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Journal of Network Intelligence 2024年第3期9卷 1441-1459页

作者： Zheng, Guang-Yuan Zhang, Fu-Quan Cheng, Chen Shan, Hong-Tao Soomro, Nouman Qadeer Liu, Yi College of Information Technology Shanghai Jian Qiao University Shanghai201306 China School of Mathematics and Computer Science Yanan University Shaanxi 716000 China School of Computer and Data Science Minjiang University No.200 Xiyuangong Road Fuzhou University Town Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Fuzhou Technology Innovation Center of intelligent Manufacturing information System Minjiang University Fuzhou350108 China Fujian Province University Fuzhou350300 China College of Business Shanghai Jian Qiao University Shanghai201306 China College of Electronic and Electrical Engineering Shanghai University of Engineering Science Shanghai201620 China Software Department Mehran University of Engineering & Technology Sindh 76062 Pakistan

Distinguishing the manifestations of pulmonary nodules poses a significant challenge in the medical field, demanding the expertise of experienced radiologists. This complexity results in the high cost and inadequacy of sample labeling. In response, this study introduces a reinforcement learning approach, denoted as TriCaps-RL, for the classification of pulmonary nodules based on CT signs. This approach initially employs the Q-value loss to train a single CapsNet. When the performance of the CapsNet plateaus and further enhancement becomes challenging, the loss function is progressively replaced by a triplet metric to augment performance. Consequently, a more nuanced differentiation among various types of pulmonary nodules is achieved. The proposed method can improve its performance through interactions with radiologists during their CT image reading process, thereby mitigating the shortage of radiologists and addressing the time-consuming issue of training sample labeling in medical image research. This paper delves into the fine-grained classification of pulmonary nodules using deep reinforcement learning based on CT imaging signs. In comparison to previous studies that primarily concentrate on the benign/malignant classification of lung nodules, this fine-grained classification of nodules founded on CT imaging signs proves to be more valuable for medical practitioners in making accurate diagnostic decisions. © 2024, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Lung cancer

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Intelligent Reflecting Surface for MIMO VLC: Joint Design of Surface Configuration and Transceiver Signal Processing

arXiv

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arXiv 2022年

作者： Sun, Shiyuan Yang, Fang Song, Jian Zhang, Rui Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Key Laboratory of Digital TV System of Guangdong Province and Shenzhen City Research Institute of Tsinghua University in Shenzhen Shenzhen518057 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China

With the capability of reconfiguring the wireless electromagnetic environment, intelligent reflecting surface (IRS) is a new paradigm for designing future wireless communication systems. In this paper, we consider optical IRS for improving the performance of visible light communication (VLC) under a multiple-input and multiple-output (MIMO) setting. Specifically, we focus on the downlink communication of an indoor MIMO VLC system and aim to minimize the mean square error (MSE) of demodulated signals at the receiver. To this end, the MIMO channel gain of the IRS-aided VLC is first derived under the point source assumption, based on which the MSE minimization problem is then formulated subject to the emission power constraints. Next, we propose an alternating optimization algorithm, which decomposes the original problem into three subproblems, to iteratively optimize the IRS configuration, the precoding and detection matrices for minimizing the MSE. Moreover, theoretical analysis on the performance of the proposed algorithm in high and low signal-to-noise ratio (SNR) regimes is provided, revealing that the joint optimization process can be simplified in such special cases, and the algorithm’s convergence property and computational complexity are also discussed. Finally, numerical results show that IRS-aided schemes significantly reduce the MSE as compared to their counterparts without IRS, and the proposed algorithm outperforms other baseline schemes. Copyright © 2022, The Authors. All rights reserved.

关键词： Mean square error

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PollenNet: A novel architecture for high precision pollen grain classification through deep learning and explainable AI

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Heliyon 2024年第19期10卷 e38596页

作者： Shamrat, F M Javed Mehedi Idna Idris, Mohd Yamani Zhou, Xujuan Khalid, Majdi Sharmin, Sharmin Sharmin, Zeseya Ahmed, Kawsar Moni, Mohammad Ali Department of Computer System and Technology Universiti Malaya Kuala Lumpur 50603 Malaysia School of Business University of Southern Queensland Toowoomba Australia Department of Computer Science and Artificial Intelligence College of Computing Umm Al-Qura University Makkah 21955 Saudi Arabia School of Information Technology Faculty of Science Engineering and Built Environment Deakin University Australia Department of Electrical and Computer Engineering University of Saskatchewan 57 Campus Drive Saskatoon S7N 5A9 SK Canada Group of Bio-photomatiχ Information and Communication Technology Mawlana Bhashani Science and Technology University Santosh Tangail 1902 Bangladesh Health Informatics Research Lab Department of Computer Science and Engineering Daffodil International University Daffodil Smart City (DSC) Birulia Savar Dhaka 1216 Bangladesh AI & Digital Health Technology Artificial Intelligence & Cyber Future Institute Charles Stuart University Bathurst 2795 NSW Australia AI & Digital Health Technology Rural Health Research Institute Charles Stuart University Orange 2800 NSW Australia

