Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability...Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches.展开更多
The innovative development of Wearable Electronics (WE) is creating exciting opportunities for application across many industries. Two sectors with high potential are healthcare and childcare. However, it is in these ...The innovative development of Wearable Electronics (WE) is creating exciting opportunities for application across many industries. Two sectors with high potential are healthcare and childcare. However, it is in these two sectors where the challenges of privacy are presumed to be of the highest. In order to ascertain the personal views of people about potential privacy problems in WE application in these two sectors, interviews with questionnaires were conducted in two different countries: Finland and the United Kingdom (UK). The results indicated that the majority of people in both countries are positive about the use of WE in healthcare and childcare environments. However, when more information is added to be read wirelessly, the attitudes become more negative. In general, the application of WE is more favorable in the UK and the reason as to the difference will make for interesting further research. Several interesting viewpoints and concerns were presented in the interviews. It can be concluded that the implementation of WE in these two sectors will require the collaboration of work on several areas and the development of versatile user studies.展开更多
With the development of wireless mobile communication technology,the demand for wireless communication rate and frequency increases year by year.Existing wireless mobile communication frequency tends to be saturated,w...With the development of wireless mobile communication technology,the demand for wireless communication rate and frequency increases year by year.Existing wireless mobile communication frequency tends to be saturated,which demands for new solutions.Terahertz(THz)communication has great potential for the future mobile communications(Beyond 5G),and is also an important technique for the high data rate transmission in spatial information network.THz communication has great application prospects in military-civilian integration and coordinated development.In China,important breakthroughs have been achieved for the key techniques of THz high data rate communications,which is practically keeping up with the most advanced technological level in the world.Therefore,further intensifying efforts on the development of THz communication have the strategic importance for China in leading the development of future wireless communication techniques and the standardization process of Beyond 5G.This paper analyzes the performance of the MIMO channel in the Terahertz(THz)band and a discrete mathematical method is used to propose a novel channel model.Then,a channel capacity model is proposed by the combination of path loss and molecular absorption in the THz band based on the CSI at the receiver.Simulation results show that the integration of MIMO in the THz band gives better data rate and channel capacity as compared with a single channel.展开更多
The binary decision diagrams （BDDs） can give canonical representation to Boolean functions; they have wide applications in the design and verification of digital systems. A new method based on cultural algorithms fo...The binary decision diagrams （BDDs） can give canonical representation to Boolean functions; they have wide applications in the design and verification of digital systems. A new method based on cultural algorithms for minimizing the size of BDDs is presented in this paper. First of all, the coding of an individual representing a BDDs is given, and the fitness of an individual is defined. The population is built by a set of the individuals. Second, the implementations based on cultural algorithms for the minimization of BDDs, i.e., the designs of belief space and population space, and the designs of acceptance function and influence function, are given in detail. Third, the fault detection approaches using BDDs for digital circuits are studied. A new method for the detection of crosstalk faults by using BDDs is presented. Experimental results on a number of digital circuits show that the BDDs with small number of nodes can be obtained by the method proposed in this paper, and all test vectors of a fault in digital circuits can also be produced.展开更多
Corona Virus Disease 2019(COVID-19) has affected millions of people worldwide and caused more than6.3 million deaths(World Health Organization, June 2022). Increased attempts have been made to develop deep learning me...Corona Virus Disease 2019(COVID-19) has affected millions of people worldwide and caused more than6.3 million deaths(World Health Organization, June 2022). Increased attempts have been made to develop deep learning methods to diagnose COVID-19 based on computed tomography(CT) lung images. It is a challenge to reproduce and obtain the CT lung data, because it is not publicly available. This paper introduces a new generalized framework to segment and classify CT images and determine whether a patient is tested positive or negative for COVID-19 based on lung CT images. In this work, many different strategies are explored for the classification task.ResNet50 and VGG16 models are applied to classify CT lung images into COVID-19 positive or negative. Also,VGG16 and ReNet50 combined with U-Net, which is one of the most used architectures in deep learning for image segmentation, are employed to segment CT lung images before the classifying process to increase system performance. Moreover, the image size dependent normalization technique(ISDNT) and Wiener filter are utilized as the preprocessing techniques to enhance images and noise suppression. Additionally, transfer learning and data augmentation techniques are performed to solve the problem of COVID-19 CT lung images deficiency, therefore the over-fitting of deep models can be avoided. The proposed frameworks, which comprised of end-to-end, VGG16,ResNet50, and U-Net with VGG16 or ResNet50, are applied on the dataset that is sourced from COVID-19 lung CT images in Kaggle. The classification results show that using the preprocessed CT lung images as the input for U-Net hybrid with ResNet50 achieves the best performance. The proposed classification model achieves the 98.98%accuracy(ACC), 98.87% area under the ROC curve(AUC), 98.89% sensitivity(Se), 97.99 % precision(Pr), 97.88%F-score, and 1.8974-seconds computational time.展开更多
