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学者姓名：孙光民
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Abstract ：
Remote photoplethysmography (rPPG), which aims at measuring heart activities without any contact, has great potential in many applications. The emergence of novel coronavirus pneumonia COVID-19 has attracted worldwide attentions. Contact photoplethysmography (cPPG) methods need to contact the detection equipment with the patient, which may accelerate the spread of the epidemic. In the future, the non-contact heart rate detection will be an urgent need. However, existing heart rate measuring methods from facial videos are vulnerable to the less-constrained scenarios (e.g., with head movement and wearing a mask). In this paper, we proposed a method of heart rate detection based on eye location of region of interest (ROI) to solve the problem of missing information when wearing masks. Besides, a model to filter outliers based on residual network was conceived first by us and the better heart rate measurement accuracy was generated. To validate our method, we also created a mask dataset. The results demonstrated that after using our method for correcting the heart rate (HR) value measured with the traditional method, the accuracy reaches 4.65 bpm, which is 0.42 bpm higher than that without correction.
Keyword ：
Eye location Eye location rPPG rPPG Heart rate Heart rate Measurement consistency Measurement consistency Residual network Residual network
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| GB/T 7714 | Zheng, Kun , Ci, Kangyi , Li, Hui et al. Heart rate prediction from facial video with masks using eye location and corrected by convolutional neural networks [J]. | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2022 , 75 . |
| MLA | Zheng, Kun et al. "Heart rate prediction from facial video with masks using eye location and corrected by convolutional neural networks" . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL 75 (2022) . |
| APA | Zheng, Kun , Ci, Kangyi , Li, Hui , Shao, Lei , Sun, Guangmin , Liu, Junhua et al. Heart rate prediction from facial video with masks using eye location and corrected by convolutional neural networks . | BIOMEDICAL SIGNAL PROCESSING AND CONTROL , 2022 , 75 . |
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Abstract ：
In medical diagnostics, the invention of the computer-aided identification method has played a significant role in making essential decisions for human diseases. Lung cancer requires a greater focus among various diagnostic processes because both men and women are affected, contributing to high mortality rates. In addition, lung cancer is one of the leading causes of death worldwide. It can be treated if diagnosed at an early stage. Detecting and classifying lung lesions is challenging for radiologists. Radiologists typically use computer-aided diagnostic systems to screen for lung cancer. In recent years, computer specialists have proposed many techniques for diagnosing lung cancer. Conventional lung cancer prediction methods have failed to maintain the precision needed because the low-quality picture affects the segmentation process. Here, we propose a well-performing method to detect and classify lung cancer. We applied the Grey Wolf Optimization algorithm with a weighted filter to reduce noise in images, followed by segmentation using watershed transformation and dilation operations. In the end, we classified lung cancer among three classes using our method that showed high performance compared to previous studies: 98.33% accuracy, 100% sensitivity, and 93.33% specificity.
Keyword ：
Grey Wolf Optimization (GWO) Grey Wolf Optimization (GWO) Classification Classification Computer-Aided Diagnostic System (CAD) System Computer-Aided Diagnostic System (CAD) System Convolutional Neural Network (CNN) Convolutional Neural Network (CNN) Computed Tomography (CT) Computed Tomography (CT)
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| GB/T 7714 | Bilal, Anas , Sun, Guangmin , Li, Yu et al. Lung nodules detection using grey wolf optimization by weighted filters and classification using CNN [J]. | JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS , 2022 , 45 (2) : 175-186 . |
| MLA | Bilal, Anas et al. "Lung nodules detection using grey wolf optimization by weighted filters and classification using CNN" . | JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS 45 . 2 (2022) : 175-186 . |
| APA | Bilal, Anas , Sun, Guangmin , Li, Yu , Mazhar, Sarah , Latif, Jahanzaib . Lung nodules detection using grey wolf optimization by weighted filters and classification using CNN . | JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS , 2022 , 45 (2) , 175-186 . |
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Abstract ：
Diabetic retinopathy (DR) is an ocular manifestation of diabetes and the leading cause of visual impairment and blindness across the globe. Early detection and treatment of DR can salvage from visual impairment. The manual screening of DR is a very laborious and time-intensive effort and heavily dependent on professional ophthalmologists. In addition, the subtle distinction among various retinal biomarkers and different grades of DR makes this recognition very challenging. To address the aforementioned problem, deep neural networks have brought many revolutions in the last few years. In this study, we proposed a novel two-stage framework for automatic DR classification. In the first stage, we employed two distinct U-Net models for optic disc (OD) and blood vessel (BV) segmentation during the preprocessing. In the second stage, the enhanced retinal images after OD and BV extraction are used as an input of transfer learning-based model VGGNet, which performs DR detection by identifying retinal biomarkers such as microaneurysms (MA), haemorrhages (HM), and exudates (EX). The proposed model achieved state-of-the-art performance with an average accuracy of 96.60%, 93.95%, 92.25% evaluated on EyePACS-1, Messidor-2, and DIARETDB0, respectively. Extensive experiments and comparison with baseline methods demonstrate that the effectiveness of the proposed approach.
