The Discrete Walsh Hadamard Transform(DWHT)has emerged as an efficient alternative to the Discrete Fourier Transform(DFT)for Orthogonal Frequency Division Multiplexing(OFDM)implementations,particularly in handling cha...The Discrete Walsh Hadamard Transform(DWHT)has emerged as an efficient alternative to the Discrete Fourier Transform(DFT)for Orthogonal Frequency Division Multiplexing(OFDM)implementations,particularly in handling channel impairments.In this article,we proposed an efficient Joint Low Complexity Regularized Zero Forcing-Wavelet Domain Equalizer(JLCRLZF-WDE)to replace the traditional Frequency Domain Equalizer(FDE)in DWHT-OFDM systems.Unlike FDE,which requires additional DFT and Inverse DFT(IDFT)computations,the proposed JLCRLZF-WDE directly operates in the Walsh domain,effectively mitigating the computational overhead.The derivation of the proposed JLCRLZF-WDE equations take the effect of the channel,Co-Carrier Frequency Offset(Co-CFO),as well as the noise into account.During the derivation of the system model equations,we assume a MultipleInput-Multiple-Output(MIMO)-OFDM communication system through a Rayleigh fading channel.The Bit Error Rate(BER)performance and computational complexity of the proposed and the conventional algorithms are compared,indicating the significance of the proposed algorithm.Simulation results confirm the superiority of the proposed equalizer,demonstrating a 23.68%±28.4%reduction in computational complexity compared to Minimum Mean Square Error(LMMSE)-FDE based on DFT,while maintaining comparable BER performance at various MIMO configuration.Furthermore,at a BER of 10^(-4),the JLCRLZF-WDE achieves performance parity with conventional Walsh domain LMMSE equalizers,whereas other equalizers require an additional Signal-to-Noise Ratio(SNR)of 3.06 d B to achieve the same performance.展开更多
The 5G cellular network aims at providing three major services:Massive machine-type communication(mMTC),ultra-reliable low-latency communications(URLLC),and enhanced-mobile-broadband(eMBB).Among these services,the URL...The 5G cellular network aims at providing three major services:Massive machine-type communication(mMTC),ultra-reliable low-latency communications(URLLC),and enhanced-mobile-broadband(eMBB).Among these services,the URLLC and eMBB require strict end-to-end latency of 1 ms while maintaining 99.999%reliability,and availability of extremely high data rates for the users,respectively.One of the critical challenges in meeting these requirements is to upgrade the existing optical fiber backhaul network interconnecting the base stations with a multigigabit capacity,low latency and very high reliability system.To address this issue,we have numerically analyzed 100 Gbit/s coherent optical orthogonal frequency division multiplexing(CO-OFDM)transmission performance over 400 km single-mode fiber(SMF)and 100 km of multi-mode fiber(MMF)links.The system is simulated over optically repeated and non-repeated SMF and MMF links.Coherent transmission is used,and the system is analyzed in a linear and non-linear regime.The system performance is quantified by bit error ratio(BER).Spectrally efficient and optimal transmission performance is achieved for 400 km SMF and 100 km MMF link.The results designate thatMMF links can be employed beyond short reach applications by using them in the existing SMF infrastructure for long haul transmission.In particular,the proposed CO-OFDM system can be efficiently employed in 5G backhaul network.The multi-gigabit capacity and lower BER of the proposed system makes it a suitable candidate especially for the eMBB and URLLC requirements for 5G backhaul network.展开更多
The adaptive control of nonlinear systems that are linear in the unknown but time-varying parameters are treated in this paper. Since satisfactory transient performance is an important factor, multiple models are requ...The adaptive control of nonlinear systems that are linear in the unknown but time-varying parameters are treated in this paper. Since satisfactory transient performance is an important factor, multiple models are required as these parameters change abruptly in the parameter space. In this paper we consider both the multiple models with switching and tuning methodology as well as multiple models with second level adaptation for this class of systems. We demonstrate that the latter approach is better than the former.展开更多
For applications such as near-field target detection and tumor hyperthermia with a fiat left-handed metamaterial （LHM） lens, a microwave will be focused in the heterogeneous and lossy medium. Different from the focu...For applications such as near-field target detection and tumor hyperthermia with a fiat left-handed metamaterial （LHM） lens, a microwave will be focused in the heterogeneous and lossy medium. Different from the focusing of a fiat LHM lens in vacuum as reported in most previous studies, the medium loss and heterogeneity will affect the focusing performance of the LHM lens. Numerical simulations indicate that the medium loss will degrade the focusing resolution, while the heterogeneity of random variability within ±30% will affect the focusing resolution to a limited extent. Both the loss and heterogeneity of the medium will shift the focal point away from the image plane. When focusing in a medium with different permittivity values, an LHM lens will also have different focusing resolutions due to different electric thicknesses.展开更多
In optical techniques,noise signal is a classical problem in medical image processing.Recently,there has been considerable interest in using the wavelet transform with Bayesian estimation as a powerful tool for recove...In optical techniques,noise signal is a classical problem in medical image processing.Recently,there has been considerable interest in using the wavelet transform with Bayesian estimation as a powerful tool for recovering image from noisy data.In wavelet domain,if Bayesian estimator is used for denoising problem,the solution requires a prior knowledge about the distribution of wavelet coeffcients.Indeed,wavelet coeffcients might be better modeled by super Gaussian density.The super Gaussian density can be generated by Gaussian scale mixture(GSM).So,we present new minimum mean square error(MMSE)estimator for spherically-contoured GSM with Maxwell distribution in additive white Gaussian noise(AWGN).We compare our proposed method to current state-of-the-art method applied on standard test image and we quantify achieved performance improvement.展开更多
