bluuebunny/crossref_metadata_2025_split huggingface dataset:
Intelligent Obstacle Avoiding AGV Using Vector Field Histogram and Supervisory Control
Authors: Bolbhat S. S. , Bhosale A. S. , Sakthivel G. , Saravanakumar D. , Sivakumar R. , Lakshmipathi J 
Published: January 2021

Abstract
Automated Guided Vehicles are used to help the process of carrying materials from one place to another. AGVs are widely used in the materials handling process in different industries hence, the reasoning behind their extremely wide usage in industries such as Automotive, manufacturing, Medical, etc. This research deals with the obstacle avoidance of Automated Guided Vehicles (AGV). This paper propose an algorithm that helps to build obstacle-free trajectories for AGV that leave the original track and again get back to the original track by avoiding the obstacle. The new path also have to act according to the dynamic & kinematic constraints of the AGV. The proposed method will be validated using simulations.
Implementation of Two-Dimensional (2D) Discrete Cosine Transform (DCT) using Reversible Gates
Authors: Aravindh Sathesh , Sakthivel S M 
Published: January 2021

Abstract
This research work presents the application-specific integrated circuit (ASIC) implementation of one dimensional (1 × 8) and two dimensional (8 × 8) discrete cosine transform using the reversible logic gates. During the DCT implementation, the integer DCT method of realization is adopted, thereby eliminating the occurrence of floating-point operations in the data path architecture. Thus, the integer mode of DCT realization using reversible logic gates in the proposed DCT architecture improves the delay, power and area of the 1D and 2D DCT data path structure when compared with the normal real DCT implementation. The proposed methodology of DCT implementation includes the construction of full adder and full subtractor using reversible gates and then extended for the realization of the DCT butterfly structure. Especially in this research method, Peres reversible gate is used for the implementation of full-adder and Thapliyal Ranganathan (TR) gate for the implementation of full subtractor, respectively. Thus, the 1D and 2D DCT implementation using reversible logic gates in 180nm technology nodes consume an overall area of 55504.311 sq.µm and 1122324.044 sq.µm respectively.
Low power SRAM using Adiabatic Logic
Authors: Anudeep Varma C , Sasipriya P 
Published: January 2021

Abstract
In this paper the design of low power SRAM has been presented. SRAM memory cell has been realized using adiabatic logic to achieve low power operation. Adiabatic SRAM has been realized using two methods such as i) gradual charging and discharging of bit-line during writing mode and ii) utilizing control transistor based adiabatic circuit i.e., Two Phase Adiabatic Dynamic Logic (2PADL) circuit for the design of memory cell. Furthermore, energy recovery of word line and bit line charge stored in the interconnect capacitances has been realized for the benefit of energy savings. Charge recovery circuits based on 2PADL has been employed in the proposed SRAM memory core to achieve energy recovery. SRAM memory array of (4×4) has been designed as a test circuit. All the circuits are implemented using 180nm CMOS technology and simulations are carried out using Cadence® Virtuoso tool. Simulation results prove that the proposed memory cell has significant amount of energy savings when compared to the conventional static CMOS SRAM cell.
Universal Verification Methodology Based Verification of UART Protocol
Authors: Kashyap Bidisha , Ravi V 
Published: January 2021

Abstract
Verification today acts as a constriction of any complex VLSI design. Bringing out improved verification efficiency is a must. Most of the computers and microcontrollers contain a number of serial data ports. These data ports are used to connect with devices such as keyboards and printers which are basically serial input and output devices. Transmission and reception of serial data from an isolated location can be done with the help of a modem connected to the serial port. UART- Universal Asynchronous Receiver and transmitter is a hardware device which facilitates serial transmission and reception of data. In this work presented here, the UART has been designed with the use of the industry standard Verilog HDL code and the verification of the protocol has been done using system Verilog code in UVM environment. The UVM based verification methodology can significantly reduce the time needed for verification.
Efficient fault diagnosis for CORDIC Algorithm by using Hamming Codes
Authors: Jain Prachi , Durai Dr. Ananiah 
Published: January 2021

