Hand-sized DNA sequencers have demonstrated excellent scientific utility. They also present exciting opportunities for the expansion of DNA analysis to a diverse range of uses and users. But this potential is currentl...
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A multi-label image classification is a challenging task as it has to map an input image to a vector of outputs. This work presents a single and efficient model to perform multi-label and multi-class classification us...
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Remarkable progress has been made in self-supervised monocular depth estimation (SS-MDE) by exploring cross-view consistency, e.g., photometric consistency and 3D point cloud consistency. However, they are very vulner...
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Remarkable progress has been made in self-supervised monocular depth estimation (SS-MDE) by exploring cross-view consistency, e.g., photometric consistency and 3D point cloud consistency. However, they are very vulnerable to illumination variance, occlusions, texture-less regions, as well as moving objects, making them not robust enough to deal with various scenes. To address this challenge, we study two kinds of robust cross-view consistency in this paper. Firstly, the spatial offset field between adjacent frames is obtained by reconstructing the reference frame from its neighbors via deformable alignment, which is used to align the temporal depth features via a depth feature alignment (DFA) loss. Secondly, the 3D point clouds of each reference frame and its nearby frames are calculated and transformed into voxel space, where the point density in each voxel is calculated and aligned via a voxel density alignment (VDA) loss. In this way, we exploit the temporal coherence in both depth feature space and 3D voxel space for SS-MDE, shifting the “point-to-point” alignment paradigm to the “region-to-region” one. Compared with the photometric consistency loss as well as the rigid point cloud alignment loss, the proposed DFA and VDA losses are more robust owing to the strong representation power of deep features as well as the high tolerance of voxel density to the aforementioned challenges. Experimental results on several outdoor benchmarks show that our method outperforms current state-of-the-art techniques. Extensive ablation study and analysis validate the effectiveness of the proposed losses, especially in challenging scenes. The code and models are available at https://***/sunnyHelen/RCVC-depth.
The advent of large language models (LLMs) has spurred considerable interest in advancing autonomous LLMs-based agents, particularly in intriguing applications within smartphone graphical user interfaces (GUIs). When ...
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Belief propagation(BP)decoding outputs soft information and can be naturally used in iterative *** list(BPL)decoding provides comparable error-correction performance to the successive cancellation list(SCL)*** this pa...
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Belief propagation(BP)decoding outputs soft information and can be naturally used in iterative *** list(BPL)decoding provides comparable error-correction performance to the successive cancellation list(SCL)*** this paper,we firstly introduce an enhanced code construction scheme for BPL decoding to improve its errorcorrection ***,a GPU-based BPL decoder with adoption of the new code construction is ***,the proposed BPL decoder is tested on NVIDIA RTX3070 and *** results show that the presented BPL decoder with early termination criterion achieves above 1 Gbps throughput on RTX3070 for the code(1024,512)with 32 lists under good channel conditions.
Genetic algorithms become widely used in various optimization as a nature-inspired algorithm. This biological-based algorithm includes three genetic operators: selection, crossover or recombination, and mutation. In t...
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Digital image steganography technique based on hiding the secret data behind of cover image in such a way that it is not detected by the human visual *** paper presents an image scrambling method that is very useful f...
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Digital image steganography technique based on hiding the secret data behind of cover image in such a way that it is not detected by the human visual *** paper presents an image scrambling method that is very useful for grayscale secret *** this method,the secret image decomposes in three parts based on the pixel’s threshold *** division of the color image into three parts is very easy based on the color channel but in the grayscale image,it is difficult to *** proposed image scrambling method is implemented in image steganography using discrete wavelet transform(DWT),singular value decomposition(SVD),and sorting *** is no visual difference between the stego image and the cover *** extracted secret image is also similar to the original secret *** proposed algorithm outcome is compared with the existed image steganography *** comparative results show the strength of the proposed technique.
In recent years, research combining max pressure (MP) with reinforcement learning for traffic signal control has become a hot topic in the field of intelligent transportation. However, existing MP methods ignore the i...
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Class imbalance is a frequently occurring issue in predictive modeling. Learning from imbalanced data is a challenging task that has attracted much interest from scholars. While a substantial amount of research has be...
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In this study, we investigate a traffic monitoring method to detect passing cars, bikes, and humans from roadsides using millimeter-wave radar during road work. Because a road worker is one of the most dangerous occup...
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