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Agricultural pests cause between 20 and 40 percent loss of global crop production every year as reported by the Food and Agriculture Organization (FAO). Therefore, smart agriculture presents the best option for farmers to apply artificial intelligence techniques integrated with modern information and communication technology to eliminate these harmful insect pests. Consequently, the productivity of their crops can be increased. Hence, this article introduces a new mobile application to automatically classify pests using a deep-learning solution for supporting specialists and farmers. The developed application utilizes faster region-based convolutional neural network (Faster R-CNN) to accomplish the recognition task of insect pests based on cloud computing. Furthermore, a database of recommended pesticides is linked with the detected crop pests to guide the farmers. This study has been successfully validated on five groups of pests; called Aphids, Cicadellidae, Flax Budworm, Flea Beetles, and Red Spider. The proposed Faster R-CNN showed highest accurate recognition results of 99.0% for all tested pest images. Moreover, our deep learning method outperforms other previous recognition methods, i.e., Single Shot Multi-Box Detector (SSD) MobileNet and traditional back propagation (BP) neural networks. The main prospect of this study is to realize our developed application for on-line recognition of agricultural pests in both the open field such as large farms and greenhouses for specific crops.

The intermittent nature associated with photovoltaic (PV) generation is a challenging problem for the optimal planning and efficient management in smart grids. A reliable forecasting model of solar irradiance can play an essential role in allowing high PV penetrations without degrading the grid performance. For this purpose, most related works either use individual forecasting models or ensemble approaches (e.g., weighted average), ignoring the interaction between the values to be aggregated and thus may worsen the forecasting reliability. Differently, in this article, we propose a reliable solar irradiance forecasting method based on long short-term memory (LSTM) models and an aggregation function based on Choquet integral. This novel combination has the following features: 1) LSTM models can achieve accurate predictions because they model the temporal changes in solar irradiance, thanks to their recurrent architecture and memory units, and 2) the Choquet integral can model the interaction between the inputs to be aggregated through a fuzzy measure. This aggregation technique can determine the largest consistency among the conflicting forecasting results, taking advantage of each individual model. To demonstrate the effectiveness of the proposed approach, we compare it with several forecasting methods using six realistic datasets collected from different sites in Finland in which solar irradiance is intermittent. The comparison reveals the high reliability of the proposed forecasting model with different sites and solar profiles.

The present work investigates heat transfer through natural convection using a series of experiments and computational modeling using Computational Fluid Dynamics (CFD) simulations in a one-meter bundle pipe with three internal pipes. The exact complex geometry is modeled where the flow channel is reduced through a spiral groove attached to a rod inside the internal tubes which was challenging compared to the flow in circular pipes in previous studies. To support the computational modeling investigations, convective heat transfer analysis is also studied through experiments with water as the production and heating fluids. Further, simulations are carried out with water-crude oil and aqueous ethylene glycol-water as the heating mediums and production fluids, respectively. Based on the heat transfer rates estimated from experimental data and CFD simulation results for the respective tubes, a modification to an existing Nusselt number is proposed for the range of temperature and flow rates used in the experiments. The proposed model, Nui = Prim Rain, was validated against experimental data and a good agreement with R2 values of more than 0.94 was achieved.

Wind speed fluctuations and load demand variations represent the big challenges against wind energy conversion systems (WECS). Besides, the inefficient measuring devices and the environmental impacts (e.g. temperature, humidity, and noise signals) affect the system equipment, leading to increased system uncertainty issues. In addition, the time delay due to the communication channels can make a gap between the transmitted control signal and the WECS that causes instability for the WECS operation. To tackle these issues, this paper proposes an adaptive neuro-fuzzy inference system (ANFIS) as an effective control technique for blade pitch control of the WECS instead of the conventional controllers. However, the ANFIS requires a suitable dataset for training and testing to adjust its membership functions in order to provide effective performance. In this regard, this paper also suggests an effective strategy to prepare a sufficient dataset for training and testing of the ANFIS controller. Specifically, a new optimization algorithm named the mayfly optimization algorithm (MOA) is developed to find the optimal parameters of the proportional integral derivative (PID) controller to find the optimal dataset for training and testing of the ANFIS controller. To demonstrate the advantages of the proposed technique, it is compared with different three algorithms in the literature. Another contribution is that a new time-domain named figure of demerit is established to confirm the minimization of settling time and the maximum overshoot in a simultaneous manner. A lot of test scenarios are performed to confirm the effectiveness and robustness of the proposed ANFIS based technique. The robustness of the proposed method is verified based on the frequency domain conditions that are driven from Hermite–Biehler theorem. The results emphases that the proposed controller provides superior performance against the wind speed fluctuations, load demand variations, system parameters uncertainties, and the time delay of the communication channels.

Bit-string generator (BSG) is based on the hardness of known number theoretical problems, such as the discrete logarithm problem with the elliptic curve (ECDLP). Such type of generators will have good randomness and unpredictability properties as it is challenged to find a solution regarding this mathematical dilemma. Hash functions in turn play a remarkable role in many cryptographic tasks to accomplish different security levels. Hash-enhanced elliptic curve bit-string generator (HEECBSG) mechanism is proposed in this study based on the ECDLP and secure hash function. The cryptographic hash function is used to achieve integrity and security of the obtained bit-strings for highly sensitive plain data. The main contribution of the proposed HEECBSG is transforming the x-coordinate of the elliptic curve points using a hash function H to generate bit-strings of any desirable length. The obtained pseudo-random bits are tested by the NIST test suite to analyze and verify its statistical and randomness properties. The resulted bit-string is utilized here for encrypting various medical images of the vital organs, i.e. the brain, bone, fetuses, and lungs. Then, extensive evaluation metrics have been applied to analyze the successful performance of the cipherimage, including key-space analysis, histogram analysis, correlation analysis, entropy analysis and sensitivity analysis. The results demonstrated that our proposed HEECBSG mechanism is feasible for achieving security and privacy purposes of the medical image transmission over unsecure communication networks.

