A Collection of Contemporary Research Articles in Electronics, Communication & Computation- Book ISBN- 978-81-948050-7-6

Authors: Atryee Bhuyan, Manisha Das, Abhishek Tamuli, Subham Chakrabarty, Smita Sarma

Abstract: The incredible digital transformation of everyday activities in the Covid-19 pandemic has simultaneously increased the demand for data security and user privacy. Cybersecurity has become a major concern in our country when cashless transactions are at an all-time high along with some other important sectors such as education, work, agriculture, health or entertainment. The cryptographic algorithm known as the “Advanced Encryption Standard (AES)†algorithm is one of the most commonly used symmetric key cryptographic algorithms, where more randomization in secret keys increases the security as well as the complexity of the algorithm. AES is a linear, pipelined and symmetric block cipher where a combination of key generation and S-Box operations make the attackers exhaustive to search for the right one to crack. In this work, a system generator by Xilinx is used to implement 128-bit AES cryptographic algorithm. The synthesis results show the utilization of 121 slice registers and 2.81% LUTs.

Keywords: Bandgap, PTAT, CTAT, opamp. AES (Advanced Encryption Standard), Cryptography, LUT (Look-up Table).

Authors: Sarjana Srivastava, Shiva Nand Singh, Jayendra Kumar

Abstract: In this paper, we present the idea of automatic and demand-based water level controller and indicator using Arduino. For the purpose of water level indication, we use the conducting property of water. There are wires placed at a different level in the tank, and no two wires touch each when water reaches a specific height, the water acts as conducting medium, and the wires get electrically connected. The water level is checked for both the source tank and the overhead tank. This is done to prevent dry running of the motor; the motor gets on only when the source tank is filled, else the motor does not start. There are two inputs taken using logic states(‘0’ and ‘1’). The user input determines the level to which the tank is to be filled (it can be one-fourth, half, three-fourth or full). Like on a day when consumption is too much, the user can fill the tank completely, and if the consumption on a particular day has to be minimal, the user can store water for one-fourth only. The motor gets ON only when the water level falls below one-fourth, i.e., LOW. The motor is connected to a relay switch (single pole double throw). The LCD screen is connected to the Arduino, which shows (i) status of the motor and (ii) the level of water, and if the sump is low, that also gets displayed on the screen. If there is any error in the connection of the circuit, then the buzzer starts to beep. So, the whole setup described above leads to the construction of an innovative, demand-based water level controller and indicator.

Keywords: Arduino; Arduino IDE, Conducting property, Water level indicator, Water wastage.

Author: Hemanshi Chugh

Abstract: This paper presents the design as well as the simulation of the I2C master-slave interface. The design was outlined using the VHDL (Verilog hardware description language).  The I2C Interface is considered to design a sensor system that enables a patient to self-monitor and check in with a trained professional if an abnormal condition is detected (clinical monitoring). From the medical perspective, the accuracy of data, the ability to perform diagnosis and the capability of an observer to assist a patient are critical issues. Collaboration with professionals in the medical field is important to the success and adoption of these systems. Hence, a sensor system is designed to enable a patient to self-monitor and check in with a trained professional if an abnormal condition is detected with the outputs displayed on LCDs. The proposed I2C interface design was prototyped using economical, easily available Xilinx Spartan3E Starter development boards manufactured by Diligent. The prototype system compiled using two I2C “slaves†(sensor and clinical end FPGA) and I2C “masterâ€.  The “master†acquires results from “slaves†and then transmits the data to a personal computer to display. The prototype system worked at 100 Kbits/sec flawlessly.

Keywords: Biomedical monitoring, I2C protocol, FPGA, Verilog.

Authors: Shemin Anto, Sherin Shaju, S Krishna Bhat, Narayanan V Eswar, Keerthana Krishnakumar, Pankaj Kumar G

Abstract: 

Due to the COVID 19 pandemic leading up to the complete shutdown has affected all sectors of the country, including the agriculture sector. With the population increasing at an exponential rate, the need for a low cost high productive, sustainable practice is increasing. With the main aim of monitoring the environmental factors, which are very influential for optimizing soil fertility levels to improve the yield of efficient crops, this model provides a method to maintain water requirement and monitor the temperature, humidity, soil moisture, etc., with the help of LoRa gateways. Data handling and tracking are accomplished using a graphical user interface.

Keywords: IoT, Irrigation, LoRa, Sensors.

