bluuebunny/crossref_metadata_2025_split huggingface dataset:
Observation of systematic deviations between Faraday cup aerosol electrometers for varying particle sizes and flow rates—results of the AEROMET FCAE workshop
Authors: Ahlawat Ajit , Seeger Stefan , Gottschalk Martin , Tuch Thomas , Wiedensohler Alfred 
Published: June 2021

Abstract
Condensation particle counters (CPCs) are widely used for the measurement of aerosol particle number concentrations in the size range from approximately 3 nm to 3 µm. For an SI-traceable calibration of the size-dependent counting efficiency, which is advisable on a regular basis and required in several applications, Faraday cup aerosol electrometers (FCAEs) are considered to be a suitable SI-traceable reference. While the volumetric aerosol inlet flowrate and the electrical current measurement in FCAEs can be related to respective SI references, inter-comparison exercises for FCAEs are still performed on a regular basis to establish reliable uncertainty budgets and to further investigate the influences of designs and operational parameters on comparability. This is strongly demanded in the international community of metrological institutes and aerosol calibration facilities around the world, which provide CPC calibrations. In the present study, the performance of FCAEs was investigated, using Ag test aerosol particles with a 30 nm particle diameter by varying the inlet flowrates from 0.5 l min−1 to 4 l min−1. From our experimental results, significant deviations were observed in FCAE currents at sample flowrates smaller than 1.5 l min−1. It is recommended that these discrepancies should be quantified before an FCAE is used for CPC calibration at low sample flowrates and small particle sizes in the sub-30 nm size range.
Improved neuronal ensemble inference with generative model and MCMC
Authors: Kimura Shun , Ota Keisuke , Takeda Koujin 
Published: June 2021

Abstract
Neuronal ensemble inference is a significant problem in the study of biological neural networks. Various methods have been proposed for ensemble inference from experimental data of neuronal activity. Among them, Bayesian inference approach with generative model was proposed recently. However, this method requires large computational cost for appropriate inference. In this work, we give an improved Bayesian inference algorithm by modifying update rule in Markov chain Monte Carlo method and introducing the idea of simulated annealing for hyperparameter control. We compare the performance of ensemble inference between our algorithm and the original one, and discuss the advantage of our method.
Blood vessel segmentation for diabetic retinopathy
Authors: Nair Arun T , Muthuvel Dr. K , Haritha K S 
Published: June 2021

Abstract
DR or Diabetic Eye Disease is a medical condition which causes blindness in people with diabetes. It is found to be a proceeding cause of preventable blindness. The lack of conduction of retinal screening examination on all diabetic patients has let to many undiagnosed and thereby untreated cases of DR. Timely and accurate diagnoses can reduce the rate vision loss if patients with DR are referred to an ophthalmologist for evaluation & treatment. This study aims to bring about a robust diagnostic technology in order to automate DR screening. For the automated DR detection, a data-driven deep learning algorithm was developed and evaluated as a novel diagnostic tool. Colour fundus images were processed by this algorithm and classified them as having DR or healthy, identifying medically relevant cases for referral. For further clinical review, all the learned information from the automated method was readily visualized through automatically generated abnormality heat map, which highlighted sub-regions within each input fundus image. This study enables to identify cases that should be referred to an ophthalmologist for further evaluation and treatment, with use a fully data-driven artificial intelligence based grading algorithm which can screen fundus photographs from diabetic patients. On a global basis, the implications of such algorithm can drastically aid to reduce the rate of vision loss caused by DR. The model is executed in two phases with the purpose of strengthening the framework of Diabetic Retinopathy (DR) recognition
An External Aid for Amyotrophic Lateral Sclerosis (ALS) and Drooping Head Syndrome (DHS) Patients
Authors: Srinivasan Viswanath , Govarthan Praveenkumar , Munirathnam Dhanalakshmi 
Published: June 2021

