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As a pandemic, the primary evaluation tool for coronavirus (COVID-19) still has serious flaws. To improve the existing situation, all facilities and tools available in this field should be used to combat the pandemic. Reverse transcription polymerase chain reaction is used to evaluate whether or not a person has this virus, but it cannot establish the severity of the illness. In this paper, we propose a simple, reliable, and automatic system to diagnose the severity of COVID-19 from the CT scans into three stages: mild, moderate, and severe, based on the simple segmentation method and three types of features extracted from the CT images, which are ratio of infection, statistical texture features (mean, standard deviation, skewness, and kurtosis), GLCM and GLRLM texture features. Four machine learning techniques (decision trees (DT), K-nearest neighbors (KNN), support vector machines (SVM), and Naïve Bayes) are used to classify scans. 1801 scans are divided into four stages based on the CT findings in the scans and the description file found with the datasets. Our proposed model divides into four steps: preprocessing, feature extraction, classification, and performance evaluation. Four machine learning algorithms are used in the classification step: SVM, KNN, DT, and Naive Bayes. By SVM method, the proposed model achieves 99.12%, 98.24%, 98.73%, and 99.9% accuracy for COVID-19 infection segmentation at the normal, mild, moderate, and severe stages, respectively. The area under the curve of the model is 0.99. Finally, our proposed model achieves better performance than state-of-art models. This will help the doctors know the stage of the infection and thus shorten the time and give the appropriate dose of treatment for this stage. [ABSTRACT FROM AUTHOR]

Massive MIMO systems with a large number of antennas at the base station (BS) may significantly boost spectrum and energy efficiency. In massive MIMO systems, it's important to have accurate channel state information (CSI) to get the most out of the large number of antennas and make sure the system works well. But because there are so many antennas at the base station (BS), the massive MIMO system has a lot of pilot overhead, which hurts system performance a lot. The spatial correlations between the signal sources in MIMO systems are low. This pattern of distribution makes it possible to use compressive sensing in massive MIMO systems to solve the channel estimation problem. In this study, we used the Shannon entropy function to come up with a new way to estimate the channel in the downlink of an FDD massive MIMO system. The Shannon entropy function is used as a sparsity regularizer for downlink channel estimation in the presented method to reduce the amount of work done by the pilot. The simulation results show that the proposed system outperforms existing compressive sensing (CS)-based channel estimation techniques in terms of NMSE performance and effectively lowers pilot overhead. [ABSTRACT FROM AUTHOR]

This study focuses on channel estimation for reconfigurable intelligent surface (RIS)-assisted mmWave systems, in which the RIS is used to facilitate base-to-user data transfer. For beamforming to work with active and passive elements, a large-size cascade channel matrix should always be known. Low training costs are achieved by using the mmWave channels' inherent sparsity. The research provides a unique compressive sensing-based channel estimation approach for reducing pilot overhead issues to a minimum. The proposed technique estimates channel data signals in a downlink for RIS-assisted mmWave systems. The mmWave systems often have a sparse distribution of signal sources due to the spatial correlations of the domains. This distribution pattern makes it possible to use compressive sensing methods to resolve the channel estimation issue. In order to decrease the pilot overhead, which is necessary to predict the channel, the proposed method extends the Re'nyi entropy function as the sparsity-promoting regularizer. In contrast to conventional compressive sensing techniques, which necessitate an initial knowledge of the signal's sparsity level, the presented method employs sparsity adaptive matching pursuit (SAMP) techniques to gradually determine the signal's sparsity level. Furthermore, it introduces a threshold parameter based on the signal's energy level to eliminate the sparsity level requirement. Extensive simulations show that the presented channel estimation approach surpasses the traditional OMP-based channel estimation methods in terms of normalized mean square error performance. In addition, the computational cost of channel estimation is lowered. Based on the simulations, our approach can estimate the channel well while reducing training overhead by a large amount. [ABSTRACT FROM AUTHOR]