Refraction index mismatch between fiat left-handed metamaterial （LHM） lens and its surrounding medium generally destroys the focusing of flat LHM lens and degrades the performance of near-field target detection by u...Refraction index mismatch between fiat left-handed metamaterial （LHM） lens and its surrounding medium generally destroys the focusing of flat LHM lens and degrades the performance of near-field target detection by using fiat LHM lens. For LHM lens of refraction index mismatch within ±30%, numerical simulations demonstrate that lenses with large refraction index may suffer less resolution degradation than lenses with small refraction index, and the enhancement of refocused microwave backscattered from target can be subsided by up to approximately 5.5 dB. The refraction index mismatch will also shift the target position in the reconstructed image so that theoretical prediction of target position needs to be modified.展开更多
Software-Defined Networking(SDN)is a new network technology that uses programming to complement the data plane with a control plane.To enable safe connection,however,numerous security challenges must be addressed.Floo...Software-Defined Networking(SDN)is a new network technology that uses programming to complement the data plane with a control plane.To enable safe connection,however,numerous security challenges must be addressed.Flooding attacks have been one of the most prominent risks on the internet for decades,and they are now becoming challenging difficulties in SDN networks.To solve these challenges,we proposed a unique firewall application built on multiple levels of packet filtering to provide a flooding attack prevention system and a layer-based packet detection system.This study offers a systematic strategy for wrapping up the examination of SDN operations.The Mininet simulator examines the effectiveness of SDN-based firewalls at various network tiers.The fundamental network characteristics that specify how SDN should operate.The three main analytical measures of the network are jitter,response time,and throughput.During regular operations,their behavior evaluates in the standard SDN conditions of Transmission Control Protocol(TCP)flooding and User Datagram Protocol(UDP)flooding with no SDN occurrences.Low Orbit Ion Cannon(LOIC)is applied to launch attacks on the transmission by the allocated server.Wireshark and MATLAB are used for the behavioral study to determine how sensitive the parameters are used in the SDN network and monitor the fluctuations of those parameters for different simulated scenarios.展开更多
We develop a simple new design for a multi-band metamaterial absorber(MTMA)for radar applications.Computer Simulation Technology(CST)Studio Suite 2018 was used for the numerical analysis and absorption study.The simul...We develop a simple new design for a multi-band metamaterial absorber(MTMA)for radar applications.Computer Simulation Technology(CST)Studio Suite 2018 was used for the numerical analysis and absorption study.The simulated results show four high peaks at 5.6 GHz,7.6 GHz,10.98 GHz,and 11.29 GHz corresponding to absorption characteristics of 100%,100%,99%,and 99%,respectively.Furthermore,two different structures were designed and compared with the proposed MTMA.The proposed structure remained insensitive for any incident angle and polarization angle from 0°to60°.Moreover,negative constitutive parameters were retrieved numerically.To support the simulated results,the proposed design was fabricated by using a computer numerical control-based printed circuit board prototyping machine and tested experimentally in a microwave laboratory.The absorption mechanism of the proposed MTMA is presented through the surface current and electric field distributions.The novelties of the proposed structure are a simple and new design,ease of fabrication,low cost,durability,suitability for real-time applications and long-term stability given the fabrication technique and non-destructive measurement method and very high absorption.The proposed structure has potential applications in C and X band frequency ranges.展开更多
Cognitive Radios (CRs) use dynamic threshold estimation (DTE) techniques to better detect primary user signals under noise uncertainty regimes. However, DTE techniques have rarely been compared before, particularly un...Cognitive Radios (CRs) use dynamic threshold estimation (DTE) techniques to better detect primary user signals under noise uncertainty regimes. However, DTE techniques have rarely been compared before, particularly under the one tier CR network (CRN) model, making it difficult to assess their comparative performance characteristics under this regime. Thus, in this paper, we have investigated the performance of some notable DTE methods under the one-tier CRN model. We used the auction game model in our investigation to compare fairly the spectrum efficiency performance of each technique. Our findings show that DTEs generally perform better than the fixed threshold method particularly under unpredictable noise uncertainty regimes. Our results show further that the channel utilization (CU) rate of the fixed threshold method, popularly used by researchers, plummets by 50.26% for a 1 dB increase in the noise uncertainty level, while the CU rate of the DTE techniques interestingly increased by an average of 4%. Our investigation will enable CR Engineers to better understand the performance characteristics of DTE techniques under the one-tier CRN model.展开更多
For the last two decades,physicians and clinical experts have used a single imaging modality to identify the normal and abnormal structure of the human body.However,most of the time,medical experts are unable to accur...For the last two decades,physicians and clinical experts have used a single imaging modality to identify the normal and abnormal structure of the human body.However,most of the time,medical experts are unable to accurately analyze and examine the information from a single imaging modality due to the limited information.To overcome this problem,a multimodal approach is adopted to increase the qualitative and quantitative medical information which helps the doctors to easily diagnose diseases in their early stages.In the proposed method,a Multi-resolution Rigid Registration(MRR)technique is used for multimodal image registration while Discrete Wavelet Transform(DWT)along with Principal Component Averaging(PCAv)is utilized for image fusion.The proposed MRR method provides more accurate results as compared with Single Rigid Registration(SRR),while the proposed DWT-PCAv fusion process adds-on more constructive information with less computational time.The proposed method is tested on CT and MRI brain imaging modalities of the HARVARD dataset.The fusion results of the proposed method are compared with the existing fusion techniques.The quality assessment metrics such as Mutual Information(MI),Normalize Crosscorrelation(NCC)and Feature Mutual Information(FMI)are computed for statistical comparison of the proposed method.The proposed methodology provides more accurate results,better image quality and valuable information for medical diagnoses.展开更多
