In this paper,we introduce a new two-dimensional nonlinear oscillator with an infinite number of coexisting limit cycles.These limit cycles form a layer-by-layer structure which is very unusual.Forty percent of these ...In this paper,we introduce a new two-dimensional nonlinear oscillator with an infinite number of coexisting limit cycles.These limit cycles form a layer-by-layer structure which is very unusual.Forty percent of these limit cycles are self-excited attractors while sixty percent of them are hidden attractors.Changing this new system to its forced version,we introduce a new chaotic system with an infinite number of coexisting strange attractors.We implement this system through field programmable gate arrays.展开更多
Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various applications.Therefore,a novel localisation algorithm is proposed for noisy range measurements in IIoT networks...Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various applications.Therefore,a novel localisation algorithm is proposed for noisy range measurements in IIoT networks.The position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position estimation.Therefore,the AMs selection algorithm for the localisation of BMs in the IIoT network is introduced.Only those AMs will participate in the localisation process,which increases the accuracy of the final location estimate.Then,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement noise.The results are compared with the state‐of‐the‐art and verified through numerous simulations.展开更多
Intracavity absorption spectroscopy is a strikingly sensitive technique that has been integrated with a two-wavelength setup to develop a sensor for human breath.Various factors are considered in such a scenario,out o...Intracavity absorption spectroscopy is a strikingly sensitive technique that has been integrated with a two-wavelength setup to develop a sensor for human breath.Various factors are considered in such a scenario,out of which Relative Intensity Noise(RIN)has been exploited as an important parameter to characterize and calibrate the said setup.During the performance of an electrical based assessment arrangement which has been developed in the laboratory as an alternative to the expensive Agilent setup,the optical amplifier plays a pivotal role in its development and operation,along with other components and their significance.Therefore,the investigation and technical analysis of the amplifier in the system has been explored in detail.The algorithm developed for the automatic measurements of the system has been effectively deployed in terms of the laser’s performance.With this in perspective,a frequency dependent calibration has been pursued in depth with this scheme which enhances the sensor’s efficiency in terms of its sensitivity.In this way,our investigation helps us in a better understanding and implementation perspective of the proposed system,as the outcomes of our analysis adds to the precision and accuracy of the entire system.展开更多
Lately,in modern smart power grids,energy demand for accurate forecast of electricity is gaining attention,with increased interest of research.This is due to the fact that a good energy demand forecast would lead to p...Lately,in modern smart power grids,energy demand for accurate forecast of electricity is gaining attention,with increased interest of research.This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand.In addition,proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network.As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman,new opportunities may arise considering the efficiency and reliability of the power system;like price-based demand response programs.These programs could either be a large scale for household,commercial or industrial users.However,excellent demand forecasting models are crucial for the deployment of these smart metering in the power grid based on good knowledge of the electricity market structure.Consequently,in this paper,an overview of the Oman regulatory regime,financial mechanism,price control,and distribution system security standard were presented.More so,the energy demand forecast in Oman was analysed,using the econometric model to forecasts its energy peak demand.The energy econometric analysis in this study describes the relationship between the growth of historical electricity consumption and macro-economic parameters(by region,and by tariff),considering a case study of Mazoon Electricity Distribution Company(MZEC),which is one of the major power distribution companies in Oman,for effective energy demand in the power grid.展开更多
Augmentation of abnormal cells in the brain causes brain tumor(BT),and early screening and treatmentwill reduce its harshness in patients.BT’s clinical level screening is usually performed with Magnetic Resonance Ima...Augmentation of abnormal cells in the brain causes brain tumor(BT),and early screening and treatmentwill reduce its harshness in patients.BT’s clinical level screening is usually performed with Magnetic Resonance Imaging(MRI)due to its multi-modality nature.The overall aims of the study is to introduce,test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans,facilitating improved accuracy.The research intends to devise a reliable framework for detecting the BT region in the twodimensional(2D)MRI slice,and identifying its class with improved accuracy.The methodology for the devised framework comprises the phases of:(i)Collection and resizing of images,(ii)Implementation and Segmentation of Convolutional Neural Network(CNN),(iii)Deep feature extraction,(iv)Handcrafted feature extraction,(v)Moth-Flame-Algorithm(MFA)supported feature reduction,and(vi)Performance evaluation.This study utilized clinical-grade brain MRI of BRATS and TCIA datasets for the investigation.This framework segments detected the glioma(low/high grade)and glioblastoma class BT.This work helped to get a segmentation accuracy of over 98%with VGG-UNet and a classification accuracy of over 98%with the VGG16 scheme.This study has confirmed that the implemented framework is very efficient in detecting the BT in MRI slices with/without the skull section.展开更多
