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Eye Blink Artifact detection With Novel Optimized Multi-Dimensional Electroencephalogram Features

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2021年 29卷 1494-1503页

作者： Wang, Jianhui Cao, Jiuwen Hu, Dinghan Jiang, Tiejia Gao, Feng Hangzhou Dianzi Univ Machine Learning I Hlth Int Cooperat Base Zhejian Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ Artificial Intelligence Inst Hangzhou 310018 Zhejiang Peoples R China Zhejiang Lab Res Ctr Intelligent Sensing Hangzhou 311100 Peoples R China Zhejiang Univ Sch Med Childrens Hosp Natl Clin Res Ctr Child HlthDept Neurol Hangzhou 310003 Peoples R China

Accurate eye blink artifact detection is essential for electroencephalogram (EEG) analysis and auxiliary analysis of nervous system diseases, especially in the presence of the frontal epileptiform discharges. In this paper, we develop a novel eye blink artifact detection algorithm based on optimally selected multi-dimensional EEG features. Specific efforts have been paid to filtering the frontal epileptiform discharges, where an unsupervised learning exploiting the EEG signal physiological characteristics and smooth nonlinear energy operator (SNEO) based on the K-means clustering has been firstly proposed. Multiple statistical EEG features derived from the frontal electrodes and other electrodes are then extracted to characterize eye blink artifacts. Discriminative feature selection scheme based on the variance filtering and Relief algorithms has been respectively studied, and the average correlation coefficient (ACC) is applied for feature optimization evaluation. The eye blink artifact detection is finally achieved based on the support vector machine (SVM) trained on the optimized EEG features. The effectiveness of the proposed algorithm is demonstrated by experiments carried out on the EEG database of 11 subjects recorded from the Children's Hospital, Zhejiang University School of Medicine (CHZU). Comparisons to several state-of-the-art (SOTA) eye blink artifact detection methods are also presented.

关键词： Electroencephalography Discharges (electric) Feature extraction Support vector machines Databases Electrodes detection algorithms Eye blink artifact detection frontal epileptiform discharge screening multi-dimensional feature extraction and optimization SNEO

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Intelligent Multi-Sensor detection System for Monitoring Indoor Building Fires

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IEEE SENSORS JOURNAL 2021年第24期21卷 27982-27992页

作者： Baek, Jaeseung Alhindi, Taha J. Jeong, Young-Seon Jeong, Myong K. Seo, Seongho Kang, Jongseok Heo, Yoseob Rutgers State Univ Dept Ind & Syst Engn Piscataway NJ 08854 USA Chonnam Natl Univ Dept Ind Engn Gwangju 61186 South Korea Chonnam Natl Univ Interdisciplinary Program Arts & Design Technol Gwangju 61186 South Korea Korea Inst Sci & Technol Informat Div Data Anal Seoul 02792 South Korea

This paper presents a novel fire detection system to monitor various types of indoor building fires. While conventional studies mainly focus on developing fire sensing systems or detection algorithms, the proposed fire detection system integrates both sensing and detection phases to effectively utilize diverse sensor signals in real-time and detect fire outbreak at an early stage. The proposed fire sensing system gathers sensor data from multiple sensor types that are sensitive to measuring various components emitted from fires. Then, the collected sensor data are utilized by a similarity matching-based fire detection algorithm that captures diverse shape patterns that exist in the sensor signals under various fire scenarios, and detects the outbreak of fires at an early stage, with low false alarms. The real-life sensor data collected by the newly developed sensing system and experimental results conducted by the proposed fire detection algorithm show the effectiveness of the proposed fire detection system.

