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
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 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
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 manuscript by Agidew et al,evaluates the critical role of family background and socioeconomic status in shaping breast cancer awareness,attitudes,and preventive behaviors,particularly in low-resource settings.Brea...The manuscript by Agidew et al,evaluates the critical role of family background and socioeconomic status in shaping breast cancer awareness,attitudes,and preventive behaviors,particularly in low-resource settings.Breast cancer continues to be a leading cause of cancer-related deaths globally,with a disproportionate impact on women in low-and middle-income countries.Recent research by Agidew et al underscores a significant association between family history of breast cancer and elevated levels of knowledge,positive attitudes,and proactive behaviors among women in Northeast Ethiopia.Building upon these findings,this editorial explores the psychological mechanisms and behavioral tendencies that drive greater awareness among women with familial exposure to the disease.Additionally,it highlights persistent socioeconomic challenges—such as limited healthcare access,education disparities,and cultural stigmas-that impede widespread preventive action,especially among women without a known family history.The editorial emphasizes the necessity of integrated public health strategies that combine culturally sensitive education,community outreach,and accessible screening services.Drawing from clinical and policy perspectives,it offers guidance on how to strengthen early detection and preventive care in under-resourced environments.Ultimately,the piece advocates for a more inclusive approach to breast cancer education and prevention that addresses both familial influence and systemic socioeconomic barriers.展开更多
Medical image analysis has become a cornerstone of modern healthcare,driven by the exponential growth of data from imaging modalities such as MRI,CT,PET,ultrasound,and X-ray.Traditional machine learning methods have m...Medical image analysis has become a cornerstone of modern healthcare,driven by the exponential growth of data from imaging modalities such as MRI,CT,PET,ultrasound,and X-ray.Traditional machine learning methods have made early contributions;however,recent advancements in deep learning(DL)have revolutionized the field,offering state-of-the-art performance in image classification,segmentation,detection,fusion,registration,and enhancement.This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks,highlighting both foundational models and recent innovations.The article begins by introducing conventional techniques and their limitations,setting the stage for DL-based solutions.Core DL architectures,including Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),Generative Adversarial Networks(GANs),Vision Transformers(ViTs),and hybrid models,are discussed in detail,including their advantages and domain-specific adaptations.Advanced learning paradigms such as semi-supervised learning,selfsupervised learning,and few-shot learning are explored for their potential to mitigate data annotation challenges in clinical datasets.This review further categorizes major tasks in medical image analysis,elaborating on how DL techniques have enabled precise tumor segmentation,lesion detection,modality fusion,super-resolution,and robust classification across diverse clinical settings.Emphasis is placed on applications in oncology,cardiology,neurology,and infectious diseases,including COVID-19.Challenges such as data scarcity,label imbalance,model generalizability,interpretability,and integration into clinical workflows are critically examined.Ethical considerations,explainable AI(XAI),federated learning,and regulatory compliance are discussed as essential components of real-world deployment.Benchmark datasets,evaluation metrics,and comparative performance analyses are presented to support future research.The article concludes with a forward-looking perspective on the role of foundation models,multimodal learning,edge AI,and bio-inspired computing in the future of medical imaging.Overall,this review serves as a valuable resource for researchers,clinicians,and developers aiming to harness deep learning for intelligent,efficient,and clinically viable medical image analysis.展开更多
The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of re...The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work, a systemic review of GAN models using the PRISMA framework is developed in detail to fill the gap by structurally evaluating GAN architectures. A wide variety of GAN models have been discussed in this review, starting from the basic Conditional GAN, Wasserstein GAN, and Deep Convolutional GAN, and have gone down to many specialized models, such as EVAGAN, FCGAN, and SIF-GAN, for different applications across various domains like fault diagnosis, network security, medical imaging, and image segmentation. The PRISMA methodology systematically filters relevant studies by inclusion and exclusion criteria to ensure transparency and replicability in the review process. Hence, all models are assessed relative to specific performance metrics such as accuracy, stability, and computational efficiency. There are multiple benefits to using the PRISMA approach in this setup. Not only does this help in finding optimal models suitable for various applications, but it also provides an explicit framework for comparing GAN performance. In addition to this, diverse types of GAN are included to ensure a comprehensive view of the state-of-the-art techniques. This work is essential not only in terms of its result but also because it guides the direction of future research by pinpointing which types of applications require some GAN architectures, works to improve specific task model selection, and points out areas for further research on the development and application of GANs.展开更多
