bluuebunny/crossref_metadata_2025 huggingface dataset:
An efficient method for high performance prediction mechanism for diabetes using enhanced Firefly algorithm and Map-Reduce
Authors: Sujatha R , Gomathi B , Padma T
Published: July 2021

Algorithms in Data mining are utilized to predict unrefined data into useful conventional information. This conservative information plays a vital role in the Health care industry. In this study we focus on the functionalities of diabetes prediction. In diabetes data we have the problem of data imbalance in predicting the accuracy. The Proposed tailored Firefly Algorithm along with Map reduce is used to augment the efficacy and precision of prediction. Comparison of Different bench mark algorithms with our new Extended Fire Fly is done and variety of classification methods are used with moto to increase the effectiveness. The new method helps to maximize the prediction of accuracy and reduces the time. The PIMA Indian Diabetic Dataset from UCI machine learning repository is utilized for our experiment results. Different metrics are used in order to prove the effectiveness.
Secure Cloud Backup for Data Sources Based on Blockchain
Authors: Mouleeswaran S K , Aruna R , Visumathi J , Gurusubramani S
Published: July 2021

Users can upload, save and download their data with cloud storage. The cloud does however handle the user’s generation of data, as well as devices created by the fast Internet development of stuff, including such webcam, and IoT and edges cameras. This growth in outsourced data volume has posed two main problems in cloud infrastructure cloud management: optimum data storage and data protection. Cloud storage employs a deductibility strategy to prevent redundant data to solve the former problem and uses cryptography to solve the latter problem. Both of these questions however are orthogonal. The convergent encryption is concurrently tackles the two problems. The conventional systems rely on web server, however which put some confidence in a private entity that may malfunction in one stage. In this paper we propose a new, transparent, authenticated online storage framework, which removes data replication from the security of the file and enhances the credibility of blockchain software. Two different approaches to data deduplication secrecy are discussed, in particular: double hazing is used and symmetrical encryption is used. We have implemented the suggested architecture in order to show stability and accessibility in spite of the increase request throughput requirements for cloud data processing.\
Impact Analysis of Security and Charging Infrastructure on the Adoption of Electric Vehicles
Authors: Mohanty Abhishek , Hallur Giri Gundu
Published: July 2021

In the Global innovation index report, 2019, India ranked 52 among the 129 nations, thus strengthening its standing in terms of technological adoption. Developed countries for long have adopted electric vehicles, and India too has initiated this. EVs have proven to be better than fossil-based traditional vehicles in terms of convenience, efficiency in operations, price, and performance. This research paper focuses on understanding the consumer perception regarding the adoption of EVs across factors like price, performance, ease of availability, security issues, efficiency, charging Infrastructure, and user experience. This paper focuses on two factors, namely security issues and charging Infrastructure, and draws an inference of how important these factors were in the respondents’ eyes. The results showed these two factors contributed to about 68% of the concerns of the Indian consumers.
Smart Autonomous Car Parking For the Modern Vehicles
Authors: Baburaj E , Tapas Bapu B R , Tamilselvi M , Dappuri Bhasker
Published: July 2021

The objective of intelligent car park management is to use advanced sensing & monitoring technology to maximize parking areas within large car parks and make car parking easy for passengers by transferring these to accessible parking spaces. An effective and insightful way to simplify parking infrastructure operations by utilizing the Internet for parking technologies. The IoT gives wireless connectivity to the device, which helps the customer control the parking area’s capacity. With the rising in automobile numbers in metropolitan areas, the main issue confronting road congestion is. This paper aims to solve the question. Typically the customer expends his time and energy to pursue the limited room accessible in a particular car park. The user is told of the parking detail. Therefore, it is reduced the waiting period of the customer searching for a car park. RFID technology is used to prevent car fraud.
A Symmetrically Diminished Interconnected Database Segmentation Framework Using Data Mining
Authors: Mohan Kumar S , Majumder Darpan , Shajin Naragunam A , Ashoka D V
Published: July 2021

Clustering is a series of mathematical learning methods for the exploration of heterogeneous partition structures grouping homogeneous data known as clusters. Clustering has successfully been implemented in many areas, such as medicine, genetics, economics, industry, and so on. We propose the notion of clustering for problems of multifactorial data processing in this article. The aim of a case study is to examine trends in 813 individuals for an issue in occupational medicine. To minimise the dimensionality of the data set, we use the key component analysis as the most widely used statistical method in factor analysis. The natural problems, particularly in the field of medicine, are mostly focused on performance criteria of a stratified kind, while PCA processes only quantitative. In comparison, consistency data are typically binary-coded, initially unnoticeable, quantitative replies. We are therefore introducing a new approach that enables theoretical and practical data to be analysed simultaneously. The idea of this approach is to project important variables on the quantitative feature space. The corresponding Clustering algorithm subspaces are then given an ideal model.
Prioritizing and Ranking the Taxonomy of Factors Critical to GDPR Compliance
Authors: Koley Shreya , Vijayakumar Bharathi S
Published: July 2021