Keyword ：
automatic diagnosis automatic diagnosis transfer learning transfer learning deep neural network deep neural network Diabetic retinopathy Diabetic retinopathy fundus images fundus images
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| GB/T 7714 | Bilal, Anas , Sun, Guangmin , Mazhar, Sarah et al. A Transfer Learning and U-Net-based automatic detection of diabetic retinopathy from fundus images [J]. | COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION , 2022 , 10 (6) : 663-674 . |
| MLA | Bilal, Anas et al. "A Transfer Learning and U-Net-based automatic detection of diabetic retinopathy from fundus images" . | COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 10 . 6 (2022) : 663-674 . |
| APA | Bilal, Anas , Sun, Guangmin , Mazhar, Sarah , Imran, Azhar , Latif, Jahanzaib . A Transfer Learning and U-Net-based automatic detection of diabetic retinopathy from fundus images . | COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION , 2022 , 10 (6) , 663-674 . |
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Abstract ：
To improve the security of authentication system and strengthen privacy protection in mobile Internet environment, this paper proposes a provably secure Chebyshev chaotic map (CCM)-based authentication scheme (CCMbAS). The proposed scheme transformed the traditional public key of Chebyshev chaotic map into a private key and combined two private keys to compute a one-time key used to encrypt authentication information. The scheme is verified using security review of BAN logic and ProVerif simulation tool. The verification results confirm that the scheme is well secured against all existing security threats. Compared with similar schemes, the proposed scheme is more efficient and secure. The security analysis shows that the proposed scheme can fulfil secure demands and ensure the security of user's information in mobile Internet environment.
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| GB/T 7714 | Zhang, Yu , Sun, Guangmin , Zhai, Peng . CCMbAS: A Provably Secure CCM-Based Authentication Scheme for Mobile Internet [J]. | MOBILE INFORMATION SYSTEMS , 2022 , 2022 . |
| MLA | Zhang, Yu et al. "CCMbAS: A Provably Secure CCM-Based Authentication Scheme for Mobile Internet" . | MOBILE INFORMATION SYSTEMS 2022 (2022) . |
| APA | Zhang, Yu , Sun, Guangmin , Zhai, Peng . CCMbAS: A Provably Secure CCM-Based Authentication Scheme for Mobile Internet . | MOBILE INFORMATION SYSTEMS , 2022 , 2022 . |
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Abstract ：
Background: Road network data are crucial in various applications, such as emergency response, urban planning, and transportation management. The recent application of deep neural networks has significantly boosted the efficiency and accuracy of road network extraction based on remote sensing data. However, most existing methods for road extraction were designed at local or regional scales. Automatic extraction of large-scale road datasets from satellite images remains challenging due to the complex background around the roads, especially the complicated land cover types. To tackle this issue, this paper proposes a land cover background-adaptive framework for large-scale road extraction. Method: A large number of sample image blocks (6820) are selected from six different countries of a wide region as the dataset. OpenStreetMap (OSM) is automatically converted to the ground truth of networks, and Esri 2020 Land Cover Dataset is taken as the background land cover information. A fuzzy C-means clustering algorithm is first applied to cluster the sample images according to the proportion of certain land use types that obviously negatively affect road extraction performance. Then, the specific model is trained on the images clustered as abundant with that certain land use type, while a general model is trained based on the rest of the images. Finally, the road extraction results obtained by those general and specific modes are combined. Results: The dataset selection and algorithm implementation were conducted on the cloud-based geoinformation platform Google Earth Engine (GEE) and Google Colaboratory. Experimental results showed that the proposed framework achieved stronger adaptivity on large-scale road extraction in both visual and statistical analysis. The C-means clustering algorithm applied in this study outperformed other hard clustering algorithms. Significance: The promising potential of the proposed background-adaptive network was demonstrated in the automatic extraction of large-scale road networks from satellite images as well as other object detection tasks. This search demonstrated a new paradigm for the study of large-scale remote sensing applications based on deep neural networks.