关键词： Sensors Sensor systems Monitoring Temperature sensors Temperature measurement detection algorithms Sensor phenomena and characterization Disaster management fire detection system fire disaster multivariate time series analysis real-time fire detection sensor network smart sensing system weighted dynamic time warping

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Real-Time Navigation for Drogue-Type Autonomous Aerial Refueling Using Vision-Based Deep Learning detection

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2021年第4期57卷 2225-2246页

作者： Garcia, Jorge Alberto Banuelos Younes, Ahmad Bani Northrup Grumman Space Syst Azusa CA 91702 USA San Diego State Univ Dept Aerosp Engn San Diego CA 92182 USA

This article develops a deep learning object detector to provide accurate six-degree-of-freedom (DoF) information of the drogue relative to a monocular camera onboard a flying unmanned aerial vehicle. An object detector helps to provide the needed information for an autonomous vehicle to dock and refuel without the need for human intervention. This object detector can detect eight different beacons by training on 8746 images of a mock drogue. Once these beacons were detected, a nonlinear least squares algorithm that uses the collinearity equations as a system model takes the beacon's location on the captured image to provide an accurate six-DoF navigation solution. These navigation solutions from the object detector were evaluated on multiple metrics and then compared to navigation solutions provided by a VICON motion tracking system. Finally, Monte Carlo analysis, using the collinearity equations as a system model, evaluates an object detector's performance with various noise levels.

关键词： Detectors Aircraft navigation Aircraft Mathematical model Measurement Object detection Feature extraction Attitude estimation detection algorithms high-order nonlinear least squares methods image processing inference algorithms machine learning algorithms Monte Carlo methods neural networks object detection pose estimation position and attitude determination

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Dim and Small Target detection in Multi-Frame Sequence Using Bi-Conv-LSTM and 3D-Conv Structure

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IEEE ACCESS 2021年 9卷 135845-135855页

作者： Liu, Xin Li, Xiaoyan Li, Liyuan Su, Xiaofeng Chen, Fansheng Univ Chinese Acad Sci Beijing Peoples R China Univ Chinese Acad Sci Hangzhou Inst Adv Study Hangzhou 310024 Peoples R China Chinese Acad Sci Key Lab Intelligent Infrared Percept Shanghai 200083 Peoples R China

Infrared dim and small target detection is widely used in military and civil fields. Traditional methods in that application rely on the local contrast between the target and background for single-frame detection. On the other hand, those algorithms depend on the motion model with fixed parameters for multi-frame association. For the great similarity of gray value and the dynamic changes of motion model parameters in the condition of low SNR and strong clutter, those methods possess weak robustness, low detection probability, and high false alarm rate. In this paper, an infrared video sequences encoding and decoding model based on Bidirectional Convolutional Long Short-Term Memory structure (Bi-Conv-LSTM) and 3D Convolutional structure (3D-Conv) is proposed, addressing the problem of high similarity and dynamic changes of parameters. For solving the problem of dynamic change in parameters, Bi-Conv-LSTM structure is used to learn the motion model of targets. And for the problem of low local contrast, 3D-Conv structure is adopted to extend receptive field in the time dimension. In order to improve the precision of detection, the Decoding part is divided into two different full connections with distinctive active function. Simulation results show that the trajectory detection accuracy of the proposed model is more than 90% under the condition of low SNR and maneuvering motion, which is better than traditional method of 80% in DB-TBD 20% in others. Real data experiment to illustrate that that our proposed method can detect small infrared targets of a low false alarm rate and high detection probability.

关键词： Video sequences Object detection detection algorithms Signal to noise ratio Solid modeling Feature extraction Decoding Deep learning (DL) neural network (NN) dim and small target detection long short-term memory (LSTM) 3D convolutional

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Sparse Activity detection in Multi-Cell Massive MIMO Exploiting Channel Large-Scale Fading

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2021年 69卷 3768-3781页

作者： Chen, Zhilin Sohrabi, Foad Yu, Wei Univ Toronto Edward S Rogers Sr Dept Elect & Comp Engn Toronto ON M5S 3G4 Canada

