To solve the problems of a few optical fibre line fault samples and the inefficiency of manual communication optical fibre fault diagnosis,this paper proposes a communication optical fibre fault diagnosis model based ...To solve the problems of a few optical fibre line fault samples and the inefficiency of manual communication optical fibre fault diagnosis,this paper proposes a communication optical fibre fault diagnosis model based on variational modal decomposition(VMD),fuzzy entropy(FE)and fuzzy clustering(FC).Firstly,based on the OTDR curve data collected in the field,VMD is used to extract the different modal components(IMF)of the original signal and calculate the fuzzy entropy(FE)values of different components to characterize the subtle differences between them.The fuzzy entropy of each curve is used as the feature vector,which in turn constructs the communication optical fibre feature vector matrix,and the fuzzy clustering algorithm is used to achieve fault diagnosis of faulty optical fibre.The VMD-FE combination can extract subtle differences in features,and the fuzzy clustering algorithm does not require sample training.The experimental results show that the model in this paper has high accuracy and is relevant to the maintenance of communication optical fibre when compared with existing feature extraction models and traditional machine learning models.展开更多
Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up t...Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to 7G.Furthermore,it improves the array gain and directivity,increasing the detection range and angular resolution of radar systems.This study proposes two highly efficient SLL reduction techniques.These techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,respectively.The convolution process determines the element’s excitations while the GA optimizes the element spacing.For M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,respectively.When the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher SLL.Tomitigate the increased HPBWof the odd and even excitations,the element spacing is optimized using the GA.Thereby,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the SLL.Furthermore,for extreme SLL reduction,the DConv/GA is introduced.In this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation vectors.It provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.展开更多
Realtime speech communications require high efficient compression algorithms to encode speech signals. As the compressed speech parameters are highly sensitive to transmission errors, robust source and channel decodin...Realtime speech communications require high efficient compression algorithms to encode speech signals. As the compressed speech parameters are highly sensitive to transmission errors, robust source and channel decoding and demodulation schemes are both important and of practical use. In this paper, an it- erative joint souree-channel decoding and demodulation algorithm is proposed for mixed excited linear pre- diction （MELP） vocoder by both exploiting the residual redundancy and passing soft information through- out the receiver while introducing systematic global iteration process to further enhance the performance. Being fully compatible with existing transmitter structure, the proposed algorithm does not introduce addi- tional bandwidth expansion and transmission delay. Simulations show substantial error correcting perfor- mance and synthesized speech quality improvement over conventional separate designed systems in delay and bandwidth constraint channels by using the joint source-channel decoding and demodulation （JSCCM） algorithm.展开更多
Technological advancement in the field of trans- portation and communication has been happening at a faster pace in the past few decades. As the demand for high-speed transportation increases, the need for an improved...Technological advancement in the field of trans- portation and communication has been happening at a faster pace in the past few decades. As the demand for high-speed transportation increases, the need for an improved seamless communication system to handle higher data traffic in a highly mobile environment becomes imperative. This paper proposes a novel scheme to enhance the quality of service in high-speed railway （HSR） communication environment using the concept of torch nodes （TNs） and adaptive measurement aggregation （AMA）. The system was modeled using an object-oriented discrete event sim- ulator, and the performance was analyzed against the existing single-antenna scheme. The simulation results show that the proposed scheme with its minimal imple- mentation overhead can efficiently perform seamless han- dover with reduced handover failure and communication interruption probability.展开更多
Authentication of the digital image has much attention for the digital revolution.Digital image authentication can be verified with image watermarking and image encryption schemes.These schemes are widely used to prot...Authentication of the digital image has much attention for the digital revolution.Digital image authentication can be verified with image watermarking and image encryption schemes.These schemes are widely used to protect images against forgery attacks,and they are useful for protecting copyright and rightful ownership.Depending on the desirable applications,several image encryption and watermarking schemes have been proposed to moderate this attention.This framework presents a new scheme that combines a Walsh Hadamard Transform(WHT)-based image watermarking scheme with an image encryption scheme based on Double Random Phase Encoding(DRPE).First,on the sender side,the secret medical image is encrypted using DRPE.Then the encrypted image is watermarking based on WHT.The combination between watermarking and encryption increases the security and robustness of transmitting an image.The performance evaluation of the proposed scheme is obtained by testing Structural Similarity Index(SSIM),Peak Signal-to-Noise Ratio(PSNR),Normalized cross-correlation(NC),and Feature Similarity Index(FSIM).展开更多
The hyperloop idea,which is one of the most ecofriendly,low-carbon emissions,and fossil fuel-efficient modes of transportation,has recently become quite popular.In this study,a double-sided linear induction motor(LIM)...The hyperloop idea,which is one of the most ecofriendly,low-carbon emissions,and fossil fuel-efficient modes of transportation,has recently become quite popular.In this study,a double-sided linear induction motor(LIM)with 500 W of output power,60 N of thrust force and 200 V/38.58 Hz of supply voltage was designed to be used in hyperloop development competition hosted by the scientific and technological research council of turkey(TüB?TAK)rail transportation technologies institute(RUTE).In contrast to the studies in the literature,concentrated winding is preferred instead of distributed winding due to mechanical constraints.The electromagnetic design of LIM,whose mechanical and electrical requirements were determined considering the hyperloop development competition,was carried out by following certain steps.Then,the designed model was simulated and analyzed by finite element method(FEM),and the necessary optimizations have been performed to improve the motor characteristics.By examining the final model,the applicability of the concentrated winding type LIM for hyperloop technology has been investigated.Besides,the effects of primary material,railway material,and mechanical air-gap length on LIM performance were also investigated.In the practical phase of the study,the designed LIM has been prototyped and tested.The validation of the experimental results was achieved through good agreement with the finite element analysis results.展开更多
