This research presents a Human Lower Limb Activity Recognition(HLLAR)system that identifies specific activities and predicts the angles of the knees simultaneously,based on the EMG signals.The HLLAR systems streamline...This research presents a Human Lower Limb Activity Recognition(HLLAR)system that identifies specific activities and predicts the angles of the knees simultaneously,based on the EMG signals.The HLLAR systems streamlines the research on the lower limb activities.The HILLAR model includes Discrete Hermite Wavelets Transform-based Synchrosqueezing(DHWTS),Deep Two-Layer Multiscale Convolutional Neural Network(DTLMCNN),and Generalized Regression Neural Network(GRNN)as feature extraction,activity recognition,and knee angle prediction respectively.Electromyography signal-based automatic lower limb activity detection is crucial to rehabilitation and human movement analysis.Yet several of these methods face issues in feature extraction in complex data,overlapping signals,extraction of crucial parameters,and adaptation constraints.This research aims classify lower limb activities and predict knee joint angles from electromy-ography signals using HILLAR model.The model is validated on two datasets,comprising 26 subjects performing three classes of activities:walking,standing,and sitting.The proposed model obtained a classification accuracy of 99.95%,along with significant achievements in precision(99.93%),recall(99.91%),and F1-score(99.93%).The generalized regression neural network predicted angles of the knee joint with a root mean squared error of 1.25%.Robustness is demonstrated through consistent results in five-fold cross-validation and statistical significance testing(p-value=0.004,McNemar's test).Additionally,the proposed model showed superior performance over baseline methods by reducing error rates by 18%and decreasing processing time to 0.98 s.展开更多
As healthcare systems increasingly embrace digitalization,effective management of electronic health records(EHRs)has emerged as a critical priority,particularly in inpatient settings where data sensitivity and realtim...As healthcare systems increasingly embrace digitalization,effective management of electronic health records(EHRs)has emerged as a critical priority,particularly in inpatient settings where data sensitivity and realtime access are paramount.Traditional EHR systems face significant challenges,including unauthorized access,data breaches,and inefficiencies in tracking follow-up appointments,which heighten the risk of misdiagnosis and medication errors.To address these issues,this research proposes a hybrid blockchain-based solution for securely managing EHRs,specifically designed as a framework for tracking inpatient follow-ups.By integrating QR codeenabled data access with a blockchain architecture,this innovative approach enhances privacy protection,data integrity,and auditing capabilities,while facilitating swift and real-time data retrieval.The architecture adheres to Role-Based Access Control(RBAC)principles and utilizes robust encryption techniques,including SHA-256 and AES-256-CBC,to secure sensitive information.A comprehensive threat model outlines trust boundaries and potential adversaries,complemented by a validated data transmission protocol.Experimental results demonstrate that the framework remains reliable in concurrent access scenarios,highlighting its efficiency and responsiveness in real-world applications.This study emphasizes the necessity for hybrid solutions in managing sensitive medical information and advocates for integrating blockchain technology and QR code innovations into contemporary healthcare systems.展开更多
Quantum dot cellular automata(QCA)is promising nanotechnology due to the three main advantages:faster speed,nanoscale size,and ultrasmall power consumption.This paper proposed a simple data path selector cum router as...Quantum dot cellular automata(QCA)is promising nanotechnology due to the three main advantages:faster speed,nanoscale size,and ultrasmall power consumption.This paper proposed a simple data path selector cum router as the‘multiplexerchannel-demultiplexer’unit using QCA,an unavoidable building block of nano communication.A Simple 2×2 block and the extended 4×4 block of data path selectors have been proposed in this article.The layouts of the proposed designs have been verified in QCADesigner,and the energy dissipation has been evaluated using two tools,QCAPro and QCQDesigner-E(QDE).The suggested designs reached a significant improvement in cell complexity(cell count)and covered area over the existing designs.In precise,the proposed 2×2(4×4)block shows 86%(63%)lower cell complexity and 87%(37%)smaller area than the prior reported similar designs.In addition,the currently reported 2×2(4×4)unit has 86%(60%)lower QDE based energy dissipation compared with prior reported designs.展开更多
In the recent past power line communication has emerged as an attractive choice for high speed data transfer and is looked upon as inexpensive and reliable media suitable for broadband internet access, home and office...In the recent past power line communication has emerged as an attractive choice for high speed data transfer and is looked upon as inexpensive and reliable media suitable for broadband internet access, home and office automation, in-vehicle data communication etc. In this paper we present an architecture for the physical layer of a PLC transceiver based on Orthogonal Frequency Division Multiplexing (OFDM) and the impact on multipath distortion for PLC transmission in terms of bit error rate. Since there is no standard PLC channel model available, a widely accepted multipath channel model is used for simulation purpose. Simulation results as well as FPGA synthesis verify the effectiveness of the proposed architecture for PLC modem design at 110 Mbps data rate.展开更多
This manuscript explores the behavior of a junctionless tri-gate FinFET at the nano-scale region using SiGe material for the channel.For the analysis,three different channel structures are used:(a)tri-layer stack chan...This manuscript explores the behavior of a junctionless tri-gate FinFET at the nano-scale region using SiGe material for the channel.For the analysis,three different channel structures are used:(a)tri-layer stack channel(TLSC)(Si-SiGe-Si),(b)double layer stack channel(DLSC)(SiGe-Si),(c)single layer channel(SLC)(S_(i)).The I−V characteristics,subthreshold swing(SS),drain-induced barrier lowering(DIBL),threshold voltage(V_(t)),drain current(ION),OFF current(IOFF),and ON-OFF current ratio(ION/IOFF)are observed for the structures at a 20 nm gate length.It is seen that TLSC provides 21.3%and 14.3%more ON current than DLSC and SLC,respectively.The paper also explores the analog and RF factors such as input transconductance(g_(m)),output transconductance(gds),gain(gm/gds),transconductance generation factor(TGF),cut-off frequency(f_(T)),maximum oscillation frequency(f_(max)),gain frequency product(GFP)and linearity performance parameters such as second and third-order harmonics(g_(m2),g_(m3)),voltage intercept points(VIP_(2),VIP_(3))and 1-dB compression points for the three structures.The results show that the TLSC has a high analog performance due to more gm and provides 16.3%,48.4%more gain than SLC and DLSC,respectively and it also provides better linearity.All the results are obtained using the VisualTCAD tool.展开更多
Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manu-facturing environments,enabling scalable and flexible access to remote data centers over the internet.In these ...Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manu-facturing environments,enabling scalable and flexible access to remote data centers over the internet.In these environments,Virtual Machines(VMs)are employed to manage workloads,with their optimal placement on Physical Machines(PMs)being crucial for maximizing resource utilization.However,achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives,particularly in scenarios involving inter-VM communication dependencies,which are common in smart manufacturing applications.This manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle Swarm Optimization(MOPSO)algorithm,enhanced with improved mutation and crossover operators,to efficiently place VMs.This approach aims to minimize the impact on networking devices during inter-VM communication while enhancing resource utilization.The proposed algorithm is benchmarked against other multi-objective algorithms,such as Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),demonstrating its superiority in optimizing resource allocation in cloud-based environments for smart manufacturing.展开更多
At 12.8 MHz center frequency,the advanced miniaturized polymer-based planar high quality factor(Q)passive elements embedded bandpassfilter works in the L-band.Because most of the demands operate inside the spectrum,the...At 12.8 MHz center frequency,the advanced miniaturized polymer-based planar high quality factor(Q)passive elements embedded bandpassfilter works in the L-band.Because most of the demands operate inside the spectrum,the wideband or high-speed operation necessary to enhance must be acquired in microwave frequency ranges.The channel has a quiet,high-performance micro-filter with wideband rejection.Capacitors and inductors are used in the high quality factor(Q)passive components,and related networks are incorporated in thefilter.Embedded layers are concatenated using Three-Dimensional Integrated Circuit(3D-IC)integration,parasitics are removed,and interconnection losses are negotiated using de-embedding methods.A wireless application-based Liquid Crystalline Polymer(LCP)viewpoint is employed as a substrate material in this work.The polymer processes,their properties,and the incorporated high-Q Band Pass Filter Framework.The suggestedfilter model is computed and manufactured utilizing the L-band frequency spectrum,decreasing total physical length by 31%while increasing bandwidth by 45%.展开更多
Currently, communication system requires multiband small antennas for 5G mobile applications. Driven this motivation, this paper proposes a multiband patch antenna for Wi-Fi, WiMAX and 5G applications. The proposed an...Currently, communication system requires multiband small antennas for 5G mobile applications. Driven this motivation, this paper proposes a multiband patch antenna for Wi-Fi, WiMAX and 5G applications. The proposed antenna can effectively operate at 2.4 GHz as Wi-Fi, 7.8 GHz as WiMAX and 33.5 GHz as 5G communication purposes. The proposed antenna arrays have given directional radiation patterns, very small voltage standing wave ratio, high gain (VSWR) and directivity for each aforementioned systems operating frequency. This antenna is made for multiband purpose which can be effective for not only Wi-Fi and WiMAX but also 5G applications.展开更多
This paper deals with the design of bidirectional coupler for broadband power line communication and the impedance matching technique with the power line. This coupler can be used for both transmitting and receiving t...This paper deals with the design of bidirectional coupler for broadband power line communication and the impedance matching technique with the power line. This coupler can be used for both transmitting and receiving the data, acting as transceiver. The impedance mismatching problem is also solved here using line trap circuit. The coupler circuit is capable of transmitting or receiving modulated signals with carrier frequency of 15 MHz which can be used for domestic as well as distribution power networks. Laboratory prototype tested using power line network consists of electrical household appliances and results show that the circuit is able to facilitate bidirectional band pass transmission.展开更多
Machine learning and pattern recognition contains well-defined algorithms with the help of complex data, provides the accuracy of the traffic levels, heavy traffic hours within a cluster. In this paper the base statio...Machine learning and pattern recognition contains well-defined algorithms with the help of complex data, provides the accuracy of the traffic levels, heavy traffic hours within a cluster. In this paper the base stations and also the noise levels in the busy hour can be predicted. J48 pruned tree contains 23 nodes with busy traffic hour provided in east Godavari. Signal to noise ratio has been predicted at 55, based on CART results. About 53% instances provided inside the cluster and 47% provided outside the cluster. DBScan clustering provided maximum noise from srikakulam. MOR (Number of originating calls successful) predicted as best associated attribute based on Apriori and Genetic search 12:1 ratio.展开更多
＂Communication project budget＂ is a professional course of the communication engineering specialty. Combined with the requirements of the teaching reform, from three aspects of the theoretical teaching, the experime...＂Communication project budget＂ is a professional course of the communication engineering specialty. Combined with the requirements of the teaching reform, from three aspects of the theoretical teaching, the experimental teaching, and the engineering practice, the author carries on the reform and practice, to cultivate the active thinking and practical ability of the students, and the effects of the re-form are fully in line with the orientation of cultivating the highly-skillful and applied talents.展开更多