In the Industrial Internet of Things (IIoT), non-orthogonal multiple access (NOMA) has emerged as a viable multiple access method due to its superior efficiency. In this paper, a new power allocation technique for NOMA-enabled IIoT devices is presented with trade-offs between increasing energy efficiency and decreasing power consumption. We present a joint optimization of transmission rate and energy harvesting in simultaneous wireless information and power transfer (SWIPT) NOMA-enabled IIoT devices. With the power splitting (PS) approach, we examine how to improve overall transmission rate and harvested energy, simultaneously, while fulfilling the minimum rate and harvested energy needs of each IIoT device in a SWIPT-enabled NOMA system. An objective function is established by adding transmission rates obtained from information decoding and the transformed throughput from energy harvesting. The combination of management approaches with Industry 4.0 technology provides a viable strategy to decrease industrial production's energy use. Several performance metrics may be utilized to study manufacturing process optimization. The efficiency of production equipment may be measured by looking at the overall effectiveness (OE) of the equipment in use. We divide the non-convex optimization problem into two sub-problems, based on the Lagrangian duality method, and solve them to find the optimal solution for the non-convex problem. The approach is validated based on physical layer parameter settings that represent potential factory of the future scenarios. Simulation results confirm the effectiveness of the presented method in a SWIPT-enabled NOMA system, provide considerable performance gains over the classic rate maximization strategy, and demonstrate the energy efficiency of the presented method compared with the conventional system. The results show huge potential of our solutions to reduce the future huge energy demand related to factory automation. [ABSTRACT FROM AUTHOR]

Code division multiple access (CDMA) is a channel access method adopted by various radio technologies worldwide. In particular, CDMA is used as an access method in many mobile standards such as CDMA2000 and WCDMA. We address the problem of blind multiuser equalization in the wideband CDMA systems in the noisy multipath propagation environment. Herein, we propose three new blind receiver schemes based on variations of independent component analysis within several filtering structures. These adaptive blind CDMA receivers do not require knowledge of the propagation parameters or spreading code sequences of the users-they primarily exploit the natural assumption of statistical independence among the symbol signals. We also develop three semi-blind adaptive detectors by incorporating new adaptive methods into the standard Rake receiver structure. Extensive comparative case studies based on bit error rate performance are carried out as a function of (i) the number of users, (ii) the number of symbols per user, and (iii) the signal-to-noise ratio. The conventional detectors include the baseline linear minimum mean squared error detector. The results show that the proposed methods outperform other detectors in estimating the symbol signals from the received mixed CDMA signals. Moreover, the new blind detectors mitigate the multiaccess interference in CDMA. [ABSTRACT FROM AUTHOR]

A new blind detection algorithm, based on fourth-order cumulant matrices, is presented and applied to the multiuser symbol estimation problem in direct sequence code division multiple access (DS-CDMA) systems. The blind detection is to estimate multiple symbol sequences in the downlink of a DS-CDMA communication system using only the received wireless data and without any knowledge of the user spreading codes. The proposed algorithm takes advantage of higher cumulant matrix properties to reduce the computational load and enhance the performance. Bit error rate simulations of this algorithm are performed for different numbers of users, signal-to-noise ratios, and numbers of symbols per user in comparison with the FastICA and RobustICA algorithms. The results show that the proposed algorithm outperforms both ICA-based detectors in estimating the symbol signals from the received mixed signals. Moreover, the proposed blind detector is computationally fast and exhibits high convergence speed in extracting user symbols. [ABSTRACT FROM AUTHOR]

The pilot overhead provides fundamental limits on the performance of massive multiple-input multiple-output (MIMO) systems. This is because the performance of such systems is based on the failure of the presentation of accurate channel state information (CSI). Based on the theory of compressive sensing, this paper presents a novel channel estimation technique as the mean of minimizing the problems associated with pilot overhead. The proposed technique is based on the combination of the compressive sampling matching and sparsity adaptive matching pursuit techniques. The sources of the signals in MIMO systems are sparsely distributed in terms of spatial correlations. This distribution pattern enables then use of compressive sampling techniques to solve the channel estimation problem in MIMO systems. Simulation results demonstrate that the proposed channel estimation outperforms the conventional compressive sensing (CS)-based channel estimation algorithms in terms of the normalized mean square error (NMSE) performance at high signal-to-noise ratios (SNRs). Furthermore, it reduces the computational complexity of the channel estimation compared to conventional methods. In addition to the achieved performance gain in terms of NMSE, the presented method significantly reduces pilot overhead compared to conventional channel estimation techniques. [ABSTRACT FROM AUTHOR]

