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Design and Development an Interactive On-the-Job Training Monitoring and Help Desk System with SMS for college of information and communication technology

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Journal of Computer and communications 2022年第7期10卷 72-89页

作者： Apple Grace G. Oliveros college of information and communications technology Nueva Ecija University of Science and Technology Cabanatuan City Philippines

This study aimed to develop An Interactive On-the-Job Training Monitoring and Help Desk System with SMS for the college of information and communication technology Nueva Ecija University of Science and technology. The system made the OJT course procedure trouble-free by emerging a system accessible through the internet. Students have a user account, which gives them the aptitude to upload document files of their reports, thereby minimizing the time and energy spent traveling from the company’s location to the university and the other way around. Similarly, the OJT coordinators of the college are given their accounts to access and check the reports submitted by the students. The system is capable of generating reports and requirements in real-time, as long as all data is stored within the database and, therefore, the process is completed online. In addition, the system provides an interactive website that might help both students and coordinators to communicate instantaneously by having an online help desk where the students can ask related questions on their OJT course that the OJT coordinator and other students will answer. The coordinators can send a brief message service to the students enrolled within the OJT course through the utilization of the proposed system - this can be for the students who aren’t capable of opening their account more often, in order that they are still informed of the announcements they need to understand immediately. The interactive OJT help desk system with SMS can be used as a tool to help the students of the college of information and communication technology (CICT) and the OJT coordinators in their tasks more conveniently.

关键词： Short Message Service (SMS) Application Program Interface (API) On-the-Job Training PHP Bootstrap MySQL

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A bio-inspired optical imaging method for robotic perception in turbid water

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Science China Technological Sciences 2025年第1期 79-81页

作者： Xudong SUN Yafeng LI Peng LIN Xiaodi SHANG Xianping FU School of information Science and technology Dalian Maritime University college of Computer Science and technology Qingdao University

In the realm of underwater robotics, optical imaging plays a pivotal role in many scientific researches. Due to the effects of absorption and scattering,images captured in turbid water are severely degraded. Therefore, enhancing the quality of underwater optical images stands paramount in ensuring the continued advancement and efficacy of underwater robots across its multifarious applications.

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Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net(MU-Net)on Spine Magnetic Resonance Images

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 733-757页

作者： Lakshmi S V V Shiloah Elizabeth Darmanayagam Sunil Retmin Raj Cyril Department of Computer Science and Engineering College of Engineering GuindyAnna UniversityChennai600025India Department of information technology Madras Institute of TechnologyAnna UniversityChrompetChennai600044India

Lower back pain is one of the most common medical problems in the world and it is experienced by a huge percentage of people *** to its ability to produce a detailed view of the soft tissues,including the spinal cord,nerves,intervertebral discs,and vertebrae,Magnetic Resonance Imaging is thought to be the most effective method for imaging the *** semantic segmentation of vertebrae plays a major role in the diagnostic process of lumbar *** is difficult to semantically partition the vertebrae in Magnetic Resonance Images from the surrounding variety of tissues,including muscles,ligaments,and intervertebral discs.U-Net is a powerful deep-learning architecture to handle the challenges of medical image analysis tasks and achieves high segmentation *** work proposes a modified U-Net architecture namely MU-Net,consisting of the Meijering convolutional layer that incorporates the Meijering filter to perform the semantic segmentation of lumbar vertebrae L1 to L5 and sacral vertebra ***-colour mask images were generated and used as ground truth for training the *** work has been carried out on 1312 images expanded from T1-weighted mid-sagittal MRI images of 515 patients in the Lumbar Spine MRI Dataset publicly available from Mendeley *** proposed MU-Net model for the semantic segmentation of the lumbar vertebrae gives better performance with 98.79%of pixel accuracy(PA),98.66%of dice similarity coefficient(DSC),97.36%of Jaccard coefficient,and 92.55%mean Intersection over Union(mean IoU)metrics using the mentioned dataset.

关键词： Computer aided diagnosis(CAD) magnetic resonance imaging(MRI) semantic segmentation lumbar vertebrae deep learning U-Net model

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Unraveling post-growth mechanisms of monolayer CsPbBr3 nanocubes:Laser-enhanced transformations and cathodoluminescence-electron microscopy correlations

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Journal of Energy Chemistry 2025年第1期100卷 146-156页

作者： Mingxing Li Xiaoge Wang Xiaofan Cao Zhiqun He Chunjun Liang Mingxing Chen Jing Ju Fangtian You Key Laboratory of Luminescence and Optical information Ministry of Education Institute of Optoelectronic Technology Beijing Jiaotong University Beijing National Laboratory for Molecular Sciences (BNLMS) College of Chemistry and Molecular Engineering Peking University

Lead-halide perovskite nanoparticles（LHP NPs） are highly promising materials for next-generation displays and solid-state lighting due to their exceptional optical properties. However, their inherent instability presents a significant challenge. Recent advances have demonstrated that optoelectronic devices based on monolayer nanoparticle films exhibit both high luminescence efficiency and long-term *** research demonstrates that mobility limitations and anisotropic alignments in CsPbBr3nanocube monolayer films are key to their stabilization, hindering spontaneous growth through face-to-face fusion and resulting in the formation of connecting necks in a diagonal direction. Introducing laser irradiation confirmed this by significantly accelerating nanocubes growth, increasing mobility, and enhancing local structural ordering, leading to larger and more regularly shaped nanosheets. Fourier transform infrared spectroscopy and energy dispersive spectroscopy line-scan analyses indicated that laser irradiation did not disrupt the ligand structure. Transmission electron microscopy and correlative cathodoluminescence electron microscopy revealed the effects of post-growth and heterogeneous structures, including enhanced luminescence and inhomogeneous intensity in the nanosheets. These findings deepen the understanding of the post-growth mechanism of monolayer nanoparticles and the structure-emission correlation and highlight the unique role of laser irradiation in directing the formation of well-defined and regular nanostructures.

