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7th International Conference on information technology: IoT and Smart City, ICIT 2019

作者： Bilal, Muhammad Yu, Zhi Bashir, Abid Hussain, Muhammad Ali Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou310027 China Information Technology Services Department of Computer Science National Textile University Faisalabad38000 Pakistan Management Science and Engineering School of Management and Economics University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9781450376631

information technology (IT) is playing a vital role in all aspects of our lives nowadays. IT is usually used for fast communication links and transferring useful information from one place to another. Nevertheless, around the world, unfortunately, IT is not properly handled. Without IT and information and Communication technology (ICT) world will become a dark place. Most of the research in the IT field is inadequate. A subjective IT field development framework designed with the assistance of better Internet features utilized properly in all developing countries around the world. The proposed IT development framework can be used innovatively by adopting a wireless-based network in developing country growth around the world. We analyzed wired and wireless systems based on six common key Internet feature parameters for IT field improvement in developing countries around the world. In this empirical research, a total of 72 IT professional specialists included based on the statistical technique judgment sampling. The questionnaire survey results signify that speed, security, and reliability Internet features in the wired network are better than the wireless network. Moreover, mobility, coverage area, and cost relevant Internet features in the wireless network are much better than a wired network. According to expert decision-makers' strategy, the wireless-based network transmission medium is better to develop improvement in the world. Therefore, it finally concluded that developing countries could also play a significant role in the IT field as well as other fields around the world if Internet features are provided in the form of a wireless-based network properly. In the future, security and speed relevant problems will be resolved in the wireless-based system through improving routing algorithms. © 2019 Association for Computing Machinery.

关键词： Developing countries

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Stacked auto encoder based Deep Reinforcement Learning for online resource scheduling in large-scale MEC networks

arXiv

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arXiv 2020年

作者： Jiang, Feibo Wang, Kezhi Dong, Li Pan, Cunhua Yang, Kun Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Department of Computer and Information Sciences Northumbria University United Kingdom Key Laboratory of Hunan Province for New Retail Virtual Reality Technology Hunan University of Commerce Changsha China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom School of Computer Sciences and Electrical Engineering University of Essex ColchesterCO4 3SQ United Kingdom University of Electronic Science and Technology of China Chengdu China

An online resource scheduling framework is proposed for minimizing the sum of weighted task latency for all the mobile users, by optimizing offloading decision, transmission power, and resource allocation in the mobile edge computing (MEC) system. Towards this end, a deep reinforcement learning (DRL) method is proposed to obtain an online resource scheduling policy. Firstly, a related and regularized stacked auto encoder (2r-SAE) with unsupervised learning is proposed to perform data compression and representation for high dimensional channel quality information (CQI) data, which can reduce the state space for DRL. Secondly, we present an adaptive simulated annealing based approach (ASA) as the action search method of DRL, in which an adaptive h-mutation is used to guide the search direction and an adaptive iteration is proposed to enhance the search efficiency during the DRL process. Thirdly, a preserved and prioritized experience replay (2p-ER) is introduced to assist the DRL to train the policy network and find the optimal offloading policy. Numerical results are provided to demonstrate that the proposed algorithm can achieve near-optimal performance while significantly decreasing the computational time compared with existing benchmarks. It also shows that the proposed framework is suitable for resource scheduling problem in large-scale MEC networks, especially in the dynamic environment. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

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Awareness and Settlement of IT Field key Challenges for Next Generation Development in World 2019

Awareness and Settlement of IT Field Key Challenges for Next...

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Proceedings of the 2019 7th International Conference on information technology: IoT and Smart City

作者： Muhammad Bilal Zhi Yu Abid Bashir Muhammad Ali Hussain Zhejiang Provincial Key Laboratory of Service Robot College of Computer Science Zhejiang University Hangzhou China Information Technology Services Manager MIS / Deputy Director of IT Department of Computer Science National Textile University Faisalabad Pakistan Management Science and Engineering School of Management and Economics University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781450376631

关键词： information technology Internet Decision-makers Developing countries Transmission medium

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Correction to: Geometric correction method for Tibetan woodcut document images

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Multimedia Tools and Applications 2022年第11期81卷 15633-15633页