Compared to high-resolution digital-toanalog converters(DACs), deploying 1-bit DACs requires much less hardware complexity for a massive multi-user multiple-input multiple-output(MUMIMO) system. However, the feasible ...Compared to high-resolution digital-toanalog converters(DACs), deploying 1-bit DACs requires much less hardware complexity for a massive multi-user multiple-input multiple-output(MUMIMO) system. However, the feasible domain of a1-bit transmitting signal is non-continuous, and thus it is more challenging to exploit multi-user interference(MUI) by precoding. In this paper, to improve symbol decision accuracy, we investigate MUI exploitation 1-bit precoding methods for massive MU-MIMO systems under QAM modulations. Because MUIs may be constructive or destructive, we define a modified mean square error(MSE) metric for QAM constellations to jointly evaluate the effect of both MUIs and noise. Then, we model the 1-bit precoding optimization problems to minimize the sum modified MSE or the maximum modified MSE, where both the transmitting vector and receiving processing factor are optimization variables. Based on whether the receiving processing factor remains constant during the whole transmission block, two scenarios are taken into consideration. Referring to existing interference exploitation 1-bit precoding methods, we design efficient algorithms to solve the two modified MSE based problems.Compared to existing 1-bit precoding methods, our proposed methods provide better bit error rate performance, especially in more practical scenario Ⅱ(constant receiving processing factor in one block).展开更多
In this article,the multi-parameters Mittag-Leffler function is studied in detail.As a consequence,a series of novel results such as the integral representation,series representation and Mellin transform to the above ...In this article,the multi-parameters Mittag-Leffler function is studied in detail.As a consequence,a series of novel results such as the integral representation,series representation and Mellin transform to the above function,are obtained.Especially,we associate the multi-parameters Mittag-Leffler function with two special functions which are the generalized Wright hypergeometric and the Fox’s-H functions.Meanwhile,some interesting integral operators and derivative operators of this function,are also discussed.展开更多
Object detection in occluded environments remains a core challenge in computer vision(CV),especially in domains such as autonomous driving and robotics.While Convolutional Neural Network(CNN)-based twodimensional(2D)a...Object detection in occluded environments remains a core challenge in computer vision(CV),especially in domains such as autonomous driving and robotics.While Convolutional Neural Network(CNN)-based twodimensional(2D)and three-dimensional(3D)object detection methods havemade significant progress,they often fall short under severe occlusion due to depth ambiguities in 2D imagery and the high cost and deployment limitations of 3D sensors such as Light Detection and Ranging(LiDAR).This paper presents a comparative review of recent 2D and 3D detection models,focusing on their occlusion-handling capabilities and the impact of sensor modalities such as stereo vision,Time-of-Flight(ToF)cameras,and LiDAR.In this context,we introduce FuDensityNet,our multimodal occlusion-aware detection framework that combines Red-Green-Blue(RGB)images and LiDAR data to enhance detection performance.As a forward-looking direction,we propose a monocular depth-estimation extension to FuDensityNet,aimed at replacing expensive 3D sensors with a more scalable CNN-based pipeline.Although this enhancement is not experimentally evaluated in this manuscript,we describe its conceptual design and potential for future implementation.展开更多
X(formerly known as Twitter)is one of the most prominent social media platforms,enabling users to share short messages(tweets)with the public or their followers.It serves various purposes,from real-time news dissemina...X(formerly known as Twitter)is one of the most prominent social media platforms,enabling users to share short messages(tweets)with the public or their followers.It serves various purposes,from real-time news dissemination and political discourse to trend spotting and consumer engagement.X has emerged as a key space for understanding shifting brand perceptions,consumer preferences,and product-related sentiment in the fashion industry.However,the platform’s informal,dynamic,and context-dependent language poses substantial challenges for sentiment analysis,mainly when attempting to detect sarcasm,slang,and nuanced emotional tones.This study introduces a hybrid deep learning framework that integrates Transformer encoders,recurrent neural networks(i.e.,Long Short-Term Memory(LSTM)and Gated Recurrent Unit(GRU)),and attention mechanisms to improve the accuracy of fashion-related sentiment classification.These methods were selected due to their proven strength in capturing both contextual dependencies and sequential structures,which are essential for interpreting short-form text.Our model was evaluated on a dataset of 20,000 fashion tweets.The experimental results demonstrate a classification accuracy of 92.25%,outperforming conventional models such as Logistic Regression,Linear Support Vector Machine(SVM),and even standalone LSTM by a margin of up to 8%.This improvement highlights the importance of hybrid architectures in handling noisy,informal social media data.This study’s findings offer strong implications for digital marketing and brand management,where timely sentiment detection is critical.Despite the promising results,challenges remain regarding the precise identification of negative sentiments,indicating that further work is needed to detect subtle and contextually embedded expressions.展开更多