An innovative in-flight glass melting technology with thermal plasmas was developed for the purpose of energy conservation and environment protection. In this study, modelling and experiments of argon-oxygen induction...An innovative in-flight glass melting technology with thermal plasmas was developed for the purpose of energy conservation and environment protection. In this study, modelling and experiments of argon-oxygen induction thermal plasmas were conducted to investigate the melting behaviour of granulated soda-lime glass powders injected into the plasma. A two-dimensional local thermodynamic equilibrium （LTE） model was performed to simulate the heat and momentum transfer between plasma and particle. Results showed that the particle temperature was strongly affected by the flow rate of carrier gas and the particle size of raw material. A higher flow rate of carrier gas led to lower particle temperature and less energy transferred to particles which resulted in lower vitrification. The incomplete melting of large particles was attributed to the lower central temperature of the particle caused by a larger heat capacity. The numerical analysis explained well the experimental results, which can provide valuable practical guidelines for the process control in the melting process for the glass industry.展开更多
Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)techniques.Especially,the EEG signals associated with seizure epilepsy can be detected to distinguish betwee...Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)techniques.Especially,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions.From this perspective,an automated AI technique with a digital processing method can be used to improve these signals.This paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG signals.These classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and non-seizure.In addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the brain.Thus,Hadamard coefficients of the EEG signals are obtained via the FWHT.Moreover,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings.Also,a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers.The LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,respectively.The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,respectively.The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals.Eventually,the proposed classifiers provide high classification accuracy compared to previously published classifiers.展开更多
An abnormality that develops in white blood cells is called leukemia.The diagnosis of leukemia is made possible by microscopic investigation of the smear in the periphery.Prior training is necessary to complete the mo...An abnormality that develops in white blood cells is called leukemia.The diagnosis of leukemia is made possible by microscopic investigation of the smear in the periphery.Prior training is necessary to complete the morphological examination of the blood smear for leukemia diagnosis.This paper proposes a Histogram Threshold Segmentation Classifier(HTsC)for a decision support system.The proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear images.Arithmetic operations are used to crop the nucleus based on automated approximation.White Blood Cell(WBC)segmentation is calculated using the active contour model to determine the contrast between image regions using the color transfer approach.Through entropy-adaptive mask generation,WBCs accurately detect the circularity region for identification of the nucleus.The proposed HTsC addressed the cytoplasm region based on variations in size and shape concerning addition and rotation operations.Variation in WBC imaging characteristics depends on the cytoplasmic and nuclear regions.The computation of the variation between image features in the cytoplasm and nuclei regions of the WBCs is used to classify blood smear images.The classification of the blood smear is performed with conventional machine-learning techniques integrated with the features of the deep-learning regression classifier.The designed HTsC classifier comprises the binary classifier with the classification of the lymphocytes,monocytes,neutrophils,eosinophils,and abnormalities in the WBCs.The proposed HTsC identifies the abnormal activity in the WBC,considering the color and shape features.It exhibits a higher classification accuracy value of 99.6%when combined with the other classifiers.The comparative analysis expressed that the proposed HTsC model exhibits an overall accuracy value of 98%,which is approximately 3%–12%higher than the conventional technique.展开更多
This paper starts with a literature survey that introduces possibilities of wearable electronics (WE) in different healthcare and childcare applications. Next, 24 personal interviews and an Internet forum survey were ...This paper starts with a literature survey that introduces possibilities of wearable electronics (WE) in different healthcare and childcare applications. Next, 24 personal interviews and an Internet forum survey were conducted in Finland about the use of WE in applications mentioned above. According to the results, most of the people feel positive about clothes used for wireless identification purposes in healthcare and childcare, but when more information about the person is added that can be wirelessly read, the feelings become more negative. Several important points to consider before implementation of WE for healthcare and childcare environments were brought up.展开更多