This paper studies the device activity detection problem in a multi-cell massive multiple-input multiple-output (MIMO) system, in which the active devices transmit signature sequences to multiple base stations (BSs) that are connected to a central unit (CU), and the BSs cooperate across multiple cells to detect the active devices based on the sample covariance matrices at the BSs. This paper demonstrates the importance of exploiting the knowledge of channel large-scale fadings in this cooperative detection setting through a phase transition analysis, which characterizes the length of signature sequences needed for successful device activity detection in the massive MIMO regime. It is shown that when the large-scale fadings are known, the phase transition for the multi-cell scenario is approximately the same as that of a single-cell system. In this case, the length of the signature sequences required for reliable activity detection in the multi-cell system can be made to be independent of the number of cells through cooperation, in contrast to the case where the large-scale fadings are not known. Further, this paper considers the case in which the fronthaul links between the BSs and the CU have capacity constraints and proposes a novel cooperation scheme based on the quantization of preliminary detection results at the BSs and the reconstruction of the sample covariance matrices at the CU. Simulations show that the proposed method significantly outperforms the scheme of directly quantizing the sample covariance matrices.

关键词： Fading channels Massive MIMO Covariance matrices Intercell interference Quantization (signal) detection algorithms Computer architecture Cooperative detection device activity detection massive MIMO massive random access phase transition analysis

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Jamming detection in Broadband Frequency Hopping Systems Based on Multi-Segment Signals Spectrum Clustering

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IEEE ACCESS 2021年 9卷 29980-29992页

作者： Xu, Hao Cheng, Yufan Wang, Pengyu Univ Elect Sci & Technol China Natl Key Lab Sci & Technol Commun Chengdu 611731 Peoples R China

Accurate jamming detection in the broadband frequency hopping (FH) systems plays a vital role in improving the anti-jamming performance of the communication systems. The traditional jamming detection algorithms are prone to misjudge FH communication signals as jamming signals, leading to inaccurate jamming detection. Therefore, this paper proposes a jamming detection algorithm for broadband FH systems based on multi-segment signals spectrum clustering (MSSC). According to the difference in the time-frequency characteristics between FH signals and jamming signals, the proposed algorithm accurately detects the jamming by clustering the spectral components of signals in multiple periods. In addition, a jamming detector suitable for broadband FH systems is designed and the theoretical solution to the threshold factor of signal detection based on the Welch spectrum is also given in this paper. The simulation results show that the MSSC-based jamming detection algorithm proposed in this paper can reduce the jamming false detection probability and significantly improve the estimation accuracy of jamming parameters, effectively overcoming the defects of the traditional FFT-based energy detection and the multi-hop accumulation algorithms whose detection accuracy is sensitive to FH signals.

关键词： Jamming Time-frequency analysis Detectors detection algorithms Broadband communication Bandwidth Frequency estimation Broadband frequency hopping jamming detection multi-segment signals spectrum clustering time-frequency characteristics jamming parameters

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Sequential Attack detection in Recommender Systems

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IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY 2021年 16卷 3285-3298页

作者： Aktukmak, Mehmet Yilmaz, Yasin Uysal, Ismail Univ S Florida Elect Engn Dept Tampa FL 33620 USA

Recommender systems are widely used in electronic commerce, social media and online streaming services to provide personalized recommendations to the users by exploiting past ratings and interactions. This paper considers the security aspect with quick and accurate detection of attacks by observing the newly created profiles sequentially to prevent the damage which may be incurred by the injection of new profiles with dishonest ratings. The proposed framework consists of a latent variable model, which is trained by a variational EM algorithm, followed by a sequential detection algorithm. The latent variable model generates homogeneous representations of the users given their rating history and mixed data-type attributes such as age and gender. The representations are then exploited to generate univariate statistics to be efficiently used in a CUSUM-like sequential detection algorithm that can quickly detect persistent attacks while maintaining low false alarm rates. We apply our proposed framework to three different real-world datasets and exhibit superior performance in comparison to the existing baseline algorithms for both attack profile and sequential detection. Furthermore, we demonstrate robustness to different attack strategies and configurations.