Due to the continuous increase in global energy demand,photovoltaic solar energy generation and associated maintenance requirements have significantly expanded.One critical maintenance challenge in photovoltaic instal...Due to the continuous increase in global energy demand,photovoltaic solar energy generation and associated maintenance requirements have significantly expanded.One critical maintenance challenge in photovoltaic installations is detecting hot spots,localized overheating defects in solar cells that drastically reduce efficiency and can lead to permanent damage.Traditional methods for detecting these defects rely on manual inspections using thermal imaging,which are costly,labor-intensive,and impractical for large-scale installations.This research introduces an automated hybrid system based on two specialized convolutional neural networks deployed in a cascaded architecture.The first convolutional neural network efficiently detects and isolates individual solar panels from high-resolution aerial thermal images captured by drones.Subsequently,a second,more advanced convolutional neural network accurately classifies each isolated panel as either defective or healthy,effectively distinguishing genuine thermal anomalies from false positives caused by reflections or glare.Experimental validation on a real-world dataset comprising thousands of thermal images yielded exceptional accuracy,significantly reducing inspection time,costs,and the likelihood of false defect detections.This proposed system enhances the reliability and efficiency of photovoltaic plant inspections,thus contributing to improved operational performance and economic viability.展开更多
Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks.This article offers an intriguing architecture for semantic,instance,and panoptic segmentation...Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks.This article offers an intriguing architecture for semantic,instance,and panoptic segmentation using EfficientNet-B7 and Bidirectional Feature Pyramid Networks(Bi-FPN).When implemented in place of the EfficientNet-B5 backbone,EfficientNet-B7 strengthens the model’s feature extraction capabilities and is far more appropriate for real-world applications.By ensuring superior multi-scale feature fusion,Bi-FPN integration enhances the segmentation of complex objects across various urban environments.The design suggested is examined on rigorous datasets,encompassing Cityscapes,Common Objects in Context,KITTI Karlsruhe Institute of Technology and Toyota Technological Institute,and Indian Driving Dataset,which replicate numerous real-world driving conditions.During extensive training,validation,and testing,the model showcases major gains in segmentation accuracy and surpasses state-of-the-art performance in semantic,instance,and panoptic segmentation tasks.Outperforming present methods,the recommended approach generates noteworthy gains in Panoptic Quality:+0.4%on Cityscapes,+0.2%on COCO,+1.7%on KITTI,and+0.4%on IDD.These changes show just how efficient it is in various driving circumstances and datasets.This study emphasizes the potential of EfficientNet-B7 and Bi-FPN to provide dependable,high-precision segmentation in computer vision applications,primarily autonomous driving.The research results suggest that this framework efficiently tackles the constraints of practical situations while delivering a robust solution for high-performance tasks involving segmentation.展开更多
Automated recognition of violent activities from videos is vital for public safety,but often raises significant privacy concerns due to the sensitive nature of the footage.Moreover,resource constraints often hinder th...Automated recognition of violent activities from videos is vital for public safety,but often raises significant privacy concerns due to the sensitive nature of the footage.Moreover,resource constraints often hinder the deployment of deep learning-based complex video classification models on edge devices.With this motivation,this study aims to investigate an effective violent activity classifier while minimizing computational complexity,attaining competitive performance,and mitigating user data privacy concerns.We present a lightweight deep learning architecture with fewer parameters for efficient violent activity recognition.We utilize a two-stream formation of 3D depthwise separable convolution coupled with a linear self-attention mechanism for effective feature extraction,incorporating federated learning to address data privacy concerns.Experimental findings demonstrate the model’s effectiveness with test accuracies from 96%to above 97%on multiple datasets by incorporating the FedProx aggregation strategy.These findings underscore the potential to develop secure,efficient,and reliable solutions for violent activity recognition in real-world scenarios.展开更多
This article presents a compact crab-shaped reconfigurable antenna(CSRA)designed for 5G sub-6 GHz wireless applications. The antenna achieves enhanced gain in a miniaturized form factor by incorporating a hexagonal sp...This article presents a compact crab-shaped reconfigurable antenna(CSRA)designed for 5G sub-6 GHz wireless applications. The antenna achieves enhanced gain in a miniaturized form factor by incorporating a hexagonal split-ring structure controlled via two radio frequency(RF) positive-intrinsicnegative(PIN) diodes(BAR64-02V). While the antenna is primarily designed to operate at 3.50 GHz for sub-6 GHz 5G applications, RF switching enables the CSRA to cover a broader frequency spectrum, including the S-band, X-band, and portions of the Ku-band. The proposed antenna offers several advantages: It is low-cost(fabricated on an FR-4 substrate), compact(achieving 64.07% size reduction compared to conventional designs), and features both frequency and gain reconfigurability through digitally controlled PIN diode switching. The reflection coefficients of the antenna, both without diodes and across all four switching states, were experimentally validated in the laboratory using a Keysight Field Fox microwave analyzer(N9916A, 14 GHz). The simulated radiation patterns and gain characteristics closely matched the measured values, demonstrating an excellent agreement. This study bridges the gap between traditional and next-generation antenna designs by offering a compact,cost-effective, and high-performance solution for multiband, reconfigurable wireless communication systems. The integration of double-split-ring resonators and dynamic reconfigurability makes the proposed antenna a strong candidate for various applications, including S-band and X-band systems, as well as the emerging lower 6G band(7.125 GHz–8.400 GHz).展开更多
