Disorders of consciousness(DoCs) are chronic conditions resulting usually from severe neurological deficits. The limitations of the existing diagnosis systems and methodologies cause a need for additional tools for re...Disorders of consciousness(DoCs) are chronic conditions resulting usually from severe neurological deficits. The limitations of the existing diagnosis systems and methodologies cause a need for additional tools for relevant patients with DoCs assessment, including brain-computer interfaces(BCIs). Recent progress in BCIs' clinical applications may offer important breakthroughs in the diagnosis and therapy of patients with DoCs. Thus the clinical significance of BCI applications in the diagnosis of patients with DoCs is hard to overestimate. One of them may be brain-computer interfaces. The aim of this study is to evaluate possibility of non-invasive EEG-based brain-computer interfaces in diagnosis of patients with DOCs in post-acute and long-term care institutions.展开更多
Stoichiometric hydroxyapatite(HA)nanoparticles were synthesized by a wet chemical method.Calcium nitrate tetra hydrate used as calcium source and dibasic ammonium phosphate used as phosphorous source.Calcium nitrate t...Stoichiometric hydroxyapatite(HA)nanoparticles were synthesized by a wet chemical method.Calcium nitrate tetra hydrate used as calcium source and dibasic ammonium phosphate used as phosphorous source.Calcium nitrate tetra hydrate and dibasic ammonium phosphate solutions were prepared by dissolving the salts in distilled water.Stoichiometric hydroxyapatite nanoparticles used by artificial bone powders and synthesized by a wet chemical method were analyzed using EDXRF method.The concentrations of K,Ca,Ti,V,Cr,Fe,Ni,Cu,Sr and Pb for artificial bone powders have been determined.Besides,Calcium contents were evaluated according to the agitation time and temperature in the production process.展开更多
With the rapid spread of the coronavirus disease 2019(COVID-19)worldwide,the establishment of an accurate and fast process to diagnose the disease is important.The routine real-time reverse transcription-polymerase ch...With the rapid spread of the coronavirus disease 2019(COVID-19)worldwide,the establishment of an accurate and fast process to diagnose the disease is important.The routine real-time reverse transcription-polymerase chain reaction(rRT-PCR)test that is currently used does not provide such high accuracy or speed in the screening process.Among the good choices for an accurate and fast test to screen COVID-19 are deep learning techniques.In this study,a new convolutional neural network(CNN)framework for COVID-19 detection using computed tomography(CT)images is proposed.The EfficientNet architecture is applied as the backbone structure of the proposed network,in which feature maps with different scales are extracted from the input CT scan images.In addition,atrous convolution at different rates is applied to these multi-scale feature maps to generate denser features,which facilitates in obtaining COVID-19 findings in CT scan images.The proposed framework is also evaluated in this study using a public CT dataset containing 2482 CT scan images from patients of both classes(i.e.,COVID-19 and non-COVID-19).To augment the dataset using additional training examples,adversarial examples generation is performed.The proposed system validates its superiority over the state-of-the-art methods with values exceeding 99.10%in terms of several metrics,such as accuracy,precision,recall,and F1.The proposed system also exhibits good robustness,when it is trained using a small portion of data(20%),with an accuracy of 96.16%.展开更多
Gastrointestinal diseases like ulcers, polyps’, and bleeding areincreasing rapidly in the world over the last decade. On average 0.7 millioncases are reported worldwide every year. The main cause of gastrointestinald...Gastrointestinal diseases like ulcers, polyps’, and bleeding areincreasing rapidly in the world over the last decade. On average 0.7 millioncases are reported worldwide every year. The main cause of gastrointestinaldiseases is a Helicobacter Pylori (H. Pylori) bacterium that presents in morethan 50% of people around the globe. Many researchers have proposeddifferent methods for gastrointestinal disease using computer vision techniques.Few of them focused on the detection process and the rest of themperformed classification. The major challenges that they faced are the similarityof infected and healthy regions that misleads the correct classificationaccuracy. In this work, we proposed a technique based on Mask Recurrent-Convolutional Neural Network (R-CNN) and fine-tuned pre-trainedResNet-50 and ResNet-152 networks for feature extraction. Initially, the region ofinterest is detected using Mask R-CNN which is later utilized for the trainingof fine-tuned models through transfer learning. Features are extracted fromfine-tuned models that are later fused using a serial approach. Moreover, anImproved Ant Colony Optimization (ACO) algorithm has also opted for thebest feature selection from the fused feature vector. The best-selected featuresare finally classified using machine learning techniques. The experimentalprocess was conducted on the publicly available dataset and obtained animproved accuracy of 96.43%. In comparison with state-of-the-art techniques,it is observed that the proposed accuracy is improved.展开更多
