Showing papers in "Iete Technical Review in 2022"

Five-Level Switched Capacitor Inverter for Photovoltaic Applications

Abstract: This paper proposes a switched-capacitor based single-phase five-level inverter configuration that operates under boost operation and generates a voltage that is more than the DC source voltage. The proposed five-level inverter uses a capacitor and boots the output voltage. In this proposed inverter, capacitor gets charged in parallel while it discharges in series connections so that output voltage may attain higher magnitude than the DC source voltage. Sinusoidal Pulse Width Modulation-based techniques are considered to produce the required gate pulses for operating the switching devices of the inverter. The five-level switched-capacitor inverter is combined with the PV system via DC–DC boost converters to extract the maximum power using MPPT algorithm. To verify its capability, the PV-based system is further integrated to the utility grid. The operation and performance of the suggested switched-capacitor inverter coupled with the grid-connected PV system are also analyzed by developing its model in MATLAB/Simulink environment.

A Comprehensive Survey on Certificateless Aggregate Signature in Vehicular Ad Hoc Networks

Abstract: Research related to the authentication schemes in vehicular ad hoc networks (VANETs) has received significant attention recently. They substantially impact the security and privacy aspects of the message dissemination process in the road environment. Some authentication schemes with certificateless aggregate signature (CLAS) in VANETs have been published since the first related article emerged in 2015. This paper comprehensively reviews most of them regarding their main feature, contributions, security, and performance efficiency, as the state-of-the-art of all CLAS mechanisms in VANETs. Finally, the conclusion and some open issues on the CLAS authentication scheme in VANETs are provided in this survey.

Historical Developments and Recent Advances in High-power Magnetron: A Review

Abstract: The cavity resonator is an efficient and compact high-power microwave (HPM) source. These HPM sources have applications in radar, medical science, communications, agriculture, industrial heating, non-destructive testing (NDT), and food processing systems. Various magnetrons with discrete designs emerged depending on the structure type, desired output power, and desired frequency. Continuous-wave (CW) magnetrons are being used for industrial purposes, and pulse magnetrons are used for linear accelerator (linac) devices. High power pulse magnetron is an efficient microwave source used in medical science to treat cancer. It is also used in space and defence applications for NDT. A drastic change in the development of magnetron and various advances came across to enhance efficiency, improve performance, and increase the operating power. The main objective of this study is to present an overview of the development of high-power magnetron and its subparts from 1869 to today. In the last three decades, substantial growth in the technological advancements of pulse magnetron has taken place. This study focuses on significant advances in strapping, coupling, frequency tuning, phase locking, frequency narrowing bandwidth, magnetic priming effects, cascading magnetrons, terahertz magnetrons, and software development.

Circularly Polarised Dielectric Resonator based Two Port Filtenna for Millimeter-Wave 5G Communication System

Abstract: In this paper, a circularly polarised dielectric resonator-based MIMO filtering antenna is designed and analyzed. The unique feature of the proposed antenna is the combination of circular polarisation characteristics with filtering response at mm-wave. Cylindrical ceramic excited by asymmetrical plus-shaped slot provides two important characteristics: (a) generate dual orthogonal degenerated mode with 900 phase shifts in between 27.0 GHz and 27.33 GHz; and (b) produce the radiation nulls at 25.5 and 27.75 GHz respectively. Two identical ports are placed orthogonally to provide the lesser mutual coupling i.e. approx. −30 dB. Experimental outcomes confirm the operating band of the proposed antenna from 25.5 GHz to 27.79 GHz. In between the operating frequency range, the proposed radiator provides broadside radiation characteristics due to the creation of mode. The peak gain of the proposed antenna is about 5.0 dBi. Diversity parameters (such as ECC, DG, and TARC) are within satisfactory limits. All these properties of the proposed radiator make it fit for a 5G communication system.

