bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of quantum correction on black hole shadows, photon rings, and lensing rings *
Authors: Peng Jun , Guo Minyong , Feng Xing-Hui
Published: June 2021

We calculate photon sphere

and critical curve

for a quantum corrected Schwarzschild black hole, finding that they violate universal inequalities proved for asymptotically flat black holes that satisfy the null energy condition in the framework of Einstein gravity. This violation seems to be a common phenomenon when considering quantum modification of Einstein gravity. Furthermore, we study the shadows, lensing rings, and photon rings in the quantum corrected Schwarzschild black hole. The violation leads to a larger bright lensing ring in the observational appearance of the thin disk emission near the black hole compared with the classical Schwarzschild black hole. Our analysis may provide observational evidence for the quantum effect of general relativity.
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

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

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

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

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

The 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

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

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

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

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

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

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

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

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

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

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

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

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.
Design of compact MIMO antenna with improved isolation for UWB application
Authors: sharma Ankit , Rawat Arun Singh , Verma Priya , Yadav Monika , Raj Utkarsh
Published: June 2021

In this paper, a compact dual port multiple-input-multiple-output (MIMO) antenna with a compact size of 26 ×40 mm2 is presented. In this proposed designed antenna two square-shaped with slotted cut radiating elements (Planar monopole) are used and positioned opposite to each other for improved isolation. The operating frequency of the presented antenna is 4.1GHz to 10.6 GHz. Two L-shaped stubs are introduced in the ground plane of the antenna to improve the isolation of the proposed design. The Mutual coupling between the two-port of antenna is less than-15 dB for complex impedance bandwidth and ECC is less than 0.05dB from 3.24GHz-4GHz and from 4GHz to 12GHz it is almost zero which is within operating frequency in UWB. diversity gain is high i.e., 9.95dB. from 3.7GHz to 4GHz and it is almost 10 dB from 4.1GHz to 12GHz. The efficiency and results of the antenna make it a promising antenna Applicant for use in UWB lightweight applications.
Design and Analysis of High Frequency Solar Blind Photodetectors for Communication with Red Signal
Authors: Mil’shtein S , Asthana D N , Ushakov M
Published: June 2021

Advantages associated with high-speed communication and data security bolsters the eligibility of Free Space Optical (FSO) and Visible Light Communication (VLC). In this context, characteristics like low noise equivalent power, high frequency response associated with the photodetectors play pivotal roles in determining the performance of the entire communication FSO/RF link. In this study, the authors present the design of detector sensitive to red signal coming from commercial laser with wavelength 650nm (photon energy 1.9eV), while being blind to the rest of sun spectrum. High-sensitivity to signal of commercial laser is provided by double barrier tunnelling p-i-n photodiode made up of AlxGa1-xAs heterostructure with mildly doped p-type and n-type regions. The photodetector operates as window discriminator. The quantum operation of double barrier tunnelling significantly narrows the band of detectable red light. In addition, all photons with high energy are effectively cut off by p-i-n filter, positioned at the top of the structure. This filtering layer works at the same time as a solar cell and provides reverse bias to the photodetector. Low noise equivalent power on the order of 0.2pW/√?? and cut-off frequency of 20GHz make this device eligible for FSO communication.
Enhanced Spectrum Sensing Techniques in Cognitive Radio Based Internet of Things
Authors: Leena K , Hiremath S. G
Published: June 2021

The use of open Spectrum broadcasting, which is a restricted and valuable resource, has been put under a range of constraints due to the growing interest in wireless applications. There is a huge spectrum requirement nowadays but radio spectrum is an extremely terrifying resource in a normal system. The frequency detection challenge has gained new angles with cognitive radio and artful spectrum allocation principles. However a fixed spectrum allocation needs to result in the use of spectrum as an extraordinary bit of enabled spectrum is not effectively used. To remain away from interference and optimise spectrum immersion, the Cognitive Radio (CR) based Internet of Things (IoT) concept was developed. The best strategy for effective spectrum usage is CR IoT. The fundamental goal of CR IoT would be to use the small set of resources without interfering with the main users (PUs). The different spectrum detection techniques used in CRs are explained in this paper and the advantages and disadvantages of each sensing technique are also discussed. We explained the detection of the cooperative spectrum and all its structures, and also the multi-dimensional dynamic spectrum method. From a cognitive radio point of view, different aspects of a problems of spectrum identification are concentrated and a multi-dimensional idea of spectrum sensing is introduced. Spectrum management problems are provided and techniques to empower spectral detection are presented.
Forecasting performance comparison of daily maximum temperature using ARMA based methods
Authors: Asha J , Santhosh kumar S , Rishidas S
Published: June 2021