In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting firear...In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting firearms.which is why an automated weapon detection system is needed.Various automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good results.However,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system.These models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance videos.This research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key parameter.The proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur algorithm.The proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 score.The results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are achieved.Speed reached 0.010 s per frame compared to the 0.17 s of the Faster R-CNN.It is promising to be used in the field of security and weapon detection.展开更多
The paper presents modelling and simulation of a local blast wave interaction with a tire of a logistic truck supporting military operations.In the military industry,it is desired to improve effectiveness and strength...The paper presents modelling and simulation of a local blast wave interaction with a tire of a logistic truck supporting military operations.In the military industry,it is desired to improve effectiveness and strength of vehicle components and simultaneously to minimize the risk of crew injuries.Therefore,the main goal of this paper is to present an attempt to improve blast resistance of a tire.Based on the developed,verified and validated finite element model an optimization procedure was conducted to minimize the damage of a tire subjected to a blast wave.The main issue in the performed computations was to estimate an influence of the cords angle in each layer.For this purpose,a pre-processor script was developed in order to easily modify the finite element model so that the generation process was perfo rmed automatically on the basis of optimization variables.Dynamic response of a tire subjected to blast wave in different cases(cords configurations) was analyzed.It was shown that the optimized cords angles configuration reduces tire local damage and increases its blast resistance.展开更多
The substantial increase in the efficiency of organic solar cells achieved in recent years would not have been possible without work on the synthesis of new materials and understanding the relationship between the mor...The substantial increase in the efficiency of organic solar cells achieved in recent years would not have been possible without work on the synthesis of new materials and understanding the relationship between the morphology and performance of organic photovoltaic devices.The structure of solvent-cast active layers is a result of phase separation in mixtures of donor and acceptor components.To a large extent,this process depends on the interactions between the components of the mixture.Here,we present a systematic analysis of the morphology of poly[N-9'-heptadecanyl-2,7-carbazole-alt-5,5-(4',7'-di-2-thienyl-2',1',3'-benzothiadiazole)](PCDTBT)and[6,6]-phenyl-C71-butyric acid methyl ester(PC70BM)films in terms of the ternary phase diagram.The interaction parameters between PCDTBT and four different solvents,namely chloroform,chlorobenzene,o-dichlorobenzene,and toluene,were estimated based on swelling experiments.Based on these values,ternary phase diagrams of PCDTBT:PC70BM in different solvents were calculated.The morphology of spin-coated films with different blend ratios cast from different solvents is discussed in terms of the obtained phase diagrams.展开更多
In this work,the evolutions of stresses in both phases of the Al/SiCp composite subjected to thermal cycling during in situ compression test were measured using Time of Flight neutron diffraction.It was confirmed that...In this work,the evolutions of stresses in both phases of the Al/SiCp composite subjected to thermal cycling during in situ compression test were measured using Time of Flight neutron diffraction.It was confirmed that inter-phase stresses in the studied composite can be caused by differences in the coefficient of thermal expansion for the reinforcement and matrix,leading to a different variation of phase volumes during sample heating or cooling.The results of the diffraction experiment during thermal cycling were well predicted by the Thermo-Mechanical Self-Consistent model.The experimental study of elastic-plastic deformation was carried out in situ on a unique diffractometer EPSILON-MDS(JINR in Dubna,Russia)with nine detector banks measuring interplanar spacings simultaneously in 9 orientations of scattering vector.For the first time,the performed analysis of experimental data allowed to study the evolution of full stress tensor in both phases of the composite and to consider the decomposition of this tensor into deviatoric and hydrostatic components.It was found that the novel Developed Thermo-Mechanical SelfConsistent model correctly predicted stress evolution during compressive loading,taking into account the relaxation of thermal origin hydrostatic stresses.The comparison of this model with experimental data at the macroscopic level and the level of phases showed that strengthening of the Al/SiCp composite is caused by stress transfer from the plastically deformed A12124 matrix to the elastic SiCp reinforcement,while thermal stresses relaxation does not significantly affect the overall composite properties.展开更多
