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The measurement of pig weight holds significant importance for producers as it plays a crucial role in managing pig growth, health, and marketing, thereby facilitating informed decisions regarding scientific feeding practices. On one hand, the conventional manual weighing approach is characterized by inefficiency and time consumption. On the other hand, it has the potential to induce heightened stress levels in pigs. This research introduces a hybrid 3D point cloud denoising approach for precise pig weight estimation. By integrating statistical filtering and DBSCAN clustering techniques, we mitigate weight estimation bias and overcome limitations in feature extraction. The convex hull technique refines the dataset to the pig's back, while voxel down-sampling enhances real-time efficiency. Our model integrates pig back parameters with a convolutional neural network (CNN) for accurate weight estimation. Experimental analysis indicates that the mean absolute error (MAE), mean absolute percent error (MAPE), and root mean square error (RMSE) of the weight estimation model proposed in this research are 12.45 kg, 5.36%, and 12.91 kg, respectively. In contrast to the currently available weight estimation methods based on 2D and 3D techniques, the suggested approach offers the advantages of simplified equipment configuration and reduced data processing complexity. These benefits are achieved without compromising the accuracy of weight estimation. Consequently, the proposed method presents an effective monitoring solution for precise pig feeding management, leading to reduced human resource losses and improved welfare in pig breeding. [ABSTRACT FROM AUTHOR]

This study adopted hyperspectral imaging technology combined with machine learning to detect the disease severity of stem blight through the canopy of asparagus mother stem. Several regions of interest were selected from each hyperspectral image, and the reflection spectra of the regions of interest were extracted. There were 503 sets of hyperspectral data in the training set and 167 sets of hyperspectral data in the test set. The data were preprocessed using various methods and the dimension was reduced using PCA. K−nearest neighbours (KNN), decision tree (DT), BP neural network (BPNN), and extreme learning machine (ELM) were used to establish a classification model of asparagus stem blight. The optimal model depended on the preprocessing methods used. When modeling was based on the ELM method, the disease grade discrimination effect of the FD−MSC−ELM model was the best with an accuracy (ACC) of 1.000, a precision (PREC) of 1.000, a recall (REC) of 1.000, an F1-score (F1S) of 1.000, and a norm of the absolute error (NAE) of 0.000, respectively; when the modeling was based on the BPNN method, the discrimination effect of the FD−SNV−BPNN model was the best with an ACC of 0.976, a PREC of 0.975, a REC of 0.978, a F1S of 0.976, and a mean square error (MSE) of 0.072, respectively. The results showed that hyperspectral imaging of the asparagus mother stem canopy combined with machine learning methods could be used to grade and detect stem blight in asparagus mother stems. [ABSTRACT FROM AUTHOR]

For CNN-based visual action recognition, the accuracy may be increased if local key action regions are focused on. The task of self-attention is to focus on key features and ignore irrelevant information. So, self-attention is useful for action recognition. However, current self-attention methods usually ignore correlations among local feature vectors at spatial positions in CNN feature maps. In this paper, we propose an effective interaction-aware self-attention model which can extract information about the interactions between feature vectors to learn attention maps. Since the different layers in a network capture feature maps at different scales, we introduce a spatial pyramid with the feature maps at different layers for attention modeling. The multi-scale information is utilized to obtain more accurate attention scores. These attention scores are used to weight the local feature vectors of the feature maps and then calculate attentional feature maps. Since the number of feature maps input to the spatial pyramid attention layer is unrestricted, we easily extend this attention layer to a spatio-temporal version. Our model can be embedded in any general CNN to form a video-level end-to-end attention network for action recognition. Several methods are investigated to combine the RGB and flow streams to obtain accurate predictions of human actions. Experimental results show that our method achieves state-of-the-art results on the datasets UCF101, HMDB51, Kinetics-400, and untrimmed Charades. [ABSTRACT FROM AUTHOR]

We address the problem of unsupervised discovery of action classes in video data. Different from all existing methods thus far proposed for this task, we present a space-time link analysis approach which consistently matches or exceeds the performance of traditional unsupervised action categorization methods in various datasets. Our method is inspired by the recent success of link analysis techniques in the image domain. By applying these techniques in the space-time domain, we are able to naturally take into account the spatiotemporal relationships between the video features, while leveraging the power of graph matching for action classification. We present a comprehensive set of experiments demonstrating that our approach is capable of handling cluttered backgrounds, activities with subtle movements, and video data from moving cameras. State-of-the-art results are reported on standard datasets. We also demonstrate our method in a compelling surveillance application with the goal of avoiding fraud in retail stores.

