Achieving good recognition results for License plates is challenging due to multiple adverse factors. For instance, in Malaysia, where private vehicle （e.g., cars） have numbers with dark background, while public veh...Achieving good recognition results for License plates is challenging due to multiple adverse factors. For instance, in Malaysia, where private vehicle （e.g., cars） have numbers with dark background, while public vehicle （taxis/cabs） have numbers with white background. To reduce the complexity of the problem, we propose to classify the above two types of images such that one can choose an appropriate method to achieve better results. Therefore, in this work, we explore the combination of Convolutional Neural Networks （CNN） and Recurrent Neural Networks namely, BLSTM （Bi-Directional Long Short Term Memory）, for recognition. The CNN has been used for feature extraction as it has high discriminative ability, at the same time, BLSTM has the ability to extract context information based on the past information. For classification, we propose Dense Cluster based Voting （DCV）, which separates foreground and background for successful classification of private and public. Experimental results on live data given by MIMOS, which is funded by Malaysian Government and the standard dataset UCSD show that the proposed classification outperforms the existing methods. In addition, the recognition results show that the recognition performance improves significantly after classification compared to before classification.展开更多
Arecanut disease identification is a challenging problem in the field of image processing.In this work,we present a new combination of multi-gradient-direction and deep con-volutional neural networks for arecanut dise...Arecanut disease identification is a challenging problem in the field of image processing.In this work,we present a new combination of multi-gradient-direction and deep con-volutional neural networks for arecanut disease identification,namely,rot,split and rot-split.Due to the effect of the disease,there are chances of losing vital details in the images.To enhance the fine details in the images affected by diseases,we explore multi-Sobel directional masks for convolving with the input image,which results in enhanced images.The proposed method extracts arecanut as foreground from the enhanced images using Otsu thresholding.Further,the features are extracted for foreground information for disease identification by exploring the ResNet architecture.The advantage of the proposed approach is that it identifies the diseased images from the healthy arecanut images.Experimental results on the dataset of four classes(healthy,rot,split and rot-split)show that the proposed model is superior in terms of classification rate.展开更多
Achieving high recognition rate for license plate images is challenging due to multi-type images. We present new symmetry features based on stroke width for classifying each input license image as private, taxi, cursi...Achieving high recognition rate for license plate images is challenging due to multi-type images. We present new symmetry features based on stroke width for classifying each input license image as private, taxi, cursive text, when they expand the symbols by writing and non-text such that an appropriate optical character recognition （OCR） can be chosen for enhancing recognition performance. The proposed method explores gradient vector flow （GVF） for defining symmetry features, namely, GVF opposite direction, stroke width distance, and stroke pixel direction. Stroke pixels in Canny and Sobel which satisfy the above symmetry features are called local candidate stroke pixels. Common stroke pixels of the local candidate stroke pixels are considered as the global candidate stroke pixels. Spatial distribution of stroke pixels in local and global symmetry are explored by generating a weighted proximity matrix to extract statistical features, namely, mean, standard deviation, median and standard deviation with respect the median. The feature matrix is finally fed to an support vector machine （SVM） classifier for classification. Experimental results on large datasets for classification show that the proposed method outperforms the existing methods. The usefulness and effectiveness of the proposed classification is demonstrated by conducting recognition experiments before and after classification.展开更多
Graphology-based handwriting analysis to identify human behavior,irrespective of applications,is interesting.Unlike existing methods that use characters,words and sentences for behavioural analysis with human interven...Graphology-based handwriting analysis to identify human behavior,irrespective of applications,is interesting.Unlike existing methods that use characters,words and sentences for behavioural analysis with human intervention,we propose an automatic method by analysing a few handwritten English lowercase characters from a to z to identify person behaviours.The proposed method extracts structural features,such as loops,slants,cursive,straight lines,stroke thickness,contour shapes,aspect ratio and other geometrical properties,from different zones of isolated character images to derive the hypothesis based on a dictionary of Graphological rules.The derived hypothesis has the ability to categorise the personal,positive,and negative social aspects of an individual.To evaluate the proposed method,an automatic system is developed which accepts characters from a to z written by different individuals across different genders and age groups.This automatic privacy projected system is available on the website(http://subha.pythonanywhere.com).For quantitative evaluation of the proposed method,several people are requested to use the system to check their characteristics with the system automatic response based on his/her handwriting by choosing to agree or disagree options.The automatic system receives 5300 responses from the users,for which,the proposed method achieves 86.70%accuracy.展开更多
