An optimized approach for extensive segmentation and classification of brain MRI

With the significant contribution in medical image processing for an effective diagnosis of critical health condition in human, there has been evolution of various methods and techniques in abnormality detection and classification process. An insight to the existing approaches highlights that potential amount of work is being carried out in detection and segmentation process but less effective modelling towards classification problems. This manuscript discusses about a simple and robust modelling of a technique that offers comprehensive segmentation process as well as classification process using Artificial Neural Network. Different from any existing approach, the study offers more granularities towards foreground/background indexing with its comprehensive segmentation process while introducing a unique morphological operation along with graph-believe network for ensuring approximately 99% of accuracy of proposed system in contrast to existing learning scheme

Integrated Modelling Approach for Enhancing Brain MRI with Flexible Pre-Processing Capability

The assurance of an information quality of the input medical image is a critical step to offer highly precise and reliable diagnosis of clinical condition in human. The importance of such assurance becomes more while dealing with important organ like brain. Magnetic Resonance Imaging (MRI) is one of the most trusted mediums to investigate brain. Looking into the existing trends of investigating brain MRI, it was observed that researchers are more prone to investigate advanced problems e.g. segmentation, localization, classification, etc considering image dataset. There is less work carried out towards image preprocessing that potential affects the later stage of diagnosing. Therefore, this paper introduces a novel model of integrated image enhancement algorithm that is capable of solving different and discrete problems of performing image pre-processing for offering highly improved and enhanced brain MRI. The comparative outcomes exhibit the advantage of its simplistic implemetation strategy

The Network Slicing and Performance Analysis of 6G Networks using Machine Learning

6G technology is designed to provide users with faster and more reliable data  transfer as compared to the current 5G technology. 6G is rapidly evolving and provides a large bandwidth, even in underserved areas. This technology is extremely anticipated and is currently booming for its ability to deliver massive network capacity, low latency, and a highly improved user experience. Its scope is immense, and it’s designed to connect everyone and everything in the world. It includes new deployment models and services with extended user capacity. This study proposes a network slicing simulator that uses hardcoded base station coordinates to randomly distribute client locations to help analyse the performance of a particular base station architecture. When a client wants to locate the closest base station, it queries the simulator, which stores base station coordinates in a K-Dimensional tree. Throughout the simulation, the user follows a pattern that continues until the time limit is achieved. It gauges multiple statistics such as client connection ratio, client count per second, Client count per slice, latency, and the new location of the client. The K-D tree handover algorithm proposed here allows the user to connect to the nearest base stations after fulfilling the required criteria. This algorithm ensures the quality requirements and decides among the base stations the user connects to

Improved space vector modulation with reduced switching vectors for multi-phase matrix converter

Multi-phase converter inherits numerous advantages, namely superior fault tolerance, lower per-leg power rating and higher degree of freedom in control. With these advantages, this thesis proposes an improved space vector modulation (SVM) technique to enhance the ac-to-ac power conversion capability of the multi-phase matrix converter. The work is set to achieve two objectives. First is to improve the SVM of a three-to-seven phase single end matrix converter by reducing number of space vector combinations. Second is to use the active vector of the SVM to eliminate the common-mode voltage due to the heterogeneous switching combination of a dual three-to-five phase matrix converter. In the first part, the proposed technique utilizes only 129 out of 2,187 possible active space vectors. With the reduction, the SVM switching sequence is greatly simplified and the execution time is shortened. Despite this, no significant degradation in the output and the input waveform quality is observed from the MATLAB/Simulink simulation and the hardware prototype. The results show that the output voltage can reach up to 76.93% of the input voltage, which is the maximum physical limit of a three-to-seven phase matrix converter. In addition, the total harmonics distortion (THD) for the output voltage is measured to be below 5% over the operating frequency range of 0.1 Hz to 300 Hz. For the second part, the common-mode voltage elimination is based on the cancellation of the resultant vectors (that causes the common-mode to be formed), using a specially derived active vectors of the dual matrix converter. The elimination strategy is coupled with the ability to control the input power factor to unity. The proposed concept is verified by the MATLAB/Simulink simulation and is validated using a 5 kW three-to-five phase matrix converter prototype. The SVM switching algorithm itself is implemented on a dSPACE-1006 digital signal processor platform. The results prove that the common-mode voltage is successfully eliminated from the five-phase induction motor winding. Furthermore, the output phase voltage is boosted up to 150% of the input voltage in linear modulation range

Event Plane Dependent Dihadron Correlations with Harmonic Vⁿ Subtraction in Au + Au Collisions at √ˢᴺᴺ = 200 GeV