Assessing the behaviour and concentration of waste pollutants deposited between two parallel plates is essential for effective environmental management.Determining the effectiveness of treatment methods in reducing po...Assessing the behaviour and concentration of waste pollutants deposited between two parallel plates is essential for effective environmental management.Determining the effectiveness of treatment methods in reducing pollution scales is made easier by analysing waste discharge concentrations.The waste discharge concentration analysis is useful for assessing how effectively wastewater treatment techniques reduce pollution levels.This study aims to explore the Casson micropolar fluid flow through two parallel plates with the influence of pollutant concentration and thermophoretic particle deposition.To explore the mass and heat transport features,thermophoretic particle deposition and thermal radiation are considered.The governing equations are transformed into ordinary differential equations with the help of suitable similarity transformations.The Runge-Kutta-Fehlberg’s fourthfifth order technique and shooting procedure are used to solve the reduced set of equations and boundary conditions.The integration of a neural network model based on the Levenberg-Marquardt algorithm serves to improve the accuracy of predictions and optimize the analysis of parameters.Graphical outcomes are displayed to analyze the characteristics of the relevant dimensionless parameters in the current problem.Results reveal that concentration upsurges as the micropolar parameter increases.The concentration reduces with an upsurge in the thermophoretic parameter.An upsurge in the external pollutant source variation and the local pollutant external source parameters enhances mass transport.The surface drag force declines for improved values of porosity and micropolar parameters.展开更多
This paper proposes a linear companding transform(CT)using either a single inflection point or two inflection points to reduce the peakto-average power ratio(PAPR)in orthogonal timefrequency space(OTFS)signals.The CT ...This paper proposes a linear companding transform(CT)using either a single inflection point or two inflection points to reduce the peakto-average power ratio(PAPR)in orthogonal timefrequency space(OTFS)signals.The CT strategically compresses higher amplitudes and enhances lower amplitudes based on carefully chosen scaling factors and points of inflection.With these selected parameters,the CT effectively reduces peak power while maintaining average power,leading to a substantial decrease in PAPR.We analyze noise changes in the inverse companding transform(ICT)process.The analysis reveals that the ICT amplifies less than 20%of the total noise.A convolutional encoder and soft decision Viterbi decoding algorithm are utilized in the OTFS system to improve the detection performance.We present simulation results focusing on PAPR reduction and bit error rate(BER)performance.These results demonstrate that the CT with two inflection points outperforms both the single inflection point case and the existingμ-law companding,clipping,peak windowing,unique OTFS frame structure,selected mapping,and partial transmit sequence methods,achieving significant PAPR reduction and BER performance.展开更多
Wireless Sensor Network(WSN)comprises a set of interconnected,compact,autonomous,and resource-constrained sensor nodes that are wirelessly linked to monitor and gather data from the physical environment.WSNs are commo...Wireless Sensor Network(WSN)comprises a set of interconnected,compact,autonomous,and resource-constrained sensor nodes that are wirelessly linked to monitor and gather data from the physical environment.WSNs are commonly used in various applications such as environmental monitoring,surveillance,healthcare,agriculture,and industrial automation.Despite the benefits of WSN,energy efficiency remains a challenging problem that needs to be addressed.Clustering and routing can be considered effective solutions to accomplish energy efficiency in WSNs.Recent studies have reported that metaheuristic algorithms can be applied to optimize cluster formation and routing decisions.This study introduces a new Northern Goshawk Optimization with boosted coati optimization algorithm for cluster-based routing(NGOBCO-CBR)method for WSN.The proposed NGOBCO-CBR method resolves the hot spot problem,uneven load balancing,and energy consumption in WSN.The NGOBCO-CBR technique comprises two major processes such as NGO based clustering and BCO-based routing.In the initial phase,the NGObased clustering method is designed for cluster head(CH)selection and cluster construction using five input variables such as residual energy(RE),node proximity,load balancing,network average energy,and distance to BS(DBS).Besides,the NGOBCO-CBR technique applies the BCO algorithm for the optimum selection of routes to BS.The experimental results of the NGOBCOCBR technique are studied under different scenarios,and the obtained results showcased the improved efficiency of the NGOBCO-CBR technique over recent approaches in terms of different measures.展开更多
Physiological signals such as electroencephalogram(EEG)signals are often corrupted by artifacts during the acquisition and processing.Some of these artifacts may deteriorate the essential properties of the signal that...Physiological signals such as electroencephalogram(EEG)signals are often corrupted by artifacts during the acquisition and processing.Some of these artifacts may deteriorate the essential properties of the signal that pertains to meaningful information.Most of these artifacts occur due to the involuntary movements or actions the human does during the acquisition process.So,it is recommended to eliminate these artifacts with signal processing approaches.This paper presents two mechanisms of classification and elimination of artifacts.In the first step,a customized deep network is employed to classify clean EEG signals and artifact-included signals.The classification is performed at the feature level,where common space pattern features are extracted with convolutional layers,and these features are later classified with a support vector machine classifier.In the second stage of the work,the artifact signals are decomposed with empirical mode decomposition,and they are then eliminated with the proposed adaptive thresholding mechanism where the threshold value changes for every intrinsic mode decomposition in the iterative mechanism.展开更多
