The ascent of the metaverse signifies a profound transformation in our digital landscape, ushering in a complex network of interlinked virtual domains and digital spaces. In this burgeoning metaverse, a paradigm shift...The ascent of the metaverse signifies a profound transformation in our digital landscape, ushering in a complex network of interlinked virtual domains and digital spaces. In this burgeoning metaverse, a paradigm shift is seen in how people engage, collaborate, and become immersed in digital environments. An especially intriguing concept taking root within this metaverse landscape is that of digital twins. Initially rooted in industrial and Internet of Things(IoT) contexts, digital twins are now making their mark in the metaverse, presenting opportunities to elevate user experiences, introduce novel dimensions of interaction, and seamlessly bridge the divide between the virtual and physical realms. Digital twins, conceived initially to replicate physical entities in real-time, have transcended their industrial origins in this new metaverse context. They no longer solely replicate physical objects but extend their domain to encompass digital entities, avatars, virtual environments, and users. Despite the vital contributions of digital twins in the metaverse, there has been no research that has explored the applications and scope of digital twins in the metaverse comprehensively. However, there are a few papers focusing on some particular applications. Addressing this research gap, we present an in-depth review of the pivotal role of application digital twins in the metaverse. We present 15 digital twin applications in the metaverse, ranging from simulation and training to emergency preparedness. This study outlines the critical limitations of integrating digital twins and metaverse and several future research directions.展开更多
Across the world, we are currently witnessing the deployments of 4 G LTE-Advanced and the 5 G research is reaching its peak point. The 5 G research mainly concentrates on addressing some of the existing OFDM based LTE...Across the world, we are currently witnessing the deployments of 4 G LTE-Advanced and the 5 G research is reaching its peak point. The 5 G research mainly concentrates on addressing some of the existing OFDM based LTE problems along with use of non-contiguous fragmented spectrum. Universal Filtered Multi Carrier(UFMC) has been considered as one of the candidate waveform for the 5 G communications because it provides robustness against the Inter Symbol Interference(ISI), and Inter Carrier Interference(ICI) and is suitable for low latency scenarios. In this paper, a novel approach is proposed to use Kaiser-Bessel filter based pulse shaping instead of standard Dolph-Chebyshev filter for UFMC based waveform to reduce the spectral leakage into nearby sub-bands. In this paper, UFMC system is simulated using MATLAB software, a comparative study for Dolph-Chebyshev and Kaiser-Bessel filters are performed and the results are also presented in terms of power spectrum density(PSD) analysis, Complementary Cumulative Distribution Function(CCDF) analysis, and Adjacent Channel Power Ratio(ACPR) analysis. The simulated results show a better power spectral density and lower sidebands for UFMC(Kaiser Based window), when compared with UFMC(Dolph-Chebyshev) and conventional OFDM.展开更多
In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead t...In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.展开更多
Global Positioning System(GPS)measurements of integrated water vapor(IWV)for two years(2014 and 2015)are presented in this paper.Variation of IWV during active and break spells of Indian summer monsoon has been studie...Global Positioning System(GPS)measurements of integrated water vapor(IWV)for two years(2014 and 2015)are presented in this paper.Variation of IWV during active and break spells of Indian summer monsoon has been studied for a tropical station Hyderabad(17.4°N,78.46°E).The data is validated with ECMWF Re-Analysis(ERA)91 level data.Relationships of IWV with other atmospheric variables like surface temperature,rain,and precipitation efficiency have been established through cross-correlation studies.A positive correlation coefficient is observed between IWV and surface temperature over two years.But the coefficient becomes negative when only summer monsoon months(June,July,August,and September)are considered.Rainfall during these months cools down the surface and could be the reason for this change in the correlation coefficient.Correlation studies between IWV-precipitation,IWVprecipitation efficiency(P.E),and precipitation-P.E show that coefficients are-0.05,-0.10 and 0.983 with 95%confidence level respectively,which proves that the efficacy of rain does not depend only on the level of water vapor.A proper dynamic mechanism is necessary to convert water vapor into the rain.The diurnal variations of IWV during active and break spells have been analyzed.The amplitudes of diurnal oscillation and its harmonics of individual spell do not show clear trends but the mean amplitudes of the break spells are approximately double than those of the active spells.The amplitudes of diurnal,semidiurnal and ter-diurnal components during break spells are 1.08 kg/m^(2),0.52 kg/m;and 0.34 kg/m;respectively.The corresponding amplitudes during active spells are 0.68 kg/m^(2),0.41 kg/m;and 0.23 kg/m;.展开更多
Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure(IOP),which damages the vision of eyes.So,detecting and classifying Glaucoma is an important and demanding task in recent days.For...Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure(IOP),which damages the vision of eyes.So,detecting and classifying Glaucoma is an important and demanding task in recent days.For this purpose,some of the clustering and segmentation techniques are proposed in the existing works.But,it has some drawbacks that include ineficient,inaccurate and estimates only the affected area.In order to solve these issues,a Neighboring Differential Clustering(NDC)-Intensity V ariation Making(IVM)are proposed in this paper.The main intention of this work is to extract and diagnose the abnormal retinal image by identifying the optic disc.This work includes three stages such as,preprocessing,clustering and segmentation.At first,the given retinal image is preprocessed by using the Gaussian Mask Updated(GMU)model for eliminating the noise and improving the quality of the image.Then,the cluster is formed by extracting the threshold and patterns with the help of NDC technique.In the segmentation stage,the weight is calculated for pixel matching and ROI extraction by using the proposed IVM method.Here,the novelty is presented in the clustering and segmentation processes by developing NDC and IVM algorithms for accurate Glaucoma identification.In experiments,the results of both existing and proposed techniques are evaluated in terms of sensitivity,specificity,accuracy,Hausdorff distance,Jaccard and dice metrics.展开更多
