Topical Drifts in Intelligent Computing

Diagnosis of skin cancer at an early stage poses a great challenge even in the twenty-first century due to complex and expensive diagnostic techniques currently used for detection. Furthermore, traditional detection techniques are highly dependent on human interpretation. In case of fatal diseases such as melanoma, detection in early stages plays a vital role in determining the probability of getting cured. Several techniques such as dermoscopy, thermography and sonography are used for skin cancer detection, but every technique has its own limitations. Also, it is not feasible for every suspected patient to receive intensive screening by dermatologists. These limitations suggest the need for development of a simpler, cheaper, minimal invasive and accurate methodology independent of human intervention for skin cancer detection. Advancements in various computer vision algorithms have led to their extensive use in the area of bioinformatics. Therefore, this research paper aims to resolve the problem of early detection of skin cancer with a higher accuracy than existing methodologies using computer vision.

Cyber-security has become a major concern with rapid evolution of technology. To counter numerous novel attacks on a regular basis, organizations use intrusion detection systems (IDS). An IDS is often used for monitoring network traffic for detecting any anomaly or data breach. These systems, often called network intrusion detection systems (NIDS) or anomaly-based network intrusion detection systems (A-NIDS), generate alerts when any suspicious activity or anomaly is detected in the network. Machine learning (ML) and deep learning (DL)-based models provide an efficient way to detect these intrusions. Although various algorithms available in these domains can be used for building an A-NIDS which can efficiently detect intrusions, it is important to analyze their performance so as to determine the most suitable model depending on the need of the organization or user. This paper focuses on a comparative study of Naive Bayes classifier, K-neighbors classifier, logistic regression, random forest, gradient boost, SVM and XGBoost algorithms based on their efficiency, accuracy, time complexity and real-time applicability using parameters such as training time, prediction accuracy and confusion matrix. By implementing some of these models and training them on the Knowledge Discovery and Data Mining Tools Competition 1999 (KDD 1999) Dataset, the authors analyzed their performance on various parameters so as to determine which of these algorithms are the most suitable for building an anomaly-based network intrusion detection system. Random forest proved to be the most efficient and robust model with an expeditious computation and therefore suitable for building A-NIDS models for real-time security.

In this paper, we present a fully automatic acute lymphoblastic leukemia detection method based on deep neural networks. ALL is a condition affecting the leukocytes. Children tend to be prone to have this melanoma. The fundamental problem with this type of cancer is that, unlike other cancers, it does not create tumors, making it extremely difficult to identify. Prior to automation, manual microscopic testing procedures were used, but they were time-consuming and error-prone. To overcome this issue, many automated systems were introduced, which used machine learning techniques. But, because we are dealing with medical information, we may require better efficiency and accuracy, so as an improvement, our proposed system employed several convolutional neural networks architectures. In the proposed system, five such CNN algorithms were implemented to classify and separate the cancerous and non-cancerous cells. The system accepts the blood cell image from the user and predicts whether the cell contains one/more blasts depending upon the prediction value obtained from the CNN algorithm on the stained cell image. As a result, it significantly reduces the research costs, increases the speed of testing, and can be a lifesaver for millions of cancer patients.

There can be some faults, while the development of the software. If these faults are not recognized and corrected at any early stage of software development, then it leads to software failure and increase in development cost. So, the most important task is to deal with these software faults. Various models have been proposed to detect the fault proneness of the software using static code metrics. In this paper, we have used support vector machine with poly-kernel and normalized poly-kernel to predict the fault proneness, and both static code metrics and process metrics have been used as independent variables. AUC measure is used as performance evaluation measure to predict the fault proneness. We have also analyzed the results using 3D bar graph and Friedman test. The result of this study shows that in case of both kernels, process metric has positive impact on predicting fault proneness, or we can say that process metric is as good as static code metrics.

In the wake of the pandemic that we are all facing these days, we all are advised to maintain some specified social distance from other people in order to keep ourselves safe. The CoVID-19 pandemic started showing its symptoms at the end of 2019 and is still killing thousands of people every day. Although the scientists have been successful in preparing the medicines for it, it is better to take some precautionary measures ourselves only. We are battling from it today as well. So, our tool is just a medium to make this battle a little easier. This will help us to monitor the distance between two objects, here, people, and whether they are at a safe distance from each other or not. This can also be helpful for the officials if they have to keep an eye on everyone and if they are following the proper guidelines to prevent COVID-19 from spreading. This will help us to detect the objects, in this case, people, and track their movements. Anyone can track whether people are maintaining a proper distance from each other or not. We are using three algorithms, object detection, object tracking, and distance measure algorithm, mainly to detect the objects, then track them, and then to analyze the distance between them.

In this paper, we have present the first universal dependency treebank for low resource definitely endangered Himachali Kangri (ISO 639-3xnr) language. The goal of the Kangri Universal Dependency Treebank (KDTB) is to establish a large, syntactically annotated treebank that will aid cross-lingual learning and typological research as well as serve as a significant resource in the development of language technology tools. KDTB data contain syntactic annotation according to dependency-constituency schema, as well as morphological tags. The Kangri UD treebank (KDTB) consists of 2249 tokens and 1108 vocabulary (288 sentences).

Machine translation (MT) is automatically converting a piece of text from one language to another. The work provides a comparison and analysis of the phrase-based statistical machine translation (PBSMT) system using monolingual corpora that interpret English text to Hindi. MT quality has improved dramatically over the previous two decades, making it appealing for usage in the translation industry. The importance of MT cannot be overstated. The application of MT systems has been bounded due to the dependency on the bilingual corpus for a range of language pairings. The current paper presents a comparison of two possible cross-lingual word embedding mapping approaches utilizing monolingual datasets for the SMT methodology. When translating from a language to some other language without supervision, inter-lingual word embedding is crucial. We have implemented and compared two cross-lingual mapping approaches are employed in this paper: adversarial training and self-learning methods. The experimental findings for several evaluation methodologies, such as BLEU, METEOR, METEOR-Hi, TER, WER, MER, and NIST, show that the PBSMT approach delivers superior translation using the self-learning method than the adversarial training method for English–Hindi.

Chakraborty, Partha Nawar, Farah Chowdhury, Humayra AfrinSentiment analysis refers to the extraction of the underlying sentiment or emotion associated with an opinion. It is a part of the difficult field in the processing of natural languages (NLP) with several applications in the business, education, and political sectors. Unlike other languages, the amount of research performed on opinion mining for Bangla text is very small. There is also a lack of proper NLP tools for Bangla text processing. To bridge this gap, in this manuscript, a classification system has been developed for predicting the polarity of Bangla text data (i.e., positive, neutral, negative) using the two most efficient algorithms SVM and random forest for opinion mining. In this study, we experimented with unigrams, bigrams, and trigrams to illustrate how contextual information affects the overall performance of the classifiers. The dataset we used in this paper is imbalanced, which resembles natural characteristics of opinions in day-to-day life.