In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending netw...In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches.展开更多
There are several challenges to inner ear drug delivery and imaging due to the existence of tight biological barriers to the target structure and the dense bone surrounding it. Advances in imaging and nanomedicine may...There are several challenges to inner ear drug delivery and imaging due to the existence of tight biological barriers to the target structure and the dense bone surrounding it. Advances in imaging and nanomedicine may provide knowledge for overcoming the existing limitations to both the diagnosis and treatment of inner ear diseases. Novel techniques have improved the efficacy of drug delivery and targeting to the inner ear, as well as the quality and accuracy of imaging this structure. In this review, we will describe the pathways and biological barriers of the inner ear regarding drug delivery, the beneficial applications and limitations of the imaging techniques available for inner ear research, the behavior of engineered nanomaterials in inner ear applications, and future perspectives for nanomedicine-based inner ear imaging.展开更多
Future networks communication scenarios by the 2030s will include notable applications are three-dimensional(3D)calls,haptics communications,unmanned mobility,tele-operated driving,bio-internet of things,and the Nanoi...Future networks communication scenarios by the 2030s will include notable applications are three-dimensional(3D)calls,haptics communications,unmanned mobility,tele-operated driving,bio-internet of things,and the Nanointernet of things.Unlike the current scenario in which megahertz bandwidth are sufficient to drive the audio and video components of user applications,the future networks of the 2030s will require bandwidths in several gigahertzes(GHz)(from tens of gigahertz to 1 terahertz[THz])to perform optimally.Based on the current radio frequency allocation chart,it is not possible to obtain such a wide contiguous radio spectrum below 90 GHz(0.09 THz).Interestingly,these contiguous blocks of radio spectrum are readily available in the higher electromagnetic spectrum,specifically in the Terahertz(THz)frequency band.The major contribution of this study is discussing the substantial issues and key features of THz waves,which include(i)key features and significance of THz frequency;(ii)recent regulatory;(iii)the most promising applications;and(iv)possible open research issues.These research topics were deeply investigated with the aim of providing a specific,synopsis,and encompassing conclusion.Thus,this article will be as a catalyst towards exploring new frontiers for future networks of the 2030s.展开更多
Deep Learning(DL)is a subfield of machine learning that significantly impacts extracting new knowledge.By using DL,the extraction of advanced data representations and knowledge can be made possible.Highly effective DL...Deep Learning(DL)is a subfield of machine learning that significantly impacts extracting new knowledge.By using DL,the extraction of advanced data representations and knowledge can be made possible.Highly effective DL techniques help to find more hidden knowledge.Deep learning has a promising future due to its great performance and accuracy.We need to understand the fundamentals and the state‐of‐the‐art of DL to leverage it effectively.A survey on DL ways,advantages,drawbacks,architectures,and methods to have a straightforward and clear understanding of it from different views is explained in the paper.Moreover,the existing related methods are compared with each other,and the application of DL is described in some applications,such as medical image analysis,handwriting recognition,and so on.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
Coronavirus disease 2019(COVID-19)is a global pandemic where several comorbidities have been shown to have a significant effect on mortality.Patients with diabetes mellitus(DM)have a higher mortality rate than non-DM ...Coronavirus disease 2019(COVID-19)is a global pandemic where several comorbidities have been shown to have a significant effect on mortality.Patients with diabetes mellitus(DM)have a higher mortality rate than non-DM patients if they get COVID-19.Recent studies have indicated that patients with a history of diabetes can increase the risk of severe acute respiratory syndrome coronavirus 2 infection.Additionally,patients without any history of diabetes can acquire newonset DM when infected with COVID-19.Thus,there is a need to explore the bidirectional link between these two conditions,confirming the vicious loop between“DM/COVID-19”.This narrative review presents(1)the bidirectional association between the DM and COVID-19,(2)the manifestations of the DM/COVID-19 loop leading to cardiovascular disease,(3)an understanding of primary and secondary factors that influence mortality due to the DM/COVID-19 loop,(4)the role of vitamin-D in DM patients during COVID-19,and finally,(5)the monitoring tools for tracking atherosclerosis burden in DM patients during COVID-19 and“COVID-triggered DM”patients.We conclude that the bidirectional nature of DM/COVID-19 causes acceleration towards cardiovascular events.Due to this alarming condition,early monitoring of atherosclerotic burden is required in“Diabetes patients during COVID-19”or“new-onset Diabetes triggered by COVID-19 in Non-Diabetes patients”.展开更多
