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International audience; A metasurface consisting of a 2D periodic square lattice of metallic square ring resonators is considered for supporting spoof surface plasmon polariton (SSPP) modes. First, SSPP modes are highlighted by simulated dispersion curves. The dispersion curves are analyzed by considering hybridization of modes in either a patch or an aperture array with frequency shifts. Hybridization of modes is also supported by means of surface current and electric field mappings showing consistent symmetric and anti-symmetric fields. Second, it is shown that a dipole antenna located near the metasurface is able to generate SSPP modes. The transmission spectra between two antennas located at each extremity of the metasurface show transmission enhancements compared with propagation in free space. The transmission enhancements are shown for different locations of the antennas either facing each other or positioned in diagonal. Finally, the transmission enhancement is connected to SSPP modes by comparing the transmission spectra and the measured dispersion curves with each other. Specifically, two transmission windows measured for an embroidered metasurface are related to the cutoff frequency of the two SSPP modes supported by the metasurface as evidenced by the measured dispersion curves.

Switching transients arises due to random switching of power apparatus results in degradation of thermal and dielectric health of equipment and system as a whole. Controlled switching or point on wave control has emerged as excellent solution for minimizing the charging transients and can aid the better condition monitoring of circuit breaker. Different switching strategies are in place based on different design and connection configurations. Each methodology has certain advantages and dis-advantages. The performance of control switching is dependent on the dielectric performance of insulating medium and circuit breaker performance. The effect of variations in insulation dielectric properties and breaker mechanical performance has been discussed in depth in this paper. Selection and performance of feedbacks used in controlled switching also play a major role in target evaluation and needs proper assessment. Review of different controlled switching methodologies in respect of transformer and shunt reactor have been discussed in paper augmented with field cases. Based on the review of available controlled switching methodologies and field experience, the acceptance criteria have been proposed.

Internet of Things (IoT) is a novel paradigm and a popular buzzword in academia and industry. However, the development of IoT systems in emerging economies such as India is still in its infancy due to poor knowledge of key success factors (KSFs) in implementing IoT systems. Therefore, this work tries to ascertain and examine the KSFs in the execution of IoT systems in the Indian context. Six main factors and twenty-four sub-factors to adopt IoT systems were recognised by literature support and experts’ feedback. Further, analytical hierarchy process is adopted to examine the KSFs in the adoption of IoT systems. The results suggested that ‘Connectivity (C3)’ main factor is the key significant factor to adopt IoT in the Indian perspective. The present research could assist policy practitioners, academicians and researchers in the understanding of various KSFs and their priority in the execution of IoT in the Indian context.

Human activity recognition (HAR) is the method of detecting the physical activity of a person. It has a huge scope in the medical domain for supervision and health analysis. With the help of artificial intelligence, it can be performed using regularly available smartphone devices. For healthcare, HAR is often a part of an IoT framework. Using a cloud-based IoT system ensures maximum resource usage and data storage but comes with the challenges of high latency and bandwidth consumption. To avoid this, an edge-based system has been proposed in this work, which ensures minimum interaction of the device with the cloud and also makes emergency actions possible to be taken due to low latency of the system. A comparative study has been done on different state-of-the-art machine learning models. Also, to ensure minimum resource requirement, optimized neural network models have been used, and it has been shown how they require less storage as compared to traditional methods.

The protection of DC power circuits against short circuits in the load is considered and a possibility of preventing emergencies is demonstrated during operation of pulse power supplies feeding the AC electric arc, which is a complex load for output stages of an inverter. The proposed scheme was implemented and tested in an arc power supply for the development of 3D-printing technology using local segregation electric arc melting.

