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LSRN-AED: lightweight super-resolution network based on asymmetric encoder–decoder

Soft Computing 2024年第13-14期28卷 8513-8525页

作者： Huang, Shuying Li, Wei Yang, Yong Wan, Weiguo Lai, Houzeng School of Software Tiangong University Tianjin300387 China School of Software and Internet of Things Engineering Jiangxi University of Finance and Economics Nanchang330032 China School of Computer Science and Technology Tiangong University Tianjin300387 China

Due to limited memory and computing resources, the application of deep neural networks on embedded and mobile devices is still a great challenge. To tackle this problem, this paper proposes a lightweight super-resolution network based on asymmetric encoder–decoder (LSRN-AED), which achieves better performance while reducing model parameters and computation. On the basis of rethinking the roles of encoder and decoder, an asymmetric encoder–decoder (AED) composed of complex encoders and simple decoders is designed to achieve feature extraction and reconstruction. Here, the decoder only adopts one inverted residual block, which can reduce the computational cost of the model and the redundancy of mapping features. For the encoder, inspired by the Transformer structure, an epiphany encoder is designed to realize the feature extraction and representation. In the encoder, a multi-way epiphany attention module (MEAM) is constructed, in which inverted residual blocks are used to replace traditional residual blocks to extract features and reduce model complexity. To realize the selection and enhancement of spatial features, an epiphany attention block (EAB) is designed by exploiting depth-wise convolutions which can learn the significant spatial information of the feature maps. Experimental results demonstrate that the proposed LSRN-AED can achieve better performance at lower parameter cost and outperform some existing state-of-the-art lightweight models. For example, compared to the advanced SMSR method, the proposed LSRN-AED has better evaluation metrics while reducing the number of parameters by 45%, 44%, and 44%, and FLOPs by 44%, 42%, and 41% on the × 2/3/4 SR tasks, respectively. The code will be published on GitHub after our paper is accepted for publication. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Feature extraction

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UAV-Assisted Maritime Data Collection via Optical Communications using Deep Reinforcement Learning 9

UAV-Assisted Maritime Data Collection via Optical Communicat...

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9th International Conference on Big Data Computing and Communications, BigCom 2023

作者： Ma, Saisai Li, Mengzhen Deng, Tinglong Wang, Jinglong Ruby, Rukhsana Luo, Hanjiang Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350331240

Ocean data collection via unmanned aerial vehicles (UAVs) has received widespread attention due to its flexibility and low cost. To further improve the efficiency of large maritime data collection between the UAVs and the ocean surface buoys, optical wireless transmission is considered as a promising technique because of its low latency and high bandwidth. However, the waves and other disturbances in the complex oceanic environment result in drift and instability of the buoy, which deteriorate and even interrupt the line-of-sight (LOS) optical transmission. To tackle these challenges, in this paper, we propose a reliable data collection scheme based on a deep reinforcement learning (DRL) approach. We first model the optical channel and calculate the maximum transmission range that satisfies a pre-defined bit error rate (BER) to ensure the quality of service (QoS). Then, we formulate the data collection procedure as a Markov decision process (MDP) aiming at maximizing the received signal intensity and minimizing the energy consumption, in which the system uncertainty caused by the oceanic environmental disturbances is taken into account. Finally, we propose a beam pointing adjustment algorithm based on deep deterministic policy gradient (DDPG) approach to alleviate the performance deterioration and maintain a stable LOS communication. Through extensive simulations, the results demonstrate that the proposed scheme is effective and achieves reliable data collection via the optical links. © 2023 IEEE.