COVID-19 is spreading widely across the globe right now. India just went through the second wave of COVID-19 pandemic and has lost more than 425,000 people to this pandemic till date. Most of the other countries have gone through the second wave, with some countries experiencing third and fourth waves. In such difficult times, there is a shortage of resources everywhere. Planning is the need of the hour, and all the countries are expanding their resources, keeping future demands in mind. Some of the states of India, like Kerala, are also expecting imminent danger of the third wave. In this study, we are predicting the arrival and peak of the third wave in Kerala. We also provide the mathematical models and theoretical background to reach such expected dates. Prediction of this type helps to suggest the preparation needed to tackle the upcoming disaster. Governments can prepare themselves so that there is minimal damage to life in future.

Communication is a way of exchanging thoughts through emotions. In this paper, we have proposed a method where human speech is converted into digital input. The digitized sound is then fed into the proposed models, and the voice of every person is classified into discrete emotional characteristics by its pitch, intensity, timbre, speech rate, and pauses. In the proposed method, we have drawn a comparative study between sentiment analysis of human speech using deep convolutional neural network (CNN) and dense deep neural networks (DNNs). In this method, multiscale area attention is applied in deep CNN as well as dense DNN to obtain emotional characteristics with wide range of granularities and therefore, the classifier can predict a wide range of emotions on a broad scale classification.

Image captioning refers to a task wherein an image is seen by a computer to give an output as “what it sees.” Image captioning would mean giving an intelligent system an image, say of a dog running across a field to receive a statement back as “dog running across a field” or something similar down its lines. The image caption generator is a deep learning model built using the power of recurrent neural networks (RNNs) which are powered by the long-term and short-term (LTSM) units. The model also uses transfer learning to enable build a better and faster deep learning model. The model is built using the “Flicker8k” dataset and uses the inherent power of a convolutional neural network to predict suitable caption for an image. The seamless integration of computer vision into the model and its high-quality prediction makes it highly usable and in-demand with respect to the current trends in the industry.

Nowadays, deep neural networks are one of the ongoing trends which are having their uses in various kinds of fields. One of the most important applications of neural networks is the object detection framework. Object detection in railways field is the novel one, which includes the detection of obstacles on the railway tracks. But some researchers deployed this model using you only look once (YOLO) and single shot detector (SSD). We found these models to produce a lesser accuracy when compared to that of faster R-CNN. The previously mentioned models consume some time for training. To overcome the existing drawbacks, the upgraded model is being deployed by using faster R-CNN, which comprises two modules, namely regional proposal network (RPN) and fast R-CNN. It helps by detecting the obstacles such as branches, boulders, iron rods, animals, vehicles, and people. So, with the help of both the models (faster R-CNN and YOLO), we can detect the obstacles present on the track. Finally, with the quantitative and qualitative comparisons made on these two models, we chose the best fit model for this purpose. Hence, this provides the novel idea to prevent railway accidents as much as possible.

Work anxiety is linked with decreased job commitment and satisfaction. Yet, work anxiety, like other mental health problems, is not physically diagnosable. The lack of diagnosis and cure of job anxiety leads to lower levels of economic productivity and adds to the mental health epidemic. This study proposes a machine learning model to predict a corporate professional’s level of work anxiety using their tweets by identifying linguistic markers associated with work anxiety. The Twitter API was used to create a dataset of over 15,000 corporate professionals. Thousands of tweets were collected from these users over 3 periods of time (May–August 2019, May–August 2020, and January-April 2021). After conducting sentiment and linguistic analysis, tweets from 90 random users (manually labelled for their job anxiety scores according to the job anxiety scale) were used to train/test an SVM regression model. The model achieved an RMSE of 0.2 and an accuracy of 83%. This approach has the potential to enable early detection of work anxiety and alert individuals about their mental health.

The development and execution of an online mobile app suitable for detecting offers and showing relevant details on a digital augmented reality display via a smart phone’s camera are discussed in this paper. The client–server design of this system is two-tiered. Initially, a deal should be recorded on our platform by an entity that is distinct from users. Deal Teal AR uses the camera view to overlay electronic data on areas around you in the current world, based on the position you are facing. Deal Teal AR displays a picture via your phone’s camera when you touch it. Deal Teal’s database of businesses-restaurants, hotels, places of interest, and much more is often used to generate cards that display information or bargains. The bargains are placed in the current environment and become active, thanks to powerful augmented reality technology like computer vision and object recognition. A virtual terrain modeling platform with deep learning was also employed to increase the application’s productivity and construction recognition abilities.

As the present world is devastated by the massive outbreak of coronavirus (COVID-19), Bangladesh is experiencing significant damage in all sectors, especially as a developing country. This paper presents the development of a Web-based corona emergency portal, which will help ordinary people find out all the necessary emergency information in this regard. The Web portal comprises the COVID-19 tracker, corona e-commerce, and blood plasma bank, which are developed with React.js, Node.js and Firebase. The COVID-19 tracker is a much-needed option for this pandemic where daily updates on COVID-19 will be posted. The blood plasma bank aims to serve people with emergency blood plasma, which is very rare to find in critical situations. The developed Web-based system is user-friendly and efficient. Under these ongoing circumstances, this portal aims to be a one-stop service for all types of corona-related services for Bangladesh during this unexpected period of the COVID-19 pandemic.

The coronavirus disease (COVID-19) also known as SARS-Cov-2 has largely impacted the entire globe physically, economically, and psychologically. The detection of the virus in early stages is extremely crucial for faster recovery in patients and curbing its spread as its nature is highly contagious. Although several techniques are present today for the detection of coronavirus, they are laborious in nature, costly, require experts from medical science, and the accuracy is questionable in some of the traditional methods. This brings the need to search for a faster and reliable technique. Computer vision produced remarkable results in predicting the onset of various diseases, and the use of machine learning in healthcare has increased tremendously owing to the fast speed and high accuracy of results with minimal human intervention. Hence, this research paper aims to develop a computer vision-based artificial intelligence model that can predict the occurrence of coronavirus using electron microscopic images of the samples. In order to achieve the goal, YOLO v3 object detection algorithm using non-maxima suppression is used to classify whether a particular sample has coronavirus or not. It is proved that the proposed algorithm works faster than existing methodologies with considerably higher accuracy for detection of coronavirus.

Cloud computing is the way by which we connect to servers, large systems into a distributed secure manner without worrying about local memory limits. Here, in this paper, we proposed a Novel distributed database architectural model for mobile cloud computing (NDDAMMCC). Accelerating the exponential growth of wireless technologies and Internet which are following Nielsen’s Law of Internet Bandwidth, we are in the new era of cloud computing. In the recent technological era, smart mobile devices play a big role in all sort of day-by-day human needs. The applicability is so huge that the number of apps install on a mobile system becomes a hazard due to local memory limitations for mobile phone users and demands an alternative approach to solve this local memory problems. Mobile cloud computing (MCC) is the ultimate mechanism to this issue, and our model presents a promising path in this new kind of cloud computing technology.