In this work,the microstructure of titania coating fabricated on the surface of hydrostatically extruded titanium grade 4 with the use of the micro-arc oxidation method was studied.The surface topography and microstru...In this work,the microstructure of titania coating fabricated on the surface of hydrostatically extruded titanium grade 4 with the use of the micro-arc oxidation method was studied.The surface topography and microstructure investigations performed with atomic force microscopy and scanning and transmission electron microscopy revealed that,by using an Na_(2)HPO_(4)electrolyte,a well-adherent porous coating is produced on the top surface and side walls of the extruded rod.The distribution of chemical elements was analyzed by using energy dispersive X-ray spectroscopy.The chemical elements dissolved in the electrolyte(Na,P and O)incorporated into the coating.Sodium locates preferentially in the outer part of the coating,while phosphorus and oxygen are distributed throughout the whole coating.The most relevant finding shows that a grain refinement caused by a hydrostatic extrusion provoked an increase in density of high-angle grain boundaries(HAGB),which in turn secured the formation of a continuous amorphous layer close to the substrate.The presence of this layer compensates for the effect of anisotropic substrate,producing a comparable and homogenous microstructure with a large number of micropores.展开更多
A new implementation of the image registration algorithm based on the mutual information is presented for the case of medical images. The registration is achieved if the maximum of the mutual information is attained. ...A new implementation of the image registration algorithm based on the mutual information is presented for the case of medical images. The registration is achieved if the maximum of the mutual information is attained. In this maximization process optimal values of five parameters of an affine transformation are searched.展开更多
Underwater images degraded due to low contrast and visibility issues.Therefore,it is important to enhance the images and videos taken in the underwater environment before processing.Enhancement is a way to improve or ...Underwater images degraded due to low contrast and visibility issues.Therefore,it is important to enhance the images and videos taken in the underwater environment before processing.Enhancement is a way to improve or increase image quality and to improve the contrast of degraded images.The original image or video which is captured through image processing devices needs to improve as there are various issues such as less light available,low resolution,and blurriness in underwater images caused by the normal camera.Various researchers have proposed different solutions to overcome these problems.Dark channel prior(DCP)is one of the most used techniques which produced a better Peak Signal to Noise Ratio(PSNR)value.However,DCP has some issues such as it tends to darken images,reduce contrast,and produce halo effects.The proposed method solves these issues with the help of contrast-limited adaptive histogram equalization(CLAHE)and the Adaptive Color Correction Method.The proposed method was assessed using Japan Agency for Marine-Earth Science and Technology(JAMSTEC),and some images were collected from the internet.The measure of entropy(MOE),Measure of Enhancement(EME),Mean Square Error(MSE),and PSNR opted as performance measures during experiments.The values of MSE and PSNR achieved by the proposed framework are 0.26 and 32 respectively which shows better results.展开更多
研究了有关癌症分类的基因选择问题。开发了集成的基于平滑剪切绝对偏差罚分的SVM—特征选择方法,直接最小化分类器的性能。为解决优化问题,应用了突函数差异算法(difference of convex functionsal-gorithms,DCA)这一进行非突连续优化...研究了有关癌症分类的基因选择问题。开发了集成的基于平滑剪切绝对偏差罚分的SVM—特征选择方法,直接最小化分类器的性能。为解决优化问题,应用了突函数差异算法(difference of convex functionsal-gorithms,DCA)这一进行非突连续优化的通用框架,致使连续线性规划算法有限收敛。真实数据集上的先验实验表明算法达到了预想目标:在压缩大量属性的同时,保持了较小分类差错。展开更多
The γ-U phase alloys can be retained down to low temperatures with less required alloying concentration by using the splat-cooling technique with a cooling rate better than 10^6 K/s. Doping with 15 at.% Mo, Pt, Pd, N...The γ-U phase alloys can be retained down to low temperatures with less required alloying concentration by using the splat-cooling technique with a cooling rate better than 10^6 K/s. Doping with 15 at.% Mo, Pt, Pd, Nb leads to a stabilization of the cubic γ-U phase, while it requires much higher Zr concentrations （≥30 at.% Zr）. All U-T splats become superconducting with Tc in the range of 0.61-2.11 K. A good agreement of the experimentally observed specific-heat jump at Tc with that from BCS theory prediction was obtained for U-15 at.% Mo consisting of the γ-U phase with an ideal bcc A2 structure.展开更多
The mechanical and microstructural properties as well as crystallographic textures of asymmetrically rolled low carbon steel were studied.The modelling of plastic deformation was carried out in two scales:in the macro...The mechanical and microstructural properties as well as crystallographic textures of asymmetrically rolled low carbon steel were studied.The modelling of plastic deformation was carried out in two scales:in the macro-scale,using the finite elements method,and in the crystallographic scale,using the polycrystalline deformation model.The internal stress distribution in the rolling gap was calculated using the finite elements method and these stresses were then applied to the polycrystalline elasto-plastic deformation model.Selected mechanical properties,namely residual stress distribution,deformation work,applied force and torques,and bend amplitude,were calculated.The diffraction measurements,X-ray and electron backscatter diffraction,enabled the examination of texture heterogeneity and selected microstructure characteristics.The predicted textures agree well with those determined experimentally.The plastic anisotropy of cold rolled ferritic steel samples,connected with texture,was expressed by Lankford coefficient.展开更多
