The rapid industrial growth and increasing population have led to significant pollution and deterioration of the natural atmospheric environment.Major atmospheric pollutants include NO_(2)and CO_(2).Hence,it is impera...The rapid industrial growth and increasing population have led to significant pollution and deterioration of the natural atmospheric environment.Major atmospheric pollutants include NO_(2)and CO_(2).Hence,it is imperative to develop NO_(2)and CO_(2)sensors for ambient conditions,that can be used in indoor air quality monitoring,breath analysis,food spoilage detection,etc.In the present study,two thin film nanocomposite(nickel oxide-graphene and nickel oxide-silver nanowires)gas sensors are fabricated using direct ink writing.The nano-composites are investigated for their structural,optical,and electrical properties.Later the nano-composite is deposited on the interdigitated electrode(IDE)pattern to form NO_(2)and CO_(2)sensors.The deposited films are then exposed to NO_(2)and CO_(2)gases separately and their response and recovery times are determined using a custom-built gas sensing setup.Nickel oxide-graphene provides a good response time and recovery time of 10 and 9 s,respectively for NO_(2),due to the higher electron affinity of graphene towards NO_(2).Nickel oxide-silver nanowire nano-composite is suited for CO_(2)gas because silver is an excellent electrocatalyst for CO_(2)by giving response and recovery times of 11 s each.This is the first report showcasing NiO nano-composites for NO_(2)and CO_(2)sensing at room temperature.展开更多
Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive met...Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive method for determining cardiac health.Various health practitioners use the ECG signal to ascertain critical information about the human heart.In this article,swarm intelligence approaches are used in the biomedical signal processing sector to enhance adaptive hybrid filters and empirical wavelet transforms(EWTs).At first,the white Gaussian noise is added to the input ECG signal and then applied to the EWT.The ECG signals are denoised by the proposed adaptive hybrid filter.The honey badge optimization(HBO)algorithm is utilized to optimize the EWT window function and adaptive hybrid filter weight parameters.The proposed approach is simulated by MATLAB 2018a using the MIT-BIH dataset with white Gaussian,electromyogram and electrode motion artifact noises.A comparison of the HBO approach with recursive least square-based adaptive filter,multichannel least means square,and discrete wavelet transform methods has been done in order to show the efficiency of the proposed adaptive hybrid filter.The experimental results show that the HBO approach supported by EWT and adaptive hybrid filter can be employed efficiently for cardiovascular signal denoising.展开更多
In recent decades,brain tumors have emerged as a serious neurological disorder that often leads to death.Hence,Brain Tumor Segmentation(BTS)is significant to enable the visualization,classification,and delineation of ...In recent decades,brain tumors have emerged as a serious neurological disorder that often leads to death.Hence,Brain Tumor Segmentation(BTS)is significant to enable the visualization,classification,and delineation of tumor regions in Magnetic Resonance Imaging(MRI).However,BTS remains a challenging task because of noise,non-uniform object texture,diverse image content and clustered objects.To address these challenges,a novel model is implemented in this research.The key objective of this research is to improve segmentation accuracy and generalization in BTS by incorporating Switchable Normalization into Faster R-CNN,which effectively captures the fine-grained tumor features to enhance segmentation precision.MRI images are initially acquired from three online datasets:Dataset 1—Brain Tumor Segmentation(BraTS)2018,Dataset 2—BraTS 2019,and Dataset 3—BraTS 2020.Subsequently,the Switchable Normalization-based Faster Regions with Convolutional Neural Networks(SNFRC)model is proposed for improved BTS in MRI images.In the proposed model,Switchable Normalization is integrated into the conventional architecture,enhancing generalization capability and reducing overfitting to unseen image data,which is essential due to the typically limited size of available datasets.The network depth is increased to obtain discriminative semantic features that improve segmentation performance.Specifically,Switchable Normalization captures the diverse feature representations from the brain images.The Faster R-CNN model develops end-to-end training and effective regional proposal generation,with an enhanced training stability using Switchable Normalization,to perform an effective segmentation in MRI images.From the experimental results,the proposed model attains segmentation accuracies of 99.41%,98.12%,and 96.71%on Datasets 1,2,and 3,respectively,outperforming conventional deep learning models used for BTS.展开更多
Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural habitats.However,the effects of urbanization on LULC of different cro...Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural habitats.However,the effects of urbanization on LULC of different crop types are less concerned.The study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region,Mexico,from 1994 to 2024,and predicted the LULC in 2034 using remote sensing data,with the goals of sustainable land management and climate resilience strategies.Despite increasing urbanization and drought,the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this region.Using Landsat imagery,we assessed crop attributes through indices such as normalized difference vegetation index(NDVI),normalized difference water index(NDWI),normalized difference moisture index(NDMI),and vegetation condition index(VCI),alongside watershed delineation and spectral features.The random forest model was applied to classify LULC,providing insights into both historical and future trends.Results indicated a significant decline in vegetation cover(109.13 km^(2))from 1994 to 2024,accompanied by an increase in built-up land(75.11 km^(2))and bare land(67.13 km^(2)).Projections suggested a further decline in vegetation cover(41.51 km^(2))and continued urban land expansion by 2034.The study found that paddy crops exhibited the highest values,while common bean and maize performed poorly.Drought analysis revealed that mildly dry areas in 2004 became severely dry in 2024,highlighting the increasing vulnerability of agriculture to climate change.The study concludes that sustainable land management,improved water resource practices,and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the area.These findings contribute to the understanding of how remote sensing can be effectively used for long-term agricultural planning and environmental sustainability.展开更多