In this article, a novel planar, compact, and wideband dual band-stop filter (DBSF) with controllable stop-bands are proposed. The first band-stop region is generated by using shunt connected radial shaped resonator to microstrip line. However, the novelty is achieved by generating another band-stop region by means of defect created within the radial stub on the same plane. This defect is created by etching some pattern from shunt connected radial stub, which results in compact filter configuration. The two rejection bands can be independently tuned by varying the dimensional parameters of radial stub and the defected region inside the radial stub, respectively. Further, cascading of the defected radial stub is done in order to achieve improved pass-band region between two stop-bands as well as stop-band regions. The proposed DBSF is designed, fabricated, and tested for WLAN bands at 2.4 GHz and 5.4 GHz. The proposed filter shows good in-band and out-of-band response up to 10 GHz. The experimental results agree well with simulation results, thus validating the proposed design.

The high-resolution time-to-digital converters (TDC) are currently being implemented in ASIC and FPGA technologies. The methods to implement TDC in ASIC and FPGA technologies are: delay line, Vernier oscillator, and multi-phase clock methods. The TDC implementation has challenges due to spread of delays, delay mismatches, unpredictable place, and route (P&R) delays. The calibration is a crucial aspect to realize high-resolution and robust TDC under process, voltage, and temperature (PVT) variations. This paper describes various time interval measurement methods and their calibration techniques. The unique calibration methodology developed using fewer resources for multi-channel TDCs is also described in this paper. The calibration techniques can be used across technologies of implementation. The TDC using Vernier oscillator method in 0.35 µm CMOS technology having least significant bit (LSB) of 127 ps and Xilinx Spartan-3 FPGA having LSB of 110 ps have been implemented. The delay line method having LSB of 72 ps is implemented in Spartan-6 FPGA.

A prototype high-speed frontend readout ASIC, designed in 180 nm CMOS process for tracking and precision time-tagging applications in high energy physics experiments, is presented. This ASIC comprises four readout channels, each consisting of a three-stage voltage amplifier, an on-chip analog cable driver and a comparator with LVDS driver. The amplifier and comparator are AC coupled externally. In this ASIC, potential distribution method (PDM) is used to design the high-speed amplifier, cable driver, and comparator stages. This method has proven to be an efficient way of optimizing the target specifications trade-offs. The ASIC exhibited a total voltage gain of ~ 71 and maximum output swing of ~ 600 mV across 50 Ω load for both the input polarities with power consumption of ~ 20 mW/channel. The timing precision of the overall FEE channel is measured to be ~ 530 ps RMS with comparator overdrive of around three times the threshold voltage.

This paper presents the development of a high-gain photo sensor in 0.35 µm commercial high voltage (HV) CMOS technology. The photo sensor consists of an array of avalanche diodes (APD), each operating in the Geiger-Muller (GM) region together with a passive quenching network, connected in parallel. The primary motivation behind this development is the possibility of monolithic integration of photo sensors and readout electronics in the commercial CMOS process, which is an attractive solution in many areas. Various promising photo sensor structures specific to CMOS technology are implemented, incorporated in a test vehicle (TV), and fabricated in HV CMOS technology. The TCAD simulation, physical design, electrical, and optical characterization of the photo sensor structure that shows the best result among the various structures are presented herewith in this paper.

This paper proposes a simple low-profile high-gain wideband antenna for Ku-band satellite applications that utilize a block of left-handed metamaterial for gain enhancement over conventional patch antenna. This antenna is designed and simulated using Ansys HFSS. It was found that performances of the simple patch antenna are improved by inserting left-handed material superstrate. The proposed antenna performs in Ku-band which is applicable to satellite applications. The proposed antenna resonates in 11.97 GHz. The left-handed material is realized by incorporating a layer of array of metallic rings with the conventional rectangular patch antenna.

Cardiac arrhythmia is a result of irregular beating of the heart and occurs when the electrical impulses in our heart fail to send signals regularly. Depending on the type, arrhythmia can cause fainting or dizziness and in some cases, even heart failure which can be fatal. Early and quick detection of arrhythmia can be quite beneficial in many situations, and thus, there has been a surge in research on using artificial intelligence to counter this problem. Until recently, researchers have been using traditional machine learning algorithms to process ECG signals and classify them into different types of arrhythmias. In this manuscript, we propose a novel convolutional neural network (CNN) architecture to classify ECG signals after converting them into two-dimensional images. Our process mirrors the procedure used by medical practitioners in the real world to detect arrhythmia. We also implemented three popular CNNs—DenseNet 169, ResNet-50, and MobileNet V1 to evaluate and compare our proposed model against them. Our model demonstrated better performance than these networks on our evaluation metrics and will be useful for future tasks of ECG arrhythmia classification.

Custom designed microstrip antennas are currently the preferred choice of creative antenna designers because of its record low better linear gain and efficiency, high bandwidth, favorable radiation pattern and easy to manufacture as they can be printed into the circuit board directly. This paper proposes a uniquely packed microstrip antenna of dimension 39.5 × 31 mm2 for Bluetooth-enabled devices which have myriad of applications ranging from healthcare, Internet of things and entertainment. The antenna structure is simple and consists of concentric elliptical strip-shaped patch designed on an inexpensive FR4 substrate. The dielectric constant and thickness of substrate is 4.4 and 1.6 mm, respectively, to match the microstrip material dimension so that devices can be used over a broad temperature range. The presented antenna operates at 2.4 GHz resonances frequency, transmits and receives linearly polarized radiation which are omnidirectional in nature, provides stable gain covering 2.38–2.42 GHz. VSWR of 2 is obtained signifying a good impedance matching with 50Ω microstrip feedline and SMA connector. Design and analysis is done by HFSS 19.0 simulation software. Measurements of parameters of fabricated antenna are in conformity with simulation values. Equivalent circuit analysis result is presented to along with experimental and simulated results to authorize the need for Bluetooth-enabled devices.

Tunable electromagnetic bandgap (EBG) variation can be observed when nano-fishnet with elliptical void structure is embedded inside triangular lattice. Being a double-negative metamaterial, it offers unique variation of mid-band frequency with increasing fill factor, which is monotonically increasing, when variation is limited within feasible mechanical limit. Highest EBG is observed at 46% fill factor, and corresponding field patterns are obtained computed inside first Brillouin zone. Only a magnetic polarized bandgap is generated, whereas a quasi-electrical polarized bandgap is not even found. Results are critically important for photonic filter design using metamaterials.

