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Simultaneous improving luminescence intensity and stability of CsPbBr_(3):SCN−@Eu/Zr-Uio-66-NH_(2) with tunable emissions from blue to green and applications in indoor photovoltaics

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Nano Research 2024年第8期17卷 6879-6887页

作者： Minze Li Yao Sun Honglai Lu Peifen Zhu Ruihong Wang Guofeng Wang Key Laboratory of Functional Inorganic Material Chemistry Ministry of EducationSchool of Chemistry and Materials ScienceHeilongjiang UniversityHarbin 150080China Department of Electrical Engineering and Computer Science University of MissouriColumbiaMO 65211USA

The construction of stable and efficient materials that emit blue and green light remains a *** the blue light materials reported,metal-organic framework(MOF)materials are rarely reported as blue phosphors due to their weak luminescence *** on the construction of CsPbBr_(3)@MOF(CPB@MOF),an innovative idea was proposed to simultaneously enhance the green luminescence of CPB and the blue luminescence of MOF through the interaction between CPB and MOF for the first *** expected,the blue luminescence from CPB:7%SCN−@0.5%MOF:Eu as well as the green luminescence from 5%CPB:7%SCN−@MOF:Eu was sufficient to construct high-performance light-emitting diode(LED)devices and further excite solar cells to generate stable photoelectric *** white LED(WLED)device with excellent color quality(color rendering index(CRI)=96.2)and correlated color temperature(CCT=9688 K)can be constructed by using the obtained blue-emitting CPB:7%SCN-@0.5%MOF:Eu,green-emitting 5%CPB:7%SCN−@MOF:Eu,and red-emitting PPB:30%Mn^(2+).The density functional theory(DFT)theoretical calculation results indicate that the p orbital of Pb plays the major role in the conduction band,and the p orbital of Br plays the major role in the valance band of CPB and CPB:SCN−.While the p orbital of O plays the major role in both the conduction band and valance band of *** heat capacity of CPB and CPB:SCN−separately reaches the Dulong–Petit limit at 200 and 400 K,indicating that the thermal stability of CsPbBr_(3)increases after SCN−doping.

关键词： CsPbBr_(3):SCN−@Uio-66-NH_(2) composites luminescence stability white light-emitting diodes(WLEDs) indoor photovoltaics density functional theory(DFT)calculation

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DGeC: Dynamically and Globally Enhanced Convolution

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 921-933页

作者： Zhang, Zihang Liu, Yuling Zhou, Zhili Yang, Gaobo Liao, Xin Wu, Q. M. Jonathan Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Guangzhou University Institute of Artificial Intelligence and Blockchain Guangzhou510006 China University of Windsor Department of Electrical and Computer Engineering WindsorON250101 Canada

We explore the reasons for the poorer feature extraction ability of vanilla convolution and discover that there mainly exist three key factors that restrict its representation capability, i.e., regular sampling, static aggregation, and limited receptive field. With the cost of extra parameters and computations, existing approaches merely alleviate part of the limitations. It drives us to seek a more lightweight operator to further improve the extracted image features. Through a closer examination of the convolution process, we discover that it is composed of two distinct interactions: spatial-wise interaction and channel-wise interaction. Based on this discovery, we decouple the convolutional blocks into these two interactions which not only reduces the parameters and computations but also enables a richer ensemble of interactions. Then, we propose the dynamically and globally enhanced convolution (DGeC), which includes several components as follows: a dynamic area perceptor block (DAP) that dynamically samples spatial cues, an adaptive global context block (AGC) that introduces the location-aware global image information, and a channel attention perceptor block (CAP) that merges different channel-wise features. The experiments on ImageNet for image classification and on COCO-2017 for object detection validate the effectiveness of DGeC. As a result, our proposed method consistently improves the performance with fewer parameters and computations. In particular, DGeC achieves a 3.1% improvement in top-1 accuracy on ImageNet dataset compared to ResNet50. Moreover, with Faster RCNN and RetinaNet, our DGeC-ResNet50 also consistently outperforms ResNet and ResNeXt. © 2024 IEEE.

