212 research outputs found

    Automated cropping intensity extraction from isolines of wavelet spectra

    Get PDF
    Timely and accurate monitoring of cropping intensity (CI) is essential to help us understand changes in food production. This paper aims to develop an automatic Cropping Intensity extraction method based on the Isolines of Wavelet Spectra (CIIWS) with consideration of intra- class variability. The CIIWS method involves the following procedures: (1) characterizing vegetation dynamics from time–frequency dimensions through a continuous wavelet transform performed on vegetation index temporal profiles; (2) deriving three main features, the skeleton width, maximum number of strong brightness centers and the intersection of their scale intervals, through computing a series of wavelet isolines from the wavelet spectra; and (3) developing an automatic cropping intensity classifier based on these three features. The proposed CIIWS method improves the understanding in the spectral–temporal properties of vegetation dynamic processes. To test its efficiency, the CIIWS method is applied to China’s Henan province using 250 m 8 days composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series datasets. An overall accuracy of 88.9% is achieved when compared with in-situ observation data. The mapping result is also evaluated with 30 m Chinese Environmental Disaster Reduction Satellite (HJ-1)-derived data and an overall accuracy of 86.7% is obtained. At county level, the MODIS-derived sown areas and agricultural statistical data are well correlated (r2 = 0.85). The merit and uniqueness of the CIIWS method is the ability to cope with the complex intra-class variability through continuous wavelet transform and efficient feature extraction based on wavelet isolines. As an objective and meaningful algorithm, it guarantees easy applications and greatly contributes to satellite observations of vegetation dynamics and food security efforts

    Grasp Stability Assessment Through Attention-Guided Cross-Modality Fusion and Transfer Learning

    Full text link
    Extensive research has been conducted on assessing grasp stability, a crucial prerequisite for achieving optimal grasping strategies, including the minimum force grasping policy. However, existing works employ basic feature-level fusion techniques to combine visual and tactile modalities, resulting in the inadequate utilization of complementary information and the inability to model interactions between unimodal features. This work proposes an attention-guided cross-modality fusion architecture to comprehensively integrate visual and tactile features. This model mainly comprises convolutional neural networks (CNNs), self-attention, and cross-attention mechanisms. In addition, most existing methods collect datasets from real-world systems, which is time-consuming and high-cost, and the datasets collected are comparatively limited in size. This work establishes a robotic grasping system through physics simulation to collect a multimodal dataset. To address the sim-to-real transfer gap, we propose a migration strategy encompassing domain randomization and domain adaptation techniques. The experimental results demonstrate that the proposed fusion framework achieves markedly enhanced prediction performance (approximately 10%) compared to other baselines. Moreover, our findings suggest that the trained model can be reliably transferred to real robotic systems, indicating its potential to address real-world challenges.Comment: Accepted by IROS 202

    Multimodal Fish Feeding Intensity Assessment in Aquaculture

    Full text link
    Fish feeding intensity assessment (FFIA) aims to evaluate the intensity change of fish appetite during the feeding process, which is vital in industrial aquaculture applications. The main challenges surrounding FFIA are two-fold. 1) robustness: existing work has mainly leveraged single-modality (e.g., vision, audio) methods, which have a high sensitivity to input noise. 2) efficiency: FFIA models are generally expected to be employed on devices. This presents a challenge in terms of computational efficiency. In this work, we first introduce an audio-visual dataset, called AV-FFIA. AV-FFIA consists of 27,000 labeled audio and video clips that capture different levels of fish feeding intensity. To our knowledge, AV-FFIA is the first large-scale multimodal dataset for FFIA research. Then, we introduce a multi-modal approach for FFIA by leveraging single-modality pre-trained models and modality-fusion methods, with benchmark studies on AV-FFIA. Our experimental results indicate that the multi-modal approach substantially outperforms the single-modality based approach, especially in noisy environments. While multimodal approaches provide a performance gain for FFIA, it inherently increase the computational cost. To overcome this issue, we further present a novel unified model, termed as U-FFIA. U-FFIA is a single model capable of processing audio, visual, or audio-visual modalities, by leveraging modality dropout during training and knowledge distillation from single-modality pre-trained models. We demonstrate that U-FFIA can achieve performance better than or on par with the state-of-the-art modality-specific FFIA models, with significantly lower computational overhead. Our proposed U-FFIA approach enables a more robust and efficient method for FFIA, with the potential to contribute to improved management practices and sustainability in aquaculture

    Locally advanced head and neck squamous cell carcinoma treatment efficacy and safety: a systematic review and network meta-analysis

    Get PDF
    Head and neck squamous cell carcinoma (HNSCC) accounts for approximately 3% of new cancer cases and 3% of all deaths worldwide. Most HNSCC patients are locally advanced (LA) at diagnosis. The combination of radiotherapy (RT), chemotherapy, targeted therapy, and immunotherapy are the primary LA-HNSCC treatment options. Nevertheless, the choice of optimal LA-HNSCC treatment remains controversial. We systematically searched public databases for LA-HNSCC-related studies and assess treatment effectiveness and safety by assessing the objective response rate (ORR), ≥3 adverse events (AEs), overall survival (OS), progression-free survival (PFS), disease-free survival (DFS), local-region control (LRC), and disease-specific survival (DSS). 126 randomized controlled clinical trials (RCTs) were included in this study. We show that concurrent RT with nimotuzumab or conventional concurrent chemo-radiotherapy (CCRT) had significantly better efficacy and long-term survival without increasing AEs than RT alone. Accelerated fractionated radiotherapy (AFRT) showed better efficiency than conventional fractionated RT, although it had higher AEs. In addition, concurrent cetuximab combined with RT failed to show a significant advantage over RT alone.Trial registration: PROSPERO CRD42022352127

    LRRC8 family proteins within lysosomes regulate cellular osmoregulation and enhance cell survival to multiple physiological stresses

    Get PDF
    LRRC8 family proteins on the plasma membrane play a critical role in cellular osmoregulation by forming volume-regulated anion channels (VRACs) necessary to prevent necrotic cell death.We demonstrate that intracellular LRRC8 proteins acting within lysosomes also play an essential role in cellular osmoregulation. LRRC8 proteins on lysosome membranes generate large lysosomal volume-regulated anion channel (Lyso-VRAC) currents in response to low cytoplasmic ionic strength conditions. When a double-leucine L706L707 motif at the C terminus of LRRC8A was mutated to alanines, normal plasma membrane VRAC currents were still observed, but Lyso-VRAC currents were absent. We used this targeting mutant, as well as pharmacological tools, to demonstrate that Lyso-VRAC currents are necessary for the formation of large lysosome-derived vacuoles, which store and then expel excess water to maintain cytosolic water homeostasis. Thus, Lyso-VRACs allow lysosomes of mammalian cells to act as the cell`s “bladder.” When Lyso-VRAC current was selectively eliminated, the extent of necrotic cell death to sustained stress was greatly increased, not only in response to hypoosmotic stress, but also to hypoxic and hypothermic stresses. Thus Lyso-VRACs play an essential role in enabling cells to mount successful homeostatic responses to multiple stressors
    • …
    corecore