Time-Modulated Arrays: A Review

Abstract: In the modern communication era, antenna arrays play a vital role, demanding more constrained antenna arrays with high gain, ultra-low sidelobes, and other characteristics. The introduction of the fourth dimension, time in antenna arrays, reduces the effort needed for synthesis and offers flexibility in designing high-performance antenna arrays. The fourth-dimensional antenna with time as the fourth dimension is also known as Time-Modulated Antenna Array (TMAA). The modulation of the antenna array by periodic time sequence generates harmonic radiation patterns. This paper represents a review of TMAA from its evolution to the current progress. The virtue of time modulation provides multi-beam radiation from TMAA, which finds its application in numerous fields like secure communication, tracking of elements, and simultaneous scanning in azimuthal and elevation planes. The exploitation of TMAA in the fields requires synthesis of the radiation pattern, making it suitable for the application by reducing the sidelobe and sideband levels, radiating sum-difference patterns, multi-tone radiation, Wireless Power Transfer, antenna failure correction, and many more. Furthermore, TMAA with an optimized timing sequence may find a place in future generations’ communication.

Bi-directional DC/DC Converters Used in Interfacing ESSs for RESs and EVs: A Review

Abstract: Bi-directional, DC/DC converters find a wide range of applications such as, electric vehicles, plug-in-hybrid electric vehicles, electric vehicle charging stations, residential micro-grid systems, uninterruptable power supplies, charging/discharging of battery storage, the realisation of a hybrid combination of battery with renewable energy sources, etc. Bi-directional DC/DC converter topologies are divided mainly in two categories namely, Isolated and Non-Isolated. Isolated and Non-Isolated topologies are further sub-categorised into nine and sixteen groups, respectively. Respective configurations, table summary, comparative plots of the component count, efficiency and voltage gain are also presented in this paper. Bi-directional DC/DC converters are being developed to improve voltage gain, efficiency, minimise size and cost, increase reliability and introduce modularity. In this work, a detailed topology-wise review is carried out along with respective applications, highlighting comparative features, modern technology trends and future scopes which throw light on specific applications. Based on the review, this paper intends to provide a logical and categorised approach to highlight various technical aspects in this domain.

Fiber Bragg Gratings in Healthcare Applications: A Review

Abstract: Fiber Bragg grating FBG is a periodic refractive change induced inside the fiber’s core due to exposure to optical radiation. Being miniature, EMI (electromagnetic interference) resistant, and sensitive to physical conditions, such as temperature, strain, and relative humidity; FBGs have gained huge attention and their implementation can be witnessed in many domains varying from oil and mining to medicine and healthcare application. This paper presents an overview regarding the application of FBGs as potential sensors in monitoring various vital physiological signals and activities such as body temperature, heart rate, cardiorespiratory analysis and more. Intricate and delicate observations such as pressure and strain mapping in prosthetics, bone health monitoring, and measuring small-scale forces in medical instruments such as spinal needles have also been discussed in detail. Various methodologies and topologies of implementing FBGs for processing and analyzing physiological signals have also been presented. To provide a better and suitable insight into tracking the progress regarding the implementation of FBGs in medicine and healthcare, thematic research in chronological order for the past decade is presented in this work. An elaborate discussion stating various issues and problems such as determination of an appropriate index for plantar pressure monitoring, cross-sensitivity of FBGs, choice of polymer sheaths, the dependence of gait analysis on the type of foot and size, etc., have also been addressed. At last, in the wake of evaluating past works and future clinical applications, a conclusory remark paving a pathway for future possibilities and applications is additionally presented in this paper.

Assessment of Power System Security for Different Bus Systems through FVSI/RFVSI and Fuzzy Logic Approaches

Abstract: A power grid in a nation or continent integrates utilities by generating stations and loads in order to meet power requirements. When such colossal power systems are involved in the transfer of power, the voltage stability issue typically crops up. Voltage collapse may lead to blackouts. Blackouts can only be reduced by focusing on the steady-state and dynamic operation of power systems. A possible solution may be to increase the power system infrastructure in order to endure these blackouts. Therefore, we need to distinguish the weak and less reliable buses or lines as soon as possible in the operation of the power system so that necessary actions can be taken for smooth operation or maintaining steady-state equilibrium. The weak elements may suffer outages, and that may lead to cascading outages. In this study, contingency ranking is carried out using two evaluation approaches, namely a pre-developed Fast Voltage Stability Index (FVSI) and a new Reduced Fast Voltage Stability Index (RFVSI) or the classical approach and the soft computing approach based on Fuzzy Logic (FL). The methods have been used for different bus systems such as IEEE-14, IEEE-30, and a practical Indian 62 bus system. The RFVSI is a reduced and faster version of FVSI. The outcomes of the two approaches were compared. It is found that the Fuzzy Logic (FL) approach computes the contingency ranking faster compared to the classical approaches, whereas the classical approach based on the RFVSI method calculates more correctly the contingencies and reliable results are produced.

