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arXiv 2023年

作者： Liu, Ruiqi Hua, Meng Guan, Ke Wang, Xiping Zhang, Leyi Mao, Tianqi Zhang, Di Wu, Qingqing Jamalipour, Abbas The State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518055 China The Wireless and Computing Research Institute ZTE Corporation Beijing100029 China The Department of Electrical Engineering City University of Hong Kong 999077 Hong Kong The State Key Laboratory of Advanced Rail Autonomous Operation The School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China The Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing100081 China The School of Electrical and Information Engineering Zhengzhou University Zhengzhou450001 China The School of Electrical Engineering Korea University Seoul02841 Korea Republic of Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China School of Electrical and Information Engineering University of Sydney CamperdownNSW2006 Australia

With the emergence of communication services with stringent requirements such as autonomous driving or on-flight Internet, the sixth-generation (6G) wireless network is envisaged to become an enabling technology for future transportation systems. In this paper, two ways of interactions between 6G networks and transportation are extensively investigated. On one hand, the new usage scenarios and capabilities of 6G over existing cellular networks are firstly highlighted. Then, its potential in seamless and ubiquitous connectivity across the heterogeneous space-air-ground transportation systems is demonstrated, where railways, airplanes, high-altitude platforms and satellites are investigated. On the other hand, we reveal that the introduction of 6G guarantees a more intelligent, efficient and secure transportation system. Specifically, technical analysis on how 6G can empower future transportation is provided, based on the latest research and standardization progresses in localization, integrated sensing and communications, and security. The technical challenges and insights for a road ahead are also summarized for possible inspirations on 6G enabled advanced transportation. © 2023, CC BY-NC-ND.

关键词： Wireless networks

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Explore Contextual information for 3D Scene Graph Generation

arXiv

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arXiv 2022年

作者： Liu, Yuanyuan Long, Chengjiang Zhang, Zhaoxuan Liu, Bokai Zhang, Qiang Yin, Baocai Yang, Xin The Department of Electronic Information and Electrical Engineering Dalian University of Technology Dalian116024 China Meta Reality Labs BurlingameCA94010 United States The Key Lab of Advanced Design and Intelligent Computing Dalian University Ministry of Education Dalian116622 China The Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Faculty of Information Technology Beijing University of Technology Beijing100124 China

3D scene graph generation (SGG) has been of high interest in computer vision. Although the accuracy of 3D SGG on coarse classification and single relation label has been gradually improved, the performance of existing works is still far from being perfect for fine-grained and multi-label situations. In this paper, we propose a framework fully exploring contextual information for the 3D SGG task, which attempts to satisfy the requirements of fine-grained entity class, multiple relation labels, and high accuracy simultaneously. Our proposed approach is composed of a Graph Feature Extraction module and a Graph Contextual Reasoning module, achieving appropriate information-redundancy feature extraction, structured organization, and hierarchical inferring. Our approach achieves superior or competitive performance over previous methods on the 3DSSG dataset, especially on the relationship prediction sub-task. Copyright © 2022, The Authors. All rights reserved.

关键词： Feature extraction

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Establishing an Interactive Virtual Library for Medical Manuscript Preservation Using KNN/SVM and an Amazon Elastic MapReduce Model

Establishing an Interactive Virtual Library for Medical Manu...

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ACS/IEEE International Conference on Computer systems and Applications

作者： Hassen Hamdi Rim Zarrouk Ramzi Mahmoudi Narjes Benameur Multimedia InfoRmation systems and Advanced Computing Laboratory MIRACL University of Sfax Tunisia Laboratoire Technologie Imagerie Mé dicale LTIM-LR12ES06 FMM FSG University of Gafsa University of Monastir Monastir Tunisie Laboratoire d'Informatique Gaspard-Monge Unité Mixte CNRS-UMLV-ESIEE UMR8049 ESIEE Paris Cité Descartes Université Paris-Est Noisy Le Grand France Laboratoire Technologie Imagerie Médicale - LTIM-LR12ES06 Laboratory of Biophysics and Medical Technologies University of Tunis ElManar Higher Institute of Medical Technologies of Tunis Tunisia

