237 research outputs found

    Spike-EVPR: Deep Spiking Residual Network with Cross-Representation Aggregation for Event-Based Visual Place Recognition

    Full text link
    Event cameras have been successfully applied to visual place recognition (VPR) tasks by using deep artificial neural networks (ANNs) in recent years. However, previously proposed deep ANN architectures are often unable to harness the abundant temporal information presented in event streams. In contrast, deep spiking networks exhibit more intricate spatiotemporal dynamics and are inherently well-suited to process sparse asynchronous event streams. Unfortunately, directly inputting temporal-dense event volumes into the spiking network introduces excessive time steps, resulting in prohibitively high training costs for large-scale VPR tasks. To address the aforementioned issues, we propose a novel deep spiking network architecture called Spike-EVPR for event-based VPR tasks. First, we introduce two novel event representations tailored for SNN to fully exploit the spatio-temporal information from the event streams, and reduce the video memory occupation during training as much as possible. Then, to exploit the full potential of these two representations, we construct a Bifurcated Spike Residual Encoder (BSR-Encoder) with powerful representational capabilities to better extract the high-level features from the two event representations. Next, we introduce a Shared & Specific Descriptor Extractor (SSD-Extractor). This module is designed to extract features shared between the two representations and features specific to each. Finally, we propose a Cross-Descriptor Aggregation Module (CDA-Module) that fuses the above three features to generate a refined, robust global descriptor of the scene. Our experimental results indicate the superior performance of our Spike-EVPR compared to several existing EVPR pipelines on Brisbane-Event-VPR and DDD20 datasets, with the average Recall@1 increased by 7.61% on Brisbane and 13.20% on DDD20.Comment: 14 pages, 10 figure

    A robust and physical BSIM3 non-quasi-static transient and AC small-signal model for circuit simulation

    Full text link

    Investigation of bio-aerosol dispersion in a tunnel-ventilated poultry house

    Get PDF
    Bio-aerosol concentrations in poultry houses must be controlled to provide adequate air quality for both birds and workers. High concentrations of airborne bio-aerosols would affect the environmental sustainability of the production and create environmental hazards to the surroundings via the ventilation systems. Previous studies demonstrate that several factors including the age of the birds, the housing configuration, the humidity and temperature would strongly affect the indoor concentration of bio-aerosols. However, limited studies are performed in the literature to investigate the bio-aerosol dispersion pattern inside poultry buildings. In order to fill a gap of the understanding of the bio-aerosol dispersion behavior, experimental measurements of the indoor bio-aerosol distribution are performed in a tunnel-ventilated poultry house in this paper. Meanwhile a three-dimensional computational fluid dynamics (CFD) model is built and validated to further investigate the effect of flow pattern, turbulence and vortex on the dispersion and deposition of the bio-aerosols. Furthermore, bio-aerosols with various diameters are also examined in the CFD model. It is found that higher concentrations of bio-aerosols are detected at the rear part of the house and strong turbulent flow resulting from the ventilation inlets enhances the diffusion and dispersion of bio-aerosols. Local vortex or disturbed flow is responsible for higher local concentration due to the re-suspension of settled bio-aerosols, which suggests that careful attentions should be paid to these locations during cleaning and disinfection. Results from present study contribute to the optimization of design and operation of the poultry houses from the standing point of reducing airborne bio-aerosol concentrations

    Gravity Data Reveal New Evidence of an Axial Magma Chamber Beneath Segment 27 in the Southwest Indian Ridge

    Get PDF
    Hydrothermal systems are integral to mid-ocean ridge activity; they form massive seafloor sulfide (SMS) deposits rich in various metallic elements, which are potential mineral resources. Since 2007, many hydrothermal fields have been discovered along the ultraslow-spreading Southwest Indian Ridge (SWIR). The Duanqiao hydrothermal field is located at segment 27’s axis between the Indomed and Gallieni transform faults; tomography models reveal an obvious low-velocity anomaly beneath it, indicating a possible axial magma chamber (AMC). However, confirmation of an AMC’s existence requires further study and evidence. In this study, we first calculated the gravity effect to identify the heterogeneous distribution of crustal density beneath segment 27 and the surrounding area. Next, we used the gravity-inversion method to obtain the crustal density structure beneath the study area. The results indicate that a thickened crust and low-density crustal materials exist beneath segment 27. The low-density anomaly in the lower crust beneath the Duanqiao hydrothermal field suggests the existence of an AMC covered with a cold and dense upper crust. The density results identify several faults, which provide potential channels for magma migration. In addition, the melt migrates westward and redistributes laterally toward the segment’s western end. However, when migrating toward the segment’s eastern end, the melt is affected by a rapid cooling mechanism. Therefore, the segment’s ends present different density features and morphologies of nontransform discontinuities (NTDs
    • …
    corecore