A novel nanocomposite thin films based on azo dye methyl red (MR) hosted in polymethylmethacrylate (PMMA) incorporated with benzyl dimethyl ketal (BDK) as a photo-initiator and doped with 2.5% and 5% of silica NPs are prepared by dip coating technique on fused silica substrates. We investigate the optical, chemical and thermal properties of (PMMA–BDK–MR)/silica NPs nanocomposite thin films by performing UV–Vis spectrometer, Fourier transform infrared spectroscopy (FTIR) and Thermogravimetric analyser (TGA). The purpose of inserting the azo dye MR in the composite is to stimulate the nanocomposite to perform the cis ↔ trans cycles through illumination ↔ thermal relaxation for Write/Read/Erase optical data storage and UV-light sensors. Our UV–Vis and FTIR findings confirm the hysteresis cycle of trans- and cis-states. Consequently, (PMMA–BDK–MR)/silica NPs thin films could be successfully used in Write/Read/Erase optical data storage and UV-light sensors. The TGA measurements show that the weight loss of the nanocomposites is reversibly proportional to the silica NPs concentration indicating the reinforcement of physicochemical bonding density as the concentration of silica NPs is increased. [ABSTRACT FROM AUTHOR]

Hybrid material of poly-methyle-methacrylate (PMMA)-benzyl-dimethyl-ketal-azo dye methyl red (MR) thin film composites doped by various concentrations of ZnO NPs have been dip coated on glass substrates. The existence of azo dye (MR) in the composite was intentional to give the solution the desired pH level and to induce the cis ↔ trans cycles through illumination ↔ thermal relaxation. The optical and structural properties of the as-prepared thin films are investigated in relevance to ZnO NPs contents and the period of UV light illumination (short- and long-wavelength UV light). We found that the PMMA-BDK-MR doped by 1% ZnO NPs could be successfully used in optical data storage by implementing this composite in the WRITE/READ/ERASE cycle repeatedly. Specifically, it was found that the absorption band of the PMMA-BDK-MR doped by 1% ZnO NPs thin film lies between 350 and 600 nm upon illumination with 366 nm UV light for 10 s. Thermogravimetric analysis (TGA) was used to investigate the thermal stability of PMMA-BDK-MR/ZnO NPs. The TGA results are presented and interpreted. Furthermore, we investigated annealed poly-methyle-methacrylate-benzyl-dimethyl-ketal-methyl-red (PMMA-BDK-MR) doped by 1% ZnO NPs thin films. The trans–cis cycle has been repeated sequentially to confirm the hysteresis behavior for possible data READ/WRITE or ERASE memory applications. [ABSTRACT FROM AUTHOR]

Power generation and grid stability have become key issues in the last decade. The high penetration of large capacity wind generation into the electric power grid has led to serious concerns about its influence on the dynamic behavior of power systems. The low-voltage ride-through (LVRT) capability of wind turbines during grid faults is one of the core requirements to ensure stability in the power grid during transient conditions. The doubly-fed induction generators (DFIGs) offer several advantages when utilized in wind turbines, but discussions about their LVRT capabilities are limited. This paper presents a comprehensive study of the LVRT of grid-connected DFIG-based wind turbines. It provides a detailed investigation of the transient characteristics and the dynamic behavior of DFIGs during symmetrical and asymmetrical grid voltage sags. A detailed theoretical study supported by computer simulations is provided. This paper also provides a new rotor-side control scheme for DFIG-based wind turbines to enhance its LVRT capability during severe grid voltage sags. The proposed control strategy focuses on mitigating the rotor-side voltage and current transients during abnormal grid conditions, without any additional cost or reliability issues. As a result, the DFIG performance is improved and utility company standards are fulfilled. Computer simulations are used to verify the expanded ride-through capability of the novel strategy and its effective performance compared to the conventional control schemes. [ABSTRACT FROM AUTHOR]

