Along with the development of the Industrial Internet of Things, human activity recognition (HAR) has received widespread attention in many fields. Support Vector Machine (SVM), is widely used by researchers for human activity recognition. However, the inherent difference of signal properties from different sensors and various orientations is potentially lost when using the vector-based SVM for human activity recognition. What's more, the outlier sensitivity problem of the standard SVM reduces the accuracy of human activity recognition. To tackle this problem, we present a tensor-based feature representation model and a weighted support tensor machine (WSTM) for human activity recognition. Specifically, tensor-based representations are first used to model features from different sensors and various orientations to retain the latent relationship. In addition, the weighted support tensor machine is proposed to classify the human activities in tensor space while avoiding the outlier sensitivity problem. Experimental results demonstrate the proposed WSTM algorithm.

Stereo image Super-Resolution (SR) aims to enhance image resolution by leveraging complementary information in stereo pairs. Convolutional Neural Networks (CNNs), widely used in stereo image SR for their strong local pattern extraction capabilities, often fail to capture long-range dependencies critical for stereo correspondence. On the other hand, Swin Transformers have demonstrated superior performance in modeling long-range dependencies for stereo image SR tasks. However, their computational complexity scales quadratically with the window size, leading to a trade-off between global receptive fields and computational efficiency. To tackle these challenges, we propose StereoMamba+, a novel stereo image SR method designed to adaptively capture both local and global dependencies in stereo pairs. Leveraging the Mamba architecture as its backbone, StereoMamba+ integrates an Adaptive State Space Module (ASSM) that efficiently extracts and fuses global and local features, maintaining linear computational complexity. Additionally, a Gated Enhanced Feed-Forward Network (GEFN) selectively amplifies essential features and depth cues, and a Residual Frequency Block (RFB) is employed to capture global features in the frequency domain. To further enhance stereo correspondence, we introduce a Stereo Bi-Directional Cross Attention Module (SBCAM), aligning unique features along both horizontal and vertical epipolar lines to improve stereo consistency. Extensive experiments demonstrate that our proposed StereoMamba+ method achieves state-of-the-art performance on 2× and 4× stereo image SR tasks, delivering PSNR improvements of up to 0.45dB, while maintaining competitive parameter efficiency compared to existing methods.

Stereo image super-resolution (SR) aims to enhance image resolution by leveraging the complementary information from stereo image pairs. While convolutional neural network (CNN)-based methods have traditionally dominated this field, they struggle with capturing long-range dependencies. Transformer-based approaches have shown improvements by better modeling long-range dependencies, but their computational complexity scales quadratically with respect to the window length. To address these challenges, in this paper we propose StereoMamba, a new stereo image super-resolution method built on Structured State Space Models (SSMs). StereoMamba leverages the Mamba architecture to effectively capture long-range dependencies and inter-view correlations in stereo image pairs. Additionally, we introduce a Stereo Bi-directional Cross-Attention Module (SBCAM) to further improve stereo view correlation. Extensive experiments show that StereoMamba consistently surpasses state-of-the-art methods across several public datasets.

Abstract The airport line plays an important role in the airport landside traffic connection, which greatly relieves the urban road pressure. Studying the characteristics and laws of the time and space distribution of the airport line passenger flow will provide basic support for the airport line's capacity configuration, passenger flow control, and air‐track coordinated operation, and will also provide quantitative basis for the layout of urban terminal buildings. This paper proposes a method to analyse the temporal and spatial distribution characteristics of airport line passenger flow based on AFC card swiping data. By mining and extracting the travel behaviour data of subway airport line air passengers, the distribution characteristics of airport line passenger flow are analysed and visualized from two aspects of passenger travel time and space. Finally, a case study is carried out based on the actual data of Daxing airport express. The results show that: (1) the travel time of departure and return passengers is concentrated in 60–90 min; (2) The departure and arrival points of passengers are concentrated within the Fourth Ring Road, and a large number of air passenger flows are gathered and evacuated at hubs such as Beijing South Railway Station and Beijing West Railway Station.

Timely road condition inspection and maintenance are key components of infrastructure management for smart cities, as they reduce traffic congestion, accidents and repairing costs. Traditional road inspection methods that employ vibrations and/or laser scanning for detecting road deterioration use expensive equipment and dedicated municipality vehicles. Recently, computer vision techniques and artificial intelligence are emerging as alternative solutions to traditional approaches for road condition detection, offering more flexibility, higher accuracy, and overall lower cost. In this paper, we utilize convolutional neural network-based and vision transformer-based object detection models to accurately identify road deteriorations namely, potholes, cracks, and alligators. We compare four different state-of-the-art models in terms of detection accuracy and speed. Performance evaluations have shown that, on the same dataset the Swin Transformer model outperformed the other state-of-the-art methods by a substantial margin. With 74% detection accuracy, and 42 frames per second processing speed Swin Transformer exceled over EfficentDet, YOLOv4, and YOLOX. We also present a new comprehensive and balanced large-scale road condition dataset of 27,298 annotated images, captured by ordinary car cameras.

Video surveillance techniques like scene segmentation are playing an increasingly important role in multimedia Internet-of-Things (IoT) systems. However, existing deep learning-based methods face challenges in both accuracy and memory when deployed on edge computing devices with limited computing resources. To address these challenges, a tensor-train video scene segmentation scheme that compares the local background information in regional scene boundary boxes in adjacent frames is proposed. Compared to the existing methods, the proposed scheme can achieve competitive performance in both segmentation accuracy and parameter compression rate. In detail, first, an improved faster region convolutional neural network (faster-RCNN) model is proposed to recognize and generate a large number of region boxes with foreground and background to achieve boundary boxes. Then, the foreground boxes with sparse objects are removed and the rest are considered as optional background boxes used to measure the similarity between two adjacent frames. Second, to accelerate the training efficiency and reduce memory size, a general and efficient training way using tensor-train decomposition to factor the input-to-hidden weight matrix is proposed. Finally, experiments are conducted to evaluate the performance of the proposed scheme in terms of accuracy and model compression. Our results demonstrate that the proposed model can improve the training efficiency and save the memory space for the deep computation model with good accuracy. This work opens the potential for the use of artificial intelligence methods in edge computing devices for multimedia IoT systems.

Supervised machine learning algorithms, especially classification algorithms, have been widely used in data analysis of industrial big data. Among them, the support vector machine (SVM) has achieved great success in the binary classification of some areas like image processing, computer vision, and pattern recognition. However, an SVM cannot achieve the desirable classification results for heterogeneous and high-dimensional data generated from thousands of industrial sensors in physical environments, because the traditional vector-based and feature-aligned SVM algorithm may result in loss of structural information and rich context information. Although the support tensor machine (STM) has extended the traditional vector-based SVM to tensor space, it fails to deal with multiple classification problems. Therefore, designing a general multiple classification algorithm for heterogeneous and high-dimensional data is a challenging but promising topic. To achieve this goal, this article presents a support multimode tensor machine (SMTM) algorithm by applying the multimode product to generalize the formulation of the STM. Furthermore, this article presents an efficient algorithm to train the parameters. Experiments conducted on various data sets validate the better performance of the SMTM over other algorithms in the multiple classification and imply the potential of the proposed model for multiple classification on industrial big data.