Electrification and sustainable energy uses are increasing in Papua New Guinea (PNG) over the last few decades. The bulk of PNG’s population (85%) lives in isolated and dispersed villages in the rural areas. Most of ...Electrification and sustainable energy uses are increasing in Papua New Guinea (PNG) over the last few decades. The bulk of PNG’s population (85%) lives in isolated and dispersed villages in the rural areas. Most of these isolated and dispersed areas are still yet to be connected to an electricity supply.?Papua New Guinea (PNG) is richly endowed with natural resources, but exploitation has been hampered by rugged terrain, land tenure issues, and the high cost of developing infrastructure. The study is focused on mapping of enriched renewable energy zones of the entire country. Different variables related to renewable, like surface albedo index, earth skin temperature, solar?insolation incident, and wind speed are used for this purpose. Three interpolation approaches,?like inverse distance weighted averaging, thin-plate smoothing splines, and kriging, are evaluated to interpolate all variables. Rating and weight sum overlay operation is applied to derive potential?renewable energy zones in this equatorial country. Results show that potential renewable energy distribution is high in Papua New Guinea on the March and September equinoxes. Yearly average distribution of renewable energy source variables is significantly higher in most areas of Manus, New?Ireland, North Solomon, West New Britain, Northern, Central and Milne Bay;a larger portion of East New Britain;the northern part of West and East Sepik, Central, Morobe and eastern part of Madang province. The potential renewable energy distribution data can help to establish sustainable energy production in the country.展开更多
Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive met...Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive method for determining cardiac health.Various health practitioners use the ECG signal to ascertain critical information about the human heart.In this article,swarm intelligence approaches are used in the biomedical signal processing sector to enhance adaptive hybrid filters and empirical wavelet transforms(EWTs).At first,the white Gaussian noise is added to the input ECG signal and then applied to the EWT.The ECG signals are denoised by the proposed adaptive hybrid filter.The honey badge optimization(HBO)algorithm is utilized to optimize the EWT window function and adaptive hybrid filter weight parameters.The proposed approach is simulated by MATLAB 2018a using the MIT-BIH dataset with white Gaussian,electromyogram and electrode motion artifact noises.A comparison of the HBO approach with recursive least square-based adaptive filter,multichannel least means square,and discrete wavelet transform methods has been done in order to show the efficiency of the proposed adaptive hybrid filter.The experimental results show that the HBO approach supported by EWT and adaptive hybrid filter can be employed efficiently for cardiovascular signal denoising.展开更多
Optical fiber Bragg grating(FBG)sensors have advanced significantly in the last several years.The use of innovative FBG in temperature and pressure measurement is examined in this study.The benefits of FBGs,such as th...Optical fiber Bragg grating(FBG)sensors have advanced significantly in the last several years.The use of innovative FBG in temperature and pressure measurement is examined in this study.The benefits of FBGs,such as their compact size,low weight,resilience to corrosion,immunity to electromagnetic interference,distributed sensing,and remote monitoring,have brought attention to the growing research in this field of structural health monitoring of civil infrastructures.展开更多
Jamming and spoofing detection of global navigation satellite systems(GNSS)is of great importance.Civil and military aerial platforms use GNSS as main navigation systems and these systems are main target of threat att...Jamming and spoofing detection of global navigation satellite systems(GNSS)is of great importance.Civil and military aerial platforms use GNSS as main navigation systems and these systems are main target of threat attacks.In this paper a simple method based on different empirical probability density functions of successive received signal powers and goodness of fit tech-nique is proposed for airborne platforms such as unmanned aerial vehicle(UAV),in no fading envi-ronment.The two different paths between UAV-satellite and UAV-threat,experience different empirical probability density functions which can be used to distinguish between authentic and threat signals.Simulation results including detection and false alarm probabilities show good perfor-mance of proposed method as well as low computational burden.展开更多
Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications...Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices.The e-healthcare application solely depends on the IoT and cloud computing environment,has provided several characteristics and applications.Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing,which led to quick exhaustion of energy.In this view,this paper introduces a new energy efficient cluster enabled clinical decision support system(EEC-CDSS)for embedded IoT environment.The presented EECCDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process.The EEC-CDSS model incorporates particle swarm optimization with levy distribution(PSO-L)based clustering technique,which clusters the set of IoT devices and reduces the amount of data transmission.In addition,the IoT devices forward the data to the cloud where the actual classification procedure is performed.For classification process,variational autoencoder(VAE)is used to determine the existence of disease or not.In order to investigate the proficient results analysis of the EEC-CDSS model,a wide range of simulations was carried out on heart disease and diabetes dataset.The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.展开更多
