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In this article, a novel ultrahigh voltage gain dc–dc boost converter with expandable diode-capacitor voltage multiplier (VM) cells is presented. The diode-capacitor VM cells and coupled inductors are employed in the presented topology to provide a higher voltage gain. Also, the main and the auxiliary power switches of the presented converter operate with zero voltage switching. The coupled inductors' leakage inductances control the rates of the current drop in the voltage multiplier diodes, which decreases their reverse recovery losses markedly. Moreover, the voltage stresses on all capacitors and semiconductors are reduced significantly. In this converter, the number of voltage multiplier cells, and the coupled inductors turn ratios provide three degrees of design freedom. These degrees of freedom are used to set the desired voltage stresses of the semiconductors within the desired range and to provide a high voltage gain at optimum duty cycles. The design and theoretical analysis of the presented converter are discussed. Finally, the performance of the presented converter is validated using a 500 W, 40 V/380 V laboratory prototype converter, and the experimental results confirm the theoretical calculation.

The introduction of doubly fed induction generators (DFIGs) has facilitated the utilization of wind energy to a great extent and constituted distributed generation (DG) systems in remote places. Therefore, long transmission lines are required to interconnect with the utility grid and, consequently, different short-circuit faults interrupt this transmission. Use of different fault current limiters (FCLs) minimizes the effect of faults and allows normal operation with minimum interruption in power flow. In this study, a series-parallel resonance-type fault current limiter (SPRFCL) is presented for enhancing the low-voltage ride-through (LVRT) capability of DFIG-based wind farms. The SPRFCL preserves the nominal voltage and power quality within the permissible limit during normal operation and during disturbances irrespective of the type of fault. The effectiveness of the proposed SPRFCL is validated by simulating both symmetrical and asymmetrical faults. Alongside the SPRFCL, two state-of-the-art FCLs—the parallel resonance-type fault current limiter (PRFCL) and the capacitive bridge-type fault current limiter (CBFCL)—are considered to investigate and compare the relative performances. Several graphical and numerical studies assure the efficacy of the proposed SPRFCL in wind farm application in multiple aspect. Moreover, the stunning total harmonic distortion (THD) values with the proposed technique signifies the excellency over its competitors. Additionally, the sub-synchronous resonance (SSR) analysis confirms the supremacy of SPRFCL for series compensated lines.

Electrification of rural islands is still a major challenge for many countries due to the inadequate and inchoate distribution of fossil fuels in conventional grid system. Even if these problems are eliminated, the high electricity cost makes rural people ineligible for electricity services. In addition to tackling environmental pollution, microgrids (MG) could be the appropriate solution to reduce these economic barriers. Aiming to mitigate the impediment of rural electrification, this study proposes a hybrid microgrid consists of renewable energy sources, natural gas generator, battery storage, and electric vehicle charging station where a case study is performed with and without wind turbine as a renewable energy source. The MG is simulated for two coastal areas of Bangladesh, and sensitivity analysis has taken place by varying the load demand. MG without wind energy is found more affordable than MG with wind energy. The winning case configuration estimated the cost of energy of 0.157$ and the carbon dioxide (CO2) emission of 6666.47 kg/y, which is a betimes step in ensuring pollution-free power supply as well as electrification in powerless areas. The results lead to the conclusion that the proposed model can provide affordable alongside eco-friendly electrification.

High frequency magnetic-link (HFML) is widely used in medium/high voltage power electronic converters for various applications. In this paper, a 440 V, 100 kHz compact HFML is designed with ANSYS/Maxwell software environment where the amorphous material is used as the magnetic core. The HFML is analyzed with solid toroidal cores with various winding configurations. A comparison of the HFML is performed for both the conventional copper winding and superconducting winding. Yttrium barium copper oxide material-based superconductor tape is applied to obtain the maximum power of the HFML. In addition, suitable winding configuration is selected to ensure possible maximum electrical power transfer while minimizing the magnetic saturation in the core. Simulation results show the electrical parameters of the HFML along with its magnetic flux density and power. The proposed HFML design with distributed winding is validated experimentally with a small-scale prototype where it is found that the maximum power transfer capability is increased more than 48% than that of the conventional one.

This paper proposes a capacitive bridge-type superconducting fault current limiter (CB-SFCL) to address the most concerning issue with the grid connected hybrid power system by improving the transient performance. The hybrid system incorporates a doubly fed induction generator (DFIG) based wind farm, a solar photovoltaic (PV) system and a synchronous generator (SG) based power system. The CB-SFCL incorporates a high temperature superconductor (HTS) along with a power capacitor to provide adequate reactive power support before and after the fault. The capacitor is kept inactive during normal operation by a control circuit to ensure seamless operation. During fault, the capacitor gets connected in series with the HTS and suppress the fault current. The performance of the CB-SFCL is investigated by proper graphical and mathematical analyses and conclusions are obtained by comparing them with that of the conventional bridge-type superconducting fault current limiter (BSFCL) and the capacitive bridge-type fault current limiter (CBFCL). The analyses support the theoretical superiority of the CB-SFCL over the BSFCL and the CBFCL by a satisfying margin.

Solar energy is a potential clean source of energy to meet our thermal and electrical energy demands but its penetration is hindered by the factors such as intermittency of solar radiation, lower thermal efficiency, and capital requirement for the solar energy systems. Improving the thermal performance of the solar collectors and effectively collecting the thermal energy from photovoltaic panels can pave the way to promote clean energy utilization. Heat pipe, being a passive energy system with a high heat transfer rate ability, can aid in ameliorating the performance of solar collectors as well as photovoltaic panels. This review study is proposed to discuss the theoretical and experimental aspects of the design and integration of heat pipes with various solar applications including solar thermal, freshwater production, and photovoltaic-thermal systems. In addition, numerical models relevant to heat pipe and solar energy systems are highlighted. An elaborate analysis of various influencing factors on the thermal performance of heat pipe integrated solar energy systems is also presented. The critical observations from experimental aspects are elucidated, and the future scope of heat pipe systems are also substantiated. This review encourages the selection of a particular heat pipe and the heat transfer enhancement method to attain higher energy conversion rate and the productivity corresponding to various solar energy systems.

