Ebook: Recent Developments in Electronics and Communication Systems

The First International Conference on Recent Developments in Electronics and Communication Systems (RDECS-2022)> was held from 22–23 July 2022>. It was organized by the Dept. of Electronics and Communication Engineering, Aditya Engineering College, ADB Road, Aditya Nagar, Surampalem-533437, A. P.>

The primary goal of RDECS-2022 was to change ideas in existing areas, encourage interaction between academia and industry, and to promote the collaborative research activities involving scientists, engineers, professionals, researchers, and students that play a major role in almost all fields of scientific growth. The conference also aimed to provide an arena for showcasing the advancements and research endeavours being undertaken in all parts of the world.

Transdisciplinary engineering outperforms inter or multi-disciplinary approaches to problem-solving in the real world. Specifically, transdisciplinary engineering solutions try to address issues where the solution is not evident during the initial stages in the multidisciplinary area. Given the significance of these issues and the context in which they arise, the research community must work with members of various other scientific groups to find solutions, because no single field or expert has all the information necessary to solve complex problems. Working together has always been crucial, but it now takes even more effort than before. Collaboration among interdisciplinary fields provides the free flow of information and the exchange of ideas essential to finding a solution. The context in which an engineering problem is to be solved, or the solution to be implemented, should be considered by both academics and engineers. Bringing in experts from different scientific disciplines can help to validate engineering solutions.

We have had the privilege to host an assembly of world-renowned scholars and experts who gathered for the conference and graced us with their words of wisdom as keynote speakers in their fields of expertise. We are proud to say that a large number of technical papers with rich content, comprising ground-breaking research from participants from various institutes, were submitted for presentation at the conference.

We would like to thank our respected Chairman, Dr. N. Sesha Reddy>, chief patron, for his continued support and guidance.

We also wish to express our deep sense of gratitude to our Vice-Chairman, Dr. N. Satish Reddy>, also a patron, for his keen interest in promoting research at the Aditya Engineering College (A)>, and for being a constant inspiration to achieve greater heights.

We have been fortunate to be guided by our Principal, Dr. M. Sreenivasa Reddy>, Conference General Chair>, who has given us his constant support and much encouragement.

I would also like to thank our conference chairs, Dr. Sanjeev Kumar and Dr. Mahesh Kumar Singh, as well as our conveners, Dr. Sridevi Gamini and Mr. V. Satyanarayana.

Special thanks also go to the organising team, faculties, staff members and students; this event could not have been successful without them. Thanks also go to Dr. N. Radha, HoD (ECE), Aditya Engineering College (A), to the reviewers for their constructive suggestions to improve the manuscripts, and to IOS Press> for their constructive comments and suggestions, which have helped us to produce the best quality work in the form of this publication.

Dr. KVS Ramachandra Murthy>

Professor & Dean (R&D)

Aditya Engineering College (A), Surampalem

Conference Chair, RDECS-22

Data mining is a growing domain. Wherever we are going, data mining Techniques are available to collect important information from the warehouse or from databases. In every sector, they need to keep data secure. Maintaining a balanced state of data is very crucial. Whenever a class tends to classify, the specimens in the class can be grouped as the majority group and the minority group. The majority group consists of higher number of data when compared with the data distributed in the minority group. This paper proposes the Randomized-Ensemble with Smote (RESMOTE) methodology to handle the class imbalance problem. This paper addresses imbalance issues and proposes a class imbalance solution using a sampling technique combined with certain classification metrics.

Now a days, web content over the World Wide Web is growing fast, it has become tougher to satisfy the necessities of the client’s queries results. This paper provides a technique for advising a sequence of queries that are similar with the client’s input search. The related searches are based on past given searches by the clients itself. This method tells about the clustering process where syntactical or semantically similar searches in groups are found. This also proposes some queries which are similar or related to the queries submitted by the client to get the required information which is relevant and efficient. This method does not only detect the similar and related searches and queries but can also rank them considering the similarity measure. And this technique is executed using real data sets from search engine query log. It gives queries in websites like Google, yahoo, Bing etc.