In this paper,we propose a new image fusion algorithm based on two-dimensional Scale-Mixing Complex Wavelet Transform(2D-SMCWT).The fusion of the detail 2D-SMCWT cofficients is performed via a Bayesian Maximum a Poste...In this paper,we propose a new image fusion algorithm based on two-dimensional Scale-Mixing Complex Wavelet Transform(2D-SMCWT).The fusion of the detail 2D-SMCWT cofficients is performed via a Bayesian Maximum a Posteriori(MAP)approach by considering a trivariate statistical model for the local neighboring of 2D-SMCWT coefficients.For the approx imation coefficients,a new fusion rule based on the Principal Component Analysis(PCA)is applied.We conduct several experiments using three different groups of multimodal medical images to evaluate the performance of the proposed method.The obt ained results prove the superiority of the proposed method over the state of the art fusion methods in terms of visual quality and several commonly used metrics.Robustness of the proposed method is further tested against different types of noise.The plots of fusion met rics establish the accuracy of the proposed fusion method.展开更多
Automated Facial Expression Recognition(FER)serves as the backbone of patient monitoring systems,security,and surveillance systems.Real-time FER is a challenging task,due to the uncontrolled nature of the environment ...Automated Facial Expression Recognition(FER)serves as the backbone of patient monitoring systems,security,and surveillance systems.Real-time FER is a challenging task,due to the uncontrolled nature of the environment and poor quality of input frames.In this paper,a novel FER framework has been proposed for patient monitoring.Preprocessing is performed using contrast-limited adaptive enhancement and the dataset is balanced using augmentation.Two lightweight efficient Convolution Neural Network(CNN)models MobileNetV2 and Neural search Architecture Network Mobile(NasNetMobile)are trained,and feature vectors are extracted.The Whale Optimization Algorithm(WOA)is utilized to remove irrelevant features from these vectors.Finally,the optimized features are serially fused to pass them to the classifier.A comprehensive set of experiments were carried out for the evaluation of real-time image datasets FER-2013,MMA,and CK+to report performance based on various metrics.Accuracy results show that the proposed model has achieved 82.5%accuracy and performed better in comparison to the state-of-the-art classification techniques in terms of accuracy.We would like to highlight that the proposed technique has achieved better accuracy by using 2.8 times lesser number of features.展开更多
The COVID-19 outbreak and its medical distancing phenomenon have effectively turned the global healthcare challenge into an opportunity for Telecare Medical Information Systems.Such systems employ the latest mobile an...The COVID-19 outbreak and its medical distancing phenomenon have effectively turned the global healthcare challenge into an opportunity for Telecare Medical Information Systems.Such systems employ the latest mobile and digital technologies and provide several advantages like minimal physical contact between patient and healthcare provider,easy mobility,easy access,consistent patient engagement,and cost-effectiveness.Any leakage or unauthorized access to users’medical data can have serious consequences for any medical information system.The majority of such systems thus rely on biometrics for authenticated access but biometric systems are also prone to a variety of attacks like spoong,replay,Masquerade,and stealing of stored templates.In this article,we propose a new cancelable biometric approach which has tentatively been named as“Expression Hash”for Telecare Medical Information Systems.The idea is to hash the expression templates with a set of pseudo-random keys which would provide a unique code(expression hash).This code can then be serving as a template for verication.Different expressions would result in different sets of expression hash codes,which could be used in different applications and for different roles of each individual.The templates are stored on the server-side and the processing is also performed on the server-side.The proposed technique is a multi-factor authentication system and provides advantages like enhanced privacy and security without the need for multiple biometric devices.In the case of compromise,the existing code can be revoked and can be directly replaced by a new set of expression hash code.The well-known JAFFE(The Japanese Female Facial Expression)dataset has been for empirical testing and the results advocate for the efcacy of the proposed approach.展开更多
文摘Disorders of consciousness(DoCs) are chronic conditions resulting usually from severe neurological deficits. The limitations of the existing diagnosis systems and methodologies cause a need for additional tools for relevant patients with DoCs assessment, including brain-computer interfaces(BCIs). Recent progress in BCIs' clinical applications may offer important breakthroughs in the diagnosis and therapy of patients with DoCs. Thus the clinical significance of BCI applications in the diagnosis of patients with DoCs is hard to overestimate. One of them may be brain-computer interfaces. The aim of this study is to evaluate possibility of non-invasive EEG-based brain-computer interfaces in diagnosis of patients with DOCs in post-acute and long-term care institutions.
