An Automated Approach of CT Scan Image Processing for Brain Tumor Identification and Evaluation

Brain Tumor identification and evaluation requires Computed Tomography (CT) scan and image processing in medical diagnosis. The Manual methods for the detection of abnormal cell growths in brain tissue is both time consuming and non-reliable. This paper initiates with a discussion of a clinical diagnosis case of normal brain tissue and other with tumor affected images. The affected area is identified first with manual approach and further an automated approach is discussed using NI Lab VIEW software for locating the exact position and its evaluation. The described method provides a better way of diagnosing brain tumor in a quick and reliable automated manner. In the view of this, an automatic segmentation of brain MR images is needed to correctly segment White Matter (WM), Cerebrospinal fluid (CSF) and Gray Matter (GM) tissues of brain in a shorter span of time. The manual segmentation of brain tumor is abstruse job and may provide erroneous results

Performance of Vector-Controlled PMSM Drive Without Using Current Sensors

The current sensorless vector-controlled permanent-magnet synchronous motor (PMSM) drive using a single sensor (i.e., speed sensor) is presented in this work. The current sensors are removed, and the estimated currents are used to close the current loop to minimize the drive cost and make it fault-tolerant against current sensor failure. A classical vector-control PMSM drive requires at least three sensors, i.e., two current sensors and one speed/position sensor. This paper presents a new current estimation technique that is free from inverter switching states, an integrator, and differentiator terms. The drive is suitable for retrofit applications, as it does not require any additional hardware. The reference voltages (vds and vqs) are used to estimate the rotor reference frame currents (i.e., iqs and ids). The presented algorithm depends on the stator resistance (Ɍs). The online Ɍs estimation algorithm is used for compensation to overcome the effect of the Ɍs on the estimated currents. The sensitivity analysis for the currents against the speed is verified and presented. The speed loop is closed with actual speed information, which will try to maintain the reference speed under any circumstances. The proposed current sensorless PMSM drive was validated using MATLAB/Simulink and also verified on a hardware prototype. The presented technique was verified for various operation conditions, and some of the extensive results are presented.publishedVersio

Investigation of CeO2 nanoparticles on the performance enhancement of InsulatingOils

Integrating nanotechnology in dielectric fluid significantly inhibits losses and boosts overall dielectric fluid performance. There has been research done on the effects of introducing various nanoparticles, such as titania, alumina, silica nanodiamonds, etc. In this paper, a novel nanoparticle, Ceria (CeO2), has been used, and its properties were examined using the FTIR (Fourier Transform Infrared) spectrum, the XRD (X-ray Diffrac-tion) spectrum, the SEM (Scanning Electron Microscopy), and the TEM (Transmission Electron Microscopy). This paper illustrates an efficient dielectric fluid prepared by the successful dispersion of Cerium Oxide (CeO2) nanoparticles in various concentrations into four commercial oils, namely mineral oil, rapeseed oil, synthetic ester oil, and soybean oil, to enhance and improve their dielectric characteristics. The performance investigation emphasises breakdown strength enhancement and other dielectric properties of the colloidal solution comprising different nanoparticle (NP) concentrations. Various commercial oils are used as a base in nano-oil to diversify their applicability as dielectric fluids by measuring the correlation in dielectric parame-ters and statistically assessing their applicability with normal and Weibull distributions. The obtained ex-perimental data sets were analysed using the Statistics and Machine Learning Toolbox in MATLAB. The aging measurement has been done only on mineral oil, and results were matched using a predictive model of statistics and the Machine Learning Toolbox in MATLAB. Well-dispersed CeO2 NPs in the insulating oils lead to a significant increase in AC breakdown strength. The effect of ageing on the dielectric properties of nano oils yields better results than conventionally aged oil. It has been observed that the breakdown voltage is enhanced by up to 30 % for mineral oil at an optimal concentration of 0.01 g/L, 9% for synthetic ester oil at 0.03 g/L, 18% for rapeseed oil at 0.02 g/L, and 19% for soybean oil at 0.03 g/L nanoparticle concentration. Following the dispersion of CeO2 nanoparticles, the dielectric constant of all insulating oils has also signifi-cantly improved. The overall experimental results are promising and show the potential of the CeO2 NPs-based nano oil as an efficient and highly performing dielectric oil for different power applications.publishedVersio

A Composite Wavelets and Morphology Approach for ECG Noise Filtering

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Power Management of Hybrid Grid System With Battery Deprivation Cost Using Artificial Neural Network

Continuous power supply in an integrated electric system supplied by solar energy and battery storage can be optimally maintained with the use of diesel generators. This article discusses the optimum setting-point for isolated wind, photo-voltaic, diesel, and battery storage electric grid systems. Optimal energy supply for hybrid grid systems means that the load is sufficient for 24 h. This study aims to integrate the battery deprivation costs and the fuel price feature in the optimization model for the hybrid grid. In order to count charge-discharge cycles and measure battery deprivation, the genetic algorithm concept is utilized. To solve the target function, an ANN-based algorithm with genetic coefficients can also be used to optimize the power management system. In the objective function, a weight factor is proposed. Specific weight factor values are considered for simulation studies. On the algorithm actions, charging status, and its implications for the optimized expense of the hybrid grid, the weight factor effect is measured.This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program.Scopu

Early Detection of Alzheimer’s Disease Using Polar Harmonic Transforms and Optimized Wavelet Neural Network