To use machine learning to discover web application security issues (ML). Web applications are notoriously difficult to analyses due to their variety and bespoke construction. Machine learning protects websites and other digital assets. It uses human-labeled data to deliver automated analytical tools with web-programming semantics. We introduce Mitch as the black box ML solution for detecting Cross-Site Request Forgery (CSRF). With Mitch’s help, we discovered 35 new CSRFs across 20 major websites and 3 new CSRFs in live production applications. In the end, working code will be utilized to prove Mitch’s worth.

This paper discusses the modelling and control aspects of an enclosed lowest converter-based shunt active power filter. In an AC distribution network, the shunt active power filter reduces harmonic interference. Using an interleaved buck converter architecture prevents the typical inverter’s shoot-through problem. The P-Q control method is utilised to provide compensating current. One loop controls the dc-link voltage while the other regulates the voltage mode. This technique uses a twin-loop architecture. For example, the park transformation and inverse park transformation are used in the current loop to take the load current into account. Current is controlled using hysteresis using a current limiting device. PWM is used to create switching pulses by the gate driver, a digital signal processor. All three parameters of a non-ideal voltage source can be figured out. For an unbalanced nonlinear load, the voltage at the source, the load’s current, and the source’s current have all been provided.

If you read that quantum machine learning applications solve some traditional machine learning problems at an amazing speed, be sure to check if they return quantum results. Quantum outputs, such as magnitude-encoded vectors, limit the use of applications and require additional specifications on how to extract practical and useful results. In satellite images, degradation of image contrast and color quality is a common problem, which brings great difficulties for information extraction and visibility of image objects. This is due to atmospheric conditions, which can cause a loss of color and contrast in the image. Regardless of the band, image enhancement plays a vital role in presenting details more clearly. The research includes new computational algorithms that accelerate multispectral satellite imagery to improve land cover mapping and study feature extraction. The first method is a quantum Fourier transform algorithm based on quantum computing, and the second method is a parallel modified data algorithm based on parallel computing. These calculation algorithms are applied to multispectral images for improvement. The quantum-processed image is compared with the original image, and better results are obtained in terms of visual interpretation of the extracted information. Quantitatively evaluate the performance of the improved method and evaluate the applicability of the improved technology to different land types.

In India, the power demand increases rapidly. The availability of coal, natural gases, and other fossil fuel availability decreases day by day. So, there should be a shortage of fossil fuel energy sources in the future. The alternative one for this is renewable energy sources like wind, solar, biomass and tidal, etc. In this paper, it gives the data about the availability of wind energy sources in India. Wind energy is available everywhere but the power generation depends on wind velocity. Every wind turbine was designed for different wind velocities. The minimum speed of wind turbines is between 3–4 m/s and the maximum speed of wind turbines at 25 m/s. good analysing tools are required to increase the efficiency of wind turbines and solve the problems associated with them. This paper gives the data of geological survey around India of required wind velocity available areas in India.

To determine the capabilities of this technology, we refer to different research papers related to this topic. This literature-based research could assist practitioners in devising responses to relevant issues and combating the COVID-19 pandemic. This paper examines the position of IoT-based technology in COVID-19. It looks at state-of-the-art architectures, networks, implementations, and industrial IoT-based solutions for combating COVID-19 in three stages: early detection, quarantine, and recovery. Since 2020 was a challenging year for all of us, and during this pandemic, we all realized that social gatherings had to be avoided, and the serious issue was to handle it. So to tackle this and ease the handling of the Corona Virus, we developed an automatic door that monitors an individual’s temperature and whether the person is wearing a mask. In the absence of a mask, it clicks a picture of the person and stores it in the database for future reference.

The approximate multipliers allow saving power and area by deploying many other contemporary, error flexible, compute intensive application. In this manuscript, first discussed an original minimally biased approximate integer multiplier design method that can be configured with an error. The proposed MBM architecture by combining by an approximated ‘Log’ biased numeral multiplier of a specific error reduction mechanism. After that analysed a place of original estimated floating point (FP) multiplier. These are showing to facilitate these FP multipliers is on the Pareto obverse on the region designs space against power and error. Here used the 45-nm criterion cell library to synthesis the designs. When compared to the precise version we designed MBM integer offers 84% power reduction and 78% area reduction. The proposed estimated FP multiplier offers improved error effectiveness than the precise scaling. The discussed FP multiplier offers 57% power and 25% area improvements. It isa smaller amount of 4% error bias, 8% mean error and 28% of peak error.

