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Stationary Marine Target detection With Time-Series SAR Imagery

IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 6406-6413页

作者： An, Wentao Lin, Mingsen Yang, Haijun Minist Nat Resources Natl Satellite Ocean Applicat Serv Key Lab Space Ocean Remote Sensing & Applicat Beijing 100081 Peoples R China Hainan Trop Ocean Univ Remote Sensing Sanya 572022 Peoples R China

Stationary marine targets, such as oil rigs and offshore wind turbines, usually show up like bright spots without or with trivial position shifts in multitemporal synthetic aperture radar (SAR) imagery. They will trigger considerable false alarms in target detection applications tasked with ship detection, if they are not identified. An algorithm for stationary marine target detection, developed based on the assumption that the apparent positions of a stationary target in multitemporal imagery are also stationary or nearly so, is proposed in this article. This algorithm requires a strict time-series of SAR images in temporal sequence as input. For each input SAR image, all targets on sea surface are initially detected with an iterative cell-averaging constant false-alarm rate detection algorithm, and their longitude and latitude positions are then used to identify whether they are stationary targets. Under this algorithm, a stationariness index with five levels ("unknown target," "suspected new stationary target," "stationary target," "suspected removed stationary target," and "removed target") is defined for each target and must be iteratively updated with the latest level of identification. The proposed algorithm is promising for monitoring the status of stationary marine targets over a large sea area, because the processing of all input SAR images follows the same procedure and meanwhile the stationariness index is generated and kept updated. Two examples with GF-3 and RADARSAT-2 images are presented to illustrate the effectiveness of the proposed algorithm in detecting both offshore wind turbines and oil rigs.

关键词： Radar polarimetry Object detection Oils Synthetic aperture radar Indexes detection algorithms Databases Offshore oil rig detection offshore wind turbine detection stationary marine target detection synthetic aperture radar (SAR) target detection

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Unsupervised Eye Blink Artifact detection From EEG With Gaussian Mixture Model

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2021年第8期25卷 2895-2905页

作者： Cao, Jiuwen Chen, Long Hu, Dinghan Dong, Fang Jiang, Tiejia Gao, Weidong Gao, Feng Hangzhou Dianzi Univ Machine Learning I Hlth Int Cooperat Base Zhejian Hangzhou 310018 Zhejiang Peoples R China Hangzhou Dianzi Univ Artificial Intelligence Inst Hangzhou 310018 Zhejiang Peoples R China Zhejiang Lab Res Ctr Intelligent Sensing Hangzhou 311100 Zhejiang Peoples R China Zhejiang Univ City Coll Sch Informat & Elect Engn Hangzhou 310015 Zhejiang Peoples R China Zhejiang Univ Childrens Hosp Natl Clin Res Ctr Child Hlth Dept NeurolSch Med Hangzhou 310003 Zhejiang Peoples R China Chinese Univ Hong Kong Sch Sci & Engn Shenzhen Hong Kong Peoples R China

Eye blink is one of the most common artifacts in electroencephalogram (EEG) and significantly affects the performance of the EEG related applications, such as epilepsy recognition, spike detection, encephalitis diagnosis, etc. To achieve an accurate and efficient eye blink detection, a novel unsupervised learning algorithm based on a hybrid thresholding followed with a Gaussian mixture model (GMM) is presented in this paper. The EEG signal is priliminarily screened by a cascaded thresholding method built on the distributions of signal amplitude, amplitude displacement, as well as the cross channel correlation. Then, the channel correlation of the two frontal electrodes (FP1, FP2), the fractal dimension, and the mean of amplitude difference between FP1 and FP2, are extracted to characterize the filtered EEGs. The GMM trained on these features is applied for the eye blink detection. The performance of the proposed algorithm is studied on two EEG datasets collected by the Temple University Hospital (TUH) and the Children's Hospital, Zhejiang University School of Medicine (CHZU), where the datasets are recorded from epilepsy and encephalitis patients, and contain a lot of eye blink artifacts. Experimental results show that the proposed algorithm can achieve the highest detection precision and F1 score over the state-of-the-art methods.