Lightweight deep learning models are increasingly required in resource-constrained environments such as mobile devices and the Internet of Medical Things(IoMT).Multi-head convolution with channel attention can facilit...Lightweight deep learning models are increasingly required in resource-constrained environments such as mobile devices and the Internet of Medical Things(IoMT).Multi-head convolution with channel attention can facilitate learning activations relevant to different kernel sizes within a multi-head convolutional layer.Therefore,this study investigates the capability of novel lightweight models incorporating residual multi-head convolution with channel attention(ResMHCNN)blocks to classify medical images.We introduced three novel lightweight deep learning models(BT-Net,LCC-Net,and BC-Net)utilizing the ResMHCNN block as their backbone.These models were crossvalidated and tested on three publicly available medical image datasets:a brain tumor dataset from Figshare consisting of T1-weighted magnetic resonance imaging slices of meningioma,glioma,and pituitary tumors;the LC25000 dataset,which includes microscopic images of lung and colon cancers;and the BreaKHis dataset,containing benign and malignant breast microscopic images.The lightweight models achieved accuracies of 96.9%for 3-class brain tumor classification using BT-Net,and 99.7%for 5-class lung and colon cancer classification using LCC-Net.For 2-class breast cancer classification,BC-Net achieved an accuracy of 96.7%.The parameter counts for the proposed lightweight models—LCC-Net,BC-Net,and BT-Net—are 0.528,0.226,and 1.154 million,respectively.The presented lightweight models,featuring ResMHCNN blocks,may be effectively employed for accurate medical image classification.In the future,these models might be tested for viability in resource-constrained systems such as mobile devices and IoMT platforms.展开更多
Sparse vector coding(SVC)is emerging as a potential technology for short packet communications.To further improve the block error rate(BLER)performance,a uniquely decomposable constellation group-based SVC(UDCG-SVC)is...Sparse vector coding(SVC)is emerging as a potential technology for short packet communications.To further improve the block error rate(BLER)performance,a uniquely decomposable constellation group-based SVC(UDCG-SVC)is proposed in this article.Additionally,in order to achieve an optimal BLER performance of UDCG-SVC,a problem to optimize the coding gain of UDCG-based superimposed constellation is formulated.Given the energy of rotation constellations in UDCG,this problem is solved by converting it into finding the maximized minimum Euclidean distance of the superimposed constellation.Simulation results demonstrate the validness of our derivation.We also find that the proposed UDCGSVC has better BLER performance compared to other SVC schemes,especially under the high order modulation scenarios.展开更多
Generalised pre-coding quadrature spatial modulation(GPQSM)is recently proposed to increase the spectral efficiency(SE)of GPSM,which extends the transmitted symbols into in-phase/quadrature domains.In this paper,a nov...Generalised pre-coding quadrature spatial modulation(GPQSM)is recently proposed to increase the spectral efficiency(SE)of GPSM,which extends the transmitted symbols into in-phase/quadrature domains.In this paper,a novel scheme named non-orthogonal multiple access(NOMA)-aided GPQSM(NOMA-GPQSM),which incorporates the GPQSM scheme into the multi-user communication networks with assist of NOMA,is proposed to further improve the SE and system performance.In NOMA-GPQSM,one base station(BS)is set to serve K users,where user 1 is closest to the BS,and user K is farthest from the BS.In addition,a low-complexity detection method is proposed to reduce the high detection complexity of the maximum-likelihood(ML)detection in successive interference cancellation(SIC)method for all users by NOMA-GPQSM.The theoretical analysis of the BER performance for all users is also derived.Simulation results show that near users achieve relatively good performance,and far users achieve acceptable performance by adjusting power factors for all users in NOMA-GPQSM.展开更多
Recently,bidirectional quantum teleportation has attracted a great deal of research attention.However,existing bidirectional teleportation schemes are normally discussed on the basis of perfect quantum environments.In...Recently,bidirectional quantum teleportation has attracted a great deal of research attention.However,existing bidirectional teleportation schemes are normally discussed on the basis of perfect quantum environments.In this paper,we first put forward a bidirectional teleportation scheme to transport three-qubit Greenberger-Horne-Zeilinger（GHZ） states based on controled-not（CNOT） operation and single-qubit measurement.Then,we generalize it to the teleportation of multi-qubit GHZ states.Further,we discuss the influence of quantum noise on our scheme by the example of an amplitude damping channel,then we obtain the fidelity of the teleportation.Finally,we utilize the weak measurement and the corresponding reversing measurement to protect the quantum entanglement,which shows an effective enhancement of the teleportation fidelity.展开更多
Multi-qubit entanglement states are the key resources for various multipartite quantum communication tasks. For a class of generalized three-qubit quantum entanglement, W-like state, we demonstrate that the weak measu...Multi-qubit entanglement states are the key resources for various multipartite quantum communication tasks. For a class of generalized three-qubit quantum entanglement, W-like state, we demonstrate that the weak measurement and the reversal measurement are capable of suppressing the amplitude damping decoherence by reducing the initial damping factor into a smaller equivalent damping factor. Furthermore, we propose an iteration method in the weak measurement and the reversal measurement to enhance the success probability of the total measurements. Finally, we discuss how the number of the iterations influences the overall effect of decoherence suppression, and find that the ＂half iteration＂ method is a better option that has more practical value.展开更多
The emergence of self-driving technologies implies that a future vehicle will likely become an entertainment center that demands personalized multimedia contents with very high quality. The surge of vehicular content ...The emergence of self-driving technologies implies that a future vehicle will likely become an entertainment center that demands personalized multimedia contents with very high quality. The surge of vehicular content demand brings significant challenges for the fifth generation(5G) cellular communication network. To cope with the challenge of massive content delivery, previous studies suggested that the 5G mobile edge network should be designed to integrate communication, computing, and cache(3C) resources to enable advanced functionalities such as proactive content delivery and in-network caching. However, the fundamental benefits achievable by computing and caching in mobile communications networks are not yet properly understood. This paper proposes a novel theoretical framework to characterize the tradeoff among computing, cache, and communication resources required by the mobile edge network to fulfill the task of content delivery. Analytical and numerical results are obtained to characterize the 3C resource tradeoff curve. These results reveal key insights into the fundamental benefits of computing and caching in vehicular mobile content delivery networks.展开更多