An Empirical Review on Evaluating the Impact of Image Segmentation on the Classification Performance for Skin Lesion Detection

Abstract: The presence of artifacts limits the accuracy of detecting skin lesions. The current study presents an extensive appraisal of the impact of eight existing image-segmentation methods on the performance of 10 deep-learning-based models to detect and classify lesions. An empirical review was conducted using dual experimentation- with unsegmented original images, and with segmented images processed using eight segmentation methods on four skin lesion datasets. The learner’s performance was assessed using standard evaluation measures. The results show superior classification performance, achieved with segmented images compared to original images. Otsu’s Binarization approach with ResNet50 model outperforms with an accuracy of 91.9% on ISIC2017 dataset.

On the Reliability of Industrial Internet of Things from Systematic Perspectives: Evaluation Approaches, Challenges, and Open Issues

Abstract: In the light of Industry 4.0 revolution, the Industrial Internet of Things (IIoT) allows digitalizing manufacturing processes and increasing the digital connectivity and automation in the smart factories and industrial systems. The reliability of a system is considered as a key performance indicator that defines how accurately and perfectly the system works. Ensuring reliability in the IIoT systems presents several challenges due to the presence of uncertain and dynamics in the industry environment as well as the complex nature of industrial systems. While there are no standard frameworks proposed and applied for the IIoT systems with multiple reliability levels, the challenges reveal many promising opportunities for the research and practical implementation including advancing technologies and systematic designs such as algorithms, architectures, and devices. However, there are a few works studying the reliability-related aspects of the industrial systems in the existing literature, they lack a framework to conduct the study and examination of reliability of IIoT systems in a holistic manner. In this regard, this article will elucidate and survey several performance-improvement methods and techniques for better reliability and further challenges to be overcome for highly reliable IIoT systems. Finally, by defining several open research issues, we expect to draw more attention for both the academic researchers and practitioners toward designing and developing reliable solutions and frameworks.

Formulation of Switching Instant for Improved Dynamic Performance in the Predictive Current Control Technique

Abstract: This paper analyzes the dynamic performance of the predictive current control (PCC) scheme for a single-phase grid-connected (SPGC) voltage source inverter (VSI). VSI performance and its operating conditions, such as steady-state and dynamic-state, have been analyzed. Conventionally a linear current error (eL)-based control technique is used, which has poorer current tracking during dynamic conditions. In this paper, a novel control technique has been proposed that utilizes a non-linear current error (eNL) based on the instantaneous grid voltage and DC-link voltage to calculate the switching instant, which gives better tracking performance during the dynamic condition. A mathematical model of digitally controlled PCC predicts the load current for all switching states of the inverter. Calculations of these predicted current values are done by using a cost function. The switching state with minimum cost function is selected and applied to the inverter for improved dynamic performance. The effect of parameter variations, such as load current, DC-link voltage etc., has been investigated. The performance of the proposed method has been tested at different sampling frequencies. The effectiveness of the PCC technique is simulated in specialized power electronics software and experimentally validated by a proto-type model using an FPGA controller.

A Novel Image Focus Metric based on Power of Companion Matrix and Gerschgorin Circle Bound

Abstract: ABSTRACT In this paper, a simple and efficient no-reference image quality assessment metric is proposed. It is based on the concept of polynomial coefficients, power of companion matrix, and Gerschgorin circles bound. The polynomial coefficient-based companion matrix captures the main features and dynamics of an image. Moreover, the Gerschgorin circles bound is used to define the focus metric. The proposed focus metric is tested on various real as well as synthetic image data sets. It is observed that the presented metric is unimodal to noise and invariant to the contrast changes that occur due to the variation in illumination effect. Moreover, it is robust under the varying salt-and-pepper and Gaussian noise. The performance of the proposed focus metric is also compared with the various existing focus metrics.