关键词： Reinforcement learning

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CMCF-Net: An End-to-End Context Multiscale Cross-Fusion Network for Robust Copy-Move Forgery Detection

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IEEE Transactions on Multimedia 2024年 26卷 6090-6101页

作者： Xiong, Lizhi Xu, Jianhua Yang, Ching-Nung Zhang, Xinpeng Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology Engineering Research Center of Digital Forensics Ministry of Education Nanjing210044 China Nanjing University of Information Science and Technology Software Engineering Nanjing210044 China National Dong Hwa University Department of Computer Science and Information Engineering Hualien974301 Taiwan Fudan University School of Computer Science Shanghai200433 China

Image copy-move forgery detection (CMFD) has become a challenging problem due to increasingly powerful editing software that makes forged images increasingly realistic. Existing algorithms that directly connect multiple scales of features in the encoder part may not effectively aggregate contextual information, resulting in poor performance. In this paper, an end-to-end context multiscale cross-fusion network (CMCF-Net) is proposed to detect image copy-move forgery. The proposed network consists of a multiscale feature extraction fusion (MSF) module and a multi-information fusion decoding (MFD) module. Multiscale information is efficiently extracted and fused in the MSF module utilizing stacked-scale feature fusion, which improves the network's forgery localization ability on objects of different scales. The MFD module employs contextual information combination and weighted fusion of multiscale information to guide the network in obtaining relevant clues from correlated information at multiple different scales. Experimental results and analysis have demonstrated that the proposed CMCF-Net achieves the best localization results with higher robustness. © 1999-2012 IEEE.

关键词： Feature extraction

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African Bison Optimization Algorithm:A New Bio-Inspired Optimizer with engineering Applications

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computers, Materials & Continua 2024年第10期81卷 603-623页

作者： Jian Zhao Kang Wang Jiacun Wang Xiwang Guo Liang Qi School of Science University of Science and Technology LiaoningAnshan114051China Team of Artificial Intelligence Theory and Application University of Science and Technology LiaoningAnshan114051China Department of Computer Science and Software Engineering Monmouth UniversityWest Long BranchNJ 07764USA Information and Control Engineering College Liaoning Petrochemical UniversityFushun113000China Department of Computer Science and Technology Shandong University of Science and TechnologyQingdao266590China

This paper introduces the African Bison Optimization(ABO)algorithm,which is based on biological *** is inspired by the survival behaviors of the African bison,including foraging,bathing,jousting,mating,and *** foraging behavior prompts the bison to seek a richer food source for *** bison find a food source,they stick around for a while by bathing *** jousting behavior makes bison stand out in the population,then the winner gets the chance to produce offspring in the mating *** eliminating behavior causes the old or injured bison to be weeded out from the herd,thus maintaining the excellent *** above behaviors are translated into ABO by mathematical *** assess the reliability and performance of ABO,it is evaluated on a diverse set of 23 benchmark functions and applied to solve five practical engineering problems with *** findings from the simulation demonstrate that ABO exhibits superior and more competitive performance by effectively managing the trade-off between exploration and exploitation when compared with the other nine popular metaheuristics algorithms.

关键词： Optimization metaheuristics African bison optimization engineering problems

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Multi-UAVs Collaborative Search Scheme in Marine Environments using Deep Reinforcement Learning 11

Multi-UAVs Collaborative Search Scheme in Marine Environment...

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11th International Conference on Advanced Cloud and Big Data, CBD 2023

作者： Guo, Kai Luo, Hanjiang Tao, Hang Ruby, Rukhsana Qin, Zhizun Liu, Kui Shandong University of Science and Technology College of Computer Science and Engineering Qingdao China Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350345346

Unmanned aerial vehicles (UAVs) have mobility in harsh environments and the flexibility to modify their flight altitude to acquire information in an adaptive manner, so these vehicles can be leveraged to solve the informative path planning (IPP) problem. As most of the research on IPP currently are on static targets and terrain detection tasks, in this paper, we focus on the search for dynamic targets on the sea surface, and study the issue of collaborative search with multiple granularities using a swarm of UAVs in marine environments. To capture the objects of interest (OOI), we design a binary classifier for UAVs to sense OOI based on a depth perception model of cameras. Afterwards, we propose a collaborative search scheme using the multi-agent deep deterministic policy gradient (MADDPG) approach to enable a swarm of UAVs to simultaneously maintain formation coordination while adapting to digital pheromone strength. The experimental results show that the proposed scheme can effectively detect targets on the sea surface in a multi-granular collaborative manner. © 2023 IEEE.