This paper provides a comprehensive bibliometric exposition on deepfake research,exploring the intersection of artificial intelligence and deepfakes as well as international collaborations,prominent researchers,organi...This paper provides a comprehensive bibliometric exposition on deepfake research,exploring the intersection of artificial intelligence and deepfakes as well as international collaborations,prominent researchers,organizations,institutions,publications,and key themes.We performed a search on theWeb of Science(WoS)database,focusing on Artificial Intelligence and Deepfakes,and filtered the results across 21 research areas,yielding 1412 articles.Using VOSviewer visualization tool,we analyzed thisWoS data through keyword co-occurrence graphs,emphasizing on four prominent research themes.Compared with existing bibliometric papers on deepfakes,this paper proceeds to identify and discuss some of the highly cited papers within these themes:deepfake detection,feature extraction,face recognition,and forensics.The discussion highlights key challenges and advancements in deepfake research.Furthermore,this paper also discusses pressing issues surrounding deepfakes such as security,regulation,and datasets.We also provide an analysis of another exhaustive search on Scopus database focusing solely on Deepfakes(while not excluding AI)revealing deep learning as the predominant keyword,underscoring AI’s central role in deepfake research.This comprehensive analysis,encompassing over 500 keywords from 8790 articles,uncovered a wide range of methods,implications,applications,concerns,requirements,challenges,models,tools,datasets,and modalities related to deepfakes.Finally,a discussion on recommendations for policymakers,researchers,and other stakeholders is also provided.展开更多
Recently,a new worldwide race has emerged to achieve a breakthrough in designing and deploying massive ultra-dense low-Earth orbit(LEO)satellite constellation(SatCon)networks with the vision of providing everywhere In...Recently,a new worldwide race has emerged to achieve a breakthrough in designing and deploying massive ultra-dense low-Earth orbit(LEO)satellite constellation(SatCon)networks with the vision of providing everywhere Internet coverage from space.Several players have started the deployment phase with different scales.However,the implementation is in its infancy,and many investigations are needed.This work provides an overview of the stateof-the-art architectures,orbital patterns,top players,and potential applications of SatCon networks.Moreover,we discuss new open research directions and challenges for improving network performance.Finally,a case study highlights the benefits of integrating SatCon network and non-orthogonal multiple access(NOMA)technologies for improving the achievable capacity of satellite end-users.展开更多
On 13 August 2010, a catastrophic debris flow with a volume of 1.17 million m3 occurred in Xiaojiagou Ravine near Yingxiu town of Wenchuan county in Sichuan Province, China. The main source material was the landslide ...On 13 August 2010, a catastrophic debris flow with a volume of 1.17 million m3 occurred in Xiaojiagou Ravine near Yingxiu town of Wenchuan county in Sichuan Province, China. The main source material was the landslide deposits retained in the ravine during the 2008 Wenchuan earthquake. This paper describes a two-dimensional hybrid numerical method that simulates the entire process of the debris flow from initiation to transportation and finally to deposition. The study area is discretized into a grid of square zones. A two dimensional finite difference method is then applied to simulate the rainfall-runoff and debris flow runout processes. The analysis is divided into three steps; namely, rainfall-runoff simulation, mixing water and solid materials, and debris flow runout simulation. The rainfall-runoff simulation is firstly conducted to obtain the cumulative runoff near the location of main source material and at the outlet of the first branch. The water and solid materials are then mixed to create an inflow hydrograph for the debris flow runout simulation. The occurrence time and volume of the debris flow can be estimated in this step. Finally the runout process of the debris flow is simulated. When the yield stress is high, it controls the deposition zone. When the yield stress is medium or low, both yield stress and viscosity influence the deposition zone. The flow velocity is largely influenced by the viscosity. The estimated yield stress by the equation, ty = pghsinO, and the estimated viscosity by the equation established by Bisantino et al. （2010） provide good estimates of the area of the debris flow fan and the distribution of deposition depth.展开更多
This paper is mainly concerned with the coupling dynamic analysis of a complex spacecraft consisting of one main rigid platform, multiple liquid-filled cylindrical tanks, and a number of flexible appendages. Firstly, ...This paper is mainly concerned with the coupling dynamic analysis of a complex spacecraft consisting of one main rigid platform, multiple liquid-filled cylindrical tanks, and a number of flexible appendages. Firstly, the carrier potential function equations of liquid in the tanks are deduced according to the wall boundary conditions. Through employ- ing the Fourier-Bessel series expansion method, the dynamic boundaries conditions on a curved free-surface under a low-gravity environment are transformed to general simple differential equations and the rigid-liquid coupled sloshing dynamic state equations of liquid in tanks are obtained. The state vectors of rigid-liquid coupled equations are composed with the modal coordinates of the relative potential func- tion and the modal coordinates of wave height. Based on the B ernoulli-Euler beam theory and the D＇Alembert＇s prin- ciple, the rigid-flexible coupled dynamic state equations of flexible appendages are directly derived, and the coordi- nate transform matrixes of maneuvering flexible appendages are precisely computed as time-varying. Then, the cou- pling dynamics state equations of the overall system of the spacecraft are modularly built by means of the Lagrange＇s equations in terms of quasi-coordinates. Lastly, the cou-piing dynamic performances of a typical complex spacecraft are studied. The availability and reliability of the presented method are also confirmed.展开更多
The traffic explosion and the rising of diverse requirements lead to many challenges for traditional mobile network architecture on flexibility, scalability, and deployability. To meet new requirements in the 5 G era,...The traffic explosion and the rising of diverse requirements lead to many challenges for traditional mobile network architecture on flexibility, scalability, and deployability. To meet new requirements in the 5 G era, service based architecture is introduced into mobile networks. The monolithic network elements(e.g., MME, PGW, etc.) are split into smaller network functions to provide customized services. However, the management and deployment of network functions in service based 5 G core network are still big challenges. In this paper, we propose a novel management architecture for 5 G service based core network based on NFV and SDN. Combined with SDN, NFV and edge computing, the proposed framework can provide distributed and on-demand deployment of network functions, service guaranteed network slicing, flexible orchestration of network functions and optimal workload allocation. Simulations are conducted to show that the proposed framework and algorithm are effective in terms of reducing network operating cost.展开更多
基金This paper is supported by State Grid Gansu Electric Power Company Science and Technology Project(20220515003).