In limited feedback-based CloudRAN(C-RAN) systems,the inter-cluster and intra-cluster interference together with the quantification error can seriously deteriorates the system spectral efficiency.We,in this paper,prop...In limited feedback-based CloudRAN(C-RAN) systems,the inter-cluster and intra-cluster interference together with the quantification error can seriously deteriorates the system spectral efficiency.We,in this paper,propose an efficient three-phase framework and corresponding algorithms for dealing with this problem.Firstly,a greedy scheduling algorithm based on the lower bound of the ergodic rate is performed for generating an elementary cluster in the first phase.And then the elementary cluster is divided into many small clusters according to the following proposed algorithms based on the short term instantaneous information in the second phase.In the end,based on the limited feedback two zero-forcing(ZF) precoding strategies are adopted for reducing the intra-cluster interference in the third phase.The provided Monte Carlo simulations show the effectiveness of our proposed algorithms in the respect of system spectral efficiency and average user rate.展开更多
Cloud computing has developed as an important information technology paradigm which can provide on-demand services. Meanwhile,its energy consumption problem has attracted a grow-ing attention both from academic and in...Cloud computing has developed as an important information technology paradigm which can provide on-demand services. Meanwhile,its energy consumption problem has attracted a grow-ing attention both from academic and industrial communities. In this paper,from the perspective of cloud tasks,the relationship between cloud tasks and cloud platform energy consumption is established and analyzed on the basis of the multidimensional attributes of cloud tasks. Furthermore,a three-way clustering algorithm of cloud tasks is proposed for saving energy. In the algorithm,f irst,t he cloud tasks are classified into three categories according to the content properties of the cloud tasks and resources respectively. Next,cloud tasks and cloud resources are clustered according to their computation characteristics（ e. g. computation-intensive,data-intensive）. Subsequently,greedy scheduling is performed. The simulation results showthat the proposed algorithm can significantly reduce the energy cost and improve resources utilization,compared with the general greedy scheduling algorithm.展开更多
A system model based on joint layer mechanism is formulated for optimal data scheduling over fixed point-to-point links in OFDMA ad-hoc wireless networks. A distributed scheduling algorithm （DSA） for system model op...A system model based on joint layer mechanism is formulated for optimal data scheduling over fixed point-to-point links in OFDMA ad-hoc wireless networks. A distributed scheduling algorithm （DSA） for system model optimization is proposed that combines the randomly chosen subcarrier according to the channel condition of local subcarriers with link power control to limit interference caused by the reuse of subcarrier among links. For the global fairness improvement of algorithms, a global power control scheduling algorithm （GPCSA） based on the proposed DSA is presented and dynamically allocates global power according to difference between average carrier-noise-ratio of selected local links and system link protection ratio. Simulation results demonstrate that the proposed algorithms achieve better efficiency and fairness compared with other existing algorithms.展开更多
The radio-over-fibre （ROF） uplink, which combines the merit of optical fibre with that of microwave technology, can supply the high capacity of communication. However, there are two major issues： nonlinear distorti...The radio-over-fibre （ROF） uplink, which combines the merit of optical fibre with that of microwave technology, can supply the high capacity of communication. However, there are two major issues： nonlinear distortion of the optical link and the multipath dispersion of the wireless channel, affecting the performance of the system. We propose an equalizer based on hybrid neural networks. The compensation needs no estimation of the channel. The simulated result shows that the ROF uplink can be adequately compensated and the performance of the equalizer depends on the channel noise.展开更多
Currently,the growth of micro and nano(very large scale integration-ultra large-scale integration)electronics technology has greatly impacted biomedical signal processing devices.These high-speed micro and nano techno...Currently,the growth of micro and nano(very large scale integration-ultra large-scale integration)electronics technology has greatly impacted biomedical signal processing devices.These high-speed micro and nano technology devices are very reliable despite their capacity to operate at tremendous speed,and can be designed to consume less power in minimum response time,which is particularly useful in biomedical products.The rapid technological scaling of the metal-oxide-semi-conductor(MOS)devices aids in mapping multiple applications for a specific purpose on a single chip which motivates us to design a sophisticated,small and reliable application specific integrated circuit(ASIC)chip for future real time medical signal separation and processing(digital stetho-scopes and digital microelectromechanical systems(MEMS)microphone).In this paper,ASIC level implementation of the adaptive line enhancer design using adaptive filtering algorithms(least mean square(LMS)and normalized least mean square(NLMS))integrated design is used to separate the real-time auscultation sound signals effectively.Adaptive line enhancer(ALE)design is imple-mented in Verilog hardware description language(HDL)language to obtain both the network and adaptive algorithm in cadence Taiwan Semiconductor Manufacturing Company(TSMC)90 nm standard cell library environment for ASIC level implementation.Native compiled simulator(NC)sim and RC lab were used for functional verification and design constraints and the physical design is implemented in Encounter to obtain the Geometric Data Stream(GDS II).In this architecture,the area occupied is 0.08 mm,the total power consumed is 5.05 mW and the computation time of the proposed system is 0.82μs for LMS design and the area occupied is 0.14 mm,the total power consumed is 4.54 mW and the computation time of the proposed system is 0.03μs for NLMS design that will pave a better way in future electronic stethoscope design.展开更多
The paper deals with two-dimensional （2D） channel estimation of Orthogonal Frequency Division Multiplexing （OFDM） system ill slow fading wireless channel. We concentrate on two channel estimation schemes： Least S...The paper deals with two-dimensional （2D） channel estimation of Orthogonal Frequency Division Multiplexing （OFDM） system ill slow fading wireless channel. We concentrate on two channel estimation schemes： Least Square （LS）＋Weighted BiLinear （WBL） and LS＋Linear Minimum Mean-Squared Error （LMMSE） where the first method is proposed in this paper. After theory analysis and simulation in Typical Urban （TU） channel, we find that LS＋LMMSE achieves the optimal perform- anee by exploiting prior knowledge of channel whereas LS＋WBL, without requiring channel knowl- edge and with only half of the computational amount of LS＋LMMSE, approaches LS＋LMMSE in Bit Error Ratio （BER） performance when the distance of two adjoining pilot symbols along frequency direction is sufficiently small. This makes LS＋WBL very suitable for wideband wireless applications.展开更多
