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Incremental Massive Random Access Exploiting the Nested Reed-Muller Sequences

IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2021年第5期20卷 2917-2932页

作者： Wang, Jue Zhang, Zhaoyang Zhong, Caijun Hanzo, Lajos Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Int Joint Innovat Ctr Haining 314400 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netw Hangzhou 310027 Peoples R China Univ Southampton Dept Elect & Comp Sci Southampton SO17 1BJ Hants England

Massive machine-type communication (mMTC) is expected to provide reliable and low-latency connectivity for an enormous number of devices, which turn active sporadically or frequently. In this highly dynamic situation, it is crucial to design efficient random access (RA) procedures to cope both with the flood of simultaneous access requests and with the potential access failures. In this article, by exploiting the large sequence space, the excellent correlation property and especially the elegant nested structure of Reed-Muller (RM) sequences, we propose a new RA scheme, which facilitates both instantaneous access for newly active users and incremental access for the existing users who suffer from detection failures. In particular, when a failure occurs, the user continues accessing the channel employing an expanded RM sequence, which is combined with the previously received ones at the access point (AP) to form a longer sequence so as to attain potentially better detection probability. Furthermore, a recursive detection algorithm is designed for jointly detecting the resultant RM sequences and the channel coefficients of both the newly active users and the existing ones. The performance of the proposed algorithm is analyzed in detail. Our simulation results validate the analysis and show the scheme's superior access probability, access latency and computational complexity.

关键词： Wireless communication Symmetric matrices detection algorithms Probability density function Matrix converters Resource management Protocols mMTC massive random access Reed-Muller sequences multi-user detection

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A Privacy-Preserving Iot-Based Fire Detector

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IEEE ACCESS 2021年 9卷 51393-51402页

作者： Altowaijri, Abdullah H. Alfaifi, Mohammed S. Alshawi, Tariq A. Ibrahim, Ahmed B. Alshebeili, Saleh A. King Saud Univ Elect Engn Dept Riyadh 11421 Saudi Arabia King Saud Univ KACST TIC Radio Frequency & Photon RFTONICS Riyadh 11421 Saudi Arabia

Fire detection has been an issue of interest to researchers due to its significant damage to lives and property within a very short time. One of the recent solutions developed to detect fire is to use Internet-of-Things (IoT) devices equipped with cameras for surveillance. The captured videos of surroundings may be processed by the IoT devices themselves or at the cloud. The latter case is required if the detection algorithm is computationally demanding. However, the use of cloud has a flaw. In fact, using the cloud could pose the threat of having the privacy of a place violated, either through hacking or unauthorized access to the footage of the place where the cloud is installed. In this paper, a fire detection system that preserves the privacy of surroundings, while maintaining a high level of accuracy for fire detection is proposed. The proposed system makes use of the cloud for fire detection;and that is achieved by sending to the cloud features extracted from the video captured by the IoT device, instead of sending the actual footage. Binary video descriptors and Convolutional Neural Network (CNN) have been used to develop the fire detection algorithm. The video descriptors are used to extract features, while the CNN is used for classification. Videos with real fire and non-fire scenes have been used in this development. Results show that the performance of proposed fire detection algorithm can achieve 97.5% classification accuracy, that outperforms the state-of-the art algorithms which make direct use of raw videos. Therefore, the proposed fire detector is as reliable as other available systems, with the advantage of having a privacy-preserving capability. It is also demonstrated that the proposed video descriptors can be implemented for real-time processing using an IoT device, Raspberry Pi 4 platform, with an average processing speed of 100ms per frame, which well satisfies practical needs.