Authors: Maninder Bir Singh Gulshan, Manasvi Grover, Priyansha Chichra, Rajiv Sharma

Abstract: 

Building a map and localization of mobile robots is an elementary and key complication in research on robotics. This paper is a thorough implementation of PyRobotics algorithms for simultaneous localization and mapping (SLAM) robots under numerous geographical surroundings. The basic idea and attributes of SLAM, along with the features and categorization of map representation without any human interference locating itself based on probability theory using PyRobotics algorithm, are analyzed in the paper.  This paper provides the recent development, features, implementation, recent issues and algorithms of SLAM. Finally, these PyRobotics algorithms are implemented on the Hardware Setup using Python Integrated Development and Learning Environment (IDLE) and Robot Operating System (ROS) - kinetic.

Keywords: SLAM, PyRobotics, LiDAR, KF, EKF, orchestrating.

Authors: Kolliboina Samba, Irfan Mansuri, Sunny Kumar Singh, Raushan Kumar, Anumeha

Abstract: In this paper, we have implemented the recent trends in developing Smart helmet system and accident alert system. Drinking and driving is the major cause behind the road accidents in today’s world. It is found out that more than 60% of road accidents are happened due to drunk driving. Drinking and driving not only affects the person who is drunk but also the ones sitting in the same vehicle or the people and vehicles on road. So we are taking an initiative to stop these accidents. The research also helps us to understand the smart helmet system and accident alert system evolved over the period and currently by using the technology like Internet of Things (IoT). The helmet makes use of a microcontroller STM32F103C8T6 and SIM7000e module (which is capable to get GPS locations). If the accident occurs then the vehicle sends our location to fixed number to get response immediately.

Keywords: Smart helmet; location tracking; collision sensing; alcohol sensing; Internet of things.

Authors: Asim Kumar, Shiv Kumar Choubey, Harshit Naman

Abstract: The Internet of things has revolutionized almost every domain of industry. Healthcare is also one of them. The healthcare system in India is not quite up to the mark when we compare it with the healthcare systems of European countries. We have not more than ninety-five thousand I.C.U beds. This number is relatively less if we look at the large population of our country. Patients admitted to I.C.U are very critical, and healthcare professionals need all the possible information about the patients in real-time, like body temperature, E.C.G, blood pressure, heart pulse. A manual collection of this data is a very tedious task. IoT based patient monitoring system will be beneficial in transferring all the information to a database in real-time. This database will alert doctors and health professionals if the values cross a threshold value.

The patient monitoring system will be a raspberry pi based system. This system will collect the patient's data using a few sensors and transfer the data using a wifi module; we can use the ESP866 module. This data can be stored in a remote database or the hospital's database. This data will be accessible to the officials of the hospital.

Keywords: Internet of Things, Raspberry Pi, ESP8266, Microcontrollers, R.F. Transmitters and Receivers.

Authors: Ankita Rayon, Rashmi Kumari, Priyanka Joshi

Abstract: The enormous usage of wearable devices has been trending in recent years. There is a massive demand for sensitive and stretchable wearable strain sensors in the field of medical practice, robotics, sports, entertainment, prosthetics, etc. The paper describes polymer nanocomposite based wearable strain sensors for bio-monitoring the motions. With the excellent features of a highly stretchable polymer matrix and nanomaterials conductivity, nanocomposite based strain sensors have become superior in the market.

Keywords: bio-monitoring, polymer, nanocomposite, strain.

Authors: Nishant Gautam, Dr. P.K. Krishnan Namboori

Abstract: Cancer is one of the deadliest diseases which is proven lethal. It is one of the major reasons for the millions of fatalities all across the world. Out of this, the Brain Tumor study is very sensitive and challenging. The main challenge is diagnosing the disease at the early level and finding the personalised treatment based on the genomic sequence of a person. With this research, we decided to find an individual level treatment among the Indian population by the Genomics and Epigenomics and Pharmacogenomics approach.

Keywords: Genomics, Epigenomics, Pharmacogenomics, Single nucleotide polymorphism, DNA methylation, Brain tumor.