Objective To detect the change of brain activity under different depth of anesthesia (DOA) noninvasively. Methods The Lempel-Ziv complexity C(n) was used to analyze EEG and its four components (delta, theta, alpha, be...Objective To detect the change of brain activity under different depth of anesthesia (DOA) noninvasively. Methods The Lempel-Ziv complexity C(n) was used to analyze EEG and its four components (delta, theta, alpha, beta), which was recorded from SD rats under different DOA. The relationship between C(n) and DOA was studied. Results The C(n) of EEG will decrease while the depth of anesthesia increasing and vice versa. It can be used to detect the change of DOA sensitively. Compared with power spectrum, the change of C(n) is opposite to that of power spectrum. Only the C(n) of delta rhythm has obvious variations induced by the change of DOA, and the variations of delta is as similar as the EEG's. Conclusion The study shows that the desynchronized EEG is replaced by the synchronized EEG when rat goes into anesthesia state from awake, that is just the reason why complexity and power spectrum appear corresponding changes under different DOA. C(n) of delta rhythm dynamic change leads to the change of EEG, and the delta rhythm is the dominant rhythm during anesthesia for rats.展开更多
As the number of wireless applications and devices grows,higher standards for the quality of service and navigation performance of mobile networks are required.Numerous critical applications,including unmanned aerial ...As the number of wireless applications and devices grows,higher standards for the quality of service and navigation performance of mobile networks are required.Numerous critical applications,including unmanned aerial vehicles,internet of things,digital twin,and military systems,require reliable communication and accurate navigation services.To meet these requirements,the development of the sixth generation(6G)network is necessary.6G networks provide seamless 3-dimensional coverage in space-air-ground-sea area,as well as deep coupling of communication,sensing,and computation.6G networks will be combined with low Earth orbit(LEO)satellites to construct a universal and intelligent integrated system of communication,sensing,and computing by utilizing the benefits of LEO satellites,such as miniaturization,modularity,large bandwidth,low latency,and wide area coverage.One of the critical construction tasks in this system is the integration of communication and navigation(ICAN),which can break the limitations of the global navigation satellite system,provide high-precision,robust navigation capability,and enable high-quality communication services.In this article,a comprehensive survey is presented for ICAN technologies toward LEO-enabled 6G networks(LEO-ICAN),including the framework design,system implementation,and key technologies.We also highlight the challenges and opportunities ahead faced by the LEO-ICAN system.Finally,the prospect development and future research trends are discussed,and a few ideas for practical and effective LEO-ICAN solutions are provided.This survey provides a reference for the theoretical design,technological innovation,and system implementation of LEO-ICAN,which is capable of coping with the demand of massive access and global seamless coverage in the upcoming 6G network era.展开更多
Cognitive radio network(CRN)uses the available spectrum resources wisely.Spectrum sensing is the central element of a CRN.However,spectrum sensing is susceptible to multiple security breaches caused by malicious users...Cognitive radio network(CRN)uses the available spectrum resources wisely.Spectrum sensing is the central element of a CRN.However,spectrum sensing is susceptible to multiple security breaches caused by malicious users(MUs).These attackers attempt to change the sensed result in order to decrease network performance.In our proposed approach,with the help of blockchain-based technology,the fusion center is able to detect and prevent such criminal activities.The method of our model makes use of blockchain-based MU detection with SHA-3 hashing and energy detection-based spectrum sensing.The detection strategy takes place in two stages:block updation phase and iron out phase.The simulation results of the proposed method demonstrate 3.125%,6.5%,and 8.8%more detection probability at−5 dB signal-to-noise ratio(SNR)in the presence of MUs,when compared to other methods like equal gain combining(EGC),blockchain-based cooperative spectrum sensing(BCSS),and fault-tolerant cooperative spectrum sensing(FTCSS),respectively.Thus,the security of cognitive radio blockchain network is proved to be significantly improved.展开更多
In response to real-world scenarios,the domain generalization(DG)problem has spurred considerable research in person re-identification(ReID).This challenge arises when the target domain,which is significantly differen...In response to real-world scenarios,the domain generalization(DG)problem has spurred considerable research in person re-identification(ReID).This challenge arises when the target domain,which is significantly different from the source domains,remains unknown.However,the performance of current DG ReID relies heavily on labor-intensive source domain annotations.Considering the potential of unlabeled data,we investigate unsupervised domain generalization(UDG)in ReID.Our goal is to create a model that can generalize from unlabeled source domains to semantically retrieve images in an unseen target domain.To address this,we propose a new approach that trains a domain-agnostic expert(DaE)for unsupervised domain-generalizable person ReID.This involves independently training multiple experts to account for label space inconsistencies between source domains.At the same time,the DaE captures domain-generalizable information for testing.Our experiments demonstrate the effectiveness of this method for learning generalizable features under the UDG setting.The results demonstrate the superiority of our method over state-of-the-art techniques.We will make our code and models available for public use.展开更多
文摘The rapid industrial growth and increasing population have led to significant pollution and deterioration of the natural atmospheric environment.Major atmospheric pollutants include NO_(2)and CO_(2).Hence,it is imperative to develop NO_(2)and CO_(2)sensors for ambient conditions,that can be used in indoor air quality monitoring,breath analysis,food spoilage detection,etc.In the present study,two thin film nanocomposite(nickel oxide-graphene and nickel oxide-silver nanowires)gas sensors are fabricated using direct ink writing.The nano-composites are investigated for their structural,optical,and electrical properties.Later the nano-composite is deposited on the interdigitated electrode(IDE)pattern to form NO_(2)and CO_(2)sensors.The deposited films are then exposed to NO_(2)and CO_(2)gases separately and their response and recovery times are determined using a custom-built gas sensing setup.Nickel oxide-graphene provides a good response time and recovery time of 10 and 9 s,respectively for NO_(2),due to the higher electron affinity of graphene towards NO_(2).Nickel oxide-silver nanowire nano-composite is suited for CO_(2)gas because silver is an excellent electrocatalyst for CO_(2)by giving response and recovery times of 11 s each.This is the first report showcasing NiO nano-composites for NO_(2)and CO_(2)sensing at room temperature.