With a fiber pigtailed electro-optic modulator as the active Q-switch, mode-hopping-free single-longitudinal-mode pulses are generated by a ring cavity fiber laser based on the injection seeding technique. The experimental results show that the single-longitudinal-mode oscillating properties of the laser may be determined by the power and the center frequency of the injection seed rather than the Q-switch waveform and modulation frequency. With the injected seed power of 200 μW, single-longitudinal-mode Q-switched pulses with the repetition rate and the pulse width from 1 to 100 kHz, and 50 to 400 ns are achieved with the laser. The output pulse linewidth can be narrowed effectively by reducing the coupling ratio of the output coupler of the laser. A stable single-longitudinal-mode pulse stream with a linewidth of 1.5 MHz is obtained with our laser, which may be the narrowest linewidth for a Q-switched fiber laser so far to our best knowledge. [ABSTRACT FROM PUBLISHER]

Background: Exergames are promising exercise tools for improving health. To the best of our knowledge, no systematic review has compared the effects of commercial exergames and conventional exercises on improving executive functions (EFs) in children and adolescents., Objective: This study aimed to investigate the effects of commercial exergames and conventional exercises on improving EFs in children and adolescents., Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, 5 randomized controlled trial (RCT) databases (PubMed, Web of Science, Scopus, PsycINFO, and SPORTDiscus) were searched from their inception to July 7, 2022, to identify relevant RCTs. The Cochrane Collaboration tool was used to evaluate the risk of bias for each study. GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) was used to evaluate the overall quality of evidence., Results: In total, 8 RCTs including 435 children and adolescents were included in the analysis. Commercial exergames had no significant benefit on overall EFs compared to conventional exercises (Hedges g=1.464, 95% CI -0.352 to 3.280; P=.06). For core EFs, there was no evidence to suggest that commercial exergames are more beneficial for improving cognitive flexibility (g=0.906, 95% CI -0.274 to 2.086; P=.13), inhibitory control (g=1.323, 95% CI -0.398 to 3.044; P=.13), or working memory (g=2.420, 95% CI -1.199 to 6.038; P=.19) than conventional exercises. We rated the evidence for overall EFs, cognitive flexibility, inhibitory control, and working memory as being of very low quality due to inconsistency (large heterogeneity) and imprecision (low number of people). Additionally, no effects of the intervention were observed in the acute and chronic groups., Conclusions: We do not have strong evidence to support the benefit of commercial exergaming on EFs because we did not observe a Hedges g close to 0 with tight CIs. Further research is needed to confirm this hypothesis., Trial Registration: PROSPERO CRD42022324111; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=324111., (©Jinlong Wu, Zhuang Xu, Haowei Liu, Xiaoke Chen, Li Huang, Qiuqiong Shi, Linman Weng, Yemeng Ji, Hao Zeng, Li Peng. Originally published in JMIR Serious Games (https://games.jmir.org), 19.10.2023.)

The operation mechanism and the pulse property of an actively Q-switched erbium-doped fiber laser based on an acousto-optic modulator (AOM) switch with the injection seeding technique are investigated. Our results show that the Q-switched pulses can be locked to oscillate near a fixed frequency higher than that of the seed laser, though the frequency-shift effect of the AOM impedes stable cavity mode oscillations. The operation mechanism of such Q-switch fiber lasers can be explained by the mutual locking-in among the shifted frequency components originated from the injected coherence seed with the help of the gain dynamics of the Q-switch cavity. Moreover, narrow-linewidth Q-switched pulses with different repetition rates can be obtained with different cavity lengths for incredibly stable output pulses without any use of cavity-stabilized techniques.