The combining microelectronic devices and associated technologies onto a single silicon chip poses a substantial challenge.However,in recent years,the area of silicon photonics has experienced remarkable advancements ...The combining microelectronic devices and associated technologies onto a single silicon chip poses a substantial challenge.However,in recent years,the area of silicon photonics has experienced remarkable advancements and notable leaps in performance.The performance of silicon on insulator(SOI)based photonic devices,such as fast silicon optical modulators,photonic transceivers,optical filters,etc.,have been discussed.This would be a step forward in creating standalone silicon photonic devices,strengthening the possibility of single on-chip nanophotonic integrated circuits.Suppose an integrated silicon photonic chip is designed and fabricated.In that case,it might drastically modify these combined photonic component costs,power consumption,and size,bringing substantial,perhaps revolutionary,changes to the next-generation communications sector.Yet,the monolithic integration of photonic and electrical circuitry is a significant technological difficulty.A complicated set of factors must be carefully considered to determine which application will have the best chance of success employing silicon-based integrated product solutions.The processing limitations connected to the current process flow,the process generation(sometimes referred to as lithography node generation),and packaging requirements are a few of these factors to consider.This review highlights recent developments in integrated silicon photonic devices and their proven applications,including but not limited to photonic waveguides,photonic amplifiers and filters,onchip photonic transceivers,and the state-of-the-art of silicon photonic in multidimensional quantum systems.The investigated devices aim to expedite the transfer of silicon photonics from academia to industry by opening the next phase in on-chip silicon photonics and enabling the application of silicon photonic-based devices in various optical systems.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
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
Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart ...Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices.Furthermore,the IoT plays a key role in multiple domains,including industrial automation,smart homes,and intelligent transportation systems.However,an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness.To address these issue,this research proposes a Modified Walrus Optimization Algorithm(MWaOA)for effective resource management in smart IoT systems.In the proposed MWaOA,a crowding process is incorporated to maintain diversity and avoid premature convergence thereby enhancing the global search capability.During resource allocation,the MWaOA prevents early convergence,which aids in achieving a better balance between the exploration and exploitation phases during optimization.Empirical evaluations show that the MWaOA reduces energy consumption by approximately 4% to 34%and minimizes the response time by 6% to 33% across different service arrival rates.Compared to traditional optimization algorithms,MWaOA reduces energy consumption by 5% to 30%and minimizes the response time by 4% to 28% across different simulation epochs.The proposed MWaOA provides adaptive and robust resource allocation,thereby minimizing transmission cost while considering network constraints and real-time performance parameters.展开更多
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
Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation...Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation.The objective of this review is to evaluate the advances,relevances,and limitations of GANs in medical imaging.An organised literature review was conducted following the guidelines of PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses).The literature considered included peer-reviewed papers published between 2020 and 2025 across databases including PubMed,IEEE Xplore,and Scopus.The studies related to applications of GAN architectures in medical imaging with reported experimental outcomes and published in English in reputable journals and conferences were considered for the review.Thesis,white papers,communication letters,and non-English articles were not included for the same.CLAIM based quality assessment criteria were applied to the included studies to assess the quality.The study classifies diverse GAN architectures,summarizing their clinical applications,technical performances,and their implementation hardships.Key findings reveal the increasing applications of GANs for enhancing diagnostic accuracy,reducing data scarcity through synthetic data generation,and supporting modality translation.However,concerns such as limited generalizability,lack of clinical validation,and regulatory constraints persist.This review provides a comprehensive study of the prevailing scenario of GANs in medical imaging and highlights crucial research gaps and future directions.Though GANs hold transformative capability for medical imaging,their integration into clinical use demands further validation,interpretability,and regulatory alignment.展开更多
This paper presents a performance study of the distributed coordination function （DCF） of 802.11 networks considering erroneous channel and capture effects under non-saturated traffic conditions employing a basic ac...This paper presents a performance study of the distributed coordination function （DCF） of 802.11 networks considering erroneous channel and capture effects under non-saturated traffic conditions employing a basic access method.The aggregate throughput of a practical wireless local area network （WLAN） strongly depends on the channel conditions.In a real radio environment,the received signal power at the access point from a station is subjected to deterministic path loss,shadowing,and fast multipath fading.The binary exponential backoff （BEB） mechanism of IEEE 802.11 DCF severely suffers from more channel idle time under high bit error rate （BER）.To alleviate the low performance of IEEE 802.11 DCF,a new mechanism is introduced,which greatly outperforms the existing methods under a high BER.A multidimensional Markov chain model is used to characterize the behavior of DCF in order to account both non-ideal channel conditions and capture effects.展开更多
