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RISS 인기검색어
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- 저자 : Abdullah Jan (검색결과 5 건)
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Deep Learning-based Depth Map Estimation: A Review
Abdullah Jan,Safran Khan,서수영 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.1
In this technically advanced era, we are surrounded by smartphones, computers, and cameras, whichhelp us to store visual information in 2D image planes. However, such images lack 3D spatial information aboutthe scene, which is very useful for scientists, surveyors, engineers, and even robots. To tackle such problems,depth maps are generated for respective image planes. Depth maps or depth images are single image metric whichcarries the information in three-dimensional axes, i.e., xyz coordinates, where z is the object’s distance from cameraaxes. For many applications, including augmented reality, object tracking, segmentation, scene reconstruction,distance measurement, autonomous navigation, and autonomous driving, depth estimation is a fundamental task. Much of the work has been done to calculate depth maps. We reviewed the status of depth map estimation usingdifferent techniques from several papers, study areas, and models applied over the last 20 years. We surveyeddifferent depth-mapping techniques based on traditional ways and newly developed deep-learning methods. Theprimary purpose of this study is to present a detailed review of the state-of-the-art traditional depth mappingtechniques and recent deep learning methodologies. This study encompasses the critical points of each methodfrom different perspectives, like datasets, procedures performed, types of algorithms, loss functions, and well-known evaluation metrics. Similarly, this paper also discusses the subdomains in each method, like supervised,unsupervised, and semi-supervised methods. We also elaborate on the challenges of different methods. At theconclusion of this study, we discussed new ideas for future research and studies in depth map research.
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Perspective Insights of Exosomes in Neurodegenerative Diseases: A Critical Appraisal
Jan, Arif Tasleem,Malik, Mudasir A.,Rahman, Safikur,Yeo, Hye R.,Lee, Eun J.,Abdullah, Tasduq S.,Choi, Inho Frontiers Media S.A. 2017 FRONTIERS IN AGING NEUROSCIENCE Vol.9 No.-
<P>Exosomes are small membranous entities of endocytic origin. Their production by a wide variety of cells in eukaryotes implicates their roles in the execution of essential processes, especially cellular communication. Exosomes are secreted under both physiological and pathophysiological conditions, and their actions on neighboring and distant cells lead to the modulations of cellular behaviors. They also assist in the delivery of disease causing entities, such as prions, α-syn, and tau, and thus, facilitate spread to non-effected regions and accelerate the progressions of neurodegenerative diseases. The characterization of exosomes, provides information on aberrant processes, and thus, exosome analysis has many clinical applications. Because they are associated with the transport of different cellular entities across the blood-brain barrier (BBB), exosomes might be useful for delivering drugs and other therapeutic molecules to brain. Herein, we review roles played by exosomes in different neurodegenerative diseases, and the possibilities of using them as diagnostic biomarkers of disease progression, drug delivery vehicles and in gene therapy.</P>
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Uniaxial Strain-Controlled Ferroelastic Domain Evolution in BiFeO<sub>3</sub>
Alsubaie, Abdullah,Sharma, Pankaj,Lee, Jin Hong,Kim, Jeong Yong,Yang, Chan-Ho,Seidel, Jan American Chemical Society 2018 ACS APPLIED MATERIALS & INTERFACES Vol.10 No.14
<P>We investigate the effect of variable uniaxial tensile strain on the evolution of 71° ferroelastic domains in (001)-oriented epitaxial BiFeO<SUB>3</SUB> (BFO) thin films using piezoresponse force microscopy (PFM). For this purpose, a newly designed bending stage has been employed, which allows tensile bending as wells as in situ PFM characterization. In situ PFM imaging reveals polarization-strain correlations at the nanoscale. Specifically, ferroelastic domains with in-plane polarization along the direction of applied tensile strain expand, whereas the adjoining domains with orthogonal in-plane polarization contract. The switching is mediated by significant domain wall roughening and opposite displacement of the successive walls. Further, the domains with long-range order are more susceptible to an applied external mechanical stimulus compared to the domains, which exhibit short-range periodicity. In addition, the imprint state of film reverses direction under applied tensile strain. Finally, the strain-induced changes in the domain structure and wall motion are fully reversible and revert to their as-grown state upon release of the applied stress. The strain-induced non-180° polarization rotation constitutes a route to control connected functionalities, such as magnetism, via coupled in-plane rotation of the magnetic plane in multiferroic BFO thin films.</P> [FIG OMISSION]</BR>
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Noor Sharizat Abdullah,Tan Hui Jan,Rabani Remli,Shahizon Azura Mohamad Mukari,Norlinah Mohamed Ibrahim 대한파킨슨병및이상운동질환학회 2020 Journal Of Movement Disorders Vol.13 No.3
Anti-GABAB receptor (GABABR) encephalitis commonly presents with seizures and limbic encephalitis and, rarely, rapidly progressive dementia. Here, we report a patient with Parkinson’s disease (PD) who developed anti-GABABR encephalitis leading to a presentation of an atypical corticobasal syndrome.
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Structural Crack Detection Using Deep Learning: An In-depth Review
Safran Khan,Abdullah Jan,Suyoung Seo 대한원격탐사학회 2023 大韓遠隔探査學會誌 Vol.39 No.4
Crack detection in structures plays a vital role in ensuring their safety, durability, and reliability. Traditional crack detection methods sometimes need significant manual inspections, which are laborious,expensive, and prone to error by humans. Deep learning algorithms, which can learn intricate features from large-scale datasets, have emerged as a viable option for automated crack detection recently. This study presents an in-depth review of crack detection methods used till now, like image processing, traditional machine learning, anddeep learning methods. Specifically, it will provide a comparative analysis of crack detection methods usingdeep learning, aiming to provide insights into the advancements, challenges, and future directions in this field. To facilitate comparative analysis, this study surveys publicly available crack detection datasets and benchmarkscommonly used in deep learning research. Evaluation metrics employed to check the performance of differentmodels are discussed, with emphasis on accuracy, precision, recall, and F1-score. Moreover, this study providesan in-depth analysis of recent studies and highlights key findings, including state-of-the-art techniques, novelarchitectures, and innovative approaches to address the shortcomings of the existing methods. Finally, this studyprovides a summary of the key insights gained from the comparative analysis, highlighting the potential of deeplearning in revolutionizing methodologies for crack detection. The findings of this research will serve as a valuableresource for researchers in the field, aiding them in selecting appropriate methods for crack detection and inspiringfurther advancements in this domain.
ScienceON
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