Over the last 10 years, there has been an exponential growth of Internet services over cellular and wireless network. This has resulted in multiple wireless networks: macro-cell, micro-cell, femto-cell, legacy networks, etc. co-existing simultaneously. Multihop cellular networks have been well-investigated to support the heterogeneous wireless network scenario. However, this results in a very distributed and a non-optimum distribution of radio resources, leading to sub-optimal throughput of the network. Keeping this in mind, this paper proposes a three-hop heterogeneous wireless network architecture, wherein each hop would be from same network or other networks. Importantly, this paper proposes and provides a detailed analysis of a specific cluster-based architecture: three-hop network. This is the first instance where a three-hop architecture is analyzed. Accordingly, in every cell, three communicating pairs communicate using a given radio resource, but for only $${1/3}^\mathrm{{rd}}$$ the time slot period. This three-hop design enables a frequency reuse ratio of one. A comprehensive performance analysis indicates that the capacity obtained with a three-hop architecture is up to 20% higher than that can be obtained by two-hop architecture and 40% higher than traditional single-hop network. This is an extremely important result. It indicates the improvement that can be obtained in using specific design for multihop-based next generation LTE-advanced network.

Recently, there is growing interest in the Internet of Bio-Nano Things (IoBNT) based on biological communication with the advent of communication engineering and nanotechnology. One of the IoBNT's significant challenges is modeling and simulating of bio-cyber interface for linking electromagnetic signal- based internet to the biochemical signal-based bio-nano-network. Gaining an understanding of the bio-cyber interface is vital for the design of the IoBNT framework. In this paper, an artificial neural network (ANN) approach is proposed for parameter estimation of the bio-cyber interface. This choice's motivation is the complexity of the mathematical model of the bio-cyber interface system and the possibility of using ANN methods in this perspective. The idea in this work is for ANN and mathematical modeling to complement each other. The proposed approach is based on two directions: using a non-linear least square for fitting electro-bio and bio-electro interface model parameters, then applying ANN to the output of the first direction to learn the design behind the model parameters(to acquire the parameter estimator). The research work proves that ANN can learn (train) from the model parameters. Furthermore, the training network can predict the model parameters for a given system. The results show that ANN achieves effective performance for parameter estimation of the bio-cyber interface. Finally,the performance analysis of the proposed bio-cyber interface device is evaluated by employing a pharmacokinetics compartmental model for molecule diffusion over the blood vessel system.

The results of the analysis of the features of the power exchange process in switched capacitors are presented. The methods are outlined to ensure high efficiency of charge pumping. The features of switching processes in transient operation modes are investigated. Circuit modeling made it possible to establish the sources of losses accompanying power conversion and heat release. The recommendations are made to optimize parameters of converter components and generate control signals.

A method is proposed for constructing a radiotelemetric system for remote reading of information with a hydroacoustic channel, intended for the rapid acquisition of data from sunken emergency carriers without lifting them. To assess the technical feasibility of such a system, theoretical calculations of the efficiency of individual subsystems were carried out and mathematical modeling was conducted. Based on the simulation results, the possibility of constructing and the main parameters of the system (throughput, the required number of elements, the type of modulation and error-correcting coding) has been established.

Studies have shown that heating ventilation and air-conditioning (HVAC) systems are the major contributors to the high energy consumption of buildings. This has some serious deleterious effects on the environment. Thus, to reduce and optimize the energy consumption, it is important for the HVACs to work efficiently at all times. Existing techniques have focused on the optimal usage of HVACs for minimizing the energy consumption of buildings. This paper offers a pragmatic and holistic solution for saving energy in smart buildings by detecting faulty HVACs using power consumption and temperature data only. The developed approach is applied to a building consisting of forty-eight HVACs and is situated in Mumbai. A total of thirty-one HVACs were detected to be faulty. The results indicate that about 46% of energy can be saved if all the HVACs operating in the building are free of faults. This is an impressive saving in energy consumption, which demonstrates the effectiveness of this novel approach.

There has been an astonished growth in the evolving technology. When it comes to the word technology, the most predominant usage by the society is Internet. There are many channels in the Internet through which data is transmitted. Having such rapid and fast growth, wide communication media increases the exposure to security threats causing unintellectual ingress of information. Steganography is the main aspect of communicating in an aspect which hides the extent of the communication. Steganalysis is another important concern in data concealing which is the art of identifying the existence of steganography. Critical review has been presented on steganalysis and detecting the steganography in images without embedding and extracting the payloads in the images. RS steganalysis and a classifier are used in this paper to detect the hidden payload in bulk images.