Abstract
The attractiveness of CORDIC Algorithm lies in the fact that by simple shift-add operations, it can perform several computing tasks such as the calculation of logarithmic, hyperbolic and trigonometric functions, real and complex multiplications, square-root, Eigen value estimation and many others. As a consequence, CORDIC has been used diversely for applications in diverse areas such as communication systems, 3- D graphics, signal and image processing apart from general scientific and technical computation. The digital computation is very much prone to errors; hence the error detection and correction became important for this algorithm. In this work it is achieved by integrating the Hamming codes to the CORDIC algorithm and hence achieving the error-free CORDIC algorithm. The proposed design are benchmarked through Xilinx FPGA and hence the reliability constraints be achieved.
Design of 9:2 compressor using FinFET
Authors: Kolluru Jyotsna , Viswanadhapalli Teja , Annis Fathima A 
Published: January 2021

Abstract
Multiplier is an essential component that is used commonly in any digital circuits. In general, for any digital circuits, multipliers occupy larger area; have long latency and their power consumption is more. Thereby, enhancing the performance of multipliers is quite important. Compressors are used to reduce the partial products so that the improved performance of multipliers can be achieved. In this paper, a new structure of 9:2 compressor is proposed in which the circuit is implemented with different combinations of 3:2, 4:2, 5:2 and 6:2 compressors. In this work, instead of bulk CMOS technology, circuits are implemented using FINFETs as they provide better gate control property, lower short channel effects. All the compressor circuits have been implemented using Cadence Virtuoso Simulator with FINFET 32nm technology. All the circuits are implemented to analyze the performance in terms of power, delay and transistor count.
Design and Implementation of 16bit SRAM Using Quantum Dot Cellular Automata
Authors: Janardan N , Guruviah Velmathi 
Published: January 2021

Abstract
The era of CMOS technology at the nanoscale will be going to replace with an unimaginable future by the opportunity of Quantum-dot cellular automata (QCA). The largest possible scope for constructing an elegant and efficient memory design is there with QCA technology. The suggested methodology and execution of a brand-new reminiscence cell shape primarily depends on QCA with a minimum delay, area, and complications is provided to develop a static random-access memory. This paper affords the circuit diagram and its execution of a 16-bit Static RAM with a new shape in QCA. Because of having powerful pipelining structure with in the QCA, this SRAM is very fast in executing operations. With a new shape of 4-bit width design for the 16-bit SRAM has applied in QCA. The ability of resulting read/write operations often with minimal delay is possible with this kind of SRAM design. Having a smaller number of majority gates and cells, the design of 16-bit decoders and multiplexers are represented. For getting rid from the adjoining hassle of intersecting labels, the new signal distribution network has been used to implement the SRAM with 2-to-4 decoders and 4-to-1 mux’s. The Quantum do CA-primarily depend on Static RAM cell turned into comparison with the Static RAM cell depended totally on CMOS. Consequences observed that the suggested Static RAM is more systematic in phrases of area, complexity, clock frequency, latency, throughput, and power in take.
A Hybrid Approach to Enhance Underwater Images
Authors: Tyagi Aryan , Graceline Jasmine S 
Published: January 2021

Abstract
Underwater pictures are barely visible due to effects of dispersion and absorption. Due to poor visibility it is very difficult to study underwater environment. Many Techniques have been used to increase the quality of degraded underwater images. This research paper follows the approach which has two major steps. First step is color correction based on grey world hypothesis. The second step is dehazing technique which is used to eradicate camera haziness. The approach suggested is ideal for use in real time application.
Efficiënt load balancing by optimized flexi max-min algorithm
Authors: Praveen Kumar P , Kanchana Devi V 
Published: January 2021