关键词： detection algorithms Recommender systems Data models Computational modeling History Hidden Markov models Data integration Recommender systems cyber-attack detection quickest detection latent variable model variational inference

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A Novel Robust Step detection Algorithm for Foot-Mounted IMU

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IEEE SENSORS JOURNAL 2021年第4期21卷 5331-5339页

作者： Liu, Xin Li, Ning Xu, Geng Zhang, Yonggang Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China

Step detection is very important for the foot-mounted Pedestrian Dead Reckoning (PDR) system. To deal with the false detection caused by variant stepping modes and occasional disturbance, a novel robust step detection algorithm is proposed. The proposed algorithm detects the number of steps and zero-velocity phase using data from inertial sensors mounted on the user's foot. It includes two parts: a novel anti-fault detection algorithm and a novel zero-velocity detection method, which are used to reduce the over-detection and miss-detection of steps, respectively. Field tests show that the proposed approach is robust under varying motion speeds in different step modes for different persons and the average accuracy of the proposed algorithm is 99.94% in all motion modes, which significantly exceeds conventional estimation approaches in terms of step detection accuracy.

关键词： Legged locomotion Acceleration detection algorithms Foot Phase detection Pedestrian navigation step detection gait analysis zero-velocity detection

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Target detection in Clutter/Interference Regions Based on Deep Feature Fusion for HFSWR

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 5581-5595页

作者： Wu, Maokai Zhang, Ling Niu, Jiong Wu, Q. M. Jonathan Ocean Univ China Coll Engn Qingdao 266100 Peoples R China Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada

High-frequency surface wave radar (HFSWR) is of great significance for maritime detection, but in the HFSWR echo signal, ship targets are often submerged in a variety of clutter and interference, making it difficult to detect vessels. In this paper, we propose an intelligent detection algorithm for targets concealed in strong clutter and complex interference environments. The algorithm has two stages: preprocessing and target detection. In the preprocessing stage, faster region-based convolutional neural networks Faster R-CNN are designed to identify and locate clutter and interference regions in the range Doppler spectrum;in the target detection stage, a two-level cascade algorithm is proposed. First, an extremum detection algorithm is proposed to identify suspicious target points in the clutter/interference regions, including real and false target points, to quickly obtain potential target positions. Second, in consideration of the characteristics of radar targets, two lightweight networks are designed to extract the CNN features and the stacked autoencoder features of the potential target locations. Then, fusion features are obtained and sent to an extreme learning machine that acts as a second-level classifier to distinguish between real and false target points. Experiments show that the proposed HFSWR target-detection algorithm has better performance for vessel detection in clutter/interference regions than the current mainstream detection algorithms.

关键词： Feature extraction Clutter Object detection Doppler effect Classification algorithms detection algorithms Detectors Convolutional neural network (CNN) extremum detection feature fusion high-frequency surface wave radar (HFSWR) stacked autoencoder (SAE) target detection

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A Community Structure Enhancement-Based Community detection Algorithm for Complex Networks

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2021年第5期51卷 2833-2846页

作者： Su, Yansen Liu, Chunlong Niu, Yunyun Cheng, Fan Zhang, Xingyi Anhui Univ Sch Comp Sci & Technol Key Lab Intelligent Comp & Signal Proc Minist Educ Hefei 230601 Peoples R China China Univ Geosci Sch Informat Engn Beijing 100083 Peoples R China

Community detection has been recognized as one of the most important tools to discover useful information hidden in complex networks which is usually hard to be obtained by simple observations. Existing community detection algorithms have demonstrated their effectiveness on a variety of complex networks, most of them, however, suffer from the scalability issue on complex networks without a clear community structure due to the challenge in the detection of ambiguous community structure. To address this issue, in this paper, we propose a community structure enhancement method, termed CSE, for community detection in complex networks. In the proposed CSE, the community structure of a network is enhanced by adding links between the nodes possibly belonging to the same community and reducing links between those belonging to different communities, thereby converting an ambiguous community structure into a structure much clearer than the original one. The experimental results show the superior performance of the proposed CSE over five state-of-the-art community detection algorithms on both synthetic benchmark networks and real-world networks, especially for those without a clear community structure.

关键词： detection algorithms Complex networks Partitioning algorithms Clustering algorithms Tools Signal processing algorithms Community detection community structure enhancement complex network

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