Optical plastic lenses are progressively substituting glass optics due to their lightweight and low costs. Polycarbonate (PC) is considered as one of the leading optical materials due to its good mechanical and optical properties. Ultra-precision machining is the most suited process to develop PC optical components. Nevertheless, ultra-precision machining is considered as one of the deterministic processes to ensure the surface quality required for optical components. However, it is important to understand the behavior of the material during each stage of manufacturing. PC offers many challenges during its processing to achieve the nanometric finish and sub-micron form accuracies. In this article, the various issues of PC machining are discussed through experimental investigations. The effect of machining variables, that is, machining parameters, cutting temperature, and vacuum clamping on surface quality is studied. The results show the importance of the selection of optimum conditions for machining PC with good surface quality. The PC optical component is developed with surface finish ( Ra) 18.1 nm and profile accuracies ( Pv) of 0.116 µm. The study is helpful to understand the various issues involved in PC machining and hence to minimize their effects on surface quality.

Wireless community networks (WCNs) are large, heterogeneous, dynamic, and decentralized networks. Such complex characteristics raise different challenges, such as the effect of wireless communications on the performance of networks and routing protocols. The prediction approaches of link quality (LQ) can improve the performance of routing algorithms of WCNs while avoiding weak links. The prediction of LQ in WCNs can be a complex task because of the fluctuated nature of LQ measurements due to the dynamic wireless environment. In this paper, a deep learning based approach is proposed to accurately predict LQ in WCNs. Specifically, we propose the use of two variants of deep recurrent neural network (RNN): long short-term memory recurrent neural networks (LSTM-RNN) and gated recurrent unit (GRU). The positive feature of the proposed variants is that they can handle the fluctuating nature of LQ due to their ability to learn and exploit the context in LQ time-series. The experimental results on data collected from a real-world WCN show that the proposed LSTM-RNN and GRU models accurately predict LQ in WCNs compared to related methods. The proposed approach could be a helpful tool for accurately predicting LQ, thereby improving the performance of routing protocols of WCNs.

The need for freeform surfaces are widely recognized in the optical, aerospace, biomedical, and automotive industries. However, the fabrication of freeform surfaces is very difficult and expensive because of the involvement of advanced, multi-axis, dedicated machining processes. In many applications, the machining surface needs further polishing to reduce the surface roughness. The main aim of this work is to investigate the flexible pad polishing process for the finishing of the freeform surface. To achieve this, a flexible pad polishing setup is developed in two-axis configuration that can be integrated with a diamond turning machine or any other computer numerical control (CNC) machine and capable to polish the conventional rotationally symmetric surfaces as well as freeform surfaces. The polishing parameters for the developed polishing head are optimized for the nano-finishing of the freeform mold surface of Stavax ESR steel. The effectiveness of the current polishing setup is demonstrated by measuring the improvement in surface roughness height and profile. Test results reveal that the improvement on the freeform surface is significant, that is, surface roughness ( Ra) reduces from 220 nm to 25 nm while keeping the profile within the desired tolerance.

In this paper, novel and efficient analytical closed-form expressions are proposed for the optimal allocation of multiple capacitors in distribution systems to maximize the total cost reduction (CR) while considering power losses. The proposed expressions are novel since they can directly solve the allocation problem without requiring iterative processes or optimization algorithms. Specifically, two analytical closed-form expressions are introduced to determine the optimal number, locations, and sizes of multiple capacitors. The first analytical expression computes directly the optimal sizes of multiple capacitors where it is employed for the optimal sizing of capacitors for all possible combinations of locations. In turn, the best combination is then assigned by using a second analytical expression which directly evaluates all the combinations in terms of their contribution in CR. Unlike the existing methods/expressions that utilize sensitivity factors or optimize each capacitor individually, the proposed analytical closed-form expressions involve a unified mathematical model for multiple capacitors. The proposed direct approach is tested using a 69-bus distribution system. The accuracy and efficacy of the proposed analytical closed-form expressions are verified by comparisons with existing methods and intensive simulations of various allocation scenarios.

Multiple object tracking (MOT) at road intersections is a major research area in the field of intelligent transportation systems (ITS). In dynamic scenes like urban traffic, there is a variety of moving objects ranging from simple pedestrians to large trucks. Occlusion due to the interaction between these moving objects is one of the major problems in traffic tracking and monitoring at intersections. This paper presents an occlusion handling approach using an efficient feature-based multiple object tracking technique. A considerable amount of research work has been done in the area of occlusion handling but most of the proposed techniques can either handle occlusion of only two objects or fail to segment the individual vehicles throughout occlusion. The proposed approach uses radius nearest neighbour (RNN) classification to group the unmatched feature points along with matched feature points to update the object model. In order to validate the efficiency of this method, it is tested on four different urban traffic sequences from the Urban Tracker dataset and four traffic sequences from the Ko-PER Intersection Dataset. Experimental results depict the tracking approach of RNN based scheme compared to the other schemes for better occlusion handling and individual object segmentation.

Groundwater from the shallow water table aquifer is the main supply of drinking water for residents in Goly Region, White Nile State, Sudan. The residents have complained about groundwater quality....

Visual odometry (VO) is one of the promising techniques that estimates pose using the camera and does not necessarily require other sensor aiding. With increasing automation and the use of miniaturized systems such as mobile devices, wearable gadgets, & gaming consoles, demand for efficient algorithms have risen. In this paper, an attempt is made to remove the redundant features from the VO pipeline that do not have a significant effect on the estimation process. A probabilistic approach based on fast mutual information (MI) computation is suggested here as the basis for removing features. The MI value acts as a beacon for selecting distinct features while eliminating the redundant ones, thus improving the overall system speed and reducing storage requirements. The proposed MI-based feature selection framework for VO has been experimented on the KITTI vision benchmark suite and EuRoC MAV datasets available publicly. The estimated trajectory results have shown that the proposed technique is better in terms of computational efficiency and has similar accuracy as compared to the normal VO pipeline. Further investigations have also been carried out over the VSLAM framework to test its applicability in a real-time system.

Freeform surfaces are gaining popularity in vast engineering applications including optical engineering, aerospace, automotive, and biomedical. Success of freeform surfaces lies with its complex de...