Highly surface active super paramagnetic colloidal suspensions of nano crystalline ferrofluid have been synthesized through wet-chemical route. Entrapment of magnetic domains presented in the nano ferrofluid in a poly...Highly surface active super paramagnetic colloidal suspensions of nano crystalline ferrofluid have been synthesized through wet-chemical route. Entrapment of magnetic domains presented in the nano ferrofluid in a polymer matrix like poly vinyl alcohol film was accomplished by developing polymer composite film in between two magnetic poles by solvent casting method. Similarly poly vinyl alcohol-ferrofluid composite films were also developed in the absence of magnetic field. Atomic force microscopy image of nano-composite film makes it clear that the film developed in the absence of magnetic field possesses randomly oriented domains, whereas film developed with magnetic field shows well aligned flux lines. The characteristics and nature of forces acting between magnetic domains along the magnetic flux lines were explored from magnetic force microscopy imaging. The number of flux lines developed in the polymer matrix was observed to be directly proportional to applied external magnetic field. Approximate number of magnetic lines passing through unit area of composite film was evaluated from line profile data analysis of atomic force microscopy image. The particle sizes of the nanoparticles encapsulated in the polymer matrix were found to be in the range of 10- 20 nm. Scanning electron microscopy micrographs confirm aggregation of ferrofluid particles of ribbon like morphology along the magnetic flux lines. Magnetic properties of the entrapped nanoparticles in polymer matrix film were analyzed using vibrating sample magnetometer at room temperature. The super paramagnetic nature and other magnetic properties were evaluated from the hysteresis loop.展开更多
Brain Hemorrhagic stroke is a serious malady that is caused by the drop in blood flow through the brain and causes the brain to malfunction.Precise segmentation of brain hemorrhage is crucial,so an enhanced segmentati...Brain Hemorrhagic stroke is a serious malady that is caused by the drop in blood flow through the brain and causes the brain to malfunction.Precise segmentation of brain hemorrhage is crucial,so an enhanced segmentation is carried out in this research work.The brain image of various patients has taken using an MRI scanner by the utilization of T1,T2,and FLAIR sequence.This work aims to segment the Brain Hemorrhagic stroke using deep learning-based Multi-resolution UNet(multires UNet)through morphological operations.It is hard to precisely segment the brain lesions to extract the existing region of stroke.This crucial step is accomplished by this proposed MMU-Net methodology by precise segmentation of stroke lesions.The proposed method efficiently determines the hemorrhagic stroke with improved accuracy of 95%compared with the existing segmentation techniques such as U-net++,ResNet,Multires UNET and 3D-ResU-Net and also provides improved performance of 2D and 3D U-Net with an enhanced outcome.The performancemeasure of the proposed methodology acquires an improved accuracy,precision ratio,sensitivity,and specificity rate of 0.07%,0.04%,0.04%,and 0.05%in comparison to U-net,ResNet,Multires UNET and 3D-ResU-Net techniques respectively.展开更多
The difference between circuit design stage and time requirements has broadened with the increasing complexity of the circuit.A big database is needed to undertake important analytical work like statistical method,hea...The difference between circuit design stage and time requirements has broadened with the increasing complexity of the circuit.A big database is needed to undertake important analytical work like statistical method,heat research,and IR-drop research that results in extended running times.This unit focuses on the assessment of test strength.Because of the enormous number of successful designs for currentmodels and the unnecessary time required for every test,maximum energy ratings with all tests cannot be achieved.Nevertheless,test safety is important for producing trustworthy findings to avoid loss of output and harm to the chip.Generally,effective power assessment is only possible in a limited sample of pre-selected experiments.Thus,a key objective is to find the experiments that might give the worst situations again for testing power.It offers a machine-based circuit power estimation(MLCPE)system for the selection of exams.Two distinct techniques of predicting are utilized.Firstly,to find testings with power dissipation,it forecasts the behavior of testing.Secondly,the changemovement and energy data are linked to the semiconductor design,identifying small problem areas.Several types of algorithms are utilized.In particular,the methods compared.The findings show great accuracy and efficiency in forecasting.That enables such methods suitable for selecting the worst scenario.展开更多
In the digital era,the Narrowband Internet of Things(Nb-IoT)influ-ences the massive Machine-Type-Communication(mMTC)features to establish secure routing among the 5G/6G mobile networks.It supports global coverage to th...In the digital era,the Narrowband Internet of Things(Nb-IoT)influ-ences the massive Machine-Type-Communication(mMTC)features to establish secure routing among the 5G/6G mobile networks.It supports global coverage to the low-cost IoT devices distributed in terrestrial networks.Its key traffic char-acteristics include robust uplink,moderate data rate/device,extremely high energy efficiency,prolonging device lifetime,and Quality of Service(QoS).This paper proposes a Deep Reinforcement Learning(DRL)combined software-defined air interface algorithm applied on the switching system,satisfying the user require-ment and enabling them with the network resources to extend quality of service by choosing the most appropriate quality of service metric.In this framework,Non-Orthogonal Multiple Accesses(NOMA)and Rate-Splitting Multiple Access(RSMA)are combined to accommodate massive(Nb-IoT)devices that can be uti-lized the entire resource(frequency band)for tackling the unknown dynamics pro-hibitive.The proposed algorithm instantly assigns the network resources per user requirements and enhances selecting the best quality of service metric optimiza-tion.Therefore,it has potential benefits of high scalability,low latency,energy efficiency,and spectrum utility.展开更多