文摘Stoichiometric hydroxyapatite(HA)nanoparticles were synthesized by a wet chemical method.Calcium nitrate tetra hydrate used as calcium source and dibasic ammonium phosphate used as phosphorous source.Calcium nitrate tetra hydrate and dibasic ammonium phosphate solutions were prepared by dissolving the salts in distilled water.Stoichiometric hydroxyapatite nanoparticles used by artificial bone powders and synthesized by a wet chemical method were analyzed using EDXRF method.The concentrations of K,Ca,Ti,V,Cr,Fe,Ni,Cu,Sr and Pb for artificial bone powders have been determined.Besides,Calcium contents were evaluated according to the agitation time and temperature in the production process.
基金support provided from the Deanship of Scientific Research at King Saud University through the,Research Group No.(RG-1435-050.)。
文摘With the rapid spread of the coronavirus disease 2019(COVID-19)worldwide,the establishment of an accurate and fast process to diagnose the disease is important.The routine real-time reverse transcription-polymerase chain reaction(rRT-PCR)test that is currently used does not provide such high accuracy or speed in the screening process.Among the good choices for an accurate and fast test to screen COVID-19 are deep learning techniques.In this study,a new convolutional neural network(CNN)framework for COVID-19 detection using computed tomography(CT)images is proposed.The EfficientNet architecture is applied as the backbone structure of the proposed network,in which feature maps with different scales are extracted from the input CT scan images.In addition,atrous convolution at different rates is applied to these multi-scale feature maps to generate denser features,which facilitates in obtaining COVID-19 findings in CT scan images.The proposed framework is also evaluated in this study using a public CT dataset containing 2482 CT scan images from patients of both classes(i.e.,COVID-19 and non-COVID-19).To augment the dataset using additional training examples,adversarial examples generation is performed.The proposed system validates its superiority over the state-of-the-art methods with values exceeding 99.10%in terms of several metrics,such as accuracy,precision,recall,and F1.The proposed system also exhibits good robustness,when it is trained using a small portion of data(20%),with an accuracy of 96.16%.
基金Supporting Project number (RSP2022R458),King Saud University,Riyadh,Saudi Arabia.
文摘Gastrointestinal diseases like ulcers, polyps’, and bleeding areincreasing rapidly in the world over the last decade. On average 0.7 millioncases are reported worldwide every year. The main cause of gastrointestinaldiseases is a Helicobacter Pylori (H. Pylori) bacterium that presents in morethan 50% of people around the globe. Many researchers have proposeddifferent methods for gastrointestinal disease using computer vision techniques.Few of them focused on the detection process and the rest of themperformed classification. The major challenges that they faced are the similarityof infected and healthy regions that misleads the correct classificationaccuracy. In this work, we proposed a technique based on Mask Recurrent-Convolutional Neural Network (R-CNN) and fine-tuned pre-trainedResNet-50 and ResNet-152 networks for feature extraction. Initially, the region ofinterest is detected using Mask R-CNN which is later utilized for the trainingof fine-tuned models through transfer learning. Features are extracted fromfine-tuned models that are later fused using a serial approach. Moreover, anImproved Ant Colony Optimization (ACO) algorithm has also opted for thebest feature selection from the fused feature vector. The best-selected featuresare finally classified using machine learning techniques. The experimentalprocess was conducted on the publicly available dataset and obtained animproved accuracy of 96.43%. In comparison with state-of-the-art techniques,it is observed that the proposed accuracy is improved.
基金We deeply acknowledge Taif University for Supporting and funding this study through Taif University Researchers Supporting Project Number(TURSP-2020/115),Taif University,Taif,Saudi Arabia.