作者： Hu, Jian Xiawu, Lijia Qiao, Shaojie Tan, Wenrong Yin, Feng Liu, Tao Han, Nan Key Laboratory for Computer Systems of State Ethnic Affairs Commission Southwest MinZu University Chengdu China Minzu Language’s Information Processing Laboratory Southwest MinZu University Chengdu China School of Computer Science and Technology Southwest MinZu University Chengdu China Southwest Nationalities Research Academy Southwest MinZu University Chengdu China School of Software Engineering Chengdu University of Information Technology Chengdu China Automatic Software Generation and Intelligence Service Key Laboratory of Sichuan Province Chengdu University of Information Technology Chengdu China Library Southwest MinZu University Chengdu China School of Management Chengdu University of Information Technology Chengdu China

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Multiple Spatial information Weighted Fuzzy Clustering for Image Segmentation

Multiple Spatial Information Weighted Fuzzy Clustering for I...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xiangdao Liu Jin Zhou Hui Jiang C. L. Philip Chen Tong Zhang Lin Wang Shiyuan Han Yuehui Chen Shandong Provincial Key Laboratory of Network based Intelligent Computing University of Jinan Jinan China Development and Test Center Chinabond Fintech Information Technology Co. Ltd. Beijing China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

For image segmentation, fuzzy clustering methods with single spatial information cannot ensure robustness to the image corrupted by different noises. In this paper, to figure out this problem, we propose a multiple spatial information weighted fuzzy clustering method, in which the original pixel intensity and its two spatial information, the mean and median of neighbors within a local window, are combined with different weights to obtain precise segmentation results of noise images. And the entropy-regularized method is employed to optimize the weight of each term to handle the images with different noise. What's more, the kernelization of the proposed method is presented to relief the impact of outliers. It is worth noting that our methods can be further extended by combining with other spatial information. Experiments on synthetic images and natural images show the superiority and efficiency of the proposed methods.

关键词： Image segmentation Clustering methods Scalability Conferences Robustness Entropy Cybernetics

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Optimization of Missile Path Planning Based on APMO-HV Algorithm

Optimization of Missile Path Planning Based on APMO-HV Algor...

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2019 IEEE International Conference on Unmanned Systems, ICUS 2019

作者： Zhang, Hu Wang, Shuai Liu, Tonglin Zhou, Aimin Zhang, Yi Beijing Electro-mechanical Engineering Institute Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China Changzhou University School of Mechanical Engineering Changzhou China East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing School of Computer Science and Technology Shanghai China

ISBN: (纸本)9781728137926

In this paper, with considerations of low efficiency of missile path planning (MPP) by traditional aggregation technology, it uses affinity propagation based multi-objective evolutionary algorithm with hypervolume environment selection (APMO-HV) to solve the problem of MPP after establishing the MPP model. The experimental part compares and analyzes APMO-HV with six state-of-the-art algorithms, and applies it to address the MPP problem. The experimental results show that compared with the other six algorithms, APMO-HV has achieved the best solution performance in both the GLT test suite and MPP problem. This not only validates the effect of the proposed algorithm, but also enriches and improves the research results of MPP. © 2019 IEEE.

关键词： Evolutionary algorithms

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MDCN: Multi-scale Dense Cross Network for image super-resolution

arXiv

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arXiv 2020年

作者： Li, Juncheng Fang, Faming Li, Jiaqian Mei, Kangfu Zhang, Guixu Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai200062 China School of Computer Science and Technology East China Normal University Shanghai200062 China Chinese University of Hong Kong ShenZhen Hong Kong

Convolutional neural networks have been proven to be of great benefit for single-image super-resolution (SISR). However, previous works do not make full use of multi-scale features and ignore the inter-scale correlation between different upsampling factors, resulting in sub-optimal performance. Instead of blindly increasing the depth of the network, we are committed to mining image features and learning the inter-scale correlation between different upsampling factors. To achieve this, we propose a Multi-scale Dense Cross Network (MDCN), which achieves great performance with fewer parameters and less execution time. MDCN consists of multi-scale dense cross blocks (MDCBs), hierarchical feature distillation block (HFDB), and dynamic reconstruction block (DRB). Among them, MDCB aims to detect multi-scale features and maximize the use of image features flow at different scales, HFDB focuses on adaptively recalibrate channel-wise feature responses to achieve feature distillation, and DRB attempts to reconstruct SR images with different upsampling factors in a single model. It is worth noting that all these modules can run independently. It means that these modules can be selectively plugged into any CNN model to improve model performance. Extensive experiments show that MDCN achieves competitive results in SISR, especially in the reconstruction task with multiple upsampling factors. The code will be provided at https://***/MIVRC/MDCN-PyTorch. Copyright © 2020, The Authors. All rights reserved.

