The convergence of sixth-generation wireless networks and Internet of Things technologies create unprecedented opportunities for revolutionizing physical education through intelligent, athletic performance monitoring and enhancement systems. This paper presents a novel framework called ultra-reliable low-latency athletic intelligence, computing, and control (uRLLAIC3) that addresses the critical need for real-time physiological monitoring, performance analytics, and safety assurance in modern sports training environments. Our approach introduces two groundbreaking metrics: athletic information age (AIA) for measuring the freshness of biometric data from wearable sensors and athletic information criticality for evaluating the time-sensitive importance of physiological status updates during training sessions. Evaluation encompassed both simulation studies comparing uRLLAIC3 against six established baseline methods and real-world validation with 48 athletes across five sports disciplines over six weeks. Simulation results demonstrate that uRLLAIC3 achieves 23.7% better AIA performance than the strongest baseline method. The framework maintains superior scalability when monitoring up to 200 athletes simultaneously. Real-world validation reveals a 73% reduction in false positive emergency alerts. Emergency detection accuracy reaches 98.7% compared to 78% for conventional systems. Emergency response time improves by 94% from 10.5 seconds to 0.7 seconds. The framework achieved 99.8% system reliability while delivering 41% better energy efficiency than existing approaches.

Although there are many wearable sensors that make the acquisition of multi-modality data easier, effective feature extraction and fusion of the data is still challenging for lower limb locomotion mode recognition. In this article, a novel neural network is proposed for accurate prediction of five common lower limb locomotion modes including level walking, ramp ascent, ramp descent, stair ascent, and stair descent. First, the encoder-decoder structure is employed to enrich the channel diversity for the separation of the useful patterns from combined patterns. Second, a self-attention based cross-modality interaction module is proposed, which enables bilateral information flow between two encoding paths to fully exploit the interdependencies and to find complementary information between modalities. Third, a multi-modality fusion module is designed where the complementary features are fused by a channel-wise weighted summation whose coefficients are learned end-to-end. A benchmark dataset is collected from 10 health subjects containing EMG and IMU signals and five locomotion modes. Extensive experiments are conducted on one publicly available dataset ENABL3S and one self-collected dataset. The results show that the proposed method outperforms the compared methods with higher classification accuracy. The proposed method achieves a classification accuracy of 98.25% on ENABL3S dataset and 95.51% on the self-collected dataset. Note to Practitioners—This article aims to solve the real challenges encountered when intelligent recognition algorithms are applied in wearable robots: how to effectively and efficiently fuse the multi-modality data for better decision-making. First, most existing methods directly concatenate the multi-modality data, which increases the data dimensionality and brings computational burden. Second, existing recognition neural networks continuously compress the feature size such that the discriminative patterns are submerged in the noise and thus difficult to be identified. This research decomposes the mixed input signals on the channel dimension such that the useful patterns can be separated. Moreover, this research employs self-attention mechanism to associate correlations between two modalities and use this correlation as a new feature for subsequent representation learning, generating new, compact, and complementary features for classification. We demonstrate that the proposed network achieves 98.25% accuracy and 3.5 ms prediction time. We anticipate that the proposed network could be a general scientific and practical methodology of multi-modality signal fusion and feature learning for intelligent systems.

Prostate cancer (PCa) is a malignancy originating from epithelial cells within the prostate gland. The gold standard for diagnosing PCa is typically based on the Gleason score. However, the inherent variability in biopsy sampling and its potential discordance with radical prostatectomy outcomes can result in the misclassification of the International Society of Urological Pathology (ISUP) Gleason Group (GG). Furthermore, the employment of prostate-specific antigen (PSA) for screening, guiding biopsy decisions, and the reduction in PSA biopsy thresholds has led to a notable increase in unwarranted biopsies among patients with PCa. Consequently, developing an efficient and accurate method to predict ISUP GG is imperative. Currently, most studies focus on ISUP GG binary classification for specific GG. In this study, we leverage multiparametric Magnetic Resonance Imaging (mpMRI) images, encompassing T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC), to introduce a post-fusion based cross-attention model named PFCA-Net. The model is designed to predict ISUP GG categories, specifically GG 0/1, GG 2, GG 3, and GG 4/5. We validate our approach using three distinct deep learning classification models and a mpMRI pre-fusion classification model. Our proposed approach demonstrates exceptional performance, attaining an accuracy (ACC) of 0.9692 and an area under the curve (AUC) of 0.9986, over a dataset comprising 107 PCa patients and encompassing a total of 927 MRI images. This study emphasizes the practical value of mpMRI in distinguishing various ISUP GG categories and expediting the evaluation of PCa GG by clinicians.

