Interference significantly impacts the performance of the Global Navigation Satellite Systems(GNSS),highlighting the need for advanced interference localization technology to bolster anti-interference and defense capa...Interference significantly impacts the performance of the Global Navigation Satellite Systems(GNSS),highlighting the need for advanced interference localization technology to bolster anti-interference and defense capabilities.The Uniform Circular Array(UCA)enables concurrent estimation of the Direction of Arrival(DOA)in both azimuth and elevation.Given the paramount importance of stability and real-time performance in interference localization,this work proposes an innovative approach to reduce the complexity and increase the robustness of the DOA estimation.The proposed method reduces computational complexity by selecting a reduced number of array elements to reconstruct a non-uniform sparse array from a UCA.To ensure DOA estimation accuracy,minimizing the Cramér-Rao Bound(CRB)is the objective,and the Spatial Correlation Coefficient(SCC)is incorporated as a constraint to mitigate side-lobe.The optimization model is a quadratic fractional model,which is solved by Semi-Definite Relaxation(SDR).When the array has perturbations,the mathematical expressions for CRB and SCC are re-derived to enhance the robustness of the reconstructed array.Simulation and hardware experiments validate the effectiveness of the proposed method in estimating interference DOA,showing high robustness and reductions in hardware and computational costs associated with DOA estimation.展开更多
Quality of Service(QoS)assurance in programmable IoT and 5G networks is increasingly threatened by cyberattacks such as Distributed Denial of Service(DDoS),spoofing,and botnet intrusions.This paper presents AutoSHARC,...Quality of Service(QoS)assurance in programmable IoT and 5G networks is increasingly threatened by cyberattacks such as Distributed Denial of Service(DDoS),spoofing,and botnet intrusions.This paper presents AutoSHARC,a feedback-driven,explainable intrusion detection framework that integrates Boruta and LightGBM–SHAP feature selection with a lightweight CNN–Attention–GRU classifier.AutoSHARC employs a two-stage feature selection pipeline to identify the most informative features from high-dimensional IoT traffic and reduces 46 features to 30 highly informative ones,followed by post-hoc SHAP-guided retraining to refine feature importance,forming a feedback loopwhere only the most impactful attributes are reused to retrain themodel.This iterative refinement reduces computational overhead,accelerates detection latency,and improves transparency.Evaluated on the CIC IoT 2023 dataset,AutoSHARC achieves 98.98%accuracy,98.9%F1-score,and strong robustness with a Matthews Correlation Coefficient of 0.98 and Cohen’s Kappa of 0.98.The final model contains only 531,272 trainable parameters with a compact 2 MB size,enabling real-time deployment on resource-constrained IoT nodes.By combining explainable AI with iterative feature refinement,AutoSHARC provides scalable and trustworthy intrusion detection while preserving key QoS indicators such as latency,throughput,and reliability.展开更多
The emergence of Generative Adversarial Network(GAN)techniques has garnered significant attention from the research community for the development of Intrusion Detection Systems(IDS).However,conventional GAN-based IDS ...The emergence of Generative Adversarial Network(GAN)techniques has garnered significant attention from the research community for the development of Intrusion Detection Systems(IDS).However,conventional GAN-based IDS models face several challenges,including training instability,high computational costs,and system failures.To address these limitations,we propose a Hybrid Wasserstein GAN and Autoencoder Model(WGAN-AE)for intrusion detection.The proposed framework leverages the stability of WGAN and the feature extraction capabilities of the Autoencoder Model.The model was trained and evaluated using two recent benchmark datasets,5GNIDD and IDSIoT2024.When trained on the 5GNIDD dataset,the model achieved an average area under the precisionrecall curve is 99.8%using five-fold cross-validation and demonstrated a high detection accuracy of 97.35%when tested on independent test data.Additionally,the model is well-suited for deployment on resource-limited Internetof-Things(IoT)devices due to its ability to detect attacks within microseconds and its small memory footprint of 60.24 kB.Similarly,when trained on the IDSIoT2024 dataset,the model achieved an average PR-AUC of 94.09%and an attack detection accuracy of 97.35%on independent test data,with a memory requirement of 61.84 kB.Extensive simulation results demonstrate that the proposed hybrid model effectively addresses the shortcomings of traditional GAN-based IDS approaches in terms of detection accuracy,computational efficiency,and applicability to real-world IoT environments.展开更多