Thermal management of electronic systems is the utmost concern to achieve optimum efficiency under space and weight constraints. For the optimal functioning of a system, the heat generated by the electronic components needs to be dissipated efficiently. The passive cooling technique is extensively used in electronic systems, wherein the more contact surface area of a heat source and the surroundings are utilized. This paper focuses on mathematical and simulation analysis for different types of heat sink designs for the 30 W multi-output DC–DC converter. Heat sink with inverted trapezoidal fins has resulted in efficient thermal management of the converter at its safe operating temperature of 398 K. Results show that the maximum temperature attained by the converter was 352 K which was in the safe operating zone of the converter. A comparative study of the effectiveness of heat dissipation with respect to maximum temperature attained has been discussed. Mathematical verification of Rayleigh number for different heat sink designs has also been carried out for its critical value.

Irradiation with swift heavy ions causes the deformation of Ferric nanoparticles in direction of the ion beam. Fe nanoparticles with mean diameter of about 20 nm were prepared by gas flow sputtering and subsequently confined within silica films. Two silica films wherein two different densities of Fe nanoparticles are encapsulated were irradiated with 50 MeV Ag ions with fluences of few 1014 ions.cm−2 at 300 K and normal incidence. Transmission electron microscopy analysis shows that the spherical Fe nanoparticles are deformed into prolate nanorods aligned in direction of the incident ion beam. The depth distribution profiles of irradiated particles reveal the presence of a critical fluence above which the elongation kinetics becomes dependent on the nanoparticles density. Analysis indicates that for the lower density particles, a saturation length is reached under irradiation to fluence between 3–4 × 1014 ions.cm−2. However, for the higher density, collective growth into aligned nanowires is presumed to take place. Hysteresis curves of the saturation magnetization and coercivity indicate an increasing magnetic anisotropy, which can be correlated with the deformation of nanoparticles in the direction of the ion beam.

The rapid increase in the installation of renewable energy sources, particularly solar photovoltaic (PV) sources associated with unbalanced features of distribution systems (DS), disturbs the classic control strategy of voltage regulation devices and causes voltage violation problems. This paper proposes an effective control strategy for voltage regulators in the DS based on the voltage sensitivity using a multi-agent system (MAS) architecture. The features of the unbalanced distribution system (UDS) with the PV and different types and configurations of voltage regulators are considered in the proposed strategy. The novelty of the proposed method lies in realizing both the control optimality of minimizing voltage violations and the flexibility to accommodate changes in the DS topology using an MAS scheme. An advantageous feature of using the MAS scheme is the robust control performance in normal operation and against system failure. Simulation studies have been conducted using IEEE 34-node and 123-node distribution test feeders considering high PV penetration and different sun profiles. The results show that the proposed voltage control strategy can optimally and effectively manage the voltage regulators in the UDS, which decrease their operation stresses and minimize the overall voltage deviation.

The intermittent photovoltaic (PV) units significantly affect the performance of distribution systems, and they often cause several operational problems, most importantly, voltage rise/drop. At high PV penetration, excessive tap movements of transformers and high curtailed PV power are expected to completely solve the voltage violation problem. In this paper, we propose an optimal voltage control method for distribution systems considering the number of tap movements of transformers and the active power curtailment of PV units. The objective function of the proposed method comprises: 1) voltage drop violation, 2) voltage rise violation, 3) tap movement rate (TMR) of transformers, and 4) curtailed power of PV (CPPV). A multiobjective grey wolf optimizer integrated with a Lévy mutation operator (GWO-Lévy) is formulated to accurately solve the voltage control problem. A 24-h simulation is performed on the 119-bus distribution system with PV and different types of loads. The performance of GWO-Lévy is compared with three other optimizers, finding that it achieves the best performance. The simulation results demonstrate the efficacy of the proposed method for solving the voltage violation problem with PV while simultaneously optimizing TMR and CPPV.

A novel iterative algorithm for electronic dispersion compensation (EDC) at the transmitter is described and demonstrated for intensity modulation and direct detection (IM/DD) systems. The proposed algorithm is based on the Gerchberg-Saxton (GS) algorithm, where the link dispersion performs time-to-frequency mapping, while the unconstrained phase at the direct detection receiver acts as a degree of freedom. Simulation results for a transmitter employing a single drive Mach-Zehnder modulator (MZM) operating at 28 Gb/s non-return-to-zero on-off keying (NRZ-OOK), show pre-compensation of upto 100 km of single mode fiber (SMF), with 1 dBQ margin above the forward error correction (FEC) bit error rate (BER) limit of $3.8 \times 10^{-3}$ . It is shown that with five iterations of the proposed EDC algorithm in digital-signal processing (DSP) coupled with an 8-bit 56 GSa/s digital-to-analog converter (DAC), the transmission reach is extended by a factor of 5 in unamplified SMF links. Simulations for a 56 Gb/s four-level pulse-amplitude-modulation (PAM-4) show the compensation of upto 10 km with five iterations of the EDC algorithm at 1 dBQ above the FEC limit.

This paper presents an intensive measurement and analysis of monopolar ionized fields in bundled high voltage direct current (HVDC) conductors using the finite difference method based on the full multigrid technique. The positive feature of this study is that it considers the comprehensive representation of the bundle conductor, unlike the existing studies that approximate the bundle conductor with an equivalent conductor radius. Firstly, the proposed method is compared with previous experimental results. Secondly, a flexible laboratory model for the bundled HVDC conductors is constructed. Thirdly, the laboratory model is exploited to validate the numerically computed current density distribution on the ground plane and corona current for different bundles’ numbers and different distances between bundles. Bundles of one, two, and four conductors are adopted in the experimental setup. For the same applied voltage, the results verified that the corona current decreases by increasing the bundles’ number and/or minimizing the spacing between bundles. Consequently, the obtained results confirmed that corona power losses can be minimized, without needing the traditional procedures that involve increasing either the conductor radius or its height above the ground. The results of the proposed numerical approach concurred well with the present and past laboratory results.

Worldwide, photovoltaic (PV) and electric vehicles (EVs) have intensively been integrated into distribution systems. As a result, different operational issues can be observed due to PV generation variability and EV stochastic characteristics. In this article, an optimal sizing approach of multiple PVs in the existence of EVs is proposed. The proposed approach minimizes both the total voltage deviations and overall energy losses, prevents active PV power curtailment, and considers numerous constraints of PV, EV, and the distribution system. The features of the proposed approach are the considerations of PV, EV, and load uncertainties via incorporating their probabilistic models. Besides, it models arrival/parting times of EVs, the required state of charge (SOC) of EV batteries based on initial SOCs and remaining parking periods, and controlled/uncontrolled charging. Furthermore, diverse control schemes of the interfacing PV inverter are formulated in the proposed optimization model. To effectively solve this comprehensive model with conflicting subfunctions and variables, a two-level multiobjective evolutionary algorithm based on decomposition with fuzzy sets is developed. The upper optimization level accurately optimizes the sizes of multiple PVs, while the lower one optimizes charging/discharging of EV batteries, PV inverter oversize, and PV reactive power. The results prove the effectiveness of the proposed approach.

