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6th International Conference on Signal and Image Processing, ICSIP 2021

作者： Liu, Ling Liu, Shuai Qu, Zhong Zhou, Dongyang University of Posts and Telecommunications School of Big Data and Software Engineering College of Mobile Telecommunications of Chongqing Chongqing China Chongqing University of Posts and Telecommunications College of Computer Science and Technology Chongqing China University of Posts and Telecommunications College of Mobile Telecommunications of Chongqing Department of Management Engineering Chongqing China

ISBN: (纸本)9780738133737

The ViBe (Visual Background Extraction) algorithm uses the first frame to initialize the pixel background model for each pixel, which may introduce the ghost regions in the following video frames. Although ViBe can use its background model update mechanism to eliminate the ghost regions, it is time-consuming. Firstly, a ghost elimination algorithm is proposed in this paper in order to reduce the elimination time for the ghost regions based on its properties. Secondly, a fixed global segmentation decision threshold is used to segment pixels in ViBe algorithm. However, because it cannot adapt to the dynamic background, a self-adaptive segmentation decision threshold based on single pixel is used to adjust to the different backgrounds. Finally, a self-adaptive update factor is presented, which makes the background model more reliable and adjusts to the background changes through the self-adaptive processing. The experimental result shows that this improved method can quickly eliminate the ghost regions, and adapt to the different kinds of backgrounds, ensuring more accurate results. © 2021 IEEE.

关键词： Pixels

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Policy Network Assisted Monte Carlo Tree Search for Intelligent Service Function Chain Deployment

Policy Network Assisted Monte Carlo Tree Search for Intellig...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zhihan Fu Qilin Fan Xu Zhang Xiuhua Li Sen Wang Yueyang Wang School of Big Data and Software Engineering Chongqing University Chongqing China College of Engineering Mathematics & Physical Sciences University of Exeter UK

ISBN: (纸本)9781665416597

Network function virtualization (NFV) simplies the coniguration and management of security services by migrating the network security functions from dedicated hardware devices to software middle-boxes that run on commodity servers. Under the paradigm of NFV, the service function chain (SFC) consisting of a series of ordered virtual network security functions is becoming a mainstream form to carry network security services. Allocating the underlying physical network resources to the demands of SFCs under given constraints over time is known as the SFC deployment problem. It is a crucial issue for infrastructure providers. However, SFC deployment is facing new challenges in trading off between pursuing the objective of a high revenue-to-cost ratio and making decisions in an online manner. In this paper, we investigate the use of reinforcement learning to guide online deployment decisions for SFC requests and propose a Policy network Assisted Monte Carlo Tree search approach named PACT to address the above challenge, aiming to maximize the average revenue-to-cost ratio. PACT combines the strengths of the policy network, which evaluates the placement potential of physical servers, and the Monte Carlo Tree Search, which is able to tackle problems with large state spaces. Extensive experimental results demonstrate that our PACT achieves the best performance and is superior to other algorithms by up to 30% and 23.8% on average revenue-to-cost ratio and acceptance rate, respectively.

关键词： Privacy Monte Carlo methods Service function chaining Reinforcement learning Network security Search problems software

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Simple and Efficient Knowledge Graph Attention Network for Recommendation

Simple and Efficient Knowledge Graph Attention Network for R...

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Cyber-Physical Social Intelligence (ICCSI), International Conference on

作者： Yangding Li Shaobin Fu Hao Feng Yangyang Zeng Jinghao Wang Zhihao Jiang Lvyun Zhang School of Information Science and Engineering Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Shenzhen Lazada Software Technology Company Limited Shenzhen China School of Big Data and Computer Science Hechi University Guangxi China

Existing methods for modeling recommendation systems based on knowledge graphs include embedding-based, pathbased, and propagation-based methods. The embedding-based approach is flexible but more suitable for intra-graph applications, the path-based approach can model complex relationships but has a high computational cost, and the propagation-based approach considers global information but may introduce noise. This study proposed a simple and efficient model, called SEKGAT, which comprehensive the ideology of path-based and propagation approach to personalized recommendation by aggregating the user preferences through graph attention mechanism and fusing multiple feature representations on the knowledge graph into item features through pooling aggregators. Experimental results for the CTR prediction and Top-K recommendation tasks on three datasets of real-world scenarios show that our model approach is competitive.