A brainwave classification,which does not involve any limb movement and stimulus for character-writing applications,benefits impaired people,in terms of practical communication,because it allows users to command a dev...A brainwave classification,which does not involve any limb movement and stimulus for character-writing applications,benefits impaired people,in terms of practical communication,because it allows users to command a device/computer directly via electroencephalogram signals.In this paper,we propose a new framework based on Empirical Mode Decomposition(EMD)features along with theGaussianMixtureModel(GMM)andKernel Extreme Learning Machine(KELM)-based classifiers.For this purpose,firstly,we introduce EMD to decompose EEG signals into Intrinsic Mode Functions(IMFs),which actually are used as the input features of the brainwave classification for the character-writing application.We hypothesize that EMD along with the appropriate IMF is quite powerful for the brainwave classification,in terms of character applications,because of the wavelet-like decomposition without any down sampling process.Secondly,by getting motivated with shallow learning classifiers,we can provide promising performance for the classification of binary classes,GMM and KELM,which are applied for the learning of features along with the brainwave classification.Lastly,we propose a new method by combining GMMand KELM to fuse the merits of different classifiers.Moreover,the proposed methods are validated by using the volunteer-independent 5-fold cross-validation and accuracy as a standard measurement.The experimental results showed that EMD with the proper IMF achieved better results than the conventional discrete wavelet transform(DWT)feature.Moreover,we found that the EMD feature along with the GMM/KELM-based classifier provides the average accuracy of 77.40%and 80.10%,respectively,which could perform better than the conventional methods where we use DWT along with the artificial neural network classifier in order to get the average accuracy of 80.60%.Furthermore,we obtained the improved performance by combining GMM and KELM,i.e.,average accuracy of 80.60%.These outcomes exhibit the usefulness of the EMD feature combining with GMMand KELM based classifiers for the brainwaveclassification in terms of the Character-Writing application,which do notrequire any limb movement and stimulus.展开更多
A coplanar waveguide-fed quintuple band antenna with a slotted circular-shaped radiator for wireless applications with a high isolation between adjacent bands is presented in this paper.The proposed antenna resonates ...A coplanar waveguide-fed quintuple band antenna with a slotted circular-shaped radiator for wireless applications with a high isolation between adjacent bands is presented in this paper.The proposed antenna resonates at multiple frequencies with corresponding center frequencies of 2.35,4.92,5.75,6.52,and 8.46 GHz.The intended functionality is achieved by introducing a circular disc radiator with five slots and a U-shaped slot in the feed.The proposed antenna exhibits coverage of the maximum set of wireless applications,such as satellite communication,worldwide interoperability for microwave access,wireless local area network(WLAN),long-distance radio telecommunications,and X-band/Satcom wireless applications.The simulation and measurement results of the proposed fabricated antenna demonstrate the high isolation between adjacent bands.A stable realized gain with an advantageous radiation pattern is achieved at the operating frequency bands.The proposed simple design,compact structure,and simple feeding technique make this antenna suitable for integration in several wireless communication applications,where the portability of devices is a significant concern.The proposed antenna is anticipated to be an appropriate candidate for WLAN,long-term evolution,and fifth-generation mobile communication because of its multi-operational bands and compact size for handheld devices.展开更多
In this paper,low profile frequency reconfigurable monopole antenna is designed on FR-4 substrate with a compact size of 30 mm^(3)×20 mm^(3)×1.6 mm^(3).The antenna is tuned to four different modes through th...In this paper,low profile frequency reconfigurable monopole antenna is designed on FR-4 substrate with a compact size of 30 mm^(3)×20 mm^(3)×1.6 mm^(3).The antenna is tuned to four different modes through three pin diode switches.In Mode 1(SW1 to SW3=OFF),antenna covers a wideband of 3.15–8.51 GHz.For Mode 2(SW1=ON,SW2 to SW3=OFF),the proposed antenna resonates at 3.5 GHz.The antenna shows dual band behavior and covers 2.6 and 6.4 GHz in Mode 3(SW1 and SW2=ON,SW3=OFF).The same antenna covers three different bands of 2.1,5 and 6.4 GHz when operating in Mode 4(SW1 to SW3=ON).The proposed antenna has good radiation efficiency ranges from 70%∼84%,providing adequate average gain of 2.05 dBi in mode 1,1.87 dBi in mode 2,1.4–1.75 dBi in mode 3 and 1.05–1.56 dBi in mode 4.The achieved impedance bandwidths at respective frequencies ranges from 240 to 5000 MHz.The Voltage Standing Waves Ratio(VSWR)of less than 1.5 is achieved for all operating bands.To validate the simulation results,the proposed antenna is fabricated and experimentally tested in antenna measurement laboratory.Due to its reasonably small size and support of multiple bands operation,the proposed antenna can be used in modern communication systems for supporting various applications such as fifth generation(5G)mobile and wireless local area networks(WLAN).展开更多
Many organizations apply cloud computing to store and effectively process data for various applications.The user uploads the data in the cloud has less security due to the unreliable verification process of data integ...Many organizations apply cloud computing to store and effectively process data for various applications.The user uploads the data in the cloud has less security due to the unreliable verification process of data integrity.In this research,an enhanced Merkle hash tree method of effective authentication model is proposed in the multi-owner cloud to increase the security of the cloud data.Merkle Hash tree applies the leaf nodes with a hash tag and the non-leaf node contains the table of hash information of child to encrypt the large data.Merkle Hash tree provides the efficient mapping of data and easily identifies the changesmade in the data due to proper structure.The developed model supports privacy-preserving public auditing to provide a secure cloud storage system.The data owners upload the data in the cloud and edit the data using the private key.An enhanced Merkle hash tree method stores the data in the cloud server and splits it into batches.The data files requested by the data owner are audit by a third-party auditor and the multiowner authentication method is applied during the modification process to authenticate the user.The result shows that the proposed method reduces the encryption and decryption time for cloud data storage by 2–167 ms when compared to the existing Advanced Encryption Standard and Blowfish.展开更多
