (73 μm). The proposed method was further tested in an in vivo human liver data. Results showed the reconstructed super-resolved images could resolve a microvessel of nearly 170 μm (FWHM). Adjacent microvessels with a distance of 670 μm, which cannot be resolved with power Doppler imaging, can be well-separated with the proposed method. Improved contrast ratios using the proposed method were shown compared with that of the conventional deep-ULM method. Additionally, the processing time to reconstruct a high-resolution ultrasound frame with an image size of 1024 × 512 pixels was around 16 ms, comparable to state-of-the-art deep-ULMs.
A GPU-accelerated framework for rapid estimation of scanner-specific scatter in CT for virtual imaging trials
Authors: Sharma Shobhit , Abadi Ehsan , Kapadia Anuj , Segars W Paul , Samei Ehsan
Published: March 2021

Virtual imaging trials (VITs), defined as the process of conducting clinical imaging trials using computer simulations, offer a time- and cost-effective alternative to traditional imaging trials for CT. The clinical potential of VITs hinges on the realism of simulations modeling the image acquisition process, where the accurate scanner-specific simulation of scatter in a time-feasible manner poses a particular challenge. To meet this need, this study proposes, develops, and validates a rapid scatter estimation framework, based on GPU-accelerated Monte Carlo (MC) simulations and denoising methods, for estimating scatter in single source, dual-source, and photon-counting CT. A CT simulator was developed to incorporate parametric models for an anti-scatter grid and a curved energy integrating detector with an energy-dependent response. The scatter estimates from the simulator were validated using physical measurements acquired on a clinical CT system using the standard single-blocker method. The MC simulator was further extended to incorporate a pre-validated model for a PCD and an additional source-detector pair to model cross scatter in dual-source configurations. To estimate scatter with desirable levels of statistical noise using a manageable computational load, two denoising methods using a (1) convolutional neural network and an (2) optimized Gaussian filter were further deployed. The viability of this framework for clinical VITs was assessed by integrating it with a scanner-specific ray-tracer program to simulate images for an image quality (Mercury) and an anthropomorphic phantom (XCAT). The simulated scatter-to-primary ratios agreed with physical measurements within 4.4% ± 10.8% across all projection angles and kVs. The differences of ∼121 HU between images with and without scatter, signifying the importance of scatter for simulating clinical images. The denoising methods preserved the magnitudes and trends observed in the reference scatter distributions, with an averaged rRMSE value of 0.91 and 0.97 for the two methods, respectively. The execution time of ∼30 s for simulating scatter in a single projection with a desirable level of statistical noise indicates a major improvement in performance, making our tool an eligible candidate for conducting extensive VITs spanning multiple patients and scan protocols.
Mean-field dynamics of infinite-dimensional particle systems: global shear versus random local forcing
Authors: Agoritsas Elisabeth
Published: March 2021

In infinite dimensions, many-body systems of pairwise interacting particles provide exact analytical benchmarks for the features of amorphous materials, such as the stress–strain curve of glasses under quasistatic shear. Here, instead of global shear, we consider an alternative driving protocol, as recently introduced by Morse et al 2020 (arXiv:2009.07706), which consists of randomly assigning a constant local displacement on each particle, with a finite spatial correlation length. We show that, in the infinite-dimensional limit, the mean-field dynamics under such a random forcing are strictly equivalent to those under global shear, upon a simple rescaling of the accumulated strain. Moreover, the scaling factor is essentially given by the variance of the relative local displacements of interacting pairs of particles, which encodes the presence of a finite spatial correlation. In this framework, global shear is simply a special case of a much broader family of local forcing, which can be explored by tuning its spatial correlations. We discuss the specific implications for the quasistatic driving of glasses—initially prepared at a replica-symmetric equilibrium—and how the corresponding ‘stress–strain’-like curves and elastic moduli can be rescaled onto their quasistatic-shear counterparts. These results hint at a unifying framework for establishing rigorous analogies, at the mean-field level, between different driven disordered systems.
Large deviations for Markov processes with stochastic resetting: analysis via the empirical density and flows or via excursions between resets
Authors: Monthus Cécile
Published: March 2021