关键词： Videos Feature extraction Sensors Detectors detection algorithms Cloud computing Temperature sensors Fire detection image descriptors features extraction pattern recognition convolutional neural network IoT devices

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A Pruning Optimized Fast Learn plus plus NSE Algorithm

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IEEE ACCESS 2021年 9卷 150733-150743页

作者： Chen, Yong Zhu, Yuquan Chen, Haifeng Shen, Yan Xu, Zhao Jiangsu Univ Sch Comp Sci & Commun Engn Zhenjiang 212013 Jiangsu Peoples R China China Unicom Internet Things Co Ltd Nanjing 210014 Jiangsu Peoples R China Jiangsu Univ Dept Informat Management & Informat Syst Zhenjiang 212013 Jiangsu Peoples R China Nanjing Longyuan Microelect Technol Co Ltd Nanjing 211106 Jiangsu Peoples R China

Due to the large number of typical applications, it is very important and urgent to study the fast classification learning of accumulated big data in nonstationary environments. The newly proposed algorithm, named Learn++.NSE, is one of the important research results in this research field. And a pruning version, named Learn++.NSE-Error-based, was given for accumulated big data to improve the learning efficiency. However, the studies have found that the Learn++.NSE-Error-based algorithm often encounters a situation that the newly generated base classifier is pruned in the next integration, which reduces the accuracy of the ensemble classifier. The newly generated base classifier is very important in the next ensemble learning and should be retained. Therefore, the two latest base classifiers are reserved without being pruned, and a new pruning algorithm named NewLearn++.NSE-Error-based was proposed. The experimental results on the generated dataset and the real-world dataset show that NewLearn++.NSE-Error-based can further improve the accuracy of the ensemble classifier under the premise of obtaining the same time complexity as Learn++.NSE algorithm. It is suitable for fast classification learning of long-term accumulated big data.

关键词： Classification algorithms Big Data Error analysis detection algorithms Data models Batch production systems Probability distribution Ensemble learning nonstationary environment classification algorithm big data mining

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A Maximum Likelihood Perspective of Spatial Filter Design in SSVEP-Based BCIs

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IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 2021年第9期68卷 2706-2717页

作者： Wang, Zhenyu Zhao, Xi Zhang, Min Hu, Honglin Chinese Acad Sci Shanghai Adv Res Inst Shanghai 201210 Peoples R China Univ Chinese Acad Sci Beijing Peoples R China ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China

In steady-state visual-evoked potential (SSVEP) based brain-computer interfaces (BCIs), existing detection algorithms utilizing spatial filters like task-related component analysis (TRCA) derive the spatial filters mainly through maximizing the inter-trial similarity between the combined signals over the training set. Although they achieve by far the best classification performance in SSVEP-based BCIs, some important problems are still unresolved. Especially, the mechanism of how spatial filters cancel the background noise in brain signals and optimize the signal-to-noise ratio (SNR) of SSVEPs is still not figured out. Therefore, to solve these problems, in this paper a new perspective of spatial filter design is proposed. Specifically, a linear generative signal model of SSVEP is adopted and the spatial filters are obtained automatically through maximum likelihood estimation of source signals and channel vectors. In the same time, the relation between maximum likelihood estimation and signal-to-noise ratio (SNR) maximization is discussed. Through a step-by-step formulation, this paper provides a theoretical justification for those conventional algorithms utilizing spatial filters. As for the classification performance, the proposed scheme is tested on a benchmark dataset of 35 subjects. Experiment results show that the classification performance of the proposed scheme is competitive against three benchmark algorithms, which include TRCA. Especially, the proposed scheme achieves a fair performance improvement over the benchmark methods in the cases where a shorter time window, or a larger number of electrodes, or a smaller number of training blocks are adopted.