This study aims to unfold the myriad factors essential in the viewpoint of GDPR implementation. Additionally, this study will prioritize and rank the critical factors which would ensure the successful enablement of GDPR in any organization.The approach is to prioritize and rank the underlying factors for GDPR compliance by implementing an efficient multi-criteria decision-making technique, specifically the analytical hierarchy process. A well-defined taxonomy of twenty factors has been defined that spreads across the four broad categories of critical factors essential for the enablement of GDPR in an organization. Governance and People related factors get the highest priority and rank. Factors such as issuing privacy policy on the public platform, recruitment of GDPR experts have the highest priority. Additionally, with the help of Pareto analysis, it was discovered that the top eight factors account for 80% of the total weight age out of the twenty factors. This study focuses only on the pre-established components for GDPR implementation identified from previous notable works. The scope of the study is constricted to the insight of select GDPR experts also does not incorporate a broader spectrum of the Data Privacy ecosystem. This study contributes to the prioritization of factors for GDPR compliance in a two-fold manner. One, a complete classification of underlying factors is proposed, and two, they are prioritized and ranked to serve as a rulebook for GDPR practitioners while working towards the enablement of GDPR in their organization.
Service Oriented Virtual Machine for Maximising Quality of Service in Wireless Networks
Authors: Radhika K , Murali Mohan Babu Y , Periasamy J K , Saravanan T R
Published: July 2021

A traditional big data network is a wireless multimedia network, as multiple video/audio streaming account for 70 percent of mobile traffic and the fifth generation wireless networks are projected to rise 500 times. However, it does raise various new and transparent challenges for multimedia big data communication over 5G wireless systems because multimedia big data services provide timely, throughput broadcasts through time-sensitive communication networks with restricted wireless resources. In order to address the above issues, we suggest in this paper information-centric virtualization architectures for a statistical latency consistency of content delivery across 5G wireless large media platforms for technology. In particular, three successful nominee strategies to promise the mathematical time limit for interactive large-data communications are implemented in our proposed scheme: information-centered network which extracts an optimum in-network storage location for multimedia big data; a virtualization of the network feature which transforms PHY architecture into many virtualized channels. In our architectures we build the 3 primary computer-generated machine collection besides power distribution plans to collectively simplify the application of NFV and SDN techniques in the ICN architecture: to improve performance for single users, to integrate effective combining capability with equity allocation for all users besides under non-cooperative betting Via models and computational assesses, we prove that our suggested designs and applications greatly perform better other existing systems to enable QoS provisioning with statistical delays over 5G immersive Large Data Wireless communication.
Internet Connected Modern Fire Fighting Robot
Authors: Sabhanayagam T , Senthil Kumar T , Narendra M , Martin Sahayaraj J
Published: July 2021

When technological development progresses, human interference has become less common, and machines are being used for diverse activities and humans’ health. Currently, fire misfortunes still keep happenings running, place human life and properties at risk, and find fires impossible to save lives. In these situations, a robot is used to defend human lives, property and the world from the flames. This model is an IoT robot which detects fire. The robot may be told regarding its activity and disabled. An air pump or a fire style carbon dioxide pump small stretches. Medium lengths. The combination of fire and carbon monoxide is identified via the mounted sensors that generate a graph. The research is often valuable for supplying supplementary details health officials inhaled over the quantity of hazardous gas the inhabitants of the impacted region over a span such that adequate steps can be taken to repair the injury.
Aggregate Customer Products Reviews on machine learning techniques using ontology process
Authors: Suma Christal Mary S , Josphineleela R , Sundaramoorthy K , Thenmozhi E , Mamatha K.
Published: July 2021

Online customer reviews are the best advertisement and/or communication tools for product consistency. Consumers often prefer to do some research before agreeing to buy. Customer ratings are a significant source of knowledge for influencing consumer buying choices in the present. In this paper, we discussed SigmaCR’s aim to explore machine learning techniques and skills, natural language processing and information technology to create an intelligent device that analyses consumer product feedback via the Internet and provides comprehensive product summaries and consumer feelings. It would also be a very practical method for evaluating the power of many recent research projects, including ontology-based knowledge extraction and sentimental analysis.
Sliding Mode Controller for Electric Vehicles based on Switched Boost Converter
Authors: Hussain Syed Masood , Pattanaik Balachandra , Minda Wondimagegn , Addis Denekew , Chala Bakana , Abate Abibual
Published: July 2021