BACKGROUND Extracorporeal membrane oxygenation(ECMO)is mainly applied to patients with significant cardiorespiratory failure who do not respond to existing conventional treatments.Patients that are supported with veno...BACKGROUND Extracorporeal membrane oxygenation(ECMO)is mainly applied to patients with significant cardiorespiratory failure who do not respond to existing conventional treatments.Patients that are supported with veno-arterial ECMO(VA-ECMO)are considered very-high risk patients to participate in any type of physical therapy(PT)or mobilization.However,cumulative evidence suggests that early mobilization of critically ill patients is feasible,safe,and efficient under certain circumstances.AIM To summarize the existing evidence on the impact of early mobilization and physiotherapy on VA-ECMO patients.METHODS This is a scoping review that used systematic electronic literature searches(from inception until January 2025)on MEDLINE(PubMed),PEDro,DynaMed,CINAHL,Scopus,Science direct and Hellenic Academic Libraries.Snowball searching method was also applied.Eligible studies included those reporting patients on VA-ECMO who participated in early mobilization or PT,published in English and utilized any primary evidence study design.Studies on children,animals and patients placed on any other ECMO,secondary evidence,and‘grey’literature were excluded.RESULTS A total of 316 articles were retrieved and 13 were included in the study.Of those,1 study was a randomized control trial,4 retrospective studies,4 retrospective cohort studies,1 case series and 3 case reports.The sample size of the included studies ranged from 1 to 104 VA-ECMO patients,who were ambulated or received PT inter-ventions,and mobilization frequency ranged from 2 per day to 4 per week.Mobilization of VA-ECMO patients seems to be safe regardless the cannula’s position.PT and early mobilization were associated with better weaning from mechanical ventilation,gradual reduction of inotropes and functional capacity improvement after ECMO discharge.CONCLUSION Early mobilization in VA-ECMO seems to be safe and can potentially help reduce vasoconstrictors and speed up rehabilitation times.High quality research on early mobilization in VA-ECMO patients is warranted.展开更多
Objective To explore how older patients self-manage their coronary heart disease （CHD） aider undergoing elective percutaneous transluminal coronary angioplasty （PTCA）. Methods This mixed methods study used a seque...Objective To explore how older patients self-manage their coronary heart disease （CHD） aider undergoing elective percutaneous transluminal coronary angioplasty （PTCA）. Methods This mixed methods study used a sequential, explanatory design and recruited a convenience sample of patients （n = 93） approximately three months after elective PTCA. The study was conducted in two phases. Quantitative data collected in Phase 1 by means of a self-administered survey were subject to univariate and bivariate analysis. Phase 1 findings in- formed the purposive samplhag for Phase 2 where ten participants were selected from the original sample for an in-depth interview. Qualita- tive data were analysed using thematic analysis. This paper will primarily report the findings from a sub-group of older participants （n = 47） classified as 65 years of age or older. Results 78.7% （n = 37） of participants indicated that they would manage recurring angina symptoms by taking glyceryl trinitrate and 34% （n = 16） thought that resting would help. Regardless of the duration or severity of the symptoms 40.5% （n = 19） would call their general practitioner or an emergency ambulance for assistance during any recurrence of angina symptoms. Older participants weighed less （P = 0.02） and smoked less （P = 0.01） than their younger counterparts in the study. Age did not seem to affect PTCA patients＇ likelihood of altering dietary factors such as fruit, vegetable and saturated fat consumption （P = 0.237）. Conclusions The findings suggest that older people in the study were less likely to know how to correctly manage any recurring angina symptoms than their younger counterparts but they had fewer risk factors for CHD. Age was not a factor that influenced participants＇ likelihood to alter lifestyle factors.展开更多