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DFDM: A Deep Feature Decoupling Module for Lung Nodule Segmentation

DFDM: A Deep Feature Decoupling Module for Lung Nodule Segme...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Wei Chen Qiuli Wang Sheng Huang Xiaohong Zhang Yucong Li Chen Liu School of Big Data and Software Engineering Chongqing University Chongqing China Hospital of Army Medical University Chongqing China

In this paper, we propose a novel feature decoupling method to tackle two critical problems in the lung nodule segmentation task: (i) ambiguity of nodule boundary leads to the imprecise segmentation boundary and (ii) the high false positive rate of segmentation result. Our motivation is that an accurate segmentation network needs explicitly modeling the nodule boundary and texture information, and suppressing the noise information. To do so, a novel Deep Feature Decoupling Module (DFDM) is proposed to decouple the nodule boundary, noise, and texture information from the original feature maps. The decoupled boundary and texture information is used to benefit the segmentation, and the noise information is removed from the input features to reduce the false positive rate. The proposed DFDM consists of three parallel branches, including Boundary Sensitive Branch (BSB), Noise Removal Branch (NRB), and Texture Preserving Branch (TPB) to decouple the mentioned three information, respectively. In particular, we design our BSB with a novel architecture to effectively capture the boundary information of lung nodules. We apply the proposed DFDM to the U-Net architecture and achieve convincing segmentation results on the LIDC-IDRI dataset.

关键词： Convolutional codes Image segmentation Image analysis Convolution Conferences Semantics Lung

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Multiband Cooperative Spectrum Sensing Meets Vehicular Network: Relying on CNN-LSTM Approach

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Lu, Lingyun Li, Xiang Wang, Guizhu Ni, Wei School of Software Engineering Beijing Jiaotong University Beijing100091 China School of Computer and Information Technology Beijing Jiaotong University Beijing100091 China Rizhao Big Data Development Bureau Rizhao276826 China The Commonwealth Scientific and Industrial Research Organisation Sydney Australia

A vehicular network is expected to empower all aspects of the intelligent transportation system (ITS) and aim at improving road safety and traffic efficiency. In view of the fact that spectrum scarcity becomes more severe owing to the increasing number of connected vehicles, implying spectrum sensing technology in vehicular network, i.e., cognitive vehicular network, has emerged as a promising solution to provide opportunistic usage of licensed spectrum. However, some unique features of vehicular networks, such as high movement and dynamic topology, take on high challenges for spectrum sensing. Recently, machine learning-based approaches, especially deep learning, for spectrum sensing have attracted sufficient interest. In this work, we investigate a learning-based cooperative spectrum sensing (CSS) approach for multiband spectrum sensing in the cognitive vehicular network. Specifically, we integrate two powerful deep learning models, i.e., the convolutional neural network (CNN) to exploit the features from sensing data, and the long-short-term memory (LSTM) network is then utilized to extract temporal correlations given input as the generated features by the CNN structure. Instead of the predefined decision threshold typically set in conventional approaches, our proposed approach could eliminate the impact of impertinent threshold value setting. Extensive simulations have been conducted to evaluate the effectiveness of the proposed method in achieving satisfactory spectrum sensing performance, particularly in terms of higher detection accuracy, robustness in low signal-to-noise ratio (SNR) environments, and a significant reduction in spectrum sensing time compared to other methods. © 2023 Lingyun Lu et al.