The use of space division multiplexing (SDM) technology provides an additional level of freedom within the elastic optical networks (EONs). The SDM-based EONs (SDM-b-EONs) are envisioned to provision the enormous capacity needed by the future heterogeneous traffic. However, in SDM-b-EONs, a lightpath can span multiple spaces (i.e. cores, modes or fibres), which poses new challenges in regard to the network protection. Many studies have proposed novel protection schemes for SDM-b-EONs; however, no study has considered the grooming of traffic and the overlapping of spectrum in protected SDM-b-EONs. In this study, the OverlaPping of spEctrum and gRooming of trAffic (OPERA) method has been introduced which provides online protection to a SDM-b-EON simultaneously considering advantages obtained by grooming the traffic and overlapping the spectrum. The performance evaluation results demonstrate that OPERA (1) maintains acceptable crosstalk levels while ensuring an efficient use of the network and (2) ensures the desired transmission quality simultaneously minimizing the demands blocking.

The issues of increasing the operation efficiency of mobile communication systems of next generations using adaptive noiseless coding are considered. The optimization method of the turbo code encoder and decoder operation is proposed using an adaptive choice of the state diagrams size with a new parameter of decoding uncertainty value. The method implementation allows us to provide the specified values of information reliability without reducing the paths throughput of systems and networks of 5G generation.

In this paper, complex adaptive algorithms for information processing in navigation complexes of mobile ground objects based on satellite radio navigation systems are synthesized applying Markov theory methods for optimal estimation of random processes. The algorithms make it possible to detect abnormal measurements at the output of the equipment for receiving radio signals from satellite radio navigation systems, and to additionally control the integrity of navigation data of satellite radio navigation systems based on the use of information from barometric altimeter. A structural scheme for information processing by the navigation complex is developed. A computer simulation of the obtained adaptive algorithms is carried out.

In this paper, we systematize architectural and circuit design solutions applicable in the design of digital parts of multispeed transceivers of serial channels. Such variants of solutions, which allow using most of the possibilities of available technology for expanding this range, especially in its high-frequency part, are substantiated. The SpaceFibre multi-speed serial channel transceiver was designed by CMOS technology using the proposed solutions. The manufactured transceiver operates in the range of bit rates from 5 Mbps to 3.125 Gbps and, in terms of the set of measured parameters, is not inferior to the closest foreign analogs.

—On the basis of the theory of conditional Markov processes, recursive optimal and quasi-optimal algorithms for estimating discrete-continuous Markov processes from observed digital signals are synthesized. The efficiency of the quasi-optimal algorithm is confirmed by statistical modeling.

The possibility of using millimeter-wave (MM) satellite communication lines in the Arctic to ensure reliable and high-speed communication is considered. The main problem for the application of MM waves is the possible meteorological conditions for the propagation of radio waves in the cold. Vertical profiles of meteorological data for a pair of islands in the Arctic Ocean were used to calculate molecular absorption on a possible link. A very simple model of attenuation of radio waves in rain on such a line is proposed. The satellite orbits for calculating the line attenuation margin were assumed as in the existing satellite system Gonets operating at frequencies of 300/400 MHz. The results of calculations show that satellite communications in the MM band are possible in the Arctic.

Efficient big data clustering is a requirement for massive data generating in this digitalized connected world. The traditional clustering algorithms do not scale over massively sized and highly unstructured big data. Thus, to obtain efficiency in clustering big data new architecture and programming paradigm is required. In this work, a novel MapReduce-based Fuzzy C-Medoids clustering algorithm is designed and experimented with to cluster big data repository of documents datasets. The performance of the proposed algorithm is experimentally evaluated for different-sized Hadoop cluster sizes and different-sized document datasets. The algorithm is found to be scalable and efficient in performing clustering jobs.