文摘To solve the problems of a few optical fibre line fault samples and the inefficiency of manual communication optical fibre fault diagnosis,this paper proposes a communication optical fibre fault diagnosis model based on variational modal decomposition(VMD),fuzzy entropy(FE)and fuzzy clustering(FC).Firstly,based on the OTDR curve data collected in the field,VMD is used to extract the different modal components(IMF)of the original signal and calculate the fuzzy entropy(FE)values of different components to characterize the subtle differences between them.The fuzzy entropy of each curve is used as the feature vector,which in turn constructs the communication optical fibre feature vector matrix,and the fuzzy clustering algorithm is used to achieve fault diagnosis of faulty optical fibre.The VMD-FE combination can extract subtle differences in features,and the fuzzy clustering algorithm does not require sample training.The experimental results show that the model in this paper has high accuracy and is relevant to the maintenance of communication optical fibre when compared with existing feature extraction models and traditional machine learning models.
基金Research Supporting Project Number(RSPD2023R 585),King Saud University,Riyadh,Saudi Arabia.
文摘Side lobe level reduction(SLL)of antenna arrays significantly enhances the signal-to-interference ratio and improves the quality of service(QOS)in recent and future wireless communication systems starting from 5G up to 7G.Furthermore,it improves the array gain and directivity,increasing the detection range and angular resolution of radar systems.This study proposes two highly efficient SLL reduction techniques.These techniques are based on the hybridization between either the single convolution or the double convolution algorithms and the genetic algorithm(GA)to develop the Conv/GA andDConv/GA,respectively.The convolution process determines the element’s excitations while the GA optimizes the element spacing.For M elements linear antenna array(LAA),the convolution of the excitation coefficients vector by itself provides a new vector of excitations of length N=(2M−1).This new vector is divided into three different sets of excitations including the odd excitations,even excitations,and middle excitations of lengths M,M−1,andM,respectively.When the same element spacing as the original LAA is used,it is noticed that the odd and even excitations provide a much lower SLL than that of the LAA but with amuch wider half-power beamwidth(HPBW).While the middle excitations give the same HPBWas the original LAA with a relatively higher SLL.Tomitigate the increased HPBWof the odd and even excitations,the element spacing is optimized using the GA.Thereby,the synthesized arrays have the same HPBW as the original LAA with a two-fold reduction in the SLL.Furthermore,for extreme SLL reduction,the DConv/GA is introduced.In this technique,the same procedure of the aforementioned Conv/GA technique is performed on the resultant even and odd excitation vectors.It provides a relatively wider HPBWthan the original LAA with about quad-fold reduction in the SLL.
基金Supported by the National Natural Science Foundation of China （No. 60572081 ）
文摘Realtime speech communications require high efficient compression algorithms to encode speech signals. As the compressed speech parameters are highly sensitive to transmission errors, robust source and channel decoding and demodulation schemes are both important and of practical use. In this paper, an it- erative joint souree-channel decoding and demodulation algorithm is proposed for mixed excited linear pre- diction （MELP） vocoder by both exploiting the residual redundancy and passing soft information through- out the receiver while introducing systematic global iteration process to further enhance the performance. Being fully compatible with existing transmitter structure, the proposed algorithm does not introduce addi- tional bandwidth expansion and transmission delay. Simulations show substantial error correcting perfor- mance and synthesized speech quality improvement over conventional separate designed systems in delay and bandwidth constraint channels by using the joint source-channel decoding and demodulation （JSCCM） algorithm.
文摘Technological advancement in the field of trans- portation and communication has been happening at a faster pace in the past few decades. As the demand for high-speed transportation increases, the need for an improved seamless communication system to handle higher data traffic in a highly mobile environment becomes imperative. This paper proposes a novel scheme to enhance the quality of service in high-speed railway （HSR） communication environment using the concept of torch nodes （TNs） and adaptive measurement aggregation （AMA）. The system was modeled using an object-oriented discrete event sim- ulator, and the performance was analyzed against the existing single-antenna scheme. The simulation results show that the proposed scheme with its minimal imple- mentation overhead can efficiently perform seamless han- dover with reduced handover failure and communication interruption probability.
基金Princess Nourah bint Abdulrahman University Researchers Supporting ProjectNumber (PNURSP2022R66), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
文摘Authentication of the digital image has much attention for the digital revolution.Digital image authentication can be verified with image watermarking and image encryption schemes.These schemes are widely used to protect images against forgery attacks,and they are useful for protecting copyright and rightful ownership.Depending on the desirable applications,several image encryption and watermarking schemes have been proposed to moderate this attention.This framework presents a new scheme that combines a Walsh Hadamard Transform(WHT)-based image watermarking scheme with an image encryption scheme based on Double Random Phase Encoding(DRPE).First,on the sender side,the secret medical image is encrypted using DRPE.Then the encrypted image is watermarking based on WHT.The combination between watermarking and encryption increases the security and robustness of transmitting an image.The performance evaluation of the proposed scheme is obtained by testing Structural Similarity Index(SSIM),Peak Signal-to-Noise Ratio(PSNR),Normalized cross-correlation(NC),and Feature Similarity Index(FSIM).