This paper deals with the problem of robust stability for continuous-time singular systems with state delay and parameter uncertainty. The uncertain singular systems with delay considered in this paper are assumed to ...This paper deals with the problem of robust stability for continuous-time singular systems with state delay and parameter uncertainty. The uncertain singular systems with delay considered in this paper are assumed to be regular and impulse free.By decomposing the systems into slow and fast subsystems,a robust delay-dependent asymptotic stability criteria based on linear matrix inequality is proposed, which is derived by using Lyapunov-Krasovskii functionals, neither model transformation nor bounding for cross terms is required in the derivation of our delay-dependent result. The robust delay-dependent stability criterion proposed in this paper is a sufficient condition. Finally, numerical examples and Matlab simulation are provided to illustrate the effectiveness of the proposed method.展开更多
Accurate classification of cardiac arrhythmias is a crucial task because of the non-stationary nature of electrocardiogram(ECG)signals.In a life-threatening situation,an automated system is necessary for early detecti...Accurate classification of cardiac arrhythmias is a crucial task because of the non-stationary nature of electrocardiogram(ECG)signals.In a life-threatening situation,an automated system is necessary for early detection of beat abnormalities in order to reduce the mortality rate.In this paper,we propose an automatic classification system of ECG beats based on the multi-domain features derived from the ECG signals.The experimental study was evaluated on ECG signals obtained from the MIT-BIH Arrhythmia Database.The feature set comprises eight empirical mode decomposition(EMD)based features,three features from variational mode decomposition(VMD)and four features from RR intervals.In total,15 features are ranked according to a ranker search approach and then used as input to the support vector machine(SVM)and C4.5 decision tree classifiers for classifying six types of arrhythmia beats.The proposed method achieved best result in C4.5 decision tree classifier with an accuracy of 98.89%compared to cubic-SVM classifier which achieved an accuracy of 95.35%only.Besides accuracy measures,all other parameters such as sensitivity(Se),specificity(Sp)and precision rates of 95.68%,99.28%and 95.8%was achieved better in C4.5 classifier.Also the computational time of 0.65 s with an error rate of 0.11 was achieved which is very less compared to SVM.The multi-domain based features with decision tree classifier obtained the best results in classifying cardiac arrhythmias hence the system could be used efficiently in clinical practices.展开更多
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 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 manuscript by Agidew et al,evaluates the critical role of family background and socioeconomic status in shaping breast cancer awareness,attitudes,and preventive behaviors,particularly in low-resource settings.Breast cancer continues to be a leading cause of cancer-related deaths globally,with a disproportionate impact on women in low-and middle-income countries.Recent research by Agidew et al underscores a significant association between family history of breast cancer and elevated levels of knowledge,positive attitudes,and proactive behaviors among women in Northeast Ethiopia.Building upon these findings,this editorial explores the psychological mechanisms and behavioral tendencies that drive greater awareness among women with familial exposure to the disease.Additionally,it highlights persistent socioeconomic challenges—such as limited healthcare access,education disparities,and cultural stigmas-that impede widespread preventive action,especially among women without a known family history.The editorial emphasizes the necessity of integrated public health strategies that combine culturally sensitive education,community outreach,and accessible screening services.Drawing from clinical and policy perspectives,it offers guidance on how to strengthen early detection and preventive care in under-resourced environments.Ultimately,the piece advocates for a more inclusive approach to breast cancer education and prevention that addresses both familial influence and systemic socioeconomic barriers.
文摘Medical image analysis has become a cornerstone of modern healthcare,driven by the exponential growth of data from imaging modalities such as MRI,CT,PET,ultrasound,and X-ray.Traditional machine learning methods have made early contributions;however,recent advancements in deep learning(DL)have revolutionized the field,offering state-of-the-art performance in image classification,segmentation,detection,fusion,registration,and enhancement.This comprehensive review presents an in-depth analysis of deep learning methodologies applied across medical image analysis tasks,highlighting both foundational models and recent innovations.The article begins by introducing conventional techniques and their limitations,setting the stage for DL-based solutions.Core DL architectures,including Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),Generative Adversarial Networks(GANs),Vision Transformers(ViTs),and hybrid models,are discussed in detail,including their advantages and domain-specific adaptations.Advanced learning paradigms such as semi-supervised learning,selfsupervised learning,and few-shot learning are explored for their potential to mitigate data annotation challenges in clinical datasets.This review further categorizes major tasks in medical image analysis,elaborating on how DL techniques have enabled precise tumor segmentation,lesion detection,modality fusion,super-resolution,and robust classification across diverse clinical settings.Emphasis is placed on applications in oncology,cardiology,neurology,and infectious diseases,including COVID-19.Challenges such as data scarcity,label imbalance,model generalizability,interpretability,and integration into clinical workflows are critically examined.Ethical considerations,explainable AI(XAI),federated learning,and regulatory compliance are discussed as essential components of real-world deployment.Benchmark datasets,evaluation metrics,and comparative performance analyses are presented to support future research.The article concludes with a forward-looking perspective on the role of foundation models,multimodal learning,edge AI,and bio-inspired computing in the future of medical imaging.Overall,this review serves as a valuable resource for researchers,clinicians,and developers aiming to harness deep learning for intelligent,efficient,and clinically viable medical image analysis.