The COVID-19 pandemic has devastated our daily lives,leaving horrific repercussions in its aftermath.Due to its rapid spread,it was quite difficult for medical personnel to diagnose it in such a big quantity.Patients ...The COVID-19 pandemic has devastated our daily lives,leaving horrific repercussions in its aftermath.Due to its rapid spread,it was quite difficult for medical personnel to diagnose it in such a big quantity.Patients who test positive for Covid-19 are diagnosed via a nasal PCR test.In comparison,polymerase chain reaction(PCR)findings take a few hours to a few days.The PCR test is expensive,although the government may bear expenses in certain places.Furthermore,subsets of the population resist invasive testing like swabs.Therefore,chest X-rays or Computerized Vomography(CT)scans are preferred in most cases,and more importantly,they are non-invasive,inexpensive,and provide a faster response time.Recent advances in Artificial Intelligence(AI),in combination with state-of-the-art methods,have allowed for the diagnosis of COVID-19 using chest x-rays.This article proposes a method for classifying COVID-19 as positive or negative on a decentralized dataset that is based on the Federated learning scheme.In order to build a progressive global COVID-19 classification model,two edge devices are employed to train the model on their respective localized dataset,and a 3-layered custom Convolutional Neural Network(CNN)model is used in the process of training the model,which can be deployed from the server.These two edge devices then communicate their learned parameter and weight to the server,where it aggregates and updates the globalmodel.The proposed model is trained using an image dataset that can be found on Kaggle.There are more than 13,000 X-ray images in Kaggle Database collection,from that collection 9000 images of Normal and COVID-19 positive images are used.Each edge node possesses a different number of images;edge node 1 has 3200 images,while edge node 2 has 5800.There is no association between the datasets of the various nodes that are included in the network.By doing it in this manner,each of the nodes will have access to a separate image collection that has no correlation with each other.The diagnosis of COVID-19 has become considerably more efficient with the installation of the suggested algorithm and dataset,and the findings that we have obtained are quite encouraging.展开更多
Vehicular Ad-hoc Networks(VANETs)are mobile ad-hoc networks that use vehicles as nodes to create a wireless network.Whereas VANETs offer many advantages over traditional transportation networks,ensuring security in VA...Vehicular Ad-hoc Networks(VANETs)are mobile ad-hoc networks that use vehicles as nodes to create a wireless network.Whereas VANETs offer many advantages over traditional transportation networks,ensuring security in VANETs remains a significant challenge due to the potential for malicious attacks.This study addresses the critical issue of security in VANETs by introducing an intelligent Intrusion Detection System(IDS)that merges Machine Learning(ML)–based attack detection with Explainable AI(XAI)explanations.This study ML pipeline involves utilizing correlation-based feature selection followed by a Random Forest(RF)classifier that achieves a classification accuracy of 100%for the binary classification task of identifying normal and malicious traffic.An innovative aspect of this study is the incorporation of XAI methodologies,specifically the Local Interpretable Model-agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).In addition,this research also considered key features identified by mutual information-based feature selection for the task at hand.The major findings from this study reveal that the XAI-based intrusion detection methods offer distinct insights into feature importance.Key features identified by mutual information,LIME,and SHAP predominantly relate to Transmission Control Protocol(TCP),Hypertext Transfer Protocol(HTTP),Domain Name System(DNS),and Message Queuing Telemetry Transport(MQTT)protocols,highlighting their significance in distinguishing normal and malicious network activity.This XAI approach equips cybersecurity experts with a robust means of identifying and understanding VANET malicious activities,forming a foundation for more effective security countermeasures.展开更多
Interact traffic classification is vital to the areas of network operation and management. Traditional classification methods such as port mapping and payload analysis are becoming increasingly difficult as newly emer...Interact traffic classification is vital to the areas of network operation and management. Traditional classification methods such as port mapping and payload analysis are becoming increasingly difficult as newly emerged applications （e. g. Peer-to-Peer） using dynamic port numbers, masquerading techniques and encryption to avoid detection. This paper presents a machine learning （ML） based traffic classifica- tion scheme, which offers solutions to a variety of network activities and provides a platform of performance evaluation for the classifiers. The impact of dataset size, feature selection, number of application types and ML algorithm selection on classification performance is analyzed and demonstrated by the following experiments： （1） The genetic algorithm based feature selection can dramatically reduce the cost without diminishing classification accuracy. （2） The chosen ML algorithms can achieve high classification accuracy. Particularly, REPTree and C4.5 outperform the other ML algorithms when computational complexity and accuracy are both taken into account. （3） Larger dataset and fewer application types would result in better classification accuracy. Finally, early detection with only several initial packets is proposed for real-time network activity and it is proved to be feasible according to the preliminary results.展开更多