关键词： Electroencephalography Correlation Epilepsy Electrodes detection algorithms Brain modeling Support vector machines Eye blink artifact detection unsupervised learning GMM amplitude displacement channel correlation

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Pavement Crack detection Algorithm Based on Densely Connected and Deeply Supervised Network

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IEEE ACCESS 2021年 9卷 11835-11842页

作者： Li, Haifeng Zong, Jianping Nie, Jingjing Wu, Zhilong Han, Hongyang Civil Aviat Univ China Coll Comp Sci & Technol Tianjin 300300 Peoples R China Chengdu Tianfu Int Airport Chengdu 641419 Peoples R China

In order to improve the accuracy and robustness of existing automated crack detection methods, a fully convolutional neural network for pixel-level detection based on densely connected and deeply supervised network is proposed. First, the densely connected layers are applied for enhancing the propagation and reuse of crack features. Then, the deeply supervised modules are designed to make network extract more significant features through multi-scale levels. Finally, the feature maps from different scales are fused to achieve complementarity at different levels. In addition, a class-balanced cross-entropy loss function is designed to balance backgrounds and cracks by increasing the weight of crack pixel loss. The proposed method is tested on three public datasets, and the experiments show that our method is superior to state-of-the-art methods in accuracy, speed and robustness.

关键词： Feature extraction Convolution Semantics Image edge detection detection algorithms Training Surface cracks Crack detection deep learning densely connected network deeply supervised network

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Fluctuation-Based Fade detection for Local Scene Changes

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IEEE ACCESS 2021年 9卷 149732-149743页

作者： Yoon, Sangho Lee, Chanhee Lee, Ho Sub Kim, Young Hwan Kang, Seokhyeong Pohang Univ Sci & Technol POSTECH Dept Elect Engn Pohang 790784 South Korea Daegu Univ Dept Elect Engn Gyongsan 38911 South Korea

In recent years, fade detection algorithms can classify fade scenes in massive video libraries have been developed. However, these algorithms misclassify some non-fade scenes as fade scenes, especially dissolve scenes and scenes with captions or flashing light sources. This paper proposes a new fade detection algorithm that uses similarity tendencies of luminance transitions to overcome such obstacles. To prevent detection accuracy degradation by letterboxing and captions, video frames are simplified. Then, fade candidates are detected by transition boundary detection using the angular and curvature characteristics of the luminance vectors. Finally, luminance flipping detection improves the detection accuracy by extracting the luminance retrograde phenomenon that occurs with flashing or light source movements. Through objective evaluation using F-1 score, the detection accuracy of the proposed algorithm was 0.884, which is an increase of 0.187 (21.2% improvement) compared with the average F-1 score of existing high-performance methods.

关键词： Image edge detection Histograms Feature extraction Light sources Image color analysis detection algorithms Standards Algorithm design and analysis change detection algorithms machine learning algorithms video signal processing

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A Traffic-Sign detection Algorithm Based on Improved Sparse R-cnn

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IEEE ACCESS 2021年 9卷 122774-122788页

作者： Cao, Jinghao Zhang, Junju Jin, Xin Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Jiangsu Peoples R China

Automatic traffic-sign detection is a hot topic in computer vision and one of the critical technologies of intelligent transportation. The Transformer structure has recently become a research hotspot due to its excellent performance. We hope to apply this structure to the design of traffic sign detection algorithms. Therefore, we make some improvements to Sparse R-cnn, a neural network model inspired by Transformer. Sparse R-cnn is a novel model, and its core idea is to replace hundreds of thousands of candidate anchors in the RPN network with a small set of proposal boxes. The experiments in our paper proved that the performance of the Sparse R-cnn model is better than other existing general object detection models. Based on the original Sparse R-cnn inspiration, an improved Sparse R-cnn model is proposed. First, a novel backbone for the task of traffic-sign detection is proposed. Multi-scale fusion structure is the essential method of improving the algorithm for small target detection, so improving the multi-scale capability of the backbone is a required method for designing traffic sign detection. So, we made further improvements to the existing backbone ResNest. We enhanced the multi-scale representation ability of the backbone by constructing hierarchical residual-like connections within each single radix block in the original ResNest. We call the improved backbone Res2Nest. The novel backbone proposed by us shows better performance without introducing excessive computational costs to the model. In addition, the attention mechanism is also an effective method to improve the detection of traffic signs, so we set up a branch network for recalibrating the channel feature response adaptively through the Global Average Pooling (GAP) operation and a fully connected layer. It can also be seen as the implementation of the cross-channel self-attention mechanism. After experiments by TT100K dataset, our method would attain a better accuracy and robustness.