关键词： Post-growth mechanism Heterogeneous structure Single-particle spectroscopy Laser irradiation

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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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A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika Jain Saxena Amit Pundir Sanjeev Singh Akshat Gaurav Savi Bansal Razaz Waheeb Attar Mosiur Rahman Brij B.Gupta Institute of Informatics and communication University of DelhiDelhi110021India Maharaja Agrasen college University of DelhiDelhi110096India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&Department of Computer Science and information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for information technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour

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Laser image refractive index sensor based on the combination of a coreless optical fiber and a digital camera

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Optoelectronics Letters 2025年第2期 65-68页

作者： GUO Jingzhong LAN Feng CHEN Xiang LIU Haifeng Xi'an Institute of Space Radio technology Key Laboratory of Optoelectronic Sensor and Sensing Network technology College of Electronic Information and Optical Engineering Nankai University

As a laser passes through a scattering medium,the light interacts with the irregular reflections within the medium,resulting in light scattering and the formation of *** this paper,an image sensor based on the combination of a coreless optical fiber and a digital camera is proposed for liquid refractive index sensing *** coreless fiber is used as a sensing unit,and the change in the speckle pattern is measured using the digital correlation method to detect the magnitude of the liquid's refractive *** experimental results indicate that the laser image sensing technique is capable of effectively distinguishing liquid samples with refractive indices ranging from 1.332 8 to1.390 8,with a sensing sensitivity of-1.306 ***,the laser image sensing technique,with its advantages of high experimental reproducibility,simple system design,remote over-control,holds great research significance and potential application in laser communication and sensor integration.

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Structure Optimization for Echo State Network Based on Contribution

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Tsinghua Science and technology 2019年第1期24卷 97-105页

作者： Dingyuan Li Fu Liu Junfei Qiao Rong Li college of communication Engineering Jilin University Faculty of information technology Beijing University of Technology Department of information technology Beijing Vocational College of Agriculture

Echo State Network(ESN) is a recurrent neural network with a large, randomly generated recurrent part called the dynamic reservoir. Only the output weights are modified during training. However, proper balancing of the trade-off between the structure and performance for ESN remains a difficult task. In this paper, a structure optimized method for ESN based on contribution is proposed to simplify its network structure and improve its ***, we evaluate the contribution of reservoir neurons. Second, we present a pruning mechanism to remove the unimportant connection weights of reservoir neurons with low contribution. Finally, the new output weights are learned with the pseudo inverse method. The novel optimized ESN, named C-ESN, is tested on a Lorenz chaotic time-series prediction and an actual municipal sewage treatment system. The simulation results show that the C-ESN can have better prediction and generalization performance than ESN.

关键词： neural network structural design time-series prediction

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High Frequency communication Network with Diversity: System Structure and Key Enabling Techniques

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China communications 2018年第9期15卷 46-59页

作者： Kun Xu Bin Jiang Zeyou Su Shengqing Wang Makun Guo Xiao Li Zhiyong Du college of information and communication National University of Defense Technology

High frequency sky wave communication suffers from poor performance including poor link quality and low link success rate. To enhance performance, diversity technology is proposed in the high frequency communication network(HFCN) in this ***, we present the benefits and the challenges by introducing diversity technology into the existing HFCN. Secondly, to exploit the benefits fully and overcome the challenges, we propose a system structure suitable for deploying diversity technology in HFCN in large scale,based on the cloud radio access network and software defined network. Moreover, we present a general structure for the real-time updating frequency management system that plays a more important role especially when resource consuming(e.g., frequency) diversity technology is deployed. Thirdly, we investigate the key techniques enabling diversity technology deployment. Finally, we point out the future research directions to help the HFCN with diversity work more efficiently and intelligently.

关键词： high frequency high frequency communication network diversity technology access layer and core layer frequency management system

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A Novel Spectrum Handoff-Based Sensing-Throughput Tradeoff Scheme in Cognitive Radio

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China communications 2016年第12期13卷 59-68页

作者： Xin Liu Feng Li Weidang Lu School of information and communication Engineering Dalian University of Technology college of information Engneering Zhejiang University of Technology

In cognitive radio(CR), there is a tradeoff between spectrum sensing time and throughput of secondary user(SU). In the conventional sensing-throughput tradeoff scheme, when the presence of the primary user(PU) is detected, the SU will stop forwarding data and wait to redetect the PU in the following frame, yielding great throughput loss. In order to improve the SU's throughput, a novel sensing-throughput tradeoff scheme is proposed, which allows the SU to search for a new idle channel through spectrum searching and transfer to this channel to continue communication, when the presence of the PU is detected. An optimization problem is proposed to maximize the SU's throughput in the proposed scheme through jointly optimizing the sensing time, the searching time and the number of available channels, providing that the detection probability to the PU is guaranteed. By fixing the number of channels, based on alternating direction optimization, the joint optimization algorithm of sensing time and searching time is proposed, and then the optimal number of channels is obtained through enumerative searching. The simulation results show that there exist the optimal solutions to the proposed scheme, and the proposed scheme outperforms the conventional scheme notably, with different detection probabilities and sampling frequencies.

关键词： cognitive radio energy sensing throughput sampling frequency detection probability

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