Subspace-based signal processing methods are fundamentally pre-trained Artificial Neural Networks(ANN)that provide the basic structure for numerous computer vision applications and explore the most promising Earth Obs...Subspace-based signal processing methods are fundamentally pre-trained Artificial Neural Networks(ANN)that provide the basic structure for numerous computer vision applications and explore the most promising Earth Observation Applications(EOA).This paper examines the fundamentals of subspacebased methods and explores the most promising algorithm for forecasting ionospheric signal delays,which was designed explicitly regarding signal and noise subspaces.The learning efficiency derived from the subspace-based components of Singular Spectrum Analysis(SSA)significantly influences the implementation of Linear Recurrent Formula(LRF)and ANN models.The proposed study introduces a novel enhancement to LRF and ANN methodologies for Global Positioning System(GPS)-Total Electron Content(TEC)forecasts based on SSA.The GPS-derived TEC at Bangalore(13.02°N and 77.57°E)location grid during sunspot cycle 25(2020)is considered for analysis.The SSA-LRF-ANN model demonstrates superior accuracy compared with the SSA-LRF,Autoregressive Moving Average(ARMA),and Holt-Winter(HW)models,achieving a correlation of 0.99,a Mean Absolute Error(MAE)of 0.55 TECU,a Mean Absolute Percentage Error(MAPE)of 7.06%,and a Root Mean Square Error(RMSE)of 0.75 TECU.Furthermore,the results and discussions section presents numerical illustrations that showcase the practical implementation of the SSA-LRF-ANN and its application.展开更多
The usage of electric vehicles holds a crucial role in lowering the diminishing of the ozone layer because electric vehicles are not dependent on fossil fuels.With more research,evaluation,and its characteristics on e...The usage of electric vehicles holds a crucial role in lowering the diminishing of the ozone layer because electric vehicles are not dependent on fossil fuels.With more research,evaluation,and its characteristics on electric vehicles,the infrastructure of charging points,production of electric vehicles,and network modelling,this paper provides a comprehensive overview of electric vehicles,and hybrid vehicles,including an analysis of their market growth,as well as different types of optimization used in the current scenario.In developing countries like India,the biggest barrier is their unfulfilled facility over the charging.Without renewable energy sources,vehicle-to-grid technology facilitates the enhancement of additional power requirements.The mobility factor has been considered an important and special characteristic of electric vehicles.展开更多
Integration of artificial intelligence in image processing methods has significantly improved the accuracy of the medical diagnostics pathway for early detection and analysis of kidney tumors.Computer-assisted image a...Integration of artificial intelligence in image processing methods has significantly improved the accuracy of the medical diagnostics pathway for early detection and analysis of kidney tumors.Computer-assisted image analysis can be an effective tool for early diagnosis of soft tissue tumors located remotely or in inaccessible anatomical locations.In this review,we discuss computer-based image processing methods using deep learning,convolutional neural networks(CNNs),radiomics,and transformer-based methods for kidney tumors.These techniques hold significant potential for automated segmentation,classification,and prognostic estimation with high accuracy,enabling more precise and personalized treatment planning.Special focus is given to Vision Transformers(ViTs),Explainable AI(XAI),Federated Learning(FL),and 3D kidney image analysis.Additionally,the strengths and limitations of the established models are compared with recent techniques to understand both clinical and computational challenges that remain unresolved.Finally,the future directions for enhancing diagnostic precision,streamlining physician workflows,and image-guided intervention for decision support are proposed.展开更多
Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems(ADAS)and autonomous driving,enabling robust environmental perception through precise range-Doppler and angular measurements.It...Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems(ADAS)and autonomous driving,enabling robust environmental perception through precise range-Doppler and angular measurements.It plays a pivotal role in enhancing road safety by supporting accurate detection and localization of surrounding objects.However,real-world deployment of automotive radar faces significant challenges,including mutual interference among radar units and dense clutter due to multiple dynamic targets,which demand advanced signal processing solutions beyond conventional methodologies.This paper presents a comprehensive review of traditional signal processing techniques and recent advancements specifically designed to address contemporary operational challenges in automotive radar.Emphasis is placed on direction-of-arrival(DoA)estimation algorithms such as Bartlett beamforming,Minimum Variance Distortionless Response(MVDR),Multiple Signal Classification(MUSIC),and Estimation of Signal Parameters via Rotational Invariance Techniques(ESPRIT).Among these,ESPRIT offers superior resolution for multi-target scenarios with reduced computational complexity compared to MUSIC,making it particularly advantageous for real-time applications.Furthermore,the study evaluates state-of-the-art tracking algorithms,including the Kalman Filter(KF),Extended KF(EKF),Unscented KF,and Bayesian filter.EKF is especially suitable for radar systems due to its capability to linearize nonlinear measurement models.The integration of machine learning approaches for target detection and classification is also discussed,highlighting the trade-off between the simplicity of implementation in K-Nearest Neighbors(KNN)and the enhanced accuracy provided by Support Vector Machines(SVM).A brief overview of benchmark radar datasets,performance metrics,and relevant standards is included to support future research.The paper concludes by outlining ongoing challenges and identifying promising research directions in automotive radar signal processing,particularly in the context of increasingly complex traffic scenarios and autonomous navigation systems.展开更多