We comparatively study two representative ballistic transport models of nanowire metal-oxide-semiconductor field effect transistors,i.e.the Natori model and the Jiménez model.The limitations and applicability of ...We comparatively study two representative ballistic transport models of nanowire metal-oxide-semiconductor field effect transistors,i.e.the Natori model and the Jiménez model.The limitations and applicability of both the models are discussed.Then the Jiménez model is extended to include atomic dispersion relations and is compared with the Natori model from the aspects of ballistic current and quantum capacitance.It is found that the Jiménez model can produce similar results compared with the more complex Natori model even at very small nanowire dimensions.展开更多
Internet of Things(IoT)is a popular social network in which devices are virtually connected for communicating and sharing information.This is applied greatly in business enterprises and government sectors for deliveri...Internet of Things(IoT)is a popular social network in which devices are virtually connected for communicating and sharing information.This is applied greatly in business enterprises and government sectors for delivering the services to their customers,clients and citizens.But,the interaction is success-ful only based on the trust that each device has on another.Thus trust is very much essential for a social network.As Internet of Things have access over sen-sitive information,it urges to many threats that lead data management to risk.This issue is addressed by trust management that help to take decision about trust-worthiness of requestor and provider before communication and sharing.Several trust-based systems are existing for different domain using Dynamic weight meth-od,Fuzzy classification,Bayes inference and very few Regression analysis for IoT.The proposed algorithm is based on Logistic Regression,which provide strong statistical background to trust prediction.To make our stand strong on regression support to trust,we have compared the performance with equivalent sound Bayes analysis using Beta distribution.The performance is studied in simu-lated IoT setup with Quality of Service(QoS)and Social parameters for the nodes.The proposed model performs better in terms of various metrics.An IoT connects heterogeneous devices such as tags and sensor devices for sharing of information and avail different application services.The most salient features of IoT system is to design it with scalability,extendibility,compatibility and resiliency against attack.The existing worksfinds a way to integrate direct and indirect trust to con-verge quickly and estimate the bias due to attacks in addition to the above features.展开更多
System Identification becomes very crucial in the field of nonlinear and dynamic systems or practical systems.As most practical systems don’t have prior information about the system behaviour thus,mathematical modell...System Identification becomes very crucial in the field of nonlinear and dynamic systems or practical systems.As most practical systems don’t have prior information about the system behaviour thus,mathematical modelling is required.The authors have proposed a stacked Bidirectional Long-Short Term Memory(Bi-LSTM)model to handle the problem of nonlinear dynamic system identification in this paper.The proposed model has the ability of faster learning and accurate modelling as it can be trained in both forward and backward directions.The main advantage of Bi-LSTM over other algorithms is that it processes inputs in two ways:one from the past to the future,and the other from the future to the past.In this proposed model a backward-running Long-Short Term Memory(LSTM)can store information from the future along with application of two hidden states together allows for storing information from the past and future at any moment in time.The proposed model is tested with a recorded speech signal to prove its superiority with the performance being evaluated through Mean Square Error(MSE)and Root Means Square Error(RMSE).The RMSE and MSE performances obtained by the proposed model are found to be 0.0218 and 0.0162 respectively for 500 Epochs.The comparison of results and further analysis illustrates that the proposed model achieves better performance over other models and can obtain higher prediction accuracy along with faster convergence speed.展开更多
Through the research on the existing C-MANTEC neural network and PID control technology, this paper presents an improved C-MANTEC algorithm based on PID control system. The combining of the artificial neural networks ...Through the research on the existing C-MANTEC neural network and PID control technology, this paper presents an improved C-MANTEC algorithm based on PID control system. The combining of the artificial neural networks with conventional PID control helps in exploring their respective advantages to forming the intelligent PID control. From UCI Repository cancer dataset, the developed system is tested. The results show that the scheme can not only improve the speed of the algorithm in the training process but also improve the generalization capability of the network, which further enhances the performance of PID controllers. The overall power consumed is also reduced to a greater extent.展开更多
Memristor is a newly found fourth circuit element for the next generation emerging nonvolatile memory technology. In this paper, design of new type of nonvolatile static random access memory cell is proposed by using ...Memristor is a newly found fourth circuit element for the next generation emerging nonvolatile memory technology. In this paper, design of new type of nonvolatile static random access memory cell is proposed by using a combination of memristor and complemented metal oxide semiconductor. Biolek memristor model and CMOS 180 nm technology are used to form a single cell. By introducing distinct binary logic to avoid safety margin is left for each binary logic output and enables better read/write data integrity. The total power consumption reduces from 0.407 mw (milli-watt) to 0.127 mw which is less than existing memristor based memory cell of the same CMOS technology. Read and write time is also significantly reduced. However, write time is higher than conventional 6T SRAM cell and can be reduced by increasing motion of electron in the memristor. The change of the memristor state is shown by applying piecewise linear input voltage.展开更多