关键词： Feature extraction Proposals Object detection Task analysis Heuristic algorithms Head detection algorithms Deep learning object detection traffic-sign detection self-attention mechanism improved Sparse R-cnn

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Detecting and Mitigating Collusive Interest Flooding Attacks in Named Data Networking

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IEEE ACCESS 2022年 10卷 65996-66017页

作者： Al-Share, Rama A. Shatnawi, Ahmed S. Al-Duwairi, Basheer Jordan Univ Sci & Technol Dept Network Engn & Secur Irbid 22110 Jordan Jordan Univ Sci & Technol Dept Software Engn Irbid 22110 Jordan

The large expansion in network services and applications seen in the last few years requires new network architectures to satisfy an increasing number of users and enhance content delivery. Named Data Networking (NDN) has recently appeared as a new paradigm to solve many shortcomings in the current TCP/IP architecture. Its main characteristics like stateful forwarding and in-network caching made NDN networks an efficient environment for data delivery where the data is retrieved based on content names rather than IP addresses. The NDN, by its nature, defends against the well-known Distributed Denial of Service (DDoS) attacks that take place in the traditional TCP/IP architecture. However, a special kind of DDoS attack called Collusive Interest Flooding Attack (CIFA) has appeared to overwhelm the resources of NDN routers by filling their Pending Interest Tables (PIT) with long-lasting malicious entries. The network throughput and consumer satisfaction rate are highly affected by CIFA. A lightweight yet efficient stateless CIFA detection algorithm is proposed in this research utilizing the non-parametric CUSUM algorithm;a change point detection approach that detects the point in time when a transition occurs in the network. The proposed algorithm is characterized by its low computational overhead, highly accurate detection, and quick response. To detect the malicious name prefixes and eliminate the CIFA effect, a mitigation algorithm that uses the average response time vales of all name prefixes is proposed in this research. Experimental results show that this approach detects CIFA after 199.5 ms from when an attack is launched in the large-scale topology. In addition, the mitigation approach effectively reduces the PIT utilization and increases the average consumer satisfaction rate.

关键词： Computer architecture Security Floods TCPIP Internet Throughput detection algorithms Collusive interest flooding attack (CIFA) denial of service detection and mitigation scheme named data networking (NDN) non-parametric cumulative sum (CUSUM)

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Multi-Regime Analysis for Computer Vision- Based Traffic Surveillance Using a Change-Point detection Algorithm

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IEEE ACCESS 2021年 9卷 40980-40995页

作者： Jeong, Seungyun Roh, Seungbin Sohn, Keemin Chung Ang Univ Dept Urban Engn Seoul 06974 South Korea

As a result of significant advances in deep learning, computer vision technology has been widely adopted in the field of traffic surveillance. Nonetheless, it is difficult to find a universal model that can measure traffic parameters irrespective of ambient conditions such as times of the day, weather, or shadows. These conditions vary recurrently, but the exact points of change are inconsistent and unpredictable. Thus, the application of a multi-regime method would be problematic, even when separate sets of model parameters are prepared in advance. In the present study we devised a robust approach that facilitates multi-regime analysis. This approach employs an online parametric algorithm to determine the change-points for ambient conditions. An autoencoder was used to reduce the dimensions of input images, and reduced feature vectors were used to implement the online change-point algorithm. Seven separate periods were tagged with typical times in a given day. Multi-regime analysis was then performed so that the traffic density could be separately measured for each period. To train and test models for vehicle counting, 1,100 video images were randomly chosen for each period and labeled with traffic counts. The measurement accuracy of multi-regime analysis was much higher than that of an integrated model trained on all data.

关键词： Feature extraction Traffic control Surveillance Density measurement Training Detectors detection algorithms Change-point algorithm autoencoder traffic surveillance multi-regime model traffic density

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A Robust Algorithm Based on Link Label Propagation for Identifying Functional Modules From Protein-Protein Interaction Networks

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第3期19卷 1435-1448页

作者： Jiang, Hao Zhan, Fei Wang, Congtao Qiu, Jianfeng Su, Yansen Zheng, Chunhou Zhang, Xingyi Zeng, Xiangxiang Anhui Univ Sch Comp Sci & Technol Minist Educ Key Lab Intelligent Comp & Signal Proc Hefei 230601 Peoples R China Hunan Univ Sch Informat Sci & Engn Changsha 410082 Peoples R China

Identifying functional modules in protein-protein interaction (PPI) networks elucidates cellular organization and mechanism. Various methods have been proposed to identify the functional modules in PPI networks, but most of these methods do not consider the noisy links in PPI networks. They achieve a competitive performance on the PPI networks without noisy links, but the performance of these methods considerably deteriorates in the noisy PPI networks. Furthermore, the noisy links are inevitable in the PPI networks. In this paper, we propose a novel link-driven label propagation algorithm (LLPA) to identify functional modules in PPI networks. The LLPA first find link clusters in PPI networks, and then the functional modules are identified from the link clusters. Two strategies aimed to ensure the robustness of LLPA are proposed. One strategy involves the proposed LLPA updating the link labels in accordance with the designed weight of the link, which can reduce the incidence of noisy links. The other strategy involves the filtration of some noisy labels from the link clusters to further reduce the influence of noisy links. The performance evaluation on three real PPI networks shows that LLPA outperforms other eight state-of-the-art detection algorithms in terms of accuracy and robustness.