文摘Cardiovascular diseases are the world’s leading cause of death;therefore cardiac health of the human heart has been a fascinating topic for decades.The electrocardiogram(ECG)signal is a comprehensive non-invasive method for determining cardiac health.Various health practitioners use the ECG signal to ascertain critical information about the human heart.In this article,swarm intelligence approaches are used in the biomedical signal processing sector to enhance adaptive hybrid filters and empirical wavelet transforms(EWTs).At first,the white Gaussian noise is added to the input ECG signal and then applied to the EWT.The ECG signals are denoised by the proposed adaptive hybrid filter.The honey badge optimization(HBO)algorithm is utilized to optimize the EWT window function and adaptive hybrid filter weight parameters.The proposed approach is simulated by MATLAB 2018a using the MIT-BIH dataset with white Gaussian,electromyogram and electrode motion artifact noises.A comparison of the HBO approach with recursive least square-based adaptive filter,multichannel least means square,and discrete wavelet transform methods has been done in order to show the efficiency of the proposed adaptive hybrid filter.The experimental results show that the HBO approach supported by EWT and adaptive hybrid filter can be employed efficiently for cardiovascular signal denoising.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(NRF-2022R1A2C2012243).
文摘In recent decades,brain tumors have emerged as a serious neurological disorder that often leads to death.Hence,Brain Tumor Segmentation(BTS)is significant to enable the visualization,classification,and delineation of tumor regions in Magnetic Resonance Imaging(MRI).However,BTS remains a challenging task because of noise,non-uniform object texture,diverse image content and clustered objects.To address these challenges,a novel model is implemented in this research.The key objective of this research is to improve segmentation accuracy and generalization in BTS by incorporating Switchable Normalization into Faster R-CNN,which effectively captures the fine-grained tumor features to enhance segmentation precision.MRI images are initially acquired from three online datasets:Dataset 1—Brain Tumor Segmentation(BraTS)2018,Dataset 2—BraTS 2019,and Dataset 3—BraTS 2020.Subsequently,the Switchable Normalization-based Faster Regions with Convolutional Neural Networks(SNFRC)model is proposed for improved BTS in MRI images.In the proposed model,Switchable Normalization is integrated into the conventional architecture,enhancing generalization capability and reducing overfitting to unseen image data,which is essential due to the typically limited size of available datasets.The network depth is increased to obtain discriminative semantic features that improve segmentation performance.Specifically,Switchable Normalization captures the diverse feature representations from the brain images.The Faster R-CNN model develops end-to-end training and effective regional proposal generation,with an enhanced training stability using Switchable Normalization,to perform an effective segmentation in MRI images.From the experimental results,the proposed model attains segmentation accuracies of 99.41%,98.12%,and 96.71%on Datasets 1,2,and 3,respectively,outperforming conventional deep learning models used for BTS.
基金supported by the Deanship of Research and Graduate Studies at the King Khalid University(RGP2/287/46)the Princess Nourah bint Abdulrahman University Researchers Supporting Project(PNURSP2025R733)+1 种基金the Princess Nourah bint Abdulrahman University Research Supporting Project(RSPD2025R787)the King Saud University,Saudi Arabia.
文摘Challenges in land use and land cover(LULC)include rapid urbanization encroaching on agricultural land,leading to fragmentation and loss of natural habitats.However,the effects of urbanization on LULC of different crop types are less concerned.The study assessed the impacts of LULC changes on agriculture and drought vulnerability in the Aguascalientes region,Mexico,from 1994 to 2024,and predicted the LULC in 2034 using remote sensing data,with the goals of sustainable land management and climate resilience strategies.Despite increasing urbanization and drought,the integration of satellite imagery and machine learning models in LULC analysis has been underutilized in this region.Using Landsat imagery,we assessed crop attributes through indices such as normalized difference vegetation index(NDVI),normalized difference water index(NDWI),normalized difference moisture index(NDMI),and vegetation condition index(VCI),alongside watershed delineation and spectral features.The random forest model was applied to classify LULC,providing insights into both historical and future trends.Results indicated a significant decline in vegetation cover(109.13 km^(2))from 1994 to 2024,accompanied by an increase in built-up land(75.11 km^(2))and bare land(67.13 km^(2)).Projections suggested a further decline in vegetation cover(41.51 km^(2))and continued urban land expansion by 2034.The study found that paddy crops exhibited the highest values,while common bean and maize performed poorly.Drought analysis revealed that mildly dry areas in 2004 became severely dry in 2024,highlighting the increasing vulnerability of agriculture to climate change.The study concludes that sustainable land management,improved water resource practices,and advanced monitoring techniques are essential to mitigate the adverse effects of LULC changes on agricultural productivity and drought resilience in the area.These findings contribute to the understanding of how remote sensing can be effectively used for long-term agricultural planning and environmental sustainability.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation(IITP)-Innovative Human Resource Development for Local Intellectualization program grant funded by the Korea government(MSIT)(IITP-2025-RS-2023-00259678)by INHA UNIVERSITY Research Grant.