New conditions are derived for the l2-stability of time-varying linear and nonlinear discrete-time multiple-input multipleoutput （MIMO） systems, having a linear time time-invariant block with the transfer function F...New conditions are derived for the l2-stability of time-varying linear and nonlinear discrete-time multiple-input multipleoutput （MIMO） systems, having a linear time time-invariant block with the transfer function F（z）, in negative feedback with a matrix of periodic/aperiodic gains A（k）, k = 0,1, 2,... and a vector of certain classes of non-monotone/monotone nonlinearities φp（-）, without restrictions on their slopes and also not requiring path-independence of their line integrals. The stability conditions, which are derived in the frequency domain, have the following features： i） They involve the positive definiteness of the real part （as evaluated on ｜z｜ = 1） of the product of Г （z） and a matrix multiplier function of z. ii） For periodic A（k）, one class of multiplier functions can be chosen so as to impose no constraint on the rate of variations A（k）, but for aperiodic A（k）, which allows a more general multiplier function, constraints are imposed on certain global averages of the generalized eigenvalues of （A（k ＋ 1）,A（k））, k = 1, 2 iii） They are distinct from and less restrictive than recent results in the literature.展开更多
In scaled CMOS processes, the single-event effects generate missing output pulses in Delay-Locked Loop (DLL). Due to its effective sequence detection of the missing pulses in the proposed Error Correction Circuit (ECC...In scaled CMOS processes, the single-event effects generate missing output pulses in Delay-Locked Loop (DLL). Due to its effective sequence detection of the missing pulses in the proposed Error Correction Circuit (ECC) and its portability to be applied to any DLL type, the ECC mitigates the impact of single-event effects and completes its operation with less design complexity without any concern about losing the information. The ECC has been implemented in 180 nm CMOS process and measured the accuracy of mitigation on simulations at LETs up to 100 MeV-cm<sup>2</sup>/mg. The robustness and portability of the mitigation technique are validated through the results obtained by implementing proposed ECC in XilinxArtix 7 FPGA.展开更多
This paper presents an automated POCOFAN-POFRAME algorithm thatpartitions large combinational digital VLSI circuits for pseudo exhaustive testing. In thispaper, a simulation framework and partitioning technique are pr...This paper presents an automated POCOFAN-POFRAME algorithm thatpartitions large combinational digital VLSI circuits for pseudo exhaustive testing. In thispaper, a simulation framework and partitioning technique are presented to guide VLSIcircuits to work under with fewer test vectors in order to reduce testing time and todevelop VLSI circuit designs. This framework utilizes two methods of partitioningPrimary Output Cone Fanout Partitioning (POCOFAN) and POFRAME partitioning todetermine number of test vectors in the circuit. The key role of partitioning is to identifyreconvergent fanout branch pairs and the optimal value of primary input node N andfanout F partitioning using I-PIFAN algorithm. The number of reconvergent fanout andits locations are critical for testing of VLSI circuits and design for testability. Hence, theirselection is crucial in order to optimize system performance and reliability. In the presentwork, the design constraints of the partitioned circuit considered for optimizationincludes critical path delay and test time. POCOFAN-POFRAME algorithm uses theparameters with optimal values of circuits maximum primary input cone size (N) andminimum fan-out value (F) to determine the number of test vectors, number of partitionsand its locations. The ISCAS’85 benchmark circuits have been successfully partitioned,the test results of C499 shows 45% reduction in the test vectors and the experimentalresults are compared with other partitioning methods, our algorithm makes fewer testvectors.展开更多
Emerging 5G communication solutions utilize the millimeter wave(mmWave)band to alleviate the spectrum deficit.In the mmWave range,Multiple Input Multiple Output(MIMO)technologies support a large number of simultaneous...Emerging 5G communication solutions utilize the millimeter wave(mmWave)band to alleviate the spectrum deficit.In the mmWave range,Multiple Input Multiple Output(MIMO)technologies support a large number of simultaneous users.In mmWave MIMO wireless systems,hybrid analog/digital precoding topologies provide a reduced complexity substitute for digital precoding.Bit Error Rate(BER)and Spectral efficiency performances can be improved by hybrid Minimum Mean Square Error(MMSE)precoding,but the computation involves matrix inversion process.The number of antennas at the broadcasting and receiving ends is quite large for mm-wave MIMO systems,thus computing the inverse of a matrix of such high dimension may not be practically feasible.Due to the need for matrix inversion and known candidate matrices,the classic Orthogonal Matching Pursuit(OMP)approach will be more complicated.The novelty of research presented in this manuscript is to create a hybrid precoder for mmWave communication systems using metaheuristic algorithms that do not require matrix inversion processing.The metaheuristic approach has not employed much in the formulation of a precoder in wireless systems.Five distinct evolutionary algorithms,such as Harris–Hawks Optimization(HHO),Runge–Kutta Optimization(RUN),Slime Mould Algorithm(SMA),Hunger Game Search(HGS)Algorithm and Aquila Optimizer(AO)are considered to design optimal hybrid precoder for downlink transmission and their performances are tested under similar practical conditions.According to simulation studies,the RUN-based precoder performs better than the conventional algorithms and other nature-inspired algorithms based precoding in terms of spectral efficiency and BER.展开更多
The 12-lead ECG aids in the diagnosis of myocardial infarction and is helpful in the prediction of cardiovascular disease complications.It does,though,have certain drawbacks.For other electrocardiographic anomalies su...The 12-lead ECG aids in the diagnosis of myocardial infarction and is helpful in the prediction of cardiovascular disease complications.It does,though,have certain drawbacks.For other electrocardiographic anomalies such as Left Bundle Branch Block and Left Ventricular Hypertrophy syndrome,the ECG signal withMyocardial Infarction is difficult to interpret.These diseases cause variations in the ST portion of the ECG signal.It reduces the clarity of ECG signals,making itmore difficult to diagnose these diseases.As a result,the specialist is misled into making an erroneous diagnosis by using the incorrect therapeutic technique.Based on these concepts,this article reviews the different procedures involved in ECG signal pre-processing,feature extraction,feature selection,and classification techniques to diagnose heart disorders such as LeftVentricularHypertrophy,Bundle Branch Block,andMyocardial Infarction.It reveals the flaws and benefits in each segment,as well as recommendations for developing more advanced and robustmethods for diagnosing these diseases,which will increase the system’s accuracy.The current issues and prospective research directions are also addressed.展开更多