Markov processes with stochastic resetting towards the origin generically converge towards non-equilibrium steady-states. Long dynamical trajectories can be thus analyzed via the large deviations at level 2.5 for the joint probability of the empirical density and the empirical flows, or via the large deviations of semi-Markov processes for the empirical density of excursions between consecutive resets. The large deviations properties of general time-additive observables involving the position and the increments of the dynamical trajectory are then analyzed in terms of the appropriate Markov tilted processes and of the corresponding conditioned processes obtained via the generalization of Doob’s h-transform. This general formalism is described in detail for the three possible frameworks, namely discrete-time/discrete-space Markov chains, continuous-time/discrete-space Markov jump processes and continuous-time/continuous-space diffusion processes, and is illustrated with explicit results for the Sisyphus random walk and its variants, when the reset probabilities or reset rates are space-dependent.
Statistical Analysis of Wind Speed Data and Assessment of Wind Power Density Using Weibull Distribution Function (Case Study: Four Regions in Iraq)
Authors: Jabbar R. I.
Published: March 2021

Assessment of accurate wind power potential is very essential before planning to implement wind power conversion system in a particular site. In order to determine the wind power potential, a statistical analysis of wind speed measurements is required. The main objective of this paper is to analyse the daily measured wind speed data at the height of 10 m and 50 m over the ground for four regions (Al-Rutba, Sinjar, Al-Qa’im and Al-Bayji) in Iraq using the Weibull distribution function. The wind data that is used in this research is collected from NASA surface meteorology. The Weibull parameters for the individual month have also been computed. This statistical analysis is programmed in MATLAB environment to evaluate the monthly and yearly wind power density of the selected areas. In addition, the wind power classes for the four locations will be determined. The results explain the months that have maximum power wind generation in the four regions. According to the calculated yearly wind power density, it can be concluded that the selected areas are between marginal to fair for wind power generation.
Radix-p Multiple Valued Logic Function Simplification using Higher Radix Representation
Authors: Al-Wardi Zaid
Published: March 2021

Conventional Boolean logic, which is based on radix-2 binary logic, is no longer suitable to represent complex digital systems. As digital systems continue to grow in complexity, more compact representations become essential to describe these systems. One such possibility is to use multiple-valued logic, with radices larder than two. Many non-conventional technologies are considered as candidates to replace conventional semiconductor based switching circuit technology for computational systems such. Interestingly, some of these technologies are inherently multiple valued. Multiple-valued logic design and optimization methodologies are not yet elaborated, as compared to the well-established binary logic design and optimization and testing scheme. In this paper we are proposing an algorithm, with which functions to describe digital systems are specified and simplified using Multiple valued logic. Representing a digital system with different radices come out with different equivalent specifications for that system. A designer can choose the most suitable specification for his design constrains. The influence of radix increase on the complexity of the algorithm and the resulting specification is also demonstrated and discussed in this paper.
A Novel Kite Cross Hexagonal Search Algorithm for Fast Block Motion Estimation
Authors: Hamood Tareq Zaid , Abdulmunem Matheel Emaduldeen
Published: March 2021

The performance quality and searching speed of Block Matching (BM) algorithm are affected by shapes and sizes of the search patterns used in the algorithm. In this paper, Kite Cross Hexagonal Search (KCHS) is proposed. This algorithm uses different search patterns (kite, cross, and hexagonal) to search for the best Motion Vector (MV). In first step, KCHS uses cross search pattern. In second step, it uses one of kite search patterns (up, down, left, or right depending on the first step). In subsequent steps, it uses large/small Hexagonal Search (HS) patterns. This new algorithm is compared with several known fast block matching algorithms. Comparisons are based on search points and Peak Signal to Noise Ratio (PSNR). According to results obtained in this paper, KCHS needs less search time than others algorithms and gives very acceptable performance quality.
Right Truncated of Mixture Topp-Leone with Exponential Distribution
Authors: Hashim Hassaneen Abd Aljabar , Al-Khafaji Kareema Abed Al-kadim
Published: March 2021