The article by Wu et al highlights the growing incidence of esophageal tumor patients,particularly in China,where the high frequency and death rate are significant problems.The article also examined the impact of heal...The article by Wu et al highlights the growing incidence of esophageal tumor patients,particularly in China,where the high frequency and death rate are significant problems.The article also examined the impact of health insurance on treatment availability and patient outcomes,demonstrating that the type of insurance can affect the financial burden on patients.This study investigates the effects of different types of health care coverage,namely Urban Employee Basic Medical Insurance vs Urban-Rural Resident Basic Medical Insurance,and the personal spending ratio on treatment decisions and survival outcomes.The database used is derived from esophageal tumor patient continuation from Chongqing University Hospital in China.A total of 2543 patients were included in the study,allowing for the formation of research cohorts.Patient information included demographic characteristics.The study followed various processes to maintain consistency,including data sources,inclusion and exclusion criteria,follow-up duration,health insurance,and statistical analysis.The average age at diagnosis ranged from 57-74 years,and predominantly included men,married people,and those of Han ethnic background,comprising 2088 and 2519 individuals,respectively.Upon controlling for age,sex,relationship status,country of origin,pathological evaluation,tumor stage,and biochemical indicators,individuals who had Urban Employee Basic Medical Insurance exhibited a higher propensity to opt for radiotherapy,chemotherapy,immunotherapy,and targeted therapy compared to those covered by the Urban-Rural Resident Basic Medical Insurance.During the follow-up phase of the study,a total of 1438 deaths were documented,with 1106 ascribed to esophageal cancer.Additionally,individuals with Urban-Rural Resident Basic Medical Insurance had a significantly elevated risk of esophageal cancer,particularly mortality,compared to those without Urban-Rural Resident Basic Medical Insurance.展开更多
文摘This research presents a Human Lower Limb Activity Recognition(HLLAR)system that identifies specific activities and predicts the angles of the knees simultaneously,based on the EMG signals.The HLLAR systems streamlines the research on the lower limb activities.The HILLAR model includes Discrete Hermite Wavelets Transform-based Synchrosqueezing(DHWTS),Deep Two-Layer Multiscale Convolutional Neural Network(DTLMCNN),and Generalized Regression Neural Network(GRNN)as feature extraction,activity recognition,and knee angle prediction respectively.Electromyography signal-based automatic lower limb activity detection is crucial to rehabilitation and human movement analysis.Yet several of these methods face issues in feature extraction in complex data,overlapping signals,extraction of crucial parameters,and adaptation constraints.This research aims classify lower limb activities and predict knee joint angles from electromy-ography signals using HILLAR model.The model is validated on two datasets,comprising 26 subjects performing three classes of activities:walking,standing,and sitting.The proposed model obtained a classification accuracy of 99.95%,along with significant achievements in precision(99.93%),recall(99.91%),and F1-score(99.93%).The generalized regression neural network predicted angles of the knee joint with a root mean squared error of 1.25%.Robustness is demonstrated through consistent results in five-fold cross-validation and statistical significance testing(p-value=0.004,McNemar's test).Additionally,the proposed model showed superior performance over baseline methods by reducing error rates by 18%and decreasing processing time to 0.98 s.
基金funded by Multimedia University,Cyberjaya,Selangor,Malaysia(Grant Number:PostDoc(MMUI/240029)).
文摘As healthcare systems increasingly embrace digitalization,effective management of electronic health records(EHRs)has emerged as a critical priority,particularly in inpatient settings where data sensitivity and realtime access are paramount.Traditional EHR systems face significant challenges,including unauthorized access,data breaches,and inefficiencies in tracking follow-up appointments,which heighten the risk of misdiagnosis and medication errors.To address these issues,this research proposes a hybrid blockchain-based solution for securely managing EHRs,specifically designed as a framework for tracking inpatient follow-ups.By integrating QR codeenabled data access with a blockchain architecture,this innovative approach enhances privacy protection,data integrity,and auditing capabilities,while facilitating swift and real-time data retrieval.The architecture adheres to Role-Based Access Control(RBAC)principles and utilizes robust encryption techniques,including SHA-256 and AES-256-CBC,to secure sensitive information.A comprehensive threat model outlines trust boundaries and potential adversaries,complemented by a validated data transmission protocol.Experimental results demonstrate that the framework remains reliable in concurrent access scenarios,highlighting its efficiency and responsiveness in real-world applications.This study emphasizes the necessity for hybrid solutions in managing sensitive medical information and advocates for integrating blockchain technology and QR code innovations into contemporary healthcare systems.
文摘Quantum dot cellular automata(QCA)is promising nanotechnology due to the three main advantages:faster speed,nanoscale size,and ultrasmall power consumption.This paper proposed a simple data path selector cum router as the‘multiplexerchannel-demultiplexer’unit using QCA,an unavoidable building block of nano communication.A Simple 2×2 block and the extended 4×4 block of data path selectors have been proposed in this article.The layouts of the proposed designs have been verified in QCADesigner,and the energy dissipation has been evaluated using two tools,QCAPro and QCQDesigner-E(QDE).The suggested designs reached a significant improvement in cell complexity(cell count)and covered area over the existing designs.In precise,the proposed 2×2(4×4)block shows 86%(63%)lower cell complexity and 87%(37%)smaller area than the prior reported similar designs.In addition,the currently reported 2×2(4×4)unit has 86%(60%)lower QDE based energy dissipation compared with prior reported designs.
文摘In the recent past power line communication has emerged as an attractive choice for high speed data transfer and is looked upon as inexpensive and reliable media suitable for broadband internet access, home and office automation, in-vehicle data communication etc. In this paper we present an architecture for the physical layer of a PLC transceiver based on Orthogonal Frequency Division Multiplexing (OFDM) and the impact on multipath distortion for PLC transmission in terms of bit error rate. Since there is no standard PLC channel model available, a widely accepted multipath channel model is used for simulation purpose. Simulation results as well as FPGA synthesis verify the effectiveness of the proposed architecture for PLC modem design at 110 Mbps data rate.