PurposeDisturbances in terms of major crises such as pandemics, fluctuations in demand and oil price, energy consumption and supply chain can significantly impair the maintenance programs effectiveness and efficiency. Hence, there is an urgent need for an agile asset performance management (AAPM) framework.Design/methodology/approachThis paper's main objective is to design a comprehensive framework for an AAPM system that sustains the desired asset performance by reacting efficiently, quickly and intelligently to the changes in the operating context parameters and asset health conditions. Such a framework is adaptive to changes in scenarios and aims to systematise the decision support process, considering different objectives.FindingsThe development of the proposed framework has led to identifying an innovative way of seamless integration between crucial reliability and asset management tools. Also, the methodology implementation is expected to promote the practical use of its reliability tools and enable asset stakeholders to break silo working for clear communication around asset performance.Originality/valueThe implementation of the AAPM framework follows a new approach developed during this research and coined by the authors as the “8S approach.”

Advancements in diamond turning technology with tool servo configurations enables the generation of precise freeform surfaces. However, the profile accuracy is mainly limited due to non-availability of an efficient tool path compensation techniques and precise alignment methods. The aim of this study is focused on developing a tool path compensation routine for slow tool servo machining of freeform optics. A seven-order polynomial freeform surface, designed for hyperspectral imaging is selected for experimentation. Alignment strategy by utilizing the available fiducials is presented to ensure the precise re-mounting of surface during machining and metrology. The contact type profilometer is used to measure the fabricated surface by taking 25 numbers of two-dimensional raster scans at an interval of 0.5 mm. The scans are then stitched to get the 3D surface measurement. The residual form error map is used to compensate the tool path. Significant reduction in form error, i.e., from peak to valley (PV) of 9.27 to 0.75 μm with surface finish (Ra) of 11.82 nm, is achieved by performing four machining iterations of compensation. The simulation studies are also presented to investigate the effects of various misalignments on manufacturing accuracies. The developed compensation process is effective for fast convergence of form error and to manufacture the precise freeform optics for various imaging and non-imaging applications.

The multiplexed resistive arrays simplify the data collection with reduced connections and circuit complexity. However, the effect of crosstalk in these cases becomes inevitable. The crosstalk is caused due to the leakage currents in the multiplexer channels as well as the parasitic paths among the non-selected elements in the array. This paper analyses a circuit arrangement capable of suppressing crosstalk in 2D resistive sensor arrays. The proposed circuit employs transistors and diodes to modify simple voltage feedback commonly used for crosstalk reduction. The paper includes simulation results, approximate mathematical model for the “element under measurement” and other practical aspects of the circuit arrangement, such as the array size or the bias resistance. Finally, the circuit is compared (based on the simulation results) with two known circuit arrangement from the literature. The suggested circuit is clearly capable of eliminating crosstalk better than previously known methods. The analysis also reveals that the circuit is sensitive to temperature variations and must be modeled with temperature calibration factors for better model accuracy.

The penetration of photovoltaic (PV) has obviously been increased in distribution systems throughout the world. To sufficiently assess the energy losses with PV, comprehensive simulations with high time-resolution data are required. These simulations have a heavy computational burden, which makes it difficult to analyze distribution systems and evaluate PV impacts with fine resolutions. To cope with this issue, most related works down-sample, cluster, or quantize the full data to reduce the computational time on the expense of the accuracy. In this paper, we propose a fast yet accurate energy-loss assessment approach in distribution systems using machine learning. The unique feature of the proposed approach is that it uses all data to estimate losses, which yields accurate results close to the exact solutions in a very short time. The simulation results demonstrate that the proposed approach extremely reduces the computational time of energy-loss estimation with high accuracy rates. The speedup of the proposed approach with respect to power flow simulations for a yearlong at a 30-s time resolution is 28 691 (99.9965 $\%$ reduction in computational time). The effectiveness of the proposed approach is also illustrated by applying it to optimize the PV size for minimizing energy losses.

Nowadays, biomaterials have attained great importance in medical sector owing to the urgent requirement of the material systems which can be suitable for different sorts of human body treatments. Among the various biomaterials available, commercially, polymeric materials occupy great importance because of their near-to-organ characteristics, biocompatibility, corrosion resistance, and other essential mechanical features. The current applications of biopolymers include scaffolding, load-bearing implants, intraocular lenses, artificial heart valves, cardiovascular prostheses, cardiopulmonary bypass, hemodialysis, and dental and orthopedic applications. Although there exist numerous established fabrication routes for biopolymers, yet, their post processing is often required in order to achieve required geometrical and topographical performances. Therefore, machining of the processed biopolymers is often required. The aim of this article was to present a crisp review of the machining of different biopolymers. Further, being an ultraprecision machining operation, specific attention has been paid on the diamond turning technique as well as a case study has been performed to study the effect of process parameters on the resulting characteristics. Overall, the current work will provide a unique platform which could be helpful for young biomedical engineers and scientists focusing on precision surface engineering.

Dual mode detection of an explosive 2,4,6-trinitrophenol (TNP) within a gathering of potential interferers likely to be present in real samples was reported herein driven by a pyrido-indole based commercial molecule, norharmane (NHM). Interestingly, the explosive was detected through spectroscopic change, naked eye and also on TLC plate under UV light. As NHM was allowed to react with various nitro compounds under 1:1, 1:2 and 1:n situations, only TNP reacted, implying high-fidelity molecular recognition. Exclusive selectivity towards TNP induced by NHM still belongs to a rare category observation in the history of explosive sensing. Ratiometric spectroscopic responses were reversible and reusable up to three consecutive cycles with virtually no loss of efficiency and producing almost same spectroscopic signature. 1H NMR titration and computational results revealed ground state proton transfer phenomenon as underlying mechanism behind ratiometric spectroscopic responses. Finally, employing ratiometric responses, blueprint of a unique sensing device was designed for instantaneous detection of TNP.