文摘The growing spectrum of Generative Adversarial Network (GAN) applications in medical imaging, cyber security, data augmentation, and the field of remote sensing tasks necessitate a sharp spike in the criticality of review of Generative Adversarial Networks. Earlier reviews that targeted reviewing certain architecture of the GAN or emphasizing a specific application-oriented area have done so in a narrow spirit and lacked the systematic comparative analysis of the models’ performance metrics. Numerous reviews do not apply standardized frameworks, showing gaps in the efficiency evaluation of GANs, training stability, and suitability for specific tasks. In this work, a systemic review of GAN models using the PRISMA framework is developed in detail to fill the gap by structurally evaluating GAN architectures. A wide variety of GAN models have been discussed in this review, starting from the basic Conditional GAN, Wasserstein GAN, and Deep Convolutional GAN, and have gone down to many specialized models, such as EVAGAN, FCGAN, and SIF-GAN, for different applications across various domains like fault diagnosis, network security, medical imaging, and image segmentation. The PRISMA methodology systematically filters relevant studies by inclusion and exclusion criteria to ensure transparency and replicability in the review process. Hence, all models are assessed relative to specific performance metrics such as accuracy, stability, and computational efficiency. There are multiple benefits to using the PRISMA approach in this setup. Not only does this help in finding optimal models suitable for various applications, but it also provides an explicit framework for comparing GAN performance. In addition to this, diverse types of GAN are included to ensure a comprehensive view of the state-of-the-art techniques. This work is essential not only in terms of its result but also because it guides the direction of future research by pinpointing which types of applications require some GAN architectures, works to improve specific task model selection, and points out areas for further research on the development and application of GANs.
文摘The combining microelectronic devices and associated technologies onto a single silicon chip poses a substantial challenge.However,in recent years,the area of silicon photonics has experienced remarkable advancements and notable leaps in performance.The performance of silicon on insulator(SOI)based photonic devices,such as fast silicon optical modulators,photonic transceivers,optical filters,etc.,have been discussed.This would be a step forward in creating standalone silicon photonic devices,strengthening the possibility of single on-chip nanophotonic integrated circuits.Suppose an integrated silicon photonic chip is designed and fabricated.In that case,it might drastically modify these combined photonic component costs,power consumption,and size,bringing substantial,perhaps revolutionary,changes to the next-generation communications sector.Yet,the monolithic integration of photonic and electrical circuitry is a significant technological difficulty.A complicated set of factors must be carefully considered to determine which application will have the best chance of success employing silicon-based integrated product solutions.The processing limitations connected to the current process flow,the process generation(sometimes referred to as lithography node generation),and packaging requirements are a few of these factors to consider.This review highlights recent developments in integrated silicon photonic devices and their proven applications,including but not limited to photonic waveguides,photonic amplifiers and filters,onchip photonic transceivers,and the state-of-the-art of silicon photonic in multidimensional quantum systems.The investigated devices aim to expedite the transfer of silicon photonics from academia to industry by opening the next phase in on-chip silicon photonics and enabling the application of silicon photonic-based devices in various optical systems.
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.
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Recently,the Internet of Things(IoT)technology has been utilized in a wide range of services and applications which significantly transforms digital ecosystems through seamless interconnectivity between various smart devices.Furthermore,the IoT plays a key role in multiple domains,including industrial automation,smart homes,and intelligent transportation systems.However,an increasing number of connected devices presents significant challenges related to efficient resource allocation and system responsiveness.To address these issue,this research proposes a Modified Walrus Optimization Algorithm(MWaOA)for effective resource management in smart IoT systems.In the proposed MWaOA,a crowding process is incorporated to maintain diversity and avoid premature convergence thereby enhancing the global search capability.During resource allocation,the MWaOA prevents early convergence,which aids in achieving a better balance between the exploration and exploitation phases during optimization.Empirical evaluations show that the MWaOA reduces energy consumption by approximately 4% to 34%and minimizes the response time by 6% to 33% across different service arrival rates.Compared to traditional optimization algorithms,MWaOA reduces energy consumption by 5% to 30%and minimizes the response time by 4% to 28% across different simulation epochs.The proposed MWaOA provides adaptive and robust resource allocation,thereby minimizing transmission cost while considering network constraints and real-time performance parameters.