To reduce temperature sensitivity of the fibre Bragg grating （FBG） chemical sensor, a simple method is proposed by measuring the peak wavelength difference between an etched FBG and an un-etched one in an optical fi...To reduce temperature sensitivity of the fibre Bragg grating （FBG） chemical sensor, a simple method is proposed by measuring the peak wavelength difference between an etched FBG and an un-etched one in an optical fibre. Thermal characteristics and chemical sensitivity of the sensor are experimentally investigated. The experimental results indicate that the etched FBG and the rest one have almost the same thermal response, and concentration changes of the surrounding chemical solutions can be detected by measuring the peak wavelength difference between them. The sensor has been used to measure the concentrations of propylene glycol solutions and sugar solutions, and it could detect 0.7% and 0.45% concentration changes for them with an optical spectrum analyser in resolution of 10pm.展开更多
文摘The Discrete Walsh Hadamard Transform(DWHT)has emerged as an efficient alternative to the Discrete Fourier Transform(DFT)for Orthogonal Frequency Division Multiplexing(OFDM)implementations,particularly in handling channel impairments.In this article,we proposed an efficient Joint Low Complexity Regularized Zero Forcing-Wavelet Domain Equalizer(JLCRLZF-WDE)to replace the traditional Frequency Domain Equalizer(FDE)in DWHT-OFDM systems.Unlike FDE,which requires additional DFT and Inverse DFT(IDFT)computations,the proposed JLCRLZF-WDE directly operates in the Walsh domain,effectively mitigating the computational overhead.The derivation of the proposed JLCRLZF-WDE equations take the effect of the channel,Co-Carrier Frequency Offset(Co-CFO),as well as the noise into account.During the derivation of the system model equations,we assume a MultipleInput-Multiple-Output(MIMO)-OFDM communication system through a Rayleigh fading channel.The Bit Error Rate(BER)performance and computational complexity of the proposed and the conventional algorithms are compared,indicating the significance of the proposed algorithm.Simulation results confirm the superiority of the proposed equalizer,demonstrating a 23.68%±28.4%reduction in computational complexity compared to Minimum Mean Square Error(LMMSE)-FDE based on DFT,while maintaining comparable BER performance at various MIMO configuration.Furthermore,at a BER of 10^(-4),the JLCRLZF-WDE achieves performance parity with conventional Walsh domain LMMSE equalizers,whereas other equalizers require an additional Signal-to-Noise Ratio(SNR)of 3.06 d B to achieve the same performance.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(Nos.2019R1A4A1023746,2019R1F1A1060799)the Strengthening R&D Capability Program of Sejong University。
文摘The 5G cellular network aims at providing three major services:Massive machine-type communication(mMTC),ultra-reliable low-latency communications(URLLC),and enhanced-mobile-broadband(eMBB).Among these services,the URLLC and eMBB require strict end-to-end latency of 1 ms while maintaining 99.999%reliability,and availability of extremely high data rates for the users,respectively.One of the critical challenges in meeting these requirements is to upgrade the existing optical fiber backhaul network interconnecting the base stations with a multigigabit capacity,low latency and very high reliability system.To address this issue,we have numerically analyzed 100 Gbit/s coherent optical orthogonal frequency division multiplexing(CO-OFDM)transmission performance over 400 km single-mode fiber(SMF)and 100 km of multi-mode fiber(MMF)links.The system is simulated over optically repeated and non-repeated SMF and MMF links.Coherent transmission is used,and the system is analyzed in a linear and non-linear regime.The system performance is quantified by bit error ratio(BER).Spectrally efficient and optimal transmission performance is achieved for 400 km SMF and 100 km MMF link.The results designate thatMMF links can be employed beyond short reach applications by using them in the existing SMF infrastructure for long haul transmission.In particular,the proposed CO-OFDM system can be efficiently employed in 5G backhaul network.The multi-gigabit capacity and lower BER of the proposed system makes it a suitable candidate especially for the eMBB and URLLC requirements for 5G backhaul network.
文摘The adaptive control of nonlinear systems that are linear in the unknown but time-varying parameters are treated in this paper. Since satisfactory transient performance is an important factor, multiple models are required as these parameters change abruptly in the parameter space. In this paper we consider both the multiple models with switching and tuning methodology as well as multiple models with second level adaptation for this class of systems. We demonstrate that the latter approach is better than the former.
基金Supported by the National Natural Science Foundation of China under Grant 60771041, the Department of Personnel of Jiangsu Province under Grant 06-E-040, and the Department of Education of Jiangsu Province under Grant 05KJB510012.
文摘For applications such as near-field target detection and tumor hyperthermia with a fiat left-handed metamaterial （LHM） lens, a microwave will be focused in the heterogeneous and lossy medium. Different from the focusing of a fiat LHM lens in vacuum as reported in most previous studies, the medium loss and heterogeneity will affect the focusing performance of the LHM lens. Numerical simulations indicate that the medium loss will degrade the focusing resolution, while the heterogeneity of random variability within ±30% will affect the focusing resolution to a limited extent. Both the loss and heterogeneity of the medium will shift the focal point away from the image plane. When focusing in a medium with different permittivity values, an LHM lens will also have different focusing resolutions due to different electric thicknesses.
文摘In optical techniques,noise signal is a classical problem in medical image processing.Recently,there has been considerable interest in using the wavelet transform with Bayesian estimation as a powerful tool for recovering image from noisy data.In wavelet domain,if Bayesian estimator is used for denoising problem,the solution requires a prior knowledge about the distribution of wavelet coeffcients.Indeed,wavelet coeffcients might be better modeled by super Gaussian density.The super Gaussian density can be generated by Gaussian scale mixture(GSM).So,we present new minimum mean square error(MMSE)estimator for spherically-contoured GSM with Maxwell distribution in additive white Gaussian noise(AWGN).We compare our proposed method to current state-of-the-art method applied on standard test image and we quantify achieved performance improvement.