The advancement of science and technology is making life easy to live. Nowadays, the world is accepting automation over manual systems. We are already in the future, possibly due to the latest emerging internet technology, IoT (Internet of things). In this coronial period, we humans have understood the importance of hygiene. Nowadays, we care more about making the environment cleaner than ever. In this period of social distancing, we want everything contactless and safe, so here we have come up with an intelligent dustbin! No need to touch the dustbin anymore. Just bring the piece of garbage in front of the dustbin. The lid of the dustbin will open on its own as soon as it detects the amount of waste. This paper presents an automatic light, fan, and intelligent dustbin controller system using IoT. We aim to provide the users remote access to their home appliances. With various sensors tech, we are taking this even further. The whole circuitry used in this project is made with components like Arduino UNO, NodeMCU WiFi module, Servo Motor, Ultrasonic sensor, etc. This project acts as an example of how positively IOT can impact our lives.

In this new era, security is being the major concern in day to day life. To secure the data many security models are been introduced. Advanced Encryption Systems such as encryption, decryption methods are having a drawback, that it uses a key to store the data in its devices. The key can be easily cloned by any 3^rd party person and there is a chance of losing data and the device security. PUF (Physically Unclonable Function) is mainly used for device authentication. It helps to identify the data and its device while performing authentication process. In front-end the PUF is used for device authentication where as in back-end LFSR (Linear-Feedback-Shift-Register) is used to generate random numbers which helps to increase more security. Previously the PUF based LFSR is been implemented and observed the results. In this project the PUF is implemented using LP LFSR (Low power Linear Feedback Shift Register) in order to show more improvement in terms of security by increasing the randomness.

Despite the Cloud Environment’s (CE) numerous and obvious improvements in healthcare, these issues continue to impede CE’s development. In order to fully understand and utilise such issues, the utmost serious thinking is required. Global, national, and local health information are in demand. Setting up the required security protocols for handling security issues and breaches is essential for getting the most out of healthcare services. The Modular Encryption Standard (MES) is therefore being investigated for this research in order to provide requirement-based health data protection in light of the tiering of security efforts. According to the presentation evaluation, the suggested work outperforms other regularly used calculations against the security of health data in the CE environment in terms of enhanced performance and practical subjective security ensuring approaches.

High speed and power are required for modern applications, and low power consumption and a small integration area. So, this system will be consumed less power. To manipulate these problems either way of computing should be changed by another technique or the transistor should be changed by another device. One selective computing is stochastic. While loaded computing contributes high hardware cost, high speed. Stochastic computing accuracy is less than binary computing circuits. They are having some implementation basic operations of stochastic computing. They are complementary operation, multiplication operation, addition operation, subtraction operation, and division operation. These operations having some logic gates i.e., AND gate, NOT gate, XOR gate, an XNOR gate. And implement the hardware Tripartite synapse. It can be divided into parts. They are Astrocyte, Synapse, Input spiking, LIF neuron, and output spiking. Using stochastic circuits and conversion processes, this paper demonstrates the fundamental notions of stochastic computing. implementation of arithmetic operations and hardware tripartite synapse and tripartite synapse hardware architecture.

On Earth, there are millions of people, and everyone of them has emotions that they exhibit through their behaviors and conduct. Some emotions have a negative impact on human functioning and can lead to mental diseases that can deteriorate over time if they are not diagnosed early. Identifying these mental difficulties is a difficult process that is important in order to provide them with assistance in fixing the issue before it develops. Observing the manner in which individuals express their emotions in either writing or social media conversations is one such method. In the study, we evaluate two mathematical algorithms that imitate social media user activity and emotional fluctuations. In this review, we utilize two datasets for widespread mental disorders. Anorexia with depression. The results reveal the existence and variety of emotions exhibited by individuals on social media with depression and anorexia through the use of two visualizations that highlight pertinent information.