基金the Istanbul Technical University Scientific Research Projects Unit with grant number MGA-2022-43948。
文摘The hyperloop idea,which is one of the most ecofriendly,low-carbon emissions,and fossil fuel-efficient modes of transportation,has recently become quite popular.In this study,a double-sided linear induction motor(LIM)with 500 W of output power,60 N of thrust force and 200 V/38.58 Hz of supply voltage was designed to be used in hyperloop development competition hosted by the scientific and technological research council of turkey(TüB?TAK)rail transportation technologies institute(RUTE).In contrast to the studies in the literature,concentrated winding is preferred instead of distributed winding due to mechanical constraints.The electromagnetic design of LIM,whose mechanical and electrical requirements were determined considering the hyperloop development competition,was carried out by following certain steps.Then,the designed model was simulated and analyzed by finite element method(FEM),and the necessary optimizations have been performed to improve the motor characteristics.By examining the final model,the applicability of the concentrated winding type LIM for hyperloop technology has been investigated.Besides,the effects of primary material,railway material,and mechanical air-gap length on LIM performance were also investigated.In the practical phase of the study,the designed LIM has been prototyped and tested.The validation of the experimental results was achieved through good agreement with the finite element analysis results.
文摘Compared to high-resolution digital-toanalog converters(DACs), deploying 1-bit DACs requires much less hardware complexity for a massive multi-user multiple-input multiple-output(MUMIMO) system. However, the feasible domain of a1-bit transmitting signal is non-continuous, and thus it is more challenging to exploit multi-user interference(MUI) by precoding. In this paper, to improve symbol decision accuracy, we investigate MUI exploitation 1-bit precoding methods for massive MU-MIMO systems under QAM modulations. Because MUIs may be constructive or destructive, we define a modified mean square error(MSE) metric for QAM constellations to jointly evaluate the effect of both MUIs and noise. Then, we model the 1-bit precoding optimization problems to minimize the sum modified MSE or the maximum modified MSE, where both the transmitting vector and receiving processing factor are optimization variables. Based on whether the receiving processing factor remains constant during the whole transmission block, two scenarios are taken into consideration. Referring to existing interference exploitation 1-bit precoding methods, we design efficient algorithms to solve the two modified MSE based problems.Compared to existing 1-bit precoding methods, our proposed methods provide better bit error rate performance, especially in more practical scenario Ⅱ(constant receiving processing factor in one block).
基金Supported by The National Undergraduate Innovation Training Program(Grant No.202310290069Z).
文摘In this article,the multi-parameters Mittag-Leffler function is studied in detail.As a consequence,a series of novel results such as the integral representation,series representation and Mellin transform to the above function,are obtained.Especially,we associate the multi-parameters Mittag-Leffler function with two special functions which are the generalized Wright hypergeometric and the Fox’s-H functions.Meanwhile,some interesting integral operators and derivative operators of this function,are also discussed.
文摘Object detection in occluded environments remains a core challenge in computer vision(CV),especially in domains such as autonomous driving and robotics.While Convolutional Neural Network(CNN)-based twodimensional(2D)and three-dimensional(3D)object detection methods havemade significant progress,they often fall short under severe occlusion due to depth ambiguities in 2D imagery and the high cost and deployment limitations of 3D sensors such as Light Detection and Ranging(LiDAR).This paper presents a comparative review of recent 2D and 3D detection models,focusing on their occlusion-handling capabilities and the impact of sensor modalities such as stereo vision,Time-of-Flight(ToF)cameras,and LiDAR.In this context,we introduce FuDensityNet,our multimodal occlusion-aware detection framework that combines Red-Green-Blue(RGB)images and LiDAR data to enhance detection performance.As a forward-looking direction,we propose a monocular depth-estimation extension to FuDensityNet,aimed at replacing expensive 3D sensors with a more scalable CNN-based pipeline.Although this enhancement is not experimentally evaluated in this manuscript,we describe its conceptual design and potential for future implementation.
文摘X(formerly known as Twitter)is one of the most prominent social media platforms,enabling users to share short messages(tweets)with the public or their followers.It serves various purposes,from real-time news dissemination and political discourse to trend spotting and consumer engagement.X has emerged as a key space for understanding shifting brand perceptions,consumer preferences,and product-related sentiment in the fashion industry.However,the platform’s informal,dynamic,and context-dependent language poses substantial challenges for sentiment analysis,mainly when attempting to detect sarcasm,slang,and nuanced emotional tones.This study introduces a hybrid deep learning framework that integrates Transformer encoders,recurrent neural networks(i.e.,Long Short-Term Memory(LSTM)and Gated Recurrent Unit(GRU)),and attention mechanisms to improve the accuracy of fashion-related sentiment classification.These methods were selected due to their proven strength in capturing both contextual dependencies and sequential structures,which are essential for interpreting short-form text.Our model was evaluated on a dataset of 20,000 fashion tweets.The experimental results demonstrate a classification accuracy of 92.25%,outperforming conventional models such as Logistic Regression,Linear Support Vector Machine(SVM),and even standalone LSTM by a margin of up to 8%.This improvement highlights the importance of hybrid architectures in handling noisy,informal social media data.This study’s findings offer strong implications for digital marketing and brand management,where timely sentiment detection is critical.Despite the promising results,challenges remain regarding the precise identification of negative sentiments,indicating that further work is needed to detect subtle and contextually embedded expressions.
