We designed a multifunctional all-dielectric high-sensitivity Fano resonance biosensor. The sensor features an asymmetric structure, comprising an elliptical ring and a rectangular frame, symmetrically aligned along t...
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Long Short-Term Memory (LSTM) networks are particularly useful in recommender systems since user preferences change over time. Unlike traditional recommender models which assume static user-item interactions, LSTM mod...
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Person re-identification is a prevalent technology deployed on intelligent *** have been remarkable achievements in person re-identification methods based on the assumption that all person images have a sufficiently h...
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Person re-identification is a prevalent technology deployed on intelligent *** have been remarkable achievements in person re-identification methods based on the assumption that all person images have a sufficiently high resolution,yet such models are not applicable to the open *** real world,the changing distance between pedestrians and the camera renders the resolution of pedestrians captured by the camera *** low-resolution(LR)images in the query set are matched with high-resolution(HR)images in the gallery set,it degrades the performance of the pedestrian matching task due to the absent pedestrian critical information in LR *** address the above issues,we present a dualstream coupling network with wavelet transform(DSCWT)for the cross-resolution person re-identification ***,we use the multi-resolution analysis principle of wavelet transform to separately process the low-frequency and high-frequency regions of LR images,which is applied to restore the lost detail information of LR ***,we devise a residual knowledge constrained loss function that transfers knowledge between the two streams of LR images and HR images for accessing pedestrian invariant features at various *** qualitative and quantitative experiments across four benchmark datasets verify the superiority of the proposed approach.
Research on neural radiance fields for novel view synthesis has experienced explosive growth with the development of new models and *** NeRF(Neural Radiance Fields)algorithm,suitable for underwater scenes or scatterin...
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Research on neural radiance fields for novel view synthesis has experienced explosive growth with the development of new models and *** NeRF(Neural Radiance Fields)algorithm,suitable for underwater scenes or scattering media,is also *** underwater 3D reconstruction systems still face challenges such as long training times and low rendering *** paper proposes an improved underwater 3D reconstruction system to achieve rapid and high-quality 3D ***,we enhance underwater videos captured by a monocular camera to correct the image quality degradation caused by the physical properties of the water medium and ensure consistency in enhancement across ***,we perform keyframe selection to optimize resource usage and reduce the impact of dynamic objects on the reconstruction *** pose estimation using COLMAP,the selected keyframes undergo 3D reconstruction using neural radiance fields(NeRF)based on multi-resolution hash encoding for model construction and *** terms of image enhancement,our method has been optimized in certain scenarios,demonstrating effectiveness in image enhancement and better continuity between consecutive frames of the same *** terms of 3D reconstruction,our method achieved a peak signal-to-noise ratio(PSNR)of 18.40 dB and a structural similarity(SSIM)of 0.6677,indicating a good balance between operational efficiency and reconstruction quality.
With the vast development of Internet technology 2.0, millions of people share their opinions on different social networking sites. To obtain the necessary information from the large volume of user-generated data, the...
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With the vast development of Internet technology 2.0, millions of people share their opinions on different social networking sites. To obtain the necessary information from the large volume of user-generated data, the attention on sentiment analysis among the research community is growing. The growth and prominence of sentiment analysis are synchronized with an increase in social media and networking sites. Users generally use natural language for speaking, writing, and expressing their views based on various sentiment orientations, ratings, and the features of different products, topics, and issues. This helps produce ambiguity at the end of the customer's decision based on criticism to form an opinion based on such comments. To overcome the challenges of usergenerated content such as noisy, irrelevant information and fake reviews, there is a significant demand for a practical methodology that emphasizes the need for sentiment analysis. This study presents an exhaustive survey of the existing methodologies. It highlights the challenges and performance factors of various sentiment analysis approaches, including text preprocessing, opinion spam detection, and aspect level sentiment analysis. Users use social media as a medium for their activities and are passionate about their posts on social networking platforms on various issues, topics, and events. Sentiment analysis plays a significant role in online e-commerce servicing sites in which users share their views and rating on products and services. With the help of sentiment analysis, companies identify customer dissatisfaction and enhance the quality of the products and services. This study seeks diverse methods and performance measures on various application domains in sentiment analysis. The paper presents an exhaustive review that provides an overview of the pros and cons of the existing techniques and highlights the current techniques in sentiment analysis, namely text preprocessing, opinion spam detection, and
Deep learning has revolutionized various fields but faces challenges such as limitations owing to vanishing gradients and the inability to handle non-differentiable loss functions and layers. Our study proposes Parall...
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GaN as a typical third generation wide-bandwidth semiconductor material is widely used in various power devices and sensor devices. However, depletion devices are conventional GaN-based HEMTs, and in practical applica...
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Land use and land cover changes has become crucial for natural resource management and monitoring, particularly in urban planning. Remote sensing and geographical information systems analyze the changes in land use an...
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Skewed distributions appear in many real-world classification problems. Skewed distributions, underrepresented classes, and multiple overlapping regions in multiclass imbalanced datasets deteriorate the performance of...
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Federated learning is an emerging privacy-preserving distributed learning paradigm,in which many clients collaboratively train a shared global model under the orchestration of a remote *** current works on federated l...
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Federated learning is an emerging privacy-preserving distributed learning paradigm,in which many clients collaboratively train a shared global model under the orchestration of a remote *** current works on federated learning have focused on fully supervised learning settings,assuming that all the data are annotated with ground-truth ***,this work considers a more realistic and challenging setting,Federated Semi-Supervised Learning(FSSL),where clients have a large amount of unlabeled data and only the server hosts a small number of labeled *** to reasonably utilize the server-side labeled data and the client-side unlabeled data is the core challenge in this *** this paper,we propose a new FSSL algorithm for image classification based on consistency regularization and ensemble knowledge distillation,called *** algorithm uses the global model as the teacher in consistency regularization methods to enhance both the accuracy and stability of client-side unsupervised learning on unlabeled ***,we introduce an additional ensemble knowledge distillation loss to mitigate model overfitting during server-side retraining on labeled *** experiments on several image classification datasets show that our EKDFSSL outperforms current baseline methods.
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