A Systematic Review on Muscle Stimulation Techniques

Abstract: The conventional procedure in retraining a muscle has been physiotherapy, but persistence is required, which often proves to be a limitation in most patients. The electrical stimulation technique has great potential in comparison to conventional methods that are employed in retraining muscles. The neuromuscular electrical stimulation method uses a device that sends electrical impulses to neurones. This input causes the muscle to contract. It can be used to re-educate or retrain a muscle. In this review article, how different types of stimulation strategies can be used for re-educating muscles in different diseased conditions is studied. The force and quality of the muscle contraction depend upon several parameters such as frequency, amplitude, pulse duration and waveforms of stimuli. The Analysis of parameters involved in different stimulation techniques can help in understanding how a particular muscle can be stimulated in different diseased conditions. There are several studies that have reported the efficacy of muscle stimulation strategies. In this review, we investigated 30 research studies that used the muscle stimulation method for re-educating muscle and discussed many considerations in context to muscle stimulation techniques: the stimulation strategies and parameters, electrodes and results. With this review, we investigated a potential intervention for re-educating muscle and tried to recognise the limitations and benefits of the current strategies involved.

A Proposal, Design, and Analysis of Planar Feed Gasket Antennas with Modified Substrate for Multiband Vehicular Applications

Abstract: The Sierpinski Gasket fractal antennas can provide the best generic multiband behaviour among the several planar feed monopole antennas available for use in automotive applications. The Gasket geometries used in antennas can be classical, scale perturbed, or both scale and flare perturbed. When used in the planar feed configuration, the classical and perturbed Gasket fractal antennas suffer from considerable reflections at one or more multiband frequencies, rendering these frequencies unsuitable for usage. Therefore, a design modification in these antennas is essential. A novel design modification has been proposed in the Sierpinski Gasket fractal antennas in the present research work by utilizing metamaterial engineered substrates. It has been found that the metamaterials not only reduce reflections but these materials also lower Gasket antenna frequencies, thereby, facilitating reduction in the antenna size. The findings have been validated through the experimental investigations. The comprehensive design analysis presented in the present paper shall prove highly beneficial to the antenna designers in rapidly customizing the vehicular antennas.

An Enhanced RBMT: When RBMT Outperforms Modern Data-Driven Translators

Abstract: Although prominent translators, such as Google, Yahoo Babel Fish, Bing, etc., perform better when translating most widely used languages, they tend to commit fundamental mistakes in working with low-resource languages such as Bengali, Romanian, Arabic, etc. Such translators (e.g. Google Translate) use different data-driven translation approaches, such as neural machine translation (NMT), statistical machine translation (SMT), etc., to develop their polyglot translation system. However, performances of these data-driven approaches entirely rely on the attainability of significantly large parallel corpora of the translating language pairs. As a consequence, numerous popular languages, such as Bengali, remain barely explored not only in machine translation but also in other fields of natural language processing. Therefore, the target of this study is to explore effective translation from Bengali to English by accomplishing several Bengali language processing tasks. To be precise, we adopt a basic rule-based machine translator for translating from Bengali to English. Next, we enhance its performance by considering the veracious interpretation of the Bengali names as subjects (and nouns) in a sentence. Besides, we propose a Bengali verb identification and optimization technique by root-word detection (stemming) of the Bengali verbs. Finally, we unfold the efficacy of our proposed techniques through a comparative analysis with popular data-driven translators using a novel customized dataset focusing on Bengali-to-English translation.

Studying the Effect of Syntactic Simplification on Text Summarization

Abstract: The need for automatic text summarization (ATS) is increased manifold in recent times due to the overwhelming growth of textual data available in electronic form. However, existing ATS systems suffer from two major shortcomings. Summarizers of extractive type, that is, the ones which select important sentences of the documents in their original form as the output, tend to copy some irrelevant or unimportant parts of the input text in the output summary. On the other hand, abstractive summarizers, that is, the ones that produce a gist of the limited size of the original document, often fail to include important contents in the generated summary. Simplification of the input texts before submitting them to the ATS system(s) may obliterate the above difficulties. The present work examines the effectiveness of simplification of input for five different known ATS systems. In this work, DEPSYM++ simplifier has been used for the above purpose, which carries out four different kinds of simplification on sentences of the input text corresponding to the presence of appositive clause, relative clause, conjoint clause, and passive voice. The results obtained are found to be very encouraging when experiments were carried out on three different gold data sets and under different evaluation metrics commonly used for performance evaluation for summarizers.