关键词： Long short-term memory

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HyperDiff: Masked Diffusion Model with High-efficient Transformer for Hyperspectral Image Cross-Scene Classification

HyperDiff: Masked Diffusion Model with High-efficient Transf...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Pei Zhang Dong Wang Chanyue Wu Jing Yang Lei Kang Zongwen Bai Ying Li Qiang Shen School of Computer Science Northwestern Polytechnical University Xi’an China Shaanxi Key Laboratory of Intelligent Processing for Big Energy Data Yan’an University Yan’an China School of Automation and Software Engineering Shanxi University Taiyuan China Qingdao Topscomm Communication Co. Ltd Qingdao China Department of Computer Science Aberystwyth University UK

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Hyperspectral Image (HSI) cross-scene classification is a challenging task in remote sensing, particularly when real-time processing of Target Domain (TD) HSI is required, and data cannot be reused for training. While deep learning methods have shown promising results, the generalization ability of HSI representations remains limited, mainly due to class label imbalance. This paper introduces a dual-stage learning framework based on transfer learning to enhance classification accuracy in the TD. The framework includes a self-supervised learning stage and a supervised fine-tuning stage. The self-supervised stage focuses on learning robust representations by leveraging inherent structures within HSI data, while the fine-tuning stage uses training labels to extract semantic information. A masked diffusion model predicts masked tokens from unmasked ones, capturing both high-level structures and fine details in HSI data. An efficient spatiospectral Transformer, which removes self-attention from the decoder, is proposed to enhance the self-supervised process. This design allows mask tokens to obtain information from visible tokens without interacting with each other, reducing sequence length and computational costs. By decoding each mask token conditionally independently, only a subset of masked tokens is processed. Extensive experiments on two public HSI datasets demonstrate that the proposed method outperforms state-of-the-art techniques.

关键词： Training Transfer learning Self-supervised learning Transformers Diffusion models Real-time systems Decoding Computational efficiency Speech processing Hyperspectral imaging

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A data Quality Assessment Framework for AI-enabled Wireless Communication

arXiv

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arXiv 2022年

作者： Tang, Hanning Yang, Liusha Zhou, Rui Liang, Jing Wei, Hong Wang, Xuan Shi, Qingjiang Luo, Zhi-Quan Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China Wireless Research Department Huawei Company Shanghai China School of Software Engineering Tongji University Shanghai China

Using artificial intelligent (AI) to re-design and enhance the current wireless communication system is a promising pathway for the future sixth-generation (6G) wireless network. The performance of AI-enabled wireless communication depends heavily on the quality of wireless air-interface data. Although there are various approaches to data quality assessment (DQA) for different applications, none has been designed for wireless air-interface data. In this paper, we propose a DQA framework to measure the quality of wireless air-interface data from three aspects: similarity, diversity, and completeness. The similarity measures how close the considered datasets are in terms of their statistical distributions;the diversity measures how well-rounded a dataset is, while the completeness measures to what degree the considered dataset satisfies the required performance metrics in an application scenario. The proposed framework can be applied to various types of wireless air-interface data, such as channel state information (CSI), signal-to-interference-plus-noise ratio (SINR), reference signal received power (RSRP), etc. For simplicity, the validity of our proposed DQA framework is corroborated by applying it to CSI data and using similarity and diversity metrics to improve CSI compression and recovery in Massive MIMO systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Channel state information

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Algorithm of Controllable Fractal Image Based on IFS Code

Algorithm of Controllable Fractal Image Based on IFS Code

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Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Xuejiao Tao Sen Bai Chun Liu Change Zhu Haojun Chen Yi Yan School of Software Chongqing Institute of Engineering Chongqing China School of Big Data and Artificial Intelligence Chongqing Institute of Engineering Chongqing China

ISBN: (数字)9781728190044

ISBN: (纸本)9781728190051

Simulated generation of fractal images is useful in various fields such as information steganography, art decoration, film and television scenes. However, the generation mechanism of fractal images has not been clarified, the function of generation parameters is not clear, and the generated fractal object is insufficiently controllable, which limit the application. Therefore, it is of great significance to explore the role of various affine transformation parameters in the IFS system to obtain the method of generating free and controllable fractal images. Based on the principle of generating fractal images by iterated function system, this paper analyzed the influence of the generation of each affine transformation of IFS code on the appearance of the fractal image. Fractal images with different transformation characteristics were generated by analyzing the types and variation law of affine transformation. The controllability parameters of the IFS code were adjusted to simulate different forms of fractal trees and fractal mountains, and to construct more natural fractal forest and fractal mountains based on this, so as to provide more controllable fractal images for application fields such as information steganography.