Daily maximum temperature of four different regions in Kerala, India, from 01/01/2019 to 31/12/2020, is recorded and is used for modelling and forecasting. The forecasting methods used are Autoregressive integrated moving average (ARIMA), Seasonal Autoregressive integrated moving average (SARIMA) and Autoregressive fractional integrated moving average (ARFIMA). The comparison of forecasting performance was based on percentage accuracy, mean squared error (MSE) and mean absolute error (MAE). The models used can capture the variations of time series data. All the models exhibit reasonably good performance in predicting the daily maximum temperature. ARFIMA model gives the least forecast errors compared to other models.
EMG Signal Classification for Detecting Neuromuscular Disorders
Authors: Ahmed Tanvir , Islam Md. Kafiul
Published: June 2021

Electromyography (EMG) is a technique for evaluating and recording the electrical activity produced by skeletal muscles to see muscle condition. Nervous system always controls the muscle activities such as relaxation or contraction. An efficient analysis of electromyography (EMG) signals plays an inevitable role in the diagnosis of neuromuscular disorders, prosthesis, and several related applications. Our aim in this study is to differentiate neuromuscular disorder patients from healthy people based on EMG signals. The EMG signals used in this research were recorded from biceps. Artificial Neural Network (ANN) was used for the classification. Eleven features were extracted from the EMG signals for the classification purpose. A comparative analysis was done based on the results. The outcome of this study encourages possible extension of this approach to improve stronger, more resilient and effective implementations.
Graphene-gated GaAs OPFET photodetector and oscillator for 5G applications
Authors: Gaitonde J V , Lohani R B
Published: June 2021

With the advancement of technology, the RF communication bandwidth is switching towards 5G and 6G communications. To relieve the congestion of traffic imposed on RF communication, operating in the optical domain or integrating RF and optical communication is imperative. The core components in this scenario are the oscillators as transmitters and photodetectors as receivers. These devices should be capable of high-speed and high-gain operation simultaneously. In this paper, the potential of graphene-gated GaAs front-illuminated OPFET (Optical Field Effect Transistor) as oscillator and detector towards 5G applications is explored. The OPFET device exhibits an oscillation frequency of 1.63 GHz to 1.8 GHz, tuned with optical illumination. The gain can be varied between 3.94 dB to 4.5 dB at the oscillation frequency. Under photodetection mode of operation, the device exhibits a maximum 3-dB bandwidth of 2.234 GHz, an fT
of 5.33 GHz at the bandwidth frequency, and a responsivity of 3.3 × 106 A/W at a photon flux density of 1019/m2-s. A drain bias voltage of 3.94 V and a gate bias voltage of 0 V are applied in both cases. The device responses are contrasted with that of the Au (gold) gated OPFET. The explored devices have great potential for sub-6 GHz 5G applications.
New Carry Maskable Adder using Modified Full Swing GDI
Authors: Rajkumar Gollu , Pandey Neeta
Published: June 2021

Addition is an essential arithmetic operation that is used extensively in processing of digital information. Now a days approximate computing is emerging methodology in area of low power VLSI design that trades off accuracy for faster and power efficient designs. Accuracy Configurable Approximate Adders is an attractive option that provides a flexibility to user to configure adder as accurate or approximate. This paper presents a new Carry Maskable Adder realization based on Full Swing-Modified Gate Diffusion Input (GDI) technique. Proposed design is functionally verified through simulations in LTspice XVII. The performance comparison of proposed design with CMOS based counterpart show that the percentage reduction in delay is 32% while power consumption is reduced by 45%.
Substrate Integrated Waveguide based Leaky Wave Antenna
Authors: Ayushi  , Tripathi Ashutosh , Nafis Areeba , Omer Saksham , Agarwal Ruchi
Published: June 2021

Leaky Wave Antennas (LWAs) allow electromagnetic wave to leak along its structure. LWAs being low profile have been effortless to fabricate. The SIW can be designed by inserting two rows of metallic via on a dielectric material which work as a wall to prevent the radiation. CRLH property can be generated by cutting the slot on the upper stub which provide the capacitance.

