一种基于模糊成像机理的QR码图像快速盲复原方法.

A fast blind restoration method of QR code images was proposed based on a blurred imaging mechanism. On the basis of the research on the centroid invariance of the blurred imaging diffuse light spots, the circular finder pattern is designed. When the image is blurred, the centroid of the pattern and the position of the QR code symbol can be quickly detected by methods such as connected components. Moreover, combined with step edge characteristics, gradient and intensity characteristics, edge detection technology, and optical imaging mechanism, the defocus radius of the blurred QR code image can be quickly and accurately estimated. Furthermore, the Wiener filter is applied to restore the QR code image quickly and effectively. Compared with the other algorithms, the proposed method has improved deblurring results in both structural similarity and peak signal-to-noise ratio, especially in the recovery speed. The average recovery time is 0.329 2 s. Experimental results show that this method can estimate the defocus radius with high accuracy and can quickly realize the blind restoration of QR code images. It has the advantages of rapidity and robustness, which are convenient for embedded hardware implementation and suitable for barcode identification-related industrial Internet of Things application scenarios

Shallow-Water-Equation Model for Simulation of Earthquake-Induced Water Waves

A shallow-water equation (SWE) is used to simulate earthquake-induced water waves in this study. A finite-difference method is used to calculate the SWE. The model is verified against the models of Sato and of Demirel and Aydin with three kinds of seismic waves, and the numerical results of earthquake-induced water waves calculated using the proposed model are reasonable. It is also demonstrated that the proposed model is reliable. Finally, an empirical equation for the maximum water elevation of earthquake-induced water waves is developed based on the results obtained using the model, which is an improvement on former models

Vidu: a Highly Consistent, Dynamic and Skilled Text-to-Video Generator with Diffusion Models

We introduce Vidu, a high-performance text-to-video generator that is capable of producing 1080p videos up to 16 seconds in a single generation. Vidu is a diffusion model with U-ViT as its backbone, which unlocks the scalability and the capability for handling long videos. Vidu exhibits strong coherence and dynamism, and is capable of generating both realistic and imaginative videos, as well as understanding some professional photography techniques, on par with Sora -- the most powerful reported text-to-video generator. Finally, we perform initial experiments on other controllable video generation, including canny-to-video generation, video prediction and subject-driven generation, which demonstrate promising results.Comment: Project page at https://www.shengshu-ai.com/vid

Chromosome-level genome assembly of a high-altitude-adapted frog (Rana kukunoris) from the Tibetan plateau provides insight into amphibian genome evolution and adaptation

Background The high-altitude-adapted frog Rana kukunoris, occurring on the Tibetan plateau, is an excellent model to study life history evolution and adaptation to harsh high-altitude environments. However, genomic resources for this species are still underdeveloped constraining attempts to investigate the underpinnings of adaptation. Results The R. kukunoris genome was assembled to a size of 4.83 Gb and the contig N50 was 1.80 Mb. The 6555 contigs were clustered and ordered into 12 pseudo-chromosomes covering similar to 93.07% of the assembled genome. In total, 32,304 genes were functionally annotated. Synteny analysis between the genomes of R. kukunoris and a low latitude species Rana temporaria showed a high degree of chromosome level synteny with one fusion event between chr11 and chr13 forming pseudo-chromosome 11 in R. kukunoris. Characterization of features of the R. kukunoris genome identified that 61.5% consisted of transposable elements and expansions of gene families related to cell nucleus structure and taste sense were identified. Ninety-five single-copy orthologous genes were identified as being under positive selection and had functions associated with the positive regulation of proteins in the catabolic process and negative regulation of developmental growth. These gene family expansions and positively selected genes indicate regions for further interrogation to understand adaptation to high altitude. Conclusions Here, we reported a high-quality chromosome-level genome assembly of a high-altitude amphibian species using a combination of Illumina, PacBio and Hi-C sequencing technologies. This genome assembly provides a valuable resource for subsequent research on R. kukunoris genomics and amphibian genome evolution in general.Peer reviewe