基金Supported by the National Natural Science Foundation of China under Grant 60771041, Department of Personnel of Jiangsu Province under Grant 06-E-060, and Department of Education of Jiangsu Province under Grant 05KJB510012.
文摘Refraction index mismatch between fiat left-handed metamaterial （LHM） lens and its surrounding medium generally destroys the focusing of flat LHM lens and degrades the performance of near-field target detection by using fiat LHM lens. For LHM lens of refraction index mismatch within ±30%, numerical simulations demonstrate that lenses with large refraction index may suffer less resolution degradation than lenses with small refraction index, and the enhancement of refocused microwave backscattered from target can be subsided by up to approximately 5.5 dB. The refraction index mismatch will also shift the target position in the reconstructed image so that theoretical prediction of target position needs to be modified.
基金supported in part by the Research Committee of Hamdard University Karachi Pakistan(www.hamdard.edu.pk)the Office of Research Innovation&Commercialization(ORIC)of Dawood University of Engineering&Technology Karachi Pakistan(www.duet.edu.pk).
文摘Software-Defined Networking(SDN)is a new network technology that uses programming to complement the data plane with a control plane.To enable safe connection,however,numerous security challenges must be addressed.Flooding attacks have been one of the most prominent risks on the internet for decades,and they are now becoming challenging difficulties in SDN networks.To solve these challenges,we proposed a unique firewall application built on multiple levels of packet filtering to provide a flooding attack prevention system and a layer-based packet detection system.This study offers a systematic strategy for wrapping up the examination of SDN operations.The Mininet simulator examines the effectiveness of SDN-based firewalls at various network tiers.The fundamental network characteristics that specify how SDN should operate.The three main analytical measures of the network are jitter,response time,and throughput.During regular operations,their behavior evaluates in the standard SDN conditions of Transmission Control Protocol(TCP)flooding and User Datagram Protocol(UDP)flooding with no SDN occurrences.Low Orbit Ion Cannon(LOIC)is applied to launch attacks on the transmission by the allocated server.Wireshark and MATLAB are used for the behavioral study to determine how sensitive the parameters are used in the SDN network and monitor the fluctuations of those parameters for different simulated scenarios.
文摘We develop a simple new design for a multi-band metamaterial absorber(MTMA)for radar applications.Computer Simulation Technology(CST)Studio Suite 2018 was used for the numerical analysis and absorption study.The simulated results show four high peaks at 5.6 GHz,7.6 GHz,10.98 GHz,and 11.29 GHz corresponding to absorption characteristics of 100%,100%,99%,and 99%,respectively.Furthermore,two different structures were designed and compared with the proposed MTMA.The proposed structure remained insensitive for any incident angle and polarization angle from 0°to60°.Moreover,negative constitutive parameters were retrieved numerically.To support the simulated results,the proposed design was fabricated by using a computer numerical control-based printed circuit board prototyping machine and tested experimentally in a microwave laboratory.The absorption mechanism of the proposed MTMA is presented through the surface current and electric field distributions.The novelties of the proposed structure are a simple and new design,ease of fabrication,low cost,durability,suitability for real-time applications and long-term stability given the fabrication technique and non-destructive measurement method and very high absorption.The proposed structure has potential applications in C and X band frequency ranges.
文摘Cognitive Radios (CRs) use dynamic threshold estimation (DTE) techniques to better detect primary user signals under noise uncertainty regimes. However, DTE techniques have rarely been compared before, particularly under the one tier CR network (CRN) model, making it difficult to assess their comparative performance characteristics under this regime. Thus, in this paper, we have investigated the performance of some notable DTE methods under the one-tier CRN model. We used the auction game model in our investigation to compare fairly the spectrum efficiency performance of each technique. Our findings show that DTEs generally perform better than the fixed threshold method particularly under unpredictable noise uncertainty regimes. Our results show further that the channel utilization (CU) rate of the fixed threshold method, popularly used by researchers, plummets by 50.26% for a 1 dB increase in the noise uncertainty level, while the CU rate of the DTE techniques interestingly increased by an average of 4%. Our investigation will enable CR Engineers to better understand the performance characteristics of DTE techniques under the one-tier CRN model.
文摘For the last two decades,physicians and clinical experts have used a single imaging modality to identify the normal and abnormal structure of the human body.However,most of the time,medical experts are unable to accurately analyze and examine the information from a single imaging modality due to the limited information.To overcome this problem,a multimodal approach is adopted to increase the qualitative and quantitative medical information which helps the doctors to easily diagnose diseases in their early stages.In the proposed method,a Multi-resolution Rigid Registration(MRR)technique is used for multimodal image registration while Discrete Wavelet Transform(DWT)along with Principal Component Averaging(PCAv)is utilized for image fusion.The proposed MRR method provides more accurate results as compared with Single Rigid Registration(SRR),while the proposed DWT-PCAv fusion process adds-on more constructive information with less computational time.The proposed method is tested on CT and MRI brain imaging modalities of the HARVARD dataset.The fusion results of the proposed method are compared with the existing fusion techniques.The quality assessment metrics such as Mutual Information(MI),Normalize Crosscorrelation(NCC)and Feature Mutual Information(FMI)are computed for statistical comparison of the proposed method.The proposed methodology provides more accurate results,better image quality and valuable information for medical diagnoses.
基金the SUT research and development fund,and in part by the National Natural Science Foundation of China under Grant 61771333All subjects gave their informed consent for inclusion before they participated in the study.The study was conducted in accordance with the Declaration of Helsinki and the protocol was approved by the Ethics Committee of Suranaree University of Technology(License EC-61-14 COA No.16/2561).