In this paper, a periodic CRLH based C-shaped SIW-LWA is proposed which operates in a single band between the frequencies of 13.2 to 15.5 GHz. With the help of SIW and CRLH property designed antenna scans in continuous backward to forward direction with suppressed stop band. This paper shows a substrate integrated waveguide (SIW) based on leaky wave antenna (LWA) including better gain, efficiency and backward to forward beam steering. The periodic C-shaped slot substrate integrated waveguide leaky wave antenna (SIW-LWA) are made-up and the calculated and replicated results acknowledges marvellously. It seems that there is a good contract between them. The beam steering of SIW-LWA is from-60 degree to 29 degree when frequency sweeps from 13.2 GHz to 15.5 GHz. The SIW-LWA offers the peak value of gain as 5.733 dB and peak value of efficiency are 77.545%. The broad side radiation will be at 14 GHz. Below 14 GHz the antenna works as a LH material and scans negative angles and above 14 GHz antenna works as RH material and scans positive angle.

This antenna, substrate integrated waveguide leaky wave antenna (SIW-LWA), is proposed only to obtained better beam steering so as to use for such application where beam scanning is used.
Hybrid energy harvesting based on piezoelectric and electromagnetic systems
Authors: Inba Rexy A , Nancy Mary J S , Priyaadarshini K G , Rachael Therese
Published: June 2021

The aim of this paper is to boost the power output of the hybrid energy harvester effectively and to provide electrical optimization for a hybrid energy harvesting system that produces two electrical outputs from a single source of mechanical input namely vibration. Two different electrical outputs are produced by using the concept of piezoelectricity and electromagnetic induction. In this paper, an electrical equivalent is obtained for the standalone piezoelectric system and the electromagnetic system. The rectification of the piezoelectric system along with its results is discussed. Different circuits for amplification is studied and simulated to improve the voltage. All the practical experimental outputs and the different software used to gain these outputs are recorded. The various applications of the hybrid system are reviewed and discussed.
The influence of the extraneous field on the conductivity tensor in cylindrical quantum wire with electron-optical phonon scattering
Authors: Ngoc Hoang Van
Published: June 2021

This work calculates conductivity tensors in cylindrical quantum wires with parabolic potential, with the presence of an electromagnetic wave field, a laser field, and is only considered for the case of electron-optical phonon scattering. By using the quantum kinetic equation for the electron in the quantum wire with the parabolic potential, combined with the presence of two external fields, the expression of conductivity tensor is computed for the case of optical electron-phonon scattering. This conductivity tensor expression depends on the electromagnetic wave frequency, the laser field frequency, the laser field amplitude, and the quantities that characterize the cylindrical quantum wire with parabolic potential. The figure of the dependence of the conductivity tensor to the external field will be plotted, examined, and analyzed for the quantum wire GaAs / GaAsAl.
Self-Adaptive Firefly Algorithm for solving Unit Commitment Problem in Power System
Authors: Ranganathan Selvarasu , Velmurugan V , Panjamoorthy Palanivel , Venugopal Ellappan
Published: June 2021

Unit Commitment is one of the crucial challenges in power system for selecting generation units to operate the system economically. It is the necessary to propose efficient solution techniques to solve unit commitment problem. In this research work a new Self-Adaptive Firefly Algorithm is planned to solve unit commitment problem. The SAFA identifies the best generating units to be functioning and offers the amount of load distribution among the units to minimize the operating cost. In order to validate SAFA an IEEE ten unit system is considered. The simulation is carried out using Matlab software and the solutions are presented. The solutions indicates that the efficiency of proposed SAFA than other solution techniques.
An Efficient Technique to Extricate Keypoints from a digital image
Authors: Dhanaraj Rachel Selva , Sridevi M
Published: June 2021

With the immense technology emerging in this period, the transmitted image information across the network can be confined easily, maneuver, and then disseminated. For any labor done on processing images, it is mandatory to detect suitable meaningful keypoints. There are existing methods that perceive key points from an image but they fail to detect key points from a smooth textured area or they do not detect enough keypoints. A method is proposed using an improved version of the Scale Invariant Feature Transform (SIFT) technique that is capable of diagnosing key points even in smooth areas as well as administer key points towards the whole image evenly.
An Approach for Morse Code Translation from Eye Blinks Using Tree Based Machine Learning Algorithms and OpenCV
Authors: Sumanth Naga Deepak G , Rohit B , Akhil Ch , Sai Surya Chandra Bharath D , Prakash Kolla Bhanu
Published: June 2021