MXenes,a unique class of two-dimensional(2D)transition metal carbides,nitrides,and carbonitrides,have garnered significant interest due to their exceptional chemical,mechanical,and electrical properties.While recent s...MXenes,a unique class of two-dimensional(2D)transition metal carbides,nitrides,and carbonitrides,have garnered significant interest due to their exceptional chemical,mechanical,and electrical properties.While recent studies predominantly focus on MXenes'applications in catalysis,energy storage and harvesting,photocatalysis,and lightweight materials,their potential in biomedicine is comparatively understated.This review aims to bridge this gap by providing a comprehensive and up-to-date overview of MXenes in biomedical applications,specifically highlighting advanced uses such as photothermal therapy and photodynamic therapy for cancer treatment,as well as their roles in biomedical imaging and as contrast agents for tumor visualization.We examine the synthesis and chemical modifications of MXenes,including functionalization,etching,and exfoliation techniques that enable tailored properties for biomedicine.This article highlights MXenes'advantages,including high surface area,tunable surface chemistry,and biocompatibility,while also addressing challenges and future research directions to unlock their full biomedical potential.This focused exploration of MXenes in cutting-edge biomedicine sets this review apart,highlighting its significance in advancing MXenes'role in modern biomedical research.展开更多
Recent advancements in hydrogel-based flexible materials have revolutionized wound healing and monitoring strategies.These materials offer promising solutions for medical treatment and real-time diagnostics.Their rich...Recent advancements in hydrogel-based flexible materials have revolutionized wound healing and monitoring strategies.These materials offer promising solutions for medical treatment and real-time diagnostics.Their rich water content,biocompatibility,and tunable properties closely mimic the natural extracellular matrix,supporting wound regeneration.Unlike traditional wound healing materials,hydrogel-based systems address critical issues such as material stability and toxicity while integrating advanced monitoring devices.This review highlights the latest innovations in hydrogel-based wound healing materials.It focuses on flexibility,biocompatibility,and potential for integration with smart monitoring systems.The review covers design principles and fabrication techniques for hydrogel-based nanofibers,elastomers,and conducting polymers.It also discusses the development of electronic skin and innovative wound dressings.In addition,the review explains how sensing capabilities,stimuli-responsive functions,and antibacterial agents are incorporated into these materials.Finally,the article examines challenges and future directions in the field.It emphasizes the transformative potential of multifunctional hydrogel-based materials for improving wound healing and continuous monitoring.展开更多
基金This research work was supported by the Faculty of Computer Science and Information Technology, the University of Malaya under a special allocation of Post Graduate Funding for the RP036B-15AET project. The work described in this paper was supported by the Natural Science Foundation of China under grant no. 61672273, and the Science Foundation for Distinguished Young Scholars of Jiangsu under grant no. BK20160021.
文摘Achieving good recognition results for License plates is challenging due to multiple adverse factors. For instance, in Malaysia, where private vehicle （e.g., cars） have numbers with dark background, while public vehicle （taxis/cabs） have numbers with white background. To reduce the complexity of the problem, we propose to classify the above two types of images such that one can choose an appropriate method to achieve better results. Therefore, in this work, we explore the combination of Convolutional Neural Networks （CNN） and Recurrent Neural Networks namely, BLSTM （Bi-Directional Long Short Term Memory）, for recognition. The CNN has been used for feature extraction as it has high discriminative ability, at the same time, BLSTM has the ability to extract context information based on the past information. For classification, we propose Dense Cluster based Voting （DCV）, which separates foreground and background for successful classification of private and public. Experimental results on live data given by MIMOS, which is funded by Malaysian Government and the standard dataset UCSD show that the proposed classification outperforms the existing methods. In addition, the recognition results show that the recognition performance improves significantly after classification compared to before classification.
文摘Arecanut disease identification is a challenging problem in the field of image processing.In this work,we present a new combination of multi-gradient-direction and deep con-volutional neural networks for arecanut disease identification,namely,rot,split and rot-split.Due to the effect of the disease,there are chances of losing vital details in the images.To enhance the fine details in the images affected by diseases,we explore multi-Sobel directional masks for convolving with the input image,which results in enhanced images.The proposed method extracts arecanut as foreground from the enhanced images using Otsu thresholding.Further,the features are extracted for foreground information for disease identification by exploring the ResNet architecture.The advantage of the proposed approach is that it identifies the diseased images from the healthy arecanut images.Experimental results on the dataset of four classes(healthy,rot,split and rot-split)show that the proposed model is superior in terms of classification rate.