A general least mean square interference technique is provided for effective adaptive filtering. The gradient adaptive learning rate methodology can now handle non-stationary data with the Interference normalised least mean square technique. Because of issues like duplicate talk and echo route variance, echo cancellation is made more difficult because the learning rate must be adjusted. Frequency domain echo cancelers learn at different rates, which can be altered in a novel fashion. Normalized least mean square method normalised learning rate under noise is used to calculate an optimal learning rate. This double-talk detection technique exceeds the competition while also being incredibly simple to implement. A number of least mean square (LMS)-type algorithms have been investigated in place of their recursive equivalents of IVM or TLS/DLS, which involve large calculations. As a result of these findings, we provide a consistent LMS type technique for the data least squares estimate problem. This unique approach normalizes step size and estimates the variance of the noise in a heuristic manner using the geometry of the mean squared error function, resulting in rapid convergence and robustness against environmental noise.

Developing countries like India is witnessing an increasing economic growth, rapid population in addition to industrialization leading to an increased rate of land use and cover. In order to better utilize the land and natural resource is essential to classify and analyse the land use and cover. Machine Learning and Deep Learning techniques are considered to be one of the effective and efficient ways for analysing and classifying the land use & cover. Here, in this paper, methodology for land use & cover classification – analysis of rural and urban regions of Bengaluru is been proposed. The proposed system’s main objective is to monitor the land cover changes of Bengaluru district including its rural and urban region for classifying the land cover into its exact classes. Classification algorithms such as SVM (Support Vector Machine), RF (Random Forest), KNN (K – Nearest Neighbor) and DT (Decision Tree) are used in the preprocessing of images and model created is tested using CNN. The Landsat datasets from usgs earth explorer is used. Performance evaluation of these algorithms are done based on their accuracy rates and efficiency. The proposed system shows that CNN classifies the land cover classes efficiently because of its highest accuracy and efficiency rates when compared with other algorithms.

In the digital signal processor, IoT, image process and network systems power are more consumed. To avoid this problem there must be effective in hardware applications like area, less power consumption, accuracy in output, speed, and error. Overall, the basic need is the low power requirement. Most of the 3-D graphics are because of multiplication and division. To develop efficiency in hardware logarithm multiplier is the best solution. It is excellent in processing multiplication operations. So that it is maintaining a crucial role in different applications from the past years. But it is a lack to explain complete history in development. Hence, this paper finalizes the complete development steps, involvement performance, and complete error analysis. It is for technical issues, LNS is used majorly. The overall importance of LNS is explained. Error calculation techniques such as antilogarithmic converters, VHDL, and logarithmic approximation are used. The usage of different techniques like Mitchell’s approximation and iterative pipeline architecture is to design the hardware components. This paper gives the best result to future designers.

This paper focuses on modeling forward and inverse kinematics of a 12-DOF bipedal robot and parametrizing its body trajectory to generate different gaits on 3D terrain.The 12-DOF kinematic chain represents the lower body part of the humanoid robot. The Cartesian coordinate is assigned to each link of the biped robot using the Denavit-Hartenberg (DH) convention. One step of the bipedal walk is divided into three walk phases depending on whether one foot or both feet are in contact with the ground.Time parameterized cubic splines construct the biped robot’s mid-hip and swinging foot trajectory.The inverse kinematic determines the values of the joint angles corresponding to hip and swinging foot frame trajectory using the geometric relation between foot ankle point, knee position,and hip position. The complete one-step gait of the biped robot is represented in the form of a stick diagram.The proposed method is a geometrical approach to parameterize the gait of a biped robot for one step of the walk in terms of hip and swinging foot trajectory optimization is required to determine energy optimal balanced gait, which we envisage as our future task.

This paper is intended to explore the research done on identifying the diseased plants and crops using Machine Learning (ML) and Deep Learning (DL) techniques during last 10 years using bibliometric methods. In this study, we used Scopus database to analyze on “Plant disease” or “Crop disease” using “Machine Learning” or “Deep Learning” or “Neural Networks”. This paper focuses on the importance of ML and DL techniques in identifying plant or crop diseases. The database collected from the Scopus is analyzed using VOSviewer software of version 1.6.16. The study is limited to publications from conferences, journals with subject areas are limited to Computer Science, Engineering and languages limited to English and Chinese. Scopus search outputs 824 articles on Plant or Crop diseases with ML, DL and Neural Networks covering conference papers and journal articles. Statistics showed that more articles were published during the last five years and major contributions were from India. By analyzing database on Authors, Subject area, Keywords, Affiliation, Source type it is evident that there is plenty of research scope in this area. Network analysis on diverse parameters specifies that there is a good scope to do research in this topic using advanced deep learning techniques.