This article presents a dual-port circular patch CPW (coplanar waveguide)-fed multiple-input-multiple-output (MIMO) antenna for ISM band (5.8 GHz) applications. The antenna achieves an impedance bandwidth of 1.64 GHz (5.22–6.86 GHz). The optimized dimension of the MIMO antenna is 30 mm × 16 mm. The MIMO structure is obtained by putting the antenna elements orthogonally and fed independently. The matching and isolation of the MIMO antenna are improved by using a rectangular stub associated with the feed and ground plane. The individual antenna has gain and radiation efficiency of 2.52 dBi and 92%, respectively. The antenna has a stable radiation characteristic at 5.8 GHz and co- and cross-polarization are also studied. The performance characteristics of the proposed antenna are dissected as far as the envelope correlation coefficient (ECC), diversity gain (DG), mean effective gain (MEG), total active reflection coefficient (TARC), isolation between the ports, and the values are 0.28, 9.90 dB, ±3 dB, −7 dB, 12 dB, respectively.

In this paper, extended gate and gate-stack-based charge-plasma gate-all-around TFET (EG-GS-CP-GAA) is designed for the first time. The device characteristics of EG-GS-CP-GAA are compared with the conventional device (CP-GAA). The proposed device shows lower IOFF, improved ION and ION/IOFF. The dual-gate materials in the proposed structure improve the drain current ratio. The presented device holds a subthreshold slope within the Boltzmann limit (<60 mV/decade). The linearity parameters, gm2 and gm3, have been compared between both the devices. Various analog parameters, like cut-off-frequency (fT), transconductance factor (TGF) (gm/Id) and transconductance frequency product (TFP) (TGF * fT), have also been compared. The linearity and analog parameters are improved in EG-GS-CP-GAA when compared with CP-GAA. To understand the device physics, electric field, potential and carrier concentrations have been analysed. Thus, the proposed device has better electrical, linearity and analog performance than the conventional structure.

Due to numerous economic innovations and population growth, the electricity demand steadily grew. Meeting the need for electricity is a problem for producing energy focused solely on fossil fuels which eventually lead to numerous environmental issues, such as carbon footprints. Alternative sources of electricity can be used to satisfy the need for power users worldwide. This research study focused on providing a solution to the problem of tracking maximum power points in the solar cell using fuzzy logic. Considering short-circuit current Isc 7.34 A with open-circuit voltage Voc 0.6 V and irradiance used for measurement Ir0 is 1000 with quality factor N 1.5; when used for the modeling of a bifacial solar cell, the efficiency of the system was found to be ranging from 90 to 97% only because the fuzzy-based logic controller is used. Also, the average duty cycle of the system 0.5 is achieved. The models have been checked for validation and linked to multiple models to create an optimal power model using fuzzy logic.

The 3D geometry structure along with the nanopillar array photovoltaics gives unique properties for effective solar cells comparing to the ordinary solar cell. It is found that with the use of GaSb, the infrared spectrum can be absorbed by the nanopillars having large interface area. A combination of AlGaSb and GaSb is analyzed for designing the infrared solar cell. Our simulation results indicate that GaSb-based infrared solar cell has higher efficiency as compared to that of Si-based nanowire solar cell. The comparison between the GaSb and Si nanowires is done regarding the I–V characteristics, efficiency, short-circuit current and open-circuit voltage. GaSb works in the infrared region of the solar spectrum and is simulated using TCAD. The efficiency of the infrared solar cell increases from 5.6 to 12.7% when the illumination increases from 1 to 100 suns. Charge carriers are collected along the radial structures as indicated by short-circuit current scaling. Simulation of the solar cell gives an open-circuit voltage, Voc of 0.34 V under 100 suns, a short-circuit current (Isc) of 0.31 nA under 1 sun and a fill factor of 71.0%. The results are verified with already published experimental data.

Over a century, electromagnetic shielding (EMI) has been restricted to a very small frequency. The curiosity comes from the necessity to safeguard the radio receiver’s circuits and equipment from EMI and the radiated field. The development of wireless, and satellite technologies possess potential health hazards directly or indirectly. Therefore, it is necessary to find solutions to effectively isolate interference. The major aim is to keep all of the notch filter’s properties, while ventilation needs to compel the enclosure to pass a specific spectrum of EMI. This research paper describes the plan and manufacture of a super slim adaptable EMI safeguard equipped for dismissing a couple of bothersome frequentness. The outline starts with a plan of a small part, which allows us to effectively calculate the ring’s initial geometric measurements, and then uses full-wave electromagnetic modeling to correct the measurements and the intended frequency response’s end. Various EMI shielding geometric patterns with concentric rings are studied and discussed. Based on these findings, a screen-printed radical-thin and malleable EMI shield were created. This paper demonstrates a strong relationship between measurement and simulation.

Circular planar array antenna with minimized peak side lobe levels is desired in many advanced wireless applications. In this paper, an innovative strategy is applied to the designing problem of the planar array antenna having concentric rings that are uniformly weighted with circular aperture using a human intelligence-based meta-heuristic algorithm, namely teaching–learning-based optimization. The objective of this work is to design the concentric circular array antenna with constraints like number of array elements as well as radius of each ring and then finally present an analysis of the same using different examples. Four cases with different number of rings are presented considering different constraints individually. Firstly, concentric circular array antenna with 5 and 6 rings is discussed with optimized ring radii. Secondly, with optimized number of array elements, 7 and 8 rings are taken into account. Results are superior in the example having rings in larger number. The statistical data for every design are also presented to showcase the effectiveness of the simulation approach. The result comparison of the proposed work with state of the art further confirms the effectiveness of the proposed design.

In recent years, the study of microstrip patch antennas (MPA) has made great progress because of its advantages in terms of weight, volume, cost, fabrication, and dimension. This paper presents a novel radial folded microstrip patch antenna, which operates at 3.55 GHz frequency. The microstrip design composed radial folded resonator with partial ground plane. The folded structure is used in order to achieve the compactness, and partial ground is used to suppress the high order harmonic. The proposed antenna shows mono narrowband behavior with the –10 dB bandwidth of 480 MHz (3.3–3.78 GHz), which is one of the 5G bands used for Worldwide Interoperability for Microwave Access (WiMAX) application. The obtained results through simulation depict return loss below –30 dB, VSWR below 0.6, and peak gain of 5.1 dB at operating frequency of 3.55 GHz. These results are obtained through HFSS software.

The gain enhancement of a rectangular open-ended waveguide (OEW) with a finite circular ground plane in the mmWave frequency range is presented in this paper. The circular ground plane, made of dielectric (FR-4) sandwiched by two perfect electric conductor (PEC) sheets, is placed on the open end of a standard WR-28 waveguide. Two optimized slots are etched out on the radiating front side of PEC sheets of the circular ground plane such that the slots expose the FR-4 substrate. In this way, the OEW with a slotted ground plane provides a gain of 14.93 dBi as compared to the gain of 5.74 dBi by a standard WR-28 waveguide. The full-wave simulator, Ansys HFSS, is used to carry out simulations and optimizations of the antenna structure.