文摘In recent years,the number of Gun-related incidents has crossed over 250,000 per year and over 85%of the existing 1 billion firearms are in civilian hands,manual monitoring has not proven effective in detecting firearms.which is why an automated weapon detection system is needed.Various automated convolutional neural networks(CNN)weapon detection systems have been proposed in the past to generate good results.However,These techniques have high computation overhead and are slow to provide real-time detection which is essential for the weapon detection system.These models have a high rate of false negatives because they often fail to detect the guns due to the low quality and visibility issues of surveillance videos.This research work aims to minimize the rate of false negatives and false positives in weapon detection while keeping the speed of detection as a key parameter.The proposed framework is based on You Only Look Once(YOLO)and Area of Interest(AOI).Initially,themodels take pre-processed frames where the background is removed by the use of the Gaussian blur algorithm.The proposed architecture will be assessed through various performance parameters such as False Negative,False Positive,precision,recall rate,and F1 score.The results of this research work make it clear that due to YOLO-v5s high recall rate and speed of detection are achieved.Speed reached 0.010 s per frame compared to the 0.17 s of the Faster R-CNN.It is promising to be used in the field of security and weapon detection.
基金The research was carried out under a research grant no.RMN 723the support of the Interdisciplinary Centre for Mathematical and Computational Modelling(ICM)University of Warsaw under grant no GB73-19.This support is gratefully acknowledged.
文摘The paper presents modelling and simulation of a local blast wave interaction with a tire of a logistic truck supporting military operations.In the military industry,it is desired to improve effectiveness and strength of vehicle components and simultaneously to minimize the risk of crew injuries.Therefore,the main goal of this paper is to present an attempt to improve blast resistance of a tire.Based on the developed,verified and validated finite element model an optimization procedure was conducted to minimize the damage of a tire subjected to a blast wave.The main issue in the performed computations was to estimate an influence of the cords angle in each layer.For this purpose,a pre-processor script was developed in order to easily modify the finite element model so that the generation process was perfo rmed automatically on the basis of optimization variables.Dynamic response of a tire subjected to blast wave in different cases(cords configurations) was analyzed.It was shown that the optimized cords angles configuration reduces tire local damage and increases its blast resistance.
基金the National Science Center(No.UMO-2013/11/B/ST5/04473)the European Regional Development Fund in the framework of the Polish Innovation Economy Operational Program(Contract No.POIG.02.01.00-12-023/08).
文摘The substantial increase in the efficiency of organic solar cells achieved in recent years would not have been possible without work on the synthesis of new materials and understanding the relationship between the morphology and performance of organic photovoltaic devices.The structure of solvent-cast active layers is a result of phase separation in mixtures of donor and acceptor components.To a large extent,this process depends on the interactions between the components of the mixture.Here,we present a systematic analysis of the morphology of poly[N-9'-heptadecanyl-2,7-carbazole-alt-5,5-(4',7'-di-2-thienyl-2',1',3'-benzothiadiazole)](PCDTBT)and[6,6]-phenyl-C71-butyric acid methyl ester(PC70BM)films in terms of the ternary phase diagram.The interaction parameters between PCDTBT and four different solvents,namely chloroform,chlorobenzene,o-dichlorobenzene,and toluene,were estimated based on swelling experiments.Based on these values,ternary phase diagrams of PCDTBT:PC70BM in different solvents were calculated.The morphology of spin-coated films with different blend ratios cast from different solvents is discussed in terms of the obtained phase diagrams.
基金supported by grants from the National Science Centre,Poland(NCN)No.UMO-2017/25/B/ST8/00134 and UMO2015/19/D/ST8/00818supported by the Polish-JINR Programme 2017(item 24)supported by the Federal Ministry for Education and Research in Germany。
文摘In this work,the evolutions of stresses in both phases of the Al/SiCp composite subjected to thermal cycling during in situ compression test were measured using Time of Flight neutron diffraction.It was confirmed that inter-phase stresses in the studied composite can be caused by differences in the coefficient of thermal expansion for the reinforcement and matrix,leading to a different variation of phase volumes during sample heating or cooling.The results of the diffraction experiment during thermal cycling were well predicted by the Thermo-Mechanical Self-Consistent model.The experimental study of elastic-plastic deformation was carried out in situ on a unique diffractometer EPSILON-MDS(JINR in Dubna,Russia)with nine detector banks measuring interplanar spacings simultaneously in 9 orientations of scattering vector.For the first time,the performed analysis of experimental data allowed to study the evolution of full stress tensor in both phases of the composite and to consider the decomposition of this tensor into deviatoric and hydrostatic components.It was found that the novel Developed Thermo-Mechanical SelfConsistent model correctly predicted stress evolution during compressive loading,taking into account the relaxation of thermal origin hydrostatic stresses.The comparison of this model with experimental data at the macroscopic level and the level of phases showed that strengthening of the Al/SiCp composite is caused by stress transfer from the plastically deformed A12124 matrix to the elastic SiCp reinforcement,while thermal stresses relaxation does not significantly affect the overall composite properties.