Keyword ：
unsupervised clustering unsupervised clustering road extraction road extraction deep learning models deep learning models land cover land cover
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| GB/T 7714 | Li, Yu , Liang, Hao , Sun, Guangmin et al. A Land Cover Background-Adaptive Framework for Large-Scale Road Extraction [J]. | REMOTE SENSING , 2022 , 14 (20) . |
| MLA | Li, Yu et al. "A Land Cover Background-Adaptive Framework for Large-Scale Road Extraction" . | REMOTE SENSING 14 . 20 (2022) . |
| APA | Li, Yu , Liang, Hao , Sun, Guangmin , Yuan, Zifeng , Zhang, Yuanzhi , Zhang, Hongsheng . A Land Cover Background-Adaptive Framework for Large-Scale Road Extraction . | REMOTE SENSING , 2022 , 14 (20) . |
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Abstract ：
The center of human settlements is in the cities, which must have high-quality habitats for their inhabitants. Many megachallenges of urbanization, population development, global advancement, environmental destruction, traffic management, and climate change must be addressed. This study is aimed at understanding how to maintain balanced land development in rapidly urbanizing towns to solve this challenge and mobility issues. Climate and weather forecasts, land cover, environmental indices, nonoptical and optical wavelengths, water history, and air quality are only some of the datasets available on Google Earth Engine, a publicly usable data repository. Machine learning techniques, i.e., random forest (RF), support vector machine (SVM), and classification and regression tree (CART), are used to monitor spatial-temporal change regarding water, vegetation, and urbanization for Pakistan from 2013 to 2021 using Landsat 8. The detection of urban land suitability concerning multiple metrics such as ecological response variables, environmental tension, socio-economic development potential, and natural resource potential is also found. Dataset features were classified as bands in the Google Earth Engine. Moreover, for 2020 and 2021, classification results showing the change in water, vegetation, and urbanization are also represented concerning China Pakistan Economic Corridor (CPEC) highway and the railway track to monitor and control traffic and its management.
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| GB/T 7714 | Mazhar, Sarah , Sun, Guangmin , Bilal, Anas et al. Digital and Geographical Feature Detection by Machine Learning Techniques Using Google Earth Engine for CPEC Traffic Management [J]. | WIRELESS COMMUNICATIONS & MOBILE COMPUTING , 2022 , 2022 . |
| MLA | Mazhar, Sarah et al. "Digital and Geographical Feature Detection by Machine Learning Techniques Using Google Earth Engine for CPEC Traffic Management" . | WIRELESS COMMUNICATIONS & MOBILE COMPUTING 2022 (2022) . |
| APA | Mazhar, Sarah , Sun, Guangmin , Bilal, Anas , Li, Yu , Farhan, Muhammad , Awan, Hamid Hussain . Digital and Geographical Feature Detection by Machine Learning Techniques Using Google Earth Engine for CPEC Traffic Management . | WIRELESS COMMUNICATIONS & MOBILE COMPUTING , 2022 , 2022 . |
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Abstract ：
Water is a vital component of life that exists in a variety of forms, including oceans, rivers, ponds, streams, and canals. The automated methods for detecting, segmenting, and mapping surface water have improved significantly with the advancements in satellite imagery and remote sensing. Many strategies and techniques to segment water resources have been presented in the past. However, due to the variant width and complex appearance, the segmentation of the water channel remains challenging. Moreover, traditional supervised deep learning frameworks have been restricted by the scarcity of water channel datasets that include precise water annotations. With this in mind, this research presents the following three main contributions. Firstly, we curated a new dataset for water channel mapping in the Pakistani region. Instead of employing pixel-level water channel annotations, we used a weakly trained method to extract water channels from VHR pictures, relying only on OpenStreetMap (OSM) waterways to create sparse scribbling annotations. Secondly, we benchmarked the dataset on state-of-the-art semantic segmentation frameworks. We also proposed AUnet, an atrous convolution inspired deep learning network for precise water channel segmentation. The experimental results demonstrate the superior performance of the proposed AUnet model for segmenting using weakly supervised labels, where it achieved a mean intersection over union score of 0.8791 and outperformed state-of-the-art approaches by 5.90% for the extraction of water channels.