Recently,automatic diagnosis of diabetic retinopathy(DR)from the retinal image is the most significant ressearch topic in the medical applications.Diabetic macular edema(DME)is the.major reason for the loss of vision ...Recently,automatic diagnosis of diabetic retinopathy(DR)from the retinal image is the most significant ressearch topic in the medical applications.Diabetic macular edema(DME)is the.major reason for the loss of vision in patients suffering fom DR.Early identification of the DR enables to prevent the vision loss and encourage diabetic control activities.Many techniques are.developed to diagnose the DR.The major drawbacks of the existing techniques are low accuracy and high time complexity.To owercome these issues,this paper propases an enhanced particle swarm optimization differential evolution feature selection(PSO DEFS)based feature selection approach with biometric aut hentication for the identification of DR.Initially,a hybrid median filter(HMF)is used for pre processing the input images.Then,the pre-processed images are embedded with each other by using least significant bit(LSB)for authentication purpose.Si-multaneously,the image features are extracted using convoluted local tetra pattern(CLTrP)and Tamura features.Feature selection is performed using PSO DEFS and PSO-gravitational search algorithm(PSO GSA)to reduce time complexity.Based on some performance metrics,the PSO-DEFS is chosen as a better choice for feature selection.The feature selection is performed based on the fitness value.A multi-relevance vector machine(M-RVM)is introduced to dlassify the 13 normal and 62 abnormal images among 75 images from 60 patients.Finally,the DR patients are further dassified by M-RVM.The experimental results exhibit that the proposed approach achieves better accuracy,sensitivity,and specificity than the exist ing techniques.展开更多
文摘The ascent of the metaverse signifies a profound transformation in our digital landscape, ushering in a complex network of interlinked virtual domains and digital spaces. In this burgeoning metaverse, a paradigm shift is seen in how people engage, collaborate, and become immersed in digital environments. An especially intriguing concept taking root within this metaverse landscape is that of digital twins. Initially rooted in industrial and Internet of Things(IoT) contexts, digital twins are now making their mark in the metaverse, presenting opportunities to elevate user experiences, introduce novel dimensions of interaction, and seamlessly bridge the divide between the virtual and physical realms. Digital twins, conceived initially to replicate physical entities in real-time, have transcended their industrial origins in this new metaverse context. They no longer solely replicate physical objects but extend their domain to encompass digital entities, avatars, virtual environments, and users. Despite the vital contributions of digital twins in the metaverse, there has been no research that has explored the applications and scope of digital twins in the metaverse comprehensively. However, there are a few papers focusing on some particular applications. Addressing this research gap, we present an in-depth review of the pivotal role of application digital twins in the metaverse. We present 15 digital twin applications in the metaverse, ranging from simulation and training to emergency preparedness. This study outlines the critical limitations of integrating digital twins and metaverse and several future research directions.
文摘Across the world, we are currently witnessing the deployments of 4 G LTE-Advanced and the 5 G research is reaching its peak point. The 5 G research mainly concentrates on addressing some of the existing OFDM based LTE problems along with use of non-contiguous fragmented spectrum. Universal Filtered Multi Carrier(UFMC) has been considered as one of the candidate waveform for the 5 G communications because it provides robustness against the Inter Symbol Interference(ISI), and Inter Carrier Interference(ICI) and is suitable for low latency scenarios. In this paper, a novel approach is proposed to use Kaiser-Bessel filter based pulse shaping instead of standard Dolph-Chebyshev filter for UFMC based waveform to reduce the spectral leakage into nearby sub-bands. In this paper, UFMC system is simulated using MATLAB software, a comparative study for Dolph-Chebyshev and Kaiser-Bessel filters are performed and the results are also presented in terms of power spectrum density(PSD) analysis, Complementary Cumulative Distribution Function(CCDF) analysis, and Adjacent Channel Power Ratio(ACPR) analysis. The simulated results show a better power spectral density and lower sidebands for UFMC(Kaiser Based window), when compared with UFMC(Dolph-Chebyshev) and conventional OFDM.
基金supported by the Center for Mining,Electro-Mechanical Research of Hanoi University of Mining and Geology(HUMG),Hanoi,Vietnam。
文摘In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.
基金research fellowship offered by ISRO under RESPOND program[No.ISRO/RES/2/406/16-17]。
文摘Global Positioning System(GPS)measurements of integrated water vapor(IWV)for two years(2014 and 2015)are presented in this paper.Variation of IWV during active and break spells of Indian summer monsoon has been studied for a tropical station Hyderabad(17.4°N,78.46°E).The data is validated with ECMWF Re-Analysis(ERA)91 level data.Relationships of IWV with other atmospheric variables like surface temperature,rain,and precipitation efficiency have been established through cross-correlation studies.A positive correlation coefficient is observed between IWV and surface temperature over two years.But the coefficient becomes negative when only summer monsoon months(June,July,August,and September)are considered.Rainfall during these months cools down the surface and could be the reason for this change in the correlation coefficient.Correlation studies between IWV-precipitation,IWVprecipitation efficiency(P.E),and precipitation-P.E show that coefficients are-0.05,-0.10 and 0.983 with 95%confidence level respectively,which proves that the efficacy of rain does not depend only on the level of water vapor.A proper dynamic mechanism is necessary to convert water vapor into the rain.The diurnal variations of IWV during active and break spells have been analyzed.The amplitudes of diurnal oscillation and its harmonics of individual spell do not show clear trends but the mean amplitudes of the break spells are approximately double than those of the active spells.The amplitudes of diurnal,semidiurnal and ter-diurnal components during break spells are 1.08 kg/m^(2),0.52 kg/m;and 0.34 kg/m;respectively.The corresponding amplitudes during active spells are 0.68 kg/m^(2),0.41 kg/m;and 0.23 kg/m;.
