Deep Learning-Based Mask Identification System Using ResNet Transfer Learning Architecture

Recently,the coronavirus disease 2019 has shown excellent attention in the global community regarding health and the economy.World Health Organization(WHO)and many others advised controlling Corona Virus Disease in 2019.The limited treatment resources,medical resources,and unawareness of immunity is an essential horizon to unfold.Among all resources,wearing a mask is the primary non-pharmaceutical intervention to stop the spreading of the virus caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)droplets.All countries made masks mandatory to prevent infection.For such enforcement,automatic and effective face detection systems are crucial.This study presents a face mask identification approach for static photos and real-time movies that distinguishes between images with and without masks.To contribute to society,we worked on mask detection of an individual to adhere to the rule and provide awareness to the public or organization.The paper aims to get detection accuracy using transfer learning from Residual Neural Network 50(ResNet-50)architecture and works on detection localization.The experiment is tested with other popular pre-trained models such as Deep Convolutional Neural Networks(AlexNet),Residual Neural Networks(ResNet),and Visual Geometry Group Networks(VGG-Net)advanced architecture.The proposed system generates an accuracy of 98.4%when modeled using Residual Neural Network 50(ResNet-50).Also,the precision and recall values are proved as better when compared to the existing models.This outstanding work also can be used in video surveillance applications.

Liquefaction proneness of stratified sand-silt layers based on cyclic triaxial tests

Most studies on liquefaction have addressed homogeneous soil strata using sand or sand with fine content without considering soil stratification.In this study,cyclic triaxial tests were conducted on the stratified sand specimens embedded with the silt layers to investigate the liquefaction failures and void-redistribution at confining stress of 100 kPa under stress-controlled mode.The loosening of underlying sand mass and hindrance to pore-water flow caused localized bulging at the sand-silt interface.It is observed that at a silt thickness of 0.2H(H is the height of the specimen),nearly 187 load cycles were required to attain liquefaction,which was the highest among all the silt thicknesses with a single silt layer.Therefore,0.2H is assumed as the optimum silt thickness(t_(opt)).The silt was placed at the top,middle and bottom of the specimen to understand the effect of silt layer location.Due to the increase in depth of the silt layer from the top position(capped soil state)to the bottom,the cycles to reach liquefaction(N_(cyc,L))increased 2.18 times.Also,when the number of silt layers increased from single to triple,there was an increase of about 880%in N_(cyc,L).The micro-characterization analysis of the soil specimens indicated silty materials transported in upper sections of the specimen due to the dissipated pore pressure.The main parameters,including thickness(t),location(z),cyclic stress ratio(CSR),number of silt layers(n)and modified relative density(D_(r,m)),performed significantly in governing the lique-faction resistance.For this,a multilinear regression model is developed based on critical parameters for prediction of N_(cyc,L).Furthermore,the developed constitutive model has been validated using the data from the present study and earlier findings.

Multilayer Neural Network Based Speech Emotion Recognition for Smart Assistance

Day by day,biometric-based systems play a vital role in our daily lives.This paper proposed an intelligent assistant intended to identify emotions via voice message.A biometric system has been developed to detect human emotions based on voice recognition and control a few electronic peripherals for alert actions.This proposed smart assistant aims to provide a support to the people through buzzer and light emitting diodes(LED)alert signals and it also keep track of the places like households,hospitals and remote areas,etc.The proposed approach is able to detect seven emotions:worry,surprise,neutral,sadness,happiness,hate and love.The key elements for the implementation of speech emotion recognition are voice processing,and once the emotion is recognized,the machine interface automatically detects the actions by buzzer and LED.The proposed system is trained and tested on various benchmark datasets,i.e.,Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS)database,Acoustic-Phonetic Continuous Speech Corpus(TIMIT)database,Emotional Speech database(Emo-DB)database and evaluated based on various parameters,i.e.,accuracy,error rate,and time.While comparing with existing technologies,the proposed algorithm gave a better error rate and less time.Error rate and time is decreased by 19.79%,5.13 s.for the RAVDEES dataset,15.77%,0.01 s for the Emo-DB dataset and 14.88%,3.62 for the TIMIT database.The proposed model shows better accuracy of 81.02%for the RAVDEES dataset,84.23%for the TIMIT dataset and 85.12%for the Emo-DB dataset compared to Gaussian Mixture Modeling(GMM)and Support Vector Machine(SVM)Model.

Design and Simulation of Ring Network-on-Chip for Different Configured Nodes

The network-on-chip(NoC)technology is frequently referred to as a front-end solution to a back-end problem.The physical substructure that transfers data on the chip and ensures the quality of service begins to collapse when the size of semiconductor transistor dimensions shrinks and growing numbers of intellectual property(IP)blocks working together are integrated into a chip.The system on chip(SoC)architecture of today is so complex that not utilizing the crossbar and traditional hierarchical bus architecture.NoC connectivity reduces the amount of hardware required for routing and functions,allowing SoCs with NoC interconnect fabrics to operate at higher frequencies.Ring(Octagons)is a direct NoC that is specifically used to solve the scalability problem by expanding each node in the shape of an octagon.This paper discusses the ring NoC design concept and its simulation in Xilinx ISE 14.7,as well as the communication of functional nodes.For the field-programmable gate array(FPGA)synthesis,the performance of NoC is evaluated in terms of hardware and timing parameters.The design allows 64 to 256 node communication in a single chip with‘N’bit data transfer in the ring NoC.The performance of the NoC is evaluated with variable nodes from 2 to 256 in Digilent manufactured Virtex-5 FPGA hardware.