Authors: Jayendra Kumar, Vasanth Subramanyam, Sourabh Kumar, Siva Rama Krishna

Abstract: In open-pit mines, blasting is common, and it will be optimal if there are rock fragments that can be easily transported and moved without any further blasting or crushing. Optimal blasting is of quite an importance for reducing the production cost, hence size analysis of blast fragmentation needs to be done. In this paper, the indirect method is discussed, i.e., an optical method that uses image analysis using MATLAB to analyze grain size and to find parameters like average grain radius and average standard deviation of grain radius. A brief discussion of all topics related to the algorithms and their implementation in MATLAB are discussed thoroughly. Image segmentation plays a major role in scene understanding and further in many applications. Since segmentation is the primary step for getting the size analysis, hereby, in this paper, the development of suitable and appropriate trailing of all the applicable segmentation algorithms to get better results of rock segmentation outputs is discussed. Extracting regions information from the segmented images and calculating certain parameters for size analysis of rock fragments are done to achieve optimal blasting and reduce production cost.

Keywords: - Segmentation, Blob detection, Binary image, Watershed Algorithm.

Authors: Ayesha Heena, Nagashettappa Biradar, Najmuddin M Maroof

Abstract: The basic goal in the processing and analysis of echocardiographic images is segmentation. Manually segmented echo images are time-consuming, inconvenient and liable to human subjectivity. There has been a great deal of interest in Computer-Aided Diagnosis (CAD) based or automated segmentation techniques. A lot of work has been done on segmentation techniques in recent years, and numerous studies have been published. In spite of many published studies, the relative merits of the various methods remain unclear. In this paper, a comparative analysis of different segmentation techniques used for medical images in general and echocardiographic images, in particular, is being presented, described, discussed and reviewed. Regarding the utility of these methods, the reasons for the lack of definitive conclusions are explained. Finally, the comparative analysis aims to identify the best within the available segmentation methods and develop a proposed method that can be used for echocardiographic images with promising results.

Keywords: Medical Image Processing, Computer-aided diagnosis, Echocardiographic images, Segmentation techniques

Authors: Tareeq Zaid, Nagashettappa Biradar, Mahesh V Sonth

Abstract: One of the essential variables in approving the cardio vascular malady is through the estimation of intima media thickness (IMT) from ultrasound imaging. Impediments in evaluating the IMT are fundamentally worried about heterogeneous force varieties for various tissues. In this paper, a focal point based element descriptor for the most part SURF and MESH procedures are executed which is approved with the IMT thickness assessed therein. Exploratory outcomes show improved execution with mean squared error of than 16.0 in SURF when contrasted with MESH based descriptor.

Keywords: Ultrasound imaging, Intima Media Thickness, SURF, MSER.

Authors: Rajeshwari S. Patil, Ambaji S. Jadhav, Nagashettappa Biradar

Abstract: Breast cancer is a major disease, which is usually seen in women. Early researches have shown that early detection and suitable treatment might increase the life span. Those researches have also proven that detection of small lesions at an early stage improves prognosis, which leads to a decrease in the mortality rate. Mammography is the best approach used for screening the disease. The present paper plans to introduce an automatic breast cancer detection approach using four phases as "pre-processing, segmentation, feature extraction, and classification." Here, the median filtering approach is used for eliminating the noise present in the mammogram image. Later, the segmentation of the tumor is done by the optimized region growing approach, which is the advanced version of the traditional region growing algorithm. Furthermore, the features like “Grey Level Co-occurrence Matrix (GLCM)†and Gray-Level “Run-Length Matrix (GRLM)†are extracted from the segmented tumor during feature extraction. Once the feature extraction is done, the features are subjected to a classifier named Fuzzy logic classifier. The threshold of the region growing algorithm and the membership function of the fuzzy classifier is optimally tuned with the help of the Crow Search Algorithm (CSA) named as “Improved Awareness Probability-based CSA†(IAP-CSA). The analysis shows that the proposed IAP-CSA is acquiring the best results in breast cancer detection and classifying the normal, benign, and malignant images.

Keywords: Mammogram Image, Breast Cancer Detection, Optimized Region Growing Algorithm, Optimized Fuzzy Classifier, Improved Awareness Probability-based Crow Search Algorithm (IAP-CSA).

Authors: Sanjukta Bhowmik, Bishanka Brata Bhowmik, Sujata Dash, Papiya Debbarma, Harjeet Nath

Abstract: The novel coronavirus (SARS-CoV-2) is contaminating individuals and spreading effectively from individual to individual. The infection has spread to almost all the countries in the world. To slow the expanding worldwide spread of the SARS- CoV-2 infection, proper disinfection procedures are required. Ultraviolet radiation (UV) technology has a notable antiviral impact. UVC is another part of the range comprised of a shorter, more vivacious frequency of light. It is especially acceptable at decimating hereditary material – regardless of whether in people or viral particles. This paper discusses utilizing UV frameworks for its disinfection as a possible adjunctive methodology for patients experiencing extreme COVID 19 diseases and the idea of Ultraviolet germicidal illumination (UVGI) to make the N95 mask reusable. This paper also discusses various kind of techniques for how UV light can be used to fight against COVID-19 diseases. It can ensure that we can avoid liquid damages by using UV techniques.