In this paper the truncated distribution from the right was found for our distribution resulting from the mixing of two Topp Leone with the exponential distributions. The CDF and PDF are derived, as are the survival, moment and hazard functions. Two methods for estimating the parameters were derived, namely the maximum likelihood method and the moment method, as well as in the data analysis an algorithm was constructed and applied in the MATLAB.
On (gαφ, - Regularisation of English Letters Partition
Authors: Alabaaweee Alaa I. , Mahmood Ammar S.
Published: March 2021

Any partition it has two characteristics, the first is called regular and the second is regularisation, both of which were introduced by James in 1976. Through this research, we will try to present a new type that greatly serves the proposed model of English letters that was presented by Mahmood and Mahmood in 2019.
A Computational Method for Nonlinear Fredholm Integro-Differential Equations Using Haar Wavelet Collocation Points
Authors: Swaidan Waleeda , Ali Haleema S.
Published: March 2021

Haar wavelet collocation points method is developed to the computational solution for nonlinear Fredholm integral and integro-differential equations on interval [0, tf ] using Leibnitz-Haar wavelet collocation points method. Essential principle is transmutation of the integral equation to equivalent higher order differential equation together with initial conditions. The transmutation is carried out using the Leibniz law. Haar wavelet collocation points and its operational matrix is employed to transform the higher order differential equation to a set of algebraic equations, then resolving these equations usage MATLAB program to calculate the demanded Haar coefficients. The computational results of the proposed approach is presented in four problems and make a simulation against the accurate solution. In addition, Error analysis is exhibited the proficiency of the proposed technique and when Haar wavelet resolutions increases the results are close to the accurate solutions.
Human Face Identification using Moments and Transformations
Authors: Hashim Asaad Noori , Taher Jannah Raad
Published: March 2021

Face recognition one of the most promising techniques such that the importance of biometric user identification is increasing every day, several methods have been suggested to perform it such as classification, deep learning, statistical, moments, etc., this work describes different approaches to develop biometric technique, based on the moments and transformation. The proposed method for face recognition is based on Legendre moments and singular value decomposition for feature extraction. Local approaches by dividing images into several blocks (overlapped blocks and non-overlapped) have been adopted to gain the best recognition rate, as well as normalization using the Z-score method, which has been used. The outcomes experimental showed that the suggested system is effective, it has been tested using ORL face image databases with 10 cases and achieved a recognition rate from 85.27-100%, also, applied on FEI Brazil face database with 10 cases and achieved a recognition rate from to 79.77-100%.
Asymptotic Regional Boundary Gradient Observability in Diffusion Neumann Problem
Authors: Al-Janabi Asmaa N. , Jaafar Zainab A.
Published: March 2021

The principle objective of this paper is to determine the concept of asymptotic regional boundary gradient observability to the case of diffusion system with Neumann boundary conditions. More precisely, for linear distributed parameter diffusion systems, we show that the sensors characterizations allow to reconstruct regional boundary gradient state in a boundary target subregion of the considered systems domain. Moreover, various interesting results associated with the selection of sensors are explained in specific and customized position situations respect to the asymptotic regional boundary gradient observability notion to be realized. Finally, we was clarified the existence of a dynamical systems that is not asymptotic boundary gradient observable in whole the boundary domain, but it may be performed on a part of boundary domain.
An Analytical Appraisal for Supervised Classifiers’ Performance on Facial Expression Recognition Based on Relief-F Feature Selection
Authors: Abdulrazaq Maiwan B. , Mahmood Mayyadah R. , Zeebaree Subhi R. M. , Abdulwahab Mohammad H. , Zebari Rizgar R. , Sallow Amira B.
Published: March 2021