基金financially supported by the Institute of Metallurgy and Materials Science of the Polish Academy of Sciences within the statutory work Z-4/2021partly supported by the EU Project POWR.03.02.00–00-I004/16。
文摘In this work,the microstructure of titania coating fabricated on the surface of hydrostatically extruded titanium grade 4 with the use of the micro-arc oxidation method was studied.The surface topography and microstructure investigations performed with atomic force microscopy and scanning and transmission electron microscopy revealed that,by using an Na_(2)HPO_(4)electrolyte,a well-adherent porous coating is produced on the top surface and side walls of the extruded rod.The distribution of chemical elements was analyzed by using energy dispersive X-ray spectroscopy.The chemical elements dissolved in the electrolyte(Na,P and O)incorporated into the coating.Sodium locates preferentially in the outer part of the coating,while phosphorus and oxygen are distributed throughout the whole coating.The most relevant finding shows that a grain refinement caused by a hydrostatic extrusion provoked an increase in density of high-angle grain boundaries(HAGB),which in turn secured the formation of a continuous amorphous layer close to the substrate.The presence of this layer compensates for the effect of anisotropic substrate,producing a comparable and homogenous microstructure with a large number of micropores.
文摘A new implementation of the image registration algorithm based on the mutual information is presented for the case of medical images. The registration is achieved if the maximum of the mutual information is attained. In this maximization process optimal values of five parameters of an affine transformation are searched.
基金Researchers Supporting Project Number(RSP2022R458),King Saud University,Riyadh,Saudi Arabia.
文摘Underwater images degraded due to low contrast and visibility issues.Therefore,it is important to enhance the images and videos taken in the underwater environment before processing.Enhancement is a way to improve or increase image quality and to improve the contrast of degraded images.The original image or video which is captured through image processing devices needs to improve as there are various issues such as less light available,low resolution,and blurriness in underwater images caused by the normal camera.Various researchers have proposed different solutions to overcome these problems.Dark channel prior(DCP)is one of the most used techniques which produced a better Peak Signal to Noise Ratio(PSNR)value.However,DCP has some issues such as it tends to darken images,reduce contrast,and produce halo effects.The proposed method solves these issues with the help of contrast-limited adaptive histogram equalization(CLAHE)and the Adaptive Color Correction Method.The proposed method was assessed using Japan Agency for Marine-Earth Science and Technology(JAMSTEC),and some images were collected from the internet.The measure of entropy(MOE),Measure of Enhancement(EME),Mean Square Error(MSE),and PSNR opted as performance measures during experiments.The values of MSE and PSNR achieved by the proposed framework are 0.26 and 32 respectively which shows better results.
文摘研究了有关癌症分类的基因选择问题。开发了集成的基于平滑剪切绝对偏差罚分的SVM—特征选择方法,直接最小化分类器的性能。为解决优化问题,应用了突函数差异算法(difference of convex functionsal-gorithms,DCA)这一进行非突连续优化的通用框架,致使连续线性规划算法有限收敛。真实数据集上的先验实验表明算法达到了预想目标:在压缩大量属性的同时,保持了较小分类差错。
基金supported by the Czech Science Foundation under the Grant No.15-01100Ssupported within the program of Czech Research Infrastructures(No.LM2011025)+2 种基金supported by the Grant Agency of the Charles University under the Project No.1332314supported by the Czech-Polish cooperation in the scope of Czech-Polish project7AMB14PL036(9004/R14/R15)European Regional Development Fund under the Infrastructure and Environment Programme
文摘The γ-U phase alloys can be retained down to low temperatures with less required alloying concentration by using the splat-cooling technique with a cooling rate better than 10^6 K/s. Doping with 15 at.% Mo, Pt, Pd, Nb leads to a stabilization of the cubic γ-U phase, while it requires much higher Zr concentrations （≥30 at.% Zr）. All U-T splats become superconducting with Tc in the range of 0.61-2.11 K. A good agreement of the experimentally observed specific-heat jump at Tc with that from BCS theory prediction was obtained for U-15 at.% Mo consisting of the γ-U phase with an ideal bcc A2 structure.
