Distributed fiber optic vibration sensing system has been widely used in safety monitoring with distinct advantages, and the feature extraction and classification methods of fiber optic signals directly determine the real-time performance and reliability of the monitoring system. The existing research feature extraction methods are unidimensional and time consuming, which cannot balance the goals of high accuracy and low time consumption for safety monitoring systems. In this paper, we propose an efficient recognition framework for fusing signal time-frequency features, called TFF-CNN, based on Gramian Angular Difference Fields(GADF) and FFT co-generation matrix(FFTT) to manually extract two-dimensional time-frequency image features of fiber optic signals, taking the significant advantages of CNN in image processing and combining a two-channel model and a fusion module that simulates human decision-making behavior. The accuracy of TFF-CNN is 99.30% and the detection response time is only 0.6s. Compared with other methods in this field, TFF-CNN has the advantages of low false alarm rate and short time consumption, which is more suitable for deployment in security monitoring field with distributed fiber optic sensing system. Index Terms: Distributed optical fiber vibration sensing system(DVS), Two-dimensional multi-feature, CNN, intrusion detection, real-time monitoring

Video Anomaly Detection (VAD) is a critical technology for intelligent surveillance systems and remains a challenging task in the signal processing community. An intuitive idea for VAD is to use a two-stream network to learn appearance and motion normality, respectively. However, existing approaches usually design a network architecture for the appearance stream with effort, then apply a similar architecture to the motion stream, ignoring the unique appearance and motion characteristics. In this paper, we propose STGCN-FFP, an unsupervised Spatial-Temporal Graph Convolutional Networks (STGCN) boosted Flow-Frame Prediction model. Specifically, we first design an STGCN-based memory module to extract and memorize normal patterns for optical flow, which is more suitable for learning motion normality. Then, we use a memory-augmented auto-encoder to model normal appearance patterns. Finally, the latent representation of two streams is fused to predict future frames, boosting the model to learn spatial-temporal normality. To our knowledge, STGCN-FFP is the first work applying STGCN to uniquely model the motion normality. Our method performs comparably to the state-of-the-art methods on three benchmarks.

Estimating the complete 3D point cloud from a partial input is a key challenge in 3D vision. Existing point cloud completion networks overlook the long-range, hierarchical features and object details of the incomplete point cloud. To this end, we propose Hierarchical Feature Fusion Network (HFF-Net) for precise and detailed point cloud completion. To succeed at this task, HFF-Net estimates the missing Point Agents (PAs) by designing a topology-aware transformer-based encoder-decoder network with Multi-level Feature Learning (MFL), which hierarchically exploits the various regional and detailed information. Further, to make better utilization of the hierar-chical information captured from MFL, we devise the Hier-archical Features Fusion (HFF) module to convert them into cross-regional features. Besides, the predicted PAs is utilized by a multi-resolution output module to recover the missing point cloud in a coarse-to-fine manner. Experiments indi-cate that HFF-Net performs favorably against state-of-the-art (SOTA) approaches on both the new-proposed and existing datasets.

Artificial intelligence (AI) and 5G system have been two hot technical areas that are changing the world. On the deep convergence of computing and communication, networking systems of AI (NSAI) is presenting a paradigm shift, where distributed AI becomes immersive in all elements of the network, i.e., cloud, edge, and terminal devices, which make AI virtually operating as a networking system. On the other hand, by the evolution of the communication systems, a network is becoming a service-specific system interweaved with AI, i.e., the network operates as an AI system, enabling real-time smart services. With the developing technology trends of “AI as a network and network as an AI,” the ecosystem of NSAI can be presenting the next-generation waves of both AI systems and B5G-6G communication networks. In this article, we mainly aim to provide a comprehensive survey on the system architecture, key technologies, application scenarios, challenges, and opportunities of NSAI, which can shed light on the future developments of both telecommunications and AI computing. The contributions of this article also include: 1) providing a unified framework for the deep convergence of computing and communications, where the network and application/service can be jointly optimized as a single integrated system and 2) suggesting the roadmap and open research problems in realizing the online-evolutive integration of cyberspace, physical world, and human society, toward the ubiquitous brain networks (UBNs), which are requiring the joint efforts from both research communities of computing and communication.

Point cloud completion is a generation and estimation issue derived from the partial point clouds, which plays a vital role in the applications of 3D computer vision. The progress of deep learning (DL) has impressively improved the capability and robustness of point cloud completion. However, the quality of completed point clouds is still needed to be further enhanced to meet the practical utilization. Therefore, this work aims to conduct a comprehensive survey on various methods, including point-based, view-based, convolution-based, graph-based, generative model-based, transformer-based approaches, etc. And this survey summarizes the comparisons among these methods to provoke further research insights. Besides, this review sums up the commonly used datasets and illustrates the applications of point cloud completion. Eventually, we also discussed possible research trends in this promptly expanding field.