Abstract
Every year thousands of people get affected by Amyotrophic Lateral Sclerosis and drooping head conditions that disable the people to have control over their head and neck extensor muscle. Commercially available supports impart four major hazards to the patients: suffocation, skin irritation, difficulty in swallowing, and neck soreness/pain. To reduce these adverse effects, a unique three-way support system that supports the head, neck, and chin is designed to arrest head movements such as flexion, extension, rotation and abduction. The proposed design is built incorporating essential biomechanics and ergonomics principles that provides required thrust in bringing the head upright with minimal stress zones. The system consists of a base panel, head panel and chin cap that are fabricated using polypropylene and the anterior and posterior support frames are fabricated using aluminum flat respectively. The supporting components are integrated using straps, made up of Nylon-Velcro material, facilitating effective head immobilization as well as easy portability. Coverings are provided with liner foam sheets that provide cushion support and protective covering for the users. Total deformation, safety factor and fatigue characteristics of the structural elements of the support are studied in a 3D modeling environment to understand the load distribution on the aid during actual usage through finite element simulations. No human data is collected during the simulation and all force acting on body is based on FEA simulation. The simulation results prove that the net force acting on the body-torso is negligible with minimal stress effects. This ensures all the mentioned hazards are greatly reduced in the proposed design in comparison with prior-arts
Image Fusion using Multi Resolution transforms with Human Visual System
Authors: Vadhi Radhika , Swamy Kilari Veera 
Published: June 2021

AbstractThe Quality of vision is a practical objective in the image processing.One of the applications of image processing procedures is Image Fusion. To get the subjective vision of a image by gathering the best data from source images of a similar scene/picture and spot them in a solitary void image is the Image Fusion process. A simple and versatile approaches are statistical measures, that are applicable in any kind of image/signal processing techniques. In the first step, image is decomposed using Discrete Wavelet Transform (DWT). Mathematical computation of Smoothness is proposed in transform domain. This metric is used to select important information from multiple source images resulting to fused image. Further, Human Visual System (HVS) is also explored for fusion. Here all sub bands of DWT are multiplied with HVS weights. Highest response sub band is identified from various sub bands of multiple images using HVS. These sub bands are selected to get the fused image. Smoothness based fusion technique identifies the good texture information and leave the noise affected portions from the multiple source images. HVS based fusion technique identifies the visually important information from the multiple source images. The registered multi-focus and medical images are considered as source images. The experimental results shows that proposed fusion techniques are good in terms of popular fusion metrics.
Identification of Autism Spectrum Disorder using Deep Neural Network
Authors: Mohanty Ashima Sindhu , Parida Priyadarsan , Patra K C 
Published: June 2021

Abstract
One of the acute neuro developmental disorders throughout the world today is the Autism Spectrum disorder (ASD). It is lifelong disorder which affects the behaviour and communication skill of an individual. According to world health organization 2019 report, the number of individuals diagnosed with ASD is increasing creating a threat as it is analogous to significant health care cost. Early recognition can considerably reduce the effect. In order to get rid of the time consuming and expensive diagnosis procedures for ASD, a mobile based ASD screening tool known as ASDTest app was developed. The app recorded over 1400 number of instances covering toddler, child, adolescent and adult. It is available publicly in Kaggle and UCI Machine Learning repository for research purpose. The paper gives a new approach for identification of ASD using a deep classifier. The ASD identification works in the following steps. Feature analysis explains ASD traits thereby improving the efficiency of screening process. Further, Machine Learning (ML) classifier models report ASD class type with evaluation parameters. In this analysis, an attempt is made for the incorporation of Principal Component Analysis (PCA) for feature dimension reduction followed by the usage of Deep Neural Network (DNN) for classification of ASD class type. The data upon which the techniques are applied are collected from Kaggle and UCI ML repository. The experiment result indicates that, PCA in combination with DNN provide clinically acceptable output for effective ASD identification.
Micro-Doppler signature based target recognition using covariance based S-transform
Authors: Shubham Abhinav , Sharma Sunil Datt 
Published: June 2021