Abstract
Load Balancing is required to distribute tasks across Virtual Machines on one hand and on the other hand in a cloud Environment. Here, a new task scheduling algorithm: ‘Optimized Flexi Max-Min Scheduling Algorithm’ is proposed. The algorithm maintains a data structure which is modeled after a Binary Search Tree (BST) for estimations, enhanced searching, task allocation, and migration of tasks. Cloud-Sim is used to model and simulate the cloud computing environment in order to obtain simulated data. The result of this experiment shows that the proposed algorithm outperforms the traditional Max-Min task scheduling algorithms.
Comparative analysis of Human Activity Recognition and object detection
Authors: Sunil Suthar M. , Nithya Darisini P.S. 
Published: January 2021

Abstract
Human Activity Recognition (HAR) is the ability to interpret human body gesture or motion and determine human activity or action. Most of the tasks can be done automatically if they can be recognized via HAR system. HAR is an essential part of various scientific research contexts and the daily defence system i.e. surveillance, healthcare and human computer interaction (HCI). In computer vision, images and videos are analysed using different techniques. While recognition of activity in the real time environment the speed of the technique matters because if the system can detect the object in the images and videos at real time then it can be forwarded for the activity recognition process. How correctly the technique detects the object and differentiates the human object and other object and how much time it is taking to give the correct object, the number of frames it processes per second all matters. This paper deals with the comparison of the performance of YOLO (You Only Look Once) and Faster R-CNN in recognizing the human activity and detecting object.
Detection of DDoS Attacks Using Supervised Learning Technique
Authors: Prriyadarshini M A , Renuka Devi S 
Published: January 2021

Abstract
The development in digital technologies has been amplified by the drastic growth in the field of digital media applications and it changes the entire life style of the people. The electronic devices and digital contents are becoming more notable and popular among the users. As an emerging research area, security has attracted a lot of attention towards security professionals, experts and practitioners. In spite of this increased interest, this field still faces manifold challenges and issues. Such a fast development in digitization contains a block with attacks from an indirect security attack called Distributed Denial of service. An attack that damages the targets, services or network. The main objective of this paper is to improve the detection rate of the DDoS attacks and to overcome these threats in order to provide a safe network. So, a novel method is proposed in this paper to detect the DDoS attacks using support vector machine. A simulation of a network traffic is created to track and capture the patterns from any type of DDoS attacks with the help of rule creation and blacklisting. Further, using rule creation and SVM model, the simulation result based on different instances of time shows the observations of the real time requests that improves the efficiency of detecting the DDoS attacks.
Predictive analysis on hyperspectral images using deep learning
Authors: Sarah Rajkumar E , Pattabiraman V 
Published: January 2021

Abstract
In the field of remote sensing, identification of hyperspectral images (HSI) has become a trending topic. Hyperspectral visualization also struggles with a non-linear relationship between the spectral data obtained and the actual material in the image. In the recent years new machine learning algorithms have been established as an efficient feature extraction method to tackle non-linear hitches efficiently and commonly cast-off in a variety of image classification tasks. In addition, deep learning was applied to identify the features of the HSI and demonstrated good performance inspired by various positive applications. This research paper provides us with an ordered review of deep learning Hyperspectral image classification literature available and it compares multiple propositions. Exclusively, we summarize the main HSI classification challenges that conventional machine learning approaches have not been able to solve successfully, and also incorporate the benefits of deep learning to address these issues. This study improved data abstraction with minimized uncertainty and enhanced HSI classification performance. Firstly, a CNN model is built to understand the HSI“s spectral functionality. The CNN is used as a pixel classifier; therefore, it operates only in the spectral domain.
Disaster Management in Smart Cities using IoT and Big Data
Authors: Ramesh Akil , Rajkumar S , Jenila Livingston L M 
Published: January 2021