The clustering of big data is a challenging task. The traditional clustering algorithms are inefficient for clustering big data. The recent researches in this field suggest that the traditional clustering algorithms needed to be redesigned for the modern architecture of computing. This wok has proposed a novel MapReduce-based fuzzy C-means algorithm for big document data clustering. The algorithm is extensively experimented with using different sizes of document datasets and executed over the Hadoop cluster of different sizes. The proposed algorithm’s efficiency is compared against serial traditional fuzzy C-means and MapReduce-based K-means algorithms. The proposed design of the fuzzy C-means algorithm is scaled well with the Hadoop platform and documents big datasets and resulted in a performance gain.

In recent years, distance education based on network environment has made great progress in China. The combination of network technology and teaching activities has brought new enlightenment to the education field. Under this background, this paper designs the distance teaching platform of network English course for communication engineering specialty, optimizes the design function of network English course platform, analyzes the common forms of the network English Course Teaching of communication engineering specialty and the new requirements of new forms for Distance English teachers, and probes into how to improve the teaching water of network English course teachers of communication engineering specialty In order to promote the further development and popularization of Distance English education.

The development of an original electromytostimulator is presented, and the functionality of the proposed device is investigated. The main feature of this device is to receive an output signal of increased power, as well as its use in a semi-automatic mode. This electromyostimulator was created at the Scientific and Technical Center of Unique Instrumentation, Russian Academy of Sciences on the basis of previous developments in this area. A trial experiment was carried out to test this device in the training process of highly qualified athletes.

The research of complex networks facilitates the progress of various disciplines, including biology, chemistry, social science, computer, and communication engineering. Recently, it is popular to utilize complex networks to study the communication networks, such as designing efficient routing strategies and robust communication networks. However, exploiting the advantages of communication networks to investigate networks in various disciplines beyond telecommunications is still in infancy. Because of this situation, this paper proposes an information-defined network (IDN) framework by which a complex network can be abstracted as a communication network associated with multiple intelligent agents. Specifically, each component and dynamic process in this framework can be defined by information. We show that the IDN framework promotes the research of unsolved problems in the current complex network field, especially for detecting new interaction types in real-world networks.

The wireless body area networks (WBAN) enable to communicate with the on-body wireless devices and systems. For on-body applications, the key requirement for the antennas is the antennas flexibility to mount the antennas on the body. Wearable antennas are fabricated on a flexible substrate to make these antennas suitable for mounting on the human body. Due to the wearable feature of these antennas, they are used in many on-body applications. The wearable characteristic also makes these antennas suitable for many on-body medical applications. This paper presents the technical review of the WBAN, WBAN frequency bands, wearable antenna fundamentals, flexible substrate characteristics, design and development of wearable antennas for medical applications. The wearable antennas are fabricated using the fabrics. The review of the material properties of various flexible substrates is given in detail. Due to the presence of the air in the gaps of fabrics, the dielectric constants of these materials are very low. Detailed analysis of antenna performance due to flexible substrate material characteristics is also discussed. The developments of wearable antennas for WBAN medical applications are presented. The paper also focuses on the design considerations, fabrication methods, challenges, and proposed solutions for the wearable printed antennas.

Specialized methods and tools (high-level models) that can significantly speed up the calculations of high-speed serial channel transceivers have been developed. A detailed analysis of the time spent for the calculations and measurements of high-speed transceivers and the benefits from the application of the proposed specialized methods and tools during the design of these devices is presented. Examples of calculations of the designed transceiver are given with determining the main parameters characterizing the quality of their work, including the relative number of errors during reception.

The present study proposes a software program with a GUI graphic setting based on the capabilities of MATLAB and PSpice software packages for describing and modeling the function of non-isolated DC-DC converters, which are of high importance for students of electrical engineering. In the light of its simple user interface, the proposed program can make a considerable contribution to educating the modeling of non-isolated basic switching convertors, investigating their function, and simulating their time and frequency domains. Accordingly, it makes it possible to observe the effects of load variations, input voltage variations, convertor element values, and tolerances on the function of convertors. Also, the software can automatically build convertor models in MATLAB and their equivalent electronic circuit in PSpice. Thus, the function of convertors can be studied by the MATLAB model and comparing the results to the electronic circuit simulation results in PSpice. Ease of use and avoiding the user involvement in the software, on the one hand, and the ability of complete modeling of non-isolated DC-DC convertors to help engineers design controllers for convertors and observe the results, on the other hand, are the most important features of the proposed software program.