文摘Lightweight deep learning models are increasingly required in resource-constrained environments such as mobile devices and the Internet of Medical Things(IoMT).Multi-head convolution with channel attention can facilitate learning activations relevant to different kernel sizes within a multi-head convolutional layer.Therefore,this study investigates the capability of novel lightweight models incorporating residual multi-head convolution with channel attention(ResMHCNN)blocks to classify medical images.We introduced three novel lightweight deep learning models(BT-Net,LCC-Net,and BC-Net)utilizing the ResMHCNN block as their backbone.These models were crossvalidated and tested on three publicly available medical image datasets:a brain tumor dataset from Figshare consisting of T1-weighted magnetic resonance imaging slices of meningioma,glioma,and pituitary tumors;the LC25000 dataset,which includes microscopic images of lung and colon cancers;and the BreaKHis dataset,containing benign and malignant breast microscopic images.The lightweight models achieved accuracies of 96.9%for 3-class brain tumor classification using BT-Net,and 99.7%for 5-class lung and colon cancer classification using LCC-Net.For 2-class breast cancer classification,BC-Net achieved an accuracy of 96.7%.The parameter counts for the proposed lightweight models—LCC-Net,BC-Net,and BT-Net—are 0.528,0.226,and 1.154 million,respectively.The presented lightweight models,featuring ResMHCNN blocks,may be effectively employed for accurate medical image classification.In the future,these models might be tested for viability in resource-constrained systems such as mobile devices and IoMT platforms.
基金supported by the National Science Fundation of China(NSFC)under grant 62001423the Henan Provincial Key Research,Development and Promotion Project under grant 212102210175the Henan Provincial Key Scientific Research Project for College and University under grant 21A510011.
文摘Sparse vector coding(SVC)is emerging as a potential technology for short packet communications.To further improve the block error rate(BLER)performance,a uniquely decomposable constellation group-based SVC(UDCG-SVC)is proposed in this article.Additionally,in order to achieve an optimal BLER performance of UDCG-SVC,a problem to optimize the coding gain of UDCG-based superimposed constellation is formulated.Given the energy of rotation constellations in UDCG,this problem is solved by converting it into finding the maximized minimum Euclidean distance of the superimposed constellation.Simulation results demonstrate the validness of our derivation.We also find that the proposed UDCGSVC has better BLER performance compared to other SVC schemes,especially under the high order modulation scenarios.
基金supported by National Nature Science Foundation of China(No.61701127,No.61871139,No.61631004,No.62071319)the International Collaborative Research Program of Guangdong Science and Technology Department(No.2020A0505100061).
文摘Generalised pre-coding quadrature spatial modulation(GPQSM)is recently proposed to increase the spectral efficiency(SE)of GPSM,which extends the transmitted symbols into in-phase/quadrature domains.In this paper,a novel scheme named non-orthogonal multiple access(NOMA)-aided GPQSM(NOMA-GPQSM),which incorporates the GPQSM scheme into the multi-user communication networks with assist of NOMA,is proposed to further improve the SE and system performance.In NOMA-GPQSM,one base station(BS)is set to serve K users,where user 1 is closest to the BS,and user K is farthest from the BS.In addition,a low-complexity detection method is proposed to reduce the high detection complexity of the maximum-likelihood(ML)detection in successive interference cancellation(SIC)method for all users by NOMA-GPQSM.The theoretical analysis of the BER performance for all users is also derived.Simulation results show that near users achieve relatively good performance,and far users achieve acceptable performance by adjusting power factors for all users in NOMA-GPQSM.
基金Project supported by the National Natural Science Foundation of China(Grant No.61172071)the Scientific Research Program Funded by Shaanxi Provincial Education Department,China(Grant No.16JK1711)+1 种基金the International Scientific Cooperation Program of Shaanxi Province,China(Grant No.2015KW-013)the Natural Science Foundation Research Project of Shaanxi Province,China(Grant No.2016JQ6033)
文摘Recently,bidirectional quantum teleportation has attracted a great deal of research attention.However,existing bidirectional teleportation schemes are normally discussed on the basis of perfect quantum environments.In this paper,we first put forward a bidirectional teleportation scheme to transport three-qubit Greenberger-Horne-Zeilinger（GHZ） states based on controled-not（CNOT） operation and single-qubit measurement.Then,we generalize it to the teleportation of multi-qubit GHZ states.Further,we discuss the influence of quantum noise on our scheme by the example of an amplitude damping channel,then we obtain the fidelity of the teleportation.Finally,we utilize the weak measurement and the corresponding reversing measurement to protect the quantum entanglement,which shows an effective enhancement of the teleportation fidelity.
基金supported by the National Natural Science Foundation of China(Grant No.61172071)the International Scientific Cooperation Program of Shaanxi Province,China(Grant No.2015KW-013)the Scientific Research Program Funded by Shaanxi Provincial Education Department,China(Grant No.16JK1711)
文摘Multi-qubit entanglement states are the key resources for various multipartite quantum communication tasks. For a class of generalized three-qubit quantum entanglement, W-like state, we demonstrate that the weak measurement and the reversal measurement are capable of suppressing the amplitude damping decoherence by reducing the initial damping factor into a smaller equivalent damping factor. Furthermore, we propose an iteration method in the weak measurement and the reversal measurement to enhance the success probability of the total measurements. Finally, we discuss how the number of the iterations influences the overall effect of decoherence suppression, and find that the ＂half iteration＂ method is a better option that has more practical value.