The theoretical possibility of enhancing the modulation bandwidth of a semiconductor laser using digital signal processing (DSP) and a digital-to-analog converter (DAC) is investigated. Simulation results show the feasibility of modulating a distributed feedback (DFB) laser designed for 2.5-Gb/s operation at modulation speeds reaching 56-Gb/s without injection locking or changing the laser cavity design, and at a power penalty less than 6 dB. The utilization of a 6-bit DAC operating at two samples per symbol, improves the modulation speeds of the DFB laser by a factor of 2.5, at the same power penalty, overcoming the intrinsic limitations imposed by the electron–photon resonance inside the laser cavity.

High transmission optical filters require specific filter characteristics and set of optimisation methods since precision in output performance plays a vital role in such applications. In this paper, an improved genetic algorithm approach to optimise high transmission optical filter is described. High transmission of around 99% in the wavelength region of 525–575 nm without any dips in between is the main performance requirement desired. The optimisation parameters are layer thickness, refractive index and number of layers and design wavelength is 550 nm in visible region. The method integrates the Taguchi method and genetic algorithm. First, Taguchi method is used to reduce the number of design experiments and find the minimum possible number of optimised set of values that represents the quality performance of the system. Then, genetic algorithm is applied to search the optimal design parameters. The performance is determined in terms of transmittance. Three levels for each optimisation parameter were considered. The total number of design experiments to be performed (in worst case) using genetic algorithm only with three optimisation variables and three levels is approximately 33 = 27 for obtaining the best design that matches the desired performance whereas Taguchi integrated genetic method helped in finding the best matching design with 9 design experiments. The best transmission performance was obtained for 24 layers, with thickness range 10–123.75 nm and high and low refractive index of 2.1 and 1.38. This method is a very robust approach that can be used for design parameter optimisation of optical filters for high transmission applications.

The traditional cellular technology is not profitable to serve the sparsely populated and clustered remote rural areas because of huge network set-up cost and lesser return from low density of rural population. To provide broadband service in those remote rural areas, in this paper, we consider a TV white space (TVWS)-based hierarchical network architecture where sparsely located clustered rural pieces of user equipment (UEs) are connected to far-off central base station (CBS) via intermediate TVWS nodes, known as the pieces of customer premises equipment (CPEs) through TVWS link. The CBS has direct broadband connectivity to the Internet through optical fiber backhaul. In such a network architecture, we adopt a two-stage market model among UEs, CPEs, CBS, and propose a pricing-based Stackelberg game to distribute the total available data rate at CBS among CPEs and then to the UE. Extensive simulations have been carried out to study the data-rate distribution, and the behavior of utilities of CPE, CBS, and UE with variation of different parameters for a particular network scenario. The study of the outcome suggests that using this simple price exchange mechanism, it is possible, based on each UE's willingness-to-pay and the total available data rate, to distribute data rate optimally among the UEs where each of the network entities behaves selfishly.

The penetration of renewable distributed generations (DG) is recently being increased in distribution systems. Due to the intermittent nature of these generation units, several technical problems are expected based on the environmental conditions and load profiles, such as voltage rise and increasing losses. A traditional way to alleviate these technical problems is to oversize their interfacing inverters for releasing their capacities to inject/absorb further reactive power. In the literature, the DG inverter is normally oversized by a certain percentage (e.g., 10%) for this purpose. In turn, an optimization-based method is proposed in this paper to calculate the optimal oversize of the interfaced inverter employed in various utility-owned DG types to regulate voltages and to reduce losses with minimum total costs. The proposed method considers the active power curtailment (APC) feature in the DG inverter and the transformer taps. Different control schemes of the interfaced inverter are considered and incorporated in the proposed optimization model. The simulation results demonstrate the effectiveness of the proposed method compared with existing methods.

Although the wind farms based on squirrel cage induction generators (SCIG) is cheaper than the wind farms based on doubly fed induction generators (DFIG), it is always in desperate need for reactiv...

Ultra-precision machining is used to generate the high quality surfaces in terms of nano-metric surface finish and sub-micron profile accuracy for various engineering applications. Due to unique properties of diamond i.e. high hardness, good heat conductivity and low friction co-efficient it is most suitable tool material for ultra-precision machining. Advancements in material science and growing needs for modern applications leads to development of new materials. Silicon is one of the prominent materials used for various optical applications. However processing of silicon by machining is quite difficult due to its hard and brittle nature. Diamond tool wear is one of the major issues for ultra-precision machining which leads to increase the process cost significantly. Control of tool wear mainly depends on machining parameter selection and understanding of tool wear mechanism. Wear measurement of diamond tool is essential to understand the wear mechanism, process behavior and tool life quantification. Wear measurement of nano-metric cutting edge is one of the challenging tasks. In current study machining experiments are performed on Silicon. Tool cutting edge is analyzed by profilometric measurements at different orientations. Wear of cutting edge, rake face and flank face is measured. Measurement results are very helpful for understanding the possible reasons of wear and hence to optimize the machining conditions to minimize the tool wear.

This paper proposes an optimal voltage regulation scheme (OVRS) for distribution systems with rich photovoltaic (PV). Various regulation devices are optimally controlled in a coordinated manner: PV inverter, D-STATCOM, step voltage regulator (SVR), and on-load-tap-changer (OLTC). A data structure algorithm is proposed to split the distribution system into layered zones considering the radial structure of the system. The solution process of the proposed scheme is accomplished by a meta-heuristic optimizer. OVRS addresses the voltage violations while yielding a coordinated operation of the various control devices. The proposed OVRS involves three control levels to completely prevent voltage violations. In the first control level, the PV inverter and D-STATCOM mitigate rapidly the local voltage deviation through injecting/absorbing optimized reactive power. The second control level is a decentralized-based control scheme that utilizes the voltage control devices in each zone to handle the voltage violations if any. For each zone, the control devices in the upper-stream zones (parent zones) are managed by the third control level to ensure cooperative control actions. The simulation results on the 119-bus distribution system, with clear, low fluctuation, and high fluctuation of solar radiation profiles, demonstrate the effectiveness of the proposed OVRS.