The M-distance-based recommendation system (MBR) is one of the most successful types of recommendation systems. In the training phase of MBR, the average of ratings given to each item is used to determine similar items. To estimate the active user’s rating on an item in the test phase of MBR, only rated items of the active user are necessary, whereas in MBR, all nearest neighbors of that item are examined and then, unrated neighbors of the active user are ignored. In most datasets, the number of unrated items is very high and, therefore, the most of nearest neighbors are unrated, examining all nearest neighbors in the test phase of MBR is unnecessary and time-wasting. In this paper, a new data structure is proposed to improve the test time of MBR. In the training phase, the rated items of each user are stored in this data structure. By employing this data structure in the test phase, it will be then unnecessary to examine unrated items. Strictly speaking, the nearest neighbor of each unrated item of the active user is determined in the test phase by examining the small set of rated items of the active user stored previously in the data structure. According to the experiments conducted on five real datasets, the runtime of our proposed method is 2.05, 10.68, 132.19 and 21.77 times less than that of MBR for the datasets ML-1 m, ML-10 m, Douban and EachMovie, respectively.

Nowadays, due to modernization or advancement in the Internet of Things (IoT) especially in the Healthcare area, we want to take care of our elders with some monitoring equipment, and the Internet of Things can play a significant role in it. The motivation of writing this paper is to collect the information of various existing Internet of Things Architecture and Communication Techniques used in Healthcare Monitoring System to observe that how efficiently, different researchers have used it. So we have studied different real-time health monitoring system based on diseases which are common in elderly people like diabetes, blood pressure, heart disease, sleep apnea, and cancer, etc. In this real-time health monitoring system, researchers introduced many new measures, communication techniques like ZigBee, Long-Range Wide Area Network (LoRawan), Radio Frequency Identification (RFID). Apart from this, it was also observed that remote monitoring system in Healthcare is incomplete without data processing and early prediction in such diseases. Though, Machine learning provides efficient techniques to extract knowledge from diagnostic medical datasets collected from the patients. That is why we highlighted the current role of various Machine Learning algorithms like Support Vector Machine, K-Nearest Neighbor, Random Forest, etc., for processing of Healthcare data and also helpful to predict the output more precisely.

Electrical energy theft is a major concern for power distribution companies as it results in an increase in non-technical loss. This overloads the distribution network and also forces power Distribution Company to increase tariff on legal consumers to meet these losses. Direct hooking on distribution overhead line or tapping’s from underground cables remains perpetual issue which should be stringently annihilated. The objective of this research work is to design and develop online electrical energy theft detection and prevention scheme (E2TDPS) with the available infrastructure in field. Proposed E2TDPS collects online data from the digital energy meters connected in the premises of legal consumers and at distribution transformer. From this data, proposed algorithm determines the voltage regulation and non-technical loss in the distribution network. It identifies the pilferage section and estimates the power stolen by illegal consumers. It can send the date, time and locations of power theft on the cellular phone of utility engineers. Thereby, the utilities can arrange raids at theft locations to catch the miscreant. The prototype is tested in the laboratory, and the results demonstrate that the proposed E2TDPS works satisfactorily under diverse operating conditions.

New Radio-Unlicensed (NR-U) is a technology that allows fifth generation mobile networks to utilize unlicensed frequency bands, in particular, the 5 GHz band. To increase throughput, an NR-U base station (gNB) can simultaneously operate in several frequency channels within the 5 GHz band. However, the gNB cannot always transmit data through one channel and, at the same time, perform the channel access procedure in another channel due to the out-of-band emission problem, which leads to channel underutilization. As a result, the throughput of NR-U networks can heavily depend on the out-of-band emission level. In this paper, by means of simulation, the impact of out-of-band emissions on NR-U performance is studied when NR-U uses all channel access methods defined in the specification. It is shown that the out-of-band emission level affects the choice of channel access method optimal in terms of throughput. We also show that out-of-band emissions do not always have a negative impact on NR-U performance; sometimes they make it possible to achieve more than 30% throughput gain.