In this paper, a non‐isolated coupled‐inductor high step‐down converter with extended duty cycle is proposed, which operates under discontinuous conduction mode. The soft‐switching condition is provided for all the switches and diodes, which results in the reduction of the switching losses, and the losses related to diode reverse recovery problem. Also, a self‐drive circuit is used to automatically generate the ON and OFF gate signals of the synchronous rectifier, which recovers the gate energy and reduces the complexity. In this topology, only a single coupled inductor is proposed which decreases the size and cost of the proposed converter. A 200 W laboratory prototype is implemented, and its results confirm the validity of theoretical analysis and advantages of the proposed converter over previous structures.

Human beings around the globe have been suffering from a devastating novel pandemic and public health emergency, coronavirus disease 2019 (COVID-19), for more than one and a half years due to the deadly and highly pathogenic severe acute respiratory coronavirus 2 (SARS-CoV-2) infection worldwide. Notably, no effective treatment strategy has been approved for the complete recovery of COVID-19 patients, though several vaccines have been rolled out around the world upon emergency use authorization. After the emergence of the COVID-19 outbreak globally, plenty of clinical investigations commenced to screen the safety and efficacy of several previously approved drugs to be repurposed against the SARS-CoV-2 pathogen. This concise review aims at exploring the current status of the clinical efficacy and safety profile of several antiviral medications for the treatment of patients with COVID-19 and other respiratory complications caused by SARS-CoV-2 infection. The paper covers all kinds of human studies (January 2020 to June 2021) except case reports/series to highlight the clear conclusion based on the current clinical evidence. Among the promising repositioned antivirals, remdesivir has been recommended in critical conditions to mitigate the fatality rate and improve clinical conditions. In addition, boosting the immune system is believed to be beneficial in treating COVID-19 patients, so interferon type I might exert immunomodulation through its antiviral effects by stimulating interferon-stimulated gene (ISG). However, more extensive clinical studies covering all ethnic groups globally are warranted based on current data to better understand the clinical efficacy of the currently proposed repurposed drugs against COVID-19.

Reading analysis can relay information about user's confidence and habits and can be used to construct useful feedback. A lack of labeled data inhibits the effective application of fully-supervised Deep Learning (DL) for automatic reading analysis. We propose a Self-supervised Learning (SSL) method for reading analysis. Previously, SSL has been effective in physical human activity recognition (HAR) tasks, but it has not been applied to cognitive HAR tasks like reading. We first evaluate the proposed method on a four-class classification task on reading detection using electrooculography datasets, followed by an evaluation of a two-class classification task of confidence estimation on multiple-choice questions using eye-tracking datasets. Fully-supervised DL and support vector machines (SVMs) are used as comparisons for the proposed SSL method. The results show that the proposed SSL method is superior to the fully-supervised DL and SVM for both tasks, especially when training data is scarce. This result indicates the proposed method is the superior choice for reading analysis tasks. These results are important for informing the design of automatic reading analysis platforms.

This article proposes a multiport magnetic bus (MMB)-based novel wind-wave hybrid ocean energy technology (HOET). The ocean waves are mixed frequency waves and generate unique frequency spectra. The wave energy converters (WECs) connected to the linear permanent magnet generators generate voltages with varying amplitudes and frequencies due to the variation of the wind speed and direction. The proposed MMB of the proposed compact wind-wave HOET can provide galvanic isolation and allow a high-frequency operation of the MMB. A decoupled voltage and current control architecture is proposed to regulate the voltage and current waveforms in the high-frequency inverter modules of the MMB that can integrate multiple WECs. An Archimedes wave swing-based conversion method is utilized for the modeling and the design of the proposed WEC. A damping controller is designed to extract the maximum power from the irregular waves. For the wind-energy generation system, a doubly fed induction generator-based technology is considered. The simulation and the laboratory-scale experimental results validate the potential and the applicability of the proposed wind-wave HOET as an effective means to harness ocean energy.

Drug-drug interaction is a notable concern among physicians when prescribing multi-therapy to the patients as concomitant administration of multi-drugs might cause unexpected adverse drug reactions. The main objective of this research is to predict a potential drug-drug interaction between two frequently used drugs by diabetic patients, an antidiabetic drug (linagliptin) and a proton pump inhibitor (rabeprazole sodium). Here, several in vitro techniques, including thermal (melting point, thermogravimetric analysis [TGA]), morphological (scanning electron microscopy [SEM] and X-ray powder diffraction [XRPD] analysis), highly sophisticated synchronous fluorescence, and in silico methods were applied to anticipate the potential drug-drug interaction between these stated drugs quickly. The melting point and TGA study revealed thermochemical properties, thermal stability profiles, and degradation patterns upon temperature rising of the formed complex and these precursor drugs. The SEM and XRPD have provided the morphological changes like particle shape and size distribution of the desired molecule that might be caused due to the potential drug-drug interactions. Besides, the drastic reduction of the quenching rate constant of linagliptin during interaction with bovine serum albumin in synchronous fluorescence also endorsed the potential drug-drug interaction. Furthermore, the drug-receptor docking analysis demonstrated that the binding affinity of the precursor ligands might be reduced due to the predicted drug-drug interaction. However, the current evidence warrants extensive investigation to confirm the above-stated potential drug-drug interaction in the larger animal model. Finally, clinical data need to be closely monitored during the treatment of diabetic patients prescribed with linagliptin and rabeprazole sodium.