Abstract
Disasters are the most vulnerable factors which are affecting the human life and environment. Smart cities are mainly affected by disasters. This shows the requirement of an efficient disaster management system in smart cities. This paper deals with disaster management technique used in smart cities to detect disasters like building fire, pollution in atmosphere, route blockage using Big Data Analysis and Internet of Things (IoT). A combination of Hadoop environment is used in disaster reliant smart cities. IoT is used as a front end for collecting real time data sets and Big data analysis for data pre-processing and aggregation. This work evaluates the threshold capacity of each data which is collected for these disasters using Z-score normalization and if collected parameter exceed above the threshold value, then alert message is sent to public service sector such as fire station, police station and hospital. Different challenges faced by this evaluation are discussed in this manuscript.
Snippet generation using textbook corpus - an NLP approach based on BERT
Authors: Moses Andrew , Bharadwaja Kumar G. 
Published: January 2021

Abstract
In today’s technology-driven world, most millennials are tech-savvy. They have neither the time nor the interest in reading textbooks, newspapers or journals. They would like to immediately get instant answers and clarifications for all their doubts and questions. On many occasions, we are unable to find the exact word or meaning which we are searching for. So, if we have a clear, concise summary of a piece of literature, and we could understand what it contains with just a glimpse, we would be able to save a lot of time. This paper dwells about utilizing Natural Language Processing (NLP) to summarize a given text/textbook/paper. The state-of-the-art technology in this field has been demonstrated by Google’s Bidirectional Encoder Representations from Transformers (BERT), one of the latest developments in NLP. BERT is believed to understand English better than other models because of its underlying bidirectional architecture. The present proposal is to use BERT as a sentence similarity extractor. By applying the TextRank algorithm, the sentences holding the most important information are extracted. This comes under the domain of extractive summarization. Abstractive summarization is much talked about, but since Google BERT is not built for generating text, we are utilizing it in a different way to achieve the requirement. This paper intends to discuss the use of BERT for the gen-next kids which will save time and initiate further interest for researchers in developing new programs continuously in the future.
Discrimination between the final state of t

t¯

H and t

t¯

A using neural network
Authors: Pratama H P , Sumowidagdo S , Primulando R , Mart T 
Published: January 2021

Abstract
The Higgs Boson discovered in 2012 at the Large Hadron Collider (LHC) is still assumed to be the Standard Model Higgs Boson -a scalar particle. But there are still possibilities for a pseudo-scalar Higgs Boson. Currently, LHC has not been able to discriminate whether the particle is a scalar, pseudo-scalar, or mixed-scalar-pseudoscalar particle. The focus of this research is in the formulation of the neural network algorithm to discriminate between event signals from a scalar Higgs Boson and pseudo-scalar Higgs Boson in the Higgs production channel of associate production of Higgs Boson with a pair of top-antitop quark in a proton-proton collision pp → t

t

¯

H with center of mass energy

s

=
13
Tev

with only Higgs decaying to a pair of bottom-antibottom quark had been considered. The set of discriminating variables used is the 3-momentum of all the final state particles. Discriminating efficiency of 0.412 was obtained which showed relatively small discriminating power.
Synthesis and characterization of Fe-doped ZnO/Graphene nanocomposites and their photocatalytic efficiency to degrade methyl orange
Authors: Pratiwi M I , Afifah N , Saleh R 
Published: January 2021

Abstract
Various amounts (3, 5, and 10 wt.%) of graphene were coupled with Fe-doped ZnO nanoparticles by the co-precipitation method. The Fe:ZnO/G nanocomposites structural and vibration modes were studied using X-ray Diffraction (XRD), Raman, and Fourier Transform Infrared (FTIR) spectroscopies. The spectra showed that the intensity of graphene increased with its amount. The thermal stability of the nanocomposites was examined by the Differential Thermal/Thermogravimetric Analysis (DTA/TGA). The DTA curves indicated that increasing graphene’s loading could increase their thermal stability. Their photocatalytic activities were also checked under UV light irradiation where methyl orange was used as the model of organic pollutant.
Prediction model of exacerbations in patients with Chronic Obstructive Pulmonary Disease (COPD) at RSCM
Authors: Amelia V , Siswantining T , Kamelia T 
Published: January 2021