关键词： Motion planning

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A Survey of Personalized Medicine Recommendation

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International Journal of Crowd science 2024年第2期8卷 77-82页

作者： Zhu, Fanglin Cui, Lizhen Xu, Yonghui Qu, Zhe Shen, Zhiqi School of Software Shandong University Jinan250101 China Shandong University Jinan250101 China School of Computer Science and Engineering Nanyang Technological University 639798 Singapore

Mining potential and valuable medical knowledge from massive medical data to support clinical decision-making has become an important research field. Personalized medicine recommendation is an important research direction in this field, aiming to recommend the most suitable medicines for each patient according to the health status of the patient. Personalized medicine recommendation can assist clinicians to make clinical decisions and avoid the occurrence of medical abnormalities, so it has been widely concerned by many researchers. Based on this, this paper makes a comprehensive review of personalized medicine recommendation. Specifically, we first make clear the definition of personalized medicine recommendation problem;then, starting from the key theories and technologies, the personalized medicine recommendation algorithms proposed in recent years are systematically classified (medicine recommendation based on multi-disease, medicine recommendation with combination pattern, medicine recommendation with additional knowledge, and medicine recommendation based on feedback) and in-depth analyzed;and this paper also introduces how to evaluate personalized medicine recommendation algorithms and some common evaluation indicators;finally, the challenges of personalized medicine recommendation problem are put forward, and the future research direction and development trends are prospected. © The author(s) 2024.

关键词： Decision making

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UKF‐MOT:An unscented Kalman filter‐based 3D multi‐object tracker

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 1031-1041页

作者： Meng Liu Jianwei Niu Yu Liu Collective Intelligence&Collaboration Laboratory China North Artificial Intelligence and Innovation Research InstituteBeijingChina China North Vehicle Research Institute BeijingChina State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijingChina State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijingChina School of Computer Science and Engineering Beihang UniversityBeijingChina

Multi‐object tracking in autonomous driving is a non‐linear *** better address the tracking problem,this paper leveraged an unscented Kalman filter to predict the object's *** the association stage,the Mahalanobis distance was employed as an affinity metric,and a Non‐minimum Suppression method was designed for *** the detections fed into the tracker and continuous‘predicting‐matching’steps,the states of each object at different time steps were described as their own continuous *** conducted extensive experiments to evaluate tracking accuracy on three challenging datasets(KITTI,nuScenes and Waymo).The experimental results demon-strated that our method effectively achieved multi‐object tracking with satisfactory ac-curacy and real‐time efficiency.

关键词： autonomous vehicle transportation

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Gastrointestinal Diseases Classification Using Deep Transfer Learning and Features Optimization

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computers, Materials & Continua 2023年第4期75卷 2227-2245页

作者： Mousa Alhajlah Muhammad Nouman Noor Muhammad Nazir Awais Mahmood Imran Ashraf Tehmina Karamat College of Applied Computer Science King Saud University(Almuzahmiyah Campus)Riyadh11543Saudi Arabia Department of Computer Science HITEC UniversityTaxila47080Pakistan Department of Computer Engineering HITEC UniversityTaxila47080Pakistan Department of Software Engineering Foundation University IslamabadIslamabad44000Pakistan