This article presents an optimal design procedure and development of a multiwinding high-frequency transformer for application in the cascaded modules of a grid-connected modular multilevel inverter. The transformer should be designed for a certain value of inductances and high efficiency because of its effect on the performance of the entire modular structure. To realize such an accurate design procedure with minimum computation effort and design complexity, a three-stage method including initial design using lumped-parameter modeling, optimal design using particle swarm optimization with reluctance network modeling and finite element method is proposed. An amorphous-based toroidal core transformer is designed, developed, and tested under different load conditions and frequency range to validate the design procedure.

Electrolytic capacitors are commonly used as a dc-link in power electronics circuits and especially in renewable energy system applications. However, their life expectancy and reliability can be severely affected by the ripple current causing increased thermal stresses. Further, the switching of power electronics and the presence of harmonic components can increase the ripple current that may shorten the lifetime of the electrolytic capacitors. This article proposes the use of a shunt active dc filter to regulate the dc capacitor current to a near-constant value and hence mitigate the events that can reduce the capacitor life expectancy. The proposed filter reduces the ripple in the capacitor dc current irrespective of the frequency spectrum of the ripple. The design and the operation principles of the proposed filter are presented and discussed. The results from the simulation and experimental studies demonstrate the effectiveness of the proposed technique.

This paper introduces eight novel interleaved non-isolated dc-dc converters with ultra-high step-up and zero voltage switching (ZVS) capabilities for renewable energy systems. To increase the voltage gain, the proposed converters benefit coupled inductors, high-frequency (HF) transformer, and voltage multiplier (VM) techniques. In comparison to other converters, which just benefit coupled inductors or HF transformers, these combinations of the techniques make an additional degree of freedom to achieve high voltage gains (more than 25 without extreme duty cycle). Besides, two active clamp circuits, including two stages of switch-capacitor VM cells, not only increase the voltage gain of the proposed converters but also act as an auxiliary circuit to provide ZVS. Moreover, the stored energy in the leakage inductances is absorbed and passed to the output by the clamp capacitors. The input current ripple is reduced by applying the interleaved technique. The voltage stresses across the power switches are clamped to lower values and can be controlled by the turn ratios of the coupled inductors and the HF transformer. The theoretical performance of the proposed converters is fully explained. Also, the proposed converters are compared with more than twenty latest interleaved high step-up and ultra-high step-up dc-dc converters. Finally, a 1 kW, 20 V/500 V laboratory prototype is built to prove the advantages of the proposed converters.

Electroencephalography (EEG) captures the electrical activities of human brain. It is an easy and cost effective tool to characterize motor imager (MI) task used in brain computer interface (BCI) implementation. The MI task is represented by short time trial of multichannel EEG. In this paper, the raw EEG trial is regenerated using narrowband signals obtained from individual channel. Each channel of EEG trial is decomposed into a set of subband signals using multivariate discrete wavelet transform. The selected subbands are organized in two different ways namely vertical arrangement of subbands (VaS) and horizontal arrangement of subbands (HaS) to regenerate the trials. The features are extracted from each of the arrangements using common spatial pattern (CSP). An optimum number of features are used to classify the motor imagery tasks represented by EEG trials. The effectiveness of two classifiers- linear discriminant analysis (LDA) and support vector machine (SVM) are studied. The performances of the proposed methods are evaluated using publicly available benchmark datasets. The experimental results show that it performs better than the recently developed algorithms.

To maximize renewable energy usage to combat climate change, the penetration of electric vehicles (EVs) has increased significantly in developed countries. This can cause serious power quality issues, such as increased voltage imbalance and neutral currents, which severely impact the operation of power systems. Although the power quality issue is not a new problem, it requires an improved strategy for the growing penetration of photovoltaic solar energy and EVs in low-voltage distribution grids and their uncoordinated operation. This article presents a new control strategy to reduce the number of coordinated EVs to mitigate voltage unbalance and compensate for the neutral current. The proposed control strategy consists of two controllers arranged in a hierarchical structure with the central controller at the top layer and the local controller at the bottom layer. It is evident that the proposed control strategy reduces the number of EVs that need to be coordinated, and further, EV coordination is not required if the grid imbalance is less. This new hierarchical control strategy can improve power quality and reduce data processing overhead and computational complexity.

Recently, electroencephalogram-based emotion recognition has become crucial in enabling the Human-Computer Interaction (HCI) system to become more intelligent. Due to the outstanding applications of emotion recognition, e.g., person-based decision making, mind-machine interfacing, cognitive interaction, affect detection, feeling detection, etc., emotion recognition has become successful in attracting the recent hype of AI-empowered research. Therefore, numerous studies have been conducted driven by a range of approaches, which demand a systematic review of methodologies used for this task with their feature sets and techniques. It will facilitate the beginners as guidance towards composing an effective emotion recognition system. In this article, we have conducted a rigorous review on the state-of-the-art emotion recognition systems, published in recent literature, and summarized some of the common emotion recognition steps with relevant definitions, theories, and analyses to provide key knowledge to develop a proper framework. Moreover, studies included here were dichotomized based on two categories: i) deep learning-based, and ii) shallow machine learning-based emotion recognition systems. The reviewed systems were compared based on methods, classifier, the number of classified emotions, accuracy, and dataset used. An informative comparison, recent research trends, and some recommendations are also provided for future research directions.

© 2007-2012 IEEE. The increasing penetration of distributed generations (DGs) and electric vehicles (EVs) offers not only several opportunities but also introduces many challenges for the distribution system operators (DSOs) regarding power quality. This article investigates the network performances due to uncoordinated DG and EV distribution. It also considers power quality-related performances such as the neutral current, energy loss, voltage imbalance, and bus voltage as a multiobjective optimization problem. The differential evolution optimization algorithm is employed to solve the multiobjective optimization problem to coordinate EV and DG in a distribution grid. This article proposed a method to coordinate EV and DG distribution. The proposed method allows DSOs to jointly optimize the phase sequence and optimal dispatch of DGs to improve the network's performance. If the network requires further improvement, the EV charging or discharging rate is coordinated for a particular location. The efficacy of the proposed method is tested in an Australian low-voltage distribution grid considering the amount of imbalance due to higher penetration of DG and EV. It is observed that the proposed method reduces voltage unbalance factor by up to 98.24%, neutral current up to 94%, and energy loss by 59.45%, and improve bus voltage by 10.42%.