Keyword ：
segmentation segmentation Google Earth Engine Google Earth Engine deep learning deep learning Landsat-8 satellite Landsat-8 satellite water channel extraction water channel extraction remote sensing remote sensing
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| GB/T 7714 | Mazhar, Sarah , Sun, Guangmin , Bilal, Anas et al. AUnet: A Deep Learning Framework for Surface Water Channel Mapping Using Large-Coverage Remote Sensing Images and Sparse Scribble Annotations from OSM Data [J]. | REMOTE SENSING , 2022 , 14 (14) . |
| MLA | Mazhar, Sarah et al. "AUnet: A Deep Learning Framework for Surface Water Channel Mapping Using Large-Coverage Remote Sensing Images and Sparse Scribble Annotations from OSM Data" . | REMOTE SENSING 14 . 14 (2022) . |
| APA | Mazhar, Sarah , Sun, Guangmin , Bilal, Anas , Hassan, Bilal , Li, Yu , Zhang, Junjie et al. AUnet: A Deep Learning Framework for Surface Water Channel Mapping Using Large-Coverage Remote Sensing Images and Sparse Scribble Annotations from OSM Data . | REMOTE SENSING , 2022 , 14 (14) . |
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Abstract ：
Emotion recognition from macroexpression and microexpression has been widely used in applications such as human-computer interaction, learning status evaluation, and mental disorder diagnosis. However, due to the complexity of human macroexpressions, recognizing macroexpressions with high accuracy is a challenging task. Moreover, the short duration and low movement intensity of microexpressions make its recognition more difficult. For MM-FER (macro and microfacial expression recognition), the key information can be more efficiently expressed by a graph. In this article, a novel framework based on graph neural network named SSGNN (spatial and spectral domain features based on a graph neural network) is designed to extract spatial and spectral domain features from facial images for MM-FER, which can efficiently recognize both macroexpressions and microexpressions under the same model. SSGNN consists of two parts, SPAGNN and SPEGNN, which are used to extract spectral and spatial domain features, respectively. Experiments proved that jointly using the spectral and spatial information extracted by SSGNN can largely improve the performance of MM-FER when the training sample is limited. First, the influences of different neighbors and samples to the model performance was analyzed. Then, the contribution of SPAGNN and SPEGNN were evaluated. It was discovered that fusing the result of SPAGNN and SPEGNN at decision level further improved the performance of MM-FER. Experiment proved that SSGNN can recognize microexpression acquired by various sensors with higher accuracy under different image resolutions and image formats than the compared state-of-the-art methods in most cases. A cross-dataset experiment demonstrated the generalization ability of SSGNN.