An Empirical Analysis of Machine Learning Algorithms for Solar Power Forecasting in a High Dimensional Uncertain Environment

Abstract: In the fabric of energy generation, solar power is the most promising clean energy solution as an alternative to non-renewable energy sources. However, solar power’s dependency on environmental factors adds uncertainty to energy production. In such a scenario, solar power forecasting provides an edge to mitigate this uncertainty and improves overall system stability. Recently, machine learning (ML) models have been extensively deployed for designing and forecasting solar power. However, data pre-processing, forecast horizon, and performance evaluation of ML algorithms have to be carefully evaluated to find an accurate model. This paper provides an empirical comparison of different generation ML models for solar power forecasting, which can help understand future research on which method to adopt, depending on the ML model’s strengths and weaknesses. Therefore, an effective forecasting method is designated in aspects such as performance errors, convergence time, and computational complexity. So, this work rates different ML models on error performance metrics and convergence time. Moreover, cross-fold validations and hyperparameter are also examined for the top five performing models for a comprehensive evaluation and to give more intuitive and calibrated insight into various stakeholders working in the solar power plant modeling field.

A Brief Review on mm-Wave Antennas for 5G and Beyond Applications

Abstract: Due to exponential growth in wireless communication in the last decade, which continues towards future applications, mm-wave has shown tremendous potential and has been established as the backbone of most modern-day high data rate communication systems. Despite many challenges associated with this technology, several inherent advantages have established it as an attractive and essential choice for high data rate communication systems. It is well known that antennas are crucial integral components of any communication system. Most modern-day mm-wave communication devices require a low profile, wideband, and highly efficient antenna system. Recent advancements in research and development in this field have been reported through many publications. This article reviews the mm-wave antennas with briefs about their characterization setup, fabrication technologies and some of the associated challenges.

RecurrentHAR: A Novel Transfer Learning-Based Deep Learning Model for Sequential, Complex, Concurrent, Interleaved, and Heterogeneous Type Human Activity Recognition

Abstract: Smartphone sensor-based Human Activity Recognition (HAR) is a significant facilitator for many real-world applications such as smart homes, personal healthcare, and illness detection because it has the potential to discover activity patterns in daily life. While solutions for detecting sequential activities like walking or running are already matured, however recognizing additional forms of complex, concurrent, interleaved, and heterogeneous human activities remains a research challenge. Furthermore, several solutions have been developed for specific types of activity complexity. According to the best of our knowledge, there is no common solution for detecting all types of action-based activities. In this paper, we address the problem of recognizing all types of action-based human activities i.e. sequential, complex, concurrent, interleaved, and heterogeneous activities. The intention behind this paper is to present a generic methodology for HAR by leveraging transfer learning and evaluating the performance on a plethora of HAR datasets. In this regard, we have proposed a model called RecurrentHAR, which stands for Recurrent layers for HAR, a novel adversarial knowledge transfer approach that uses the Gated Recurrent Units (GRUs) architecture for smartphone sensor-based HAR. The performance of the proposed model is evaluated through extensive experiments using three public datasets namely WISDM (sequential activities), PAMAP2 (complex, concurrent, and interleaved activities), and KU-HAR (Heterogeneous activities), and the RecurrentHAR model has outperformed compared with other state-of-the-art approaches. The proposed model achieved 96.26%, 94.77%, and 98.98% of F1-score for WISDM, PAMAP2, and KU-HAR datasets, respectively. The experimental results provide insight into the applicability of the proposed model and future research possibilities.