Keywords: Ultra-violate technology, Coronavirus 2, 4-log virus inactivation, N95 face mask.

Authors: Abhinav Shubham, Soumendu Das

Abstract: The novel Coronavirus 2019 (COVID-19) continues to spread exponentially, especially in India, with total cases crossing the 20 lakh mark as of today. It has deeply affected daily lives, public health, and the economy of the whole world. A vital step in tackling COVID-19 is a successful screening of infected patients as soon as possible and treating them. There is a need for supplementary diagnostic tools apart from RT-PCR, which is easy to use and less contagious. Significant findings have proven that Chest X-rays (CXR) in combination with Deep learning algorithms for Image Processing are vital in finding infected patients. In this paper, we have explored the proposed methodologies of classifying the CXR images taken from various sources into COVID-19 positive and negative classes.

Keywords: COVID-19 detection, COVID-19 classification, Chest X-ray (CXR), Radiographs, Convolution Neural Networks (CNN), Deep learning.

Authors: Sagar Rai, Aditya Anand, Kunal Singh

Abstract: Breast cancer, abbreviated as BC, is one of the most prominent cancers among females globally, consisting of the major percentage of the new cancerous cases and the disease-related fatalities in the world among the gender. This makes the disease a major health-related issue in the current world. Disease’s early diagnosis highly upgrades the prognosis and result in a high survival rate among women. This is mainly due to the fact that the early diagnosis may promote timely clinical treatment. Additionally, the correct classification of benign (not risky) tumors saves the patients from going to unnecessary treatments. The unique advantages of Machine Learning (ML) to detect complex relations and critical features have a major advantage over any other traditional method for correct classification of the disease tumor. Research shows that an expert physician can diagnose a case of breast cancer with an accuracy of 79 percent while the accuracy of 91% or above is achieved by using machine learning algorithms. In the conducted project, we have performed various operations (data pre-processing and feature selection) on the raw data collected from the UCI repository to get meaningful data from the raw data. We then trained various Machine Learning models on the meaningful data to achieve great accuracy in the classification of the breast tumor as dangerous or not. The study’s main aim was to find an algorithm that has a good cross-validation score along with a high cross-validation score. K-fold cross-validation was used for testing the trained model. This ensured that the model was neither highly biased neither had a high variance. Application programming interface (API) support for the model using Flask is also provided for cross-language usage of the trained model.

Keywords: Breast Cancer, Machine Learning, Malignant, Benign, Tumor, Cross Validation, K-Folds, Flask, API

 

Authors: Ramesh Naik M, Dr. Jayendra Kumar, Vasanth Subramanyam

Abstract: H.264 standard is the most recent and widely used standard for developing a video codec. Enhanced compression and network friendliness made it very useful in teleconferencing applications. This paper presents F to develop a windows application that could perform video communication. And we are designing the codec part of it. For performance improvement, we have made use of two kinds of parallel processing. One is Data parallelism, and another is Thread-level parallelism. To implement the first technique, we used processor-specific SIMD instructions, and for the second technique we made use of the Windows thread libraries.

Keywords: Data and Thread level parallelism, JM reference software, SIMD instructions

Authors: Jayendra Kumar, Anumeha, Arvind R.Yadav, M. Ramesh Naik

Abstract: Fake News is false information about any existing original news content or intentionally fabricated for any specific purpose. Since the spread of news is more being used in an online manner, it’s challenging to detect fake news automatically before it leads to any serious damage. Many researches have been done to differentiate between fake or real news content, using different dataset and algorithms. We introduce a comparative experiment on various classification algorithms and develop an efficient machine learning model to detect whether the news is fake or real. The experiment is done on two different formats of the dataset, which are mostly affected by fake news content, i.e., blog based (Face- book post) and linguistic-based news content. Thus, developing an efficient model with high accuracy and detecting the veracity of news in different format of news. We achieve an accuracy of 97.5% with the linguistic dataset and 75.5% with the Facebook post-based dataset.

Keywords: Machine learning, Fake news detection, Classifier algorithm, Facebook post, Linguistic news.