Abstract
Micro-Doppler signature analysis play an important role for target recognition such as fixed-target, vibrating target, moving human, vehicular motion, etc. Therefore, various signal processing based methods are reported for target recognition. Recently, Short time Fourier transform (STFT), has been applied for target recognition. In this work, we have applied Covariance based S-transform for analysis of fixed target, vibrating target. Also, the performance of Covariance based S-transform method has been compared with STFT.
Enhancement of Clustering Techniques Efficiency for WSN using LEACH Algorithm
Authors: Srividhya G. , Nagarajan R. , Kannadhasan S. 
Published: June 2021

Abstract
The Low Energy Adaptive Hierarchy is perhaps the most popular hierarchical protocol (LEACH). LEACH is a hierarchical structure that implements the creation of a disseminated cluster; nodes select themselves as the head node with any probability at any point in it. In view of the fact that a wireless node may only be configured with a restricted power supply, energy consumption is considered to be the key design target. In many implementations, it is not feasible to substitute energy sources, and therefore the life cycle of the node shows a heavy dependency on the battery life. With the aim of leveraging the network life cycle by applying clustering designs, several algorithms have been published. The paper presents numerous routing protocol definitions to save resources and prolong the existence of the sensor network. WSN applications, such as communication architectures, security and management, still have several problems to be resolved. We will close the distance between technology and implementation by addressing these problems.
Comparative performance analysis based short channel effects for TG Nano FinFETs
Authors: Renthlei Zohmingmawia , Dhar Rudra Sankar , Nanda Swagat 
Published: June 2021

Abstract
The scaling down of CMOS technology has been carried out successfully following the Moore’s law for the last five decades. One of the main challenges for a nano scaled device are the Short Channel Effects (SCEs). It has begun to reach the limit of Silicon material; hence high-k dielectric materials are introduced to challenge the SCEs. Particularly HfO2 is used for this study. The high k dielectric material has been compared with the well-known SiO2 in a 3D SOI Tri-Gate (TG) FinFET device which is modelled and simulated in SILVACO TCAD tools. TG SOI FinFETs having 22nm,14nm and 10 nm channel lengths are developed. Since the device are in nano regime, short channel device parameters such as threshold voltage, subthreshold swing (SS), Ion current, Ioff current, Ion/Ioff ratio and DIBL are analysed for the two materials developed in TG SOI FinFET. The comparison shows that high k dielectric materials shows better results in reducing current leakage and drain induced barrier leakage than that of SiO2.
Development of LED based OSL dosimeters for personal monitoring
Authors: Yadav P. J. 
Published: June 2021

Abstract
A subset of solid state materials have long been used as integrating dosimeters because they store energy deposited as a result of their interactions with ionizing radiation and then, when stimulated appropriately, release a proportionate amount of visible or near-visible light. During the 1960s, thermoluminescent dosimeters (TLDs), for which heat is used to extract the stored dosimetric signal, began to replace the photographic film as occupational dosimeters of record and for medical dosimetry. At the end of the twentieth century, a viable optically stimulated luminescent (OSL) material was developed which is now gaining in popularity as both an occupational and medical dosimeter. In this paper we give the designing and study of LED based OSL dosimeters for personal monitoring
Comparison study on speech emotion prediction using machine learning
Authors: Reakaa S D , Haritha J 
Published: June 2021