In this paper,a novel cancellable biometrics technique calledMulti-Biometric-Feature-Hashing(MBFH)is proposed.The MBFH strategy is utilized to actualize a single direction(non-invertibility)biometric shape.MBFH is a t...In this paper,a novel cancellable biometrics technique calledMulti-Biometric-Feature-Hashing(MBFH)is proposed.The MBFH strategy is utilized to actualize a single direction(non-invertibility)biometric shape.MBFH is a typical model security conspire that is distinguished in the utilization of this protection insurance framework in numerous sorts of biometric feature strategies(retina,palm print,Hand Dorsum,fingerprint).A more robust and accurate multilingual biological structure in expressing human loneliness requires a different format to record clients with inseparable comparisons from individual biographical sources.This may raise worries about their utilization and security when these spread out designs are subverted as everybody is acknowledged for another biometric attribute.The proposed structure comprises of four sections:input multi-biometric acquisition,feature extraction,Multi-Exposure Fusion(MEF)and secure hashing calculation(SHA-3).Multimodal biometrics systems that are more powerful and precise in human-unmistakable evidence require various configurations to store a comparative customer that can be contrasted with biometric wellsprings of people.Disparate top words,biometrics graphs can’t be denied and change to another request for positive Identifications(IDs)while settling.Cancellable biometrics is may be the special procedure used to recognize this issue.展开更多
Handwriting is a unique and significant human feature that distinguishes them from one another.There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols...Handwriting is a unique and significant human feature that distinguishes them from one another.There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification.However,such systems are susceptible to forgery,posing security risks.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.Our innovative method is intricately designed,encompassing five distinct phases:data collection,preprocessing,feature extraction,significant feature selection,and classification.One key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting(BHW),setting the foundation for our comprehensive approach.Post-preprocessing,we embarked on an exhaustive feature extraction process,encompassing integration with kinematic,statistical,spatial,and composite features.This meticulous amalgamation resulted in a robust set of 91 features.To enhance the efficiency of our system,we employed an analysis of variance(ANOVA)F test and mutual information scores approach,meticulously selecting the most pertinent features.In the identification phase,we harnessed the power of cutting-edge deep learning models,notably the Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM).These models underwent rigorous training and testing to accurately discern individuals based on their handwriting characteristics.Moreover,our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM,capitalizing on fine motor features for enhanced individual classifications.Crucially,our experimental results underscore the superiority of our approach.The CNN,BiLSTM,and hybrid models exhibited superior performance in individual classification when compared to prevailing state-of-the-art techniques.This validates our method’s efficacy and underscores its potential to outperform existing technologies,marking a significant stride forward in the realm of individual identification through handwriting analysis.展开更多
Deeply etched rib waveguides on silicon on insulator platform were not addressed well in research publications. We have analyzed single mode condition and polarization independence of a deeply etched rib waveguide （D...Deeply etched rib waveguides on silicon on insulator platform were not addressed well in research publications. We have analyzed single mode condition and polarization independence of a deeply etched rib waveguide （DE-RW） structure from biosensing perspective. With this rib structure, an asymmetrically etched integrated optic directional coupler has been numerically modeled to have the same coupling length for quasi- TE and TM modes. The coupling coefficients with the glucose solution as an upper cladding were calculated using a full vector mode solver, and the bulk refractive index sensitivity of the sensor was found as 28.305 × 10^-2/RIU for a fundamental quasi-TE mode.展开更多
We experimentally demonstrate a novel quasi-bound state in the continuum(BIC) resonance in the mid-infrared wavelength region with the resonant electric field confined as a slot mode within a low-refractive-index medi...We experimentally demonstrate a novel quasi-bound state in the continuum(BIC) resonance in the mid-infrared wavelength region with the resonant electric field confined as a slot mode within a low-refractive-index medium sandwiched between high-index layers.The structures studied here comprise coupled amorphous germanium guided-mode resonance(GMR) structures with a top one-dimensional grating layer and bottom uniform layer separated by a low-index silicon nitride layer.The slot-mode profile within the silicon nitride layer with mode field confinement >30% is achieved as a solution to the electromagnetic wave propagation through the coupled GMR structure with the dominant field component being perpendicular to the layers.The quasi-BIC resonance in symmetric 1D grating structures can be observed even at normal incidence when considering a realistic excitation beam with finite angular spread.The measured transmission peak is found to redshift(remain almost unchanged)under classical(full-conical) mounting conditions.The highest quality factor of ~400 is experimentally extracted at normal incidence under a classical mounting condition with a resonance peak at 3.41 μm wavelength.Such slot-mode GMR structures with appropriately chosen low-index intermediate layers can find applications in resonantly enhanced sensing and active photonic devices.展开更多