Pollen grains play a critical role in environmental, agricultural, and allergy research despite their tiny dimensions. The accurate classification of pollen grains remains a significant challenge, mainly attributable to their intricate structures and the extensive diversity of species. Traditional methods often lack accuracy and effectiveness, prompting the need for advanced solutions. This study introduces a novel deep learning framework, PollenNet, designed to tackle the intricate challenge of pollen grain image classification. The efficiency of PollenNet is thoroughly evaluated through stratified 5-fold cross-validation, comparing it with cutting-edge methods to demonstrate its superior performance. A comprehensive data preparation phase is conducted, including removing duplicates and low-quality images, applying Non-local Means Denoising for noise reduction, and Gamma correction to adjust image brightness. Furthermore, Explainable AI (XAI) is utilized to enhance the interpretability of the model, while Receiver Operating Characteristic (ROC) curve analysis serves as a quantitative method for evaluating the model's capabilities. PollenNet demonstrates superior performance when compared to existing models, with an accuracy of 98.45 %, precision of 98.20 %, specificity of 98.40 %, recall of 98.30 %, and f1-score of 98.25 %. The model also maintains low Mean Squared Error (0.03) and Mean Absolute Error (0.02) rates. The ROC curve analysis, the low False Positive Rate (0.016), and the False Negative Rate (0.017) highlight the reliability and dependability of the model. This study significantly improves the efficacy of classifying pollen grains, indicating an important advancement in the application of deep learning for ecological research. © 2024 The Authors

关键词： Deep learning Explainable AI Image processing Pollen grain classification PollenNet model

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LDGCN: An Edge-End Lightweight Dual GCN Based on Single-Channel EEG for Driver Drowsiness Monitoring

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IEEE Transactions on Consumer Electronics 2025年

作者： Huang, Jingwei Wang, Chuansheng Huang, Jiayan Fan, Haoyi Xue, Xingsi Zhang, Fuquan Fuzhou University College of Computer and Big Data Fuzhou China Polytechnic University of Catalonia Department of Automatic Control Barcelona Spain Putian University New Engineering Industry College Putian China Fujian University of Technology Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian Fuzhou350118 China Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Fuzhou China Minjiang University College of Computer and Control Engineering Fuzhou China Sichuan Conservatory of Music Digital Media Art Key Laboratory of Sichuan Province Chengdu China Minjiang University Fuzhou Technology Innovation Center of Intelligent Manufacturing information System Fuzhou China Fuzhou China

Driver drowsiness electroencephalogram (EEG) signal monitoring can promptly alert drivers of their drowsiness in edge-end Consumer Electronics (CE), thereby reducing the probability of traffic accidents. Graph convolutional networks (GCNs) have shown significant advancements in processing the non-stationary, time-varying, and non-Euclidean nature of EEG signals. However, the existing single-channel EEG adjacency graph construction process lacks interpretability, which hinders the ability of GCNs to effectively extract adjacency graph features, thus affecting the performance of drowsiness monitoring. To address this issue, we propose an edge-end Lightweight Dual Graph Convolutional Network (LDGCN). Specifically, we are the first to incorporate neurophysiological knowledge to design a Baseline Drowsiness Status Adjacency Graph (BDSAG), which characterizes driver drowsiness status. Additionally, to express more features within limited EEG data, we introduce the Augmented Graph-level Module (AGM). This module captures global and local information at the graph level, ensuring that BDSAG features remain intact while enhancing effective feature expression capability. To further enhance the application of CE, we apply Adaptive Pruning Optimization (APO) to the channels and neurons of LDGCN, which nearly halves the inference latency. Experiments on benchmark datasets demonstrate that LDGCN offers the best trade-off between monitoring performance and hardware resource utilization, outperforming existing state-of-the-art algorithms. © 1975-2011 IEEE.