Face expression recognition technology is one of the most recently developed fields in machine learning and has profoundly helped its users through forensic, security, and biometric applications. Many researchers and program developers have allocated their time and energy to figure out various techniques which would add to the technology’s functionality and accuracy. Face expression recognition is a complicated computational process in which is implemented via analyzing changes in facial traits that follow different emotional reactions. This paper endeavors to inspect accuracy ratio of six classifiers based on Relief-F feature selection method, relying on the utilization of the minimum quantity of attributes. The classifiers in which the paper attempts to inspect are Multi-Layer Perceptron, Random Forest, Decision Tree, Support Vector Machine, K-Nearest Neighbor, and Radial Basis Function. The experiment illustrates that K-Nearest Neighbor is the most accurate classifier with the total accuracy ratio of 94.93% amongst the rest when applied on CK+ Dataset.
On θ-Continuity in Micro Topological Spaces
Authors: Jassim Rana H. , Rasheed Reem O. , Faris Hairan I.
Published: March 2021

Every year different types of topological spaces are introduced by many topologists. Nowadays available topologies are supra topology, ideal topology, bitopology, fuzzy topology, Fine topology, nano topology and so on. Nano topology introduced by Thivagar, using this nano topology we introduced micro topology and also study the concepts of θ - micro open sets and θ – micro continuous and some of their properties are investigated.
Estimate Probability Distribution of Monthly Maximum Daily Rainfall of Iraq
Authors: Nassif Wedyan G. , Al-Taai Osama T. , Mohammed Ali J. , AL-Shamarti Hasanain K. A.
Published: March 2021

Precipitation is very important to humankind, as it represents a pure source of water beside the earth. Therefore, many researchers are interested in rain. In this study, the monthly data for the amount of rainfall for fourteen stations (Baghdad, Basra, Diwania, Kirkuk, Smaw, Tikirit, Rutb, Mousl, Alialgarbi, talafer, Kut, samara, Hali, and Nasiriya) in Iraq was collected from the General Authority for Meteorology and Seismic Monitoring, the maximum understanding of precipitation behavior, was tested the data according to tests Kolmogorov-Smirnov. Anderson, Chi-Squared, The 20 probability distributions were applied to find the appropriate distribution of this data. Was found that the Mosul station has the largest amount of rain from other stations and that this data follows the distribution Log-Gamma Which is considered the best distribution according to the tests, while Basra station takes to distribute Burr (4p), while, Rutba Station takes logistical distribution while Baghdad Log-Pearson 3 Station, despite the maximum monthly precipitation, Baghdad has more statistics than Rutba.
The Relationships Between Quantum Logic Maps, and T-norms on D-posets via Probabilistic Space Operations
Authors: Al-Adilee Ahmed , Al-Mousawi Mustafa G.
Published: March 2021

In this paper, We propose several constructions of quantum logic maps, and T-norms on d-poset. Afterwards, we examine the relationships of those constructions to the probability space operations. We have presented several relations, proofs, and examples of each structure.
Predicting Autism Spectrum Disorder (ASD) for Toddlers and Children Using Data Mining Techniques
Authors: Musa Rasool Azeem , Manaa Mehdi Ebady , Abdul-Majeed Ghassan
Published: March 2021

Autism Spectrum Disorder (ASD) is a contemporary disease that has recently spread among toddlers and children. Many researchers have been interested to determine the features of the autism. However, this kind of studies is costly in term of the gathering information from several sources. In this paper, we introduced and applied a novel and early prediction techniques based on the using of data mining and machine learning tools. It is difficult to determine the features of any autism ages. In this paper, we used data mining predication techniques which play an integral role to predict the symptoms of autism for any age group. The data of this study is AQ-10 dataset which are involved for toddlers and children. The results present a superior performance for ASD classification. Random forest, Decision Tree, Support Vector Machine, and Naive Bayes the accuracy of 1.0 with the features selected by correlation technique.
Evaluation and Realization of Memristor emulator spice
Authors: Ahmed Zahraa Khaled , Taha Faris Hassan
Published: March 2021