关键词： Convolutional neural networks

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Exploratory Analysis of Zero-Shot Lunar Crater Detection using SAM for Chandrayaan-2 Images 3

Exploratory Analysis of Zero-Shot Lunar Crater Detection usi...

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3rd International Conference on Communication, Control, and Intelligent Systems, CCIS 2024

作者： Koranglekar, Kaustubh Prateek, K. Kumar, Shankar Biswas, Dheeraj Kumar Khanna, Nitin Department of Mechatronics Bhilai India Department of Electrical Engineering Bhilai India Department of Computer Science and Engineering Bhilai India

ISBN: (纸本)9798331528201

This paper explores the application of a zero-shot lunar crater detection method based on the Segment Anything Model (SAM) for high-resolution Digital Terrain Model (DTM) images from ISRO's Chandrayaan-II mission. A pre-trained transformer-based instance segmentation model, SAM, is used to segment and identify lunar craters without requiring training data. The proposed method is capable of detecting craters in varying crater sizes and shapes, highlighting its potential for generalizable planetary feature detection. This work investigates the applicability of general-purpose deep learning models in planetary exploration and offers insights for efficient lunar surface analysis. © 2024 IEEE.

关键词： Lunar surface analysis

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Development of Dementia Care Training System Using AR and Large Language Model

Development of Dementia Care Training System Using AR and La...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Miyawaki, Tomoya Nishiura, Yuuki Fukuda, Ryouta Nakashima, Kazuto Kurazume, Ryo Graduate School of Information Science and Electrical Engineering Kyushu University Japan Kyushu University Faculty of Engineering Department of Electrical Engineering and Computer Science Japan Kyushu University Faculty of Information Science and Electrical Engineering Japan

ISBN: (纸本)9781665410205

We have developed HEARTS, a dementia care training system using augmented reality based on Humanitude. Humanitude is a multimodal comprehensive care technique for dementia, and has attracted attention as a method to reduce the burden on both caregivers and patients. However, the HEARTS developed so far could not evaluate 'speaking' skills based on the content of conversations among 'seeing,' 'touching,' and 'speaking,' all of which are fundamental skills in Humanitude. Therefore, we attempted a new quantitative evaluation of trainees' 'speaking' skills by estimating the emotional value of conversational content using GPT-4 named HEARTS 5. A survey of caregivers was conducted using the developed system and was well received by the participants. We also developed a HEARTS 5 conversational version based on HEARTS 5, with the addition of GPT-4 conversation generation and Azure Text-to-Speech. © 2024 IEEE.

关键词： Augmented reality

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Occluded human pose estimation based on limb joint augmentation

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Neural Computing and Applications 2025年第3期37卷 1241-1253页

作者： Han, Gangtao Song, Chunxiao Wang, Song Wang, Hao Chen, Enqing Wang, Guanghui School of Electrical and Information Engineering Zhengzhou University Zhengzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Department of Computer Science Toronto Metropolitan University Toronto Canada

Human pose estimation aims at locating the specific joints of humans from the images or videos. While existing deep learning-based methods have achieved high positioning accuracy, they often struggle with generalization in occlusion scenarios. In this paper, we propose an occluded human pose estimation framework based on limb joint augmentation to enhance the generalization ability of the pose estimation model on the occluded human bodies. Specifically, the occlusion blocks are at first employed to randomly cover the limb joints of the human bodies from the training images, imitating the scene where the objects or other people partially occlude the human body. Trained by the augmented samples, the pose estimation model is encouraged to accurately locate the occluded keypoints based on the visible ones. To further enhance the localization ability of the model, this paper constructs a dynamic structure loss function based on limb graphs to explore the distribution of occluded joints by evaluating the dependence between adjacent joints. Extensive experimental evaluations on two occluded datasets, OCHuman and CrowdPose, demonstrate significant performance improvements without additional computation cost during inference. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Joints (anatomy)