In this paper, we propose a methodology and models for physical modeling of electromagnetic interference using the example of the interference-resistance analysis of vehicle electronic devices under a powerful electromagnetic effect of the contact network of electric transport. Physical models and an experimental stand for physical modeling of electromagnetic interference in a communication line were developed. The obtained results allow implementing a practical method for predicting the interference resistance of vehicle electronic devices when exposed to a magnetic field of an electric-vehicle contact network based on physical modeling.

Predictive Modeling problems deal with high-dimensional data; however, the curse of dimensionality presents an obstacle to the use of many methods for their solutions. In many applications, real-world data occupy only a very small part of high-dimensional observation space whose intrinsic dimension is essentially lower than that of the space. A popular model for such data is the manifold model, according to which the data lie on an unknown low-dimensional manifold (Data Manifold) embedded in the ambient high-dimensional space. Predictive modeling problems, which are studied under this assumption, are called manifold estimation problems. The general goal of such problems is to identify the low-dimensional structure of multidimensional data from a given dataset. If dataset points are sampled according to an unknown probability measure on the data manifold, there is a need to model manifolds when solving various machine learning problems. We provide a short survey of such problems and outline some approaches to their solution.

Transportation is a driving force behind economic development and well-being of all people around the world. An urban transport system is one of the most critical infrastructures for urban development. Impacts of a poor urban transport are manifested in terms of congestion, delays, pollution, accidents, high-energy consumption, low productivity of resources, community severances and inadequate access to the service. In urban areas, despite the construction of flyovers and roads, the roads continue to face traffic congestion, environmental pollution and lack of mass transportation facility resulting in heavy shift of commuters to intermediate and private vehicle ownership. Therefore, it is necessary to make transport system attractive and user friendly in terms of safety, reliability, travel time and comfort, which can be achieved using intelligent transportation system (ITS). This study has been intended to develop a methodology for development and operation of intelligent transportation system namely Presto ITS mobile application. The Android application, Presto ITS, developed works along with the Urban Traffic Data Management Centre to help reduce the congestion on the road networks by allowing the users to make informed decisions about route choices. This helps in reducing further inflow of vehicles in an already congested road network and thus allowing distribution of vehicles among the different routes resulting in lesser overall congestion. This system when installed on an overall city wide basis will drastically help in reducing the congestion and evenly distributed traffic.

In recent times, scores of innovations are taking place in the field of unmanned aerial vehicle (UAV) technology that includes continuous development of design of on-board processing, memory, storage, and communication capabilities. Since UAVs are now being used extensively in many areas of expertise that include commercial, military, civilian, agricultural, and environmental applications, it is very essential that these UAVs communicate efficiently. The communication may be of the UAV-to-UAV (U2U) type or it could be with the already existing prevalent networking infrastructures UAV-to-Infrastructure (U2I) type of communication. A new prototype is developed based on the insect flight dynamism. These insect wings are very thin membranous structure. They are subjected to many forces and follow aerodynamic flying and hovering. The current research trend on this subject is the simulation of insect wing structure and utilization to create a prototype for the development of unmanned micro air vehicles. In the present study, three flying insects such as dragon fly, cicada, and jewel beetle are selected and the wing aerodynamic parameters are analyzed by mathematical models. The comparative scanning of the data revealed that cicada wing structure is their aerodynamic characteristics may serve as a prototype. The results are discussed with relation to the base data for the flapping-wing system in micro air vehicle. In this paper, we have tried to put forward an approach that is capable of providing real-time positioning and tracking of a UAV. The approach has three parts: tracking device, backend server, and mobile app.