Because of its high adsorption capacity, biochar has been used to stabilize metals when remediating contaminated soils; to date, however, it has seldom been used to remediate contaminated sediment. A biochar was used ...Because of its high adsorption capacity, biochar has been used to stabilize metals when remediating contaminated soils; to date, however, it has seldom been used to remediate contaminated sediment. A biochar was used as a stabilization agent to remediate Cu-and Pb-contaminated sediments, collected from three locations in or close to Beijing. The sediments were mixed with a palm sawdust gasified biochar at a range of weight ratios(2.5%, 5%, and 10%) and incubated for 10, 30, or 60 days. The performance of the different treatments and the heavy metal fractions in the sediments were assessed using four extraction methods, including diffusive gradients in thin films, the porewater concentration, a sequential extraction, and the toxicity characteristic leaching procedure. The results showed that biochar could enhance the stability of heavy metals in contaminated sediments. The degree of stability increased as both the dose of biochar and the incubation time increased. The sediment p H and the morphology of the metal crystals adsorbed onto the biochar changed as the contact time increased. Our results showed that adsorption,metal crystallization, and the p H were the main controls on the stabilization of metals in contaminated sediment by biochar.展开更多
Single image super-resolution has attracted increasing attention and has a wide range of applications in satellite imaging, medical imaging, computer vision, security surveillance imaging, remote sensing, objection de...Single image super-resolution has attracted increasing attention and has a wide range of applications in satellite imaging, medical imaging, computer vision, security surveillance imaging, remote sensing, objection detection, and recognition. Recently, deep learning techniques have emerged and blossomed, producing " the state-of-the-art” in many domains. Due to their capability in feature extraction and mapping, it is very helpful to predict high-frequency details lost in low-resolution images. In this paper, we give an overview of recent advances in deep learning-based models and methods that have been applied to single image super-resolution tasks. We also summarize, compare and discuss various models from the past and present for comprehensive understanding and finally provide open problems and possible directions for future research.展开更多
The manufacturing of nanomaterials by the electrospinning process requires accurate and meticulous inspection of related scanning electron microscope(SEM)images of the electrospun nanofiber,to ensure that no structura...The manufacturing of nanomaterials by the electrospinning process requires accurate and meticulous inspection of related scanning electron microscope(SEM)images of the electrospun nanofiber,to ensure that no structural defects are produced.The presence of anomalies prevents practical application of the electrospun nanofibrous material in nanotechnology.Hence,the automatic monitoring and quality control of nanomaterials is a relevant challenge in the context of Industry 4.0.In this paper,a novel automatic classification system for homogenous(anomaly-free)and non-homogenous(with defects)nanofibers is proposed.The inspection procedure aims at avoiding direct processing of the redundant full SEM image.Specifically,the image to be analyzed is first partitioned into subimages(nanopatches)that are then used as input to a hybrid unsupervised and supervised machine learning system.In the first step,an autoencoder(AE)is trained with unsupervised learning to generate a code representing the input image with a vector of relevant features.Next,a multilayer perceptron(MLP),trained with supervised learning,uses the extracted features to classify non-homogenous nanofiber(NH-NF)and homogenous nanofiber(H-NF)patches.The resulting novel AE-MLP system is shown to outperform other standard machine learning models and other recent state-of-the-art techniques,reporting accuracy rate up to92.5%.In addition,the proposed approach leads to model complexity reduction with respect to other deep learning strategies such as convolutional neural networks(CNN).The encouraging performance achieved in this benchmark study can stimulate the application of the proposed scheme in other challenging industrial manufacturing tasks.展开更多
Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially in...Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process.In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.展开更多
Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it...Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.展开更多
Visual degradation of captured images caused by rainy streaks under rainy weather can adversely affect the performance of many open-air vision systems.Hence,it is necessary to address the problem of eliminating rain s...Visual degradation of captured images caused by rainy streaks under rainy weather can adversely affect the performance of many open-air vision systems.Hence,it is necessary to address the problem of eliminating rain streaks from the individual rainy image.In this work,a deep convolution neural network(CNN)based method is introduced,called Rain-Removal Net(R2N),to solve the single image de-raining issue.Firstly,we decomposed the rainy image into its high-frequency detail layer and lowfrequency base layer.Then,we used the high-frequency detail layer to input the carefully designed CNN architecture to learn the mapping between it and its corresponding derained high-frequency detail layer.The CNN architecture consists of four convolution layers and four deconvolution layers,as well as three skip connections.The experiments on synthetic and real-world rainy images show that the performance of our architecture outperforms the compared state-of-the-art de-raining models with respects to the quality of de-rained images and computing efficiency.展开更多