Copper is an interesting material for printed electronics inks because, for example, of its good conductivity and lower raw material price compared to silver. However, post-processing Cu inks is challenging because of...Copper is an interesting material for printed electronics inks because, for example, of its good conductivity and lower raw material price compared to silver. However, post-processing Cu inks is challenging because of non-conductive copper oxide. In this work, inkjet-printed Cu nanoparticle structures were sintered on a polyimide substrate with a continuous-wave 808-nm diode laser. Laser sintering was tested by varying the sintering parameters (optical power and scanning velocity), and the electrical resistance of the samples was measured. A minimum sheet resistance of approx.90 mΩ/□ was obtained. All tests were run in room conditions. Sintered structures were then analyzed from SEM images. Results showed that laser sintering produces good repeatability, that a scanning velocity increment positively affects the process window, and that multiple sintering cycles do not increase conductivity.展开更多
This paper investigates the effect of inflow, outflow and shock waves in a single lane highway traffic flow problem. A constant source term has been introduced to demonstrate the inflow and outflow. The classical Ligh...This paper investigates the effect of inflow, outflow and shock waves in a single lane highway traffic flow problem. A constant source term has been introduced to demonstrate the inflow and outflow. The classical Lighthill Whitham and Richards (LWR) model combined with the Greenshields model is used to obtain analytical and numerical solutions. The model is treated as an IBVP and numerical solutions are presented using Lax Friedrichs scheme. Godunov method is also used to present shock wave analysis. The numerical procedures adopted in this investigation yield results which are very much consistent with real life scenario in terms of traffic density and velocity.展开更多
In this paper, we summarize some recent activities in the field of metamaterial research at the National University of Singapore (NUS). Integral equations are applied for electromagnetic modelling of supernatural mate...In this paper, we summarize some recent activities in the field of metamaterial research at the National University of Singapore (NUS). Integral equations are applied for electromagnetic modelling of supernatural materials. Some special charac- teristics of the metamaterials are shown. Moreover, quasi-static Lorentz theory and numerical method (i.e., the method of moments for solving the electric field integral equation) and the transmission line theory are both presented to obtain the effective consti- tutive relations of metamaterials, respectively. Finally, feasibility of fabricating metamaterials based on analysis of equivalent transmission line model in the microwave spectrum and even higher is also shown and correspondingly some broad-bandwidth and low-loss metamaterial structures are designed and synthesized.展开更多
Cancelable biometrics are required in most remote access applications that need an authentication stage such as the cloud and Internet of Things(IoT)networks.The objective of using cancelable biometrics is to save the...Cancelable biometrics are required in most remote access applications that need an authentication stage such as the cloud and Internet of Things(IoT)networks.The objective of using cancelable biometrics is to save the original ones from hacking attempts.A generalized algorithm to generate cancelable templates that is applicable on both single and multiple biometrics is proposed in this paper to be considered for cloud and IoT applications.The original biometric is blurred with two co-prime operators.Hence,it can be recovered as the Greatest Common Divisor(GCD)between its two blurred versions.Minimal changes if induced in the biometric image prior to processing with co-prime operators prevents the recovery of the original biometric image through a GCD operation.Hence,the ability to change cancelable templates is guaranteed,since the owner of the biometric can pre-determine and manage the minimal change induced in the biometric image.Furthermore,we test the utility of the proposed algorithm in the single-and multi-biometric scenarios.The multi-biometric scenario depends on compressing face,fingerprint,iris,and palm print images,simultaneously,to generate the cancelable templates.Evaluation metrics such as Equal Error Rate(EER)and Area and Receiver Operator Characteristic curve(AROC)are considered.Simulation results on single-and multi-biometric scenarios show high AROC values up to 99.59%,and low EER values down to 0.04%.展开更多
文摘Reliable Cluster Head(CH)selectionbased routing protocols are necessary for increasing the packet transmission efficiency with optimal path discovery that never introduces degradation over the transmission reliability.In this paper,Hybrid Golden Jackal,and Improved Whale Optimization Algorithm(HGJIWOA)is proposed as an effective and optimal routing protocol that guarantees efficient routing of data packets in the established between the CHs and the movable sink.This HGJIWOA included the phases of Dynamic Lens-Imaging Learning Strategy and Novel Update Rules for determining the reliable route essential for data packets broadcasting attained through fitness measure estimation-based CH selection.The process of CH selection achieved using Golden Jackal Optimization Algorithm(GJOA)completely depends on the factors of maintainability,consistency,trust,delay,and energy.The adopted GJOA algorithm play a dominant role in determining the optimal path of routing depending on the parameter of reduced delay and minimal distance.It further utilized Improved Whale Optimisation Algorithm(IWOA)for forwarding the data from chosen CHs to the BS via optimized route depending on the parameters of energy and distance.It also included a reliable route maintenance process that aids in deciding the selected route through which data need to be transmitted or re-routed.The simulation outcomes of the proposed HGJIWOA mechanism with different sensor nodes confirmed an improved mean throughput of 18.21%,sustained residual energy of 19.64%with minimized end-to-end delay of 21.82%,better than the competitive CH selection approaches.