Recently, the Internet of Things (IoT) has an important role in the growth and development of digitalized electric power stations while offering ambitious opportunities, specifically real-time monitoring and cybersecurity. In this regard, this paper introduces a novel IoT architecture for the online monitoring of the gas-insulated switchgear (GIS) status instead of the traditional observation methods. The proposed IoT architecture is derived from the concept of the cyber-physic system (CPS) in Industry 4.0. However, the cyber-attacks and the classification of the GIS insulation defects represent the main challenges against the implementation of IoT topology for the online monitoring and tracking of the GIS status. For this purpose, advanced machine learning techniques are utilized to detect cyber-attacks to conduct the paradigm and verification. Different test scenarios on various defects in GIS are performed to demonstrate the effectiveness of the proposed IoT architecture. Partial discharge pulse sequence features are extracted for each defect to represent the inputs for IoT architecture. The results confirm that the proposed IoT architecture based on the machine learning technique, that is the extreme gradient boosting (XGBoost), can visualize all defects in the GIS with different alarms, besides showing the cyber-attacks on the networks effectively. Furthermore, the defects of GIS and the fake data due to the cyber-attacks are recognized and presented on the dashboard of the proposed IoT platform with high accuracy and more clarified visualization to enhance the decision-making about the GIS status.

Worldwide, the hosting capacity of renewable energy sources (RES) is remarkably expanded in distribution systems. One of the most auspicious RES is wind turbine systems (WTSs), which can improve the performance of distribution systems. In turn, the integration of high WTS penetrations can also deviate the system operation away from the standard condition. To tackle this issue, we propose a method for enhancing the hosting capacity of multiple WTSs considering their intermittent generations in distribution systems. The proposed method considers the operation of the on-load tap changer (OLTC), allowing to solve voltage problems efficiently. Especially, the proposed method optimises the charging/discharging power of electric vehicles (EVs), which can contribute positively to regulating WTS intermittent generation. Additionally, the reactive power support of WTSs, complying with the IEEE 1547:2018 standard, is incorporated in the planning model of WTSs. To solve such an optimisation problem, a bi-level optimisation algorithm is developed based on the gravitational search algorithm. Comprehensive simulation results are performed on the 69-bus distribution feeder interconnected to four EV stations. Based on the results, the proposed approach can efficiently enhance/increase the hosting capacity of WTSs in distribution systems, thanks to the consideration of OLTC, reactive power support of WTSs and EVs.

Internet of Things (IoT) is the utmost assuring framework to facilitate human life with quality and comfort. IoT has contributed significantly to numerous application areas. The stormy expansion of smart devices and their credence for data transfer using wireless mechanics boosts their susceptibility to cyber-attacks. Consequently, the rate of cybercrime is increasing day by day. Hence, the study of IoT security threats and possible corrective measures can benefit the researchers to identify appropriate solutions to deal with various challenges in cybercrime investigation. IoT forensics plays a vital role in cybercrime investigations. This review paper presents an overview of the IoT framework consisting of IoT architecture, protocols, and technologies. Various security issues at each layer and corrective measures are also discussed in detail. This paper also presents the role of IoT forensics in cybercrime investigation in various domains like smart homes, smart cities, automated vehicles, healthcare, etc. Along with the role of advanced technologies like Artificial Intelligence, Machine Learning, Cloud computing, Edge computing, Fog computing, and Blockchain technology in cybercrime investigation are also discussed. At last, various open research challenges in IoT to assist cybercrime investigation are explained, which provide a new direction for further research., 39 pages

The early detection of the transformer faults with high accuracy rates guarantees the continuous operation of the power system networks. Dissolved gas analysis (DGA) is a technique that is used to detect or diagnose the transformer faults based on the dissolved gases due to the electrical and thermal stresses influencing the insulating oil. Many attempts are accomplished to discover an appropriate technique to correctly diagnose the transformer fault types, such as the Duval Triangle method, Rogers' ratios method, and IEC standard 60599. In addition, several artificial intelligence, classification, and optimization techniques are merged with the previous methods to enhance their diagnostic accuracy. In this article, a novel approach is proposed to enhance the diagnostic accuracy of the transformer faults based on introducing new gas concentration percentages limits and gases' ratios that help to separate the conflict between the diverse transformer faults. To do so, an optimization model is established which simultaneously optimizes both gas concentration percentages and ratios so as to maximize the agreement of the diagnostic faults with respect to the actual ones achieving the high diagnostic accuracy of the transformer faults. Accordingly, an efficient teaching-learning based optimization (TLBO) is developed to accurately solve the optimization model considering training datasets (Egyptian chemical laboratory and literature). The proposed TLBO algorithm enhances diagnostic accuracy at a significant level, which is higher than some of the other DGA techniques that were presented in the literature. The robustness of the proposed optimization-based approach is confirmed against uncertainty in measurement where its accuracy is not affected by the uncertainty rates. To prove the efficacy of the proposed approach, it is compared with five existing approaches using an out-of-sample dataset where a superior agreement rate is reached for the different fault types.

In this study, the potential reuse of kitchen wastewater (KWW) in irrigation was analyzed to reduce the present freshwater demand. To know the suitability of KWW for irrigation, the samples were first collected from an educational institute in India and then characterized according to its physical, chemical, and bacteriological properties. The characterized data were then compared with the standard limit for irrigation Food and Agriculture Organization (FAO in Water quality for agriculture. Irrigation and drainage paper 29, M56, 1994) and the US Salinity Laboratory (USSL). Apart from the above irrigation standards, the characterized data were also compared with sodium adsorption ratio (SAR), residual sodium carbonate, sodium percentage (Na%), magnesium hazard (MH), Kelly’s ratio (KR), and permeability index to get better clarity. From the characterization, it was found that carbonate, fluoride, chromium, and Escherichia coli were absent, whereas parameters like pH, chloride, iron, copper, magnesium, lead, nickel, sodium, calcium, zinc, aluminum, and sodium adsorption ratio were within the permissible limit. The result obtained from the USSL classification system suggested that 30.77% of KWW samples are safe for irrigation. Moreover, its quality was found to be safe for irrigation based on SAR, Na%, KR, and MH. For better decision making of KWW reuse in irrigation, the output of Mamdani fuzzy inference system (MFIS) was compared with the USSL classification system. The overall agreement between USSL and MFIS was found to be 55.6% for KWW.