This paper proposes electric vehicle energy conversion of DC-DC by using switched capacitor step-up converter and the implemented system’s control techniques. In this system, the proposed switched boost capacitor converter is interfaced with a power source, attaining high power gain that could not be achieved by the existing systems like VSI and boost converter. In this proposed system, the large inductor is ignored and the capacitor that used for filtering purpose. Because of this, the system is directed to low cost and high density. The switched capacitor regulated converter is applicable for all the power conversion such as dc-ac, dc-dc. In this paper, the proposed Sliding mode controller (SMC) controls the BLDC motor in the vehicle’s inverter. The proposed system results are obtained and verified using MATLAB/Simulink.
Learning Based Download of Health Care Confidentiality Apps Iot with Power Storage
Authors: Viswanathan C , Sailaja P , Manimala G , Prasanna Srinivasan V
Published: July 2021

Remote cloud computing aims to provide a decent standard of computer-intensive experience for the healthcare of Internet of Things (IoT) users through the use of electricity. We suggest a privacy-aware download framework for strengthening learning (RL) to help IoT devices secure consumer privacy and privacy habits. In particular, this scheme allows an IoT system to choose a rate of discharge to maximise the measurement efficiency, protect the privacy of users and save the liveliness of the IoT device, deprived of understanding the confidentiality leak, IoT power requirements besides advanced machine model. In this software, transfer learning is used to minimise random experimentation during the initial education process besides a Dyna architecture is applied that offers virtual download experience to speed up the learning process. The recognised channel state model is used to further boost download quality in a state-learning system following decision. In the sense of the degree of anonymity, energy use and computing latency, we deliver the efficiency bound for three standard IoT offload scenarios. This scheme will reduce the delay of measurements, conserve energy usage and increase the level of privacy of an IoT healthcare device relative to the benchmarking scheme.
Vision Platform Lesion Quality Measurement of Accessible Endoscopic Images Using Machine Learning Techniques
Authors: Sammeta Naresh , Duraichi N , Velmurugan P , Lalitha V
Published: July 2021

Through use of non-mydriatic retinal computed radiography which led to invaluable applications of handheld fundus cameras is illustrated by telemedicine and the medical “big data” age of dermatology. Even then, non-mydriatic visual acuity is more resistant to distortion in the case of portable fundus photography, such as irregular lighting, reduction of light, boring and poor contrast. These distortions are known as distortions of standardised content. This paper presents a methodology capable of identifying fair-generic images that would’ve been useful particularly for the collection of clear meaningful and interpretable data by inexperienced people. The algorithm relies on three features: the multi-channel feeling, only visible blur and the visual field function, which senses lighting and distortion of colour, blurred and low distortion of contrast respectively. A total of 536 photographs were classified by one senior and two junior ophthalmicists separately, 280 from private data bases and 256 from public collections, thereby splitting three calculation sizes of consistency and general quality into two groups. In order to evaluate the performance of the proposed algorithm, binary classifications were performed via the support vector machine and the decision tree and receptor operating function (ROC) curves were obtained and scheduled. The experimental findings indicate that the overall grade sensitivity of 87,45 percent at 91,66 percent with a Correlation coefficient region of 0,9450 indicates the importance of the implementation of the human-vision method algorithm, particularly with low-cost ophthalmic telemedicine’s, in order to assess the photographic performance, the non-mydriatic images.
Energy Storage System with Dynamic Voltage Restorer Integrated For Wind Energy System
Authors: Pattanaik Balachandra , Hussain Syed Masood , Karthikeyan V , Giftson Samuel G
Published: July 2021

The permanent magnet induction generator (PMSG) based wind system that integrates with dynamic voltage restorer (DVR) and the energy storage system (ESS) for backup power purpose is explained in this paper. The output power transmission of the wind energy generation is interfaced with the dynamic voltage restorer in series. The energy storage system and the dc-link capacitor are parallel to the PMSG based wind system. The proposed system is to control the fluctuations in wind power and compensate the disturbed grid voltages. The bidirectional converter controls the power flow in both directions as the power flow from the source to battery and battery to the voltage source inverter (VSI) compensates the grid voltage disturbances through the voltage injection performance and transformer. The DVR-based energy storage system results for the wind energy conversion system are validated using in MATLAB/Simulink.
Raspberry pi based secured cloud data
Authors: Prasath kumar S , Rayavel P , Anbarasi N , Renukadevi B , Maalini D
Published: July 2021

In the growing digital world, Cloud Storage plays a major role. Cloud Storage is preferred for storing enormous amounts of data that our system cannot hold. But the cost of using private Cloud Storage services will be costly and our data is under the control of others. The main objective of our idea is that our data will be within ourselves rather than sending the data to unknown Cloud Storage providers and which is also cost effective. We are using the hard disks as our own Cloud Storage which will be controlled by raspberry pi and it can be accessed from anywhere. It is also more secure of using the material cloud with ourselves rather than depending on a private Cloud Storage service which may or may not be secure but here our data is only with ourselves as we will implement our suitable security measures. The design of our project is also much compact and portable as it gives a major advantage, we can carry it anywhere. The main motto of our project is to give a user personal Cloud Storage of his own in a cost-efficient manner which will be much more reliable than any other material cloud services.
Machine Learning-Based Optimization Technique for Forecasting the Solar Radiation
Authors: Jayaprakash S , Soundara Bala S , MadhusudhanaRao G , Murugesan R
Published: July 2021