In monolayer transition metal dichalcogenide semiconductors,valley coherence degrades rapidly due to a combination of fast scattering and inter-valley exchange interaction.This leads to a sub-picosecond valley coheren...In monolayer transition metal dichalcogenide semiconductors,valley coherence degrades rapidly due to a combination of fast scattering and inter-valley exchange interaction.This leads to a sub-picosecond valley coherence time,making coherent manipulation of exciton a highly challenging task Using monolayer MoS_(2) sandwiched between top and bottom graphene,here we demonstrate fully valley-coherent excitons by observing~100%degree of linear polarization in steady state photoluminescence.This is achieved in this unique design through a combined effect of(a)suppression in exchange interaction due to enhanced dielectric screening,(b)reduction in exciton lifetime due to a fast inter-layer transfer to graphene,and(c)operating in the motional narrowing regime.We disentangle the role of the key parameters affecting valley coherence by using a combination of calculation(solutions of Bethe-Salpeter and Maiallle-Silva-Sham equations)and a careful choice of design of experiments using four different stacks with systematic variation of screening and exciton lifetime.To the best of our knowledge,this is the first report in which the excitons are found to be valley coherent in the entire lifetime in monolayer semiconductors,allowing optical readout of valley coherence possible.展开更多
In recent years,modern metropolitan areas are the main indicators of economic growth of nation.In metropolitan areas,number and frequency of vehicles have increased tremendously,and they create issues,like traffic con...In recent years,modern metropolitan areas are the main indicators of economic growth of nation.In metropolitan areas,number and frequency of vehicles have increased tremendously,and they create issues,like traffic congestion,accidents,environmental pollution,economical losses and unnecessary waste of fuel.In this paper,we propose traffic management system based on the prediction information to reduce the above mentioned issues in a metropolitan area.The proposed traffic management system makes use of static and mobile agents,where the static agent available at region creates and dispatches mobile agents to zones in a metropolitan area.The migrated mobile agents use emergent intelligence technique to collect and share traffic flow parameters(speed and density),historical data,resource information,spatio-temporal data and so on,and are analyzes the static agent.The emergent intelligence technique at static agent uses analyzed,historical and spatio-temporal data for monitoring and predicting the expected patterns of traffic density(commuters and vehicles)and travel times in each zone and region.The static agent optimizes predicted and analyzed data for choosing optimal routes to divert the traffic,in order to ensure smooth traffic flow and reduce frequency of occurrence of traffic congestion,reduce traffic density and travel time.The performance analysis is performed in realistic scenario by integrating NS2,SUMO,OpenStreatMap(OSM)and MOVE tool.The effectiveness of the proposed approach has been compared with the existing approach.展开更多
Optical microscopy with optimal axial resolution is critical for precise visualization of two-dimensional flat-top structures.Here,we present sub-diffraction-limited ultrafast imaging of hexagonal boron nitride(hBN)na...Optical microscopy with optimal axial resolution is critical for precise visualization of two-dimensional flat-top structures.Here,we present sub-diffraction-limited ultrafast imaging of hexagonal boron nitride(hBN)nanosheets using a confocal focus-engineered coherent anti-Stokes Raman scattering(cFE-CARS)microscopic system.By incorporating a pinhole with a diameter of approximately 30μm,we effectively minimized the intensity of side lobes induced by circular partial pi-phase shift in the wavefront(diameter,d0)of the probe beam,as well as nonresonant background CARS intensities.Using axial-resolution-improved cFE-CARS(acFE-CARS),the achieved axial resolution is 350 nm,exhibiting a 4.3-folded increase in the signal-to-noise ratio compared to the previous case with 0.58 d0 phase mask.This improvement can be accomplished by using a phase mask of 0.24 d0.Additionally,we employed nonde-generate phase matching with three temporally separable incident beams,which facilitated cross-sectional visualization of highly-sample-specific and vibration-sensitive signals in a pump-probe fashion with subpicosecond time resolution.Our observations reveal time-dependent CARS dephasing in hBN nanosheets,induced by Raman-free induction decay(0.66 ps)in the 1373 cm^(−1) mode.展开更多
文摘In this paper,we introduce a new two-dimensional nonlinear oscillator with an infinite number of coexisting limit cycles.These limit cycles form a layer-by-layer structure which is very unusual.Forty percent of these limit cycles are self-excited attractors while sixty percent of them are hidden attractors.Changing this new system to its forced version,we introduce a new chaotic system with an infinite number of coexisting strange attractors.We implement this system through field programmable gate arrays.