关键词： Correlation Training detection algorithms Signal to noise ratio Maximum likelihood estimation Task analysis Maximum likelihood detection BCI detection algorithm maximum likelihood estimation SNR maximization SSVEP spatial filter

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Research on Detecting Bearing-Cover Defects Based on Improved YOLOv3

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IEEE ACCESS 2021年 9卷 10304-10315页

作者： Zheng, Zehao Zhao, Ji Li, Yue Univ Sci & Technol Liaoning Sch Comp Sci & Software Engn Anshan 114051 Peoples R China

Detecting defects, which is a branch of target detection in the field of computer vision, is widely used in factory production. To solve the problems in existing detection algorithms that relate to their insensitivity to large or medium defect targets on bearing covers, their difficulty in detecting subtle defects effectively and their lack of real-time detection, in this work, we establish a large-scale bearing-cover defect dataset and propose an improved YOLOv3 network model. The proposed model is divided into four submodels: the bottleneck attention network (BNA-Net), the attention prediction subnet model, the defect localization subnet model, and the large-size output feature branch. To test the generality, robustness and practicability of the new model, we design a comparative experiment under abnormal illumination conditions. We design an ablation experiment to verify the validity of the proposed submodules. The experimental results show that our model solves the problem of the YOLOv3 algorithm's insensitivity to medium or large targets and satisfies real-time detection conditions. The mAP result is 69.74%, which is 16.31%, 13.4%, 13%, 10.9%, and 7.2% more than that of YOLOv3, EfficientDet-D2, YOLOv5, YOLOv4, and PP-YOLO, respectively.

关键词： Feature extraction Convolution Production detection algorithms Object detection Deep learning Real-time systems Attention convolutional neural network defect detection multiscale feature fusion YOLOv3

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Successive-Coded Spatial Shift Keying Modulation for MIMO Wireless Communications

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IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第10期69卷 6516-6528页

作者： Wu, Liang Zhang, Zaichen An, Bo Dang, Jian Liu, Huaping Wang, Jiangzhou Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Oregon State Univ Sch Elect Engn & Comp Sci Corvallis OR 97331 USA Univ Kent Sch Engn & Digital Arts Canterbury CT2 7NT Kent England

Spatial domain modulation employs spatial degree of freedom to convey information bits. Traditional spatial domain modulation schemes include spatial modulation, spatial shift keying (SSK), and variations of these two schemes. To fully make use of the spatial degree of freedom and improve the achievable data rate of traditional spatial domain modulation schemes, we propose a successive-coded SSK (SC-SSK) modulation scheme. The core of the proposed SC-SSK scheme is a multiple step coded modulation strategy, in which different information bits are mapped to the designed pattern in each step. The constellation of the transmit signals is designed. A QR decomposition based successive detection algorithm is proposed for this particular coded modulation strategy. The achievable rate, the error rate performance, and the computational complexity of the proposed SC-SSK scheme are analyzed in detail. Simulations are carried out to compare the proposed the SC-SSK scheme with the reference spatial domain modulation schemes, and reveal the advantages of the proposed SC-SSK scheme.

关键词： Modulation Transmitting antennas Detectors GSM Receiving antennas Slot antennas detection algorithms Achievable rate multiple-input and multiple-output (MIMO) successive coded (SC) spatial shift keying (SSK)

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Performance evaluation of land cover change detection algorithms using remotely sensed data

Performance evaluation of land cover change detection algori...

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International Conference on Circuit, Power and Computing Technologies (ICCPCT)

作者： L. Antony Jude A. Suruliandi Dept. of CSE M S University Tamilnadu

ISBN: (纸本)9781479924059

Remote sensing has long been used as a means of detecting and classifying the different types of data attribute present in the land cover. In general, remote sensing is widely used across variety of real-time applications to identify the change information of geographical areas. The Objective of this work is to study three land cover change detection algorithms such as Image Differencing method, Auto-Correlation function method and Distance Analysis method. In this paper, land cover change is obtained by change detection method from the sequence of annual pattern dataset of multi-date temporal datasets. This work presents a performance comparison of change detection algorithms based on performance metrics Change data and False alarm. From the evaluated results, Autocorrelation function method performs better than other Image differencing and Distance analysis method.