Functional near-infrared (fNIR)-based motor imagery (MI) classification is an interesting challenge for the brain–computer interface (BCI) implementation. The success of the fNIR-BCI mostly depends on the classification performance. Usually, machine learning-based complex algorithms are used to classify the fNIR-MI data which often proves impractical to program in small hardware. Therefore, the practical small and low price hardware-based BCI system demands simple algorithm that can provide satisfactory classification accuracy. This paper proposes an fNIR-MI data classifier utilizing the multiple linear regression model (MLRM). In this work, two-class fNIR-MI data were acquired for feature extraction and classification. The predictive model for classification was prepared by the proposed method. Additionally, the same dataset was classified by the widely used classification method—support vector machine (SVM) and linear discriminant analysis (LDA). The results reveal that the proposed multiple linear regression model-based classifier (MLRMC) shows almost equivalent results compared to the SVM and LDA on the same features, although MLRM is a much simpler algorithm than that of the others. Therefore, the outcomes of this research work claim that the proposed MLRMC is a good choice as a classifier for fNIR-like simple signal classification.

Background: Studies of the pharmacotherapeutic pattern on schizophrenic patients are few clinical therapy is one of the bases for the assessment of the treatment of Schizophrenia. Objectives: This study aimed to analyze the demographics of these patients along with current trends of drug prescribing practice in Bangladesh. Methods: This survey was conducted over 6 months at the National Institute of Mental Health, Dhaka, Bangladesh, and Bangabandhu Sheikh Mujib Medical University, Dhaka, Bangladesh. A total of 250 subjects were investigated and their prescriptions were verified to obtain necessary information regarding pharmacotherapy. Non-cooperation and negative consent lead to exclusion from the study sample. Results: Out of 250 patients, the frequency of female patients was higher (54.4%) than that of the male patients (45.6%). Therefore, the gender ratio was female-biased. We found that 63.6% of the patients suffering from schizophrenia were above 25 years old. The treatment approach of schizophrenia includes pharmacotherapy and or psychotherapy. In this study, we found that the highest medication used in the treatment was antipsychotic drugs (n=388, 46.58%) followed by anticholinergic drugs (n=217, 26.05%) and anxiolytic drugs (n=172, 20.65%). Conclusion: The overall prescribing method for schizophrenia in Bangladesh complies with the authorized practiced guidelines and treatment pattern of the developed countries as well as authorized guidelines.

Bearing failure often occurs in doubly fed induction generator (DFIG)-based wind turbines which are usually subject to electrical corrosion effects. Fault diagnosis method based on electrical signals has been paid much attention as the method is noninvasive and cost-effective. This paper describes the use of the modulation signal bispectrum (MSB) detector for diagnosing bearing faults in DFIGs of wind turbines. The major theoretical principles involved with the MSB method are presented and it is shown how the amplitude and phase relationships of the stator current signals caused by torque oscillations can be effectively revealed. Since the MSB result is obtained by averaging results from each record, overlapped segmentation is proposed to improve computational accuracy with limited data. On-site experimental results obtained from 1.5-MW wind turbines corroborate that these faults can be detected, in the current MSB, by the identification of a spectral component at the fundamental frequency and the characteristic frequency. Compared with the other data processing methods based on second-order cumulants, the MSB detector can avoid misdiagnosis by containing phase information of stator current. Owing to relatively high accuracy, the proposed current-based MSB method can identify incipient bearing corrosion failure in DFIG-based wind turbines without additional sensors, which also has great potential in other industrial applications.

Presurgical investigations for categorizing focal patterns are crucial, leading to localization and surgical removal of the epileptic focus. This paper presents a machine learning approach using information theoretic features extracted from high-frequency subbands to detect the epileptic focus from interictal intracranial electroencephalogram (iEEG). It is known that high-frequency subbands (>80 Hz) include important biomarkers such as high-frequency oscillations (HFOs) for identifying epileptic focus commonly referred to as the seizure onset zone (SOZ). In this analysis, the multi-channel interictal iEEG signals were splitted into segments and each segment was decomposed into multiple high-frequency subbands. The different types of entropy were calculated for each of the subbands and the sparse linear discriminant analysis (sLDA) was applied to select the prominent entropy features. Due to the imbalance of SOZ and non-SOZ channels in iEEG data, the use of machine learning techniques is always tricky. To deal with the imbalanced learning problem, an adaptive synthetic oversampling approach (ADASYN) with radial basis function kernel-based SVM was used to detect the focal segments. Finally, the epileptic focus was identified based on detection of focal segments on SOZ and non-SOZ channels. Eight patients were examined to observe the efficiency of the automatic detector. The experimental results and statistical tests indicate that the proposed automatic detector can identify the epileptic focus accurately and efficiently.

The high-frequency magnetic linked solid-state transformer (SST) has the tremendous potential to solve the issues related to interfacing the grid with renewable energy sources and the non-linear loads as well as to provide the additional control functionalities. Several H-bridges are usually connected in series for the medium voltage grid integration of the SST. The number of the dual active bridge modules and the number of high-frequency magnetic links (HFMLs) usually increase with the increase of the number of series connected H-bridges. This increases the possibility of parameter mismatches and system instability. Therefore, the multiple-active bridge (MAB) dc-dc converter concept has been proposed for next generation SST, where multiple H-bridges share the same HFML. This configuration reduces the number of HFML and increases the cross-coupling power transfer capability. However, the design process of the MAB converter involves a multi-physics research in the field of power electronics, magnetics, switching, control, and energy management. In this paper, the detailed analytical modelling and the control technique of the silicon carbide (SiC)-based MAB converter are investigated. The small and large signal average models of the converter are developed and a voltage balance controller is designed for the better understanding of the controller characteristics. The effect of connecting several H-bridges to a single HFML is investigated in detail. Moreover, to reduce the switching loss, the converter topology adopts the SiC-based switching devices instead of the traditional silicon (Si)-based devices and the nanocrystalline magnetic material is used for the HFML design due to its low specific core loss. A scaled down laboratory prototype of the MAB converter is implemented in the laboratory and can be utilized as a basic building block for the next generation SST.