Solar radiation measurement determines how often electricity a given area absorbs from the sun. This light is the key source of energy for conversion into solar thermal and photovoltaic plants. The radiation incident is not stable and relies on the temperature records, contributing to intermittent activity and electricity supply changes. This justifies designing a method to forecast and estimate incident radiation to predict improvements in photovoltaic systems’ performance. In this paper, the support vector machine (SVM) based machine learning is proposed to improve solar radiation prediction accuracy. The designed system results are compared with existing models that predicted the radiation and the global solar radiation is predicted accurately with efficient.
Design and Implementation of a Deep Convolutional Neural Networks Hardware Accelerator
Authors: Sekar K , Gopinath S , Sakthivel K , Lalitha S
Published: July 2021

In a wide variety of cognitive tasks, deep Convolutionary neural networks have recently demonstrated very high precision, and because of this, researchers have attracted considerable attention. Dedicated hardware semiconductors are crucial to improving their efficiency, given the enormous computation complexity of CNNs. The FPGA’s energy usage, computational strength and resilience make it a good tool for CNN additional hardware. This article suggests a coprocessor design with energy-efficient deep convolution neural networks for multi-object detection applications image processing and analysis algorithms. It can support both Convolutionary layers and fully linked layers to speed up various mobile deep learning algorithms. Also, it facilitates optimal and batches normalization activities through a separate arrangement of the pooling module. Besides, in this coprocessor, a reconfigurable activation function module serving four nonlinear functions is also realized. To draw out their connections and distinctions, we group the works into many groups. This paper is useful for artificially intelligent, hardware engineering and machine design experts, making it ideal for smart devices.
Bidirectional converter with hybrid UPQC implementation for power quality improvement
Authors: Alexander Jeevanantham Y , Annie Isabella L , Kumar KNaresh , Ellis Chandla
Published: July 2021

This paper presents a power quality enhancement strategy based on an energy storage device using the active power line cleanser in energy systems. The PQ challenges can be fixed in this framework by using the efficiency of the power compensation. For distributed generators, the transmission line is used to preserve the shunt inverter’s DC connection capacitor and dynamic voltage regulator. The renewable power source is used to provide the supply for bidirectional converter which controlled by using MPPT method. This proposed system consists of dynamic voltage restorer, bidirectional power flow converter, PV arrays, shunt inverter, shunt active power filter control, and super capacitor energy storage system. The PQ troubles of sag and swells are addressed at distribution system and achieve the compensation. The simulation designs are carried out in order to validate the proposed system topology results with their performance.
Fault Detection in the Wind Farm Turbine Using Machine Learning Based On SVM Algorithm
Authors: Muthukrishnan S , Pallekonda Arun Kumar , Saravanan R , Meenakshi B
Published: July 2021

In this paper, for failure identification and insulation in a horizontal axis variable speed turbine made up of three sheets and one total converter, the vector support machines (SVM) is used. Data is based on the SVM method and so know-how is robust. It is also focused on the reduction of systemic risk this increases generalization and encourages method non-linearity accounting for the use of modular kernels. A radial function as a kernel has been used in this work. Various parts of the process, including actuators, sensors and process failures, have been investigated.
High voltage gain and conversion using bidirectional converter in microgrid and electric vehicle systems
Authors: Annie Isabella L. , Alexander Jeevanantham Y. , Ellis Chandla , Kameshwaran R.
Published: July 2021

The objective of this paper is to achieve high voltage gain conversion ratio in batteries used for electric vehicles (EV). The bidirectional converter topology is proposed in this paper which is converting bidirectional direct current to direct current. In this proposed system, the interleaved buck-boost converter (IB2C) with two phases is utilized with simpler control circuit to accomplish the high voltage conversion. This bidirectional converter is used in two states as charging and discharging states. The system is performing as a voltage doubler two stage boost converter for achieving high boost conversion ratio (48V to 450) during discharge state. The topology in charging state, the high voltage buck conversion (385 to 32) is achieved while the converter performs like a voltage divider. The performance of the proposed converter system is verified and validated successfully using the environment of MATLAB/Simulink.
An Efficient Framework of Human Voice Verification for Robotic Applications
Authors: Goel Anuj Kumar , Satyanarayana T , Jabirullah Mohammad
Published: July 2021