文摘Achieving high recognition rate for license plate images is challenging due to multi-type images. We present new symmetry features based on stroke width for classifying each input license image as private, taxi, cursive text, when they expand the symbols by writing and non-text such that an appropriate optical character recognition （OCR） can be chosen for enhancing recognition performance. The proposed method explores gradient vector flow （GVF） for defining symmetry features, namely, GVF opposite direction, stroke width distance, and stroke pixel direction. Stroke pixels in Canny and Sobel which satisfy the above symmetry features are called local candidate stroke pixels. Common stroke pixels of the local candidate stroke pixels are considered as the global candidate stroke pixels. Spatial distribution of stroke pixels in local and global symmetry are explored by generating a weighted proximity matrix to extract statistical features, namely, mean, standard deviation, median and standard deviation with respect the median. The feature matrix is finally fed to an support vector machine （SVM） classifier for classification. Experimental results on large datasets for classification show that the proposed method outperforms the existing methods. The usefulness and effectiveness of the proposed classification is demonstrated by conducting recognition experiments before and after classification.
基金The work described in this paper was supported by the Science Foundation for Distinguished Young Scholars of Jiangsu under grant no.BK20160021the Natural Science Foundation of China under grant nos.61672273 and 61272218.
文摘Graphology-based handwriting analysis to identify human behavior,irrespective of applications,is interesting.Unlike existing methods that use characters,words and sentences for behavioural analysis with human intervention,we propose an automatic method by analysing a few handwritten English lowercase characters from a to z to identify person behaviours.The proposed method extracts structural features,such as loops,slants,cursive,straight lines,stroke thickness,contour shapes,aspect ratio and other geometrical properties,from different zones of isolated character images to derive the hypothesis based on a dictionary of Graphological rules.The derived hypothesis has the ability to categorise the personal,positive,and negative social aspects of an individual.To evaluate the proposed method,an automatic system is developed which accepts characters from a to z written by different individuals across different genders and age groups.This automatic privacy projected system is available on the website(http://subha.pythonanywhere.com).For quantitative evaluation of the proposed method,several people are requested to use the system to check their characteristics with the system automatic response based on his/her handwriting by choosing to agree or disagree options.The automatic system receives 5300 responses from the users,for which,the proposed method achieves 86.70%accuracy.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean Government(MSIT:2022R1A5A8023404)the 2024 Global Joint Research Program,funded by the Pukyong National University(202412210001)U.P.acknowledges VIEP,BUAP,for financial help extended through Grant(00262).
文摘MXenes,a unique class of two-dimensional(2D)transition metal carbides,nitrides,and carbonitrides,have garnered significant interest due to their exceptional chemical,mechanical,and electrical properties.While recent studies predominantly focus on MXenes'applications in catalysis,energy storage and harvesting,photocatalysis,and lightweight materials,their potential in biomedicine is comparatively understated.This review aims to bridge this gap by providing a comprehensive and up-to-date overview of MXenes in biomedical applications,specifically highlighting advanced uses such as photothermal therapy and photodynamic therapy for cancer treatment,as well as their roles in biomedical imaging and as contrast agents for tumor visualization.We examine the synthesis and chemical modifications of MXenes,including functionalization,etching,and exfoliation techniques that enable tailored properties for biomedicine.This article highlights MXenes'advantages,including high surface area,tunable surface chemistry,and biocompatibility,while also addressing challenges and future research directions to unlock their full biomedical potential.This focused exploration of MXenes in cutting-edge biomedicine sets this review apart,highlighting its significance in advancing MXenes'role in modern biomedical research.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korean government(MSIT)(2022R1A5A8023404)the 2024 Global Joint Research Program,funded by the Pukyong National University(202412210001)U.P.acknowledges the support extended by VIEP-BUAP.
文摘Recent advancements in hydrogel-based flexible materials have revolutionized wound healing and monitoring strategies.These materials offer promising solutions for medical treatment and real-time diagnostics.Their rich water content,biocompatibility,and tunable properties closely mimic the natural extracellular matrix,supporting wound regeneration.Unlike traditional wound healing materials,hydrogel-based systems address critical issues such as material stability and toxicity while integrating advanced monitoring devices.This review highlights the latest innovations in hydrogel-based wound healing materials.It focuses on flexibility,biocompatibility,and potential for integration with smart monitoring systems.The review covers design principles and fabrication techniques for hydrogel-based nanofibers,elastomers,and conducting polymers.It also discusses the development of electronic skin and innovative wound dressings.In addition,the review explains how sensing capabilities,stimuli-responsive functions,and antibacterial agents are incorporated into these materials.Finally,the article examines challenges and future directions in the field.It emphasizes the transformative potential of multifunctional hydrogel-based materials for improving wound healing and continuous monitoring.
