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Transforming Education with Photogrammetry:Creating Realistic 3D Objects for Augmented Reality Applications

Computer Modeling in Engineering & Sciences 2025年第1期142卷 185-208页

作者： Kaviyaraj Ravichandran Uma Mohan Department of Computational intelligence School of ComputingSRM Institute of Science and TechnologyKattankulathur603203India

Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different *** increased use of mobile devices has an even greater *** the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in ***,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational ***,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)*** 3D objects for augmented reality education applications can be challenging and time-consuming for the *** address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)*** for augmented and virtual reality can then utilize these created 3D *** evaluated the proposed pipeline based on the usability of the 3D object and performance ***,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry *** analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above *** study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.

关键词： Augmented reality education immersive learning 3D object creation photogrammetry and StructureFromMotion

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Decision making module based on stochastic magnetic tunnel junctions

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 202-209页

作者： Yifan Miao Li Zhao Yajun Zhang Zhe Yuan Center for Advanced Quantum Studies and Department of Physics Beijing Normal University institute for Nanoelectric Devices and Quantum computing Fudan University interdisciplinary Center for Theoretical Physics and information Sciences Fudan University

in biological neural systems, noise is ubiquitous but does not affect the correct decisions made in the complex cognitive ***-making in biological neural system is typically achieved by accumulating input information over a period of *** by recent developments in neurosciences, we design a decision-making module based on spintronic devices, utilizing superparamagnetic tunnel junctions as artificial neurons. The feasibility of this decision-making module is verified through circuit simulations. Taking a multi-layer perceptron as an example, the module significantly improves the accuracy of the perceptron in the handwritten digit recognition task. Furthermore, the spintronic decision-making module offers advantages over the conventional pooling methods, such as adaptive decision time, high performance and the absence of analog-to-digital conversion. The decision-making module is flexible to be integrated into artificial neural networks and provides a general yet effective solution to enhance performance against device noise.

关键词： neuromorphic computing spintronics magnetic tunnel junction neural networks

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vSpec: workload-adaptive operating system specialization for virtual machines in cloud computing

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Science China(information Sciences) 2016年第9期59卷 47-62页

作者： Xinkui ZHAO Jianwei YiN Zuoning CHEN Sheng HE College of Computer Science Zhejiang University National Parallel computing Engineering Research Center Jiangnan institute of computing Technology

in general, operating systems(OSs) are designed to mediate access to device hardware by applications. They process different kinds of system calls using an indiscriminate kernel with the same *** in cloud computing platforms are constructed from service components. Each of the service components is assigned separately to an individual virtual machine(VM), which leads to homogeneous system calls on each VM. in addition, the requirements for kernel function and configuration of system parameters from different VMs are different. Therefore, the suit-to-all design incurs an unnecessary performance overhead and restricts the OS’s processing capacity in cloud computing. in this paper, we propose an adaptive model for cloud computing to resolve the conflict between generality and performance. Our model adaptively specializes the OS of a VM according to the resource-consuming characteristics of workloads on the VM. We implement a prototype of the adaptive model, v Spec. There are five classes of VM: CPU-intensive, memory-intensive, i/O-intensive, networkintensive and compound, according to the resource-consuming characteristics of the workloads running on the VMs. v Spec specializes the OS of a VM according to the VM class. We perform comprehensive experiments to evaluate the effectiveness of v Spec on benchmarks and real-world applications.

关键词： workload classification operating system specialization workload-aware cloud computing v Spec system performance

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Endoscopy-assisted lightweight diagnosis system based on transformers for colon polyp detection

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Optoelectronics Letters 2025年第1期21卷 57-64页

作者： FAN Weiming YU Jiahui JU Zhaojie School of Automation and Electrical Engineering Shenyang Ligong University Department of Biomedical Engineering Zhejiang University School of computing University of Portsmouth

The integration of endoscopy has significantly propelled the diagnosis and treatment of gastrointestinal diseases, with colonoscopy establishing itself as the primary method for early diagnosis and preventive care in colorectal cancer(CRC). Although deep learning holds promise in mitigating missed polyp rates, modern endoscopy examinations pose additional challenges, such as image blurring and atomizing. This study explores lightweight yet powerful attention mechanisms, introducing the spatial-channel transformer(SCT), an innovative approach that leverages spatial channel relationships for attention weight calculation. The method utilizes rotation operations for inter-dimensional dependencies, followed by residual transformation, encoding inter-channel and spatial information with minimal computational overhead. Extensive experiments on the CVC-Clinic DB polyp detection dataset, addressing endoscopy pitfalls, underscore the superiority of our SCT over other state-of-the-art methods. The proposed model maintains high performance, even in challenging scenarios.