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Fine-Tuned Attribute Weighted Naïve Bayes with Modified Partial Instances Reduction for Gaming Disorder Classification

HighTech and Innovation Journal

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HighTech and Innovation Journal 2025年第1期6卷 67-80页

作者： Latubessy, Anastasya Wardoyo, Retantyo Musdholifah, Aina Kusrohmaniah, Sri Doctoral Program Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Informatics Engineering Faculty of Engineering Universitas Muria Kudus Indonesia Department of Computer Science and Electronics Faculty of Mathematics and Natural Science Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Psychology Faculty of Psychology Universitas Gadjah Mada Yogyakarta55281 Indonesia

Fine Tuning Attribute Weighted Naïve Bayes (FTAWNB) is a reliable modified Naïve Bayes model. Even though it is able to provide high accuracy on ordinal data, this model is sensitive to outliers. To improve the performance of FTAWNB, this research modified the Partial Instances Reduction (PIR) technique to make the FTAWNB more adaptive to outliers. Nevertheless, in contrast to the original PIR technique, which substitutes missing values for data values deemed outliers, the PIR technique suggested in this study replaces data values deemed outliers using a Naïve Bayes weighting approach. The attribute values from the outlier data are replaced with the highest probability values for the attributes in the actual class. This PIR technique is referred to as modified PIR. The FTAWNB model with modified PIR has been evaluated using the Gaming Disorder dataset. Replacing the four attributes with the least amount of information resulted in accuracy gains of 99.74%, an increase of 1.53% over the FTAWNB model. The experimental result shows that adding the modified PIR technique to the FTAWNB model can handle the outlier in the data, proving it by increasing the performance in terms of accuracy, precision, and recall without pruning the dataset used. © 2025, Ital Publication. All rights reserved.

关键词： Attribute Weighted Classification Fine-Tune Gaming Disorder Instances Reduction Naïve Bayes

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Forecasting Bitcoin Prices Using Deep Learning for Consumer-Centric Industrial Applications

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IEEE Transactions on Consumer Electronics 2024年第1期70卷 1351-1358页

作者： Roy, Pradeep Kumar Kumar, Abhinav Singh, Ashish Sangaiah, Arun Kumar Indian Institute of Information Technology Surat Department of Computer Science and Engineering Surat394190 India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj211004 India KIIT Deemed to Be University School of Computer Engineering Bhubaneswar751024 India National Yunlin University of Science and Technology International Graduate Institute of AI Douliou64002 Taiwan Lebanese American University Department of Electrical and Computer Engineering Byblos1102-2801 Lebanon

As cryptocurrencies become more popular as investment vehicles, bitcoin draws interest from businesses, consumers, and computer scientists all across the world. Bitcoin is a computer file stored in digital wallet applications where each transaction is secured using strong cryptographic algorithms. It was challenging to forecast the future price of bitcoin due to its nonlinearity and extreme volatility. Several recent classic parametric models have been found with limited accuracy. To address the limitations and fill the existing research gaps, there is a need for a good prediction model which will provide the desired accuracy in the case of uncertainty and dynamism. This research suggested a deep learning-based framework for predicting and forecasting Bitcoin price. The research will be helpful for worldwide consumers and industries to take their decision on whether to invest or not. The research utilizes Yahoo! finance dataset for the period of 01-03-2016 to 26-02-2021 having 1828 samples. The experimental outcomes of the proposed Long Short-Term Memory (LSTM) model outperformed similar deep learning models by securing minimum loss and confirming that it can be used for future price prediction of the cryptocurrencies, which is helpful for the buyer to take their decision. © 1975-2011 IEEE.