The Internet of things(IoT)is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision.Despite several a...The Internet of things(IoT)is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision.Despite several advantages,the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals.A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and industries.To overcome the security challenges of IoT networks,this article proposes a lightweight deep autoencoder(DAE)based cyberattack detection framework.The proposed approach learns the normal and anomalous data patterns to identify the various types of network intrusions.The most significant feature of the proposed technique is its lower complexity which is attained by reducing the number of operations.To optimally train the proposed DAE,a range of hyperparameters was determined through extensive experiments that ensure higher attack detection accuracy.The efficacy of the suggested framework is evaluated via two standard and open-source datasets.The proposed DAE achieved the accuracies of 98.86%,and 98.26%for NSL-KDD,99.32%,and 98.79%for the UNSW-NB15 dataset in binary class and multi-class scenarios.The performance of the suggested attack detection framework is also compared with several state-of-the-art intrusion detection schemes.Experimental outcomes proved the promising performance of the proposed scheme for cyberattack detection in IoT networks.展开更多
基金the financial support from the National Key Research and Development Program of China(No.2023YFB3907001)the National Natural Science Foundation of China(Nos.U2233217,62371029)the UK Engineering and Physical Sciences Research Council(EPSRC),China(Nos.EP/M026981/1,EP/T021063/1 and EP/T024917/)。
文摘Interference significantly impacts the performance of the Global Navigation Satellite Systems(GNSS),highlighting the need for advanced interference localization technology to bolster anti-interference and defense capabilities.The Uniform Circular Array(UCA)enables concurrent estimation of the Direction of Arrival(DOA)in both azimuth and elevation.Given the paramount importance of stability and real-time performance in interference localization,this work proposes an innovative approach to reduce the complexity and increase the robustness of the DOA estimation.The proposed method reduces computational complexity by selecting a reduced number of array elements to reconstruct a non-uniform sparse array from a UCA.To ensure DOA estimation accuracy,minimizing the Cramér-Rao Bound(CRB)is the objective,and the Spatial Correlation Coefficient(SCC)is incorporated as a constraint to mitigate side-lobe.The optimization model is a quadratic fractional model,which is solved by Semi-Definite Relaxation(SDR).When the array has perturbations,the mathematical expressions for CRB and SCC are re-derived to enhance the robustness of the reconstructed array.Simulation and hardware experiments validate the effectiveness of the proposed method in estimating interference DOA,showing high robustness and reductions in hardware and computational costs associated with DOA estimation.
文摘Quality of Service(QoS)assurance in programmable IoT and 5G networks is increasingly threatened by cyberattacks such as Distributed Denial of Service(DDoS),spoofing,and botnet intrusions.This paper presents AutoSHARC,a feedback-driven,explainable intrusion detection framework that integrates Boruta and LightGBM–SHAP feature selection with a lightweight CNN–Attention–GRU classifier.AutoSHARC employs a two-stage feature selection pipeline to identify the most informative features from high-dimensional IoT traffic and reduces 46 features to 30 highly informative ones,followed by post-hoc SHAP-guided retraining to refine feature importance,forming a feedback loopwhere only the most impactful attributes are reused to retrain themodel.This iterative refinement reduces computational overhead,accelerates detection latency,and improves transparency.Evaluated on the CIC IoT 2023 dataset,AutoSHARC achieves 98.98%accuracy,98.9%F1-score,and strong robustness with a Matthews Correlation Coefficient of 0.98 and Cohen’s Kappa of 0.98.The final model contains only 531,272 trainable parameters with a compact 2 MB size,enabling real-time deployment on resource-constrained IoT nodes.By combining explainable AI with iterative feature refinement,AutoSHARC provides scalable and trustworthy intrusion detection while preserving key QoS indicators such as latency,throughput,and reliability.