关键词： Endoscopy

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EPiDEM: A Mobile Application to Contain Coronavirus Disease 2019

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E-Health Telecommunication Systems and Networks 2020年第4期9卷 49-59页

作者： Goetz Robert Uwe Grosche Anna Katrina Javier Bolivar i-optic computing Wollongong Australia independent Researcher Quezon City Philippines

With the spread of COViD-19 across the globe, almost 33 million people became sick and almost 1 million have died (WHO). The government of the Philippines, along with the government of other countries declared movement restrictions or lockdown in the attempt to contain the virus. This resulted in loss of job and economic breakdown. The Philippines, in particular, experienced the longest and strictest lockdown. As a down side, a lot of people have lost their jobs and the country is currently facing economic recession. With the aim of containing the virus to reopen the economy, a team of experts developed a mobile application called EPiDEM. The application is available for both Android and Apple smartphones. With the use of EPiDEM, lay people can monitor their infection risk for COViD-19 and isolate themselves or get themselves tested when infection risk is high. Users can also monitor the health risk of other people that they come in contact with, so they can decide if meeting them is safe or risky. Further, the application provides a monitoring map that shows the area that is safe from COViD-19 and area where a possible outbreak might occur. The monitoring map will be helpful to both lay people, so they would know the places to avoid and to government officials, so they can quickly respond to outbreak. The teams of developers are recommending the nationwide implementation of EPiDEM to help contain the COViD-19 outbreak in the country. The application is not a new strategy of containment. instead, it’s a better wheel that utilizes advanced technology to execute the manual containment strategies that are already being implemented in the country.

关键词： COViD-19 Coronavirus Disease 2019 Mobile Application

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Cloud-Sea computing Systems:Towards Thousand-Fold improvement in Performance per Watt for the Coming Zettabyte Era

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Journal of Computer Science & Technology 2014年第2期29卷 177-181页

作者：徐志伟 institute of computing Technology Chinese Academy of Sciences

We are entering a new era of computing, characterized by the need to handle over one zettabyte （1021 bytes, or ZB） of data. The world＇s capacities to sense, transmit, store, and process information need to grow three orders of magnitude, while maintain an energy consumption level similar to that of the year 2010. in other words, we need to produce thousand-fold improvement in performance per watt. To face this challenge, in 2012 the Chinese Academy of Sciences launched a 10-year strategic priority research initiative called the Next Generation information and Communication Technology initiative （the NiCT initiative）. A research thrust of the NiCT program is the Cloud-Sea computing Systems project. The main idea is to augment conventional cloud computing by cooperation and integration of the cloud-side systems and the sea-side systems, where the ＂sea-side＂ refers to an augmented client side consisting of human facing and physical world facing devices and subsystems. The Cloud-Sea computing Systems project consists of four research tasks： a new computing model called REST 2.0 which extends the REST （representational state transfer） architectural style of Web computing to cloud-sea computing, a three-tier storage system architecture capable of managing ZB of data, a billion-thread datacenter server with high energy efficiency, and an elastic processor aiming at energy efficiency of one trillion operations per second per watt. This special section contains 12 papers produced by the Cloud-Sea computing Systems project team, presenting research results relating to sensing and REST 2.0, the elastic processor, the hyperparallel server, and the cloud-sea storage.

关键词： billion-thread server cloud-sea computing cloud-sea storage elastic processor REST 2.0

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An adaptive switching scheme for iterative computing in the cloud

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Frontiers of Computer Science 2014年第6期8卷 872-884页

作者： Yu ZHANG Xiaofei LiAO Hai JiN Li LiN Feng LU Service computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

Delta-based accumulative iterative computation （DAiC） model is currently proposed to support iterative algorithms in a synchronous or an asynchronous way. However, both the synchronous DAiC model and the asynchronous DAiC model only satisfy some given conditions, respectively, and perform poorly under other conditions either for high synchronization cost or for many redundant activations. As a result, the whole performance of both DAiC models suffers from the serious network jitter and load jitter caused by multi- tenancy in the cloud. in this paper, we develop a system, namely Hyblter, to guarantee the performance of iterative algorithms under different conditions. Through an adaptive execution model selection scheme, it can efficiently switch between synchronous and asynchronous DAiC model in order to be adapted to different conditions, always getting the best performance in the cloud. Experimental results show that our approach can improve the performance of current solutions up to 39.0%.

