Aim: Recent advances in Artificial Intelligence (AI) and the addition of Deep Learning (DL) have made it possible to analyse both real-time and historical data from the Internet of Things (IoT). Recently, IoT technolo...
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Single-pixel imaging, as an innovative imaging technique, has attracted much attention during the last decades. However, it is still a challenging task for single-pixel imaging to reconstruct high-quality images with ...
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Single-pixel imaging, as an innovative imaging technique, has attracted much attention during the last decades. However, it is still a challenging task for single-pixel imaging to reconstruct high-quality images with fewer measurements. Recently, deep learning techniques have shown great potential in single-pixel imaging especially for under-sampling cases. Despite outperforming traditional model-based methods, the existing deep learning-based methods usually utilize fully convolutional networks to model the imaging process which have limitations in long-range dependencies capturing, leading to limited reconstruction performance. In this paper, we present a transformer-based single-pixel imaging method to realize high-quality image reconstruction in undersampled situation. By taking advantage of self-attention mechanism, the proposed method is good at modeling the imaging process and directly reconstructs high-quality images from the measured one-dimensional light intensity sequence. Numerical simulations and real optical experiments demonstrate that the proposed method outperforms the state-of-the-art single-pixel imaging methods in terms of reconstruction performance and noise robustness.
Unmanned Combat Aerial Vehicles (UCAVs) are becoming a critical part of the military to automate complex missions with minimum risk and increased efficiency. Path planning is a necessary routine for UCAVs to guide the...
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E-learning environments represent digital platforms designed to facilitate online learning experiences. Recognizing the diverse learning preferences of individuals, the need for identifying and integrating multi-layer...
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Frequency conversion is pivotal in nonlinear optics and quantum optics for manipulating and translating light signals across different wavelength *** frequency conversion between two light beams with a small frequency...
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Frequency conversion is pivotal in nonlinear optics and quantum optics for manipulating and translating light signals across different wavelength *** frequency conversion between two light beams with a small frequency interval is a central *** this work,we design a pair of coupled silicon microrings wherein coupled-induced modesplitting exists to achieve a small frequency shift by the process of four-wave mixing Bragg *** an example,the signal can be up or down converted to the idler which is 15.5 GHz spaced when two pumps align with another pair of split *** results unveil the potential of coupled microring resonators for small interval frequency conversion in a high-fidelity,all-optical,and signal processing quantum frequency interface.
The Metaverse depicts a parallel digitalized world where virtuality and reality are *** has economic and social systems like those in the real world and provides intelligent services and *** this paper,we introduce th...
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The Metaverse depicts a parallel digitalized world where virtuality and reality are *** has economic and social systems like those in the real world and provides intelligent services and *** this paper,we introduce the Metaverse from a new technology perspective,including its essence,corresponding technical framework,and potential technical ***,we analyze the essence of the Metaverse from its etymology and point out breakthroughs promising to be made in time,space,and contents of the Metaverse by citing Maslow's Hierarchy of ***,we conclude four pillars of the Metaverse,named ubiquitous connections,space convergence,virtuality and reality interaction,and human-centered communication,and establish a corresponding technical ***,we envision open issues and challenges of the Metaverse in the technical *** work proposes a new technology perspective of the Metaverse and will provide further guidance for its technology development in the future.
Cloud environments often exhibit varying levels of heterogeneity arising from the diverse characteristics of cloudlets and virtual machines. This research paper focuses on addressing this heterogeneity and proposes tw...
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In this paper, we propose hardware acceleration to improve a performance of scripting programming languages for embedded developments. Scripting programming languages enable more efficient software developments and sc...
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Self-supervised learning is attracting significant attention from researchers in the point cloud processing field. However, due to the natural sparsity and irregularity of point clouds, effectively extracting discrimi...
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Self-supervised learning is attracting significant attention from researchers in the point cloud processing field. However, due to the natural sparsity and irregularity of point clouds, effectively extracting discriminative and transferable features for efficient training on downstream tasks remains an unsolved challenge. Consequently, we propose PointSmile, a reconstruction-free self-supervised learning paradigm by maximizing curriculum mutual information(CMI) across the replicas of point cloud objects. From the perspective of how-and-what-to-learn, PointSmile is designed to imitate human curriculum learning, i.e.,starting with easier topics in a curriculum and gradually progressing to learning more complex topics in the curriculum. To solve “how-to-learn”, we introduce curriculum data augmentation(CDA) of point *** encourages PointSmile to follow a learning path that starts from learning easy data samples and progresses to learning hard data samples, such that the latent space can be dynamically affected to create better embeddings. To solve “what-to-learn”, we propose maximizing both feature-and class-wise CMI to better extract discriminative features of point clouds. Unlike most existing methods, PointSmile does not require a pretext task or cross-modal data to yield rich latent representations; additionally, it can be easily transferred to various backbones. We demonstrate the effectiveness and robustness of PointSmile in downstream tasks such as object classification and segmentation. The study results show that PointSmile outperforms existing self-supervised methods and compares favorably with popular fully supervised methods on various standard architectures. The code is available at https://***/theaalee/PointSmile.
Background Most existing chemical experiment teaching systems lack solid immersive experiences,making it difficult to engage *** address these challenges,we propose a chemical simulation teaching system based on virtu...
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Background Most existing chemical experiment teaching systems lack solid immersive experiences,making it difficult to engage *** address these challenges,we propose a chemical simulation teaching system based on virtual reality and gesture *** The parameters of the models were obtained through actual investigation,whereby Blender and 3DS MAX were used to model and import these parameters into a physics *** establishing an interface for the physics engine,gesture interaction hardware,and virtual reality(VR)helmet,a highly realistic chemical experiment environment was *** code script logic,particle systems,as well as other systems,chemical phenomena were ***,we created an online teaching platform using streaming media and databases to address the problems of distance *** The proposed system was evaluated against two mainstream products in the *** the experiments,the proposed system outperformed the other products in terms of fidelity and *** The proposed system which offers realistic simulations and practicability,can help improve the high school chemistry experimental education.
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