Nowadays, the grid-connected solar photovoltaic (PV) system has been drawing significant attention due to the rapid development and the decreasing cost of solar panels. The efficiency and reliability of a grid-connected PV system mainly depend on the power conversion system and the control strategy. This article presents a new magnetic-linked 7-level active neutral point clamped (ANPC) converter, which requires less number of flying capacitors and also reduces the control complexity of the grid-connected PV system. The proposed converter topology utilizes the input dc bus voltage better than the traditional ANPC multilevel converter and also provides galvanic isolation to the grid-connected PV systems. The lack of galvanic isolation is one of the biggest problems faced by the power frequency transformerless grid-connected PV systems. The proposed magnetic-linked power converter is also validated through simulations in MATLAB/Simulink and through tests in a laboratory test platform.

Accidental falls are a major source of loss of autonomy, deaths, and injuries among the elderly. Accidental falls also have a remarkable impact on the costs of national health systems. Thus, extensive research and development of fall detection and rescue systems are a necessity. Technologies related to fall detection should be reliable and effective to ensure a proper response. This article provides a comprehensive review on state-of-the-art fall detection technologies considering the most powerful deep learning methodologies. We reviewed the most recent and effective deep learning methods for fall detection and categorized them into three categories: Convolutional Neural Network (CNN) based systems, Long Short-Term Memory (LSTM) based systems, and Auto-encoder based systems. Among the reviewed systems, three dimensional (3D) CNN, CNN with 10-fold cross-validation, LSTM with CNN based systems performed the best in terms of accuracy, sensitivity, specificity, etc. The reviewed systems were compared based on their working principles, used deep learning methods, used datasets, performance metrics, etc. This review is aimed at presenting a summary and comparison of existing state-of-the-art deep learning based fall detection systems to facilitate future development in this field.

This paper presents the design of an extended parameterisations of H ∞ controller for off grid operation of a microgrid. The microgrid consists of distributed generation units, filters and local loads. The filters are used to achieve accurate sinusoidal output voltage. However, loads which are connected to the microgrid are parametrically uncertain. Hence, it undergoes with unknown loads uncertainties. These unknown loads may create unknown loads harmonics, non-linearities which may reduce the voltage and current profile of the microgrid. As a result, the sudden rise and fall of voltage current profile damages the domestic and commercial loads. The proposed controller provides robust stability against various unknown loads and uncertainties. The design of the controller is presented using linear matrix inequality approach and satisfies the Lyapunov stability criterion. Moreover, it provides lower closed-loop H ∞ norm and has better tracking accuracy than other. For justification, several load conditions have been tested in MATLAB/SimPowerSystem Toolbox to ensure the robust stability of the proposed controller. All the results presented in the paper indicate high performance of the controller.

Achieving a reliable classification of motor imagery (MI) tasks is a major challenge in brain-computer interface (BCI) implementation. The set of relevant and discriminative features plays an important role in the classification scheme. This paper presents a supervised approach to select discriminative features for the enhancement of MI classification using multichannel electroencephalography (EEG) signal. The dimension of multiband feature space is reduced using the feature selection method. Each trial of the multichannel EEG signal representing MI tasks is decomposed into a finite set of narrowband signals. The common spatial pattern-based features are extracted from each subband. The features obtained from the multiple subbands are combined to derive a high-dimensional feature vector. The neighborhood component analysis-based feature selection method is implemented to select the features that are relevant in performing an accurate classification. It is a nearest-neighbor-based approach to learn the feature weights with regularization by maximizing the average leave-one-out classification accuracy over the labeled training data. The selected features are used to train the support vector machine for classification. The features relatively irrelevant to the classification task are discarded, yielding a reduction of feature dimension. The evaluation of the proposed method is performed using BCI Competition III dataset 4a and IV dataset 2b. Both are publicly available datasets and are used as types of benchmark data to evaluate the MI classification algorithm to implement BCI. The obtained simulation results confirm the superiority of the proposed method compared to the recently developed algorithms.

Oceanic wave energy extraction through electrical generator is one of the most interesting topics in the field of power engineering. Almost all the existing relevant review paper focus on electrical generator with the working principle of electromagnetic induction or piezoelectric or triboelectric effect. In this paper, all the existing types (based on principle of operation) of electrical generator used for wave power harvesting are discussed. This paper not only covers recent progress in electrical power generation by electro-magnetic induction, piezoelectric generator, and electrostatic induction, but also presents critical comparative review as well where suitable use and weakness of each type of generators are discussed. Moreover, the application of advanced magnetic core, winding, and permanent magnets are discussed with extensive explanation which are not focused in the existing reviews. Various new constructional features of the electrical generators such as split translator flux switching, two-point absorber, triangular coil, dual port linear generator, piezoelectric, triboelectric nanogenerator, etc. are highlighted with principles of operation. It also includes emerging human intervened optimization method for determining optimum shape of generator and cooling system which is necessary to prevent demagnetization of the permanent magnet. Finally, the way of supply the generated electrical power form the generator to load/grid is thoroughly described in a separate section that would be obvious for successful operation. The comparison among all types of generators in terms of output voltage, current, scale of power production, power-frequency characteristics, power density, cascading, and approaches are tabulated in this paper.