基金Projects(DEC-2011/01/B/ST8/07394,DEC-2011/01/D/ST8/07399)supported by the Polish National Centre for Science(NCN)The support of the Polish Ministry of Science and Higher Education and of the French ANR 05-BLAN-0383 project
文摘The mechanical and microstructural properties as well as crystallographic textures of asymmetrically rolled low carbon steel were studied.The modelling of plastic deformation was carried out in two scales:in the macro-scale,using the finite elements method,and in the crystallographic scale,using the polycrystalline deformation model.The internal stress distribution in the rolling gap was calculated using the finite elements method and these stresses were then applied to the polycrystalline elasto-plastic deformation model.Selected mechanical properties,namely residual stress distribution,deformation work,applied force and torques,and bend amplitude,were calculated.The diffraction measurements,X-ray and electron backscatter diffraction,enabled the examination of texture heterogeneity and selected microstructure characteristics.The predicted textures agree well with those determined experimentally.The plastic anisotropy of cold rolled ferritic steel samples,connected with texture,was expressed by Lankford coefficient.
文摘In this paper,we propose a new image fusion algorithm based on two-dimensional Scale-Mixing Complex Wavelet Transform(2D-SMCWT).The fusion of the detail 2D-SMCWT cofficients is performed via a Bayesian Maximum a Posteriori(MAP)approach by considering a trivariate statistical model for the local neighboring of 2D-SMCWT coefficients.For the approx imation coefficients,a new fusion rule based on the Principal Component Analysis(PCA)is applied.We conduct several experiments using three different groups of multimodal medical images to evaluate the performance of the proposed method.The obt ained results prove the superiority of the proposed method over the state of the art fusion methods in terms of visual quality and several commonly used metrics.Robustness of the proposed method is further tested against different types of noise.The plots of fusion met rics establish the accuracy of the proposed fusion method.
基金Researchers Supporting Project Number(RSP2022R458),King Saud University,Riyadh,Saudi Arabia.
文摘Automated Facial Expression Recognition(FER)serves as the backbone of patient monitoring systems,security,and surveillance systems.Real-time FER is a challenging task,due to the uncontrolled nature of the environment and poor quality of input frames.In this paper,a novel FER framework has been proposed for patient monitoring.Preprocessing is performed using contrast-limited adaptive enhancement and the dataset is balanced using augmentation.Two lightweight efficient Convolution Neural Network(CNN)models MobileNetV2 and Neural search Architecture Network Mobile(NasNetMobile)are trained,and feature vectors are extracted.The Whale Optimization Algorithm(WOA)is utilized to remove irrelevant features from these vectors.Finally,the optimized features are serially fused to pass them to the classifier.A comprehensive set of experiments were carried out for the evaluation of real-time image datasets FER-2013,MMA,and CK+to report performance based on various metrics.Accuracy results show that the proposed model has achieved 82.5%accuracy and performed better in comparison to the state-of-the-art classification techniques in terms of accuracy.We would like to highlight that the proposed technique has achieved better accuracy by using 2.8 times lesser number of features.
文摘The COVID-19 outbreak and its medical distancing phenomenon have effectively turned the global healthcare challenge into an opportunity for Telecare Medical Information Systems.Such systems employ the latest mobile and digital technologies and provide several advantages like minimal physical contact between patient and healthcare provider,easy mobility,easy access,consistent patient engagement,and cost-effectiveness.Any leakage or unauthorized access to users’medical data can have serious consequences for any medical information system.The majority of such systems thus rely on biometrics for authenticated access but biometric systems are also prone to a variety of attacks like spoong,replay,Masquerade,and stealing of stored templates.In this article,we propose a new cancelable biometric approach which has tentatively been named as“Expression Hash”for Telecare Medical Information Systems.The idea is to hash the expression templates with a set of pseudo-random keys which would provide a unique code(expression hash).This code can then be serving as a template for verication.Different expressions would result in different sets of expression hash codes,which could be used in different applications and for different roles of each individual.The templates are stored on the server-side and the processing is also performed on the server-side.The proposed technique is a multi-factor authentication system and provides advantages like enhanced privacy and security without the need for multiple biometric devices.In the case of compromise,the existing code can be revoked and can be directly replaced by a new set of expression hash code.The well-known JAFFE(The Japanese Female Facial Expression)dataset has been for empirical testing and the results advocate for the efcacy of the proposed approach.