Waves are considered to be used to decode the speech signal more efficiently. This study is an accessible and robust approach for obtaining voice recognition features. Here, we suggested a new text-related method for the identification of human voices (TDHVR) system, which utilizes the discrete wavelet transform (DWT) for low level feature extraction, Relative Spectral Algorithm (RSA) for denoising the voice signal and finally Additive Prognostication (AP) for estimating the formants. First, the proposed methods are used for voice signals, and then we construct a vector train function that includes the derived low level function and estimated formant parameters. The same technique is then applied for calculating speech signals and constructing a test feature vector. The Euclidean distance between the vectors will now be used to balance all vectors in order to distinguish the voice and voice. The simulated human voice would equal the educated person’s speech if the difference between two vectors is almost null. Computation results were compared with the LPC Scheme and revealed, that by using fifty preconfigured six voice signals, verification trials were carried out, and a best accuracy of approximately 90 percent was reached, the suggested methodology surpassed the current methodology.
Analysis on the Effects of Materials on Propagation Path in an Indoor Office Environment at 60 GHz
Authors: Shehade Karam , Susila M , Sreedevi A G , Rama Rao T
Published: July 2021

Recently, 60 GHz have been logged as a virtuous choice for high data rate indoor communications. This paper presents a millimeter-wave propagation analysis at 60 GHz and measurements in an indoor office environment of a multi-storied building. The main factors for multipath propagation in millimeter waves are delay spread, path loss, and received power. Being an indoor environment, building materials and frequency sensitivity materials (permittivity, conductivity, roughness) plays a significant role in propagation analysis. The model is compared with the International Telecommunication Union’s recommendations (ITU), including material attenuation. 20 dBi horn antenna as transmitter and 20 dBi omnidirectional antenna as the receiver, an office environment is modeled in Wireless Insite, and the effects are studied.
A Survey of Low-Latency IoT System Using FPGA Accelerator
Authors: Mahimai Don Bosco F P , Chitra E , Ryan Ebenezer S
Published: July 2021

Internet of Things has taken its place in the world of technology fairly in the previous few years. It is assumed that there will be approximately 4 billion IoT devices interconnected by the year 2030. IoT has not widespread full feathered in all the fields of application. However, the future holds a wide spectrum of implementations and dependency in IoT, which demands digital computing parameters such as faster processing of data, reduced latency and parallel processing of multiple data channel simultaneously. This publication provides a solution to satisfy these parameters, using FPGA (field-programmable gate array) accelerators in the IoT systems.

In IoT, it is necessary to achieve data-centric parameters such as higher bitrate at a seamless flow rate avoiding data congestion and data traffic. The predictability of the endpoint is another important parameter to be considered in an IoT system. In this paper, we will discuss the use of Constrained Application Protocol and speculate the possibility of enhancing the performance parameters such as latency and predictability by accelerating the cloud servers with FPGA Accelerator.
IoT-Based Vehicle Tracking and Monitoring using Raspberry Pi
Authors: Dhanne Basava , Srinivas A , Subramanya Chari K
Published: July 2021

The inspection of the powered vehicle and the following mechanisms is given and checked as an embedded board and the Android software for the tracking of school vehicles from every area A to area B. The system suggested would allow a significant use of creativity in the area of the smart phone android smart phone programme. The proposed framework utilizes the internet of things (IoT) and GPS, where IoT sends information to any guardian or owner regarding the vehicle status and GPS sends the current area of the vehicle.
Development of Advanced Healthcare System in Ambulance using IoT Environment
Authors: Agrawal Sneha , Aarthy S T , Kolangiammal S
Published: July 2021

Every year, many people, due to traffic rules’ negligence, become victims of road accidents and lose their lives. No matter which mode of transport they are from, even the ones on foot are not safe. Often there is a delay in response of EMS (Emergency Medical System), Ambulance and thus delay in treatment. This delay can cause the patients to lose their life [1]. The emergence of IoT has led to an evolution in EMS that makes our global society more streamlined and fecund. Thus, appending ambulance (EMS) with IoT makes it a smart ambulance. The bleeding person’s health state could be collected and communicated to the nearby hospital on the Internet. Thus, the doctors would know the victims’ physical condition before they arrive at the hospital itself. Shortly, the hospital’s arrangements could be made based on the patient’s health condition by detecting the parameters, like heart rate, respiratory rate, temperature, etc.
Measurement of Blood Glucose using Non-Invasive Methods – A Review
Authors: Mohanram S. , Shirly Edward A.
Published: July 2021

Diabetes mellitus has developed into a significant problem during the past decades. Identifying and controlling diabetes can be done by proper treatment and routine like a healthy diet, a weight reduction program, insulin shots treatments, vocal medicines to reduce blood glucose level. Self-glucose checking or clinical testing helps control future complications like blindness, heart disease, kidney damage, or amputations of the limbs due to the disease. Therefore, timely checkups can increase the chances of getting treatment before it is too late. Present glucose evaluating systems for raising diabetic patients are mostly intrusive, hurting, laborious, & cost a sum for frequent testers. The non-invasive glucose testing machinery helps in overcoming these boundaries. Due to this advantage, this subject has gained interest among researchers and many product manufacturing Industries. This evaluation targets to offer current information on available and upcoming trends for non-invasive and nominally invasive techniques. This review also focuses on using the different electromagnetic spectrum bands in detecting the blood glucose level.
Image Processing For Face Recognition Using HAAR, HOG, and SVM Algorithms
Authors: Maria Dominic Savio M , Deepa T , Bonasu Anudeep , Anurag Talluru Sai
Published: July 2021