Hydrogen has received tremendous global attention as an energy carrier and an energy storage system. Hydrogen carrier introduces a power to hydrogen (P2H), and power to hydrogen to power (P2H2P) facility to store the excess energy in renewable energy storage systems, with the facts of large-scale storage capacity, transportability, and multiple utilities. This work examines the techno-economic feasibility of hybrid solar photovoltaic (PV)/hydrogen/fuel cell-powered cellular base stations for developing green mobile communication to decrease environmental degradation and mitigate fossil-fuel crises. Extensive simulation is carried out using a hybrid optimization model for electric rnewables (HOMER) optimization tool to evaluate the optimal size, energy production, total production cost, per unit energy production cost, and emission of carbon footprints subject to different relevant system parameters. In addition, the throughput, and energy efficiency performance of the wireless network is critically evaluated with the help of MATLAB-based Monte-Carlo simulations taking multipath fading, system bandwidth, transmission power, and inter-cell interference (ICI) into consideration. Results show that a more stable and reliable green solution for the telecommunications sector will be the macro cellular basis stations driven by the recommended hybrid supply system. The hybrid supply system has around 17% surplus electricity and 48.1 h backup capacity that increases the system reliability by maintaining a better quality of service (QoS). To end, the outcomes of the suggested system are compared with the other supply scheme and the previously published research work for justifying the validity of the proposed system.

Renewables such as solar, wind, tide energies are the new route for alternative power sources in spite of fossil fuels or other nonrenewable energies. From the last decade, converting the variable solar dc power to constant ac power has become a challenge. This paper underpins a novel three phase transformer-less grid connected photovoltaic inverter with the control blocks applicable for all types of grid voltage ranges. This novel three-phase inverter topology not only contains very lower reduced leakage current as well as less total harmonic distortion (THD) but also improved power quality. Filter clamping approach has been owned to eliminate extra leakage current through the ground path. Besides unipolar sinusoidal pulse width modulation (SPWM) method has been used to control the power switches. As a result, the common mode voltage has been held to constant, which is also responsible for reducing leakage current to 25.42 mA. The leakage current suppression has been analyzed in details with simulation results. The switches did not affect the efficiency of this inverter so much but reduced the leakage current. Switching losses and conduction losses are very low. The THD of Grid voltage of this proposed topology is recorded 0.94% likewise, grid current THD is measured as 0.42%. To check the performance of this proposed inverter more accurately, input voltage as well as load amount of this inverter have been changed instantly in the middle of simulation. This topology has been implemented and tested via MATLAB/Simulink platform.

Now-a-days, transformer-less grid connected solar photovoltaic (PV) inverters have attracted significant attention due to their special benefits such as higher efficiency, lower cost, and easy set up mechanism. A new transformer-less inverter topology with reduced leakage current and improved performances has been presented in this paper. This inverter circuit has been fabricated with eight semiconductor devices and an LCL filter at the output side. Three switches are used in the common mode voltage (CMV) clamping brunch to enhance inverter efficiency. In the freewheeling brunch, one switch is used so that the leakage current spikes get avoided. In the freewheeling mode common mode voltage is kept constant. While zero voltage state, CMV is clamped to half of the input voltage. The proposed inverter provides only a total of 10.18 mA leakage current. The grid current THD and grid voltage THD are found to be 1.42% and 0.37%, respectively. Unipolar SPWM method has been used to control the power switches. The behavior of the proposed TLPV inverter is verified via simulation analysis performed at MATLAB/Simulink environment.

Now-a-days, renewable energy has considered a potential source of energy to ensure the sustainable developments. Solar energy plays a vital role not only as a renewable energy source but also an environment friendly energy source. Power electronic inverter is commonly used to interconnect solar photovoltaic (PV) systems to power grids or ac loads. From last decades' industries have been developing solar PV inverters to make it transformer-less compact and lightweight, efficient, and grid friendly. This paper underpins a new single phase TLPV inverter topology, reducing extra leakage current, decreasing total harmonic distortion (THD) and improving power quality. Midpoint clamping method is applied to suppress leakage current. Unipolar sinusoidal pulse width modulation technique with zero dead time have been obtained to control the power switches that also helps to reduce the leakage current plus decreases THD by creating lower ripple on the output voltage. Seven super junction insulated gate bipolar transistors are used as the power switches. 10.28 mA leakage current has been recorded for the proposed inverter. The grid voltage and grid current THD have been recorded 0.42% and 1.62%, respectively. Besides 97.28% European efficiency has been found for this proposed H7 topology. The performance of the proposed inverter has been studied via MATLAB/Simulink simulation.

Brain-computer interface (BCI) can be a hope for the people who are not capable of interacting with the external environment. Classification accuracy is a vital issue for implementing BCI system. This study aims to compare the performance of four different machine learning models for fNIRS-based BCI and find out a suitable one. We also present a method of ensemble the models for slight enhancement of accuracy from the best performing model. We use fNIRS data acquired for two different mental tasks- right-hand and lefthand motor imagery movements. The signals were taken from the prefrontal cortex of six healthy subjects using the multichannel fNIRS system. Mean, median, variance, and slope were used as features for classification. Based on the features, four different models -Quadratic discriminant analysis (QDA), Naive Bayes approach, support vector machine (SVM), and Random forest, were used to figure out the best-fit algorithm. Eventually, the average test accuracies are found as 85%, 83%, 87.5%, and 92.5%, respectively. We present a 0.4% enhancement of accuracy through ensemble the models.

Producing an average of five hundred million tweets per date, Twitter has grown as one of the most comprehensive platforms of data interpretation for the researchers. Beforehand, various researches have been conveyed on twitter data i.e., sentimental analysis. Nevertheless, not much research has been performed to classify the tweets in terms of categories so that tweets can be spread as per user preferences. In this research, we started by constructing four comprehensive classes: politics, sports, crime and natural. Next, we implemented our proposed preprocessing model on the raw twitter dataset. After that, we implemented different machine learning techniques (Random Forest, K-Nearest Neighbors, Naive Bayes, Logistic Regression, Decision Tree and Support Vector Machine) to classify the twitter data. Finally, we examined the outcomes with and without preprocessing in terms of sensitivity, specificity, and accuracy. We found that our proposed preprocessing model enhanced the performance of all the machine learning classifiers.