Kar, T. Kanungo, P. Jha, VinodShot boundary detection is the first and the most crucial step towards video content management applications including indexing, retrieval and summerisation. In this paper, an abrupt transition detection algorithm has been proposed based on phase congruency feature of the frames. The phase congruency feature is insensitive to illumination variation, change in contrast and scale. Besides this, it captures edges, corners and structural information of the frames. Motivated by this, a PC-based similarity measure is proposed for illumination insensitive video cut detection. The proposed approach is experimentally validated with standard algorithms available in the literature using TRECVid data set and other publicly available videos. The favourable results are in agreement with the proposed model.

This paper presents the results of a comparative analysis of label-free biomolecule detection using different biosensing techniques with conventional SiO2 and high-K dielectric metal-oxide-semiconductor high-electron-mobility transistor (MOSHEMT)-based biosensors. A MOSHEMT-based biosensor with high-K dielectric material to improve its detection sensitivity and selectivity. The use of high-K material decreases the substantial amount of leakage current due to the effect of quantum tunneling and improve two-dimensional electron gas (2DEG) carrier confinement. Hence, power consumption of the device is reduced and increased the gate capacitance without leakage effects. The numerical modeling is carried out using TCAD Silvaco Atlas. Different performance parameters of high-K MOSHEMT-based biosensors are studied using the simulation and compare with SiO2 MOSHEMT for the identification of biomolecules without introducing the label. AlGaN/GaN MOSHEMTs with high-K dielectric are excellent candidates for making biosensors.

In this paper, the effect of dielectric constant value and the thickness of substrate material on the performance of an inset feed semi-circular slot compact microstrip antenna for 10 GHz mobile communication, alike satellite and terrestrial applications, is reported. The performance analysis of the compact antenna achieves a resonance frequency of 9.52 GHz at reflection coefficient of −22.14 dB, voltage standing wave ratio of value 1.72, and a gain 4.9 dB. Y parameter, group delay, and directivity are also part of the analysis. The bending of electric flux lines near at the edge of patch toward ground is fringing effect. Fringing effect plays a key role in the designing of antenna as it is function of radiating patch dimension. Fringing effect on antenna’s performance due to varying dielectric constant and substrate thickness has been studied, and result parameters of antenna like S11, VSWR, 2D radiation pattern, and 3D polar plot due to variation of substrate thickness and dielectric constant have been obtained. Fringing field is enhanced due to increasing substrate height and due to decreasing substrate dielectric constants. The design, simulation, result parameters determination, and variations are done on HFSS.

Over the last decade, the continuous strife for miniaturization has led to an exponential increase in the power density of semiconductor electronics. The average operating temperature of devices has sharply increased due to the combined effects of high frequency switching and lower surface area per device. A possible way out is the introduction of with high bandgap materials or alloys as an alternative to silicon electronics. Of the different options, GaN with its high bandgap and mobility has emerged as one of the most promising materials. However, use of GaN has primarily been restricted to applications in HEMT and optoelectronic devices. In this paper, a comparative simulation study of GaN and Si field effect device has been presented using SOI FinFET as the device of choice. It has been shown that besides a higher ON current GaN shows a reduced sensitivity of ION/IOFF ratio to variations in operating temperature justifying its choice in high-power density devices. Effect of an LDD profile has been studied. Moreover, this work reports a higher susceptibility of threshold voltage of GaN-based devices to variations in dielectric. This has promising implications with respect to its use as a bio-detector.

Zinc oxide (ZnO) nanoparticles (NPs) have been successfully synthesized and characterized. Firstly, it has been synthesized the ZnO NPs and then after calcined at 400, 500 and 600 $$^\circ{\rm C}$$ ∘ C for getting better crystalline ZnO NPs. More than 70 components including alcohol, keton, steroid, terpinoid and carotinoide were present in the leaf extract which was confirmed by gas chromatography mass spectrometer (GC–MS) analysis. It has been characterized the synthesized ZnO NPs through XRD, FESEM, EDX, FTIR, DSC, TGA and VSM. The crystalline size of the XRD was around (33.58–25.8) nm. It was shown by the elemental analysis of the ZnO NPs by EDX, and it is shown that the zinc, oxygen, carbon is in the ZnO NPs. The FTIR analysis showed that the capping agents of the NPs contained the functional groups alcohol, alkene, kitone, terpinoied, organic acid. The thermal stability was determine. It was shown that the exothermic peaks ware created. It was also shown that the heat enthalpy was of ZnO NPs were 2326 j/g and 242.7 μVs/mg, respectively. Using TGA, it is shown that the percentage of weight loss was of about 10% for ZnO. It is also found that the ZnO NPs were super paramagnetic in nature with zero coercivity. It is also found that the crystalline size has been changed with calcinations temperature. But the crystallite size increases with calcinations temperature, which supported the results of XRD and VSM.

In this paper, an investigation is carried out to study the impact of tilting the electrostatic discharge (ESD) gun on discharging current waveform which is critical for studying electromagnetic compatibility (EMC) issues due to ESD and ESD compliance of electrical or electronics (E/E) modules. The impact with respect to various angles and ESD voltages are simulated, and results are compared with IEC 61000-4-2. The ESD gun is rotated in the range of ±10° in the X and Y axis, and its effects on discharge current are monitored between ESD gun tip and target through ground strap with respect to the different ESD voltage levels. It is found that the angular rotation of the gun affects the discharging current significantly. To test the discharge waveform more accurately, current target is simulated.

The main purpose of conventional CMOS is to style SRAM, but the performance of systems is affected due to increase in leakage currents and high-power dissipation. The purpose of memories is to possess short time interval, less power consumption, and low leakage currents so SRAM cells with FinFET preferable. Multi-threshold CMOS and variable threshold voltage CMOS include stacking method and circuit power gating and self-controllable threshold voltage-level techniques. This paper details the advantage of multi-threshold CMOS method in order to develop a FinFET-based SRAM cell, and then, the designed cell is differentiated in terms of active power dissipation. By using predictive technology mode (PTM) and 14 nm technology on cadence, all the simulations are performed.

A new method of improving the overall performance of a solar thermoelectric generator (STEG) using a TEG with frustum leg geometries is presented in this study. Six STEG models are proposed compromising the conventional rectangular (rect), frustum (frust)—rect, di-frust—rect, di-frust—frust, frust and di-frust legs, respectively. A comparison of the volume occupied by the total thermoelectric legs in the diverse models reveals that the models which utilized only frustum legs reduced the volume occupied by rectangular legs in a conventional TEG cell by 42%, thus implying a reduction in fabrication cost as lesser materials will be required for modules comprising the novel leg geometries. Furthermore, it follows that under the same amount of concentrated solar radiation, system 6, which utilized only di-frustum legs, provides a temperature gradient, power output density, and thermodynamic efficiencies (energy and exergy efficiencies) that are 2.1, 3.8, and 1.3 times higher than that of the conventional STEG module, respectively. The findings of this paper will greatly advance the research in optimizing the performance of contemporary STEG systems.