Abstract
Speech Emotion recognition is the process of analyzing human emotions. People vary widely in their precision at recognizing the emotion of others. Support vector machine (SVM) is one of the best algorithms for prediction and handling the voice data. In this speech emotion identification is a difficult task that includes various preprocessing steps. The proposed one, the method is based on the Cepstral Mel frequency Coefficients (MFCC) and voice signal energy as Input and use of the RAVASS emotional database function discourse. In this research work support vector machine algorithm, linear kernel processing is used to predict the human emotions.
In Feature Extraction using Librosa module to handling audio and music type of data. Librosa can extract the values of Mfcc-Mel-frequency Cepstral coefficients it’s used for identifying monosyllabic words in voice recognition. Chroma mainly used for similarity and audio matching. In voice data each data are applied to feature extraction technique. Then generated matrix format of values for calculation. In proposed Support vector machine algorithm for act as a classifier to find emotions. Mfcc, chroma, Mel features are directly send out the Support vector machine (SVM) algorithm. Its take a class of eight emotions like happy, sad, calm, angry, neutral, fearful, disgust. Based on our features (mfcc, chroma, Mel) to classify emotions. The comparison study on SVM and KNN algorithms, SVM has predicted 4% better accuracy than KNN.
Sharp transition FIR bandpass filter for processing bioelectric signals
Authors: Marchon Niyan J. S. 
Published: June 2021

Abstract
The main objective in a filter module is to improve the signal-to-noise (SNR) ratio, intelligently extract certain frequency components or separation of frequency bands among other applications. These much needed filters can help in areas of speech or biomedical signal processing. A sharp transition (ST) finite impulse response (FIR) band pass filter (BPF) is proposed for any filter order (N). The FIR filter is implemented with least passband ripple and a satisfactory stopband attenuation. The proposed filter design permits to set the fiduciary edges for any filter order. The merits of this proposed ST FIR filter includes its linearity, sharpness of the transition band and mitigating the effects of the Gibb’s phenomenon at the fiduciary edges. The accuracy and failed detections were computed to evaluate the performance of the ST FIR BPF design using the Physionet database. It is observed that the designed ST FIR BPF was able to compute precise single fetus R-peaks and maternal R-peaks. The proposed ST filter can also be used for other ranged frequency signals other than biomedical electrical signals.
Novel hybrid metamaterial to improve the performance of a beamforming antenna
Authors: Shashi Kumar D , Suganthi Sellakkutti 
Published: June 2021

Abstract
This paper investigates the design and implementation of a novel hybrid metamaterial unit cell to improve a beamforming Wi-Fi antenna’s performance. The proposed metamaterial unit cell is created on an FR-4 substrate (εγ
= 4.4) and a thickness of 1.6 mm. The metallization height of the unit cell is maintained at 0.035 mm. The designed metamaterial unit cell is simulated using HFSS Ver. 18.2 to verify the double negative behaviour. The unit cell consists of five Split Ring Resonators (SRR’s) at the bottom and a hexagonal ring of six triangles. Initially, a conventional inset fed microstrip patch antenna is designed then an array of the proposed unit cell is created and used as a superstrate to study the performance. A Three Element Antenna Array (TEAA) is designed to operate at 2.4 GHz Wi-Fi band, and the superstrate created out of the proposed unit cell is used to study its effect. Metamaterial superstrate improved the conventional Single Element Antenna (SEA) gain by approximately 2 dB. Superstrate with TEAA exhibited an improved gain of 1 dB over TEAA.
Design and Testing of Solar/Electric Cart
Authors: Mil’shtein S , Zinaddinov M , Asthana D N , Scheminger J 
Published: June 2021

Abstract
In the current study we report design and testing of a light-weight electric/solar four-wheel drive cart capable of running at speeds in the order of 30mile/hour. The cart can carry 4 passengers and transport attached additional load of about 500 kg. The battery block consists of LiFePO4 cells and provides 6000Watt power to 4 hub motor wheels, each of which are rated 1500Watt. The 600Watt solar array secured on the roof provides steady recharging for the lithium-ion batteries. The power distribution and control of hub motors is executed by special programmed microchip controllers and is very important for safe operation of this of four-wheel drive vehicle. The commercial MPPT charge controller is installed to operate the amount of power for battery recharging. During operation the battery recharge could be executed using power of solar panels. Willingness to minimize dependence on the grid which operates 120V 15A, has led us optimize the required tractive effort while minimizing the weight of the cart. The cost of experimental electric/solar four-wheel drive cart is presented by table 1. Many elements of discussed prototype are taken from the shelf, i.e. purchased at the market price. So, the commercial cost of such prototype in mass production is expected to be significantly cheaper.
Improving Accuracy of Source Localization Algorithms Using Kalman Filter Estimator
Authors: George Sherly , Nandanan Dhanya , Muralikrishna P 
Published: June 2021