基金the support from the Natural Science Foundation of China (Grant No.61571378)
文摘The emergence of self-driving technologies implies that a future vehicle will likely become an entertainment center that demands personalized multimedia contents with very high quality. The surge of vehicular content demand brings significant challenges for the fifth generation(5G) cellular communication network. To cope with the challenge of massive content delivery, previous studies suggested that the 5G mobile edge network should be designed to integrate communication, computing, and cache(3C) resources to enable advanced functionalities such as proactive content delivery and in-network caching. However, the fundamental benefits achievable by computing and caching in mobile communications networks are not yet properly understood. This paper proposes a novel theoretical framework to characterize the tradeoff among computing, cache, and communication resources required by the mobile edge network to fulfill the task of content delivery. Analytical and numerical results are obtained to characterize the 3C resource tradeoff curve. These results reveal key insights into the fundamental benefits of computing and caching in vehicular mobile content delivery networks.
基金supported by the National Natural Science Foundation of China(NSFC) under Grant(No. 61461136001)
文摘In limited feedback-based CloudRAN(C-RAN) systems,the inter-cluster and intra-cluster interference together with the quantification error can seriously deteriorates the system spectral efficiency.We,in this paper,propose an efficient three-phase framework and corresponding algorithms for dealing with this problem.Firstly,a greedy scheduling algorithm based on the lower bound of the ergodic rate is performed for generating an elementary cluster in the first phase.And then the elementary cluster is divided into many small clusters according to the following proposed algorithms based on the short term instantaneous information in the second phase.In the end,based on the limited feedback two zero-forcing(ZF) precoding strategies are adopted for reducing the intra-cluster interference in the third phase.The provided Monte Carlo simulations show the effectiveness of our proposed algorithms in the respect of system spectral efficiency and average user rate.
基金Supported by the Harbin Technology Bureau Youth Talented Project(2014RFQXJ073)China Postdoctoral Fund Projects(2014M561330)
文摘Cloud computing has developed as an important information technology paradigm which can provide on-demand services. Meanwhile,its energy consumption problem has attracted a grow-ing attention both from academic and industrial communities. In this paper,from the perspective of cloud tasks,the relationship between cloud tasks and cloud platform energy consumption is established and analyzed on the basis of the multidimensional attributes of cloud tasks. Furthermore,a three-way clustering algorithm of cloud tasks is proposed for saving energy. In the algorithm,f irst,t he cloud tasks are classified into three categories according to the content properties of the cloud tasks and resources respectively. Next,cloud tasks and cloud resources are clustered according to their computation characteristics（ e. g. computation-intensive,data-intensive）. Subsequently,greedy scheduling is performed. The simulation results showthat the proposed algorithm can significantly reduce the energy cost and improve resources utilization,compared with the general greedy scheduling algorithm.
文摘A system model based on joint layer mechanism is formulated for optimal data scheduling over fixed point-to-point links in OFDMA ad-hoc wireless networks. A distributed scheduling algorithm （DSA） for system model optimization is proposed that combines the randomly chosen subcarrier according to the channel condition of local subcarriers with link power control to limit interference caused by the reuse of subcarrier among links. For the global fairness improvement of algorithms, a global power control scheduling algorithm （GPCSA） based on the proposed DSA is presented and dynamically allocates global power according to difference between average carrier-noise-ratio of selected local links and system link protection ratio. Simulation results demonstrate that the proposed algorithms achieve better efficiency and fairness compared with other existing algorithms.
基金Supported by the National Natural Science Foundation of China under Grant No 60502001.
文摘The radio-over-fibre （ROF） uplink, which combines the merit of optical fibre with that of microwave technology, can supply the high capacity of communication. However, there are two major issues： nonlinear distortion of the optical link and the multipath dispersion of the wireless channel, affecting the performance of the system. We propose an equalizer based on hybrid neural networks. The compensation needs no estimation of the channel. The simulated result shows that the ROF uplink can be adequately compensated and the performance of the equalizer depends on the channel noise.
文摘Currently,the growth of micro and nano(very large scale integration-ultra large-scale integration)electronics technology has greatly impacted biomedical signal processing devices.These high-speed micro and nano technology devices are very reliable despite their capacity to operate at tremendous speed,and can be designed to consume less power in minimum response time,which is particularly useful in biomedical products.The rapid technological scaling of the metal-oxide-semi-conductor(MOS)devices aids in mapping multiple applications for a specific purpose on a single chip which motivates us to design a sophisticated,small and reliable application specific integrated circuit(ASIC)chip for future real time medical signal separation and processing(digital stetho-scopes and digital microelectromechanical systems(MEMS)microphone).In this paper,ASIC level implementation of the adaptive line enhancer design using adaptive filtering algorithms(least mean square(LMS)and normalized least mean square(NLMS))integrated design is used to separate the real-time auscultation sound signals effectively.Adaptive line enhancer(ALE)design is imple-mented in Verilog hardware description language(HDL)language to obtain both the network and adaptive algorithm in cadence Taiwan Semiconductor Manufacturing Company(TSMC)90 nm standard cell library environment for ASIC level implementation.Native compiled simulator(NC)sim and RC lab were used for functional verification and design constraints and the physical design is implemented in Encounter to obtain the Geometric Data Stream(GDS II).In this architecture,the area occupied is 0.08 mm,the total power consumed is 5.05 mW and the computation time of the proposed system is 0.82μs for LMS design and the area occupied is 0.14 mm,the total power consumed is 4.54 mW and the computation time of the proposed system is 0.03μs for NLMS design that will pave a better way in future electronic stethoscope design.