The fourth missing element of electrical device now are attractive in many applications, memristor is one of the elements which the resistance depends on the historical memory of the current flowing. In this paper, a memristor device were realized, implemented and evaluated using PSPICE package. Where the HP laboratories are manufactured and tests the performance. The relationship between voltage and current characteristics has been measured in different frequencies and voltage to the memristor. The results obtained are compatible with the designed micromodel with the expected performance.
HSH-algebra with application
Authors: Abbass Husein Hadi , Albermany Salah , mazel Hussein saad
Published: March 2021

In this paper we will study a new notion called HSH-algebra with his application in coding and the encryption data.
Survey: Convolution Neural networks in Object Detection
Authors: Hakim Heba , Fadhil Ali
Published: March 2021

In latest years, deep neural networks were observed to be the most influential among all innovations in the computer vision field, generating remarkable performance on image classification. Convolution neural networks (CNNs) are considering as an interesting tool for studying vision of biological because this category of artificial vision systems shows the capabilities of visual recognition similar to those of human observers. By improving the recognition performance of these models, it appears that they become more effective in prediction. Recent benchmarks have shown that deep CNNs are excellent approaches for object recognition and detection. In this paper, we are focusing on the core building blocks of convolution neural networks architecture. Different object detection methods that utilize convolution neural networks are discussed and compared. On the other hand, there is a simple summary of the common CNNs architectures.
The impact of Routing Protocols and topologies on IOT Based Systems
Authors: Hameed Saif Saad , Saleh Hadeel Mohammed
Published: March 2021

Routing protocols is a mechanism of detecting best route path from single node to multiple nodes or signal node in order to transfer data between network and it specify how routing techniques in routers communicate between routers to accomplish communication tasks. A simulation was done to compare some protocols such as ““RIP””, ““OSPF”” and “EIGRB’ to determine the best routing protocol at a specific given network topology for IoT applications. The main problem there is a different routing protocols was developed each has its performance among different IoT applications. As well a simulation was required to detect the perfect routing protocol required for IoT applications. In this project a simulation of “IoT” network that serve the “IoT” applications using different routing protocols such as ““RIP””, ““OSPF”’ and “EIGRB” and an evaluation to the performance of each routing technique was to decide which is the best routing for “IoT” applications. Opnet modular was used to implement the simulation and it is found that the ““IGRP”” has the fastest router convergence among the three protocols we are testing.
Evaluating Facebook success in Iraq: An extension of the DeLone and McLean’s model of information systems success (ISS)
Authors: Al-Azawei Ahmed , Al-Azawi Raad
Published: March 2021

With the continuous improvement in Internet and World Wide Web (WWW) technologies, people are increasingly using social network sites (SNS) such as Facebook. SNS has many different implications for individuals, societies, and organizations. Hence, it is important to evaluate the success of such technologies from the users’ point of view. This research represents one of the first empirical evaluations of Facebook success based on an extended Delone and Mclean’s information systems success (ISS) model in Iraq. The proposed model includes eight constructs namely, information quality, system quality, service quality, intrinsic motivation, perceived interaction, Facebook use, user satisfaction, and net benefits. The validity and reliability of the research instrument used to collect the research data was confirmed first. Accordingly, the partial least squares-structural equation modeling technique (PLS-SEM) was performed on data gathered from 369 Facebook users. This method is useful in identifying the association between different variables as well as the prediction ability of independent on dependent constructs. The findings show that nine out of thirteen hypothesized associations were upheld. These outcomes provide significant implications for both practice and theory.
On Soft Closure Function in Soft Ideal Spaces
Authors: Abbas Sameer Annon , Hussain Ahmed Hadi , Salman Abbas Musleh , Hussein Nabeel Ali
Published: March 2021