STAR measurements of dihadron azimuthal correlations (ΔΦ) are reported in midcentral (20-60%) Au + Au collisions at √ˢᴺᴺ = 200 GeV as a function of the trigger particle\u27s azimuthal angle relative to the event plane, Φs = | Φt- ΨEP|. The elliptic (v2), triangular (v3), and quadratic (v4) flow harmonic backgrounds are subtracted using the zero yield at minimum (ZYAM) method. It is found that a finite near-side (|ΔΦ| \u3c π/2) long-range pseudorapidity correlation (ridge) is present in the in-plane direction (Φs ~ 0). The away-side (|ΔΦ| \u3e π/2) correlation shows a modification from d+ Au data, varying with Φs. The modification may be a consequence of path-length-dependent jet quenching and may lead to a better understanding of high-density QCD

Charged-to-Neutral Correlation at Forward Rapidity in Au + Au Collisions at √s(NN)=200 GeV

Event-by-event fluctuations of the multiplicities of inclusive charged particles and photons at forward rapidity in Au + Au collisions at √s(NN) = 200 GeV have been studied. The dominant contribution to such fluctuations is expected to come from correlated production of charged and neutral pions. We search for evidence of dynamical fluctuations of different physical origins. Observables constructed out of moments of multiplicities are used as measures of fluctuations. Mixed events and model calculations are used as base lines. Results are compared to the dynamical net-charge fluctuations measured in the same acceptance. A nonzero statistically significant signal of dynamical fluctuations is observed in excess to the model prediction when charged particles and photons are measured in the same acceptance. We find that, unlike dynamical net-charge fluctuation, charge-neutral fluctuation is not dominated by correlation owing to particle decay. Results are compared to the expectations based on the generic production mechanism of pions owing to isospin symmetry, for which no significant

Measurements of Dihadron Correlations Relative to the Event Plane in Au Plus Au Collisions at √SNN= 200 GeV

Dihadron azimuthal correlations containing a high transverse momentum (pT) trigger particle are sensitive to the properties of the nuclear medium created at RHIC through the strong interactions occurring between the traversing parton and the medium, i.e. jet-quenching. Previous measurements revealed a strong modification to dihadron azimuthal correlations in Au+Au collisions with respect to p+p and d+Au collisions. The modification increases with the collision centrality, suggesting a path-length or energy density dependence to the jet-quenching effect. This paper reports STAR measurements of dihadron azimuthal correlations in mid-central (20%-60%) Au+Au collisions at √sNN = 200 GeV as a function of the trigger particle\u27s azimuthal angle relative to the event plane, ϕs = |ϕt - ψEP| .The azimuthal correlation is studied as a function of both the trigger and associated particle pT. The subtractions of the combinatorial background and anisotropic flow, assuming Zero Yield At Minimum (ZYAM), are described. The correlation results are first discussed with subtraction of the even harmonic (elliptic and quadrangular) flow backgrounds. The away-side correlation is strongly modified, and the modification varies with ϕs, with a double-peak structure for out-of-plane trigger particles. The near-side ridge (long range pseudo-rapidity Δη correlation) appears to drop with increasing ϕs while the jet-like component remains approximately constant. The correlation functions are further studied with the subtraction of odd harmonic triangular flow background arising from fluctuations. It is found that the triangular flow, while responsible for the majority of the amplitudes, is not sufficient to explain the ϕs-dependence of the ridge or the away-side double-peak structure. The dropping ridge with ϕs, could be attributed to a ϕs-dependent elliptic anisotropy; however, the physics mechanism of the ridge remains an open question. Even with a ϕs-dependent elliptic flow, the away-side correlation structure is robust. These results, with extensive systematic studies of the dihadron correlations as a function of ϕs, trigger and associated particle pT, and the pseudo-rapidity range Δη, should provide stringent inputs to help understand the underlying physics mechanisms of jet-medium interactions in high energy nuclear collisions

System-size Dependence of Transverse Momentum Correlations at √ s N N = 62.4 and 200 GeV at the BNL Relativistic Heavy Ion Collider

We present a study of the average transverse momentum (Pt) fluctuations and pt correlations for charged particles produced in Cu+Cu collisions at midrapidity for √sNN= 62.4 and 200 GeV. These results are compared with those published for Au+Au collisions at the same energies, to explore the system size dependence. In addition to the collision energy and system size dependence, the Pt correlation results have been studied as functions of the collision centralities, the ranges in Pt, the pseudorapidity η, and the azimuthal angle ϕ. The square root of the measured Pt correlations when scaled by mean pt is found to be independent of both colliding beam energy and system size studied. Transport-based model calculations are found to have a better quantitative agreement with the measurements compared to models which incorporate only jetlike correlations