Addressing on monocular visual odometry problem, this paper presents a novel end-to-end network for estimation of camera ego-motion. The network learns the latent space of optical flow (OF) and models sequential dynamics so that the motion estimation is constrained by the relations between sequential images. We compute the OF field of consecutive images and extract the latent OF representation in a self-encoding manner. A Recurrent Neural Network is then followed to examine the OF changes, i.e., to conduct sequential learning. The extracted sequential OF latent space is used to compute the regression of the 6-dimensional pose vector. Particularly, we separately train the encoder in an unsupervised manner. By this means, we avoid non-convergence during the training of the whole network and allow more generalized and effective feature representation. Substantial experiments have been conducted on KITTI and Malaga datasets, and the results demonstrate that our model outperforms most learning-based VO approaches.

Time series is very ubiquitous in both the industrial environment and real-life. Capturing the time dependency is very useful for time series analysis. Although the one-dimensional local binary pattern (1D-LBP) can analyze the Univariate time series, it cannot effectively handle the multivariate time series (MTS). The UTS can be considered as zero-order tensor time series (TTS), while the MTS can be considered as one- or higher order TTS. Each variable in MTS depends not only on its past values, but also on the other variables. In this article, we propose a temporal tensor LBP (TTLBP) operator to extract the discriminative temporal features from TTS by extending 1D-LBP operation from the scalar-wise to the tensor-wise. The TTLBP is discriminative and can handle the TTS effectively and straightforwardly. To the best of our knowledge, TTLBP is the first LBP variant for TTS analysis. Furthermore, we also propose a stricter uniform TTLBP to improve robustness and to reduce the high dimensionality. We apply the proposed TTLBP in the edge intelligence-assisted video anomaly detection system. Qualitative and quantitative comparisons demonstrate the effectiveness of the proposed TTLBP.

Local binary pattern (LBP) is one of the most successful feature descriptors. However, LBP and its variants have not been as successful as other feature descriptors in video anomaly detection (VAD). This is because LBP and its variants are mainly designed for spatial texture analysis. Although the volume LBP (VLBP) and the LBP-three orthogonal planes (LPB-TOP) have the capability of describing dynamic texture, they are seldom used as descriptors for VAD because 1) both VLBP and LBP-TOP are more suitable for natural scenes with rich dynamic textures, but sensitive to noise in the scenes with less dynamic textures, 2) the combination of motion and appearance not only limits their capability of motion characterizing but also brings the irrelevant appearance information such as background, and 3) high dimensionality is another drawback. In this paper, a new variant of the LBP called the squirrel-cage LBP (SCLBP) is proposed for VAD. By imitating the structure of squirrel cage rotor, the proposed SCLBP can be regarded as a stretched LBP in temporal direction and has two distinct features: 1) it is computed at vector-wise, rather than at pixel-wise (i.e., for the central vector with respect to its surrounding parallel vectors), and 2) the sign between two vectors is determined by the angle-based thresholding scheme. The SCLBP can effectively encode the motion information and is insensitive to noise and irrelevant disturbances caused by dynamic background and illumination change. To the best of our knowledge, The SCLBP is the first variant of the LBP specially designed for motion characterizing. The SCLBP has great flexibility, extendibility, and low dimensionality (only one-third of the LBP-TOP descriptor). The effectiveness of the proposed SCLBP descriptor is demonstrated on different public data sets and compared with the other dominant descriptors and state-of-the-art approaches in VAD.

Existing anomaly detection (AD) approaches rely on various hand‐crafted representations to represent video data and can be costly. The choice or designing of hand‐crafted representation can be difficult when faced with a new dataset without prior knowledge. Motivated by feature learning, e.g. deep leaning and the ability to directly learn useful representations and model high‐level abstraction from raw data, the authors investigate the possibility of using a universal approach. The objective is learning data‐driven high‐level representation for the task of video AD without relying on hand‐crafted representation. A deep incremental slow feature analysis (D‐IncSFA) network is constructed and applied to directly learning progressively abstract and global high‐level representations from raw data sequence. The D‐IncSFA network has the functionalities of both feature extractor and anomaly detector that make AD completion in one step. The proposed approach can precisely detect global anomaly such as crowd panic. To detect local anomaly, a set of anomaly maps, produced from the network at different scales, is used. The proposed approach is universal and convenient, working well in different types of scenarios with little human intervention and low memory and computational requirements. The advantages are validated by conducting extensive experiments on different challenge datasets.