Effective and accurate diagnosis of Alzheimer’s disease (AD), as well as early-stage detection, has gained more and more attention in recent years. For AD classification, we propose a new hybrid method for early detection of Alzheimer’s disease (AD) using Polar Harmonic Transforms (PHT) and Self-adaptive Differential Evolution Wavelet Neural Network (SaDE-WNN). The orthogonal moments are used for feature extraction from the grey matter tissues of structural Magnetic Resonance Imaging (MRI) data. Irrelevant features are removed by the feature selection process through evaluating the in-class and among-class variance. In recent years, WNNs have gained attention in classification tasks; however, they suffer from the problem of initial parameter tuning, parameter setting. We proposed a WNN with the self-adaptation technique for controlling the Differential Evolution (DE) parameters, i.e., the mutation scale factor (F) and the cross-over rate (CR). Experimental results on the Alzheimer’s disease Neuroimaging Initiative (ADNI) database indicate that the proposed method yields the best overall classification results between AD and mild cognitive impairment (MCI) (93.7% accuracy, 86.0% sensitivity, 98.0% specificity, and 0.97 area under the curve (AUC)), MCI and healthy control (HC) (92.9% accuracy, 95.2% sensitivity, 88.9% specificity, and 0.98 AUC), and AD and HC (94.4% accuracy, 88.7% sensitivity, 98.9% specificity and 0.99 AUC)

Role of Information Communication and Technology (ICT) in the Treatment of Brain Ailments

The emphasis on understanding the human brain and its functioning has been captivating since ages. Rising to this challenge, it gives us an insight of who we are. Significant and profound research is taking place in medical field to understand the structure and functioning of the brain in micro detailing aspect. There are many techniques and methodologies used in the study and diagnosis of brain ailments. ICT has proven to be a strong and supportive hand in this field for better understanding and analysis. In this paper the role of ICT in the field of neuro-sciences has been elaborated

Design and Implementation of Quad-Port MIMO Antenna with Dual-Band Elimination Characteristics for Ultra-Wideband Applications

A planar, microstrip line-fed, quad-port, multiple-input-multiple-output (MIMO) antenna with dual-band rejection features is proposed for ultra-wideband (UWB) applications. The proposed MIMO antenna design consists of four identical octagonal-shaped radiating elements, which are placed orthogonally to each other. The dual-band rejection property (3.5 GHz and 5.5 GHz corresponding to Wi-MAX and WLAN bands) was obtained by introducing a hexagonal-shaped complementary split-ring resonator (HCSRR) in the radiators of the designed antenna. The MIMO antenna was etched on low-cost FR-4 dielectric substrate of size 58 × 58 × 0.8 mm3. Isolation higher than 18 dB and envelope correlation coefficient (ECC) lesser than 0.07 was observed for the MIMO/diversity antenna in the operating range of 3–16 GHz. The presented four-port UWB MIMO antenna configuration was fabricated, and the experimental results validate the simulation outcomes

Design of Quad-Port Ultra-Wideband Multiple-Input-Multiple-Output Antenna with Wide Axial-Ratio Bandwidth

This article presents a compact, planar, quad-port ultra-wideband (UWB) multiple-input–multiple-output (MIMO) antenna with wide axial ratio bandwidth (ARBW). The proposed MIMO design consists of four identical square-shaped antenna elements, where each element is made up of a circular slotted ground plane and feed by a 50 Ω microstrip line. The circular polarization is achieved using a protruding hexagonal stub from the ground plane. The four elements of the MIMO antenna are placed orthogonally to each other to obtain high inter-element isolation. FR-4 dielectric substrate of size 45 × 45 × 1.6 mm3 is used for the antenna prototype, and a good agreement is noticed among the simulated and experimental results. The proposed MIMO antenna shows 3-dB ARBW of 52% (3.8–6.5 GHz) and impedance bandwidth (S11 ≤ −10 dB) of 144% (2.2–13.5 GHz)

Disjoint Spanning Tree Based Reliability Evaluation of Wireless Sensor Network

Wireless sensor networks (WSNs) are becoming very common in numerous manufacturing industries; especially where it is difficult to connect a sensor to a sink. This is an evolving issue for researchers attempting to contribute to the proliferation of WSNs. Monitoring a WSN depends on the type of collective data the sensor nodes have acquired. It is necessary to quantify the performance of these networks with the help of network reliability measures to ensure the stable operation of WSNs. Reliability plays a key role in the efficacy of any large-scale application of WSNs. The communication reliability in a wireless sensor network is an influential parameter for enhancing network performance for secure, desirable, and successful communication. The reliability of WSNs must incorporate the design variables, coverage, lifetime, and connectivity into consideration; however, connectivity is the most important factor, especially in a harsh environment on a large scale. The proposed algorithm is a one-step approach, which starts with the recognition of a specific spanning tree only. It utilizes all other disjoint spanning trees, which are generated directly in a simple manner and consume less computation time and memory. A binary decision illustration is presented for the enumeration of K-coverage communication reliability. In this paper, the issue of computing minimum spanning trees was addressed and it is a pertinent method for further evaluating reliability for WSNs. This paper inspects the reliability of WSNs and proposes a method for evaluating the flow-oriented reliability of WSNs. Further, a modified approach for the sum-of-disjoint products to determine the reliability of WSN from the enumerated minimal spanning trees is proposed. The proposed algorithm when implemented for different sizes of WSNs demonstrates its applicability to WSNs of various scales. The proposed methodology is less complex and more efficient in terms of reliability