Author: Dr. Madhu Sudan Chakraborty

Abstract: Signed-Digit Number Systems have been resurging as a potential contender of the conventional radix-complement number system. However, even at the present level of technical advancement, the signed-digit number systems cannot cater to all computational needs. The ordinary signed-digit output of the common signed-digit arithmetic operations is to be transformed back into the conventional radix-complement form for further processing. This transformation is called the reverse conversion, and owing to consumption of significant delay, area and power overheads, it is often projected a major performance bottleneck of the signed-digit arithmetic. Even the subsequent radix-complement to sign-magnitude conversion, whenever needed, also attracts similar high overheads. Recently, Chakraborty and Mondal proposed a generic method for the constant-time conversion of the radix-complement output of signed-digit arithmetic operations into the sign-magnitude form with low overheads in order to counteract the high overheads of the reverse conversion as a whole. However, the Chakraborty-Mondal method has been proposed with abstract, generic terms. As the binary signed-digit number system has been being subjected to the most rigorous investigations among the various classes of signed-digit number systems on various issues, in this paper, the arithmetic constructs of the Chakraborty and Mondal algorithm is strived to be elaborated in sufficient details for the binary signed-digit number system, focusing its possible adaptation by the recent reverse conversion algorithm proposed by Sahoo, Gupta, Asati and Shekhar.

Keywords: Signed-digit number systems, Reverse conversion, Computer arithmetic, Constant-time conversion, Radix-complement to sign-magnitude transformation.

Authors: Nitish Sinha, Suman Debnath, Rajat Kumar Das, Bishanka Brata Bhowmik, Sangita Choudhury

Abstract: This paper discusses the outbreak of COVID-19 in India since the ï¬rst suspected case on 30th January 2020. The doubling rate, conï¬rmed cases, recovered cases, active cases, and death per million people in India had been including in this paper. Some seriously affected states are also compared based on the current COVID-19 situation.

Keywords: Data Analysis, COVID-19, Doubling rate, SARSCov-2.

Authors: Raghwendra Kishore Singh, A.Chowdhury

Abstract: This article reports state feedback based stability augmentation system for airplane aviation .The aircraft motion in both longitudinal as well as lateral directions have been considered to improve the dynamic stability of the aircraft. The State feedback gain matrix has been derived using the Bass-Gura algorithm. Aircraft motion in longitudinal and in lateral direction have been studied with and without state feedback gain the designed algorithm is implemented on MATLAB/ SIMULINK software.

Keywords: Controllability, Observability, Transformation matrix

Authors: N Shweta, Nagendra H

Abstract: Seizure is a period of symptoms due to abnormal neuronal activity in the brain. An epileptic seizure is one of the most serious neurological disorders. Approximately 50 million people have suffered from a seizure. Electroencephalogram (EEG) is the measurement and recording of electrical activity in the brain for diagnosis purpose. Detection of epileptic seizures could be very useful for patient safety. The various method has been proposed to detect the seizure. The main objective is to analyze and detection of the seizure using a single-channel, i.e. F4-C4 (Frontal-central). In the proposed algorithm analysis of EEG signals using single-channel frontal- central (F4-C4) are formed by extracting the alpha-beta gamma delta and theta bands from the EEG signals using a bandpass filter, then the classifiers using neural network and support vector machine is used separately to detect the seizure. The experimental result shows that the proposed method effectively detects the seizure in the EEG signal using the F4-C4 Channel and also showed a reasonable accuracy in detection.

Keywords: - Electroencephalography (EEG), Seizure, Discrete Wavelet transform (DWT) Artificial neural network (ANN) Support vector machine (SVM).

Authors: Malay Chakraborty, Surya Shekhar, Mrutyunjay Rout

Abstract: Wireless Sensor Network (WSN) is one of the rising domain in modern times that is regularly improving. WSN is a resource-constrained network in terms of energy consumption. To improve the lifetime of the sensor network, the energy consumption should be as less as possible. An efficient routing routes the sensed data from source to destination with minimum energy dissipation. Hierarchical routings are one of those which routes the data with less energy consumption. Two such popular hierarchical routing protocols are named as LEACH (Low Energy Adaptive Clustering Hierarchy) and PEGASIS (Power-Efficient Gathering in Sensor Information Systems). LEACH is cluster-based, and PEGASIS is a chain-based protocol, where there are leader nodes in both cases which collect the information from the member nodes and forward the aggregated information to the Base Station (BS). In this paper, we have compared both the existing protocols based on network lifetime and discussed the required modifications which made them more efficient. 

Keywords: WSN, Routing Protocols, Clustering, LEACH, Chaining, PEGASIS