• Thermostatic loads considering the distinct features of devices. • Storing and extracting energy in thermostatic loads. • Distribute agents of thermostatic loads using mixed-integer linear programming. • Enabling long-term services using thermostatic loads. This paper proposes novel modeling and control frameworks to evaluate the underlying capability of Thermostatically Controlled Loads (TCLs). The modeling architecture is based on distributed agents responsible for monitoring and controlling a small group of TCLs. Agents are mathematically formulated as a tracking problem in a mixed-integer linear programming format. Control actions are obtained by solving the agents sequentially in a rolling horizon control architecture. A step required to avoid the curse of dimensionality dictated by the number of devices and the duration of the planning horizon. The proposed architectures provide more practical evaluations in quantifying TCLs' capability. In essence, individual devices are described by distinct parameters and different operating conditions imitating real-life devices found in actual distribution systems. In contrast to aggregate models developed in the literature, this work inherently allows devices' heterogeneity, captures the outside temperature variations, and provides control access for both set-points and devices' status. In this paper, the scope of TCLs evaluation is providing long-term services such as load-shifting and sustained load-reductions. Simulation results have shown that 1000 heterogeneous TCLs represented by air-conditioners are able to achieve both services. However, consequences on customers' comfort are expected proportional to the extracted service. For instance, a sustained average power reduction of 319 KW causes an average set-point increase of 3.8 ° F. [ABSTRACT FROM AUTHOR]

We report on optical properties of undoped ZnO and Boron doped ZnO (B-ZnO) thin films prepared by sol-gel dip coating technique. Optical transmittance and reflectance were investigated to get a deeper insight into the optical properties and optoelectronic behavior of B-ZnO thin films. The main aim of this work is to resolve the major challenge facing researchers working on thin films of finding the proper physical model that explains the correlations between the film thickness and the optical band gap. We propose a novel derived mathematical model to interpret the experimental transmittance as a function of wavelength (λ) of amorphous and crystal semiconductors and dielectric thin films. The proposed model reproduces the values of the optical band gap energy of the investigated thin films that are in good agreement with Tauc plot method and other optical models. We found that optical band gap of B-ZnO has decreased from 3.434 eV to 3.256 eV as boron concentration in ZnO films is increased from 0% to 10%. In addition, we found that Urbach energy values have drastically decreased as the concentration of boron introduced into ZnO thin films was increased. We implemented the newly derived model to reproduce optical properties such as the refractive index dispersion and oscillator parameters that were investigated using other different optical models such as Cauchy, Wemple Di-Domenico, Sellemeier, Spitzer-Fan and Drude. Our results indicate that optical parameters of (B-ZnO) thin films are strongly dependent on boron concentration. [ABSTRACT FROM AUTHOR]

Sol-gel technique is used to synthesize as-grown zinc oxide (ZnO) and iron-nickel (Fe-Ni) co-doped ZnO thin films deposited on glass substrates using dip coating technique. The structural properties and crystal imperfections of as-prepared thin films are investigated. We performed the structural analysis of films using X-ray diffraction (XRD). The XRD analysis reveal that the as-prepared films exhibit wurtzite structure. Furthermore, XRD-line profile analysis is performed to study the correlation between structural properties and imperfections of the nanocomposite thin films. The crystallite size and microstrains parameters are predicted using the Williamson–Hall method. We found that the crystallites size increases as the co-doped (Fe-Ni) concentration is increased. However, microstrains of the nanocomposite films decreases as (Fe-Ni) concentration is increased. The optical properties of the (Fe-Ni) co-doped nanocomposite films are investigated by performing UV-Vis (250 nm–700 nm) spectrophotometer measurements. We found that as the (Fe-Ni) concentration level is steadily increased, transmittance of the undoped ZnO thin films is decreased. Remarkably, refractive index of undoped ZnO thin films is found to exhibit values extending from 1.55 to1.88 that would increase as (Fe-Ni) concentration is increased. [ABSTRACT FROM AUTHOR]