A Systematic Review of Recent Machine Learning Techniques for Plant Disease Identification and Classification

Abstract: Plant disease management in agriculture science is the primary concern for every country, as the food demand is increasing fast due to an increase in population. Furthermore, modern technology has improved the efficacy and precision of disease detection in plants and animals. Plant disease identification using various image processing approaches has recently been employed on a big scale to help farmers monitor their plantation areas. Based on the perpetuation and spread, the diseases can be floral, foliar, and soilborne. Grain production is typically affected by foliar diseases, which reduce photosynthetic area, duration, and function. Soil-borne conditions include vascular wilt, root rot, and damping-off; and can exhibit symptoms such as wilting of foliage, root decay, and sudden death. This paper highlights the significant issues and challenges for leaf disease classification. A comparative study of different methods based on the agricultural product, methodology, efficiency, advantages, and disadvantages is also included. The review study analyzes the most frequently used machine learning algorithms in the last five to seven years, revealing that Support Vector Machine (SVM) has been extensively used for disease classification. An analysis of specific Techniques (Feature Extraction plus machine learning-based Classification algorithm) and their associated accuracy is also performed, demonstrating that (ORB) features combined with Linear SVM provide the highest accuracy of 99.98%.

An Improved 2-Factor Authentication Scheme for WSN Based on ECC

Abstract: The Internet of Things (IoT) stands as one of the emerging technologies because of its novel innovations and solutions for countless domains. As one of the enabling components for IoT, Wireless Sensor Network (WSN) is utilized for various real-world appliances to collect valuable raw sensed data. Here, valuable data from the sensor nodes are suitable to access with the help of a gateway node through the registered user. However, due to the resource constraints of sensor nodes and the exposed nature of the wireless frequency, security has turned out to a considerable task in WSN. Authentication is a primary security facility that can ensure the legitimacy of data access in WSN. There are numerous 2-factor verification protocols in related work based on a smartcard, public-key, and password. However, most of them have trouble reconciling proficiency and security necessities; hence, it is still challenging to implement a 2-factor authentication scheme to satisfy security topographies while preserving suitable proficiency. We designed an enhanced 2-factor verification scheme based on ECC and biometric information in this work. Our proposed scheme accomplishes distinct security features and protocol-designed logic verified with the AVISPA tool. Simulation results demonstrate that the projected scheme is robust, energy-efficient, and well-suited to WSN environments.

An Advanced Shunt Active Power Filter (SAPF) for Non-ideal Grid Using Predictive DPC

Abstract: The usage of power electronic converters and other non-linear devices is very common in this modern era because of their flexibility in control. However, they inject harmonics into the power system. To deal with the problem, the use of filters has become almost inevitable. Active power filters controlled by direct power control (DPC) have overtaken this responsibility from the passive filters because of their numerous advantages. To deal with the real-world power system grid which is far from the idealized character of being balanced and sinusoidal, the proposed method uses the extended pq theory integrated with the dual second-order generalized integrator (D-SOGI) for the correct estimation of the all-important instantaneous powers. In contrast, the conventional pq theory is used in the existing works, which doesn’t hold in a non-ideal grid. In addition to that, here, for the selection of the switching vector, an advanced and more accurate predictive DPC method which is specifically designed to operate in a non-ideal grid, replaces the static look-up tables used in the previous works. Further, to make the filter designing easier, the proposed method focuses on constant switching frequency operation eliminating the problems arising from variable switching frequency used in literature. The proposed technique is tested in MATLAB/Simulink environment and the real-time system to validate its feasibility.

DC Microgrid: A Comprehensive Review on Protection Challenges and Schemes

Abstract: The development of microgrids can prove to be a game changer in the realization of future smart grids. The increasing trends of automation in domestic loads and smart homes have increased the penetration of DC loads at the distribution level. Hence, the development of a low- or medium-voltage DC microgrid seems the need of the hour to match this changing scenario. Another point is that the various distributed generation (DG) sources like PV, batteries, fuel cells, etc., also provide energy in DC form. Hence, integrating these sources into the DC system also becomes convenient as the power conversion at each point of connection will no longer be needed. But, due to the lack of universally accepted standards and technological advancements, the implementation of DC microgrids is still limited to very small-scale usage. The unavailability of an efficient and reliable protection system is one such issue that is hindering the implementation of DC microgrids on a larger scale. Therefore, a thorough examination of the several DC microgrid protection difficulties and challenges is necessary. Thus the purpose of this article is to provide a comprehensive analysis of the protection challenges, and the currently available protection schemes for DC microgrids and to highlight the gaps for future research to enable the development of a more reliable and efficient protection system.