With the advancement of artificial intelligence (AI) techniques, fake digital content has mushroomed in recent years. The production of very realistic fake content, even by a novice computer literate, has become a cakewalk with the latest deep learning and generative adversarial network (GAN) models. The objectionable content created by the use of deepfake technology is getting exploited by bad elements to tarnish the image of celebrities. With the advancement of digital technologies, it is almost impossible to detect fake content with naked eyes and is posing a big challenge for the public. In this manuscript, we use a method to detect deepfakes by using eye reflection and comparing both eyes in terms of different metrics such as structural similarity index measure (SSIM), intersection over union (IoU), and mean-squared error (MSE). We used Flickr-Faces-HQ (FFHQ) dataset for real human eye images, and the thispersondosentexist.com Web site for GAN-synthesized images. Next, we used automatic detection of the corneal region and compared pixels of both eyes. Despite their simplicity, the methods can achieve AUC values of up to 0.9.

Today, implantable antennas are very popular among researchers, as they are used to find simple, effective, and real-time solutions for many health-related issues inside the human body. Biomedical implantable devices are receiving great attention to find solutions to different medical conditions. Implantable wireless sensors integrated with implantable antennas have many advantages like early detection of disease and continuous and real-time monitoring of health conditions. Thus, it reduces healthcare costs, improves the quality of life of an individual, and improves the accuracy of diagnostics systems. The implantable sensors are placed inside the human body to measure real-time information of the various body parameters like glucose level, body temperature, blood pressure, and ocular pressure. However, the implantable antennas used for transmitting this information face major challenges such as poor gain and large size. The human tissues due to their frequency-dependent permittivity and highly lossy nature exacerbate the performance of the implantable system. This manuscript covers an overview of the major requirements of implantable antennas, various design aspects, challenges, simulation tools, testing methods, and various applications of biomedical implantable antennas.

Bitcoin is based on peer-to-peer technology where there is no need for central authority or banks as intermediaries. The network carries out Bitcoin issuing and transaction management. It is open-source, decentralized by design, nobody owns it or controls Bitcoin, and everyone can participate. Bitcoin challenges the previous payment system through its unique properties and allows compelling use cases that were not feasible in the past. Due to its decentralized properties and lack of price control, many a time, celebrities can influence its value using their social media fan followership. Twitter is becoming common place for celebrities to share their sentiments about Bitcoin. Elon Musk, a juggernaut entrepreneur of this century, is a prominent celebrity who has had a significant influence on Bitcoin and has been instrumental in promoting and criticizing Bitcoin in past months. In this paper, we analyze how Elon Musk’s tweets affect Bitcoin prices.

As the Scaling of Devices is in turn facing many problems regarding the performance of the device and to keep Moore’s law alive a better replacement for a FINFET is a GAAFET. The strained silicon technology has made a great revolution in the last few years makes the device with enhanced mobility and reduces the short channel effects (SCEs) with less leakage current are the main parameters of a semiconductor device. The paper presents a 22 nm GAAFET device of tri-layered structure (s-Si, s-SiGe, s-Si) with a strained silicon channel of (2 nm, 4 nm, 2 nm) thickness, respectively. The biaxial strain-induced will increases the mobility of the charges. The controlling ability of the channel with four gates makes it better performance. The proposed device is compared with a silicon GAAFET using a Silvaco TCAD tool which is superior with a 22% of improvement in the drain current and less leakage current.

The state-of-the-art designs are presented in this manuscript to improve the performance parameters of a standard patch antenna for the 2.45 GHz band. Several rectangular microstrip patch antennas with slots of different shapes and sizes have been proposed with inset-fed types of feed. Optimum values are selected through the simulation for different designs to increase their performance. The proposed antennas are designed and optimized using full-wave Ansys HFSS software. The antennas show resonance frequency near 2.45 GHz which can be used in Wireless Local Area Network (WLAN)/Radio Frequency Identification (RFID) applications. The substrate used for all the designs is economical FR-4 which has a permittivity of 4.4 and thickness of 1.6 mm. The overall dimension of all the antennas is 38.22 mm × 30.25 mm × 1.6 mm. The antennas can achieve peak gain up to 3.5 dBi and are suitable for 2.45 GHz WLAN/RFID applications.

In strain technology a twisting in the MOSFET by growing heterostructure bed connected with Si/SiGe/Si layers within the system is working broadly. Developing a tri-layered HOI n-channel FinFET devices at 14 nm gate length which have double strained Si layers and in between strained SiGe with the high-k dielectric material like HfO2, ZrO2 and Si3N4 as gate oxide. The short channel effects like DIBL, Ion/Ioff, threshold voltage, etc. have been analyzed for better performance through changing the SiO2 as gate oxide with the different high permittivity materials such as HfO2, ZrO2 and Si3N4. This paper explores it considering equivalent oxide thickness calculation and optimized using SILVACO TCAD software. Also compare it with SiO2 SOI structure for both drain current and transconductance of FinFET and the result in both the drain current and transconductance are higher than SiO2 SOI device when biased in the linear region. And DIBL, Ion/Ioff are also developed by incorporating the high-k materials.

Energy is a crucial constraint for wireless sensor networks (WSNs). Among the most fundamental approaches for prolonging the lifetime of a WSN is clustering. In this work, we present a Center of Gravity (COG)-based clustering algorithm for variable clustering. The formation of clusters uses the partitioning concept which is based on the node density for formation of optimum number of clusters. In the proposed algorithm, cluster head election is based on the concept of COG. The proposed algorithm is simulated and compared with standard algorithm like LEACH. The simulation results show that there is a significant improvement in all the lifetime metrics, i.e., First Node Die out (FND) and Half Node Die out (HND).

The most limiting sections in terms of throughput are sections in areas of crossing sections, intermediate stations, station platforms, ascent and descent ferry sections, etc. The existing methods for determining the minimum passing interval cannot be applied with the coordinate system of interval regulation due to the fact that at small coordinates they do not give accurate results, since they do not take into account the dynamics of the approach of two trains. The latter circumstance requires an accurate mathematical study of the process of approaching trains, identifying the most difficult situation in terms of approach (critical moment of approach) and determining the value of the minimum interval of passing following from conditions of critical approach. For a quantitative analysis of the automatic control system of train traffic, analytical expression of the connections between the input and output characteristics of the system, to determine the degree of influence of one or another controlled characteristic on its operation, outlining the range of tasks that the system can solve, and establishing the redistribution of the system's capabilities, it is necessary to build a mathematical model of the system.