基金the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Group Project under grant number(RGP.2/245/46)funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R760)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The research team thanks the Deanship of Graduate Studies and Scientific Research at Najran University for supporting the research project through the Nama’a program,with the project code NU/GP/SERC/13/352-1。
文摘The emergence of Generative Adversarial Network(GAN)techniques has garnered significant attention from the research community for the development of Intrusion Detection Systems(IDS).However,conventional GAN-based IDS models face several challenges,including training instability,high computational costs,and system failures.To address these limitations,we propose a Hybrid Wasserstein GAN and Autoencoder Model(WGAN-AE)for intrusion detection.The proposed framework leverages the stability of WGAN and the feature extraction capabilities of the Autoencoder Model.The model was trained and evaluated using two recent benchmark datasets,5GNIDD and IDSIoT2024.When trained on the 5GNIDD dataset,the model achieved an average area under the precisionrecall curve is 99.8%using five-fold cross-validation and demonstrated a high detection accuracy of 97.35%when tested on independent test data.Additionally,the model is well-suited for deployment on resource-limited Internetof-Things(IoT)devices due to its ability to detect attacks within microseconds and its small memory footprint of 60.24 kB.Similarly,when trained on the IDSIoT2024 dataset,the model achieved an average PR-AUC of 94.09%and an attack detection accuracy of 97.35%on independent test data,with a memory requirement of 61.84 kB.Extensive simulation results demonstrate that the proposed hybrid model effectively addresses the shortcomings of traditional GAN-based IDS approaches in terms of detection accuracy,computational efficiency,and applicability to real-world IoT environments.
文摘BACKGROUND Extracorporeal membrane oxygenation(ECMO)is mainly applied to patients with significant cardiorespiratory failure who do not respond to existing conventional treatments.Patients that are supported with veno-arterial ECMO(VA-ECMO)are considered very-high risk patients to participate in any type of physical therapy(PT)or mobilization.However,cumulative evidence suggests that early mobilization of critically ill patients is feasible,safe,and efficient under certain circumstances.AIM To summarize the existing evidence on the impact of early mobilization and physiotherapy on VA-ECMO patients.METHODS This is a scoping review that used systematic electronic literature searches(from inception until January 2025)on MEDLINE(PubMed),PEDro,DynaMed,CINAHL,Scopus,Science direct and Hellenic Academic Libraries.Snowball searching method was also applied.Eligible studies included those reporting patients on VA-ECMO who participated in early mobilization or PT,published in English and utilized any primary evidence study design.Studies on children,animals and patients placed on any other ECMO,secondary evidence,and‘grey’literature were excluded.RESULTS A total of 316 articles were retrieved and 13 were included in the study.Of those,1 study was a randomized control trial,4 retrospective studies,4 retrospective cohort studies,1 case series and 3 case reports.The sample size of the included studies ranged from 1 to 104 VA-ECMO patients,who were ambulated or received PT inter-ventions,and mobilization frequency ranged from 2 per day to 4 per week.Mobilization of VA-ECMO patients seems to be safe regardless the cannula’s position.PT and early mobilization were associated with better weaning from mechanical ventilation,gradual reduction of inotropes and functional capacity improvement after ECMO discharge.CONCLUSION Early mobilization in VA-ECMO seems to be safe and can potentially help reduce vasoconstrictors and speed up rehabilitation times.High quality research on early mobilization in VA-ECMO patients is warranted.
文摘Objective To explore how older patients self-manage their coronary heart disease （CHD） aider undergoing elective percutaneous transluminal coronary angioplasty （PTCA）. Methods This mixed methods study used a sequential, explanatory design and recruited a convenience sample of patients （n = 93） approximately three months after elective PTCA. The study was conducted in two phases. Quantitative data collected in Phase 1 by means of a self-administered survey were subject to univariate and bivariate analysis. Phase 1 findings in- formed the purposive samplhag for Phase 2 where ten participants were selected from the original sample for an in-depth interview. Qualita- tive data were analysed using thematic analysis. This paper will primarily report the findings from a sub-group of older participants （n = 47） classified as 65 years of age or older. Results 78.7% （n = 37） of participants indicated that they would manage recurring angina symptoms by taking glyceryl trinitrate and 34% （n = 16） thought that resting would help. Regardless of the duration or severity of the symptoms 40.5% （n = 19） would call their general practitioner or an emergency ambulance for assistance during any recurrence of angina symptoms. Older participants weighed less （P = 0.02） and smoked less （P = 0.01） than their younger counterparts in the study. Age did not seem to affect PTCA patients＇ likelihood of altering dietary factors such as fruit, vegetable and saturated fat consumption （P = 0.237）. Conclusions The findings suggest that older people in the study were less likely to know how to correctly manage any recurring angina symptoms than their younger counterparts but they had fewer risk factors for CHD. Age was not a factor that influenced participants＇ likelihood to alter lifestyle factors.