For ages, human beings have been communicating with one another through different modes of communication. Communication is a process through which a person can communicate his/her feelings and thoughts to the other person. To communicate we can do it through either speech or sign language. The spoken language is used by abled persons, While the differently abled persons (deaf and dumb) may find it difficult to understand the same. So, for effective communication between the differently abled and abled person sign language has been developed. For private communication between two people, morse code has been developed which is highly efficient to exchange secrets. It also helps in emergencies where a person cannot communicate through hand gestures. Different methods/modes are used in morse code, but our focus is on eye blinking. Our approach towards this area has been to implement morse code using eye blinks in real-time assistance using a webcam to provide predicting power based on machine learning’s tree algorithms.
Rain Streaks Removal Using Discrete Cosine Transform and Diffusion based Technique
Authors: Riya  , Gupta Bhupendra , Lamba Subir Singh
Published: June 2021

Images acquired by the camera are sometimes affected by many environmental conditions such as fog, rain, snow, and clouds, etc., which influence the visible quality of the images and several computer vision applications. In this manuscript, we developed a new rain removal technique. Firstly, an exiting haze removal technique is used so that rain streaks could be visible and after that apply Discrete cosine transform (DCT) and diffusion-based technique to remove rain streaks from the rainy images. The experimental section shows the supremacy of the proposed method over the existing methods.
Back-Propagation Neural Network (BP-NN) model for the detection of borer pest attack
Authors: Prabhu Yogini V. , Parab Jivan S. , Naik G.M.
Published: June 2021

The multi-disciplinary agri-technologies domain have paved a way to the big data technologies, through Machine learning. Pest management is one of the most important problems facing farmers. A normal human monitoring cannot accurately predict the amount and intense of pests attacked. The issue of plant pests and diseases detection of agriculture has been tackled using the various available Neural Network (NN) techniques to process spectral data. In this manuscript, authors have presented a Back propagation Neural Network ( BP-NN) model, which was developed on data of the reflectance spectra (in range of 400 to 900 nm) cashew trees leaves infested with a borer pest attack as well asgood leaves spectra. With the help of BP-NN model the classification accuracy was foundto be 85% which is quite good. However, the accuracy of the model needs to be improved with better trainingalgorithmandlargerdataset.
Hierarchical based tumor segmentation by detection using deep learning approach
Authors: Sahaai Madona B , Jothilakshmi G R
Published: June 2021

Brain tumor is a cluster of abnormal cells that grows out of control in brain. Identifying brain tumor is challenging for doctors, since its impact will lead to danger for human life. Spotting of brain tumor using traditional methods is not accurate. Deep learning provides solution for detecting Brain Tumor in an efficient way. We have used MRI scan images. Since the image contains noise, image pre-processing work has been done to enhance the images. Deep learning methods for images works with Convolutional Neural Network (CNN). CNN has an advantage of extracting features by own. CNN has many hidden layers, where features are extracted and those features are learned for future prediction process. Single Shot Detector is used for detection of tumor region. SSD uses 8732 default bounding boxes mapped to the ground truth boxes for localisation process. Jaccard Overlap is used for match the default box with ground truth box. The detected whole tumor region is then used for segmenting the proper tumor region.
Novel design of electrostatic micro cantilever for enhanced travel range
Authors: Trivedi R R , Sharma D S
Published: June 2021

Electrostatic micro actuators are commonly deployed micro electro mechanical system (MEMS) devices due to their unpretentious construction and well-matched micro fabrication processes. The phenomenan of pull-in instability puts substantial restrictions on the execution of electrostatically driven MEMS beam type actuators by restraining the range of travel. A larger working range is desirable for a wide variety of tuning applications. In this paper, mechanism of pull-in instability and means to extend the useful working range of the microactuator by changing the design is presented. It shows drastic improvement in the results. Important conclusions are drawn from the results.
Design, assembly and use of a simple, economical water sampler for suspended sediment collection
Authors: Ansari E , Akhtar M N , Abdullah M N , Bakar E A
Published: June 2021

