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The transmission of power with the help of high voltage alternating current (HVAC) is a conventional technique. On the other hand, transferring huge power over extensive distances using high voltage direct current (HVDC) systems is a developing and advanced technique. Although from 1950, researchers and technologists have achieved significant development in HVDC transmission systems that were based on mercury-arc valves, an important milestone was completed in the 1970s, when the thyristors switching technologies were adopted in the line commutated converter (LCC) based HVDC power transmissions. Since 1997, the evolution of power transistor-based voltage source converter (VSC) technology, and then in 2010, the modular multilevel converter (MMC) technology, opened new eras in the advanced HVDC transmission systems. With the help of pulse width modulation (PWM) based VSC, and MMC technologies, the field operators can control the real and imaginary powers, individualistically. The short length asynchronous interconnections can also be possible with these technologies. This paper will discuss the mathematical analysis of advanced HVDC transmission systems, having tie lines and power converters, and HVDC transmission for bulk solar photovoltaic (PV) power generation.

In this paper, we present a novel Image Fusion Model (IFM) for ECG heart-beat classification to overcome the weaknesses of existing machine learning techniques that rely either on manual feature extraction or direct utilization of 1D raw ECG signal. At the input of IFM, we first convert the heart-beats of ECG into three different images using Gramian Angular Field (GAF), Recurrence Plot (RP) and Markov Transition Field (MTF) and then fuse these images to create a single imaging modality. We use AlexNet for feature ex-traction and classification and thus employ end-to-end deep learning. We perform experiments on PhysioNet’s MIT-BIH dataset for five different arrhythmias in accordance with the AAMI EC57 standard and on PTB diagnostics dataset for myocardial infarction (MI) classification. We achieved an state-of-an-art results in terms of prediction accuracy, precision and recall.

Global cognizance for a green environment will embark on the rising demand for Plugin Electric vehicles (PEV) in the upcoming years. In this article, an online power management scheme (RTPMS) for a solar power battery-buffer grid-connected charging facility in an educational work area is developed. This algorithm contributes to lessening the overall diurnal price of recharging the PEVs, alleviating the influence of the charging station on the power grid, while participating in shaving the rise of the load curve. The understudy paper sheds the idea of quick charging of PEV integrated with energy storage stacks, photovoltaic systems and at the least demand with the power grid subject to constraints scenarios. Forecasting and statistical parameters are considered in the RTPMS to model the many uncertainties encompasses such as the photovoltaic power, PEVs arrival-departure period, and the energy present in their car batteries during their entrance at the station. The paper demonstrate maximum profit acquired by fast electric station besides lessening the overloading on the power grid with the execution of resilient RTPMS. The efficacy of the proposed supervisory rule-based method is stamped through assigning different assignments through MATLAB code editor.

Optimization is a method for searching the best candidate solution to lessen or expand the value of the objective problem. Broadly speaking algorithms can be orgabized into four main classes, i.e. biology-based algorithms, physics-based algorithms, sociology-based algorithms, and human intelligence-based algorithms. Swarm-intelligence (SI) based algorithms appeared as a commanding family of optimization techniques. The paper aims to commence a brief review of meta-heuristic algorithms especially Particle swarm optimization (PSO) and its sister variants in short. The understudy paper covers all important aspects of swarm intelligence PSO with deep insight learning for practitioners and scholars.

To lessen the burden on the mainstream, the simplest means is to use an extreme mix of renewable resources. A grid-interactive electric-driven vehicle fast-charging area boosted by the solar power network and a couple of lithium chargeable batteries as a dynamic stationary energy storage powerhouse is valued and understudied. The scheme management is backed by pretending the fast station under variant irradiance scenarios taking consideration of the price of the power traveling waves and the battery state of health. The feasibility of deploying local stationary storage with the ultra-power charger to make it less dependent on the grid is demonstrated in the defined scenarios. The execution of the energy management strategy is carried by employing a rule-based energy management controller with the efficient algorithm scheme applies to an advanced fast car charging deck. It reveals that the process works well and has a power efficient control of the car refueling deck under study. Indeed, this project is of the best concern for reviewing and assessing several configuration and control networks of fast charging through the application of paradigms with Matlab/Simulink simulations.