As a result of evolution in science by researchers and scientists led to quantum leaps in several different fields of science, including medicine, engineering, and chemistry, physics and other Humanities and social as this evolution based on evolution in various branches of mathematics which is the infrastructure to develop the rest of science. Where dentition of (Soft Set) came to solve many problems facing humans. This is because of researcher Molodtsov in 1999 [1]. Where the definition of soft sets was based universal set, Ex
represent the all parameters of X, also the function from Ex
to P(X). The motivation behind this paper is to present the thought of soft ideal in soft set theory. The idea of soft closure is likewise presented. These ideas are talked about with a view to discover unprecedented soft topologies from the first one The fundamental structure, particularly a reason for such produced soft topologies likewise considered here. At long last, the idea of soft closure compatibility with soft topologies is presented and some proportional conditions. As well as the concept of soft Ψ
Γ
-Operator and study of the most significant properties.
Design of Cloud Computing for Educational Centers Using Private Cloud Computing: A Case Study
Authors: Ouda Ghazwan K. , Yas Qahtan M.
Published: March 2021

Cloud computing is becoming a popular approach to solve many serious problematic issues since its introduction in 2000. Cloud computing includes data storage in addition to software and hardware sharing via using network infrastructure, computers, and other resources. However, the educational centers are still considered conventional and limited in terms of their existing hardware infrastructure. A case study of cloud computing deployment in the University of Diyala-ICC (Internet and Computer Center)-will be used to illustrate the situation. This study aims to develop a basic cloud-based design for improving the efficiency of ICC laboratories, as an educational center in the University of Diyala to make it a study and research center of cloud computing for computer science students in the university. The proposed design is built via using “Software as a Service” (SaaS) model. Cloud computing in ICC laboratories is designed using Private Cloud Computing. The proposed design provided flexibility to ICC and allows to improve the capabilities of computer network and helps in managing their resources easily.
Proposed APs Distribution Optimization Algorithm: Indoor Coverage Solution
Authors: Rawaa Akram M. , Al-Nakkash Aseel H. , Salim Omar Nameer Mohammed , AlAbdullah Ali A. S
Published: March 2021

Recently, Wireless systems for indoor environments have become very important and popular. With increasing the need for mobile devices like laptops, tabs and smartphones, the optimization of WLAN coverage became very necessary to provide efficient communication with less cost. This optimization is verified by decreasing number of WiFi Access Point coverage. A multi objectives algorithm is proposed in this paper to optimize WiFi coverage in indoor environment using MatLab software. The proposed algorithm employs (BPSO) to determine the optimal number and locations for WiFi APs distribution based on predefined received power thresholds value. it has been found that the proposed algorithm is flexible using weighted multi-fitness functions. In addition, conducting site survey in real environment verifies the reliability of the proposed algorithm. Applying APD-CS algorithm in the adopted real environment increase the coverage by 64.6% and the average received power 7 dBm at Pth equal to-55 dBm.
Network intrusion detection system using deep neural networks
Authors: Maithem Mohammed , Al-sultany Ghadaa A.
Published: March 2021

In recent decades, rapid development in the world of technology and networks has been achieved, also there is a spread of Internet services in all fields over the world. Piracy numbers have increased, also a lot of modern systems were penetrated, so the developing information security technologies to detect the new attack become an important requirement. One of the most important information security technologies is an Intrusion Detection System (IDS) that uses machine learning and deep learning techniques to detect anomalies in the network. The main idea of this paper is to use an advanced intrusion detection system with high network performance to detect the unknown attack package, by using a deep neural network algorithm, also in this model, the attack detection is done by two ways (binary classification and multiclass classification). The proposed system has shown encouraging results in terms of the high accuracy (99.98% with multiclass classification and with binary classification).
Biometric identity Authentication System Using Hand Geometry Measurements
Authors: Mohammed Hesham Hashim , Baker Shatha A. , Nori Ahmed S.
Published: March 2021