International audience; The ammonium chloride effect on bismuth electrodeposition from choline chloride-urea deep eutectic solvent (DES) was investigated. Spectroscopic and thermal studies were performed on the latter in order to understand whether the addition of ammonium chloride affects the DES behavior; the results of the thermal study do not indicate any significant change except a small shift in the melting point of the DES when adding the ammonium salt (~+3 °C) and the modification of a cold crystallization peaks at low scan rate. The electrochemical behavior of bismuth was then studied by cyclic voltammetry in the deep eutectic solvent with different NH4Cl content. Bismuth electrocrystallization was studied by chronoamperometry which has shown that the bismuth electrodeposition in studied conditions evolves with a three-dimensional progressive nucleation. SEM images show that grain size of bismuth deposits decreases while adopting a well-defined geometry as ammonium salt concentration increases. EDS analysis confirms that pure bismuth thin films were obtained and X-Ray diffraction indicates that the bismuth growth follows a preferential orientation along the (012) reticular plane.

The effect of different additives on manganese and bismuth electrodeposition from choline chloride-urea (Reline) and choline chloride-ethylene glycol (Ethaline) deep eutectic solvents (DESs) was investigated. Spectroscopic and thermal studies were performed on the latters in order to understand whether some additives affect the DESs behaviors. the results of the thermal study do not indicate any significant change except a small shift in the melting point of the reline when adding ammonium chloride (~+3°C) and the modification of a cold crystallization peaks at low scan rate. The electrochemical behaviors of manganese and bismuth were then studied by cyclic voltammetry. electrocrystallization was studied by chronoamperometry which has shown that the bismuth electrodeposition evolves with a 3D progressive nucleation. The current transients for Mn reveals that the presence of thiourea enhances the nucleation rate by modifying the corresponding nucleation and growth model from 3D progressive to 3D instantaneous. EDS analysis confirms that pure bismuth thin films were obtained and X-Ray diffraction indicates that the bismuth growth follows a preferential orientation along the (012) reticular plane. While for manganese, no metal phase was observed in diffraction due to the amorphous nature of Mn deposits. Annealing treatments will therefore be necessary.

Electrical biosignals have the potential for use as biometric authenticators, owing to their ability to facilitate liveness detection and concealed nature. In this work, the viability of using surface electromyogram (sEMG) as a biometric modality for users verification is investigated. A database of multi-channel sEMG signals is created using a wearable armband from able-bodied users. Each user used his/her muscles to form a password that consists of a unique combination of specific hand gestures. A total of 18 features are extracted from the signals in order to distinguish between the users. Several features are extracted in the frequency domain after estimating the power spectral density while using the Welch&rsquo, s method. Specifically, average frequency, signal power, median frequency, Kurtosis, Deciles, coefficient of dissymmetry, and the peak frequency of the sEMG signal are considered. To further increase the accuracy of the classifier, time domain features are also extracted through segmentation of the signal into 10 segments, and then calculating both the root mean square and length of the signal. Several classifiers that are based on K-nearest Neighbors (KNN), Linear Discernment Analysis (LDA), and Ensemble of Classifiers are constructed, trained, and statistically compared, resulting in an average accuracy in 97.4%, 98.3%, and 98.5%, respectively. False acceptance rate (FAR) and False Rejection Rate (FRR) are estimated for each classifier in order to determine the effectiveness of the biometrics verification system. Although the ensemble classifier accuracy was found to be the highest, the results show that the KNN classifier exhibits a FAR of 0.2% and FRR of 2.9%. Thus, the KNN classifier was found to he the optimum classifier after the extraction of all 18 features. This work demonstrates the usefulness of sEMG as a biometric authenticator in user verification.

The increasing need for observation in seawater or ocean monitoring systems has ignited a considerable amount of interest and the necessity for enabling advancements in technology for underwater wireless tracking and underwater sensor networks for wireless communication. This type of communication can also play an important role in investigating ecological changes in the sea or ocean-like climate change, monitoring of biogeochemical, biological, and evolutionary changes. This can help in controlling and maintaining the production facilities of outer underwater grid blasting by deploying unmanned underwater vehicles (UUVs). Underwater tracking-based wireless networks can also help in maintaining communication between ships and divers, submarines, and between multiple divers. At present, the underwater acoustic communication system is unable to provide the data rate required to monitor and investigate the aquatic environment for various industrial applications like oil facilities or underwater grit blasting. To meet this challenge, an optical and magnetic tracking-based wireless communication system has been proposed as an effective alternative. Either optical or magnetic tracking-based wireless communication can be opted for according to the requirement of the potential application in sea or ocean. However, the hybrid version of optical and wireless tracking-based wireless communication can also be deployed to reduce the latency and improve the data rate for effective communication. It is concluded from the discussion that high data rate optical, magnetic or hybrid mode of wireless communication can be a feasible solution in applications like UUV-to-UUV and networks of aquatic sensors. The range of the proposed wireless communication can be extended using the concept of multihop.

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijepes.2020.105997 Voltage deviation (VD) and voltage flicker (VF) are considered common operational problems associated with high photovoltaic (PV) penetrated distribution systems. In this paper, an optimal control strategy is proposed for minimizing VD and VF in PV-rich distribution systems. The control strategy is based on proposed analytical expressions that minimize both voltage problems by optimizing the smart functions of the PV inverters and control devices simultaneously. The proposed analytical expressions are formulated based on voltage sensitivities with respect to the active and reactive power injections of PV. Specifically, a three-level control strategy with different time resolutions is proposed to significantly alleviate voltage deviation/flicker while minimizing PV active power curtailments and tap movements for transformers. These control levels are (1) local control (LC), (2) area control (AC), and (3) coordinated control (CC). LC provides rapid local control actions to minimize VD and VF, AC minimizes VD within the corresponding area individually, and CC plays a vital role to coordinate between the various control units. The proposed control strategy is assessed using high PV penetration with realistic high-resolution very-variable solar radiation datasets (10 ms). To demonstrate the accuracy and efficiency of the proposed analytical expressions, the calculated results have been compared with existing methods. Results demonstrate that the proposed control strategy effectively coordinates between the various voltage control units while minimizing VD and VF.

