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arXiv 2021年

作者： Luo, Hongming Zhou, Fei Liao, Guangsen Qiu, Guoping College of Electronics and Information Engineering Shenzhen University China Peng Cheng Laboratory Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Shenzhen Key Laboratory of Digital Creative Technology China Shenzhen Institute for Artificial Intelligence and Robotics for Society Shenzhen China School of Computer Science University of Nottingham NottinghamNG8 1BB United Kingdom Guangdong-Hong Kong Joint Laboratory for Big Data Imaging and Communication Guangdong Shenzhen China

In real-world applications, such as sharing photos on social media platforms, images are always not only sub-sampled but also heavily compressed thus often containing various artefacts. Simple methods for enhancing the resolution of such images will exacerbate the artefacts, rendering them visually objectionable. In spite of its high practical values, super-resolving compressed images is not well studied in the literature. In this paper, we propose a novel compressed image super resolution (CISR) framework based on parallel and series integration of artefacts removal and resolution enhancement. Based on a mathematical inference model for estimating a clean low-resolution (LR) image and a clean high-resolution (HR) image from a down-sampled and compressed observation, we have designed a CISR architecture consisting of two deep neural network modules: the artefacts removal module (ARM) and the resolution enhancement module (REM). The ARM and the REM work in parallel with both taking the compressed LR image as their inputs, at the same time they also work in series with the REM taking the output of the ARM as one of its inputs and the ARM taking the output of the REM as its other input. A technique called unfolding is introduced to recursively suppress the compression artefacts and restore the image resolution. A unique feature of our CISR system is that it exploits the parallel and series connections between the ARM and the REM, and recursive optimization to reduce the model's dependency on specific types of degradation thus making it possible to train a single model to super-resolve images compressed by different methods to different qualities. Experiments are conducted on a mixture of JPEG and WebP compressed images without assuming apriori compression type and compression quality factor. To demonstrate our technique's real-world application value, we have also applied the trained models directly to restore social media images which have undergone scaling and compre

关键词： Deep neural networks

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Probabilistic learning vector quantization on manifold of symmetric positive definite matrices

arXiv

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arXiv 2021年

作者： Sonna, Fengzhen Tang Feng, Haifeng Tino, Peter Si, Bailu Ji, Daxiong State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China School of computer Science University of Birmingham BirminghamB15 2TT United Kingdom School of Systems Science Beijing Normal University Beijing100875 China Institute of Marine Electronics and Intelligent Systems Ocean College Zhejiang University Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province Engineering Research Center of Oceanic Sensing Technology and Equipment Ministry of Education Zhoushan316021 China

In this paper, we develop a new classification method for manifold-valued data in the framework of probabilistic learning vector quantization. In many classification scenarios, the data can be naturally represented by symmetric positive definite matrices, which are inherently points that live on a curved Riemannian manifold. Due to the non-Euclidean geometry of Riemannian manifolds, traditional Euclidean machine learning algorithms yield poor results on such data. In this paper, we generalize the probabilistic learning vector quantization algorithm for data points living on the manifold of symmetric positive definite matrices equipped with Riemannian natural metric (affine-invariant metric). By exploiting the induced Riemannian distance, we derive the probabilistic learning Riemannian space quantization algorithm, obtaining the learning rule through Riemannian gradient descent. Empirical investigations on synthetic data, image data, and motor imagery EEG data demonstrate the superior performance of the proposed method. © 2021, CC BY-NC-SA.

关键词： Learning algorithms

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Cooperative Intelligence for Autonomous Driving

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ZTE Communications 2019年第2期17卷 44-50页

作者： CHENG Xiang DUAN Dongliang YANG Liuqing ZHENG Nanning State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Department of Electrical and Computer Engineering University of WyomingLaramieWY 82071USA Department of Electrical and Computer Engineering Colorado State UniversityFort CollinsCO 80523USA Institute of Artificial Intelligence and Robotics Xi’an Jiaotong UniversityXi’anShaanxi 710049China

Autonomous driving is an emerging technology attracting interests from various sectors in recent *** of existing work treats autonomous vehicles as isolated individuals and has focused on developing separate intelligent *** this paper,we attempt to exploit the connectivity among vehicles and propose a systematic framework to develop autonomous driving *** first introduce a general hierarchical information fusion framework for cooperative sensing to obtain global situational awareness for *** this,a cooperative intelligence framework is proposed for autonomous driving *** general framework can guide the development of data collection,sharing and processing strategies to realize different intelligent functions in autonomous driving.

关键词： autonomous driving cooperative intelligence information fusion vehicular communications and networking

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Recognition of patient groups with sleep related disorders using bio-signal processing and deep learning

arXiv

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arXiv 2021年

作者： Jarchi, Delaram Andreu-Perez, Javier Kiani, Mehrin Vysata, Oldrich Kuchynka, Jiri Prochazka, Ales Sanei, Saeid Smart Health Technologies Group School of Computer Science and Electronic Engineering University of Essex ColchesterCO4 3SQ United Kingdom Embedded and Intelligent Systems Laboratory School of Computer Science and Electronics University of Essex ColchesterCO4 3SQ United Kingdom Department of Computing and Control Engineering University of Chemistry and Technology in Prague Prague 6166 28 Czech Republic Department of Neurology Faculty of Medicine in Hradec Králové Charles University Hradec Králové500 05 Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague 6160 00 Czech Republic School of Science and Technology Nottingham Trent University NottinghamNG11 8NS United Kingdom

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem. © 2021, CC BY-NC-ND.