The amalgamation of renewable sources into the preexisting grid system gives rise to stability, frequency deviation or power mismatch issues. Electrical vehicles (EVs) can supply power to the grid to solve these issues. EV is an energy storage system that is applied as a load and also as a source. To achieve high power quality, the management of automatic generation control (AGC) at the generation side is used. The system under investigation including thermal dish Stirling solar system (TDSSS) is used which emphasized the utilization of renewable into AGC. Along with TDSSS, distributed generators (DG) are also used consisting renewable sources like photovoltaic, aqua electrolyzes, fuel cell, wind, and diesel In this paper, two area system have been studied. Area 1 includes TDSSS with EV integration, and Area 2 has DG with EV penetration. An aggregate model of EV is used in both areas. The system is analyzed for step load and random load disruptions. To control the frequency, a proportional–integral–derivative controller with a derivative filer (PIDF) is used. Particle swarm optimization (PSO) and firefly algorithm (FF-A) optimization techniques are implemented to find the control variables, and these two techniques are compared. The time-delay effect is also taken into consideration, and the system under study is demonstrated in MATLAB/Simulink environment. The simulation results show that FA has low oscillation, very small settling time and have a considerable improvement in delay margin.

Electrical vehicle (EV) as an energy storage element plays a key role in stability, frequency deviation or power mismatch issues. HVDC links are acting as DC capacitors and its electrostatic energy is used as an energy storage device. To achieve high power quality, the management of automatic generation control (AGC) at the generation side is used. Along with the preexisting energy sources like thermal and gas, EV and HVDC are penetrated the system for load frequency control (LFC). The system under investigation includes thermal and gas with EV in both areas with HVDC link is connected between two areas. Area 1 includes thermal with EV integration and Area 2 has a gas system with EV penetration. An aggregate model of EV is used in both areas. The system is analyzed for step load and random load disruptions. To control the frequency, proportional-integral (PI) controller proportional-integral-derivative (PID) controller is used. Firefly algorithm (FF-A) optimization techniques are implemented to find the control variables. The time delay effect is also taken into consideration and the system under study is demonstrated in MATLAB/Simulink environment. The simulation results show that FA has low oscillation, very small settling time and have a huge improvement in delay margin.

Due to the system nonlinearities, the expected response of a control system could not be achieved. The control function used by the controller is adaptive in nature, in order to deal with the significant changes of the parameters of the system due to environment or due to internal system disturbances. This adaptive control mechanism is dependent upon the value of adaptation gain. For higher order systems, the available range of adaptation gain becomes very less. A second order control mechanism, on the basis of the application of the MIT rule, is used in the paper. In order to achieve the desired response from the control system, introduction of a proposed compensating signal which is in addition to the normal actuating signal. This additional signal is applied to the controller and output response of system is maintained unaltered, even though there are changes in the system itself, input noise, ambiance or others likely to occur in any actual real life system. This signal, which may be called the additional actuating signal (AAS), helps in controlling output in the control system, without disturbing the standard actuating signal and the systems working environment. This technique would be useful in systems where the system parameters including the inputs cannot be fully controlled, like in an autonomous or independent control system or a physiological control system. Simulation is done in Simulink and MATLAB software.

In practice, all the systems posses’ some type of nonlinear dynamics for the intentional and inherent imperfection or properties of the system. Due to different dynamics, a nonlinear system exhibits some nonlinear phenomena, which are inevitable in any process. An approximate behavior of the nonlinear system can provide by the linearized system. But, often linearized models are inadequate or insufficient for analyzing the overall system behavior. To achieve the desired response, system parameter estimation is very much important in the face of external disturbances. For a specific value of initial conditions, structural model identification depends on the system’s dynamical beaver, output, and input. The online parameter identification process is proposed in this paper for continuous-time plants to characterize the input required for structural identification. Generalization of nonlinear observability and incorporating extended lie is considered for structural identifiability derivation. The methodology evaluates structural identifiability for time-variant system inputs, and moreover, it can be used to limit the input profile that is required to design an expected system. MATLAB ‘SIMULINK’ software tools for model simulation and analysis are more effective for the proposed work.

Building integrated photovoltaic (BIPV) installations in modern buildings necessitate a large BIPV array (LBiAr) for both off-grid and on-grid system applications. The problem of LBiAr is partial shading (PS) that reduces considerable power output (PO), which happens due to various reasons. To mitigate PS and to maximise PO of LBiAr, one of the effective solutions is the arrangement of fixed BIPV array configurations (BIPV-ArCns). This research paper proposes four different 5 × 6 fixed hybrid (Hbr) triple-tied (TrTd) BIPV array configurations (BIPV-ArCns), such as series–parallel triple-tied (Hbr-SePl TrTd), bridge-linked triple-tied (Hbr-BdLk TrTd), honey-combed triple-tied (Hbr-HnCb TrTd), and ladder triple-tied (Hbr-Ld TrTd) to improve the PO. The design, simulation and analysis of the proposed Hbr TrTd BIPV-ArCns under four diverse patterns of PS are effectively executed in Matlab/Simulink. Finally, the evaluation of performance for the Hbr TrTd BIPV-ArCns is compared and judged in respect of global maximum-power (Gl-MxPo), mismatch-loss (MsLo), fill-factor (FlFc) and efficiency (Efcy).

Zn/Cu electrodes-based electrochemical cell has been designed and developed using soil of a pot and living Bryophillum pinnatum tree for cultivation of electricity. Voc, Isc, Pmax, and rin have been studied. Firstly, Zn plate was placed in the soil of the pot, and Cu plate was placed onto the living PKL tree. Secondly, Cu plate was placed in the soil of the pot, and Zn plate was placed onto the living PKL tree. Different soil pots and different living PKL trees have been used for getting Voc, Isc, Pmax, and rin of the electrochemical cell. It is shown that the performance of the second condition is better than the first condition. This work is very new and innovative. This work can help to light the LED bulb.

The experiment is carried out to study the electrical energy harvested from the four types of living plant extracts like PKL, pandan leaf, red spinach, and green chili based on electrochemical cells and to utilize the generated electricity for lighting systems. Plant extracts were used as electrolytes where Zn and Cu plates were used as a cathode and anode in this investigation. This research work aims to find out the more sustainable energy source by comparing the sources of PKL, pandan leaf, red spinach, and green chili extracts. Here, six electrochemical cells were used and connected in a series combination. LED bulb was used as a load. From experiments, three circuit parameters (load voltage, load current, and load power) have been compared for the PKL, pandan leaf, red spinach and green chili electrochemical cells such as (i) the maximum and minimum load voltages are 5.52 V, 3.75 V, 3.74 V, and 3.73 V, respectively; and 5.18 V, 3.73 V, 3.72 V and 3.69 V, respectively; (ii) the maximum and minimum load currents are 760 mA, 10.10 mA, 7.50 mA, and 0.60 mA; and 440 mA, 9.40 mA, 7.10 mA, and 0.30 mA, respectively, and (iii) the maximum and minimum load powers are 420 mW, 26.26 mW, 18.00 mW and 0.90 mW, respectively, and 230 mW, 24.94 mW, 16.04 mW, and 0.45 mW, respectively. Electrochemistry is responsible for electricity generation. Finally, it is concluded that the electrochemical cell for the PKL extract is better than the other cells. This work is innovative.