Computer-aided diagnosis (CAD) systems are considered a powerful tool for physicians to support identification of the novel Coronavirus Disease 2019 (COVID-19) using medical imaging modalities. Therefore, this article proposes a new framework of cascaded deep learning classifiers to enhance the performance of these CAD systems for highly suspected COVID-19 and pneumonia diseases in X-ray images. Our proposed deep learning framework constitutes two major advancements as follows. First, complicated multi-label classification of X-ray images have been simplified using a series of binary classifiers for each tested case of the health status. That mimics the clinical situation to diagnose potential diseases for a patient. Second, the cascaded architecture of COVID-19 and pneumonia classifiers is flexible to use different fine-tuned deep learning models simultaneously, achieving the best performance of confirming infected cases. This study includes eleven pre-trained convolutional neural network models, such as Visual Geometry Group Network (VGG) and Residual Neural Network (ResNet). They have been successfully tested and evaluated on public X-ray image dataset for normal and three diseased cases. The results of proposed cascaded classifiers showed that VGG16, ResNet50V2, and Dense Neural Network (DenseNet169) models achieved the best detection accuracy of COVID-19, viral (Non-COVID-19) pneumonia, and bacterial pneumonia images, respectively. Furthermore, the performance of our cascaded deep learning classifiers is superior to other multi-label classification methods of COVID-19 and pneumonia diseases in previous studies. Therefore, the proposed deep learning framework presents a good option to be applied in the clinical routine to assist the diagnostic procedures of COVID-19 infection.

Recently, the penetration of photovoltaic (PV) units and plug-in electric vehicles (PEVs) has been quickly increased worldwide. Due to the intermittent nature of PV and the stochastic nature of PEVs, several operation problems can be noticed in distribution systems, including excessive energy losses and voltage violations. In this paper, an optimization-based algorithm is proposed to accurately determine the optimal locations and capacities of multiple PV units in the presence of PEVs to minimize energy losses while considering various system constraints. The proposed algorithm considers the uncertainty of PV and loads, and the stochastic nature of PEVs. Furthermore, the operational constraints of PEVs are incorporated in the optimization model: 1) arrival and departure times, 2) initial state of charge (SOC), 3) minimum preset state of charge by the owner, and 4) the time-of-use electricity tariff, and 5) different charging control schemes. The optimal PV planning model is formulated as a two-layer optimization problem that ensures an optimal PV allocation while optimizing PEV charging simultaneously. A two-layer metaheuristic method is developed to solve the optimization model considering annual datasets of the studied distribution systems. The results demonstrate the efficacy of the proposed algorithm.

In this paper, a customizable wearable 3D-printed bionic arm is designed, fabricated, and optimized for a right arm amputee. An experimental test has been conducted for the user, where control of the artificial bionic hand is accomplished successfully using surface electromyography (sEMG) signals acquired by a multi-channel wearable armband. The 3D-printed bionic arm was designed for the low cost of 295 USD, and was lightweight at 428 g. To facilitate a generic control of the bionic arm, sEMG data were collected for a set of gestures (fist, spread fingers, wave-in, wave-out) from a wide range of participants. The collected data were processed and features related to the gestures were extracted for the purpose of training a classifier. In this study, several classifiers based on neural networks, support vector machine, and decision trees were constructed, trained, and statistically compared. The support vector machine classifier was found to exhibit an 89.93% success rate. Real-time testing of the bionic arm with the optimum classifier is demonstrated.

Kitchen wastewater (KWW), a type of gray wastewater has garnered significant attention because of organic pollutants and oil and grease. It has hazardous effects on the environment, such as...

Systematic optimization of sputter parameters allowed the growth of size-tunable ferric nanoparticles on glassy-carbon substrates using gas flow sputtering technique. By variation of the sputtering pressure and particle aggregation length, Fe nanoparticles with mean diameter ranging between 20 and 150 nm were formed. Several physical and optical techniques were used to examine the size and morphology of the nanoparticles. While nanoparticles were revealed as spherical, their crystalline structure was detected only for the hematite type of Fe. By fostering the sputtering growth using Ar and He admixture flow, density variation within the grown particles was established, offering a strategy to overcome the slow growth rates in the sputter sources. Magnetization measurements taken at room temperature did not show evidence of the impact of size on the magnetic properties of the nanoparticles. Thus, saturation magnetization and coercivity values were obtained. Measurements of the linear optical properties of the sputtered nanoparticles showed a general decrease in extinction with decreasing nanoparticle size. Monotonically decreasing spectra were observed, except for a shoulder in the 300–400 nm range.

Germanium lenses are widely used in various scientific and technological applications mainly infrared optical components. The performance of these lenses depends upon the surface finish, which is challenging to achieve during ductile-regime machining. It is crucial to provide the optimum parameters to ensure the high-quality surface, without surface and subsurface damage. In the present work, an attempt has been made to analyse the effect of input machining parameters on surface finish and subsurface damage distribution, induced during the diamond turning. Taguchi’s optimization has been performed to optimize the parameters affecting the performance and surface quality of fabricated components. Abbott–Firestone (A–F) curve is utilized to study the distribution of peaks and valleys on the machined surface. Additionally, chip morphology and machining forces are analysed to study the material removal behaviour at varying parameters. Further, indentations are performed on the best and worst combinations to examine the effect of parameters selection on surface cracks propagation. It is found that surface with a greater number of valleys leads to chipping during indentation. In this study, a surface roughness of 2.3 nm is achieved with minimum subsurface deformation.

Image fusion has been widely used to combine multispectral information into an enhanced information image. The application of such enhanced information content in the field of surveillance for improving situation awareness of observer is highly recommended. When a single sensor information is used for surveillance like visible camera output during poor ambient lighting conditions, ‘hot-target’ details are not visible to the observer. The use of visible-infrared fused image is recommended during surveillance in poor ambient lighting conditions to visualise background scene details and ‘hot-target’ details simultaneously. A wrapping-based curvelet transform method is proposed for fusion of infrared and visible images. Curvelet transform is used because of its advantages over wavelet transform limitations like directional insensitivity, isotropic basis and inability to resolve curves. The approximation coefficients are fused using the principal component analysis rule while detailed coefficients are ...