关键词： TV Shape Simulation Forestry Vegetation Fractals Power electronics

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Lightweight helmet detection algorithm based on improved YOLOv5 2

Lightweight helmet detection algorithm based on improved YOL...

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2nd International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2022

作者： Li, Jianning Zhang, Junzhao Zhang, Xinyu Wang, Shuai College of Electrical and Power Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China Software Institute Nanjing University Jiangsu Nanjing210093 China Shanxi Electric Drive and Internet of Things Engineering Research Center Shanxi Taiyuan030024 China Shanxi Province 1331 Project-Smart City Based on Big Data Lighting Data Sharing and Public Service Engineering Technology Research Center Shanxi Taiyuan030024 China Shanxi Intelligent Digital Lighting Union Laboratory Shanxi Taiyuan030024 China Shanxi Intelligent Digital Cultural Tourism Industry Research Institute Shanxi Taiyuan030024 China

ISBN: (纸本)9781510660571

In the construction process, wearing safety helmet can effectively reduce the injury caused by falling objects and hard objects. In order to detect the wearing of safety helmet during the construction process, a lightweight safety helmet wearing detection algorithm YOLOv5-CS is proposed and applied to the embedded equipment at the construction site. The algorithm is based on the improvement of YOLOv5 target detection algorithm. The improved CAShuffle module is used to form the backbone feature extraction network, which reduces the size of the original algorithm and ensures the detection accuracy of the algorithm;DIoU as the loss function, which makes the algorithm more conducive to detecting small targets such as hard hats;modify dataset, add the dataset on the basis of the original dataset and mark the safety helmet and the anchor frame of the person separately, and judge whether the worker wears the safety helmet by analyzing positional relationship of the identified anchor frame, use the algorithm combining K-means and genetic algorithm to predict the anchor frame of the dataset, and use Mosaic algorithm to enhance the data. The results show that the parameter quantity of YOLOv5-CS algorithm is 2.4M, less than one third of YOLOv5;The average precision decreased by only 4.2%;640x640 pictures can be recognized at 31 frames per second on Jetson Nano, a embedded platform. © 2022 SPIE.

关键词： Signal detection

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Cooperative Sensing Via Matrix Factorization of the Partially Received Sample Covariance Matrix

Cooperative Sensing Via Matrix Factorization of the Partiall...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Rui Zhou Wenqiang Pu Licheng Zhao Ming-Yi You Qingjiang Shi Sergios Theodoridis Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China National Key Laboratory of Electromagnetic Space Security Jiaxing China School of Software Engineering Tongji University Shanghai China Department of Electronics Systems Aalborg University Denmark Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece

A fundamental problem in cognitive radio is spectrum sensing, which detects the presence of the primary users in a licensed spectrum. To boost the detection performance and robustness, the multiantenna detector has been investigated and various related methods have been developed, e.g., the energy detector, the eigenvalue arithmetic-to-geometric mean detector, and the generalized likelihood ratio test detector. Cooperative sensing, which makes use of multiple receivers distributed in different locations, has the advantage of being able to make full use of the distributed antennas and enjoy a high spatial diversity gain. However, the successful employment of cooperative sensing depends on the reliable information exchange among the cooperating receivers over a long range, which may be impractical for real-world scenarios. In this paper, we consider the scenario where each receiving node can only broadcast its received raw data in a short-range communication fashion. We propose a novel cooperative sensing scheme by allowing each node to send to the fusion center only local correlation coefficients, computed within a neighborhood. A detection algorithm, based on matrix factorization of the partially received sample covariance matrix, i.e., with missing entries, is proposed. The performance of our proposed cooperative scheme is verified via numerical experiments.

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