文摘Glaucoma is an eye disease that usually occurs with the increased Intra-Ocular Pressure(IOP),which damages the vision of eyes.So,detecting and classifying Glaucoma is an important and demanding task in recent days.For this purpose,some of the clustering and segmentation techniques are proposed in the existing works.But,it has some drawbacks that include ineficient,inaccurate and estimates only the affected area.In order to solve these issues,a Neighboring Differential Clustering(NDC)-Intensity V ariation Making(IVM)are proposed in this paper.The main intention of this work is to extract and diagnose the abnormal retinal image by identifying the optic disc.This work includes three stages such as,preprocessing,clustering and segmentation.At first,the given retinal image is preprocessed by using the Gaussian Mask Updated(GMU)model for eliminating the noise and improving the quality of the image.Then,the cluster is formed by extracting the threshold and patterns with the help of NDC technique.In the segmentation stage,the weight is calculated for pixel matching and ROI extraction by using the proposed IVM method.Here,the novelty is presented in the clustering and segmentation processes by developing NDC and IVM algorithms for accurate Glaucoma identification.In experiments,the results of both existing and proposed techniques are evaluated in terms of sensitivity,specificity,accuracy,Hausdorff distance,Jaccard and dice metrics.
文摘Highly surface active super paramagnetic colloidal suspensions of nano crystalline ferrofluid have been synthesized through wet-chemical route. Entrapment of magnetic domains presented in the nano ferrofluid in a polymer matrix like poly vinyl alcohol film was accomplished by developing polymer composite film in between two magnetic poles by solvent casting method. Similarly poly vinyl alcohol-ferrofluid composite films were also developed in the absence of magnetic field. Atomic force microscopy image of nano-composite film makes it clear that the film developed in the absence of magnetic field possesses randomly oriented domains, whereas film developed with magnetic field shows well aligned flux lines. The characteristics and nature of forces acting between magnetic domains along the magnetic flux lines were explored from magnetic force microscopy imaging. The number of flux lines developed in the polymer matrix was observed to be directly proportional to applied external magnetic field. Approximate number of magnetic lines passing through unit area of composite film was evaluated from line profile data analysis of atomic force microscopy image. The particle sizes of the nanoparticles encapsulated in the polymer matrix were found to be in the range of 10- 20 nm. Scanning electron microscopy micrographs confirm aggregation of ferrofluid particles of ribbon like morphology along the magnetic flux lines. Magnetic properties of the entrapped nanoparticles in polymer matrix film were analyzed using vibrating sample magnetometer at room temperature. The super paramagnetic nature and other magnetic properties were evaluated from the hysteresis loop.
文摘Brain Hemorrhagic stroke is a serious malady that is caused by the drop in blood flow through the brain and causes the brain to malfunction.Precise segmentation of brain hemorrhage is crucial,so an enhanced segmentation is carried out in this research work.The brain image of various patients has taken using an MRI scanner by the utilization of T1,T2,and FLAIR sequence.This work aims to segment the Brain Hemorrhagic stroke using deep learning-based Multi-resolution UNet(multires UNet)through morphological operations.It is hard to precisely segment the brain lesions to extract the existing region of stroke.This crucial step is accomplished by this proposed MMU-Net methodology by precise segmentation of stroke lesions.The proposed method efficiently determines the hemorrhagic stroke with improved accuracy of 95%compared with the existing segmentation techniques such as U-net++,ResNet,Multires UNET and 3D-ResU-Net and also provides improved performance of 2D and 3D U-Net with an enhanced outcome.The performancemeasure of the proposed methodology acquires an improved accuracy,precision ratio,sensitivity,and specificity rate of 0.07%,0.04%,0.04%,and 0.05%in comparison to U-net,ResNet,Multires UNET and 3D-ResU-Net techniques respectively.
基金supported by Dr S Karthik,SRM Institute of Science and TechnologySRM Institute of Science and Technology,Vadapalani Campus,Chennai,Tamilnadu,India。
文摘The difference between circuit design stage and time requirements has broadened with the increasing complexity of the circuit.A big database is needed to undertake important analytical work like statistical method,heat research,and IR-drop research that results in extended running times.This unit focuses on the assessment of test strength.Because of the enormous number of successful designs for currentmodels and the unnecessary time required for every test,maximum energy ratings with all tests cannot be achieved.Nevertheless,test safety is important for producing trustworthy findings to avoid loss of output and harm to the chip.Generally,effective power assessment is only possible in a limited sample of pre-selected experiments.Thus,a key objective is to find the experiments that might give the worst situations again for testing power.It offers a machine-based circuit power estimation(MLCPE)system for the selection of exams.Two distinct techniques of predicting are utilized.Firstly,to find testings with power dissipation,it forecasts the behavior of testing.Secondly,the changemovement and energy data are linked to the semiconductor design,identifying small problem areas.Several types of algorithms are utilized.In particular,the methods compared.The findings show great accuracy and efficiency in forecasting.That enables such methods suitable for selecting the worst scenario.
文摘In the digital era,the Narrowband Internet of Things(Nb-IoT)influ-ences the massive Machine-Type-Communication(mMTC)features to establish secure routing among the 5G/6G mobile networks.It supports global coverage to the low-cost IoT devices distributed in terrestrial networks.Its key traffic char-acteristics include robust uplink,moderate data rate/device,extremely high energy efficiency,prolonging device lifetime,and Quality of Service(QoS).This paper proposes a Deep Reinforcement Learning(DRL)combined software-defined air interface algorithm applied on the switching system,satisfying the user require-ment and enabling them with the network resources to extend quality of service by choosing the most appropriate quality of service metric.In this framework,Non-Orthogonal Multiple Accesses(NOMA)and Rate-Splitting Multiple Access(RSMA)are combined to accommodate massive(Nb-IoT)devices that can be uti-lized the entire resource(frequency band)for tackling the unknown dynamics pro-hibitive.The proposed algorithm instantly assigns the network resources per user requirements and enhances selecting the best quality of service metric optimiza-tion.Therefore,it has potential benefits of high scalability,low latency,energy efficiency,and spectrum utility.