关键词： Wavelet transforms

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Cover Image, Volume 6, Issue 10

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Health Science Reports 2023年第10期6卷

作者： Mohammad Faranoush Pooya Faranoush Iraj Heydari Mohammad Reza Foroughi-Gilvaee Azita Azarkeivan Ali Parsai Kia Negin Sadighnia Ali Elahinia Afsoon Zandi Mohammad Reza Rezvany Nahid Hashemi-Madani Amir Ziaee Reza Nekouian Farzaneh Rohani Pediatric Growth and Development Research Center Institute of Endocrinology Iran University of Medical Sciences Tehran Iran Cardio-Oncology Research Center Rajaie Cardiovascular Medical & Research Center Iran University of Medical Sciences Tehran Iran Nano Bio Electronic Devices Lab Cancer Electronics Research Group School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Blood Transfusion Research Center High Institute for Research and Education in Transfusion Medicine Tehran Iran Robotics Research Laboratory School of Mechanical Engineering Iran University of Science and Technology Tehran Iran Department of Otolaryngology Head & Neck Surgery Taleghani Hospital Shahid Beheshti University of Medical Sciences Tehran Iran

The cover image is based on the Research ArticleComplications in patients with transfusion dependent thalassemia: A descriptive cross‐sectional studyby Mohammad Faranoush et al.,https://***/10.1002/hsr2.1624

The cover image is based on the Research Article Complications in patients with transfusion dependent thalassemia: A descriptive cross‐sectional study by Mohammad Faranoush et al., https://***/10.1002/hsr2.1624

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UAV and Machine Learning Based Refinement of a Satellite-Driven Vegetation Index for Precision Agriculture †

arXiv

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arXiv 2020年

作者： Mazzia, Vittorio Comba, Lorenzo Khaliq, Aleem Chiaberge, Marcello Gay, Paolo Department of Electronics and Telecommunications Politecnico di Torino Corso Duca degli Abruzzi 24 Torino10129 Italy PIC4SeR Politecnico Interdepartmental Centre for Service Robotics Turin10129 Italy 10095 Italy Institute of Electronics Computer and Telecommunication Engineering National Research Council of Italy c/o Politecnico di Torino Corso Duca degli Abruzzi 24 Torino10129 Italy

Precision agriculture is considered to be a fundamental approach in pursuing a low-input, high-efficiency, and sustainable kind of agriculture when performing site-specific management practices. To achieve this objective, a reliable and updated description of the local status of crops is required. Remote sensing, and in particular satellite-based imagery, proved to be a valuable tool in crop mapping, monitoring, and diseases assessment. However, freely available satellite imagery with low or moderate resolutions showed some limits in specific agricultural applications, e.g., where crops are grown by rows. Indeed, in this framework, the satellite’s output could be biased by intra-row covering, giving inaccurate information about crop status. This paper presents a novel satellite imagery refinement framework, based on a deep learning technique which exploits information properly derived from high resolution images acquired by unmanned aerial vehicle (UAV) airborne multispectral sensors. To train the convolutional neural network, only a single UAV-driven dataset is required, making the proposed approach simple and cost-effective. A vineyard in Serralunga d’Alba (Northern Italy) was chosen as a case study for validation purposes. Refined satellite-driven normalized difference vegetation index (NDVI) maps, acquired in four different periods during the vine growing season, were shown to better describe crop status with respect to raw datasets by correlation analysis and ANOVA. In addition, using a K-means based classifier, 3-class vineyard vigor maps were profitably derived from the NDVI maps, which are a valuable tool for growers. Copyright © 2020, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Exploring the effect of image enhancement techniques on COVID‐19 detection using chest X‐rays images

arXiv

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arXiv 2020年

作者： Rahman, Tawsifur Khandakar, Amith Qiblawey, Yazan Tahir, Anas Kiranyaz, Serkan Kashem, Saad Bin Abul Islam, Mohammad Tariqul Maadeed, Somaya Al Zughaier, Susu M. Khan, Muhammad Salman Chowdhury, Muhammad E.H. Department of Biomedical Physics & Technology University of Dhaka Dhaka 1000 Bangladesh Department of Electrical Engineering Qatar University Doha2713 Qatar Faculty of Robotics and Advanced Computing Qatar Armed Forces‐Academic Bridge Program Qatar Foundation Doha24404 Qatar Dept. of Electrical Electronics and Systems Engineering Universiti Kebangsaan Malaysia BangiSelangor43600 Malaysia Department of Computer Science and Engineering Qatar University Doha2713 Qatar Department of Basic Medical Sciences College of Medicine Biomedical and Pharmaceutical Research Unit QU Health Qatar University Doha2713 Qatar University of Engineering and Technology Peshawar Pakistan

The use of computer-aided diagnosis in the reliable and fast detection of corona virus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the medical infrastructure. Chest X-ray (CXR) imaging has several advantages over other imaging techniques as it is cheap, easily accessible, fast and portable. CXR images are sometimes of poor quality and so image enhancement techniques can help the machine learning models to extract valuable discriminating features from the image. Numerous works have been reported on COVID-19 detection from smaller set of original X-ray images. However, the effect of image enhancement in COVID-19 detection was not reported in the literature. This paper explores the effect of various popular image enhancement techniques and states the © 2020, CC BY.