文摘The innovative development of Wearable Electronics (WE) is creating exciting opportunities for application across many industries. Two sectors with high potential are healthcare and childcare. However, it is in these two sectors where the challenges of privacy are presumed to be of the highest. In order to ascertain the personal views of people about potential privacy problems in WE application in these two sectors, interviews with questionnaires were conducted in two different countries: Finland and the United Kingdom (UK). The results indicated that the majority of people in both countries are positive about the use of WE in healthcare and childcare environments. However, when more information is added to be read wirelessly, the attitudes become more negative. In general, the application of WE is more favorable in the UK and the reason as to the difference will make for interesting further research. Several interesting viewpoints and concerns were presented in the interviews. It can be concluded that the implementation of WE in these two sectors will require the collaboration of work on several areas and the development of versatile user studies.
基金Hallym University Research Fund,2019(HRF-201905-013).
文摘With the development of wireless mobile communication technology,the demand for wireless communication rate and frequency increases year by year.Existing wireless mobile communication frequency tends to be saturated,which demands for new solutions.Terahertz(THz)communication has great potential for the future mobile communications(Beyond 5G),and is also an important technique for the high data rate transmission in spatial information network.THz communication has great application prospects in military-civilian integration and coordinated development.In China,important breakthroughs have been achieved for the key techniques of THz high data rate communications,which is practically keeping up with the most advanced technological level in the world.Therefore,further intensifying efforts on the development of THz communication have the strategic importance for China in leading the development of future wireless communication techniques and the standardization process of Beyond 5G.This paper analyzes the performance of the MIMO channel in the Terahertz(THz)band and a discrete mathematical method is used to propose a novel channel model.Then,a channel capacity model is proposed by the combination of path loss and molecular absorption in the THz band based on the CSI at the receiver.Simulation results show that the integration of MIMO in the THz band gives better data rate and channel capacity as compared with a single channel.
基金supported by Natural Science Foundation of Guangdong Provincial of China (No.7005833)
文摘The binary decision diagrams （BDDs） can give canonical representation to Boolean functions; they have wide applications in the design and verification of digital systems. A new method based on cultural algorithms for minimizing the size of BDDs is presented in this paper. First of all, the coding of an individual representing a BDDs is given, and the fitness of an individual is defined. The population is built by a set of the individuals. Second, the implementations based on cultural algorithms for the minimization of BDDs, i.e., the designs of belief space and population space, and the designs of acceptance function and influence function, are given in detail. Third, the fault detection approaches using BDDs for digital circuits are studied. A new method for the detection of crosstalk faults by using BDDs is presented. Experimental results on a number of digital circuits show that the BDDs with small number of nodes can be obtained by the method proposed in this paper, and all test vectors of a fault in digital circuits can also be produced.
文摘Corona Virus Disease 2019(COVID-19) has affected millions of people worldwide and caused more than6.3 million deaths(World Health Organization, June 2022). Increased attempts have been made to develop deep learning methods to diagnose COVID-19 based on computed tomography(CT) lung images. It is a challenge to reproduce and obtain the CT lung data, because it is not publicly available. This paper introduces a new generalized framework to segment and classify CT images and determine whether a patient is tested positive or negative for COVID-19 based on lung CT images. In this work, many different strategies are explored for the classification task.ResNet50 and VGG16 models are applied to classify CT lung images into COVID-19 positive or negative. Also,VGG16 and ReNet50 combined with U-Net, which is one of the most used architectures in deep learning for image segmentation, are employed to segment CT lung images before the classifying process to increase system performance. Moreover, the image size dependent normalization technique(ISDNT) and Wiener filter are utilized as the preprocessing techniques to enhance images and noise suppression. Additionally, transfer learning and data augmentation techniques are performed to solve the problem of COVID-19 CT lung images deficiency, therefore the over-fitting of deep models can be avoided. The proposed frameworks, which comprised of end-to-end, VGG16,ResNet50, and U-Net with VGG16 or ResNet50, are applied on the dataset that is sourced from COVID-19 lung CT images in Kaggle. The classification results show that using the preprocessed CT lung images as the input for U-Net hybrid with ResNet50 achieves the best performance. The proposed classification model achieves the 98.98%accuracy(ACC), 98.87% area under the ROC curve(AUC), 98.89% sensitivity(Se), 97.99 % precision(Pr), 97.88%F-score, and 1.8974-seconds computational time.