VoteSumm: A Multi-Document Summarization Scheme Using Influential Nodes of Multilayer Weighted Sentence Network

Abstract: This work proposes a sentence network-based approach for performing the task of multi-document text summarization. The sentences of the input set of documents are represented by the nodes of the network. Weighted edges are added between the nodes to represent the semantic similarity between the corresponding sentences. The network has a multilayer structure, where each layer corresponds to an individual input document. This helps in effective differentiation between the inter-document and intra-document edges. A hyperparameter, namely layering factor, has been used to alter the strength of inter-document connections through reinforcement or weakening. It is hypothesized that the summary sentence nodes must act as effective information spreaders in the sentence network. Summary generation is performed by identifying the influential nodes of the network using VoteRank scheme. A comparative study with different network measures, such as Weighted Degree, PageRank, Betweenness centrality, and Closeness centrality reveals the efficacy of the proposed VoteSumm technique for multi-document text summarization. Improved performance is observed when an additional pre-processing step of syntactic simplification is applied on the raw text. Performance is further improved when keyword information is included in the simplified texts.

An Enhanced Entity Model for Converting Relational to Non-Relational Documents in Hospital Management System Based on Cloud Computing

Abstract: In the past few years, several cloud services such as IBM cloud services, Microsoft service, Amazon web services as well as Google cloud platform services related databases are rising rapidly all over the world. In the case of a Relational database, it is easy to handle only a small amount of data. So to overcome the shortcomings, this paper proposes a Hospital Management System (HMS) using Mongo DB as a database. The Mongo DB is the classification of a document-related database that comes under the category of NoSQL database which is referred to as a non-relational database. Mongo DB aims in reducing the gap among two different types of scalable key-value databases such as fast and high scalable key-value databases. Also, this database reduces the time delay during the working of four operational modes such as selection, insertion, deletion, and updation. Furthermore, the Enhanced Entity-Relationship Model (EERM) for the HMS that is designed to manage the entire section of the hospital which includes the reception section, casualty section, details regarding the medical treatment, and employees. The HMS stores three million tuple entities and is loaded into the Mongo DB database. Also, the operation based on selection, insertion, deletion, and updation mode is evaluated. Therefore, the experimental analysis reveals that the proposed HMS using Mongo DB provides is highly efficient with less time delay, thus obtaining an effective system.

Parallel Greedy Search for Random Access in Wireless Networks

Abstract: We consider the random access problem in wireless networks such as cellular networks and massive machine type communication networks, where a large number of devices/users are connected to a base station. In order to establish communication with the base station, as a part of the random access procedure, active users select and transmit codes (pilot sequences) randomly from a codebook. Since the active users are typically a small fraction of the total users, sparse signal recovery algorithms are employed to detect the transmitted codes. In this paper, we use multiple measurement vector model for the received signal and develop a parallel greedy search algorithm and it’s stopping criteria to detect the active users. We show through simulations that the proposed scheme, compared with the existing greedy algorithms, gives better detection performance at lower false alarms levels. We study how the code length and the code word size need to be chosen in order to detect a given number of active users. We also address the scenario when there are different timing offsets for signals received from different users.

Modelling and Simulation of Ge Absorber-based Tunnel Field-Effect Phototransistor at 1550 nm