This paper presents an effective method to deal with the uncertainty problems of the magnetic levitation systems using the Fuzzy Supervised Proportional-Integral-Derivative (FSPID) controller. A magnetic levitation system is matured using a conventional PID controller and FS-PID controller. The magnetic performance of the levitation control system significantly relies on the PID controller being adopted. Owing to the fact that conventional PID controllers have constant gains, they hold some limitations as they offer uncertainty in case of an air gap and load changes. To overcome these limitations, a Fuzzy supervisory PID controller has been designed by making use of the Fuzzy inference engine. The designed FS-PID controller has the ability to intelligently tune the gains of a conventional PID controller, obtaining the desired response under various conditions. The results validate that the problem of uncertainty due to the air gap and load changes have been resolved convincingly.

The emergence of Internet-of-Things (IoT) has attracted a massive response from different stakeholders including industry, academia and research community due to the wide range of applications it provides. IoT enables devices with storage, computation and communication capabilities to connect to the Internet, thus providing various applications such as smart transportation, smart healthcare, smart industry, logistics and smart homes, to name a few. Since, IoT devices generates sensitive information, therefore, security is an important issue in IoT networks. Recently, Blockchain (BC) is proposed as a revolutionary technology which can be integrated within IoT to provide desired level of security and privacy. However, the integration of BC within IoT networks is extremely challenging. The major contribution of this study is three-fold. (1) Firstly, we proposed a tier-based architecture “Blockchain in Internet-of-ThingS (BITS)” which can support BC within IoT networks. (2) Secondly, we highlighted various applications of BC in IoT networks, and (3) Lastly, we provided various challenges and future research directions in the realization of BC in IoT networks.

To meet the fast-growing energy demand and manage the distributed resource, an intelligent energy management system plays a vital role in the electric power system. The next-generation electric power system, known as the smart grid, will incorporate a large number of renewable energy resources that fundamentally change the energy management paradigm. To manage efficiently the energy supply and demand in the power grid, further improvement in the reliability and efficiency of electrical grids can be achieved by utilizing dc distribution in microgrid systems. DC microgrid is an attractive technology in the modern electrical grid system because of its natural interface with renewable energy sources, electric loads (e.g. data center load, electrical vehicle charging load, residential dc load), solid state transformer and energy storage systems. In this paper we discussed the functional expectation of the dc microgrid, system architecture, communication structure and distributed grid intelligent system. This paper highlights the recent technology update in dc microgrid system. This paper also discusses benefits and challenges of using dc grid.

In this paper a solar photovoltaic based power generation system with improved panel efficiency using back flow channel is proposed. Main contribution of this work is to improve the electrical efficiency of a standalone PV system by reducing the panel temperature using Zinc-water (Zn-H20) nanofluid as a back channel fluid. Forcing nanofluid to flow through the tubes attached at the back of the panel reduces panel temperature considerably results in efficiency improvement of the panel. To analyze the performance of the proposed Zn-H20 nanofluid flow channel based PV Panel used in PV systems, MATLAB Simulink model is developed and simulation under varying conditions of temperature and irradiance for Sub-tropical region (27.8974° N, 78.0880° E) has been carried out. Results obtained with Zn-H20 nanofluid flow channel are compared with the system with air flow and without air flow channel PV panels. Proposed system using nanofluid shows efficiency improvement of 0.4-0.6% and 1-1.1 % to that with air flow channel and without air flow channel respectively. It is envisage that the proposed Panel system can be very useful for design and implementation of an efficient standalone solar PV system in sub-tropical region.