关键词： Noise measurement Proteins Robustness Clustering algorithms detection algorithms Signal processing algorithms Partitioning algorithms Functional modules identification link label propagation nosiy link protein-protein interaction network

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eXplainable and Reliable Against Adversarial Machine Learning in Data Analytics

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IEEE ACCESS 2022年 10卷 83949-83970页

作者： Vaccari, Ivan Carlevaro, Alberto Narteni, Sara Cambiaso, Enrico Mongelli, Maurizio Consiglio Nazl Ric CNR IEIIT Inst I-16149 Genoa Italy Univ Genoa Dept Elect Elect & Telecommun Engn & Naval Archit I-16145 Genoa Italy Politecn Torino Dept Control & Comp Engn DAUIN I-10129 Turin Italy

Machine learning (ML) algorithms are nowadays widely adopted in different contexts to perform autonomous decisions and predictions. Due to the high volume of data shared in the recent years, ML algorithms are more accurate and reliable since training and testing phases are more precise. An important concept to analyze when defining ML algorithms concerns adversarial machine learning attacks. These attacks aim to create manipulated datasets to mislead ML algorithm decisions. In this work, we propose new approaches able to detect and mitigate malicious adversarial machine learning attacks against a ML system. In particular, we investigate the Carlini-Wagner (CW), the fast gradient sign method (FGSM) and the Jacobian based saliency map (JSMA) attacks. The aim of this work is to exploit detection algorithms as countermeasures to these attacks. Initially, we performed some tests by using canonical ML algorithms with a hyperparameters optimization to improve metrics. Then, we adopt original reliable AI algorithms, either based on eXplainable AI (Logic Learning Machine) or Support Vector Data Description (SVDD). The obtained results show how the classical algorithms may fail to identify an adversarial attack, while the reliable AI methodologies are more prone to correctly detect a possible adversarial machine learning attack. The evaluation of the proposed methodology was carried out in terms of good balance between FPR and FNR on real world application datasets: Domain Name System (DNS) tunneling, Vehicle Platooning and Remaining Useful Life (RUL). In addition, a statistical analysis was performed to improve the robustness of the trained models, including evaluating their performance in terms of runtime and memory consumption.

关键词： Machine learning algorithms Reliability Adversarial machine learning Prediction algorithms Machine learning Data models Safety Object detection Machine learning detection algorithms adversarial machine learning reliable

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Sparsity Signal detection for Indoor GSSK-VLC System

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第12期70卷 12975-12984页

作者： Zuo, Ting Wang, Fasong Zhang, Jiankang Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Henan Peoples R China Bournemouth Univ Dept Comp & Informat Bournemouth BH12 5BB Dorset England

In this article, the signal detection problem in indoor visible light communication (VLC) system aided by generalized space shift keying (GSSK) is modeled as a sparse signal reconstruction problem, which has lower computational complexity by exploiting the sparse reconstruction algorithms in compressed sensing (CS). In order to satisfy the measurement matrix property to perform sparse signal reconstruction, a preprocessing approach of measurement matrix is proposed based on singular value decomposition (SVD), which theoretically guarantees the feasibility of utilizing CS based sparse signal detection method in indoor GSSK-VLC system. Then, by adopting classical orthogonal matching pursuit (OMP) algorithm and compressed sampling matching pursuit (CoSaMP) algorithm, the GSSK signals are efficiently detected in the considered indoor GSSK-VLC system. Furthermore, a more efficient detection algorithm combined with OMP and maximum likelihood (ML) is also presented especially for SSK scenario. Finally, the effectiveness of the proposed sparsity aided detection algorithms in indoor GSSK-VLC system are verified by computer simulations. The results show that the proposed algorithms can achieve better bit error rate (BER) and lower computation complexity than ML based detection method. Specifically, a signal-to-noise ratio (SNR) gain as high as 12 dB is observed in the SSK scenario and about 5 dB in case of a GSSK scenario upon employing our proposed detection methods.

关键词： Matching pursuit algorithms Visible light communication Signal detection Radio frequency Lighting detection algorithms Sparse matrices Visible light communication (VLC) generalized space shift keying (GSSK) compressed sensing (CS) maximum likelihood (ML) signal detection

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