基金supported by the Science and Technology Project of Beijing (No. D161100000216001)the National Science Foundation of China (No. 41672227)
文摘Because of its high adsorption capacity, biochar has been used to stabilize metals when remediating contaminated soils; to date, however, it has seldom been used to remediate contaminated sediment. A biochar was used as a stabilization agent to remediate Cu-and Pb-contaminated sediments, collected from three locations in or close to Beijing. The sediments were mixed with a palm sawdust gasified biochar at a range of weight ratios(2.5%, 5%, and 10%) and incubated for 10, 30, or 60 days. The performance of the different treatments and the heavy metal fractions in the sediments were assessed using four extraction methods, including diffusive gradients in thin films, the porewater concentration, a sequential extraction, and the toxicity characteristic leaching procedure. The results showed that biochar could enhance the stability of heavy metals in contaminated sediments. The degree of stability increased as both the dose of biochar and the incubation time increased. The sediment p H and the morphology of the metal crystals adsorbed onto the biochar changed as the contact time increased. Our results showed that adsorption,metal crystallization, and the p H were the main controls on the stabilization of metals in contaminated sediment by biochar.
基金the support from the Shanxi Hundred People Plan of China
文摘Single image super-resolution has attracted increasing attention and has a wide range of applications in satellite imaging, medical imaging, computer vision, security surveillance imaging, remote sensing, objection detection, and recognition. Recently, deep learning techniques have emerged and blossomed, producing " the state-of-the-art” in many domains. Due to their capability in feature extraction and mapping, it is very helpful to predict high-frequency details lost in low-resolution images. In this paper, we give an overview of recent advances in deep learning-based models and methods that have been applied to single image super-resolution tasks. We also summarize, compare and discuss various models from the past and present for comprehensive understanding and finally provide open problems and possible directions for future research.
基金supported by the European Commission,the European Social Fund and the Calabria Region(C39B18000080002)supported by the UK Engineering and Physical Sciences Research Council(EPSRC)(EP/M026981/1,EP/T021063/1,EP/T024917/1)。
文摘The manufacturing of nanomaterials by the electrospinning process requires accurate and meticulous inspection of related scanning electron microscope(SEM)images of the electrospun nanofiber,to ensure that no structural defects are produced.The presence of anomalies prevents practical application of the electrospun nanofibrous material in nanotechnology.Hence,the automatic monitoring and quality control of nanomaterials is a relevant challenge in the context of Industry 4.0.In this paper,a novel automatic classification system for homogenous(anomaly-free)and non-homogenous(with defects)nanofibers is proposed.The inspection procedure aims at avoiding direct processing of the redundant full SEM image.Specifically,the image to be analyzed is first partitioned into subimages(nanopatches)that are then used as input to a hybrid unsupervised and supervised machine learning system.In the first step,an autoencoder(AE)is trained with unsupervised learning to generate a code representing the input image with a vector of relevant features.Next,a multilayer perceptron(MLP),trained with supervised learning,uses the extracted features to classify non-homogenous nanofiber(NH-NF)and homogenous nanofiber(H-NF)patches.The resulting novel AE-MLP system is shown to outperform other standard machine learning models and other recent state-of-the-art techniques,reporting accuracy rate up to92.5%.In addition,the proposed approach leads to model complexity reduction with respect to other deep learning strategies such as convolutional neural networks(CNN).The encouraging performance achieved in this benchmark study can stimulate the application of the proposed scheme in other challenging industrial manufacturing tasks.