In this paper modified opportunistic routing (MORR) protocol is propose, which focuses on addressing routing issues for wireless sensor networks (WSNs) for prolonging network lifetime and load balancing by selecting forwarder nodes, which have maximum residual energy so that network lifetime is enhanced. Furthermore, throughout the selection of forwarder node, channel interference is also taken into account to offer consistent communication. Consequently proposed scheme minimizes the chances of collision and packet duplication at the network layer by controlling number of forwarders and transmitters. Simulation results show that our scheme outperforms in term of average number of transmissions per packet, packet delay, energy consumption and residual energy as compared to baseline solution.

The invention of Smart Grid (SG) have revolutionized the traditional electricity consumption pattern as well as distribution. The technology of communication and information have been involved in almost every domain so for Smart Grids. Production of electricity is not cheap, hence with the help of information and technology, Smart Meters (SM) play vital role to control, manage and perform optimization to utilize the electric power efficiently on consumer side and called Demand Side Management (DSM). In the proposed research paper, a Home Energy Management System (HEMS) have been proposed to optimize the home appliances to reduce the maximum cost. Elephant Herd Optimization (EHO) algorithm have been implemented along with our own hybrid of EHO algorithm. This EHO is hybrid of Genetic Algorithm (GA) and called Genetic Elephant Herd Optimization (GEHO). Results of simulation shows that GEHO scheduled the appliance more efficiently to reduce maximum cost when comparing with regular EHO and unscheduled schemes. Peak to Average Ration (PAR) also have been observed. GEHO and unscheduled have equal PAR due to scheduling of maximum appliances on either sides but EHO have small PAR. For further understanding of cost optimization different Operation Time Interval (OTI) have been applied. Trends of load and cost with all of three schemes have been discussed in detail.

Patten recognition techniques are widely used for image processing in medical imaging. It provides assistance to physicians and scientists in large scale diagnosis. In this paper, we have proposed an automated system for detecting melanoma from dermoscopic images. We detected melanoma by extracting information from region of interest (ROI) rather than the whole image composed of lesion and background. ROI of image is extracted out by using one of the segmentation techniques named as adaptive thresholding with morphological operators. After extracting ROI, we analyzed the classification results by using texture and color features on the extracted region and compared the results. The images were obtained from one of the hospitals of Portugal utilizing dermoscopy as an imaging methodology. Our extracted algorithm shows better performance than conventional thresholding and based on experimental results it can be concluded that the color features outperform the classification results but it gives more accurate results by combining color and texture features extracted from ROI.

A robust adaptive beamforming algorithm based on least square constant modulus algorithm (LSCMA) is proposed in this paper. The CMA adaptive array was designed to lock onto the desired signal having the constant modulus property. The proposed algorithm employs both the DOA information as well as the constant modulus property of the signals. This enables the beamformer to avoid self-cancellation caused by the desired signal having large power (high SNR) relative to the interference signal, while maintaining the good performance at low SNR. Simulation results demonstrate the superiority of the proposed algorithm especially for the case of desired signals having large dynamic range.

The paper holistically discusses the analysis and simulation of simplified two diode model and its comparative analysis with the one and three diode models. The two diode model presented has been grounded on the interpretation of the output current equation. This model entails four parameters and condenses the complexity and the computational time considerably. I-V and P-V curves for two diode model are plotted taking into account the variation of the atmospheric irradiance and temperature. Furthermore the two diode model is analysed by taking into account the effect of partial shading which plays an extremely detrimental role in the Solar Array output. A grave issue with partly shaded PV arrays is detouring of the maximum output voltage which acts to preponderate due to the manoeuvre of the bypass diodes present alongside the affected cell. This might cause a sizeable power loss, subject to the frequency of shading manifestation and thus leading to substantial reduction in the energy output of module. Paper presented scrutinizes the effects of six different Shading schemes on 6 × 6 array. Additionally it peers effects of Partial Shading on the different diode models in consideration. The characteristics of the diode models are comprehensively affected due to the presence of the shading effect. All the diode models for the investigation have been primed using the MATLAB simulation software. The values of various parameters used for comparison are presented in tabular form. The conclusions are deduced and the best model for simulation purpose is suggested taking into account the partial shading effect. From the outcomes and conclusions of this paper, it can be established that there exists a momentous loss in the output power because of the irregular irradiation pattern visible on the series strings.