Complex network models are helpful to explain the evolution rules of network structures, and also are the foundations of understanding and controlling complex networks. The existing studies (e.g., scale-free model, small-world model) are insufficient to uncover the internal mechanisms of the emergence and evolution of communities in networks. To overcome the above limitation, in consideration of the fact that a network can be regarded as a pattern composed of communities, we introduce Turing pattern dynamic as theory support to construct the network evolution model. Specifically, we develop a Reaction-Diffusion model according to Q-Learning technology (RDQL), in which each node regarded as an intelligent agent makes a behavior choice to update its relationships, based on the utility and behavioral strategy at every time step. Extensive experiments indicate that our model not only reveals how communities form and evolve, but also can generate networks with the properties of scale-free, small-world and assortativity. The effectiveness of the RDQL model has also been verified by its application in real networks. Furthermore, the depth analysis of the RDQL model provides a conclusion that the proportion of exploration and exploitation behaviors of nodes is the only factor affecting the formation of communities. The proposed RDQL model has potential to be the basic theoretical tool for studying network stability and dynamics.

This letter presents a system to solve the vector-to-imagery building conflation problem. To drive the system, structure outlines in high-resolution images are extracted via a shape-driven level set scheme. Shape and relative position features are then computed for the image-extracted buildings and for vector graphics buildings from a geospatial information system (GIS). These two features are used by a graph-matching procedure that finds correspondences between the image-extracted buildings and those from a GIS. Extensions of our system to vector-to-vector building conflation, generic polygonal object conflation, and image-to-image registration are also possible.

As the availability of various geospatial data increases, there is an urgent need to integrate multiple datasets to improve spatial analysis. However, since these datasets often originate from different sources and vary in spatial accuracy, they often do not match well to each other. In addition, the spatial discrepancy is often nonsystematic such that a simple global transformation will not solve the problem. Manual correction is labor-intensive and time-consuming and often not practical. In this paper, we present an innovative solution for a vector-to-imagery conflation problem by integrating several vector-based and image-based algorithms. We only extract the different types of road intersections and terminations from imagery based on spatial contextual measures. We eliminate the process of line segment detection which is often troublesome. The vector road intersections are matched to these detected points by a relaxation labeling algorithm. The matched point pairs are then used as control points to perform a piecewise rubber-sheeting transformation. With the end points of each road segment in correct positions, a modified snake algorithm maneuvers intermediate vector road vertices toward a candidate road image. Finally a refinement algorithm moves the points to center each road and obtain better cartographic quality. To test the efficacy of the automated conflation algorithm, we used U.S. Census Bureau's TIGER vector road data and U.S. Department of Agriculture's 1-m multi-spectral near infrared aerial photography in our study. Experiments were conducted over a variety of rural, suburban, and urban environments. The results demonstrated excellent performance. The average correctness measure increased from 20.6% to 95.5% and the average root-mean-square error decreased from 51.2 to 3.4 m.

Presents a suite of indexes for comprehensively evaluating the results of automated building extraction. The indexes described include detection rate, correctness, matched overlay, area omission error, area commission error, root mean square error, corner difference, area difference, perimeter difference, and shape similarity. These proposed unbiased quality measures should enable the accuracy assessment of the building extraction process to address extraction issues such as completeness, geometric accuracy, and building shape similarity.