关键词： Remote sensing Correlation detection algorithms FCC Satellites Computers Measurement

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A Cooperative Game Theory-Based Algorithm for Overlapping Community detection

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IEEE ACCESS 2020年 8卷 68417-68425页

作者： Zhou, Xu Cheng, Shichao Liu, Yanheng Jilin Univ Ctr Comp Fundamental Educ Changchun 130012 Peoples R China Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China Jilin Univ Key Lab Symbol Computat & Knowledge Engn Minist Educ Changchun 130012 Peoples R China

Community detection is of great significance and has more guiding for network analysis which has attract a lot of attention and is widely used in many areas. The process of community formation is modeled as a game in this paper, nodes in networks are taken as players, and communities are regarded as coalitions. An overlapping community detection algorithm is presented based on cooperative game theory. Furthermore, an edge weight computation is proposed for calculating Shapley value of node and coalition, and a strategy of optimal income for the individual is designed. It is for each player to choose its best coalition to stay or leave during the game process. Through choosing coalitions which can improve the value of their characteristic function, coalitions can be merged to avoid small size coalitions. It does not need any prior information about the number and the size of communities and the overlapping community structure is stable at the end of the algorithm. The experimental results both on synthetic and real networks demonstrate the good performance of the proposed method. It can detect overlapping communities more accurately.

关键词： Games Game theory detection algorithms Image edge detection Computer science Computational modeling Social network services Cooperative game Shapley value overlapping community detection

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On Followers Search

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2021年第5期8卷 1068-1082页

作者： Ni, Li Luo, Wenjian Zhu, Tao Xu, Peilan Anhui Univ Sch Comp Sci & Technol Hefei 230601 Peoples R China Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Peoples R China Harbin Inst Technol Sch Comp Sci & Technol Shenzhen 518055 Peoples R China Univ South China Sch Software Engn Hengyang 421001 Peoples R China

Although followership has been widely studied in sociology and management, the problem of finding followers has not drawn attention in the field of artificial intelligence. We refer to the problem of finding followers of a given object as followers search. In sociology, followers are close to their leaders, and leaders are superior to their followers. In this article, aimed at finding followers of a given object, we formulate followers on the basis of both superiority and closeness. The former means that the given object should be superior to followers, and the latter means that followers should be close to the given object. We present a followers search algorithm to find the followers of the given object. Furthermore, we apply the ideas of followers to the market basket and recommender system datasets. The experimental results demonstrate the rationality of the discovered followers on the market basket dataset and the improved performance of the TrustPMF algorithm by adopting followership on recommender system datasets, which indicate a promising future for followers search.

关键词： Recommender systems Itemsets Search problems Sociology Task analysis Social networking (online) detection algorithms Data intelligence followers search market basket analysis recommender systems

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Copy Move Source-Target Disambiguation Through Multi-Branch CNNs

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2021年 16卷 1825-1840页

作者： Barni, Mauro Phan, Quoc-Tin Tondi, Benedetta Univ Siena Dept Informat Engn & Math I-53100 Siena Italy Univ Trento Dept Informat Engn & Comp Sci I-38123 Trento Italy

We propose a method to identify the source and target regions of a copy-move forgery so allow a correct localisation of the tampered area. First, we cast the problem into a hypothesis testing framework whose goal is to decide which region between the two nearly-duplicate regions detected by a generic copy-move detector is the original one. Then we design a multi-branch CNN architecture that solves the hypothesis testing problem by learning a set of features capable to reveal the presence of interpolation artefacts and boundary inconsistencies in the copy-moved area. The proposed architecture, trained on a synthetic dataset explicitly built for this purpose, achieves good results on copy-move forgeries from both synthetic and realistic datasets. Based on our tests, the proposed disambiguation method can reliably reveal the target region even in realistic cases where an approximate version of the copy-move localization mask is provided by a state-of-the-art copy-move detection algorithm.

关键词： Forgery Interpolation Feature extraction Task analysis Kernel detection algorithms Training Copy-move detection and localization image forensics tampering detection and localization deep learning for forensics Siamese networks

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