文摘This manuscript explores the behavior of a junctionless tri-gate FinFET at the nano-scale region using SiGe material for the channel.For the analysis,three different channel structures are used:(a)tri-layer stack channel(TLSC)(Si-SiGe-Si),(b)double layer stack channel(DLSC)(SiGe-Si),(c)single layer channel(SLC)(S_(i)).The I−V characteristics,subthreshold swing(SS),drain-induced barrier lowering(DIBL),threshold voltage(V_(t)),drain current(ION),OFF current(IOFF),and ON-OFF current ratio(ION/IOFF)are observed for the structures at a 20 nm gate length.It is seen that TLSC provides 21.3%and 14.3%more ON current than DLSC and SLC,respectively.The paper also explores the analog and RF factors such as input transconductance(g_(m)),output transconductance(gds),gain(gm/gds),transconductance generation factor(TGF),cut-off frequency(f_(T)),maximum oscillation frequency(f_(max)),gain frequency product(GFP)and linearity performance parameters such as second and third-order harmonics(g_(m2),g_(m3)),voltage intercept points(VIP_(2),VIP_(3))and 1-dB compression points for the three structures.The results show that the TLSC has a high analog performance due to more gm and provides 16.3%,48.4%more gain than SLC and DLSC,respectively and it also provides better linearity.All the results are obtained using the VisualTCAD tool.
基金funded by Researchers Supporting Project Number(RSPD2025R 947),King Saud University,Riyadh,Saudi Arabia.
文摘Cloud computing has emerged as a vital platform for processing resource-intensive workloads in smart manu-facturing environments,enabling scalable and flexible access to remote data centers over the internet.In these environments,Virtual Machines(VMs)are employed to manage workloads,with their optimal placement on Physical Machines(PMs)being crucial for maximizing resource utilization.However,achieving high resource utilization in cloud data centers remains a challenge due to multiple conflicting objectives,particularly in scenarios involving inter-VM communication dependencies,which are common in smart manufacturing applications.This manuscript presents an AI-driven approach utilizing a modified Multi-Objective Particle Swarm Optimization(MOPSO)algorithm,enhanced with improved mutation and crossover operators,to efficiently place VMs.This approach aims to minimize the impact on networking devices during inter-VM communication while enhancing resource utilization.The proposed algorithm is benchmarked against other multi-objective algorithms,such as Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),demonstrating its superiority in optimizing resource allocation in cloud-based environments for smart manufacturing.
文摘At 12.8 MHz center frequency,the advanced miniaturized polymer-based planar high quality factor(Q)passive elements embedded bandpassfilter works in the L-band.Because most of the demands operate inside the spectrum,the wideband or high-speed operation necessary to enhance must be acquired in microwave frequency ranges.The channel has a quiet,high-performance micro-filter with wideband rejection.Capacitors and inductors are used in the high quality factor(Q)passive components,and related networks are incorporated in thefilter.Embedded layers are concatenated using Three-Dimensional Integrated Circuit(3D-IC)integration,parasitics are removed,and interconnection losses are negotiated using de-embedding methods.A wireless application-based Liquid Crystalline Polymer(LCP)viewpoint is employed as a substrate material in this work.The polymer processes,their properties,and the incorporated high-Q Band Pass Filter Framework.The suggestedfilter model is computed and manufactured utilizing the L-band frequency spectrum,decreasing total physical length by 31%while increasing bandwidth by 45%.
文摘Currently, communication system requires multiband small antennas for 5G mobile applications. Driven this motivation, this paper proposes a multiband patch antenna for Wi-Fi, WiMAX and 5G applications. The proposed antenna can effectively operate at 2.4 GHz as Wi-Fi, 7.8 GHz as WiMAX and 33.5 GHz as 5G communication purposes. The proposed antenna arrays have given directional radiation patterns, very small voltage standing wave ratio, high gain (VSWR) and directivity for each aforementioned systems operating frequency. This antenna is made for multiband purpose which can be effective for not only Wi-Fi and WiMAX but also 5G applications.
文摘This paper deals with the design of bidirectional coupler for broadband power line communication and the impedance matching technique with the power line. This coupler can be used for both transmitting and receiving the data, acting as transceiver. The impedance mismatching problem is also solved here using line trap circuit. The coupler circuit is capable of transmitting or receiving modulated signals with carrier frequency of 15 MHz which can be used for domestic as well as distribution power networks. Laboratory prototype tested using power line network consists of electrical household appliances and results show that the circuit is able to facilitate bidirectional band pass transmission.
文摘Machine learning and pattern recognition contains well-defined algorithms with the help of complex data, provides the accuracy of the traffic levels, heavy traffic hours within a cluster. In this paper the base stations and also the noise levels in the busy hour can be predicted. J48 pruned tree contains 23 nodes with busy traffic hour provided in east Godavari. Signal to noise ratio has been predicted at 55, based on CART results. About 53% instances provided inside the cluster and 47% provided outside the cluster. DBScan clustering provided maximum noise from srikakulam. MOR (Number of originating calls successful) predicted as best associated attribute based on Apriori and Genetic search 12:1 ratio.
文摘＂Communication project budget＂ is a professional course of the communication engineering specialty. Combined with the requirements of the teaching reform, from three aspects of the theoretical teaching, the experimental teaching, and the engineering practice, the author carries on the reform and practice, to cultivate the active thinking and practical ability of the students, and the effects of the re-form are fully in line with the orientation of cultivating the highly-skillful and applied talents.