关键词： Neurons

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A Versatile Data Fabric for Advanced IoT-Based Remote Health Monitoring

arXiv

引用

arXiv 2023年

作者： Buleje, Italo Siu, Vince S. Hsieh, Kuan Yu Hinds, Nigel Dang, Bing Bilal, Erhan Nguyen, Thanhnha Lee, Ellen E. Depp, Colin A. Rogers, Jeffrey L. Digital Health IBM T.J. Watson Research Center Yorktown HeightsNY United States Emerging Technology Engineering IBM T.J. Watson Research Center Yorktown Heights United States Department of Electrical and Computer Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Psychiatry University of California La Jolla San DiegoCA United States Sam and Rose Stein Institute for Research on Aging University of California La Jolla San DiegoCA United States USA IBM-UCSD Artificial Intelligence for Healthy Living Center La Jolla CA United States Veterans Affairs San Diego Healthcare System San DiegoCA United States

This paper presents a data-centric and security-focused data fabric designed for digital health applications. With the increasing interest in digital health research, there has been a surge in the volume of Internet of Things (IoT) data derived from smartphones, wearables, and ambient sensors. Managing this vast amount of data, encompassing diverse data types and varying time scales, is crucial. Moreover, compliance with regulatory and contractual obligations is essential. The proposed data fabric comprises an architecture and a toolkit that facilitate the integration of heterogeneous data sources, across different environments, to provide a unified view of the data in dashboards. Furthermore, the data fabric supports the development of reusable and configurable data integration components, which can be shared as open-source or inner-source software. These components are used to generate data pipelines that can be deployed and scheduled to run either in the cloud or on-premises. Additionally, we present the implementation of our data fabric in a home-based telemonitoring research project involving older adults, conducted in collaboration with the University of California, San Diego (UCSD). The study showcases the streamlined integration of data collected from various IoT sensors and mobile applications to create a unified view of older adults' health for further analysis and research. © 2023, CC BY-NC-ND.

关键词： Internet of things

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Rate Splitting Multiple Access for RIS-aided URLLC MIMO Broadcast Channels

arXiv

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arXiv 2024年

作者： Soleymani, Mohammad Santamaria, Ignacio Jorswieck, Eduard Di Renzo, Marco Schober, Robert Hanzo, Lajos The Signal and System Theory Group Universität Paderborn Paderborn33098 Germany The Department of Communications Engineering Universidad de Cantabria Santander39005 Spain The Institute for Communications Technology Technische Universität Braunschweig Braunschweig38106 Germany Université Paris-Saclay CNRS CentraleSupélec Laboratoire des Signaux et Systèmes Gif-sur-Yvette91192 France The Institute for Digital Communications Friedrich Alexander University of Erlangen-Nuremberg Erlangen91058 Germany The Department of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

The performance of modern wireless communication systems is typically limited by interference. The impact of interference can be even more severe in ultra-reliable and low-latency communication (URLLC) use cases. A powerful tool for managing interference is rate splitting multiple access (RSMA), which encompasses many multiple-access technologies like non-orthogonal multiple access (NOMA), spatial division multiple access (SDMA), and broadcasting. Another effective technology to enhance the performance of URLLC systems and mitigate interference is constituted by reconfigurable intelligent surfaces (RISs). This paper develops RSMA schemes for multiuser multiple-input multiple-output (MIMO) RIS-aided broadcast channels (BCs) based on finite block length (FBL) coding. We show that RSMA and RISs can substantially improve the spectral efficiency (SE) and energy efficiency (EE) of MIMO RIS-aided URLLC systems. Additionally, the gain of employing RSMA and RISs noticeably increases when the reliability and latency constraints are more stringent. Furthermore, RISs impact RSMA differently, depending on the user load. If the system is underloaded, RISs are able to manage the interference sufficiently well, making the gains of RSMA small. However, when the user load is high, RISs and RSMA become synergetic. Copyright © 2024, The Authors. All rights reserved.

关键词： Space division multiple access

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Global assessment of terrestrial productivity in response to water stress

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Science Bulletin 2024年第15期69卷 2352-2356页

作者： Qi Liu Huadong Guo Jiahua Zhang Shijie Li Ji Li Fengmei Yao Miguel D.Mahecha Jian Peng School of Computer Engineering Jiangsu University of TechnologyChangzhou 213001China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 101408China Department of Remote Sensing Helmholtz Centre for Environmental ResearchUFZLeipzig 04318Germany Remote Sensing Centre for Earth System Research Leipzig UniversityLeipzig 04103Germany Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China Remote Sensing and Digital Earth Center School of Computer Science and TechnologyQingdao UniversityQingdao 266071China Key Laboratory of Earth Observation of Hainan Province Hainan Aerospace Information Research InstituteChinese Academy of SciencesSanya 572000China School of Geographical Sciences Nanjing University of Information Science and TechnologyNanjing 210044China Key Laboratory of Water Cycle and Related Land Surface Processes Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China German Centre for Integrative Biodiversity Research(iDiv) Halle-Jena-Leipzig 04103Germany

In recent decades, climate warming has led to an increase in atmospheric water demand, a potential reduction in soil moisture and a rise in the frequency and intensity of droughts. This has intensified the effects of water stress on plants, leading to more vegetated areas experiencing water stress [1,2]. Soil moisture (SM)and vapor pressure deficit (VPD) are considered the two main factors that exert water stress on vegetation activity and terrestrial productivity, thereby affecting the global carbon cycle [3].