In recent years hand geometric dependent biometric system has shown to be the quite acceptable biometric trait and suitable for security applications. It has been recognized as an effective means of authenticating identity in a variety of commercial applications as a result of better hardware and improved algorithms. This paper purpose a hand recognition system that extract 21 features for the right hand to identify and authorize persons. The system has two main parts, the first contain the data collection, explains the basic pre-processing required and how hand geometry characteristics like fingers length, width, coordinates of the base of the fingers, and palm width are extracted to derive the features used for discrimination, While the second part include the training and testing of three artificial neural networks to perform the recognition. After features extraction, the system uses three kinds of artificial neural networks in performing the recognition process, which are feed forward back propagation NN, Elman NN, and the cascade forward neural network NN. The proposed system shows that the Recognition Rate RR for the neural networks after testing were 95%, 92%, 88% respectively.
Thermo-Mechanical evaluation of differential calorimetric scanned factors for AD60 VHDPE polymer using OriginPro LabTM Analysis tool version 8.5.1: A review comprising novel experimental optimization approach
Authors: Sharma Shubham , Sudhakara  , Misra SK , Singh J
Published: March 2021

The objective of the present work is to provides a comprehensive overview on the thermal analysis of the virgin high-density polyethylene (VHDPE) graded AD60 material by means of a differential scanning calorimetry (DSC) equipments. In order to verify possible transformations in its molecular structure and change of phases present with the variation of temperature over time. Past literature results reported the testing-analysis of the samples which was carried out through 3 distinct cycles separated into 3 stages, the first of which the material will undergo a heating of the ambient temperature up to 178°C, at a rate of 10°C/min, the second stage a cooling from the temperature of 178°C at a temperature of 43°C, with a rate of 10°/min, and the third stage the test body will undergo a new heating from 43°C to 184°C with a rate of 10°C/min. As revealed from in-depth studies, results obtained from the thermograms for the seven samples of the high-density polyethylene for the three required cycles, showed approximate values, with variations in centesimal scales that can be disregarded if the slight variations have been analyzed in the air pressure that the equipment can present. With this, it was possible to conclude the values of the parameters of the crystallization temperature (Tc), the percentage of crystallinity (% C), Melt Temperature (Tm) and melting enthalpy change (ΔHfm).
A Review on DC-DC Converters with Photovoltaic System in DC Micro Grid
Authors: Radhika S , Margaret V
Published: March 2021

Photovoltaic system is the low-cost source of electrical power in high solar energy regions. The benefits of PV system are like nonpolluting and minimum maintenance. Solar energy changes as per irradiance and temperature and also one factor which reduces the power output is the partial shading in the cells. Hence f o r th, various algo rith ms a r e p u t fo rth to obta in t h e maximum power f r o m t h e PV arrangement and dc-dc converters intend to regulate the supply. The concept of micro grid is emerging as an excellent solution for inter connecting renewable energy sources and loads. DC micro grid is a necessity in today’s world. There is wide increase in usage of DC systems in commercial, residential and industrial systems. DC micro grids are dominant in reliability, control and efficiency. Direct current architectures will be used in demand in the future electrical distribution systems. This paper reviews on all above concepts to be used in DC micro grid for future DC applications.
Intelligent Car
Authors: Suryakailash R. , Rathinakumar P. , Atchaya S. , Sripriyadharshini L
Published: March 2021

INTELLIGENT CAR (ICAR) is a car, developed on eye-tracking software using a camera. The ICAR the first Intelligent car that works with eye control and automatic driving. You can ride a car and move around where ever you want. The car drives Right, left, forward and stop using your eyes with the help of camera and coding. You need to sit facing the camera to control it. The main advantage is the car drives without any hassle in the roadways because of sensors i.e without any accidents/hitting any objects. It requires some hardware along with software along with a camera. Roads are easy and cost-effective for travelling through vehicles and walking people. Physically challenged people can also travel easily by I-Car, they need not call any person for help on the roads or depend on anyone to move from one place to another.
Circular Monopole Electromagnetic Band Gap Structured Antenna SLLOptimization using Heuristic
Authors: Aradhyula Raghavaraju , Rama Krishna T V , Madhav B T P , Rao M C , Bhavani T
Published: March 2021