Face recognition is one of the most active areas of research from the past two decades. Attempts are being made to understand how a human recognizes another human face. It is widely accepted that facial recognition can be based on structural information and nonstructural / spatial details. In the present study, he is applying differential observations using Eigen / docking characteristics of many built-in facial features and artificial neural networks. The proposed method aims to obtain a facial feature by reducing facial features such as eyes, nose, mouth, and face depending on the importance of facial features. The face recognition system developed in this paper will inform the human face and assess the current percentage of accuracy. Therefore, this work is for human facial recognition and includes a percentage of facial expressions. The implementation of this function also offers many applications such as photography, bio-metric in bank Lockers, etc.
Agriculture Resources for Plant-Leaf Disease Identification using Deep Learning Techniques
Authors: Hema L K , Vijendra Babu D. , Navaneetharajan A. , Vijayakumar K. , Dhayanithi S.
Published: July 2021

Agriculture is an essential food supply. In developing countries like India, agriculture provides farmers with large-scale livelihood opportunities. The recent advances in computer vision brought on by in-depth learning have paved the way for detecting and diagnosing plant diseases by using a camera to take pictures. This study is an important means of distinguishing different diseases in various plant species. The system has been developed to detect and classify many plant varieties, including apples, wheat, grapes, potatoes, sugar cane and tomatoes. The computer is also able to diagnose a host of herbal diseases. The experts were able to create profound learning models that identified and differentiated plant diseases and non-attention of ailments with 25000 images of infected sound plant leaves and disease. The model produced was 95,3 percent accurate, and the gadget was able to report the accuracy up to 100 percent to classify and differentiate between the plant variety and the types of diseases that were infected by the plant.
Design of High Performance ALU Using Vedic Mathematics
Authors: Swathi V , Panduga Kavitha , Kumari Gurrala Shiva
Published: July 2021

This article proposes a Vedic multiplier-based design of multiply and accumulate unit by employing UrdhvaTiryagbhyam Sutra. Further, it implements an efficacious ALU with 32-bit architecture. Simulation analysis disclosed the comparison of proposed 32-bit ALU with existing architectures. In addition, complexity in hardware, area and delay reduction demonstrated that proposed 32-bit ALU is more efficient over conventional architectures.
Low-Cost Pulse Oximeter & Heart Rate Measurement for COVID Diagnosis
Authors: Ganesh Kakumanu Vamsi Sree Sai , Ruhan Bevi A
Published: July 2021

This paper presents a portable heart rate and SpO2 blood-oxygen-level monitoring system with robust performance. The system is designed to be user interactive and kept its architecture as minimalistic as possible. The system is small in size, and user compatibility is high compared with other conventional medical devices. This system is based on a MAX30100 sensor which can produce results with high accuracy. Anyone using this device can move during the monitoring of vital signs of the heart. This paper describes the practical application cases in which this type of system is mainly used where the end-user has size and mobility requirements in the first place. A custom-designed GUI is implemented on an OLED to display the measured sensor data in real-time.
Analysis of substrate materials for millimeter wave antenna applications
Authors: Yalavarthi Usha Devi , Cheerla Sree Vardhan , Bandi Sai Dinesh , Chappidi Apoorva , Nannam Anvesh
Published: July 2021

A compact and novel multi band series fed array antenna is simulated and analyzed for antenna applications in millimeter-wave frequency ranges. The single element antenna performance is investigated for different flexible substrate materials like Rogers Ultralam, Arlon Diclad 880, Neltec NY9220, Taconic TLY, Polyethylene and Polystyrene in initial stages. The proposed series fed array antenna operates (S11<-10dB) in 14.7-20.8GHz, 23.125.8GHz, 28.5-34.8GHz and 39.3-44.8GHz frequency bands. The designed antenna exhibits negative group delay characteristics from 18.2-19.9GHz, 29.9-33.3GHz, 39.4-42GHz and 42.6-43.6GHz. It achieved a peak gain of 13.9dB at 42.5GHz and radiation efficiency of 97.5% at 33GHz. Results obtained are in fine conformity and therefore, proposed antenna is preferable for mmwave applications.
Optimizing data rate and power in BLE network for health care
Authors: Jayanthy S , Gouthamm G
Published: July 2021