Application-directed cache coherence design

Thesis (Ph. D.)--University of Rochester. Dept. of Computer Science, 2013.Chip multiprocessors continue to provide programmers with a coherent view of shared memory in hardware across all cores. At large core counts, maintaining coherence in hardware across cached copies of data is a challenge due to bandwidth and metadata storage consumption. A cache block is the basic unit for data storage and communication, chosen at design time to match average locality across a range of applications. Conventional hardware implements the coherence protocol using a fixed granularity (of a cache block) for all coherence operations. Coherence metadata is recorded for every cache block, and coherence permissions are also granted in cache block units. Metadata is typically proportional both to the number of cores and the amount of data cached. Empirical analysis shows that applications typically exhibit a small number of sharing patterns, resulting in redundant information in the metadata. Similarly, considerable bandwidth is wasted due to a mismatch between application access granularity and the fixed granularity data and coherence communication. This dissertation leverages the inherent patterns of data access and sharing behavior in applications to design protocols that eliminate the bandwidth and metadata storage waste in conventional coherence protocols. The sharing pattern-aware directory designs, which we call SPACE and SPATL, recognize and represent only one copy of the subset of sharing patterns exhibited at any given instant in an application. The resulting protocols eliminate the linear proportionality of metadata storage to the number of cores. The adaptive coherence granularity designs, which we call Protozoa, match data movement to an application’s spatial locality and access behavior, supporting fine granularity sharing without increasing metadata storage needs. The application-directed approach allows bandwidth needs to track inherent application access and sharing behavior

Tucker Decomposition-Based Network Compression for Anomaly Detection With Large-Scale Hyperspectral Images

Deep learning methodologies have demonstrated considerable effectiveness in hyperspectral anomaly detection (HAD). However, the practicality of deep learning-based HAD in real-world applications is impeded by challenges arising from limited labeled data, large-scale hyperspectral images (HSIs), and constrained computational resources. In light of these challenges, this article introduces a convolutional neural network (CNN)-based HAD model through the incorporation of Tucker decomposition, named TD-CNND. Drawing inspiration from transfer learning, the proposed model initially constructs pixel sample pairs from known labeled HSIs in the source domain, feeding them into the designed CNN to train the network learning spectral feature differences to obtain a CNN containing knowledge from the source domain. Subsequently, to prevent the need for network retraining caused by structural changes and to reduce model parameters for improving detecting timeliness, a general network compression scheme based on Tucker decomposition is applied to the CNN, where the convolutional layers of the above CNN undergo Tucker tensor decomposition to compress the network and alleviate parameter redundancy. Finally, spectral features realignment is used to recover the detection accuracy loss caused by Tucker tensor decomposition. In addition, a dual-windows structure is implemented during the detection phase, incorporating spatial information to the aforementioned spectral-level learning model, facilitating spectral-spatial collaborative HAD. Experimental evaluations using three real hyperspectral datasets and artificially expanded datasets demonstrate that, in comparison with state-of-the-art methods, the proposed TD-CNND method exhibits effectiveness and superiority in terms of both time cost and detection accuracy, where the notable advantages in terms of time cost become more pronounced with an increasing number of pixels

A novel TM01-TE01 high-power microwave mode converter

This study presents the design and simulation of a novel type of TM01-TE01 mode converter for high-power microwave applications, which efficiently converts the TM01 circular waveguide mode into the TE01 circular waveguide mode. The converter is composed of three sections: power-dividing section, twist-waveguide section and power-combining section. These sections were analyzed and discussed respectively, and the whole converter structure was studied numerically. The results show that at the central frequency 12.5 GHz, the TM01-TE01 conversion loss of the converter made of aluminum is 0.05 dB and the bandwidth for S21 > 0.25 dB is 2.73 GHz. Moreover, the power-handling capacity is over 2 GW