Heuristic algorithmis used in the design of electromagnetic band gap structured circular monopole antenna. Design to parametric analysis and optimization with respect to bandwidth enhancement is presented in this work. The proposed algorithm has been compared with famous genetic algorithm and particle swarm optimization algorithm and optimizedside lobe level has been reported in this work. The comparison between simulation and the measurement results matching is supporting the applicability of the work in real time communication modules.
Effective Microstrip Feed Line Length in Ultra-Wideband Responses and Wireless Applications
Authors: Mohan K N , Himaja Reddy K
Published: March 2021

In this article, an effective feed line technique is used instead of DGS (Defected Ground Structure) and CSRR (Complimentary Split Ring Resonator) which are commonly used for achieving UWB (Ultra-Wideband Applications) responses. A classical antenna is designed for 2.4GHz frequency with a feedline of 180° electrical length. The resonance of proposed antenna responses as UWB and it has interesting current distributions through the patch antenna. This paper avoided all aspects of existing techniques which are proposed to achieve UWB.
An Effective Brain Tumor Detection from T1w MR Images Using Active Contour Segmentation Techniques
Authors: Babu KRajesh , Ksony  , Indira NDurga , Vara Prasad K , Shameem Syed
Published: March 2021

In the medical imaging application, early detection of tumors from magnetic resonance imaging (MRI) brain images is a challenging task, it reduces the risk of human health and increases the chance of survival. The manual detection of brain tissue was requiring a lot of experience and time-consuming. Magnetic resonance images (MRI) scan helps to detect brain tumors in the early stage, and it has become a hot research topic in medical image processing. In this research article, an automated framework is presented intended to segment and extraction of brain tumors of MRI images in an efficient manner. In this work, three primary steps are involved in the suggested brain tumor segmentation system including pre-processing, image thresholding, and segmentation. This article focusses on to study the accurate brain tumor segmentation system by comparing the level set (LSM) and Chan-vese (C-V) techniques. All the experiments are conducted on Harvard datasets to validate the performance of both methods. The performance of the algorithms was calculated in terms of dice coefficient (DC), Hausdorff distance (HD), and Jaccard similarity index (JSI). It has been observed that KIFCM has performed better than the other clustering methods and Chan-Vase has shown better performance in tumor detection on selected datasets. The overall work shows that the presented framework outer performs for the detection of brain tumors with minimum distance error and loss of information over existingmethods.
Evaluation and Implementation of Particle Swarm Optimization Maximumpower Point Tracking Technique for Photo Voltaic System
Authors: Kumari Pamula Raja , Bojja Polaiah , Pragathi B. , Prasanth kumar reddy T , Vijaya Lakshmi P
Published: March 2021

The power generated from the solar PV panels by photovoltaic effect is varying on the particular day. To extract the peak power from the solar panels maximum power point tracking (MPPT) techniques are developed. The demerits of conventional MPPT techniques are slow tracking of the peak point and inaccurate setting of the peak operating voltage point VMPP. The paper proposes the various MPPT techniques and the particle swarm optimization (PSO) MPPT technique. The proposed PSO-MPPT technique improves the productivityand performance of the system. The comparison of various MPPT algorithms based on the performance characteristics are discussed in the paper. The proposed particle swarm optimization MPPT algorithm is efficient, simple and accurate which increases the panel power, by controlling the duty cycle of the switching pulse in the DC converter section under varying weather conditions. The proposed particle swarm optimization MPPT technique is simulated in MATLAB/SIMULINK.