There is a need for low power high bandwidth connected patient monitoring solution for patients in large volumes with easy deployment. The paper proposes data compression algorithms for optimizing data rate and power in Bluetooth Low energy network. The system is implemented using Bluetooth Low Energy (BLE) devices for data collection from a patient and a gateway to transfer the data to remote computer in an Internet Protocol (IP) network. The compression algorithms are optimized for the ARM microprocessor to compress the recorded values along with time stamps into a block. The sensor module is designed to be wearable continuously reads temperature and Electrocardiogram (ECG) and transmits them to a nearby gateway device. The gateway decompresses the data and uploads them to a cloud infrastructure. The system also allows for intensive monitoring of patient vitals. The collected data is processed and displayed in a moving graph in a remote web terminal. Results indicate that data rate and power has reduced by more than 45% and 3% respectively. With these results it is shown that high bandwidth monitoring solution is feasible with no extra expense in power. This system would be very beneficial to society in terms of affordable medical care as it can be used in large hospitals and remote medical camps alike where the patients can be monitored remotely.
A Photonic Based Multiband Signal Generation, Transmission and processing for 5G ROF front-haul
Authors: Kavitha C. , Manimegalai C. T.
Published: July 2021

This work proposes the generation of multi-band K-band, V-band, W-band of millimeter-wave (mm-wave) and its transmission performance through the standard SMF of various lengths. The optical frequency multiplication (OFM) principle is used for multi-band signal generation by the optical modulator’s non-linear characteristic. The frequency doubling, quadrupling, and six-tupling are done for multi-band generation. The generated multi-band is modulated with downlink data at the central station and transmitted through the conventional Radio over Fiber (RoF) network. The received signal is photo-electrically converted by heterodyne mixing with the local oscillator according to the user’s requirement. The transmission performance is analyzed at the receiver end for varying lengths of SMF. The simulation analysis result shows that the RoF system holds good for multi-band transmission of 30, 60, 90 GHz for about 40 km. The power penalty for 30, 60, 90 GHz signal from B-T-B to 40 km is -3.79 dB, -4.847 dB, -6.33 dB.
VLSI Tree-Based Inference Design Applications for Low-Power Learning
Authors: Nagaraju V , Suresh G , Uthayakumar C , Jijina G O
Published: July 2021

For the decision tree ensemble, this paper suggests a hardware architecture utilizing many feature channels. The proposed work uses the complexity of function channels for rapid identification compared to parallel processing in spatial domain scheduling to achieve conflict-free system memory. The results’ analysis demonstrates that only an FPGA implementation of the new architecture with a pedestrian sensor collated channel feature will conduct 229 thousand pulses per second at an operational value of 100 MHz while providing relatively limited resources. Checking estimation systems’ electricity-accuracy trade-offs has become central. This research evaluates the nature of data sets, investigating the outcomes of design difficulty or intensity of accuracy approximation. We improved the simulations’ precision by up to 6.7 percent by quantizing the inputs to small sizes relative to specific scenarios. The gap in model complexity was more important than source distance in terms of capacity, as we achieved reductions of up to 67 percent by reducing tree depth.
Equalization of Finite-Alphabet MMSE for All-Digital Massive MU-MIMO mm-Wave Communication
Authors: Paulchamy B , Chidambaram S , Vairaprakash S , Duraivel A N
Published: July 2021

For more than twenty years, growing the performance and efficiency of wireless communications systems using antenna arrays has been an active field of study. Wireless networks with multiple-input multiple-output are also part of the current norms and are implemented around the world. Access points or BSs with comparatively few antennas are used for standard MIMO systems, and the resulting increase in spectral efficiency was relatively modest. A Multiple-Input Multiple-Output platform’s capacity is researched where the transmitter outputs are processed and quantified by a set of limit quantizes through an analogue linear combining network. The linear mixing weights and cutoff levels are chosen from with a collection of possible combinations as a function of the transmitted signal. Millimetre-wave networking requires optimum data transmission to various computers on same moment network in combination with large multi-user actually massive. In order to guarantee efficient data transmission, the heavy insertion loss of wave propagation at su ch a faster speed needs proper channel estimation. A new channel estimation algorithm called Beam space Channel Estimation is suggested. From a set of possible configurations, the capacity of a massive stream from which antennas signals are handled by an analog channel as a part of the channel matrix, linear mixture weights and frequency modulation levels are selected. Probable implementations of specific analogue receiver designs for the combined network model, such as smart antenna selection, sign antennas output thresholding or linear output processing. To demonstrate the effectiveness of BEACHES in service and have FPGA implementation results, we are developing VLSI architecture. Our results show that for large MU-MIMOs, mm-wave communications with hundreds of antennas, specially made denoising can be done at maximum bandwidth and in an equipment format.
Design and Analysis of Dual Port Super WideBand Antenna Set for MIMO Applications
Authors: Gireesh Parvathy , Sandeep Kumar P , Malathi K , Khanra Ishani , Agarwal Aakansha , Sivakumar Kaushika
Published: July 2021

