bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

AbstractThe 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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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

Abstract
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.