ISBN: (数字)9798331518240

ISBN: (纸本)9798331518257

The objective of this study was to assess the viability of establishing an interactive Virtual National Library of Medicine (VNL-M). This research proposes a solution that leverages machine learning algorithms and cloud computing technologies, including advanced techniques such as Hadoop, MapReduce, and Cascading, to virtualize a significant number of medical manuscripts from the National Library of Tunisia. The proposed solution has two phases: •The first presents a hybrid (KNN/SVM) approach for an optical character recognition (OCR) system. Because this technology necessitates high bandwidth and computational capacity, spreading it via distributed architecture or platforms may be a viable alternative for improving performance. •The second entails considering cloud computing as an infrastructure (IaaS) to deploy virtualization techniques for the National Library of Tunisia's “medicine and health” subject area, which belongs to the “natural sciences and mathematics” class in the Dewey Decimal Classification (DDC) system. Furthermore, Cloud storing as a Service (SaaS) is employed for the storing and retrieval of enormous amounts of medical manuscript information. The proposed solution was evaluated by conducting experiments using S3 and Amazon EC2 Elstic Map Reduce with an interesting-scale dataset from a database of manuscript heritage. Lastly, the VNL-M is published as a Web service (VNL-Mweb Service).

关键词： Computers Cloud computing Machine learning algorithms Web services Databases Optical character recognition Nearest neighbor methods Libraries Mathematics Virtualization

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A New Method for Sentiment Analysis Using Contextual Auto-Encoders

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Journal of Computer Science & Technology 2018年第6期33卷 1307-1319页

作者： Hanen Ameur Salma Jamoussi Abdelmajid Ben Hamadou Multimedia Information Systems and Advanced Computing Laboratory Sfax University Sfax Technopole Sfa 3021 Tunisia

Sentiment analysis, a hot research topic, presents new challenges for understanding users＇ opinions and judg-ments expressed online. They aim to classify the subjective texts by assigning them a polarity label. In this paper, weintroduce a novel machine learning framework using auto-encoders network to predict the sentiment polarity label at theword level and the sentence level. Inspired by the dimensionality reduction and the feature extraction capabilities of theauto-encoders, we propose a new model for distributed word vector representation ＂PMI-SA＂ using as input pointwise-mutual-information ＂PMI＂ word vectors. The resulted continuous word vectors are combined to represent a sentence. Anunsupervised sentence embedding method, called Contextual Recursive Auto-Encoders ＂CoRAE＂, is also developed forlearning sentence representation. Indeed, CoRAE follows the basic idea of the recursive auto-encoders to deeply composethe vectors of words constituting the sentence, but without relying on any syntactic parse tree. The CoRAE model consistsin combining recursively each word with its context words （neighbors＇ words： previous and next） by considering the wordorder. A support vector machine classifier with fine-tuning technique is also used to show that our deep compositionalrepresentation model CoRAE improves significantly the accuracy of sentiment analysis task. Experimental results demon-strate that CoRAE remarkably outperforms several competitive baseline methods on two databases, namely, Sanders twittercorpus and Facebook comments corpus. The CoRAE model achieves an efficiency of 83.28% with the Facebook dataset and97.57% with the Sanders dataset.

关键词： sentiment analysis recursive auto-encoder stacked auto-encoder pointwise mutual information deep embed-ding representation

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PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators

arXiv

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arXiv 2023年

作者： Cong, Runmin Yang, Wenyu Zhang, Wei Li, Chongyi Guo, Chun-Le Huang, Qingming Kwong, Sam Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China College of Computer Science Nankai University Tianjin300350 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China Department of Computer Science City University of Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and blurring details, which aggravate the difficulty of downstream underwater understanding tasks. Therefore, how to obtain clear and visually pleasant images has become a common concern of people, and the task of underwater image enhancement (UIE) has also emerged as the times require. Among existing UIE methods, Generative Adversarial Networks (GANs) based methods perform well in visual aesthetics, while the physical model-based methods have better scene adaptability. Inheriting the advantages of the above two types of models, we propose a physical model-guided GAN model for UIE in this paper, referred to as PUGAN. The entire network is under the GAN architecture. On the one hand, we design a Parameters Estimation subnetwork (Par-subnet) to learn the parameters for physical model inversion, and use the generated color enhancement image as auxiliary information for the Two-Stream Interaction Enhancement subnetwork (TSIE-subnet). Meanwhile, we design a Degradation Quantization (DQ) module in TSIE-subnet to quantize scene degradation, thereby achieving reinforcing enhancement of key regions. On the other hand, we design the Dual-Discriminators for the style-content adversarial constraint, promoting the authenticity and visual aesthetics of the results. Extensive experiments on three benchmark datasets demonstrate that our PUGAN outperforms state-of-the-art methods in both qualitative and quantitative metrics. The code and results can be found from the link of https://***/proj_***. © 2023, CC BY-NC-SA.