基金supported by the New Energy and Industrial Technology Development Organization of Japan(No.A0006)
文摘An innovative in-flight glass melting technology with thermal plasmas was developed for the purpose of energy conservation and environment protection. In this study, modelling and experiments of argon-oxygen induction thermal plasmas were conducted to investigate the melting behaviour of granulated soda-lime glass powders injected into the plasma. A two-dimensional local thermodynamic equilibrium （LTE） model was performed to simulate the heat and momentum transfer between plasma and particle. Results showed that the particle temperature was strongly affected by the flow rate of carrier gas and the particle size of raw material. A higher flow rate of carrier gas led to lower particle temperature and less energy transferred to particles which resulted in lower vitrification. The incomplete melting of large particles was attributed to the lower central temperature of the particle caused by a larger heat capacity. The numerical analysis explained well the experimental results, which can provide valuable practical guidelines for the process control in the melting process for the glass industry.
基金The authors would like to thank the support of the Taif University Researchers Supporting Project TURSP 2020/34,Taif University,Taif Saudi Arabia for supporting this work.
文摘Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)techniques.Especially,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic regions.From this perspective,an automated AI technique with a digital processing method can be used to improve these signals.This paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG signals.These classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and non-seizure.In addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the brain.Thus,Hadamard coefficients of the EEG signals are obtained via the FWHT.Moreover,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG recordings.Also,a k-fold cross-validation technique is applied to validate the performance of the proposed classifiers.The LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,respectively.The computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,respectively.The results show that the LSTM classifier provides better performance than SVM in the classification of EEG signals.Eventually,the proposed classifiers provide high classification accuracy compared to previously published classifiers.
基金This research is funded by the Deanship of Scientific Research at Umm Al-Qura University,Grant Code:22UQU4281768DSR01.
文摘An abnormality that develops in white blood cells is called leukemia.The diagnosis of leukemia is made possible by microscopic investigation of the smear in the periphery.Prior training is necessary to complete the morphological examination of the blood smear for leukemia diagnosis.This paper proposes a Histogram Threshold Segmentation Classifier(HTsC)for a decision support system.The proposed HTsC is evaluated based on the color and brightness variation in the dataset of blood smear images.Arithmetic operations are used to crop the nucleus based on automated approximation.White Blood Cell(WBC)segmentation is calculated using the active contour model to determine the contrast between image regions using the color transfer approach.Through entropy-adaptive mask generation,WBCs accurately detect the circularity region for identification of the nucleus.The proposed HTsC addressed the cytoplasm region based on variations in size and shape concerning addition and rotation operations.Variation in WBC imaging characteristics depends on the cytoplasmic and nuclear regions.The computation of the variation between image features in the cytoplasm and nuclei regions of the WBCs is used to classify blood smear images.The classification of the blood smear is performed with conventional machine-learning techniques integrated with the features of the deep-learning regression classifier.The designed HTsC classifier comprises the binary classifier with the classification of the lymphocytes,monocytes,neutrophils,eosinophils,and abnormalities in the WBCs.The proposed HTsC identifies the abnormal activity in the WBC,considering the color and shape features.It exhibits a higher classification accuracy value of 99.6%when combined with the other classifiers.The comparative analysis expressed that the proposed HTsC model exhibits an overall accuracy value of 98%,which is approximately 3%–12%higher than the conventional technique.
文摘This paper starts with a literature survey that introduces possibilities of wearable electronics (WE) in different healthcare and childcare applications. Next, 24 personal interviews and an Internet forum survey were conducted in Finland about the use of WE in applications mentioned above. According to the results, most of the people feel positive about clothes used for wireless identification purposes in healthcare and childcare, but when more information about the person is added that can be wirelessly read, the feelings become more negative. Several important points to consider before implementation of WE for healthcare and childcare environments were brought up.
