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arXiv 2022年

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Zhou, Jiantao Yang, Jian Qi, Guo-Jun The Intelligent Visual Information Perception Laboratory Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China The School of Communication and Information Engineering Shanghai University Shanghai200444 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China The Research Center for Industries of the Future The School of Engineering Westlake University Hangzhou310024 China OPPO Research SeattleWA98101 United States

Recently, Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks due to the ability of global feature extraction. However, the capabilities of Transformers that need to incorporate contextual information to extract features dynamically are neglected. To address this issue, we propose a lightweight Cross-receptive Focused Inference network (CFIN) that consists of a cascade of CT Blocks mixed with CNN and Transformer. Specifically, in the CT block, we first propose a CNN-based Cross-Scale Information Aggregation Module (CIAM) to enable the model to better focus on potentially helpful information to improve the efficiency of the Transformer phase. Then, we design a novel Cross-receptive Field Guided Transformer (CFGT) to enable the selection of contextual information required for reconstruction by using a modulated convolutional kernel that understands the current semantic information and exploits the information interaction within different self-attention. Extensive experiments have shown that our proposed CFIN can effectively reconstruct images using contextual information, and it can strike a good balance between computational cost and model performance as an efficient model. Source codes will be available at https://***/IVIPLab/CFIN. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Biomonitoring Technologies Used in Aquatic Ecosystems: A systematic and Trend Analysis

Biomonitoring Technologies Used in Aquatic Ecosystems: A Sys...

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International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)

作者： Carey Louise Arroyo Matthew Dionela Mary Grace Ann Bautista Ronnie Concepcion Ryan Rhay Vicerra Bernardo Duarte Department of Manufacturing Engineering and Management De La Salle University Manila Philippines Department of Electronics and Computer Engineering De La Salle University Manila Philippines MARE—Marine and Environmental Sciences Centre & ARNET – Aquatic Research Infrastructure Network Associated Laboratory Faculdade de Ciências da Universidade de Lisboa Campo Grande Portugal Departamento de Biologia Vegetal Faculdade de Ciências Universidade de Lisboa Lisbon Portugal

Aquatic biomonitoring technologies are widely used to detect and identify hazardous chemicals and pollutants in aquatic ecosystems. With the advancement of technology, this study focuses on providing a review of current trends and challenges related to aquatic biomonitoring. In particular, the advancements made in the designs of biosensors and Biological Early Warning systems (BEWS) were presented. This includes biosensors that utilize luminescent bacteria, algae, living and/or microbial fuel cells. For BEWS, those that use fishes, daphnids, and bivalves as sentinel organisms were discussed. It was shown that biosensors and BEWS show promising potential for monitoring water quality of aquatic ecosystems as they allow for real-time monitoring while performing comparably to traditional analytical methods. Despite the various research done, there are still some issues and challenges concerning aquatic biomonitoring. BEWS and biosensors were shown to have their advantages and disadvantages. Biosensors can quantify the tested parameters but are analyte-specific and often need to be replaced upon detection. On the other hand, BEWS allow for continuous real-time monitoring but are often site-specific and require an established baseline data of the water quality parameters. To combat the issues, this study proposes designing a BEWS using bivalves for early detection and utilizing analytical methods to confirm changes in water quality. It is also recommended that further research be done to determine levels of toxicity, capacity to meet needs, and water quality baseline data for local aquatic ecosystems.

关键词： Microorganisms Ecosystems Fuel cells Algae Water quality Market research Real-time systems

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High-density implantable neural electrodes and chips for massive neural recordings

Brain-X

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Brain-X 2024年第2期2卷

作者： Wang, Longchun Suo, Yanxing Wang, Jiahao Wang, Xuanqi Xue, Kai An, Jingjing Sun, Xun Chen, Qinyu Tang, Xiaochen Zhao, Yang Ji, Bowen Liu, Jingquan Department of Micro/Nano Electronics Shanghai Jiao Tong University Shanghai China National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong University Shanghai China DCI Joint Team Collaborative Innovation Center of IFSA Shanghai Jiao Tong University Shanghai China Unmanned System Research Institute Northwestern Polytechnical University Shaanxi Xi'an China National Key Laboratory of Unmanned Aerial Vehicle Technology Shaanxi Xi'an China Ministry of Education Key Laboratory of Micro and Nano Systems for Aerospace School of Mechanical Engineering Northwestern Polytechnical University Shaanxi Xi'an China Institute of Guizhou Aerospace Measuring and Testing Technology Guizhou Guiyang China Leiden Institute of Advanced Computer Science (LIACS) Leiden University Leiden Netherlands Klipsch School of Electrical and Computer Engineering New Mexico State University Las Cruces NM United States