关键词： Discriminators

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Ultra-Fast Mini License Plate Recognition system Based-on Vision Processing Unit 20

Ultra-Fast Mini License Plate Recognition System Based-on Vi...

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Proceedings of the 2020 2nd International Conference on Big-data Service and Intelligent Computation

作者： Junhui Wang Shuangyin Ren Jiezhong He Xiaolan Ji Diqing Huang College of Computer National University of Defense Technology and State Key Laboratory of Mathematical Engineering and Advanced Computing China National Key Laboratory of Science and Technology on Information System Security Institute of Systems Engineering Academy of Military Science College of Computer National University of Defense Technology

ISBN: (纸本)9781450388399

As more embedded environments need license plate recognition systems, how to recognize car plates with high speed/accuracy and low energy has become an important and challenging problem. In this paper, we propose a ultra-Fast miNi (FaNi) license plate recognition (LPR) system. The FaNi system are divided into one training sub-system and one inference sub-system. The former are used to get some offline features; then, the latter is deployed online to recognize license numbers with nearly real-time speed. The inference system is comprised of the vision processing unit (VPU) and the display unit. These two parts are both implemented with hardware logic. Experiments show that the FaNi system can obtain high accuracy and high speed with low resource cost.

关键词： VPU Plate Recognition system FPGA

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Evaluation of Stationary Wavelet Transforms in Reconstruction of Pure High Frequency Oscillations (HFOs) 18th

Evaluation of Stationary Wavelet Transforms in Reconstructio...

引用

18th International Conference on Smart Homes and Health Telematics, ICOST 2020

作者： Guesmi, Thouraya Hadriche, Abir Jmail, Nawel Amar, Chokri Ben Institut Supérieur d’Informatique et de Multimédia Gabes University Teboulbou Tunisia Research Groups on Intelligent Machines Laboratory REGIM ENIS Sfax University Sfax Tunisia Digital Research Centre of Sfax Sfax University Sfax Tunisia Multimedia Information Systems and Advanced Computing Laboratory MIRACL Sfax University Sfax Tunisia

ISBN: (纸本)9783030515164

High frequency oscillations (HFO) from, MEG (magnetoencephalography) and intracerebral EEG are considered as effective tools to identify cognitive status and several cortical disorders especially in epilepsy diagnosis. The aim of our study is to evaluate stationary wavelet transform (SWT) technique performance in efficient reconstruction of pure epileptic high frequency oscillations, reputed as biomarkers of epileptogenic zones: generators of inter ictal epileptic discharges, and offhand seizures. We applied SWT on simulated and real database to detect non-contaminated HFO by spiky element. For simulated data, we computed the GOF of reconstruction that reaches for all studied constraint (relative amplitude, frequency, SNR and overlap) a promising results. For real data we used time frequency domain to evaluate SWT robustness of HFO reconstruction. We proved that SWT is an efficient filtering technique for separation HFO from spiky events. Our results would have an important impact on the definition of epileptogenic zones. © 2020, The Author(s).

关键词： Magnetoencephalography

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arXiv

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arXiv 2024年

作者： Ren, Yingying Li, Qiuli Guo, Yangyang Pedrycz, Witold Xing, Lining Liu, Anfeng Song, Yanjie School of Computer Electronics and Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University China School of Systems Science Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Poland Faculty of Engineering and Natural Sciences Department of Computer Engineering Sariyer Istanbul Turkey School of Electronic Engineering Xidian University Xian China School of Electronic Engineering Central South University Changsha China National Engineering Research Center of Maritime Navigation System Dalian Maritime University Dalian China