基金funded by the Spanish Ministerio de Ciencia,Innovación y Universidades,grant number RTC2019-007364-3(FPGM)by the Comunidad de Madrid through the direct grant with ref.SI4/PJI/2024-00233 for the promotion of research and technology transfer at the Universidad Autónoma de Madrid。
文摘Due to the continuous increase in global energy demand,photovoltaic solar energy generation and associated maintenance requirements have significantly expanded.One critical maintenance challenge in photovoltaic installations is detecting hot spots,localized overheating defects in solar cells that drastically reduce efficiency and can lead to permanent damage.Traditional methods for detecting these defects rely on manual inspections using thermal imaging,which are costly,labor-intensive,and impractical for large-scale installations.This research introduces an automated hybrid system based on two specialized convolutional neural networks deployed in a cascaded architecture.The first convolutional neural network efficiently detects and isolates individual solar panels from high-resolution aerial thermal images captured by drones.Subsequently,a second,more advanced convolutional neural network accurately classifies each isolated panel as either defective or healthy,effectively distinguishing genuine thermal anomalies from false positives caused by reflections or glare.Experimental validation on a real-world dataset comprising thousands of thermal images yielded exceptional accuracy,significantly reducing inspection time,costs,and the likelihood of false defect detections.This proposed system enhances the reliability and efficiency of photovoltaic plant inspections,thus contributing to improved operational performance and economic viability.
文摘Techniques in deep learning have significantly boosted the accuracy and productivity of computer vision segmentation tasks.This article offers an intriguing architecture for semantic,instance,and panoptic segmentation using EfficientNet-B7 and Bidirectional Feature Pyramid Networks(Bi-FPN).When implemented in place of the EfficientNet-B5 backbone,EfficientNet-B7 strengthens the model’s feature extraction capabilities and is far more appropriate for real-world applications.By ensuring superior multi-scale feature fusion,Bi-FPN integration enhances the segmentation of complex objects across various urban environments.The design suggested is examined on rigorous datasets,encompassing Cityscapes,Common Objects in Context,KITTI Karlsruhe Institute of Technology and Toyota Technological Institute,and Indian Driving Dataset,which replicate numerous real-world driving conditions.During extensive training,validation,and testing,the model showcases major gains in segmentation accuracy and surpasses state-of-the-art performance in semantic,instance,and panoptic segmentation tasks.Outperforming present methods,the recommended approach generates noteworthy gains in Panoptic Quality:+0.4%on Cityscapes,+0.2%on COCO,+1.7%on KITTI,and+0.4%on IDD.These changes show just how efficient it is in various driving circumstances and datasets.This study emphasizes the potential of EfficientNet-B7 and Bi-FPN to provide dependable,high-precision segmentation in computer vision applications,primarily autonomous driving.The research results suggest that this framework efficiently tackles the constraints of practical situations while delivering a robust solution for high-performance tasks involving segmentation.
基金Supported by the Research Chair of Online Dialogue and Cultural Communication,King Saud University,Saudi Arabia.
文摘Automated recognition of violent activities from videos is vital for public safety,but often raises significant privacy concerns due to the sensitive nature of the footage.Moreover,resource constraints often hinder the deployment of deep learning-based complex video classification models on edge devices.With this motivation,this study aims to investigate an effective violent activity classifier while minimizing computational complexity,attaining competitive performance,and mitigating user data privacy concerns.We present a lightweight deep learning architecture with fewer parameters for efficient violent activity recognition.We utilize a two-stream formation of 3D depthwise separable convolution coupled with a linear self-attention mechanism for effective feature extraction,incorporating federated learning to address data privacy concerns.Experimental findings demonstrate the model’s effectiveness with test accuracies from 96%to above 97%on multiple datasets by incorporating the FedProx aggregation strategy.These findings underscore the potential to develop secure,efficient,and reliable solutions for violent activity recognition in real-world scenarios.
文摘This article presents a compact crab-shaped reconfigurable antenna(CSRA)designed for 5G sub-6 GHz wireless applications. The antenna achieves enhanced gain in a miniaturized form factor by incorporating a hexagonal split-ring structure controlled via two radio frequency(RF) positive-intrinsicnegative(PIN) diodes(BAR64-02V). While the antenna is primarily designed to operate at 3.50 GHz for sub-6 GHz 5G applications, RF switching enables the CSRA to cover a broader frequency spectrum, including the S-band, X-band, and portions of the Ku-band. The proposed antenna offers several advantages: It is low-cost(fabricated on an FR-4 substrate), compact(achieving 64.07% size reduction compared to conventional designs), and features both frequency and gain reconfigurability through digitally controlled PIN diode switching. The reflection coefficients of the antenna, both without diodes and across all four switching states, were experimentally validated in the laboratory using a Keysight Field Fox microwave analyzer(N9916A, 14 GHz). The simulated radiation patterns and gain characteristics closely matched the measured values, demonstrating an excellent agreement. This study bridges the gap between traditional and next-generation antenna designs by offering a compact,cost-effective, and high-performance solution for multiband, reconfigurable wireless communication systems. The integration of double-split-ring resonators and dynamic reconfigurability makes the proposed antenna a strong candidate for various applications, including S-band and X-band systems, as well as the emerging lower 6G band(7.125 GHz–8.400 GHz).