Higher penetration of variable renewable energy sources into the grid brings down the plant load factor of thermal power plants. However, during sudden changes in load, the thermal power plants support the grid, though at higher ramping rates and with inefficient operation. Hence, further renewable additions must be backed by battery energy storage systems to limit the ramping rate of a thermal power plant and to avoid deploying diesel generators. In this paper, battery-integrated renewable energy systems that include floating solar, bifacial rooftop, and wind energy systems are evaluated for a designated smart city in India to reduce ramping support by a thermal power plant. Two variants of adaptive-local-attractor-based quantum-behaved particle swarm optimization (ALA-QPSO) are applied for optimal sizing of battery-integrated and hybrid renewable energy sources to minimize the levelized cost of energy (LCoE), battery life cycle loss (LCL), and loss of power supply probability (LPSP). The obtained results are then compared with four variants of differential evolution. The results show that out of 427 MW of the energy potential, an optimal set of hybrid renewable energy sources containing 274 MW of rooftop PV, 99 MW of floating PV, and 60 MW of wind energy systems supported by 131 MWh of batteries results in an LPSP of 0.005%, an LCoE of 0.077 USD/kW, and an LCL of 0.0087. A sensitivity analysis of the results obtained through ALA-QPSO is performed to assess the impact of damage to batteries and unplanned load appreciation, and it is found that the optimal set results in more energy sustainability.

Modern livestock selection is rapidly condensing the indigenous cattle gene pool. This trend limits the options for future genetic selection to benefit both animal well-being and farmer challenges. Here we reveal the potential of Red Chittagong cattle (RCC), a native genotype of Bangladesh, for tackling these current and pending challenges. Red Chittagong cattle are reddish in color and small in size with mature bulls and cows weighing 342 and 180 kg from birth weights of 16 and 14 kg, respectively. Whilst low mean levels of milk production of 618 L across a 228-day lactation are recorded so are high levels of milk protein (3.8%) and fat (4.8%) with offered feed types typically low in nutritive value, particularly crude protein. However, one in five cows under farm condition yield >1,000 L/lactation. Alongside high levels of milk protein and fat, other key features of this breed include resistance to common diseases and parasites with a high level of adaptation to agro-ecological conditions. As opposed to other indigenous breeds, there is currently high genetic variation in the RCC population, and associated variation in productive and reproductive traits highlighting the opportunity for development through long-term breeding programs alongside improved management conditions. Such efforts would enable this breed to become a global resource for tackling the challenges of modern animal production systems. In addition, further work is required to reveal the demographic distribution of the breed, potential production levels through the provision of improved diets and the mechanisms enabling disease resistance and digestibility of feeds.

Electrical power generation from the oceanic wave is one of the noteworthy footsteps in the field of renewable energy resource based energy conversion system. There are different technologies regarding electricity generation by using linear electrical generators along with the wave energy devices. This paper presents various control technologies of the linear generator that are widely been used for harvesting oceanic wave energy. Several controllers are discussed that are used to control the voltage, current, and power flow of the linear generator. Mathematical models of different controllers are discussed. Connection of the controller with the linear generator, input, output, outcome, and method of the controller with their pros and cons are presented as well. In addition, testing devices and their controls for the linear generator are presented with their schematic diagram. This paper would play vital role to the researcher for the future development of the linear generator for oceanic wave energy conversion.

Novel coronavirus (COVID-19) has become a global problem in recent times due to the rapid spread of this disease. Almost all the countries of the world have been affected by this pandemic that made a major consequence on the medical system and healthcare facilities. The healthcare system is going through a critical time because of the COVID-19 pandemic. Modern technologies such as deep learning, machine learning, and data science are contributing to fight COVID-19. The paper aims to highlight the role of machine learning approaches in this pandemic situation. We searched for the latest literature regarding machine learning approaches for COVID-19 from various sources like IEEE Xplore, PubMed, Google Scholar, Research Gate, and Scopus. Then, we analyzed this literature and described them throughout the study. In this study, we noticed four different applications of machine learning methods to combat COVID-19. These applications are trying to contribute in various aspects like helping physicians to make confident decisions, policymakers to take fruitful decisions, and identifying potentially infected people. The major challenges of existing systems with possible future trends are outlined in this paper. The researchers are coming with various technologies using machine learning techniques to face the COVID-19 pandemic. These techniques are serving the healthcare system in a great deal. We recommend that machine learning can be a useful tool for proper analyzing, screening, tracking, forecasting, and predicting the characteristics and trends of COVID-19.

In this paper, a Kretschmann configuration based surface plasmon resonance (SPR) sensor is numerically designed using graphene-MoS2hybrid structure TiO2-SiO2nano particles for formalin detection. In this design, the observations of SPR angle versus minimum reflectance and SPR frequency (FSPR) versus maximum transmittance (Tmax) are considered. The chitosan is used as probe legend to perform reaction with the formalin (40% formaldehyde) which acts as target legend. In this paper, both graphene and MoS2are used as biomolecular acknowledgment element (BAE) and TiO2as well as SiO2bilayers is used to improve the sensitivity of the sensor. The numerical results show that the variation of FSPR and SPR angles for inappropriate sensing of formalin is quite insignificant which confirms the absence of formalin. On the other hand, these variations for appropriate sensing are considerably significant that confirm the presence of formalin. At the end of this article, the variation of sensitivity of the proposed biosensor is measured in corresponding to the increment of a refractive index with a refractive index step 0.01 refractive index unit (RIU). In inclusion of TiO2-SiO2bilayers with graphene-MoS2, a maximum sensitivity of 85.375% is numerically calculated.