基金Supported by the Key Project of the National Natural Science Foundation of China(61036004)the National Natural Science Foundation of China under Grant Nos 61274096 and 61204043+1 种基金the Guangdong Natural Science Foundation(S2012010010533)the Fundamental Research Project of Shenzhen Science&Technology Foundation(JC201105180786A).
文摘We comparatively study two representative ballistic transport models of nanowire metal-oxide-semiconductor field effect transistors,i.e.the Natori model and the Jiménez model.The limitations and applicability of both the models are discussed.Then the Jiménez model is extended to include atomic dispersion relations and is compared with the Natori model from the aspects of ballistic current and quantum capacitance.It is found that the Jiménez model can produce similar results compared with the more complex Natori model even at very small nanowire dimensions.
文摘Internet of Things(IoT)is a popular social network in which devices are virtually connected for communicating and sharing information.This is applied greatly in business enterprises and government sectors for delivering the services to their customers,clients and citizens.But,the interaction is success-ful only based on the trust that each device has on another.Thus trust is very much essential for a social network.As Internet of Things have access over sen-sitive information,it urges to many threats that lead data management to risk.This issue is addressed by trust management that help to take decision about trust-worthiness of requestor and provider before communication and sharing.Several trust-based systems are existing for different domain using Dynamic weight meth-od,Fuzzy classification,Bayes inference and very few Regression analysis for IoT.The proposed algorithm is based on Logistic Regression,which provide strong statistical background to trust prediction.To make our stand strong on regression support to trust,we have compared the performance with equivalent sound Bayes analysis using Beta distribution.The performance is studied in simu-lated IoT setup with Quality of Service(QoS)and Social parameters for the nodes.The proposed model performs better in terms of various metrics.An IoT connects heterogeneous devices such as tags and sensor devices for sharing of information and avail different application services.The most salient features of IoT system is to design it with scalability,extendibility,compatibility and resiliency against attack.The existing worksfinds a way to integrate direct and indirect trust to con-verge quickly and estimate the bias due to attacks in addition to the above features.
文摘System Identification becomes very crucial in the field of nonlinear and dynamic systems or practical systems.As most practical systems don’t have prior information about the system behaviour thus,mathematical modelling is required.The authors have proposed a stacked Bidirectional Long-Short Term Memory(Bi-LSTM)model to handle the problem of nonlinear dynamic system identification in this paper.The proposed model has the ability of faster learning and accurate modelling as it can be trained in both forward and backward directions.The main advantage of Bi-LSTM over other algorithms is that it processes inputs in two ways:one from the past to the future,and the other from the future to the past.In this proposed model a backward-running Long-Short Term Memory(LSTM)can store information from the future along with application of two hidden states together allows for storing information from the past and future at any moment in time.The proposed model is tested with a recorded speech signal to prove its superiority with the performance being evaluated through Mean Square Error(MSE)and Root Means Square Error(RMSE).The RMSE and MSE performances obtained by the proposed model are found to be 0.0218 and 0.0162 respectively for 500 Epochs.The comparison of results and further analysis illustrates that the proposed model achieves better performance over other models and can obtain higher prediction accuracy along with faster convergence speed.
文摘Through the research on the existing C-MANTEC neural network and PID control technology, this paper presents an improved C-MANTEC algorithm based on PID control system. The combining of the artificial neural networks with conventional PID control helps in exploring their respective advantages to forming the intelligent PID control. From UCI Repository cancer dataset, the developed system is tested. The results show that the scheme can not only improve the speed of the algorithm in the training process but also improve the generalization capability of the network, which further enhances the performance of PID controllers. The overall power consumed is also reduced to a greater extent.
文摘Memristor is a newly found fourth circuit element for the next generation emerging nonvolatile memory technology. In this paper, design of new type of nonvolatile static random access memory cell is proposed by using a combination of memristor and complemented metal oxide semiconductor. Biolek memristor model and CMOS 180 nm technology are used to form a single cell. By introducing distinct binary logic to avoid safety margin is left for each binary logic output and enables better read/write data integrity. The total power consumption reduces from 0.407 mw (milli-watt) to 0.127 mw which is less than existing memristor based memory cell of the same CMOS technology. Read and write time is also significantly reduced. However, write time is higher than conventional 6T SRAM cell and can be reduced by increasing motion of electron in the memristor. The change of the memristor state is shown by applying piecewise linear input voltage.
文摘New conditions are derived for the l2-stability of time-varying linear and nonlinear discrete-time multiple-input multipleoutput （MIMO） systems, having a linear time time-invariant block with the transfer function F（z）, in negative feedback with a matrix of periodic/aperiodic gains A（k）, k = 0,1, 2,... and a vector of certain classes of non-monotone/monotone nonlinearities φp（-）, without restrictions on their slopes and also not requiring path-independence of their line integrals. The stability conditions, which are derived in the frequency domain, have the following features： i） They involve the positive definiteness of the real part （as evaluated on ｜z｜ = 1） of the product of Г （z） and a matrix multiplier function of z. ii） For periodic A（k）, one class of multiplier functions can be chosen so as to impose no constraint on the rate of variations A（k）, but for aperiodic A（k）, which allows a more general multiplier function, constraints are imposed on certain global averages of the generalized eigenvalues of （A（k ＋ 1）,A（k））, k = 1, 2 iii） They are distinct from and less restrictive than recent results in the literature.