Abstract: In this article, a semi-analytical model for germanium absorber-based SOI-tunnel field-effect phototransistor at 1550 nm has been proposed. The model works efficiently for a wide range of intensities i.e. 10−2 to 102 µW/µm2 under the application of a very low gate voltage of 0.54 V. Exhaustive simulations using numerical ATLAS 2D device simulation tool with LUMNINOUS optical module have been carried out to propose the merit of tunnel field-effect transistors (TFETs) over metal oxide semiconductor field effect transistors (MOSFETs) for optical applications. The proposed TFET design provides a way to assist the ultrasensitive optical interconnects at a very low gate bias of 0.54 V with a maximum sensitivity of 1.5 × 107, responsivity of 270 mA/W, and detectivity of 3.5 × 1012 Jones at low power density of 0.01 µW/µm2. The TFET performance has further been improved by introducing a heavily doped pocket in between source and channel, which serves as a potential alternative for photodetector applications. The proposed pocket-doped TFET design offers significant improvements in device optical response with optimized sensitivity of 4.1 × 107, a responsivity of 772 mA/W, and a detectivity of 9.9 × 1012 Jones at an optical intensity of 0.01 µW/µm2, gate bias of 0.45 V, and drain voltage of 0.6 V. Transient analysis has been carried out to optimize the response time of the phototransistor for both Ge-gated conventional TFET and pocket-doped architecture and the fall time of ∼0.22 ms has been achieved.

Highly Sensitive Refractive Index-Based Sensor for DNA Hybridization Using Subwavelength Grating Waveguide

Abstract: A biosensor for detecting DNA hybridization via refractive index sensing has been demonstrated using a subwavelength grating (SWG) waveguide. Waveguide dimensions are selected by parametric optimizations to increase the mode-analyte overlap with considerations of typical silicon-on-insulator fabrications technology. DNA layer is added with a linker layer on silicon pillars of the SWG waveguide to optimize and detect the DNA hybridization. Some essential characteristics such as mode overlap factor, change in effective refractive index, waveguide sensitivity, and shift in resonance wavelength are computed for the different dimensional parameters of the SWG waveguide in DNA hybridization along with mode field intensity and normalized power using the finite element method. The DNA hybridization is shown by variation in normalized power of interacting light in cladding region of the waveguide for 0% to 100% fractional change of dsDNA and ssDNA. Waveguide sensitivity, shift in resonance wavelength, device sensitivity, and intrinsic limit of detection are obtained to ∼ 0.157, 7.01, 605 nm/RIU, and 1.30 × 10−4 RIU, respectively, for the optimized structure of SWG waveguide in sensing of DNA hybridization, which could be suitable aspects for detecting DNA hybridization.

Antenna Array Pattern Synthesis Using Metaheuristic Algorithms: A Review

Abstract: In the field of wireless communication, antenna array plays an important role as it improves the gain, directivity, coverage area alongside provides beamsteering capability to the network that is an essential part of a 5G communication system. Antenna array pattern synthesis has received significant importance in communication as it aims at obtaining the radiation pattern close to the desired pattern. In antenna array excitation amplitude, excitation phase and distance between antenna elements are three parameters that can be optimized to obtain radiation pattern with minimum side lobe level, optimum null depth, and desired beamwidth. Metaheuristic algorithms are high-level search algorithms that have widely been explored for pattern synthesis of different array configuration. In this article author has discussed and compared the performance of different metaheuristic algorithms for uniform linear array (ULA), circular, concentric ring, conformal and planar antenna array pattern synthesis.

Solving Environment-Constrained Economic Dispatch for a Microgrid System with Varying Electricity Market Pricing Strategy: A DSM-Based Approach

Abstract: Microgrids (MG) can be considered as a reduced power system network wherein the generation, transmission, and distribution of power take place within a limited geographical area. They are employed to utilize the maximum penetration of renewable energy sources (RES). Microgrid also has advantages such as reduction of transmission losses and costs pertaining to such losses. In this research, economic dispatch, emission dispatch, combined economic emission dispatch (CEED) based on fractional programming (FP), and environment-constrained economic dispatch (ECED) were assessed. An MG system for three distinct scenarios is studied. A novel, robust, and powerful swarm-intelligence hybrid algorithm, created by amalgamating the features of the conventional gray-wolf optimizer (GWO), sine-cosine algorithm (SCA), and crow search algorithm (CSA), is implemented as the optimization tool for the study. A significant reduction in generation costs of about 4.50-9.75% was achieved throughout the study when the time-of-usage (TOU) electricity market pricing strategy was used instead of the fixed pricing strategy. Furthermore, the paper also dealt with the Demand Side Management (DSM) to facilitate the generation cost by managing the controllable load demand. The generation cost decreased by a notable amount when ECED was evaluated with a 15% and 20% DSM-based revised load curve for the MG system.