Engineers are facing challenges related to secure and steady operation of power system by virtue of the fact that wind power is variable due to its intermittent and unpredictable behaviour. In the present paper, the focus has been laid down on the latest and ongoing advancements in the arena of wind-energy forecasting methods. It is seen that there are various forecasting techniques recommended and implemented to predict the level of fluctuations of the wind. To estimate the wind energy and wind speed numerous measures have been evolved and implemented by the researchers. The literature exhibits that a lot of research is under way, in operation, accompanying, and proceeding to estimate wind-speed and power incorporating intelligent, biologically and naturally inspired computing techniques, and mathematically developed models to minimize the forecasting error. This review paper also explores the techniques utilized for integration of wind power and its economic influences on the present power systems and various techniques and guidelines for balancing the electricity power market. Various models for validation and simulation of electricity power-market to integrate wind production are also highlighted. The paper intents to be beneficial for the researchers and engineers particularly those who are working in this particular field.

The growing demand for data rate and volume in different areas and different scenarios manifest that the novel service as ultra-reliable low latency communication introduced in 5G network. High reliability at stringent latency constraints concerned with demanding areas of its use like V2V communication, industrialization that call reliability of 99.999% (i.e., five 9’s). In the mentioned work, the concept of multi-connectivity (MC) is presented to accomplish a particular goal of ultra-reliability as well as trying to maintain latency as small as possible for different heterogeneous nets. MC is one of the major competitors, due to its capability to tackle diverse element involved in it. It plays a vital role to deal with different interfering channel impairments, which results to gain aimed outage probability. Some findings regarding the paper are: (1) achieving to measure the exact outage probability gain considering SINR at the given threshold of SC over different MC diversity schemes; (2) evaluating the work advancement of dual connectivity-selection combining, dual connectivity-maximum ratio combining over single connectivity (SC) in terms of sum-rate for different UE positions. The performance is evaluated using computer simulations and considering the gain regarding MC and cooperation permitted cloud radio access network.

In this paper, we show the possibility of organizing multi-user access based on a relative transmission scheme using chaotic radio pulses as a data carrier. The maximal number of subscribers and information capacity of a multi-user system are theoretically estimated. The validity of the obtained theoretical results was confirmed by computer modeling.

Unintentional islanding is one of the serious concerns to be addressed when interfacing a distributed generation source, as it causes damage to the equipment and personnel. Many algorithms have been proposed for islanding detection in the literature which are classified into local methods (passive, active and hybrid), communication-based methods, and methods employing signal processing and classifiers techniques. Out of which, passive islanding detection methods have been mostly adopted in practice since they have an edge over other islanding detection methods (IDMs) such as there is no degradation of power quality as in active methods and are simple to implement and cheap as compared to communication-based methods. Therefore, in this paper, an attempt has been made to systematically put forward a detailed review of islanding detection methods exclusively with a passive approach. This review provides an insight into passive parameters (which are islanding detection indicators) considered by various researchers and their performance with respect to detection time (also known as run-on-time), non-detection zone, performance in a noisy environment exclusively for passive schemes employing signal processing techniques, difficulty level and process of selecting threshold value(s). Additionally, this review provides a brief account on the future role of passive schemes and also passive IDMs incorporating ride-through requirements in compliance with recent interconnection standards.

The spread of the Covid-19 virus in Indonesia is increasingly widespread and more than 1 million people have been infected with the Coronavirus. The Indonesian government is making various efforts to slow the spread and rate of the Coronavirus and one of them is by campaigning for the Sinovac vaccine. Various campaigns on the importance of vaccination as the most effective effort to prevent the Covid-19 pandemic from spreading have been carried out di Indonesia. This research uses a quantitative approach, with the nature of the research is explanatory research. This research method uses a survey research type. The population in this study were members of the Istiqomah Taklim Council, and the sample used in this study amounted to 30 people. Using non-probability sampling, with the Accidental Sampling technique. The results showed that the campaign effectiveness of using the Covid-19 Sinovac vaccine had a major or significant effect on people's behavior in receiving the vaccine. Keywords: Effectiveness, Campaign, Communication Engineering Behavior, Sinovac Vaccin

This work describes a state-of-the-art design and implementation of a hybrid self-control scheme for PM Synchronous motor drive. The hybrid scheme is developed by combining conventional self-control with V/f control. This modification leads to elimination of the closed-loop current control scheme required for conventional self-control. The dynamic performance of this drive is comparable with conventional self-control drive. However, elimination of current control loop reduces overall cost and complexity of the drive. Indigenous techniques have been developed to implement the scheme. These developed techniques are such that, which can be implemented with minimum resources to make the drive simple and cost-effective. These features make this drive suitable for applications like fan, blower, pump, E-Rickshaw, E-Scooter, etc. Experimental results are presented to validate the performance of the drive.