A planar monopole antenna with a super wideband frequency range covering a wide range of applications and compatible with MIMO applications is duly proposed. Compactness, i.e., reduction of size, is achieved following compatibility with MIMO application and also with SWB (Super Wide Band) applications. The bandwidth achieved is in the band range from 2 GHz to 20 GHz with a ratio of 1:10. Compactness in terms of size of the antenna is achieved, dimensions being 40mm*30mm. FR4 substrate is used in the design of the antenna, having a relative permittivity of 4.4. A hexagonal structure with an ellipse added on top is made as to the radiating element with at a per feed having a width of 3.1mm feeding an input of 50ohm into the antenna. Parasitic elements are introduced on the substrate for the design of the antenna.DGS techniques have been used in the semicircular structure of the partial ground plane of the antenna. A dual-port antenna supporting MIMO applications is therefore achieved by replicating the antenna described above on a horizontal plane with a 90-degree shift and hence accommodating both the vertical and horizontal polarization at the transmitting and receiving side as well.
AI Based Intrusion Detection System using Multi Sensors
Authors: Karthikeyan R , Hema L K , Vineet S , Vivek Prajapati P , Reginald Paul
Published: July 2021

In an intrusion detection system, the existing system uses single sensor that causes false alarms due to lack of accuracy and also leads to misuse of fuels that are stored in nuclear power plants/ reactors. The fuels that are used in the reactors are too expensive and high in price. The fuels use in nuclear power plants/ reactors must be protected under such circumstances that may reduce the detection and inactive secure system of false alarms under given area. In this paper multiple sensors are used to interface with Arduino to overcome false detection using KNN algorithm for the classification of nearest neighboring values that compares with the predefined and predicted values in the given database. Hence the KNN uses to forecast a new point in sample classification, a database where the data points are divided into many groups. This is the point where KNN is located in the Scikit-learning algorithm list. Therefore, the K Nearest Neighbors method stores all available cases and starting lineup cases on the basis of mutual information.
Design and Implementation of Dielectric Resonator Antenna Array for Satellite Applications
Authors: Raghavendra Ch , Neelaveni Ammal M
Published: July 2021

A dielectric resonator antenna with size 100*100*10 operating at 6GHZ is proposed. The proposed antenna is constructed using triangular monopoles on top of the cylinders and fed by using a power divider with four ports. Power is fed through the microstrip line to the power divider. The cylinders are combined with patches of triangular shape. The designed antenna has a bandwidth from 5GHZ to 9GHZwith a peak gain 26dBand for satellite uplink applications.
MR and CT Image Fusion Using Nonlinear Anisotropic Filtering in PCA Domain
Authors: Rama Kishore Reddy S , Swathi V , Anusha K
Published: July 2021

In medical science it has been commonly used for computer-aided brain surgery, Alzheimer’s therapy, tumour identification & other medical assessment. Accurate fusion algorithms can be made to ensure proper detection of diseases. The mechanism of fusion is incredibly insightful, since it transforms information from a single picture from two or more pictures into a single picture. In addition, the most common application is the use of images of the magnet resonance (MR) & the computed tomography image (CT). The objects in the source images must be reduced. A new algorithm is introduced here for image fusion. In the principal component analysis (PCA) domain, the nonlinear anisotropic filtering (NLAF) most efficiently preserves texture features of the segmented image. The source images are broken down into estimation & information layers by NLAF. The PCA support is used to measure the actual detail & approximation layers. Fusioned images are eventually generated by last detail and approximation layers linear combination. The algorithm suggested efficiency & quantitative output is evaluated by image consistency parameters, including the PSNR, entropy (E), square-root root (RMS) & structural similitude (SSIM) indices. Compared with the conventional & recent image fusion algorithms, detailed simulation findings of the suggested hybrid technique. Evaluation of efficiency shows that the proposed fusion solution is beyond the actual fusion approach.
Real Time Traffic Light Controlling System Using Morphological Operators and Fuzzy Logic
Authors: Hari Krishna B , Santosh Kumar Patra P , Kalpana Ganapuram
Published: July 2021

There is a need for development of advanced smart traffic controlling schemes due to the enhancement in urban traffic congestion. Currently, there are some traffic controlling methods based on timers or controlled by human. However, due to these systems there must be wastage of power in the night times and early morning hours. In order to address this issue, this paper introduces a novel and Traffic light management scheme in real-time with digital image recognition using contrast improvement and fluctuating logic systems for morphological operators. The proposed traffic control system provides an upgrade over traditional systems in terms of time of response, automation, stability and overall performance.
Android Application and SMS Alert Based Garbage Monitoring and Navigation System
Authors: Umapathy K , Mangayarkarasi T , Subitha D , Sivagami A
Published: July 2021

The treatment of waste is unsafe in city centres where residents are more involved, leading to germs that can lead to disease. How commonly these litter containers are used in different regions is different. Regular timing checks are not helpful. Often, they will overflow much faster than expected. In this article, a smart waste monitoring method is advised to prevent this and increase cleaning, to figure out where the waste is complete.The architecture of the method collects and transmits information through a wireless network. Under this technique, the dustbin’s waste level is measured by sensors and information is transmitted through the GSM and GPS module to the official mobile station. Wi-Fi Module is used to track waste levels online at the control room. Arduino Processor uses network modules to link sensor systems.