关键词： soil moisture terrestrial

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Cuckoo-based malware dynamic analysis

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International Journal of Performability engineering 2019年第3期15卷 772-781页

作者： Wang, Lele Wang, Binqiang Liu, Jiangang Miao, Qiguang Zhang, Jianhui National Digital Switching System Engineering and Technological Research Center Zhengzhou450002 China Nanjing Information Technology Institute Nanjing210000 China Department of Computer Science Xidian University Xi'an710071 China

Aiming at the problems of the huge number of malware currently in the big data environment, the insufficient ability of automatic malware analysis available, and the inefficiency of the classification of malicious attributes, in this paper, we propose a Cuckoo-based malware dynamic analysis system that can be extended, analyzed quickly, and has application value. The system proposes a semantic feature model based on deep learning, uses a deep recursive neural network model to describe the multi-layered aggregation relationship of program semantics, and builds a malware semantic aggregation model. The model can automatically complete the acquisition and analysis of behavioural features of unknown program samples and perform attribute discrimination on unknown program samples efficiently and accurately. © 2019 Totem Publisher, Inc.

关键词： Semantics

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A joint energy and latency framework for transfer learning over 5G industrial edge networks

arXiv

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arXiv 2021年

作者： Yang, Bo Fagbohungbe, Omobayode Cao, Xuelin Yuen, Chau Qian, Lijun Niyato, Dusit Zhang, Yan Engineering Product Development Pillar Singapore University of Technology and Design Singapore487372 Singapore Department of Electrical and Computer Engineering Credit Center Prairie View A and M University Texas A and M University System Prairie ViewTX77446 United States School of Computer Science and Engineering Nanyang Technological University Singapore Singapore University of Oslo Norway Simula Metropolitan Center for Digital Engineering Norway

In this paper, we propose a transfer learning (TL)-enabled edge-CNN framework for 5G industrial edge networks with privacy-preserving characteristic. In particular, the edge server can use the existing image dataset to train the CNN in advance, which is further fine-tuned based on the limited datasets uploaded from the devices. With the aid of TL, the devices that are not participating in the training only need to fine-tune the trained edge-CNN model without training from scratch. Due to the energy budget of the devices and the limited communication bandwidth, a joint energy and latency problem is formulated, which is solved by decomposing the original problem into an uploading decision subproblem and a wireless bandwidth allocation subproblem. Experiments using ImageNet demonstrate that the proposed TL-enabled edge-CNN framework can achieve almost 85% prediction accuracy of the baseline by uploading only about 1% model parameters, for a compression ratio of 32 of the autoencoder. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Intelligent Computing: The Latest Advances, Challenges and Future

arXiv

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arXiv 2022年

作者： Zhu, Shiqiang Yu, Ting Xu, Tao Chen, Hongyang Dustdar, Schahram Gigan, Sylvain Gunduz, Deniz Hossain, Ekram Jin, Yaochu Lin, Feng Liu, Bo Wan, Zhiguo Zhang, Ji Zhao, Zhifeng Zhu, Wentao Chen, Zuoning Durrani, Tariq S. Wang, Huaimin Wu, Jiangxing Zhang, Tongyi Pan, Yunhe Zhejiang Lab Hangzhou China TU Wien Austria Sorbonne University Paris France Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Department of Electrical and Computer Engineering University of Manitoba Manitoba Canada Faculty of Technology Bielefeld University Bielefeld Germany Chinese Academy of Engineering Beijing China Department of Electronic and Electrical Engineering University of Strathclyde Glasgow United Kingdom National University of Defense Technology Changsha China National Digital Switching System Engineering & Technological R&D Center Zhengzhou China Materials Genome Institute Shanghai University Shanghai China Zhejiang University Hangzhou China

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human-computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy and an abundance of innovations in the theories, systems, and applications of intelligent computing are expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners. Copyright © 2022, The Authors. All rights reserved.

关键词： Surveys

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