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.
基金supported by Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/540/46.
文摘Over the years,Generative Adversarial Networks(GANs)have revolutionized the medical imaging industry for applications such as image synthesis,denoising,super resolution,data augmentation,and cross-modality translation.The objective of this review is to evaluate the advances,relevances,and limitations of GANs in medical imaging.An organised literature review was conducted following the guidelines of PRISMA(Preferred Reporting Items for Systematic Reviews and Meta-Analyses).The literature considered included peer-reviewed papers published between 2020 and 2025 across databases including PubMed,IEEE Xplore,and Scopus.The studies related to applications of GAN architectures in medical imaging with reported experimental outcomes and published in English in reputable journals and conferences were considered for the review.Thesis,white papers,communication letters,and non-English articles were not included for the same.CLAIM based quality assessment criteria were applied to the included studies to assess the quality.The study classifies diverse GAN architectures,summarizing their clinical applications,technical performances,and their implementation hardships.Key findings reveal the increasing applications of GANs for enhancing diagnostic accuracy,reducing data scarcity through synthetic data generation,and supporting modality translation.However,concerns such as limited generalizability,lack of clinical validation,and regulatory constraints persist.This review provides a comprehensive study of the prevailing scenario of GANs in medical imaging and highlights crucial research gaps and future directions.Though GANs hold transformative capability for medical imaging,their integration into clinical use demands further validation,interpretability,and regulatory alignment.
文摘This paper presents a performance study of the distributed coordination function （DCF） of 802.11 networks considering erroneous channel and capture effects under non-saturated traffic conditions employing a basic access method.The aggregate throughput of a practical wireless local area network （WLAN） strongly depends on the channel conditions.In a real radio environment,the received signal power at the access point from a station is subjected to deterministic path loss,shadowing,and fast multipath fading.The binary exponential backoff （BEB） mechanism of IEEE 802.11 DCF severely suffers from more channel idle time under high bit error rate （BER）.To alleviate the low performance of IEEE 802.11 DCF,a new mechanism is introduced,which greatly outperforms the existing methods under a high BER.A multidimensional Markov chain model is used to characterize the behavior of DCF in order to account both non-ideal channel conditions and capture effects.
文摘This paper deals with the problem of robust stability for continuous-time singular systems with state delay and parameter uncertainty. The uncertain singular systems with delay considered in this paper are assumed to be regular and impulse free.By decomposing the systems into slow and fast subsystems,a robust delay-dependent asymptotic stability criteria based on linear matrix inequality is proposed, which is derived by using Lyapunov-Krasovskii functionals, neither model transformation nor bounding for cross terms is required in the derivation of our delay-dependent result. The robust delay-dependent stability criterion proposed in this paper is a sufficient condition. Finally, numerical examples and Matlab simulation are provided to illustrate the effectiveness of the proposed method.
文摘Accurate classification of cardiac arrhythmias is a crucial task because of the non-stationary nature of electrocardiogram(ECG)signals.In a life-threatening situation,an automated system is necessary for early detection of beat abnormalities in order to reduce the mortality rate.In this paper,we propose an automatic classification system of ECG beats based on the multi-domain features derived from the ECG signals.The experimental study was evaluated on ECG signals obtained from the MIT-BIH Arrhythmia Database.The feature set comprises eight empirical mode decomposition(EMD)based features,three features from variational mode decomposition(VMD)and four features from RR intervals.In total,15 features are ranked according to a ranker search approach and then used as input to the support vector machine(SVM)and C4.5 decision tree classifiers for classifying six types of arrhythmia beats.The proposed method achieved best result in C4.5 decision tree classifier with an accuracy of 98.89%compared to cubic-SVM classifier which achieved an accuracy of 95.35%only.Besides accuracy measures,all other parameters such as sensitivity(Se),specificity(Sp)and precision rates of 95.68%,99.28%and 95.8%was achieved better in C4.5 classifier.Also the computational time of 0.65 s with an error rate of 0.11 was achieved which is very less compared to SVM.The multi-domain based features with decision tree classifier obtained the best results in classifying cardiac arrhythmias hence the system could be used efficiently in clinical practices.