文摘A brainwave classification,which does not involve any limb movement and stimulus for character-writing applications,benefits impaired people,in terms of practical communication,because it allows users to command a device/computer directly via electroencephalogram signals.In this paper,we propose a new framework based on Empirical Mode Decomposition(EMD)features along with theGaussianMixtureModel(GMM)andKernel Extreme Learning Machine(KELM)-based classifiers.For this purpose,firstly,we introduce EMD to decompose EEG signals into Intrinsic Mode Functions(IMFs),which actually are used as the input features of the brainwave classification for the character-writing application.We hypothesize that EMD along with the appropriate IMF is quite powerful for the brainwave classification,in terms of character applications,because of the wavelet-like decomposition without any down sampling process.Secondly,by getting motivated with shallow learning classifiers,we can provide promising performance for the classification of binary classes,GMM and KELM,which are applied for the learning of features along with the brainwave classification.Lastly,we propose a new method by combining GMMand KELM to fuse the merits of different classifiers.Moreover,the proposed methods are validated by using the volunteer-independent 5-fold cross-validation and accuracy as a standard measurement.The experimental results showed that EMD with the proper IMF achieved better results than the conventional discrete wavelet transform(DWT)feature.Moreover,we found that the EMD feature along with the GMM/KELM-based classifier provides the average accuracy of 77.40%and 80.10%,respectively,which could perform better than the conventional methods where we use DWT along with the artificial neural network classifier in order to get the average accuracy of 80.60%.Furthermore,we obtained the improved performance by combining GMM and KELM,i.e.,average accuracy of 80.60%.These outcomes exhibit the usefulness of the EMD feature combining with GMMand KELM based classifiers for the brainwaveclassification in terms of the Character-Writing application,which do notrequire any limb movement and stimulus.
基金the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(No.2019R1A4A1023746,No.2019R1F1A1060799)and Strengthening R&D Capability Program of Sejong University.
文摘A coplanar waveguide-fed quintuple band antenna with a slotted circular-shaped radiator for wireless applications with a high isolation between adjacent bands is presented in this paper.The proposed antenna resonates at multiple frequencies with corresponding center frequencies of 2.35,4.92,5.75,6.52,and 8.46 GHz.The intended functionality is achieved by introducing a circular disc radiator with five slots and a U-shaped slot in the feed.The proposed antenna exhibits coverage of the maximum set of wireless applications,such as satellite communication,worldwide interoperability for microwave access,wireless local area network(WLAN),long-distance radio telecommunications,and X-band/Satcom wireless applications.The simulation and measurement results of the proposed fabricated antenna demonstrate the high isolation between adjacent bands.A stable realized gain with an advantageous radiation pattern is achieved at the operating frequency bands.The proposed simple design,compact structure,and simple feeding technique make this antenna suitable for integration in several wireless communication applications,where the portability of devices is a significant concern.The proposed antenna is anticipated to be an appropriate candidate for WLAN,long-term evolution,and fifth-generation mobile communication because of its multi-operational bands and compact size for handheld devices.
文摘In this paper,low profile frequency reconfigurable monopole antenna is designed on FR-4 substrate with a compact size of 30 mm^(3)×20 mm^(3)×1.6 mm^(3).The antenna is tuned to four different modes through three pin diode switches.In Mode 1(SW1 to SW3=OFF),antenna covers a wideband of 3.15–8.51 GHz.For Mode 2(SW1=ON,SW2 to SW3=OFF),the proposed antenna resonates at 3.5 GHz.The antenna shows dual band behavior and covers 2.6 and 6.4 GHz in Mode 3(SW1 and SW2=ON,SW3=OFF).The same antenna covers three different bands of 2.1,5 and 6.4 GHz when operating in Mode 4(SW1 to SW3=ON).The proposed antenna has good radiation efficiency ranges from 70%∼84%,providing adequate average gain of 2.05 dBi in mode 1,1.87 dBi in mode 2,1.4–1.75 dBi in mode 3 and 1.05–1.56 dBi in mode 4.The achieved impedance bandwidths at respective frequencies ranges from 240 to 5000 MHz.The Voltage Standing Waves Ratio(VSWR)of less than 1.5 is achieved for all operating bands.To validate the simulation results,the proposed antenna is fabricated and experimentally tested in antenna measurement laboratory.Due to its reasonably small size and support of multiple bands operation,the proposed antenna can be used in modern communication systems for supporting various applications such as fifth generation(5G)mobile and wireless local area networks(WLAN).
基金The Universiti Kebangsaan Malaysia(UKM)Research Grant Scheme FRGS/1/2020/ICT03/UKM/02/6 and GGPM-2020-028 funded this research.
文摘Many organizations apply cloud computing to store and effectively process data for various applications.The user uploads the data in the cloud has less security due to the unreliable verification process of data integrity.In this research,an enhanced Merkle hash tree method of effective authentication model is proposed in the multi-owner cloud to increase the security of the cloud data.Merkle Hash tree applies the leaf nodes with a hash tag and the non-leaf node contains the table of hash information of child to encrypt the large data.Merkle Hash tree provides the efficient mapping of data and easily identifies the changesmade in the data due to proper structure.The developed model supports privacy-preserving public auditing to provide a secure cloud storage system.The data owners upload the data in the cloud and edit the data using the private key.An enhanced Merkle hash tree method stores the data in the cloud server and splits it into batches.The data files requested by the data owner are audit by a third-party auditor and the multiowner authentication method is applied during the modification process to authenticate the user.The result shows that the proposed method reduces the encryption and decryption time for cloud data storage by 2–167 ms when compared to the existing Advanced Encryption Standard and Blowfish.