文摘In scaled CMOS processes, the single-event effects generate missing output pulses in Delay-Locked Loop (DLL). Due to its effective sequence detection of the missing pulses in the proposed Error Correction Circuit (ECC) and its portability to be applied to any DLL type, the ECC mitigates the impact of single-event effects and completes its operation with less design complexity without any concern about losing the information. The ECC has been implemented in 180 nm CMOS process and measured the accuracy of mitigation on simulations at LETs up to 100 MeV-cm<sup>2</sup>/mg. The robustness and portability of the mitigation technique are validated through the results obtained by implementing proposed ECC in XilinxArtix 7 FPGA.
文摘This paper presents an automated POCOFAN-POFRAME algorithm thatpartitions large combinational digital VLSI circuits for pseudo exhaustive testing. In thispaper, a simulation framework and partitioning technique are presented to guide VLSIcircuits to work under with fewer test vectors in order to reduce testing time and todevelop VLSI circuit designs. This framework utilizes two methods of partitioningPrimary Output Cone Fanout Partitioning (POCOFAN) and POFRAME partitioning todetermine number of test vectors in the circuit. The key role of partitioning is to identifyreconvergent fanout branch pairs and the optimal value of primary input node N andfanout F partitioning using I-PIFAN algorithm. The number of reconvergent fanout andits locations are critical for testing of VLSI circuits and design for testability. Hence, theirselection is crucial in order to optimize system performance and reliability. In the presentwork, the design constraints of the partitioned circuit considered for optimizationincludes critical path delay and test time. POCOFAN-POFRAME algorithm uses theparameters with optimal values of circuits maximum primary input cone size (N) andminimum fan-out value (F) to determine the number of test vectors, number of partitionsand its locations. The ISCAS’85 benchmark circuits have been successfully partitioned,the test results of C499 shows 45% reduction in the test vectors and the experimentalresults are compared with other partitioning methods, our algorithm makes fewer testvectors.
文摘Emerging 5G communication solutions utilize the millimeter wave(mmWave)band to alleviate the spectrum deficit.In the mmWave range,Multiple Input Multiple Output(MIMO)technologies support a large number of simultaneous users.In mmWave MIMO wireless systems,hybrid analog/digital precoding topologies provide a reduced complexity substitute for digital precoding.Bit Error Rate(BER)and Spectral efficiency performances can be improved by hybrid Minimum Mean Square Error(MMSE)precoding,but the computation involves matrix inversion process.The number of antennas at the broadcasting and receiving ends is quite large for mm-wave MIMO systems,thus computing the inverse of a matrix of such high dimension may not be practically feasible.Due to the need for matrix inversion and known candidate matrices,the classic Orthogonal Matching Pursuit(OMP)approach will be more complicated.The novelty of research presented in this manuscript is to create a hybrid precoder for mmWave communication systems using metaheuristic algorithms that do not require matrix inversion processing.The metaheuristic approach has not employed much in the formulation of a precoder in wireless systems.Five distinct evolutionary algorithms,such as Harris–Hawks Optimization(HHO),Runge–Kutta Optimization(RUN),Slime Mould Algorithm(SMA),Hunger Game Search(HGS)Algorithm and Aquila Optimizer(AO)are considered to design optimal hybrid precoder for downlink transmission and their performances are tested under similar practical conditions.According to simulation studies,the RUN-based precoder performs better than the conventional algorithms and other nature-inspired algorithms based precoding in terms of spectral efficiency and BER.
文摘The 12-lead ECG aids in the diagnosis of myocardial infarction and is helpful in the prediction of cardiovascular disease complications.It does,though,have certain drawbacks.For other electrocardiographic anomalies such as Left Bundle Branch Block and Left Ventricular Hypertrophy syndrome,the ECG signal withMyocardial Infarction is difficult to interpret.These diseases cause variations in the ST portion of the ECG signal.It reduces the clarity of ECG signals,making itmore difficult to diagnose these diseases.As a result,the specialist is misled into making an erroneous diagnosis by using the incorrect therapeutic technique.Based on these concepts,this article reviews the different procedures involved in ECG signal pre-processing,feature extraction,feature selection,and classification techniques to diagnose heart disorders such as LeftVentricularHypertrophy,Bundle Branch Block,andMyocardial Infarction.It reveals the flaws and benefits in each segment,as well as recommendations for developing more advanced and robustmethods for diagnosing these diseases,which will increase the system’s accuracy.The current issues and prospective research directions are also addressed.
文摘Recently,automatic diagnosis of diabetic retinopathy(DR)from the retinal image is the most significant ressearch topic in the medical applications.Diabetic macular edema(DME)is the.major reason for the loss of vision in patients suffering fom DR.Early identification of the DR enables to prevent the vision loss and encourage diabetic control activities.Many techniques are.developed to diagnose the DR.The major drawbacks of the existing techniques are low accuracy and high time complexity.To owercome these issues,this paper propases an enhanced particle swarm optimization differential evolution feature selection(PSO DEFS)based feature selection approach with biometric aut hentication for the identification of DR.Initially,a hybrid median filter(HMF)is used for pre processing the input images.Then,the pre-processed images are embedded with each other by using least significant bit(LSB)for authentication purpose.Si-multaneously,the image features are extracted using convoluted local tetra pattern(CLTrP)and Tamura features.Feature selection is performed using PSO DEFS and PSO-gravitational search algorithm(PSO GSA)to reduce time complexity.Based on some performance metrics,the PSO-DEFS is chosen as a better choice for feature selection.The feature selection is performed based on the fitness value.A multi-relevance vector machine(M-RVM)is introduced to dlassify the 13 normal and 62 abnormal images among 75 images from 60 patients.Finally,the DR patients are further dassified by M-RVM.The experimental results exhibit that the proposed approach achieves better accuracy,sensitivity,and specificity than the exist ing techniques.