文摘Localisation of machines in harsh Industrial Internet of Things(IIoT)environment is necessary for various applications.Therefore,a novel localisation algorithm is proposed for noisy range measurements in IIoT networks.The position of an unknown machine device in the network is estimated using the relative distances between blind machines(BMs)and anchor machines(AMs).Moreover,a more practical and challenging scenario with the erroneous position of AM is considered,which brings additional uncertainty to the final position estimation.Therefore,the AMs selection algorithm for the localisation of BMs in the IIoT network is introduced.Only those AMs will participate in the localisation process,which increases the accuracy of the final location estimate.Then,the closed‐form expression of the proposed greedy successive anchorization process is derived,which prevents possible local convergence,reduces computation,and achieves Cramér‐Rao lower bound accuracy for white Gaussian measurement noise.The results are compared with the state‐of‐the‐art and verified through numerous simulations.
基金This work was supported in part by the German Academic Exchange Service(Deutsche Akademische Austausch Dienst(DAAD)),and in part by the University of Kassel.
文摘Intracavity absorption spectroscopy is a strikingly sensitive technique that has been integrated with a two-wavelength setup to develop a sensor for human breath.Various factors are considered in such a scenario,out of which Relative Intensity Noise(RIN)has been exploited as an important parameter to characterize and calibrate the said setup.During the performance of an electrical based assessment arrangement which has been developed in the laboratory as an alternative to the expensive Agilent setup,the optical amplifier plays a pivotal role in its development and operation,along with other components and their significance.Therefore,the investigation and technical analysis of the amplifier in the system has been explored in detail.The algorithm developed for the automatic measurements of the system has been effectively deployed in terms of the laser’s performance.With this in perspective,a frequency dependent calibration has been pursued in depth with this scheme which enhances the sensor’s efficiency in terms of its sensitivity.In this way,our investigation helps us in a better understanding and implementation perspective of the proposed system,as the outcomes of our analysis adds to the precision and accuracy of the entire system.
文摘Lately,in modern smart power grids,energy demand for accurate forecast of electricity is gaining attention,with increased interest of research.This is due to the fact that a good energy demand forecast would lead to proper responses for electricity demand.In addition,proper energy demand forecast would ensure efficient planning of the electricity industry and is critical in the scheduling of the power grid capacity and management of the entire power network.As most power systems are been deregulated and with the rapid introduction and development of smart-metering technologies in Oman,new opportunities may arise considering the efficiency and reliability of the power system;like price-based demand response programs.These programs could either be a large scale for household,commercial or industrial users.However,excellent demand forecasting models are crucial for the deployment of these smart metering in the power grid based on good knowledge of the electricity market structure.Consequently,in this paper,an overview of the Oman regulatory regime,financial mechanism,price control,and distribution system security standard were presented.More so,the energy demand forecast in Oman was analysed,using the econometric model to forecasts its energy peak demand.The energy econometric analysis in this study describes the relationship between the growth of historical electricity consumption and macro-economic parameters(by region,and by tariff),considering a case study of Mazoon Electricity Distribution Company(MZEC),which is one of the major power distribution companies in Oman,for effective energy demand in the power grid.
文摘Augmentation of abnormal cells in the brain causes brain tumor(BT),and early screening and treatmentwill reduce its harshness in patients.BT’s clinical level screening is usually performed with Magnetic Resonance Imaging(MRI)due to its multi-modality nature.The overall aims of the study is to introduce,test and verify an advanced image processing technique with algorithms to automatically extract tumour sections from brain MRI scans,facilitating improved accuracy.The research intends to devise a reliable framework for detecting the BT region in the twodimensional(2D)MRI slice,and identifying its class with improved accuracy.The methodology for the devised framework comprises the phases of:(i)Collection and resizing of images,(ii)Implementation and Segmentation of Convolutional Neural Network(CNN),(iii)Deep feature extraction,(iv)Handcrafted feature extraction,(v)Moth-Flame-Algorithm(MFA)supported feature reduction,and(vi)Performance evaluation.This study utilized clinical-grade brain MRI of BRATS and TCIA datasets for the investigation.This framework segments detected the glioma(low/high grade)and glioblastoma class BT.This work helped to get a segmentation accuracy of over 98%with VGG-UNet and a classification accuracy of over 98%with the VGG16 scheme.This study has confirmed that the implemented framework is very efficient in detecting the BT in MRI slices with/without the skull section.