If the batteries are charged by clean renewable energy sources, electric vehicles (EVs) can have zero gas emission, contributing greatly toward the preservation of the green environment. In a smart micro-grid, EVs together with other distributed energy storage units can be used to supply electricity to the loads during the peak hours so as to minimize the effects of the load shedding and improve the quality of electricity. To achieve these goals, an isolated hybrid multi-port converter is required to control the power flows and balance the energy among renewable energy sources, EVs, and the grid. In this paper, a new isolated multi-port converter is proposed, which can control the power flow in multiple directions. The converter is modeled in the matlab/Simulink software environment and this validates the technology with a laboratory prototype test platform. The modeling, implementation, and results are discussed comprehensively.

This paper presents an amorphous alloy magnetic-bus-based neutral point clamped (NPC) converter for grid-connected renewable generation systems. In the proposed system, the amorphous alloy high-frequency high-power density multi-winding magnetic bus generates balanced dc supplies for the five-level (5L) NPC converter for high-quality power conversion. Compared to the traditional NPC converter topologies, the proposed magnetic-bus-based architecture does not require any control circuit for voltage balancing of the series connected capacitors. The magnetic bus inherently overcomes galvanic isolation issues and may reduce the size of the boosting inductor. In this paper, a finite control set model predictive control algorithm is derived to control the grid-connected 5L-NPC inverter for multilevel voltage synthesizing, while achieving the user-defined active and reactive power values. To verify the proposed concept, a simulation model is developed and analyzed in MATLAB/Simulink environment. To validate the technology, a scale d-down prototype test platform is developed in the laboratory with silicon carbide switching devices, which achieves high blocking voltage, low power dissipation, high switching frequency, and high-temperature operation. Based on the simulation and the experimental results, it is expected that the proposed converter will have a great potential for widespread application in renewable generation systems including superconducting generator-based wind turbines.

More than 170 countries have already established renewable energy targets to meet their national increasing energy demand and also to keep their environment sustainable. Due to a number of features, the use of the multi-megawatt solar photovoltaic (PV) power plants is becoming the preferred choice for escalating and updating the power systems all over the world. Moreover, the solar PV power plant is also the first choice for meeting rapidly growing demands; as it can be installed relatively quickly, say in 6–12 months, compared to that of the fossil-fuel-based plants that may require more than 4–5 years. The traditional low-voltage (288–690 V) converter-based system requires a step-up transformer and a line filter to interconnect a solar PV power plant with medium-voltage grids. Recently, the use of medium-voltage converters without a step-up transformer and a line filter has become more attractive for direct medium-voltage grid integration of solar PV power plants. This paper aims to review the necessity and the technical challenges in developing medium-voltage power electronic converters, including the converter circuit topologies and control techniques used in the development of medium-voltage converters to interconnect solar PV power plants to medium-voltage grids directly. In this paper, a comprehensive review of the current research activities and the possible future directions of research to develop medium-voltage converter technologies to provide for a cost-effective grid integration of solar PV power plants are presented.

This paper focuses on a review of the state of the art of future power grids, where new and modern technologies will be integrated into the power distribution grid, and will become the future key players for electricity generation, transmission, and distribution. The current power grids are undergoing an unprecedented transformation from the original design, changing the way how energy has been produced, delivered, and consumed over the past century. This new energy era includes the integration of renewable sources such as wind and solar, supported by the distributed or community energy storage, to power distribution grids through innovative high-frequency magnetic links and power-electronic converters. The use of emission free transportation, such as electric vehicles, and energy efficient technologies, such as superconducting generators and storage systems, are also rapidly emerging and will be integrated into the power grids in the foreseeable future. However, it is necessary to reconsider the current paradigms of system analysis and plan with a focus on how to achieve the most flexible, efficient, and reliable power grid for the future - the one that enables operation in a domain which is very different than the current one to deliver the services to consumers at an affordable cost.

A medium-voltage ac network is commonly used to interconnect wind turbines to the offshore substation, which can create significant power losses, as the distance between wind turbine and offshore substation can vary from 5 to 9 km. The copper-conductor-based high-voltage dc (HVDC) or high-voltage ac (HVAC) transmission lines are generally used to transmit the electrical power from the offshore substations to the onshore grids. With the recent advancement of the high-temperature superconducting (HTS) cables, it is now possible to replace the traditional HVAC/HVDC transmission lines with a medium-voltage dc (MVDC) cables. A new MVDC converter is proposed in this paper to convert the generator low ac output voltage to MVDC directly. The proposed new technology not only eliminates the requirement of low-frequency heavy and large step-up transformers and line filters, but can also eliminate the voltage conversion stages to allow the entire power transmission networks to use MVDC HTS cables. A compact, lightweight, and efficient high-frequency common magnetic link is used in the proposed converter. The magnetic link can inherently solve the isolation problem. In this paper, a scale-down prototype converter is developed in the laboratory to validate the feasibility of the proposed MVDC converter.

In this study, a novel blended state estimated adaptive controller is designed for voltage and current control of microgrid against unknown noise. The core feature of the microgrid (MG) is its ability to integrate more than one distributed energy resource into the main grid. The state of a microgrid may deteriorate due to many reasons, for example malicious cyber-attacks, disturbances, packet losses, etc. Therefore, it is necessary to achieve the true state of the system to enhance the control requirement and automation of the microgrid. To achieve the true state of a microgrid, this study proposes the use of an algorithm based on the unscented kalman filter (UKF). The proposed state estimator technique is developed using an unscented-transformation and sigma-points measurement technique capable of minimizing the mean and covariance of a nonlinear cost function to estimate the true state of a single-phase, three-phase single-source and three-phase multi-source microgrid system. The advantage of the proposed estimator over using extended kalman filter (EKF) is investigated in simulations. The results demonstrate that the use of the UKF estimator produces a superior estimation of the system compared with the use of the EKF. An adaptive PID controller is also developed and used in system conjunction with the estimator to regulate its voltage and current against the number of loads. Deviation in load parameters hamper the function of the MG system. The performance of the developed controller is also evaluated against number of loads. Results indicate the controller provides a more stable and high-tracking performance with the inclusion of the UKF in the system.