In this paper, a fractional order modified Anti Windup (AW) proportional–integral (PI) scheme is designed to generate the switching gate pulse of buck converter to control the speed of separately excited DC (SEDC) motor for low speed applications. Input saturation is a constraint on all real-time motors. Furthermore, for DC-DC power converters, the duty cycle is the natural control input. Therefore, to keep off the wind up phenomena, the output of a new modified fractional order back calculation-based AW scheme with PI controller is compared to the repetitive sawtooth waveform for switching pulse generation in closed loop speed tracking system. Here, fractional tuning gain of integrator contributes additional flexibility. To ameliorate the steady state error as well as tracking speed performance compared to the AWPI and fractional order AWPI controller, this new controller is formulated by taking the difference between AW tracking time constant and proportional gain connected to the output capacitor voltage signal. It can be observed that the new controller, with its two configurable parameters, outperforms in terms of speed tracking. Also, it can minimize the integral time absolute error (ITAE) and integral squared error (ISE). In presence of noise, both fractional order controllers perform well compared to the integer order controller by showing their potency for speed tracking as well as disturbance rejection.

One of the most remarkable menaces that attract attention in this “.com” era is cardiac surgery. To prevent this, the electric ventricular assist device (EVAD) has widely been put into practice, especially during the execution of the cardiac surgery. Previous research works present function with the exponential time delay, but this function has widely been accepted as it has proved to be helpful in creating not just polynomial form but exponential forms as well through the “Pade approximation method.” This paper aims to unveil a nonlinear analysis through the EVAD system by considering dead zone combined with saturated nonlinearity, which has resulted in the creation of a converging stable limit cycle through analysis. Ultimately, by the application of the Lyapunov stability theorem on the proposed nonlinear model, it is vivid that due to the semi-definite energy function, Sylvester’s criteria are used, and proper energy function for our proposed design has also been calculated.

The automatic voltage regulator (AVR) is regarded as a critical component of an electrical power network, and its proper operation is essential for the network’s efficient and protected process. The focus of this research is on developing an inexpensive and reliable control mechanism for AVR systems. As a result, the multi-verse optimizer (MVO)-based linear quadratic regulator (LQR) scheme has been explored and realized using MATLAB 2018a software, with the transient response stipulations equated to standard PID and two-degree of freedom (2-dof) PID control provisions. The robustness of the proposed methodology has also been explored in terms of parameter fluctuations caused due to external factors. The obtained results have demonstrated that the proposed LQR-MVO-linked AVR system has yielded better transient resolution as well as robustness when compared with existing methodologies.

It has been discussed about an observation of energy density of a PKL, aloe vera, myrobalan, lemon, and tomato electrochemical cell. The PKL, aloe vera, myrobalan, lemon, and tomato electrochemical cells are the primary cell. It can generate the electrical power from chemical reaction. Everywhere of every country needs electricity for their development. It is now proved that world needs renewable energy sources to safeguard the future earth. It can be used 24 h unlike solar energy. The each of the electro chemical cells were fabricated 2 half cells. It is obtained that the energy density of PKL electrochemical cell is the best among all other electrochemical cells.

The power system has expanded immensely during the past few years due to continuous emerging innovations and changing ideas for improvement in the system stability. Hence, it becomes very necessary to maintain different parameters of the system as per the norms without disturbing the system. For fulfilling the load demand of the consumers, most reliably and economically power systems are interconnected to each other forming multi-area control systems. Also, the conventional power system structure is getting replaced by deregulated structures for the betterment of the power quality and increasing the competition in the power sector, which is also immensely impacting the way of how the whole power sector works. The main area impacted due to these changes is load frequency control. This paper highlights the recent work done on load frequency regulation techniques most recently applied in a deregulated environment. A brief review of various control methodologies based on robust control and soft computing control techniques are discussed.

Accumulation of dust in conjugation with various environmental adversities over the surface of PV modules causes soiling phenomenon, thereby generating shading scenarios and leading to reduced irradiance available to the module. Soiling has been recorded as one of the most common detrimental factors to module health and energy output. An overview on the soiling phenomena has been discussed in this manuscript, with a brief discussion regarding the soiling of bifacial PV modules. Furthermore, a case study investigating the impact of bifacial gain of a 90° bifacial PV module installed over two different orientations East–West (E–W) and North–South (N–S) has been conducted. The results show that the performance ratio (PR) of soiled system installed at E–W orientation (1.37) is greater than PR of N–S-oriented system (0.99). The bifacial gain of a soiled system under E–W orientation (0.81) is greater than the bifacial gain of soiled system (0.34) under N–S orientation due to higher irradiance collection throughout the day.

Remote sensing is an important field in science and technology and consists of the images of the Earth taken by the means of artificial satellites or aircraft. Satellite images or high-resolution aerial images are flexible to work with and easy to monitor. Since the total area of the earth is so large, high-resolution remote sensing images produce vast amount of data, even image processing is time consuming. This project represents a combination of unsupervised and supervised process to classify high spatial resolution satellite images so that minimal human intervention is needed. For this purpose, histogram peak-based classification approach is used to classify remote sensing image into subcategories like urban land, vegetation land, water body, etc. To detect different objects, present in the image, convolutional neural network-based approach is used. The neural network model is trained using custom dataset. Then, object localization operation is performed to get the coordinates of the object present in the image. Then, histogram-based segmentation operation is performed to compute the area of different objects present in the image. After that 3D model is constructed using the coordinates obtained. Georeferencing technique is used to calculate the area of different classes observed.

The evolution of perovskite-based solar cells has recently generated a huge attention, with the goal of removing harmful lead from perovskite materials. Important goal of this work is to enhance current research by numerically simulating various lead-free perovskite solar cells (PSCs) with the SCAPS-1D software. Device simulation is carried out for five different lead-free perovskite materials in n-i-p configuration of FTO/TiO2/perovskite layer/Spiro-OMeTAD/Au and analyzed. The impacts of various perovskite material layers on solar cell performance, such as hole and electron transport layer thickness and doping concentration, have been thoroughly investigated and optimized. Among lead-free perovskite-based devices, the CsSnI3-based PSC has the highest power conversion efficiency of 32.11%. This suggests that lead-free PSCs with comparable performance could be produced experimentally in the future.