关键词： Hidden Markov models

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3D layout of the Spidergon-Donut on-chip interconnection network

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International Journal of High Performance Systems Architecture 2023年第3期11卷 137-147页

作者： Sibai, Fadi N. Asaduzzaman, Abu Elmoursy, Ali Department of Electrical and Computer Engineering Gulf University for Science and Technology P.O. Box 7207 Hawally32093 Kuwait Department of Electrical and Computer Engineering Wichita State University WichitaKS67260 United States Department of Electrical and Computer Engineering University of Sharjah P.O. Box 27272 Sharjah United Arab Emirates

3D integration promises to resolve many of the heat and die size limitations of 2D integrated circuits. A critical step in the design of 3D many-cores and MPSOCs is the layout of their 3D network-on-chip (NoC). In this paper, we explore and present multiple 3D layouts of the Spidergon-Donut (SD) NoC and estimate their longest wire lengths and cost requirements. For a total of 64 cores, the 4×2×8 and 2×4×8 placements result in the best longest wire delays, with the former higher 3D integration costs, while the second requiring larger chip area and through-silicon-vias (TSV) array costs. Such study helps in guiding 3D integration direction and weighing 3D NoC layout and placement alternatives. Copyright © 2023 Inderscience Enterprises Ltd.

关键词： Network-on-chip

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A Comprehensive Early Detection of Seasonal Plant Diseases Using a Convolutional Neural Network (CNN)

A Comprehensive Early Detection of Seasonal Plant Diseases U...

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2024 International Conference on science, engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Shema, Aisha Uppin, Chandrashekhar George, Gilbert Baze University Department of Electrical and Computer Engineering Abuja Nigeria Baze University Department of Computer Science Abuja Nigeria

ISBN: (纸本)9798350358155

Plant diseases arise as a consequence of shifts in the environment and climate, resembling natural disasters that disrupt plant growth and can even lead to the demise of plants during their development, starting from the seed stage. Detecting these disorders can be challenging without adequate knowledge and expertise, and many farmers and plantation growers lack the necessary skills, professional knowledge, and resources for accurate identification and treatment of plant issues. To address this problem, a Convolutional Neural Network (CNN) model is employed, leveraging the power of deep learning technology to appropriately recognize plant disorders and their potential remedies. The CNN model will be trained using a vast dataset from various sources, encompassing information about plant diseases. Moreover, it will be fine-tuned to identify diseases based on pictures supplied by farmers, taken through their cameras or retrieved from their galleries. Specifically, this research concentrates on the early detection of seasonal diseases in distinct plant leaves, including Apples, Corn, Tomatoes, and Potatoes. From our experiments, we obtained an accuracy of 98.89 %. Outperforming related works in the field. © 2024 IEEE.

关键词： Plant diseases

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ChebyRegNet: An Unsupervised Deep Learning Technique for Deformable Medical Image Registration 50

ChebyRegNet: An Unsupervised Deep Learning Technique for Def...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Danish, Muhammad Umair Noorchenarboo, Mohammad Narayan, Apurva Grolinger, Katarina Department of Electrical and Computer Engineering Western University LondonON Canada Department of Computer Science Western University LondonON Canada

ISBN: (纸本)9781665464543

Image registration supports a variety of clinical applications such as treatment planning, disease monitoring, and interventions. Deep learning-based deformable image registration techniques, including VoxelMorph and HyperMorph, are achieving state-of-the-art results but still exhibit considerable errors in multi-modality registrations, such as structural to functional image registration. To address these challenges, this paper proposes ChebyRegNet, a deep learning framework that leverages Chebyshev polynomials and hypernetworks, to enhance registration accuracy. Moreover, a Feature Extraction Network is added that produces deep features for the Chebyshev HyperNetwork, which is responsible for generating parameters for the Image Registration Network. This structure allows ChebyRegNet to successfully handle challenging registration tasks. Experiments show that ChebyRegNet greatly improves registration in terms of target registration errors compared to VoxelMorph and HyperMorph, achieving 0.1 versus 0.2 and 0.19 for HyperMorph and VoxelMorph, respectively. Moreover, ChebyRegNet outperforms the other two techniques in terms of the Dice Score and achieves competitive results in terms of the Jaccard Coefficient. The publicly available code for our work can be accessed at https://***/udanish50/ChebyRegNet/tree/main. © 2024 IEEE.

关键词： Chebyshev polynomials

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