文摘Electrification and sustainable energy uses are increasing in Papua New Guinea (PNG) over the last few decades. The bulk of PNG’s population (85%) lives in isolated and dispersed villages in the rural areas. Most of these isolated and dispersed areas are still yet to be connected to an electricity supply.?Papua New Guinea (PNG) is richly endowed with natural resources, but exploitation has been hampered by rugged terrain, land tenure issues, and the high cost of developing infrastructure. The study is focused on mapping of enriched renewable energy zones of the entire country. Different variables related to renewable, like surface albedo index, earth skin temperature, solar?insolation incident, and wind speed are used for this purpose. Three interpolation approaches,?like inverse distance weighted averaging, thin-plate smoothing splines, and kriging, are evaluated to interpolate all variables. Rating and weight sum overlay operation is applied to derive potential?renewable energy zones in this equatorial country. Results show that potential renewable energy distribution is high in Papua New Guinea on the March and September equinoxes. Yearly average distribution of renewable energy source variables is significantly higher in most areas of Manus, New?Ireland, North Solomon, West New Britain, Northern, Central and Milne Bay;a larger portion of East New Britain;the northern part of West and East Sepik, Central, Morobe and eastern part of Madang province. The potential renewable energy distribution data can help to establish sustainable energy production in the country.
文摘Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive method for determining cardiac health.Various health practitioners use the ECG signal to ascertain critical information about the human heart.In this article,swarm intelligence approaches are used in the biomedical signal processing sector to enhance adaptive hybrid filters and empirical wavelet transforms(EWTs).At first,the white Gaussian noise is added to the input ECG signal and then applied to the EWT.The ECG signals are denoised by the proposed adaptive hybrid filter.The honey badge optimization(HBO)algorithm is utilized to optimize the EWT window function and adaptive hybrid filter weight parameters.The proposed approach is simulated by MATLAB 2018a using the MIT-BIH dataset with white Gaussian,electromyogram and electrode motion artifact noises.A comparison of the HBO approach with recursive least square-based adaptive filter,multichannel least means square,and discrete wavelet transform methods has been done in order to show the efficiency of the proposed adaptive hybrid filter.The experimental results show that the HBO approach supported by EWT and adaptive hybrid filter can be employed efficiently for cardiovascular signal denoising.
文摘Optical fiber Bragg grating(FBG)sensors have advanced significantly in the last several years.The use of innovative FBG in temperature and pressure measurement is examined in this study.The benefits of FBGs,such as their compact size,low weight,resilience to corrosion,immunity to electromagnetic interference,distributed sensing,and remote monitoring,have brought attention to the growing research in this field of structural health monitoring of civil infrastructures.
文摘Jamming and spoofing detection of global navigation satellite systems(GNSS)is of great importance.Civil and military aerial platforms use GNSS as main navigation systems and these systems are main target of threat attacks.In this paper a simple method based on different empirical probability density functions of successive received signal powers and goodness of fit tech-nique is proposed for airborne platforms such as unmanned aerial vehicle(UAV),in no fading envi-ronment.The two different paths between UAV-satellite and UAV-threat,experience different empirical probability density functions which can be used to distinguish between authentic and threat signals.Simulation results including detection and false alarm probabilities show good perfor-mance of proposed method as well as low computational burden.
基金This research was supported by the Ministry of Trade,Industry&Energy(MOTIE),Korea Institute for Advancement of Technology(KIAT)through the Encouragement Program for The Industries of Economic Cooperation Region(P0006082)the Soonchunhyang University Research Fund.