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2023R66)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The COVID-19 pandemic has devastated our daily lives,leaving horrific repercussions in its aftermath.Due to its rapid spread,it was quite difficult for medical personnel to diagnose it in such a big quantity.Patients who test positive for Covid-19 are diagnosed via a nasal PCR test.In comparison,polymerase chain reaction(PCR)findings take a few hours to a few days.The PCR test is expensive,although the government may bear expenses in certain places.Furthermore,subsets of the population resist invasive testing like swabs.Therefore,chest X-rays or Computerized Vomography(CT)scans are preferred in most cases,and more importantly,they are non-invasive,inexpensive,and provide a faster response time.Recent advances in Artificial Intelligence(AI),in combination with state-of-the-art methods,have allowed for the diagnosis of COVID-19 using chest x-rays.This article proposes a method for classifying COVID-19 as positive or negative on a decentralized dataset that is based on the Federated learning scheme.In order to build a progressive global COVID-19 classification model,two edge devices are employed to train the model on their respective localized dataset,and a 3-layered custom Convolutional Neural Network(CNN)model is used in the process of training the model,which can be deployed from the server.These two edge devices then communicate their learned parameter and weight to the server,where it aggregates and updates the globalmodel.The proposed model is trained using an image dataset that can be found on Kaggle.There are more than 13,000 X-ray images in Kaggle Database collection,from that collection 9000 images of Normal and COVID-19 positive images are used.Each edge node possesses a different number of images;edge node 1 has 3200 images,while edge node 2 has 5800.There is no association between the datasets of the various nodes that are included in the network.By doing it in this manner,each of the nodes will have access to a separate image collection that has no correlation with each other.The diagnosis of COVID-19 has become considerably more efficient with the installation of the suggested algorithm and dataset,and the findings that we have obtained are quite encouraging.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant Number 62127802.
文摘Vehicular Ad-hoc Networks(VANETs)are mobile ad-hoc networks that use vehicles as nodes to create a wireless network.Whereas VANETs offer many advantages over traditional transportation networks,ensuring security in VANETs remains a significant challenge due to the potential for malicious attacks.This study addresses the critical issue of security in VANETs by introducing an intelligent Intrusion Detection System(IDS)that merges Machine Learning(ML)–based attack detection with Explainable AI(XAI)explanations.This study ML pipeline involves utilizing correlation-based feature selection followed by a Random Forest(RF)classifier that achieves a classification accuracy of 100%for the binary classification task of identifying normal and malicious traffic.An innovative aspect of this study is the incorporation of XAI methodologies,specifically the Local Interpretable Model-agnostic Explanations(LIME)and SHapley Additive exPlanations(SHAP).In addition,this research also considered key features identified by mutual information-based feature selection for the task at hand.The major findings from this study reveal that the XAI-based intrusion detection methods offer distinct insights into feature importance.Key features identified by mutual information,LIME,and SHAP predominantly relate to Transmission Control Protocol(TCP),Hypertext Transfer Protocol(HTTP),Domain Name System(DNS),and Message Queuing Telemetry Transport(MQTT)protocols,highlighting their significance in distinguishing normal and malicious network activity.This XAI approach equips cybersecurity experts with a robust means of identifying and understanding VANET malicious activities,forming a foundation for more effective security countermeasures.
基金Supported by the National High Technology Research and Development Programme of China (No. 2005AA121620, 2006AA01Z232)the Zhejiang Provincial Natural Science Foundation of China (No. Y1080935 )the Research Innovation Program for Graduate Students in Jiangsu Province (No. CX07B_ 110zF)
文摘Interact traffic classification is vital to the areas of network operation and management. Traditional classification methods such as port mapping and payload analysis are becoming increasingly difficult as newly emerged applications （e. g. Peer-to-Peer） using dynamic port numbers, masquerading techniques and encryption to avoid detection. This paper presents a machine learning （ML） based traffic classifica- tion scheme, which offers solutions to a variety of network activities and provides a platform of performance evaluation for the classifiers. The impact of dataset size, feature selection, number of application types and ML algorithm selection on classification performance is analyzed and demonstrated by the following experiments： （1） The genetic algorithm based feature selection can dramatically reduce the cost without diminishing classification accuracy. （2） The chosen ML algorithms can achieve high classification accuracy. Particularly, REPTree and C4.5 outperform the other ML algorithms when computational complexity and accuracy are both taken into account. （3） Larger dataset and fewer application types would result in better classification accuracy. Finally, early detection with only several initial packets is proposed for real-time network activity and it is proved to be feasible according to the preliminary results.
基金Supported by the National Basic Research and Development Programme of China under Grant No 2003 CB314906, the Key Project of the National Natural Science Foundation of China under Grant No 60331010, the Scientific Research Common Programme of Beijing Municipal Commission of Education under Grant No KM200411232005, and the Initiating Grant For PhD Degree of School of Electronic Engineering, BUPT.
文摘To reduce temperature sensitivity of the fibre Bragg grating （FBG） chemical sensor, a simple method is proposed by measuring the peak wavelength difference between an etched FBG and an un-etched one in an optical fibre. Thermal characteristics and chemical sensitivity of the sensor are experimentally investigated. The experimental results indicate that the etched FBG and the rest one have almost the same thermal response, and concentration changes of the surrounding chemical solutions can be detected by measuring the peak wavelength difference between them. The sensor has been used to measure the concentrations of propylene glycol solutions and sugar solutions, and it could detect 0.7% and 0.45% concentration changes for them with an optical spectrum analyser in resolution of 10pm.