Online teaching–learning methods have been followed by world-class universities for more than a decade to cater to the needs of students who stay far away from universities/colleges. But during the COVID-19 pandemic period, online teaching–learning helped almost all universities, colleges, and affiliated students. An attempt is made to find the effectiveness of online teaching–learning methods for university and college students by conducting an online survey. A questionnaire has been specially designed and deployed among university and college students. About 450 students from various universities, engineering colleges, medical colleges in South India have taken part in the survey and submitted responses. It was found that the following methods promote effective online learning: animations, digital collaborations with peers, video lectures delivered by faculty handling the subject, online quiz having multiple-choice questions, availability of student version software, a conducive environment at home, interactions by the faculty during lectures and online materials provided by the faculty. Moreover, online classes are more effective because they provide PPTs in front of every student, lectures are heard by all students at the sound level of their choice, and walking/travel to reach classes is eliminated.

—Numerical calculations of the matching of a resonant radio absorber (RA) have confirmed that the ideal matching of such an RA with free space can be realized using practically any dielectrics with losses at any desired frequency. The conditions are considered that provide the widest matching band of the RA, and it is shown that the ratio of the bandwidth to the RA thickness (bandwidth to thickness ratio) is about 3.7, i.e., more than for the usual single-layer Dallenbach RA (about 3.2).

With the rapid growth of P2P network, the resource searching and resource delivery are two crucial problems required to be solved in a P2P system. At the resource searching stage, a large number of random resource requests form a search request queue at the node. At the resource delivery stage, if the nodes cannot process new requests at a certain time, the queue of resource requests may be congested at the node. The behavior of each user making a search request is a random phenomenon, so it is necessary to use the knowledge of queuing theory to allocate the users who request different resources and manage these request queues to provide services efficiently, which are based on the hybrid P2P network model in this paper. Firstly, a two-dimensional Markov chain is constructed, and the steady state distribution of system is determined by using the method of matrix geometric solution. Secondly, the expressions of performance indexes, such as the probability that the local Peer Node (PN), the remote PN and Virtual Content Server (VCS) provide the users, are obtained, and the influence of different system parameters on the performance indexes are analyzed using the results of numerical calculations. Finally, in order to avoid congestion of the request queue, the social benefit function is defined, and the optimal user arrival rate of system is obtained.

The recent explosion of interest in online courses can directly be attributed to the current pandemic which has collated to the adoption of Massive Open Online Courses. While the enforced social distancing protocol inevitably demands the involvement of technology in raising awareness, it further requires the imposition of preventive restrictions on touch-based systems. Visual gestures like simple blinks can account for a significant subset of possible actions in mobile application domains. The proposed model introduces a novel approach that facilitates the automated generation of relevant lecture notes and web-linked keywords from videos through a blink-controlled interface . Functions like auto-video pausing and auto-closing of the application based on eye attribute tracking have been integrated into the system for handling context switching. The experimental results reveal 84.17 usability score, an average of 89.55% of different types of blink detection accuracy, 91.56% of text generation accuracy and 70.41% of keyword detection accuracy along with low false positives and false negatives under different lighting conditions.

A new approach to the development of microprocessor systems for fuzzy information processing using membership functions of an arbitrary type is proposed, which consists in supplementing the main fuzzy solver with a set of correcting fuzzy nodes. It is shown that the application of the approach makes it possible to reduce the complexity of the description and the complexity of the technical implementation while increasing the accuracy of fuzzy computations. Examples of using the approach in practical tasks are given.