文摘Assessing the behaviour and concentration of waste pollutants deposited between two parallel plates is essential for effective environmental management.Determining the effectiveness of treatment methods in reducing pollution scales is made easier by analysing waste discharge concentrations.The waste discharge concentration analysis is useful for assessing how effectively wastewater treatment techniques reduce pollution levels.This study aims to explore the Casson micropolar fluid flow through two parallel plates with the influence of pollutant concentration and thermophoretic particle deposition.To explore the mass and heat transport features,thermophoretic particle deposition and thermal radiation are considered.The governing equations are transformed into ordinary differential equations with the help of suitable similarity transformations.The Runge-Kutta-Fehlberg’s fourthfifth order technique and shooting procedure are used to solve the reduced set of equations and boundary conditions.The integration of a neural network model based on the Levenberg-Marquardt algorithm serves to improve the accuracy of predictions and optimize the analysis of parameters.Graphical outcomes are displayed to analyze the characteristics of the relevant dimensionless parameters in the current problem.Results reveal that concentration upsurges as the micropolar parameter increases.The concentration reduces with an upsurge in the thermophoretic parameter.An upsurge in the external pollutant source variation and the local pollutant external source parameters enhances mass transport.The surface drag force declines for improved values of porosity and micropolar parameters.
文摘This paper proposes a linear companding transform(CT)using either a single inflection point or two inflection points to reduce the peakto-average power ratio(PAPR)in orthogonal timefrequency space(OTFS)signals.The CT strategically compresses higher amplitudes and enhances lower amplitudes based on carefully chosen scaling factors and points of inflection.With these selected parameters,the CT effectively reduces peak power while maintaining average power,leading to a substantial decrease in PAPR.We analyze noise changes in the inverse companding transform(ICT)process.The analysis reveals that the ICT amplifies less than 20%of the total noise.A convolutional encoder and soft decision Viterbi decoding algorithm are utilized in the OTFS system to improve the detection performance.We present simulation results focusing on PAPR reduction and bit error rate(BER)performance.These results demonstrate that the CT with two inflection points outperforms both the single inflection point case and the existingμ-law companding,clipping,peak windowing,unique OTFS frame structure,selected mapping,and partial transmit sequence methods,achieving significant PAPR reduction and BER performance.
文摘Wireless Sensor Network(WSN)comprises a set of interconnected,compact,autonomous,and resource-constrained sensor nodes that are wirelessly linked to monitor and gather data from the physical environment.WSNs are commonly used in various applications such as environmental monitoring,surveillance,healthcare,agriculture,and industrial automation.Despite the benefits of WSN,energy efficiency remains a challenging problem that needs to be addressed.Clustering and routing can be considered effective solutions to accomplish energy efficiency in WSNs.Recent studies have reported that metaheuristic algorithms can be applied to optimize cluster formation and routing decisions.This study introduces a new Northern Goshawk Optimization with boosted coati optimization algorithm for cluster-based routing(NGOBCO-CBR)method for WSN.The proposed NGOBCO-CBR method resolves the hot spot problem,uneven load balancing,and energy consumption in WSN.The NGOBCO-CBR technique comprises two major processes such as NGO based clustering and BCO-based routing.In the initial phase,the NGObased clustering method is designed for cluster head(CH)selection and cluster construction using five input variables such as residual energy(RE),node proximity,load balancing,network average energy,and distance to BS(DBS).Besides,the NGOBCO-CBR technique applies the BCO algorithm for the optimum selection of routes to BS.The experimental results of the NGOBCOCBR technique are studied under different scenarios,and the obtained results showcased the improved efficiency of the NGOBCO-CBR technique over recent approaches in terms of different measures.
文摘Physiological signals such as electroencephalogram(EEG)signals are often corrupted by artifacts during the acquisition and processing.Some of these artifacts may deteriorate the essential properties of the signal that pertains to meaningful information.Most of these artifacts occur due to the involuntary movements or actions the human does during the acquisition process.So,it is recommended to eliminate these artifacts with signal processing approaches.This paper presents two mechanisms of classification and elimination of artifacts.In the first step,a customized deep network is employed to classify clean EEG signals and artifact-included signals.The classification is performed at the feature level,where common space pattern features are extracted with convolutional layers,and these features are later classified with a support vector machine classifier.In the second stage of the work,the artifact signals are decomposed with empirical mode decomposition,and they are then eliminated with the proposed adaptive thresholding mechanism where the threshold value changes for every intrinsic mode decomposition in the iterative mechanism.
文摘Subspace-based signal processing methods are fundamentally pre-trained Artificial Neural Networks(ANN)that provide the basic structure for numerous computer vision applications and explore the most promising Earth Observation Applications(EOA).This paper examines the fundamentals of subspacebased methods and explores the most promising algorithm for forecasting ionospheric signal delays,which was designed explicitly regarding signal and noise subspaces.The learning efficiency derived from the subspace-based components of Singular Spectrum Analysis(SSA)significantly influences the implementation of Linear Recurrent Formula(LRF)and ANN models.The proposed study introduces a novel enhancement to LRF and ANN methodologies for Global Positioning System(GPS)-Total Electron Content(TEC)forecasts based on SSA.The GPS-derived TEC at Bangalore(13.02°N and 77.57°E)location grid during sunspot cycle 25(2020)is considered for analysis.The SSA-LRF-ANN model demonstrates superior accuracy compared with the SSA-LRF,Autoregressive Moving Average(ARMA),and Holt-Winter(HW)models,achieving a correlation of 0.99,a Mean Absolute Error(MAE)of 0.55 TECU,a Mean Absolute Percentage Error(MAPE)of 7.06%,and a Root Mean Square Error(RMSE)of 0.75 TECU.Furthermore,the results and discussions section presents numerical illustrations that showcase the practical implementation of the SSA-LRF-ANN and its application.