文摘In Wireless Sensor Networks(WSNs),Clustering process is widely utilized for increasing the lifespan with sustained energy stability during data transmission.Several clustering protocols were devised for extending network lifetime,but most of them failed in handling the problem of fixed clustering,static rounds,and inadequate Cluster Head(CH)selection criteria which consumes more energy.In this paper,Stochastic Ranking Improved Teaching-Learning and Adaptive Grasshopper Optimization Algorithm(SRITL-AGOA)-based Clustering Scheme for energy stabilization and extending network lifespan.This SRITL-AGOA selected CH depending on the weightage of factors such as node mobility degree,neighbour's density distance to sink,single-hop or multihop communication and Residual Energy(RE)that directly influences the energy consumption of sensor nodes.In specific,Grasshopper Optimization Algorithm(GOA)is improved through tangent-based nonlinear strategy for enhancing the ability of global optimization.On the other hand,stochastic ranking and violation constraint handling strategies are embedded into Teaching-Learning-based Optimization Algorithm(TLOA)for improving its exploitation tendencies.Then,SR and VCH improved TLOA is embedded into the exploitation phase of AGOA for selecting better CH by maintaining better balance amid exploration and exploitation.Simulation results confirmed that the proposed SRITL-AGOA improved throughput by 21.86%,network stability by 18.94%,load balancing by 16.14%with minimized energy depletion by19.21%,compared to the competitive CH selection approaches.
基金supported by the National Natural Science Foundation of China(grant number:81170914/H1304)
文摘There are several challenges to inner ear drug delivery and imaging due to the existence of tight biological barriers to the target structure and the dense bone surrounding it. Advances in imaging and nanomedicine may provide knowledge for overcoming the existing limitations to both the diagnosis and treatment of inner ear diseases. Novel techniques have improved the efficacy of drug delivery and targeting to the inner ear, as well as the quality and accuracy of imaging this structure. In this review, we will describe the pathways and biological barriers of the inner ear regarding drug delivery, the beneficial applications and limitations of the imaging techniques available for inner ear research, the behavior of engineered nanomaterials in inner ear applications, and future perspectives for nanomedicine-based inner ear imaging.
基金the Research Program through the National Research Foundation of Korea(NRF-2019R1A2C1005920).
文摘Future networks communication scenarios by the 2030s will include notable applications are three-dimensional(3D)calls,haptics communications,unmanned mobility,tele-operated driving,bio-internet of things,and the Nanointernet of things.Unlike the current scenario in which megahertz bandwidth are sufficient to drive the audio and video components of user applications,the future networks of the 2030s will require bandwidths in several gigahertzes(GHz)(from tens of gigahertz to 1 terahertz[THz])to perform optimally.Based on the current radio frequency allocation chart,it is not possible to obtain such a wide contiguous radio spectrum below 90 GHz(0.09 THz).Interestingly,these contiguous blocks of radio spectrum are readily available in the higher electromagnetic spectrum,specifically in the Terahertz(THz)frequency band.The major contribution of this study is discussing the substantial issues and key features of THz waves,which include(i)key features and significance of THz frequency;(ii)recent regulatory;(iii)the most promising applications;and(iv)possible open research issues.These research topics were deeply investigated with the aim of providing a specific,synopsis,and encompassing conclusion.Thus,this article will be as a catalyst towards exploring new frontiers for future networks of the 2030s.
文摘Deep Learning(DL)is a subfield of machine learning that significantly impacts extracting new knowledge.By using DL,the extraction of advanced data representations and knowledge can be made possible.Highly effective DL techniques help to find more hidden knowledge.Deep learning has a promising future due to its great performance and accuracy.We need to understand the fundamentals and the state‐of‐the‐art of DL to leverage it effectively.A survey on DL ways,advantages,drawbacks,architectures,and methods to have a straightforward and clear understanding of it from different views is explained in the paper.Moreover,the existing related methods are compared with each other,and the application of DL is described in some applications,such as medical image analysis,handwriting recognition,and so on.
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).