The behaviour of river can be perceived by analysing the sediments to a substantial extent. Computation of sediment weight per unit volume of water, sediment distribution along the quantifiable parameters of water body and mensuration of amount of useful as well as detrimental contents of sediments entails for sediment water sampling. The design and outline of an effortless and uncomplicated water sampler is presented in this paper. Although a number of manual, semi-automatic and fully automatic samplers are in attendance, there is a gap in asking price of previously available samplers and the present tool. The cost of sampler can be limited by designing for its specific purpose. The present sampler can only be used for top layers of water body ( < 5 feet). But the same sampler can be modified for collection up to 10 to 15 feet. Design stresses of materials and force acting on the sampler is used to examine the safety of components used at the critical points. Manual design calculations and experimental use of the instrument in Kerian River, Malaysia evinces the approval of the design. The trouble-free design and facile use of the device will assist in suspended water monitoring for research in an economical way.
Zinc tungstate doped polyaniline as room temperature humidity sensing composite
Authors: Machappa T. , Badrunnisa S. , Ambika Prasad M V N
Published: June 2021

Zinc Tungstate (ZnWO4) a bi-metal oxide is doped in low concentration to polyaniline (PANI) by ‘In-situ’ chemical polymerization method to form a composite. The FTIR characterization technique is used to confirm the formation of polyaniline salt phase and its composites. X-RD & SEM characterization techniques are used to verify the existence of ZnWO4 in PANI matrix. The temperature dependent conductivity shows an exponential behaviour which is thermally activated; the conductivity of the composites has been enhanced by 10 Ohms when compared with pristine PANI. It is reported that electrical resistance decrease, when the composite matrix are exposed to the wide range of humidity from 20 to 90% RH. The decrease of resistance has been recognized as; the chemically adsorbed water vapour donates the electron to the sensing surface in this composite and also due to capillary condensation.
Microstructure of ITO for Transparent Electrode on Glass Slide Prepare by RF Sputtering Technique
Authors: Sangwaranatee N , Srithanachai I , Niemcharoen S
Published: June 2021

Indium tin oxide (ITO) is an absorber photovoltaic material and widely used for semiconductor work for long time. ITO has characteristics by high transparent and low resistivity that can use for electrode of photodetector. Area of photodetector has use for metal contact but if change metal contact to transparent contact will get more photocurrent. However, this paper will investigate microstructure of ITO by analyze physical properties of material by various deposite time. The results show that transparent properties show around 97% at growth 1h and annealing at 500°C. XRD results show ITO peak (222), (400), (622) and (441) at sputtering time 60 mins.
DC conductivity of Li2O and SrO doped borophosphate glasses
Authors: Thipperudra Anand , Manjunatha S , Pushpalatha H L , Prashanth Kumar M , Ravikiran Y T
Published: June 2021

Borophosphate glasses with dopants SrO and Li2O in the composition of 20(B2O3) + 30(P2O5) + (50-x) (SrO) + x (Li2O) where x = 10, 20, 30, 40 and 50 were synthesized by standard melt quenching technique. All the glass samples were annealed to extract the thermal strains presence in it if any, its amorphous nature was evidenced by XRD characterization, density of the as synthesized glass series has been calculated at the ambient temperature using Archimedes’ principle and also its molar volume was estimated. With increase in mole fraction of Li2O, density of the glass samples found deceasing and the molar volume observed to be increasing as per the trends. Using Nernst-Einstein equation, high temperature dc activation energy has been calculated.
Formation of Non-equilibrium Alloys Through Nanocluster Deposition: The Ag0.3Al0.7 Supersaturated Alloy
Authors: Pramanik S , Ghosh J , Barman P , Bhattacharyya S R , Mukhopadhyay M K , Chanda D K , Datta A
Published: June 2021

We present here results of soft landing 4 nm sized clusters of Ag from a nanocluster beam on Al for 5 mins and 15 mins. We have studied the samples after cluster deposition using X-ray Photoelectron Spectroscopy, Secondary Ion Mass Spectroscopy, and X-ray Diffraction. We have found that no Ag nanoparticles have formed, the Ag is not oxidised but the Al has undergone some hydrated oxidation. More important, we have established the formation of the supersaturated Ag0.3Al0.7 alloy with an fcc structure at ambient temperature and at Ag concentrations ∼ 0.1%-0.2% of the total atoms in the sample, for both the 5 min and 15 min irradiated samples, growing with the same orientation as the Al lattice, with traces of another phase suggestive of the hcp phase of Ag2Al. Interestingly, the crystallinity of the alloy in both cases is better than that of the Al lattice and they have a marginally lower strain. We have proposed an explanation of the strong tendency to form Ag-rich alloys by invoking a recently established structure of Al with very low bonding electron density localised at the tetrahedral interstices.