文摘The usage of electric vehicles holds a crucial role in lowering the diminishing of the ozone layer because electric vehicles are not dependent on fossil fuels.With more research,evaluation,and its characteristics on electric vehicles,the infrastructure of charging points,production of electric vehicles,and network modelling,this paper provides a comprehensive overview of electric vehicles,and hybrid vehicles,including an analysis of their market growth,as well as different types of optimization used in the current scenario.In developing countries like India,the biggest barrier is their unfulfilled facility over the charging.Without renewable energy sources,vehicle-to-grid technology facilitates the enhancement of additional power requirements.The mobility factor has been considered an important and special characteristic of electric vehicles.
文摘Integration of artificial intelligence in image processing methods has significantly improved the accuracy of the medical diagnostics pathway for early detection and analysis of kidney tumors.Computer-assisted image analysis can be an effective tool for early diagnosis of soft tissue tumors located remotely or in inaccessible anatomical locations.In this review,we discuss computer-based image processing methods using deep learning,convolutional neural networks(CNNs),radiomics,and transformer-based methods for kidney tumors.These techniques hold significant potential for automated segmentation,classification,and prognostic estimation with high accuracy,enabling more precise and personalized treatment planning.Special focus is given to Vision Transformers(ViTs),Explainable AI(XAI),Federated Learning(FL),and 3D kidney image analysis.Additionally,the strengths and limitations of the established models are compared with recent techniques to understand both clinical and computational challenges that remain unresolved.Finally,the future directions for enhancing diagnostic precision,streamlining physician workflows,and image-guided intervention for decision support are proposed.
基金supported in part by the National Science and Technology Council,Taiwan:NSTC 113-2410-H-030-077-MY2.
文摘Automotive radar has emerged as a critical component in Advanced Driver Assistance Systems(ADAS)and autonomous driving,enabling robust environmental perception through precise range-Doppler and angular measurements.It plays a pivotal role in enhancing road safety by supporting accurate detection and localization of surrounding objects.However,real-world deployment of automotive radar faces significant challenges,including mutual interference among radar units and dense clutter due to multiple dynamic targets,which demand advanced signal processing solutions beyond conventional methodologies.This paper presents a comprehensive review of traditional signal processing techniques and recent advancements specifically designed to address contemporary operational challenges in automotive radar.Emphasis is placed on direction-of-arrival(DoA)estimation algorithms such as Bartlett beamforming,Minimum Variance Distortionless Response(MVDR),Multiple Signal Classification(MUSIC),and Estimation of Signal Parameters via Rotational Invariance Techniques(ESPRIT).Among these,ESPRIT offers superior resolution for multi-target scenarios with reduced computational complexity compared to MUSIC,making it particularly advantageous for real-time applications.Furthermore,the study evaluates state-of-the-art tracking algorithms,including the Kalman Filter(KF),Extended KF(EKF),Unscented KF,and Bayesian filter.EKF is especially suitable for radar systems due to its capability to linearize nonlinear measurement models.The integration of machine learning approaches for target detection and classification is also discussed,highlighting the trade-off between the simplicity of implementation in K-Nearest Neighbors(KNN)and the enhanced accuracy provided by Support Vector Machines(SVM).A brief overview of benchmark radar datasets,performance metrics,and relevant standards is included to support future research.The paper concludes by outlining ongoing challenges and identifying promising research directions in automotive radar signal processing,particularly in the context of increasingly complex traffic scenarios and autonomous navigation systems.
文摘The article by Wu et al highlights the growing incidence of esophageal tumor patients,particularly in China,where the high frequency and death rate are significant problems.The article also examined the impact of health insurance on treatment availability and patient outcomes,demonstrating that the type of insurance can affect the financial burden on patients.This study investigates the effects of different types of health care coverage,namely Urban Employee Basic Medical Insurance vs Urban-Rural Resident Basic Medical Insurance,and the personal spending ratio on treatment decisions and survival outcomes.The database used is derived from esophageal tumor patient continuation from Chongqing University Hospital in China.A total of 2543 patients were included in the study,allowing for the formation of research cohorts.Patient information included demographic characteristics.The study followed various processes to maintain consistency,including data sources,inclusion and exclusion criteria,follow-up duration,health insurance,and statistical analysis.The average age at diagnosis ranged from 57-74 years,and predominantly included men,married people,and those of Han ethnic background,comprising 2088 and 2519 individuals,respectively.Upon controlling for age,sex,relationship status,country of origin,pathological evaluation,tumor stage,and biochemical indicators,individuals who had Urban Employee Basic Medical Insurance exhibited a higher propensity to opt for radiotherapy,chemotherapy,immunotherapy,and targeted therapy compared to those covered by the Urban-Rural Resident Basic Medical Insurance.During the follow-up phase of the study,a total of 1438 deaths were documented,with 1106 ascribed to esophageal cancer.Additionally,individuals with Urban-Rural Resident Basic Medical Insurance had a significantly elevated risk of esophageal cancer,particularly mortality,compared to those without Urban-Rural Resident Basic Medical Insurance.