Abstract
Chronic Obstructive Pulmonary Disease (COPD) is a worldwide health problem. COPD has a tendency for exacerbations. Exacerbations are worsening of acute respiratory symptoms resulting in additional therapy. Exacerbations in COPD increase the risk of death. The objective of this study is to determine the prediction model of exacerbations in patients with COPD based on factors affecting exacerbations in patients with COPD at RSCM (Rumah Sakit Cipto Mangunkusumo). The data used in this study is secondary data from the medical records of patients with COPD in RSCM. The sample was chosen using purposive sampling technique. The samples in this study are 107 patients with COPD. The method used is binary logistic regression analysis. The results of this study indicate that the factors that significantly influence the exacerbations of COPD are breathlessness, history of ICS use, and history of antibiotics use. Appropriate logistic regression model has been obtained. The result indicates that patients with COPD who have breathlessness, have history of ICS use, and have history of antibiotics use are more at risk of exacerbations than those who don’t. Accuracy test has been conducted with classification table at cut point 0.5. The prediction model has an accuracy rate of 74.77 %.
Properties of Burr distribution and its application to heavy-tailed survival time data
Authors: Hakim A R , Fithriani I , Novita M 
Published: January 2021

Abstract
Burr distribution is Burr Type XII distribution which is one among the twelve types of the continuous distributions in Burr system. It has two positive shape parameters, namely k and c. It is implied from the probability density function which can be either decreasing or unimodal, and the hazard rate function which can be either decreasing or upside-down bathtub-shaped. The other distributional properties and the moment properties of Burr distribution will be discussed in more detail. By considering these properties, we will study its tail behaviour. To estimate the parameters k and c, the maximum likelihood method will be considered. Based on the properties of the data representing the remission time of bladder cancer patients, we infer that Burr distribution is suitable to model the data. The goodness-of-fit using the Kolmogorov–Smirnov test shows that Burr distribution fits well to the data.
Application of credible value at risk in predicting Indonesia’s stock market return
Authors: Pangestika R , Novita M , Nurrohmah S 
Published: January 2021

Abstract
Risk is a probability of loss. In financial terms, loss can be interpreted as a possibility that an actual return on an investment will be lower than expected returns. Recent studies develop a new type of risk measures called credible value at risk (CrVaR). Credible value at risk is a model obtained by combining credibility theory and one of the most used risk measures, value at risk (VaR). Credibility theory is a model which gives a proper weight for both information and VaR is used to calculate maximum loss with the specific level of certainty and specific time frame. The combination of credibility theory and VaR is required to get a better value at risk estimation based on individual and group experiences. This paper discusses the model of credible value at risk, its parameter estimation, and focuses on the implementation of credible value at risk to predict the future rate of return from Indonesia’s stock market data.
Forecasting tuberculosis morbidity rate in Indonesia using autoregressive integrated moving average (ARIMA) method
Authors: Lesmono W D , Mardiyati S , Lestari D , Zili A H A 
Published: January 2021

Abstract
Tuberculosis is a disease that can affect socio-economic development. Based on data from the World Health Organization, there were 810,918 tuberculosis cases in Indonesia, which is noted as the third-highest number of tuberculosis cases in Asia in 2016. Prevention and control of tuberculosis are of considerable importance, especially in the insurance field, to cover the cost of treatment, so an accurate model of tuberculosis morbidity is needed. The method used in forecasting the tuberculosis morbidity rate is Autoregressive Integrated Moving Average (ARIMA) method. The ARIMA method is a time series method that is widely used to predict morbidity rates in the future. The data used in this study is the number of incidence morbidity tuberculosis rates that occurred in Indonesia from 2000 to 2017, which is obtained from the World Bank. The results showed that ARIMA (1, 2, 0) is the best and very accurate model to forecast the morbidity rate in Indonesia from 2018 to 2027, with the mean absolute percentage error (MAPE) is 0.1682 % and Akaike Information Criterion (AIC) values is -181.0120. The results of forecasting tuberculosis morbidity rate are expected to help insurance companies in determining the amount of premium paid by customers who suffer tuberculosis diseases.