With the rapid development of the satellite industry, the information transmission network based on communication satellites has gradually become a major and important part of the future satellite ground integration network. However, the low transmission efficiency of the satellite data relay back mission has become a problem that is currently constraining the construction of the system and needs to be solved urgently. Effectively planning the task of satellite ground networking by reasonably scheduling resources is crucial for the efficient transmission of task data. In this paper, we hope to provide a task execution scheme that maximizes the profit of the networking task for satellite ground network planning considering feeding mode (SGNPFM). To solve the SGNPFM problem, a mixed-integer planning model with the objective of maximizing the gain of the link-building task is constructed, which considers various constraints of the satellite in the feed-switching mode. Based on the problem characteristics, we propose a distance similarity-based genetic optimization algorithm (DSGA), which considers the state characteristics between the tasks and introduces a weighted Euclidean distance method to determine the similarity between the tasks. To obtain more high-quality solutions, different similarity evaluation methods are designed to assist the algorithm in intelligently screening individuals. The DSGAalso uses an adaptive crossover strategy based on similarity mechanism, which guides the algorithm to achieve efficient population search. In addition, a task scheduling algorithm considering the feed-switching mode is designed for decoding the algorithm to generate a highquality scheme. The results of simulation experiments show that the DSGA can effectively solve the SGNPFM problem. Compared to other algorithms, the proposed algorithm not only obtains higher quality planning schemes but also has faster algorithm convergence speed. The proposed algorithm improves data trans

关键词： Profitability

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Non-aligned double JPEG compression detection based on refined Markov features in QDCT domain

Non-aligned double JPEG compression detection based on refin...

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作者： Wang, Jinwei Huang, Wei Luo, Xiangyang Shi, Yun-Qing Jha, Sunil Kr. School of Computer and Software Nanjing University of Information Science and Technology NanjingJiangsu China Nanjing University of Information Science and Technology Nanjing China Shanxi Key Laboratory of Network and System Security Xidian University Shanxi China State Key Laboratory of Mathematical Engineering and Advanced Computing ZhengzhouHenan China New Jersey Institute of Technology NewarkNJ United States

Due to the widespread use of the JPEG format, non-aligned double JPEG (NA-DJPEG) compression is very common in image tampering. Therefore, non-aligned double JPEG compression detection has attracted significant attention in digital forensics in recent years. In most of the previous detection algorithms, grayscale images are used directly, or color images are first converted into grayscale images and then processed. However, it is worth noting that most tampered images are color images. To make full use of the color information in images, a detection algorithm, which uses color images directly, is put forward in this paper. The algorithm based on refined Markov in quaternion discrete cosine transform (QDCT) domain is proposed for NA-DJPEG compression detection. Firstly, color information of a given JPEG image is extracted from blocked images to construct quaternion, and then block image QDCT coefficient matrices, including amplitude and three angles (ψ, , and θ) can be obtained. Secondly, the refined Markov features are generated from the transition probability matrix in the corresponding refinement process. Our proposed refinement method not only reduces redundant features but also makes the acquired features more efficient in detection. Therefore, the refined Markov features can not only capture the intra-block correlation between block QDCT coefficients but also improve computing efficiency in real-time. Finally, support vector machine (SVM) method is employed for NA-DJPEG compression detection. The experiment results demonstrate that the proposed algorithm not only make use of color information of images, but also can achieve better detection performance with small size images (i.e., 64 × 64) outperforming state-of-the-art detection methods tested on the same dataset. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Discrete cosine transforms

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Bimodal person recognition using dorsal-vein and finger-vein images

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Procedia Computer Science 2020年 176卷 1121-1130页

作者： Amal Abbes Randa Boukhris Trabelsi Yassine Ben Ayed Multimedia InfoRmation systems and Advanced Computing Laboratory MIRACL University of Sfax Tunisia Computers Imaging Electronics and Systems Group (CIELS) from Advanced Control and Energy Management Laboratory(CEM-Lab) University of Sfax Sfax Engineering School BP W 3038 Sfax Tunisia

Nowadays, human recognition with biometric characteristic have been investigated in many researchers. However, traditional biometric methods are not always robust against counterfeit and spoof attacks. The challenge of this work is to develop a bimodal biometric system, based on more reliable and robust characteristics against counterfeiting. Indeed, biometric system based on the venous network of the hand gives higher recognition rate compared to other systems. Furthermore, the fusion of two prints minimums the error rate and counterfeits. In our biometric system, the most challenging phase is the feature extraction step. For this, we propose a new feature extraction approach based on the concatenation of the pyramid of Difference of Gaussian (DoG) and Local Line Binary Patterns (LLBP) histograms (H-DoG_LLBP). To evaluate the proposed system, we opted for Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifiers. The experimental study is based on the dorsal hand vein BOSPHORUS database and finger vein MMCBNU_6000 database. Our system presents an area under curve (AUC) equal to 0.99, and 0.001 mean square error (MSE) with ANN, and 0.0042 equal error rate (EER) with SVM.

关键词： Bimodal Biometric system hand veins

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