Gastrointestinal diseases like ulcers, polyps’, and bleeding areincreasing rapidly in the world over the last decade. On average 0.7 millioncases are reported worldwide every year. The main cause of gastrointestinaldiseases is a Helicobacter Pylori (H. Pylori) bacterium that presents in morethan 50% of people around the globe. Many researchers have proposeddifferent methods for gastrointestinal disease using computer vision *** of them focused on the detection process and the rest of themperformed classification. The major challenges that they faced are the similarityof infected and healthy regions that misleads the correct classificationaccuracy. In this work, we proposed a technique based on Mask Recurrent-Convolutional Neural Network (R-CNN) and fine-tuned pre-trainedResNet-50 and ResNet-152 networks for feature extraction. Initially, the region ofinterest is detected using Mask R-CNN which is later utilized for the trainingof fine-tuned models through transfer learning. Features are extracted fromfine-tuned models that are later fused using a serial approach. Moreover, anImproved Ant Colony Optimization (ACO) algorithm has also opted for thebest feature selection from the fused feature vector. The best-selected featuresare finally classified using machine learning techniques. The experimentalprocess was conducted on the publicly available dataset and obtained animproved accuracy of 96.43%. In comparison with state-of-the-art techniques,it is observed that the proposed accuracy is improved.

关键词： Diseases deep learning endoscopy gastrointestinal tract transfer learning

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In silico prediction methods of self-interacting proteins:an empirical and academic survey

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Frontiers of computer science 2023年第3期17卷 199-216页

作者： Zhanheng CHEN Zhuhong YOU Qinhu ZHANG Zhenhao GUO Siguo WANG Yanbin WANG College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China School of Computer Science Northwestern Polytechnical UniversityXi’an 710129China Institute of Machine Learning and Systems Biology School of Electronics and Information EngineeringTongji UniversityShanghai 201804China School of Cyber Science and Technology Zhejiang UniversityHangzhou 310058China

In silico prediction of self-interacting proteins(SIPs)has become an important part of *** is an urgent need to develop effective and reliable prediction methods to overcome the disadvantage of high cost and labor intensive in traditional biological wet-lab *** goal of our survey is to sum up a comprehensive overview of the recent literature with the computational SIPs prediction,to provide important references for actual work in the *** this review,we first describe the data required for the task of DTIs ***,some interesting feature extraction methods and computational models are presented on this topic in a timely ***,an empirical comparison is performed to demonstrate the prediction performance of some classifiers under different feature extraction and encoding ***,we conclude and highlight potential methods for further enhancement of SIPs prediction performance as well as related research directions.

关键词： proteomics self-interacting proteins feature extraction prediction model

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Leveraging Augmented Reality,Semantic-Segmentation,and VANETs for Enhanced Driver’s Safety Assistance

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computers, Materials & Continua 2024年第1期78卷 1443-1460页

作者： Sitara Afzal Imran Ullah Khan Irfan Mehmood Jong Weon Lee Mixed Reality and Interaction Lab Department of SoftwareSejong UniversitySeoul05006Korea Faculty of Engineering and Digital Technologies School of Computer ScienceAI and ElectronicsUniversity of BradfordBradfordUK

Overtaking is a crucial maneuver in road transportation that requires a clear view of the road ***,limited visibility of ahead vehicles can often make it challenging for drivers to assess the safety of overtaking maneuvers,leading to accidents and *** this paper,we consider atrous convolution,a powerful tool for explicitly adjusting the field-of-view of a filter as well as controlling the resolution of feature responses generated by Deep Convolutional Neural Networks in the context of semantic image *** article explores the potential of seeing-through vehicles as a solution to enhance overtaking ***-through vehicles leverage advanced technologies such as cameras,sensors,and displays to provide drivers with a real-time view of the vehicle ahead,including the areas hidden from their direct line of *** address the problems of safe passing and occlusion by huge vehicles,we designed a see-through vehicle system in this study,we employed a windshield display in the back car together with cameras in both *** server within the back car was used to segment the car,and the segmented portion of the car displayed the video from the front *** see-through system improves the driver’s field of vision and helps him change lanes,cross a large car that is blocking their view,and safely overtake other *** network was trained and tested on the Cityscape dataset using semantic *** transparent technique will instruct the driver on the concealed traffic situation that the front vehicle has *** our findings,we have achieved 97.1% *** article also discusses the challenges and opportunities of implementing see-through vehicles in real-world scenarios,including technical,regulatory,and user acceptance factors.

关键词： Overtaking safety augmented reality VANET V2V deep learning

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