文摘This paper provides a comprehensive bibliometric exposition on deepfake research,exploring the intersection of artificial intelligence and deepfakes as well as international collaborations,prominent researchers,organizations,institutions,publications,and key themes.We performed a search on theWeb of Science(WoS)database,focusing on Artificial Intelligence and Deepfakes,and filtered the results across 21 research areas,yielding 1412 articles.Using VOSviewer visualization tool,we analyzed thisWoS data through keyword co-occurrence graphs,emphasizing on four prominent research themes.Compared with existing bibliometric papers on deepfakes,this paper proceeds to identify and discuss some of the highly cited papers within these themes:deepfake detection,feature extraction,face recognition,and forensics.The discussion highlights key challenges and advancements in deepfake research.Furthermore,this paper also discusses pressing issues surrounding deepfakes such as security,regulation,and datasets.We also provide an analysis of another exhaustive search on Scopus database focusing solely on Deepfakes(while not excluding AI)revealing deep learning as the predominant keyword,underscoring AI’s central role in deepfake research.This comprehensive analysis,encompassing over 500 keywords from 8790 articles,uncovered a wide range of methods,implications,applications,concerns,requirements,challenges,models,tools,datasets,and modalities related to deepfakes.Finally,a discussion on recommendations for policymakers,researchers,and other stakeholders is also provided.
基金Ehab Mahmoud Mohamed is supported via funding from Prince sattam bin Abdulaziz University project number(PSAU/2025/R/1446).
文摘Recently,a new worldwide race has emerged to achieve a breakthrough in designing and deploying massive ultra-dense low-Earth orbit(LEO)satellite constellation(SatCon)networks with the vision of providing everywhere Internet coverage from space.Several players have started the deployment phase with different scales.However,the implementation is in its infancy,and many investigations are needed.This work provides an overview of the stateof-the-art architectures,orbital patterns,top players,and potential applications of SatCon networks.Moreover,we discuss new open research directions and challenges for improving network performance.Finally,a case study highlights the benefits of integrating SatCon network and non-orthogonal multiple access(NOMA)technologies for improving the achievable capacity of satellite end-users.
基金support from Sichuan Provincial Department of Transportation and Communications,the National Basic Research Program of China (Grant No.2011CB013506)the Research Grants Council of the Hong Kong SAR (Grant No.622210)
文摘On 13 August 2010, a catastrophic debris flow with a volume of 1.17 million m3 occurred in Xiaojiagou Ravine near Yingxiu town of Wenchuan county in Sichuan Province, China. The main source material was the landslide deposits retained in the ravine during the 2008 Wenchuan earthquake. This paper describes a two-dimensional hybrid numerical method that simulates the entire process of the debris flow from initiation to transportation and finally to deposition. The study area is discretized into a grid of square zones. A two dimensional finite difference method is then applied to simulate the rainfall-runoff and debris flow runout processes. The analysis is divided into three steps; namely, rainfall-runoff simulation, mixing water and solid materials, and debris flow runout simulation. The rainfall-runoff simulation is firstly conducted to obtain the cumulative runoff near the location of main source material and at the outlet of the first branch. The water and solid materials are then mixed to create an inflow hydrograph for the debris flow runout simulation. The occurrence time and volume of the debris flow can be estimated in this step. Finally the runout process of the debris flow is simulated. When the yield stress is high, it controls the deposition zone. When the yield stress is medium or low, both yield stress and viscosity influence the deposition zone. The flow velocity is largely influenced by the viscosity. The estimated yield stress by the equation, ty = pghsinO, and the estimated viscosity by the equation established by Bisantino et al. （2010） provide good estimates of the area of the debris flow fan and the distribution of deposition depth.
基金project was supported by the National Natural Science Foundation of China (Grants 11472041, 11302244, 11532002)Guangxi Natural Science Foundation (2015GXNSFBA 139013)
文摘This paper is mainly concerned with the coupling dynamic analysis of a complex spacecraft consisting of one main rigid platform, multiple liquid-filled cylindrical tanks, and a number of flexible appendages. Firstly, the carrier potential function equations of liquid in the tanks are deduced according to the wall boundary conditions. Through employ- ing the Fourier-Bessel series expansion method, the dynamic boundaries conditions on a curved free-surface under a low-gravity environment are transformed to general simple differential equations and the rigid-liquid coupled sloshing dynamic state equations of liquid in tanks are obtained. The state vectors of rigid-liquid coupled equations are composed with the modal coordinates of the relative potential func- tion and the modal coordinates of wave height. Based on the B ernoulli-Euler beam theory and the D＇Alembert＇s prin- ciple, the rigid-flexible coupled dynamic state equations of flexible appendages are directly derived, and the coordi- nate transform matrixes of maneuvering flexible appendages are precisely computed as time-varying. Then, the cou- pling dynamics state equations of the overall system of the spacecraft are modularly built by means of the Lagrange＇s equations in terms of quasi-coordinates. Lastly, the cou-piing dynamic performances of a typical complex spacecraft are studied. The availability and reliability of the presented method are also confirmed.
基金supported by China Ministry of Education-CMCC Research Fund Project No.MCM20160104National Science and Technology Major Project No.No.2018ZX03001016+1 种基金Beijing Municipal Science and technology Commission Research Fund Project No.Z171100005217001Fundamental Research Funds for Central Universities NO.2018RC06
文摘The traffic explosion and the rising of diverse requirements lead to many challenges for traditional mobile network architecture on flexibility, scalability, and deployability. To meet new requirements in the 5 G era, service based architecture is introduced into mobile networks. The monolithic network elements(e.g., MME, PGW, etc.) are split into smaller network functions to provide customized services. However, the management and deployment of network functions in service based 5 G core network are still big challenges. In this paper, we propose a novel management architecture for 5 G service based core network based on NFV and SDN. Combined with SDN, NFV and edge computing, the proposed framework can provide distributed and on-demand deployment of network functions, service guaranteed network slicing, flexible orchestration of network functions and optimal workload allocation. Simulations are conducted to show that the proposed framework and algorithm are effective in terms of reducing network operating cost.