基金supported by the National Natural Science Foundation of China(Nos.61771027,61071139,61471019,61671035)supported in part under the Royal Society of Edinburgh-National Natural Science Foundation of China(RSE-NNSFC)Joint Project(2017–2019)(No.6161101383)with China University of Petroleum(Huadong)partially supported by the UK Engineering and Physical Sciences Research Council(EPSRC)(Nos.EP/I009310/1,EP/M026981/1)
文摘Synthetic Aperture Radar(SAR) imaging systems have been widely used in civil and military fields due to their all-weather and all-day abilities and various other advantages. However, due to image data exponentially increasing, there is a need for novel automatic target detection and recognition technologies. In recent years, the visual attention mechanism in the visual system has helped humans effectively deal with complex visual signals. In particular, biologically inspired top-down attention models have garnered much attention recently. This paper presents a visual attention model for SAR target detection, comprising a bottom-up stage and top-down process.In the bottom-up step, the Itti model is improved based on the difference between SAR and optical images. The top-down step fully utilizes prior information to further detect targets. Extensive detection experiments carried out on the benchmark Moving and Stationary Target Acquisition and Recognition(MSTAR) dataset show that, compared with typical visual models and other popular detection methods, our model has increased ability and robustness for SAR target detection, under a range of Signal to Clutter Ratio(SCR) conditions and scenes. In addition, results obtained using only the bottom-up stage are inferior to those of the proposed method, further demonstrating the effectiveness and rationality of a top-down strategy. In summary, our proposed visual attention method can be considered a potential benchmark resource for the SAR research community.
基金Supported by School of Engineering, Napier University, United Kingdom, and partially supported by the National Natural Science Foundation of China (No.60273093).
文摘Genetic Algorithm (GA) is a biologically inspired technique and widely used to solve numerous combinational optimization problems. It works on a population of individuals, not just one single solution. As a result, it avoids converging to the local optimum. However, it takes too much CPU time in the late process of GA. On the other hand, in the late process Simulated Annealing (SA) converges faster than GA but it is easily trapped to local optimum. In this letter, a useful method that unifies GA and SA is introduced, which utilizes the advantage of the global search ability of GA and fast convergence of SA. The experimental results show that the proposed algorithm outperforms GA in terms of CPU time without degradation of performance. It also achieves highly comparable placement cost compared to the state-of-the-art results obtained by Versatile Place and Route (VPR) Tool.
基金This work was supported by the National Natural Science Foundation of China(Grant No.61673222)Jiangsu Universities Natural Science Research Project(Grant No.13KJA510001)Major Program of the National Social Science Fund of China(Grant No.17ZDA092).
文摘Visual degradation of captured images caused by rainy streaks under rainy weather can adversely affect the performance of many open-air vision systems.Hence,it is necessary to address the problem of eliminating rain streaks from the individual rainy image.In this work,a deep convolution neural network(CNN)based method is introduced,called Rain-Removal Net(R2N),to solve the single image de-raining issue.Firstly,we decomposed the rainy image into its high-frequency detail layer and lowfrequency base layer.Then,we used the high-frequency detail layer to input the carefully designed CNN architecture to learn the mapping between it and its corresponding derained high-frequency detail layer.The CNN architecture consists of four convolution layers and four deconvolution layers,as well as three skip connections.The experiments on synthetic and real-world rainy images show that the performance of our architecture outperforms the compared state-of-the-art de-raining models with respects to the quality of de-rained images and computing efficiency.
基金The Deanship of Scientific Research(DSR)at King Abdulaziz University(KAU),Jeddah,Saudi Arabia has funded this project,under Grant No.(IFPDP-279-22).
文摘The Internet of things(IoT)is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision.Despite several advantages,the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals.A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and industries.To overcome the security challenges of IoT networks,this article proposes a lightweight deep autoencoder(DAE)based cyberattack detection framework.The proposed approach learns the normal and anomalous data patterns to identify the various types of network intrusions.The most significant feature of the proposed technique is its lower complexity which is attained by reducing the number of operations.To optimally train the proposed DAE,a range of hyperparameters was determined through extensive experiments that ensure higher attack detection accuracy.The efficacy of the suggested framework is evaluated via two standard and open-source datasets.The proposed DAE achieved the accuracies of 98.86%,and 98.26%for NSL-KDD,99.32%,and 98.79%for the UNSW-NB15 dataset in binary class and multi-class scenarios.The performance of the suggested attack detection framework is also compared with several state-of-the-art intrusion detection schemes.Experimental outcomes proved the promising performance of the proposed scheme for cyberattack detection in IoT networks.