Construction business and structural engineering necessities an innovation for the data gathering, elucidation and examination. Augmented Intellect along with innumerable fields discover an imposing solicitation in the field of civilian industry for data seizure, administration along with presentation. This study emphases on by what means Augmented intelligence and its numerous ideologies were intermingled through an emergent extents of operational business by what means this will be influencing the construction business through an Artificial intelligence grounded solutions that are better substitutes to detect engineering design factors are described.

In this paper, an algorithm for logarithm converter and antilog converter with fast efficient error correction and efficient area is discussed. Here discussed a Binary-to-Binary logarithm conversion. As, a Binary logarithm is a replacement for an arithmetic operation like multiplication and division due to their fast response and less storage area. A different approach taken by various researchers related to this field to achieve an accurate error correction scheme in form of literature review. This is restricted, a few methods to achieve logarithmic and antilogarithm conversion have been discussing methodology. In this article discussed various parameters in which explained the various method of with respect to existing method to do the conversion of outperforms the previously reported method.

In today’s global verbal exchange has turn out to be so easy because of the integration of conversation technologies with the internet. But vision impaired people discover it extremely tough to utilize this technology because considering the device it needs visual perception. This paper ambition to create an electronic mail system that will support even visually challenged people to use these service areas to communicate without earlier exercise. By which machine will no longer permit the client utilize the console alternatively will work best on mouse operation and speech conversion to text. Even, the present device could be utilized by anyone too as an instance the one like the person who can’t even study. The device is totally primarily reached from cooperative voice feedback to be able to make it clear and efficient to apply.

Present research is focusing on applications used for Industrial safety using the application of Internet of Things (IoT). With the growing popularity of IoT wireless technologies, 5G technology has emerged as a particularly difficult and exciting research field. With the advent of 5G, this study examines the issues and possibilities that a wide range of communication firms will face. The IoT and the new and evolving technologies that enable it are thoroughly covered in this paper. Low-power networks serve a wider geographic area, and safety issues are taken into account while determining control mechanisms. Fire and spark activity detection and categorization have been performed using an IoT system in this study. Convolution Neural Network (CNN) based deep learning is being used in this research process. The evaluation metrics chosen are Precision, Accuracy and Recall value. A camera is a kind of sensing equipment that captures conventional photos as well as detecting fire and embers.

Grey wolf optimizer (GWO) is a meta-heuristic algorithm adopted by grey wolf leadership theory and hunting swarm intelligence algorithm. Besides that, three basic hunting trials are used seeking prey encircling pray and attacking prey as a result, that GWO has attracted a large research involvement from a variety of areas in a less period. The existing GWO devotes half of its iteration to exploration and another half to exploitation ignoring the importance of finding the accurate balance among the two to ensure correct estimation of the optimum solution. To address this problem a 2D based optimization of gray wolf using the Firefly algorithm (GWO-FA) is introduced to overcome this problem and obtain to exact location. The GWO-FA algorithm and other meta-heuristics are used in this study to examine the 2D locations in an anisotropy atmosphere utilizing a different approach of placing the virtual anchors with parasol projection across the moving targeted nodes. In compared to certain known algorithms, simulations results show that this approach can deliver successful outcomes. The finding of the node localization challenge shows that the suggested approach is efficient in tracking actual situation involving uncertain solution space.

All organizations that are striving for healthy living must establish and maintain updated diet regimens. A health care recommendation system uses data to give medical advice. Machine Learning analyses data to predict future requests and improve healthcare management. An intelligent health care system needs to be built using machine learning and blockchain for health care recommendation systems. This paper discusses in detail the various systems for providing high quality health services. The existing systems that are discussed are Distributed Ledger Technology (DLT), swarm intelligence learning-based systems, adaptive systems, and deep learning systems. Furthermore, various techniques, advantages, disadvantages, tools used, and their accuracy are compared and contrasted. From the studies, it is clear that block chain technology with deep learning techniques provides better accuracy than other conventional methods.