关键词： iterative algorithm computational skew communication skew cloud delta-based accumulative iterative computation

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Modification of Maxwell model for conductivity prediction of carbon nanotubes-filled polymer composites with tunneling effect

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Applied Mathematics and Mechanics(English Edition) 2025年第1期46卷 25-36页

作者： Jue ZHU Longyuan Li Ningtao ZHU Zhejiang Provincial Engineering Research Center for the Safety of Pressure Vessel and Pipeline Key Laboratory of impact and Safety Engineering (Ningbo University) Ministry of Education School of Engineering Computing and Mathematics University of Plymouth Xi'an Zhanshi Testing & Engineering Co.Ltd.

Carbon nanotubes(CNTs) have garnered great attention in recent years due to their outstanding electrical, thermal, and mechanical properties. The incorporation of small amounts of CNTs in polymers can substantially improve the sensitivity of the polymer's electrical conductivity. This paper presents a modified Maxwell model to evaluate the electrical conductivity of CNTs-filled polymer composites by introducing a transition zone to account for the tunneling effect. in this modified Maxwell model, the CNTs-filled polymer composite is modeled as a three-phase composite, consisting of a matrix(polymer), inclusions(CNTs), and a transition zone(tunneling zone). The effective electrical conductivity(EEC) of the composite is calculated based on the volume fractions and electrical conductivities of the matrix, inclusions, and transition zone. The model's validity is confirmed through the use of available test data, which demonstrates its capability to accurately capture the nonlinear conductivity behavior observed in CNTs-polymer composites. This study offers valuable insights into the design of high-performance conductive polymer nanocomposites, and enhances the understanding of electrical conduction mechanisms in CNT-dispersed polymer composites.

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Big Data with Cloud computing:Discussions and Challenges

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Big Data Mining and Analytics 2022年第1期5卷 32-40页

作者： Amanpreet Kaur Sandhu University institute of computing Chandigarh University

With the recent advancements in computer technologies,the amount of data available is increasing day by ***,excessive amounts of data create great challenges for ***,cloud computing services provide a powerful environment to store large volumes of *** eliminate various requirements,such as dedicated space and maintenance of expensive computer hardware and *** big data is a time-consuming task that requires large computational clusters to ensure successful data storage and *** this work,the definition,classification,and characteristics of big data are discussed,along with various cloud services,such as Microsoft Azure,Google Cloud,Amazon Web Services,international Business Machine cloud,Hortonworks,and MapR.A comparative analysis of various cloud-based big data frameworks is also *** research challenges are defined in terms of distributed database storage,data security,heterogeneity,and data visualization.

关键词： big data data analysis cloud computing Hadoop

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Three-Stage Transfer Learning with AlexNet50 for MRi image Multi-Class Classification with Optimal Learning Rate

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 155-183页

作者： Suganya Athisayamani A.Robert Singh Gyanendra Prasad Joshi Woong Cho School of computing Sastra Deemed to be UniversityThanjavur613401India Department of Computational intelligence SRM Institute of Science and TechnologyKattankulathur603203India Department of Ai and Software Engineering Kangwon National UniversitySamcheok25913Republic of Korea Department of Electronics Information and Communication EngineeringKangwon National UniversitySamcheok25913Republic of Korea

in radiology,magnetic resonance imaging(MRi)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological *** is particularly effective for detecting soft tissue ***,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of *** address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRi *** manuscript presents the use of the Deep AlexNet50 model for MRi classification with discriminative learning *** are three stages for learning;in the first stage,the whole dataset is used to learn the *** the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented *** method used three publicly available MRi classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National institute of Neuroscience and Hospitals brain MRi dataset(NiNS-dataset)for *** hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning ***-dataset registers maximum classification *** evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.

关键词： MRi tumors classification AlexNet50 transfer learning hyperparameter tuning optimizer

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