文摘Coronavirus disease 2019(COVID-19)is a global pandemic where several comorbidities have been shown to have a significant effect on mortality.Patients with diabetes mellitus(DM)have a higher mortality rate than non-DM patients if they get COVID-19.Recent studies have indicated that patients with a history of diabetes can increase the risk of severe acute respiratory syndrome coronavirus 2 infection.Additionally,patients without any history of diabetes can acquire newonset DM when infected with COVID-19.Thus,there is a need to explore the bidirectional link between these two conditions,confirming the vicious loop between“DM/COVID-19”.This narrative review presents(1)the bidirectional association between the DM and COVID-19,(2)the manifestations of the DM/COVID-19 loop leading to cardiovascular disease,(3)an understanding of primary and secondary factors that influence mortality due to the DM/COVID-19 loop,(4)the role of vitamin-D in DM patients during COVID-19,and finally,(5)the monitoring tools for tracking atherosclerosis burden in DM patients during COVID-19 and“COVID-triggered DM”patients.We conclude that the bidirectional nature of DM/COVID-19 causes acceleration towards cardiovascular events.Due to this alarming condition,early monitoring of atherosclerotic burden is required in“Diabetes patients during COVID-19”or“new-onset Diabetes triggered by COVID-19 in Non-Diabetes patients”.
基金M.Mäntysalo is sponsored by Academy of Finland with grant No.251882.
文摘Copper is an interesting material for printed electronics inks because, for example, of its good conductivity and lower raw material price compared to silver. However, post-processing Cu inks is challenging because of non-conductive copper oxide. In this work, inkjet-printed Cu nanoparticle structures were sintered on a polyimide substrate with a continuous-wave 808-nm diode laser. Laser sintering was tested by varying the sintering parameters (optical power and scanning velocity), and the electrical resistance of the samples was measured. A minimum sheet resistance of approx.90 mΩ/□ was obtained. All tests were run in room conditions. Sintered structures were then analyzed from SEM images. Results showed that laser sintering produces good repeatability, that a scanning velocity increment positively affects the process window, and that multiple sintering cycles do not increase conductivity.
文摘This paper investigates the effect of inflow, outflow and shock waves in a single lane highway traffic flow problem. A constant source term has been introduced to demonstrate the inflow and outflow. The classical Lighthill Whitham and Richards (LWR) model combined with the Greenshields model is used to obtain analytical and numerical solutions. The model is treated as an IBVP and numerical solutions are presented using Lax Friedrichs scheme. Godunov method is also used to present shock wave analysis. The numerical procedures adopted in this investigation yield results which are very much consistent with real life scenario in terms of traffic density and velocity.
文摘In this paper, we summarize some recent activities in the field of metamaterial research at the National University of Singapore (NUS). Integral equations are applied for electromagnetic modelling of supernatural materials. Some special charac- teristics of the metamaterials are shown. Moreover, quasi-static Lorentz theory and numerical method (i.e., the method of moments for solving the electric field integral equation) and the transmission line theory are both presented to obtain the effective consti- tutive relations of metamaterials, respectively. Finally, feasibility of fabricating metamaterials based on analysis of equivalent transmission line model in the microwave spectrum and even higher is also shown and correspondingly some broad-bandwidth and low-loss metamaterial structures are designed and synthesized.
基金This research was funded by the Deanship of Scientific Research at Princess Nourah Bint Abdulrahman University through the Fast-track Research Funding Program to support publication in the top journal(Grant No.42-FTTJ-13).
文摘Cancelable biometrics are required in most remote access applications that need an authentication stage such as the cloud and Internet of Things(IoT)networks.The objective of using cancelable biometrics is to save the original ones from hacking attempts.A generalized algorithm to generate cancelable templates that is applicable on both single and multiple biometrics is proposed in this paper to be considered for cloud and IoT applications.The original biometric is blurred with two co-prime operators.Hence,it can be recovered as the Greatest Common Divisor(GCD)between its two blurred versions.Minimal changes if induced in the biometric image prior to processing with co-prime operators prevents the recovery of the original biometric image through a GCD operation.Hence,the ability to change cancelable templates is guaranteed,since the owner of the biometric can pre-determine and manage the minimal change induced in the biometric image.Furthermore,we test the utility of the proposed algorithm in the single-and multi-biometric scenarios.The multi-biometric scenario depends on compressing face,fingerprint,iris,and palm print images,simultaneously,to generate the cancelable templates.Evaluation metrics such as Equal Error Rate(EER)and Area and Receiver Operator Characteristic curve(AROC)are considered.Simulation results on single-and multi-biometric scenarios show high AROC values up to 99.59%,and low EER values down to 0.04%.