关键词： Signal sampling

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Summary of Scene Text Detection and Recognition

Summary of Scene Text Detection and Recognition

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Yao Qin Zhi Zhang Collage of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China China

ISBN: (数字)9781728151694

ISBN: (纸本)9781728151700

In recent years, scene text recognition has received much attention, and has a wealth of application scenarios, such as: photo translation, image retrieval, scene understanding and so on. However, the text in the scene is also faced with many problems, such as: light changes, deformation text, text string recognition under background noise interference, text skew and degree of curvature, and a large number of artistic fonts. Solving the above problems will always be a challenging thing. This paper reviews some recent work on text detection, text recognition and end-to-end text detection and recognition, and finally looks forward to the future development direction.

关键词： Industrial electronics Image recognition Text recognition Interference Market research Noise measurement Strain

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Enhancing Collaborative Inference on Heterogeneous Edge Devices via Adaptive Ensemble Knowledge Distillation

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IEEE Journal on Selected Areas in Communications 2025年

作者： Wu, Shangrui Li, Yupeng Wang, Wenhua Guo, Jianxiong Fan, Wentao Liu, Qin Jia, Weijia Yu, Shui Cao, Jiannong Wang, Tian Beijing Normal-Hong Kong Baptist University Guangdong Provincial/Zhuhai Key Laboratory of IRADS Department of Computer Science Zhuhai China Hong Kong Baptist University Hong Kong Hong Kong Hong Kong Baptist University Department of Interactive Media Hong Kong Hong Kong Beijing Normal University Institute of Artificial Intelligence and Future Networks Zhuhai China Xi'an University of Posts and Telecommunications Shaanxi Key Laboratory of Information Communication Network and Security Shaanxi Xi'an China Hunan University College of Computer Science and Electronic Engineering Changsha China University of Technology Sydney School of Computer Science Sydney Australia The Hong Kong Polytechnic University Department of Computing Hong Kong Hong Kong

The integration of edge computing with deep neural networks (DNNs) is crucial for intelligent industrial cyber-physical systems. Typically, deploying DNNs on heterogeneous edge devices relies on methods like model compression and partitioning. However, these approaches often result in homogeneous models across devices. This homogeneity limits the collective capability of edge computing systems, particularly in terms of generalization to diverse data distributions and adaptation to dynamic industrial environments. In this work, we propose to treat each DNN on an edge device as an independent model, aggregating their capabilities via ensemble learning to enhance generalization and dynamic adaptability. To realize this, we introduce the Adaptive Ensemble Knowledge Distillation Framework (AEKDF), combining cloud-based model training with edge computing based collaborative inference. In the cloud, AEKDF develops an enhanced Born Again Network that generates diverse, lightweight models tailored to specific edge devices through knowledge distillation. This process ensures model diversity which is critical to effective ensemble learning. On the edge, AEKDF employs an adaptive ensemble technique that aggregates prediction logits across devices, enabling rapid adaptation to changing environments and maintaining inference efficiency. Our extensive evaluations conducted on a realistic prototype demonstrate the substantial boost in predictive performance achieved by our AEKDF, showcasing a 4% to 10% accuracy improvement on the CIFAR-100 compared to conventional single-model approaches, while maintaining low latency. © 1983-2012 IEEE.

关键词： Deep neural networks

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An improved method of algal-bloom discrimination in Taihu Lake using Sentinel-1A data 6

An improved method of algal-bloom discrimination in Taihu La...

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6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Wu, Lin Sun, Mengwei Min, Lin Zhao, Jianhui Li, Ning Guo, Zhengwei College of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China Henan Engineering Research Center of Intelligent Technology and Application Kaifeng475004 China

ISBN: (纸本)9781728129129

The algal bloom is a prominent manifestation of water pollution. Synthetic aperture radar (SAR) shows an advantage in water monitoring due to its characteristic of all-time and all-weather. The water regions where algae gather present dark in SAR image. However, dark regions may also be caused by other factors, such as low wind. This paper proposes an improved algal bloom discrimination method based on Artificial Neural Network (ANN) to recognize the dark regions of algal bloom. Taihu Lake is chosen as the research area in this study because of its serious bloom in recent years. By means of quasi-synchronous optical images, the dark region database of SAR images labeled algal bloom and non-algal bloom are obtained. Then the segmentation algorithm and region growing algorithm are used to acquire the feature from dark regions, and divided into training feature set and testing feature set. Finally, the training and testing feature set are used for ANN-based discrimination model construction and verification. According the experimental results, the overall accuracy reaches 80%, which indicates that ANN model has a good applicability in algal bloom recognition of SAR image. © 2019 IEEE.

关键词： Neural networks

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