基金Supported by the National Natural Science Foundation of China (No: 60496311).
文摘The paper deals with two-dimensional （2D） channel estimation of Orthogonal Frequency Division Multiplexing （OFDM） system ill slow fading wireless channel. We concentrate on two channel estimation schemes： Least Square （LS）＋Weighted BiLinear （WBL） and LS＋Linear Minimum Mean-Squared Error （LMMSE） where the first method is proposed in this paper. After theory analysis and simulation in Typical Urban （TU） channel, we find that LS＋LMMSE achieves the optimal perform- anee by exploiting prior knowledge of channel whereas LS＋WBL, without requiring channel knowl- edge and with only half of the computational amount of LS＋LMMSE, approaches LS＋LMMSE in Bit Error Ratio （BER） performance when the distance of two adjoining pilot symbols along frequency direction is sufficiently small. This makes LS＋WBL very suitable for wideband wireless applications.
文摘Objective To detect the change of brain activity under different depth of anesthesia (DOA) noninvasively. Methods The Lempel-Ziv complexity C(n) was used to analyze EEG and its four components (delta, theta, alpha, beta), which was recorded from SD rats under different DOA. The relationship between C(n) and DOA was studied. Results The C(n) of EEG will decrease while the depth of anesthesia increasing and vice versa. It can be used to detect the change of DOA sensitively. Compared with power spectrum, the change of C(n) is opposite to that of power spectrum. Only the C(n) of delta rhythm has obvious variations induced by the change of DOA, and the variations of delta is as similar as the EEG's. Conclusion The study shows that the desynchronized EEG is replaced by the synchronized EEG when rat goes into anesthesia state from awake, that is just the reason why complexity and power spectrum appear corresponding changes under different DOA. C(n) of delta rhythm dynamic change leads to the change of EEG, and the delta rhythm is the dominant rhythm during anesthesia for rats.
基金supported in part by the Natural Science Foundation of Fujian Province of China(grant number 2023J01001)the National Natural Science Foundation of China(grant numbers 61901403 and 62077040)+2 种基金the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University(grant number 2023D10)the Science and Technology Key Project of Fujian Province,China(grant num-ber 2021HZ021004)the Science and Technology Key Project of Xiamen(grant number 3502Z20221027).
文摘As the number of wireless applications and devices grows,higher standards for the quality of service and navigation performance of mobile networks are required.Numerous critical applications,including unmanned aerial vehicles,internet of things,digital twin,and military systems,require reliable communication and accurate navigation services.To meet these requirements,the development of the sixth generation(6G)network is necessary.6G networks provide seamless 3-dimensional coverage in space-air-ground-sea area,as well as deep coupling of communication,sensing,and computation.6G networks will be combined with low Earth orbit(LEO)satellites to construct a universal and intelligent integrated system of communication,sensing,and computing by utilizing the benefits of LEO satellites,such as miniaturization,modularity,large bandwidth,low latency,and wide area coverage.One of the critical construction tasks in this system is the integration of communication and navigation(ICAN),which can break the limitations of the global navigation satellite system,provide high-precision,robust navigation capability,and enable high-quality communication services.In this article,a comprehensive survey is presented for ICAN technologies toward LEO-enabled 6G networks(LEO-ICAN),including the framework design,system implementation,and key technologies.We also highlight the challenges and opportunities ahead faced by the LEO-ICAN system.Finally,the prospect development and future research trends are discussed,and a few ideas for practical and effective LEO-ICAN solutions are provided.This survey provides a reference for the theoretical design,technological innovation,and system implementation of LEO-ICAN,which is capable of coping with the demand of massive access and global seamless coverage in the upcoming 6G network era.
基金support and facilities offered by Vellore Institute of Technology,Vellore,India,in carrying out this research.
文摘Cognitive radio network(CRN)uses the available spectrum resources wisely.Spectrum sensing is the central element of a CRN.However,spectrum sensing is susceptible to multiple security breaches caused by malicious users(MUs).These attackers attempt to change the sensed result in order to decrease network performance.In our proposed approach,with the help of blockchain-based technology,the fusion center is able to detect and prevent such criminal activities.The method of our model makes use of blockchain-based MU detection with SHA-3 hashing and energy detection-based spectrum sensing.The detection strategy takes place in two stages:block updation phase and iron out phase.The simulation results of the proposed method demonstrate 3.125%,6.5%,and 8.8%more detection probability at−5 dB signal-to-noise ratio(SNR)in the presence of MUs,when compared to other methods like equal gain combining(EGC),blockchain-based cooperative spectrum sensing(BCSS),and fault-tolerant cooperative spectrum sensing(FTCSS),respectively.Thus,the security of cognitive radio blockchain network is proved to be significantly improved.
基金supported by the National Natural Science Foundation of China(Nos.62225113,62176188,and 623B2080)the Innovative Research Group Project of Hubei Province(No.2024AFA017).
文摘In response to real-world scenarios,the domain generalization(DG)problem has spurred considerable research in person re-identification(ReID).This challenge arises when the target domain,which is significantly different from the source domains,remains unknown.However,the performance of current DG ReID relies heavily on labor-intensive source domain annotations.Considering the potential of unlabeled data,we investigate unsupervised domain generalization(UDG)in ReID.Our goal is to create a model that can generalize from unlabeled source domains to semantically retrieve images in an unseen target domain.To address this,we propose a new approach that trains a domain-agnostic expert(DaE)for unsupervised domain-generalizable person ReID.This involves independently training multiple experts to account for label space inconsistencies between source domains.At the same time,the DaE captures domain-generalizable information for testing.Our experiments demonstrate the effectiveness of this method for learning generalizable features under the UDG setting.The results demonstrate the superiority of our method over state-of-the-art techniques.We will make our code and models available for public use.