文摘Internet of Things(IoT)has become a major technological development which offers smart infrastructure for the cloud-edge services by the interconnection of physical devices and virtual things among mobile applications and embedded devices.The e-healthcare application solely depends on the IoT and cloud computing environment,has provided several characteristics and applications.Prior research works reported that the energy consumption for transmission process is significantly higher compared to sensing and processing,which led to quick exhaustion of energy.In this view,this paper introduces a new energy efficient cluster enabled clinical decision support system(EEC-CDSS)for embedded IoT environment.The presented EECCDSS model aims to effectively transmit the medical data from IoT devices and perform accurate diagnostic process.The EEC-CDSS model incorporates particle swarm optimization with levy distribution(PSO-L)based clustering technique,which clusters the set of IoT devices and reduces the amount of data transmission.In addition,the IoT devices forward the data to the cloud where the actual classification procedure is performed.For classification process,variational autoencoder(VAE)is used to determine the existence of disease or not.In order to investigate the proficient results analysis of the EEC-CDSS model,a wide range of simulations was carried out on heart disease and diabetes dataset.The obtained simulation values pointed out the supremacy of the EEC-CDSS model interms of energy efficiency and classification accuracy.
基金supported by Taif University Researchers Supporting Project Number(TURSP-2020/215)Taif University,Taif,Saudi Arabia(www.tu.edu.sa).
文摘In this paper,a novel cancellable biometrics technique calledMulti-Biometric-Feature-Hashing(MBFH)is proposed.The MBFH strategy is utilized to actualize a single direction(non-invertibility)biometric shape.MBFH is a typical model security conspire that is distinguished in the utilization of this protection insurance framework in numerous sorts of biometric feature strategies(retina,palm print,Hand Dorsum,fingerprint).A more robust and accurate multilingual biological structure in expressing human loneliness requires a different format to record clients with inseparable comparisons from individual biographical sources.This may raise worries about their utilization and security when these spread out designs are subverted as everybody is acknowledged for another biometric attribute.The proposed structure comprises of four sections:input multi-biometric acquisition,feature extraction,Multi-Exposure Fusion(MEF)and secure hashing calculation(SHA-3).Multimodal biometrics systems that are more powerful and precise in human-unmistakable evidence require various configurations to store a comparative customer that can be contrasted with biometric wellsprings of people.Disparate top words,biometrics graphs can’t be denied and change to another request for positive Identifications(IDs)while settling.Cancellable biometrics is may be the special procedure used to recognize this issue.
基金MMU Postdoctoral and Research Fellow(Account:MMUI/230023.02).
文摘Handwriting is a unique and significant human feature that distinguishes them from one another.There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification.However,such systems are susceptible to forgery,posing security risks.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.Our innovative method is intricately designed,encompassing five distinct phases:data collection,preprocessing,feature extraction,significant feature selection,and classification.One key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting(BHW),setting the foundation for our comprehensive approach.Post-preprocessing,we embarked on an exhaustive feature extraction process,encompassing integration with kinematic,statistical,spatial,and composite features.This meticulous amalgamation resulted in a robust set of 91 features.To enhance the efficiency of our system,we employed an analysis of variance(ANOVA)F test and mutual information scores approach,meticulously selecting the most pertinent features.In the identification phase,we harnessed the power of cutting-edge deep learning models,notably the Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM).These models underwent rigorous training and testing to accurately discern individuals based on their handwriting characteristics.Moreover,our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM,capitalizing on fine motor features for enhanced individual classifications.Crucially,our experimental results underscore the superiority of our approach.The CNN,BiLSTM,and hybrid models exhibited superior performance in individual classification when compared to prevailing state-of-the-art techniques.This validates our method’s efficacy and underscores its potential to outperform existing technologies,marking a significant stride forward in the realm of individual identification through handwriting analysis.
文摘Deeply etched rib waveguides on silicon on insulator platform were not addressed well in research publications. We have analyzed single mode condition and polarization independence of a deeply etched rib waveguide （DE-RW） structure from biosensing perspective. With this rib structure, an asymmetrically etched integrated optic directional coupler has been numerically modeled to have the same coupling length for quasi- TE and TM modes. The coupling coefficients with the glucose solution as an upper cladding were calculated using a full vector mode solver, and the bulk refractive index sensitivity of the sensor was found as 28.305 × 10^-2/RIU for a fundamental quasi-TE mode.
基金Ministry of Electronics and Information Technology (Center of Excellence in Quantum Technologies,NNetra)Department of Science and Technology+1 种基金Ministry of Science and TechnologyIndia (Nano Mission, QUST Programme)。
文摘We experimentally demonstrate a novel quasi-bound state in the continuum(BIC) resonance in the mid-infrared wavelength region with the resonant electric field confined as a slot mode within a low-refractive-index medium sandwiched between high-index layers.The structures studied here comprise coupled amorphous germanium guided-mode resonance(GMR) structures with a top one-dimensional grating layer and bottom uniform layer separated by a low-index silicon nitride layer.The slot-mode profile within the silicon nitride layer with mode field confinement >30% is achieved as a solution to the electromagnetic wave propagation through the coupled GMR structure with the dominant field component being perpendicular to the layers.The quasi-BIC resonance in symmetric 1D grating structures can be observed even at normal incidence when considering a realistic excitation beam with finite angular spread.The measured transmission peak is found to redshift(remain almost unchanged)under classical(full-conical) mounting conditions.The highest quality factor of ~400 is experimentally extracted at normal incidence under a classical mounting condition with a resonance peak at 3.41 μm wavelength.Such slot-mode GMR structures with appropriately chosen low-index intermediate layers can find applications in resonantly enhanced sensing and active photonic devices.