The digital beamforming (DBF) of ultra-wideband (UWB) radar is a challenging issue and its performance is closely affected by the delay compensation filters and the aperture of the antenna array. In this paper, a high-resolution DBF method for UWB signals utilizing Hermite fractional delay filter sets combining a virtual array created by a vector extrapolation algorithm on autoregressive(AR) models is presented. Theoretical analysis indicates that the magnitude response and the group delay of the Hermite fractional delay filter sets are much more accurate than that of the traditional method such as Lagrange fractional delay Alter over the whole frequency band, and the adopted virtual array creation algorithm can extend the aperture of the original array to 6 times. Simulation results of the DBF performance using UWB linear frequency modulated (LFM) signals for two different scenarios also demonstrate the superiority of the proposed method.

The convergence rate of the power inversion (PI) algorithm utilized in null steering antenna is of vital importance to handle interferences in efficient way. This paper presents the in-depth analysis of the factors affecting the convergence rate of null steering antenna utilizing power inversion algorithm. Apart from choosing optimum value of step size, factors like number of snapshots, signal structure, DOA of signals etc can also affect the convergence rate which can degrade the performance of null steering array to null the interference efficiently. Simulations show that the properly designed array with optimized parameters can achieves faster convergence and produces deeper nulls. By choosing optimum values of these parameters null steering antenna utilizing power inversion algorithm becomes more robust and efficient.

Cognitive Radio (CR) is an emerging and promising technology which is predicted to solve the problem of spectrum shortage by utilization of the spectrum efficiently by exploiting the licensed spectrum white spaces. Video on demand (VoD) is a very popular present day service. It is a well-known fact, that video on demand internet service needs a large amount of bandwidth. CR WMNs is envisaged to provide the much needed bandwidth for video streaming as CR WMNs has the ability to access a large part of the under-utilized licensed spectrum. In CR WMNs interference is a critical issue which degrades cognitive radio wireless mesh networks (CR WMNs) performance. As the number of users rises, interference also increases resulting in low throughput. In this paper first an analytical model for CR WMNs has been presented. We have also presented an efficient VoD model and an optimizing approach which minimizes interference to provide a much needed higher network capacity to VoD services. Simulation results show effectiveness of our proposed approach as there is an increase in the number of concurrent VoD sessions.

Internet Protocol Television — IPTV is a new form of traditional TV, in which TV content is delivered using IP based networks which are managed to provide the required level of quality of service, experience, security, interactivity and reliability. Quality of Service (QoS) and Quality of Experience (QoE) are the two major factors which need to be ensured in order to achieve IPTV customer satisfaction. Channel zapping latency is the major problem which affects QoE for IPTV users. In IPTV systems while switching channels, a delay occurs due to different factors like IGMP Join/Leave latency, video steam encoding and decoding time, network jitter and traffic load. These factors hinder the process of IPTV deployment at large scale and pose scalability issues to IPTV. In order to solve IPTV channel zapping latency problem, a number of approaches have been contributed by different researchers but due to bandwidth limitation and video quality issues, every approach has its own tradeoff. In this paper, we survey the current state-of-art in this thread of research and present the insights behind various approaches that have been proposed in literature to solve the problem of increased channel zapping time. In conclusion, we also point out open research questions and future research areas.

This paper studies the uplink channel of a time division multiple access (TDMA) cellular network assisted by wireless relays. Aiming at increasing the networkpsilas transmission rate, we optimally place multiple decode-and-forward (D&F) dedicated relays within every cell. In the first part of the contribution, we consider the relays transmitting Gaussian codewords. Results show that there is almost 39% gain in the transmission rate of each cell with respect to no relaying. In the second part, we analyze the performance impact of finite set modulation at user and relays (i.e. M-PAM) . With such a modulation, the gain is slightly decreased to 27%. Finally, we show that D&F relaying provides large gains in the outage capacity at the edge of the cell, with similar performance for Gaussian and M-PAM modulation.