Keyword ：
spatial domain spatial domain Trajectory Trajectory Training data Training data Convolutional neural networks Convolutional neural networks facial expression recognition facial expression recognition Feature extraction Feature extraction Cross-dataset Cross-dataset Face recognition Face recognition Spectral analysis Spectral analysis Task analysis Task analysis microexpression microexpression spatial and spectral domain graph neural network (SSGNN) spatial and spectral domain graph neural network (SSGNN) spectral domain spectral domain
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| GB/T 7714 | Zhang, Junjie , Sun, Guangmin , Zheng, Kun et al. SSGNN: A Macro and Microfacial Expression Recognition Graph Neural Network Combining Spatial and Spectral Domain Features [J]. | IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS , 2022 , 52 (4) : 747-760 . |
| MLA | Zhang, Junjie et al. "SSGNN: A Macro and Microfacial Expression Recognition Graph Neural Network Combining Spatial and Spectral Domain Features" . | IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS 52 . 4 (2022) : 747-760 . |
| APA | Zhang, Junjie , Sun, Guangmin , Zheng, Kun , Mazhar, Sarah , Fu, Xiaohui , Li, Yu et al. SSGNN: A Macro and Microfacial Expression Recognition Graph Neural Network Combining Spatial and Spectral Domain Features . | IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS , 2022 , 52 (4) , 747-760 . |
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Abstract ：
The main purpose of this study is to observe the importance of machine vision (MV) approach for the identification of five types of skin cancers, namely, actinic-keratosis, benign, solar-lentigo, malignant, and nevus. The 1000 (200 x 5) benchmark image datasets of skin cancers are collected from the International Skin Imaging Collaboration (ISIC). The acquired ISIC image datasets were transformed into texture feature dataset that was a combination of first-order histogram and gray level co-occurrence matrix (GLCM) features. For the skin cancer image, a total of 137,400 (229 x 3 x 200) texture features were acquired on three nonover-lapping regions of interest (ROls). Principal component analysis (PCA) clustering approach was employed for reducing the dimension of feature dataset. Each image acquired twenty most discriminate features based on two different approaches of statistical features such as average correlation coefficient plus probability of error (ACC + POE) and Fisher (Fis). Furthermore, a correlation-based feature selection (CFS) approach was employed for feature reduction, and optimized 12 features were acquired. Furthermore, a classification algorithm naive bayes (NB), Bayes Net (BN), LMT Tree, and multilayer perception (MLP) using 10 K-fold cross-validation approach were employed on optimized feature datasets and the overall accuracy achieved by MLP is 97.1333%.
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| GB/T 7714 | Zareen, Syeda Shamaila , Guangmin, Sun , Li, Yu et al. A Machine Vision Approach for Classification of Skin Cancer Using Hybrid Texture Features [J]. | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE , 2022 , 2022 . |
| MLA | Zareen, Syeda Shamaila et al. "A Machine Vision Approach for Classification of Skin Cancer Using Hybrid Texture Features" . | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022 (2022) . |
| APA | Zareen, Syeda Shamaila , Guangmin, Sun , Li, Yu , Kundi, Mahwish , Qadri, Salman , Qadri, Syed Furqan et al. A Machine Vision Approach for Classification of Skin Cancer Using Hybrid Texture Features . | COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE , 2022 , 2022 . |
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Abstract ：
为了便于对建筑外墙瓷砖松动和开裂现象进行定期排查以保证周围居民的人身安全，本文提出了一种通过高分辨率相机拍摄的楼面图像进行微小缺陷自动检测的方法。首先，将原始检测任务划分为大尺度下的非墙体分割任务以及小尺度下的缺陷检测任务；其次，分别针对这些任务训练相应的深度模型并应用其进行处理；最后，将这些多尺度任务的处理结果进行融合，得到微小缺陷的最终检测结果。实验表明本文算法在精度和效率上都要明显优于单尺度方法。本文算法已在某小区实际部署运行并取得了良好的效果，具有很高的实用价值。
Keyword ：
墙面 墙面 多尺度 多尺度 卷积神经网络 卷积神经网络 缺陷 缺陷 负反馈技术 负反馈技术 高分辨率检测器 高分辨率检测器 目标检测 目标检测 滑窗 滑窗
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| GB/T 7714 | 孙光民 , 陈佳阳 , 李冰 et al. 双尺度网络高分辨率楼面影像微小缺陷检测 [J]. | 哈尔滨工程大学学报 , 2021 , (02) : 1-8 . |
| MLA | 孙光民 et al. "双尺度网络高分辨率楼面影像微小缺陷检测" . | 哈尔滨工程大学学报 02 (2021) : 1-8 . |
| APA | 孙光民 , 陈佳阳 , 李冰 , 李煜 , 闫冬 . 双尺度网络高分辨率楼面影像微小缺陷检测 . | 哈尔滨工程大学学报 , 2021 , (02) , 1-8 . |
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