Abstract
Passive geolocation of emitters provides benefits to military and civilian surveillance and in the field of aerospace. Passive geolocation can be done by exploiting some of the parameters of the signal transmitted by the source or emitter. In this paper the Time Difference Of Arrival (TDOA) of the signal at different receivers is used to estimate the position of the emitter. This paper aims at presenting the research for the development of an algorithm which can be used in a distributed network of acoustic sensors consisting of an emitter and five stationary receivers. By means of measurement of time difference of arrival of underwater acoustic signal the source position is estimated using three different algorithms. Taylor series, closed form and two stage weighted least square methods are used to estimate the stationary source location by TDOAmeasurements of received signal and compared on the basis of Cramer Rao Lower Bound (CRLB). Comparison of performance between these methods are done and analysed.Use of a kalman filter is proposed to improve the accuracy of all the three methods and the results are compared and analysed.
Automated detection of Alzheimer’s Disease using Deep Learning in MRI
Authors: Muhammed Raees P C , Thomas Vinu 
Published: June 2021

Abstract
Alzheimer’s disease (AD) is a progressive mental deterioration and incurable neurodegenerative disease that can occur in middle or old age, due to generalized degeneration of the brain. Because of the irreversible nature of the progression of Alzheimer’s disease, the early diagnosis of AD has an immense clinical, social, and economic need. This research output proposing a state-of-the-art, easy, and early automated deep learning-based system to predict AD from a large MRI dataset of normal and diseased subjects. It classified the database of 111 subjects into Mild Cognitive Impairment (MCI), Alzheimer’s disease (AD), and Normal classes. Classification tools like Support Vector Machines (SVM) and different models of Deep Neural Network (DNN) algorithms were tested. Deep learning algorithms were offering high accuracy of about 80-90% on AD prediction. For the prediction of diseases such as Alzheimer’s, Dementia, and Parkinson’s, applying highly accurate computational-automated machine learning-tools will help to diagnose the disease in the early stage itself and provide a better clinical, social and economic outcome.
Smart Phone Camera based Weighing Scale for Kitchens in Household Applications
Authors: Patil Vaishali S. , Shinde Santosh A. , Dhawale Nandkishor M. 
Published: June 2021

Abstract
Measurement of physical variables is the most important task in manufacturing, production and trading of each and every produce. E.g. our clothes cannot be stitched properly without taking bodily measurements by a tailor. Household furniture if produced oversize cannot be brought inside homes if the measurement’s of door at entry was not considered in prior. Kitchen food cooked by every fellow Indian sisters, mothers, and daughters can produce some mouthwatering tastes, only if all cooking ingredients were mixed in the right proportion’s, else many have experienced the odds, with special attention to salt, sugar, chili powder, turmeric powder, and other spices. Cooking food is an art and science both and the ingredients to be added in proportion are measured in prior using tools such as measuring spoons, cups and sometimes using weighing scales. Often measurements are carried with confidence of eyes and hands without use of tools, with special attention if one does not have a measuring tool. Not many have access to measuring scales in our society. However it is estimated that 60 % of people in India have access to Smart Phones. Modern Smart Phones are built using at least a 10’s of sensors, among which the Camera is the most popular. A modest weight measuring device can be developed based on very simple algorithms from the wide spectrum of image processing tools. This paper attempts to discuss the use of smart phones to quantify (estimate) the weight of at least one cooking ingredient used in every household. The same device can be used to estimate to identify the quality of the same ingredient in terms of size, color, nutrition, etc.