Co-relation of SARS-CoV-2 related 30-d mortality with HRCT score and RT-PCR Ct value-based viral load in patients with solid malignancy

BACKGROUND Coronavirus disease 2019(COVID-19)patients with malignancy are published worldwide but are lacking in data from India.AIM To characterize COVID-19 related mortality outcomes within 30 d of diagnosis with HRCT score and RT-PCR Ct value-based viral load in various solid malignancies.METHODS Patients included in this study were with an active or previous malignancy and with confirmed severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)infection from the institute database.We collected data on demographic details,baseline clinical conditions,medications,cancer diagnosis,treatment and the COVID-19 disease course.The primary endpoint was the association between the mortality outcome and the potential prognostic variables,specially,HRCT score,RT-PCR Ct value-based viral load,etc.using logistic regression analyses treatment received in 30 d.RESULTS Out of 131 patients,123 met inclusion criteria for our analysis.The median age was 57 years(interquartile range=19-82)while 7(5.7%)were aged 75 years or older.The most prevalent malignancies were of GUT origin 49(39.8%),hepatopancreatobiliary(HPB)40(32.5%).109(88.6%)patients were on active anticancer treatment,115(93.5%)had active(measurable)cancer.At analysis on May 20,2021,26(21.1%)patients had died.In logistic regression analysis,independent factors associated with an increased 30-d mortality were in patients with the symptomatic presentation.Chemotherapy in the last 4 wk,number of comorbidities(≥2 vs none:3.43,1.08-8.56).The univariate analysis showed that the risk of death was significantly associated with the HRCT score:for moderate(8-15)[odds ratio(OR):3.44;95%confidence interval(CI):1.3-9.12;P=0.0132],severe(>15)(OR:7.44;95%CI:1.58-35.1;P=0.0112).CONCLUSION To the best of our knowledge,this is the first study from India reporting the association of HRCT score and RT-PCR Ct value-based 30-d mortality outcomes in SARS-CoV-2 infected cancer patients.

Performance Analysis of AODV Routing forWireless Sensor Network in FPGA Hardware

Wireless sensor network(WSN)is a group of interconnected sensor nodes that work wirelessly to capture the information of surroundings.The routing of the network is a challenging task.The routing of WSN is classified as proactive,reactive,and hybrid.Adhoc on-demand distance vector(AODV)routing is an example of reactive routing based on the demand route formations among different nodes in the network.The research article emphasizes the design and simulation of the AODV routing hardware chip using very-high-speed integrated circuit hardware description language(VHDL)programming in Xilinx integrated synthesis environment(ISE)14.7 software.The performance of the chip is studied based on the field-programmable gate array(FPGA)hardware parameters such as slices,lookup table(LUTs),input/output block(IOB),flipflops,and memory for the different configurations of the network(N=10,20….100).The delay and frequency are also estimated on the Virtex-5 FPGA.The performance of the WSN with AODV routing is also analyzed based on the packet delivery ratio,throughput,delay,and control overhead.The simulation test cases verified the 8-bit,64-bit,and 128-bit data communication within the nodes.

Image smog restoration using oblique gradient profile prior and energy minimization

Removing the smog from digital images is a challenging pre-processing tool in various imaging systems.Therefore,many smog removal(i.e.,desmogging)models are proposed so far to remove the effect of smog from images.The desmogging models are based upon a physical model,it means it requires efficient estimation of transmission map and atmospheric veil from a single smoggy image.Therefore,many prior based restoration models are proposed in the literature to estimate the transmission map and an atmospheric veil.However,these models utilized computationally extensive minimization of an energy function.Also,the existing restoration models suffer from various issues such as distortion of texture,edges,and colors.Therefore,in this paper,a convolutional neural network(CNN)is used to estimate the physical attributes of smoggy images.Oblique gradient channel prior(OGCP)is utilized to restore the smoggy images.Initially,a dataset of smoggy and sunny images are obtained.Thereafter,we have trained CNN to estimate the smog gradient from smoggy images.Finally,based upon the computed smog gradient,OGCP is utilized to restore the still smoggy images.Performance analyses reveal that the proposed CNN-OGCP based desmogging model outperforms the existing desmogging models in terms of various performance metrics.

Higher Plant Proteins of Cyanobacterial Origin:Are They or Are They Not Preferentially Targeted to Chloroplasts?

Dear Editor, What does the evolutionary origin of a plant protein tell about its subcellular localization？ Naively thinking, one would assume that plant proteins that were originally encoded in the endosymbiont genome are targeted to the chloroplast. However, published data seem to support only a loose link between evolutionary origin and subcel- lular localization. About half of the Arabidopsis proteins with a detectable cyanobacterial ortholog are targeted to subcellular compartments other than the chloroplast （Martin et al., 2002）. H