文摘In monolayer transition metal dichalcogenide semiconductors,valley coherence degrades rapidly due to a combination of fast scattering and inter-valley exchange interaction.This leads to a sub-picosecond valley coherence time,making coherent manipulation of exciton a highly challenging task Using monolayer MoS_(2) sandwiched between top and bottom graphene,here we demonstrate fully valley-coherent excitons by observing~100%degree of linear polarization in steady state photoluminescence.This is achieved in this unique design through a combined effect of(a)suppression in exchange interaction due to enhanced dielectric screening,(b)reduction in exciton lifetime due to a fast inter-layer transfer to graphene,and(c)operating in the motional narrowing regime.We disentangle the role of the key parameters affecting valley coherence by using a combination of calculation(solutions of Bethe-Salpeter and Maiallle-Silva-Sham equations)and a careful choice of design of experiments using four different stacks with systematic variation of screening and exciton lifetime.To the best of our knowledge,this is the first report in which the excitons are found to be valley coherent in the entire lifetime in monolayer semiconductors,allowing optical readout of valley coherence possible.
文摘In recent years,modern metropolitan areas are the main indicators of economic growth of nation.In metropolitan areas,number and frequency of vehicles have increased tremendously,and they create issues,like traffic congestion,accidents,environmental pollution,economical losses and unnecessary waste of fuel.In this paper,we propose traffic management system based on the prediction information to reduce the above mentioned issues in a metropolitan area.The proposed traffic management system makes use of static and mobile agents,where the static agent available at region creates and dispatches mobile agents to zones in a metropolitan area.The migrated mobile agents use emergent intelligence technique to collect and share traffic flow parameters(speed and density),historical data,resource information,spatio-temporal data and so on,and are analyzes the static agent.The emergent intelligence technique at static agent uses analyzed,historical and spatio-temporal data for monitoring and predicting the expected patterns of traffic density(commuters and vehicles)and travel times in each zone and region.The static agent optimizes predicted and analyzed data for choosing optimal routes to divert the traffic,in order to ensure smooth traffic flow and reduce frequency of occurrence of traffic congestion,reduce traffic density and travel time.The performance analysis is performed in realistic scenario by integrating NS2,SUMO,OpenStreatMap(OSM)and MOVE tool.The effectiveness of the proposed approach has been compared with the existing approach.
基金National Research Foundation of Korea(2023R1A2C100531711)H.K.also acknowledges support from the DGIST R&D programs(22-CoENT-01 and 22-BT-06)funded by the Ministry of Science and ICT.V.R.acknowledges support from Department of Science and Technology(DST)Indo-Korea joint research project(INT/Korea/P-44).
文摘Optical microscopy with optimal axial resolution is critical for precise visualization of two-dimensional flat-top structures.Here,we present sub-diffraction-limited ultrafast imaging of hexagonal boron nitride(hBN)nanosheets using a confocal focus-engineered coherent anti-Stokes Raman scattering(cFE-CARS)microscopic system.By incorporating a pinhole with a diameter of approximately 30μm,we effectively minimized the intensity of side lobes induced by circular partial pi-phase shift in the wavefront(diameter,d0)of the probe beam,as well as nonresonant background CARS intensities.Using axial-resolution-improved cFE-CARS(acFE-CARS),the achieved axial resolution is 350 nm,exhibiting a 4.3-folded increase in the signal-to-noise ratio compared to the previous case with 0.58 d0 phase mask.This improvement can be accomplished by using a phase mask of 0.24 d0.Additionally,we employed nonde-generate phase matching with three temporally separable incident beams,which facilitated cross-sectional visualization of highly-sample-specific and vibration-sensitive signals in a pump-probe fashion with subpicosecond time resolution.Our observations reveal time-dependent CARS dephasing in hBN nanosheets,induced by Raman-free induction decay(0.66 ps)in the 1373 cm^(−1) mode.