关键词： Image enhancement

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Robust Online Model Adaptation by Extended Kalman Filter with Exponential Moving Average and Dynamic Multi-Epoch Strategy

arXiv

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arXiv 2019年

作者： Abuduweili, Abulikemu Liu, Changliu School of Electronics Engineering and Computer Science Peking University BeijingP. R.100871 China Robotics Institute Carnegie Mellon University PittsburghPA15213 United States

High fidelity behavior prediction of intelligent agents is critical in many applications. However, the prediction model trained on the training set may not generalize to the testing set due to domain shift and time variance. The challenge motivates the adoption of online adaptation algorithms to update prediction models in real-time to improve the prediction performance. Inspired by Extended Kalman Filter (EKF), this paper introduces a series of online adaptation methods, which are applicable to neural network-based models. A base adaptation algorithm Modified EKF with forgetting factor (MEKFλ) is introduced first, followed by exponential moving average filtering techniques. Then this paper introduces a dynamic multi-epoch update strategy to effectively utilize samples received in real time. With all these extensions, we propose a robust online adaptation algorithm: MEKF with Exponential Moving Average and Dynamic Multi-Epoch strategy (MEKFEMA-DME). The proposed algorithm outperforms existing methods as demonstrated in experiments. Copyright © 2019, The Authors. All rights reserved.

关键词： Extended Kalman filters

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Robust Lane Marking Detection Algorithm Using Drivable Area Segmentation and Extended SLT

Robust Lane Marking Detection Algorithm Using Drivable Area ...

引用

IEEE International Conference on robotics and Biomimetics

作者： Umar Ozgunalp Rui Fan Shanshan Cheng Yuxiang Sun Weixun Zuo Yilong Zhu Bohuan Xue Linwei Zheng Qing Liang Ming Liu Electrical and Electronics Engineering Cyprus International University North Cyprus Turkey Department of Electronic and Computer Engineering the Hong Kong University of Science and Technology Hong Kong SAR China Guolu Gaoke Engineering Technology Institute Co. Ltd. Beijing China Robotics and Multiperception Laboratory Robotics Institute the Hong Kong University of Science and Technology Hong Kong SAR China

In this paper, a robust lane detection algorithm is proposed, where the vertical road profile of the road is estimated using dynamic programming from the v-disparity map and, based on the estimated profile, the road area is segmented. Since the lane markings are on the road area and any feature point above the ground will be a noise source for the lane detection, a mask is created for the road area to remove some of the noise for lane detection. The estimated mask is multiplied by the lane feature map in a bird's eye view (BEV). The lane feature points are extracted by using an extended version of symmetrical local threshold (SLT), which not only considers dark light dark transition (DLD) of the lane markings, like (SLT), but also considers parallelism on the lane marking borders. The segmentation then uses only the feature points that are on the road area. A maximum of two linear lane markings are detected using an efficient 1D Hough transform. Then, the detected linear lane markings are used to create a region of interest (ROI) for parabolic lane detection. Finally, based on the estimated region of interest, parabolic lane models are fitted using robust fitting. Due to the robust lane feature extraction and road area segmentation, the proposed algorithm robustly detects lane markings and achieves lane marking detection with an accuracy of 91% when tested on a sequence from the KITTI dataset.

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3D Pose Estimation of Bin Picking Object using Deep Learning and 3D Matching 15

3D Pose Estimation of Bin Picking Object using Deep Learning...

引用

15th International Conference on Informatics in Control, Automation and robotics, ICINCO 2018

作者： Lee, Junesuk Kang, Sangseung Park, Soon-Yong School of Computer Science and Engineering Kyungpook National University Daegu Korea Republic of Intelligent Robotics Research Division Electronics and Telecommunications Research Institute Daejeon Korea Republic of

ISBN: (纸本)9789897583216

In this paper, we propose a method to estimate 3D pose information of an object in a randomly piled-up environment by using image data obtained from an RGB-D camera. The proposed method consists of two modules: object detection by deep learning, and pose estimation by Iterative Closest Point (ICP) algorithm. In the first module, we propose an image encoding method to generate three channel images by integrating depth and infrared images captured by the camera. We use these encoded images as both the input data and training data set in a deep learning-based object detection step. Also, we propose a depth-based filtering method to improve the precision of object detection and to reduce the number of false positives by pre-processing input data. ICP-based 3D pose estimation is done in the second module, where we applied a plane-fitting method to increase the accuracy of the estimated pose. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

关键词： Object detection

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