High-density neural recordings with superior spatiotemporal resolution powerfully unveil cellular-scale neural communication, showing great promise in neural science, translational medicine, and clinical applications. To achieve such, many design and fabrication innovations enhanced the electrode, chip, or both for biocompatibility improvement, electrical performance upgrade, and size miniaturization, offering several thousands of recording sites. However, an enormous gap exists along the trajectory toward billions of recording sites for brain scale resolution, posing many more design challenges. This review tries to find possible insight into mitigating the gap by discussing the latest progress in high-density electrodes and chips for neural recordings. It emphasizes the design, fabrication, bonding techniques, and in vivo performance optimization of high-density electrodes. It discusses the promising opportunities for circuit-level and architecture-level multi-channel chip design innovations. We expect that joint effort and close collaboration between high-density electrodes and chips will pave the way to high-resolution neural recording tools supporting cutting-edge neuroscience discoveries and applications. © 2024 The Author(s). Brain-X published by John Wiley & Sons Australia, Ltd on behalf of Ainuohui Medical Technology.

关键词： brain-computer interface high density implantable chips implantable electrodes massive neural recording

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Hybrid RAG-empowered Multi-modal LLM for Secure Data Management in Internet of Medical Things: A Diffusion-based Contract Approach

arXiv

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arXiv 2024年

作者： Su, Cheng Wen, Jinbo Kang, Jiawen Wang, Yonghua Su, Yuanjia Pan, Hudan Zhong, Zishao Hossain, M. Shamim School of Automation The GuangdongHongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangdong University of Technology Guangzhou510006 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Automation The Guangdong Basic Research Center of Excellence for Ecological Security and Green Development Key Laboratory for City Cluster Environmental Safety Green Development The Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation The Key Laboratory of Intelligent Detection and IoT in Manufacturing Ministry of Education Guangdong University of Technology Guangzhou510006 China School of Automation Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangdong University of Technology Guangzhou510006 China Guangzhou University of Traditional Chinese Medicine State Key Laboratory of Traditional Chinese Medicine Syndrome The Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Second Affiliated Hospital of Guangzhou University of Chinese Medicine Chinese Medicine Guangdong Laboratory Hengqin 519000 China Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh12372 Saudi Arabia

Secure data management and effective data sharing have become paramount in the rapidly evolving healthcare landscape, especially with the growing integration of the Internet of Medical Things (IoMT). The rise of generative artificial intelligence has further elevated Multi-modal Large Language Models (MLLMs) as essential tools for managing and optimizing healthcare data in IoMT. MLLMs can support multi-modal inputs and generate diverse types of content by leveraging large-scale training on vast amounts of multi-modal data. However, critical challenges persist in developing medical MLLMs, including security and freshness issues of healthcare data, affecting the output quality of MLLMs. To this end, in this paper, we propose a hybrid Retrieval-Augmented Generation (RAG)-empowered medical MLLM framework for healthcare data management. This framework leverages a hierarchical cross-chain architecture to facilitate secure data training. Moreover, it enhances the output quality of MLLMs through hybrid RAG, which employs multi-modal metrics to filter various unimodal RAG results and incorporates these retrieval results as additional inputs to MLLMs. Additionally, we employ age of information to indirectly evaluate the data freshness impact of MLLMs and utilize contract theory to incentivize healthcare data holders to share their fresh data, mitigating information asymmetry during data sharing. Finally, we utilize a generative diffusion model-based deep reinforcement learning algorithm to identify the optimal contract for efficient data sharing. Numerical results demonstrate the effectiveness of the proposed schemes, which achieve secure and efficient healthcare data management. Copyright © 2024, The Authors. All rights reserved.

关键词： Information management

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Environment-Aware AUV Trajectory Design and Resource Management for Multi-Tier Underwater Computing

arXiv

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arXiv 2022年

作者： Hou, Xiangwang Wang, Jingjing Bai, Tong Deng, Yansha Ren, Yong Hanzo, Lajos Department of Electronic Engineering Tsinghua University Beijing100084 China School of Cyber Science and Technology Beihang University Beijing100191 China Department of Engineering King’s College London LondonWC2R 2LS United Kingdom Network and Communication Research Center Peng Cheng Laboratory Shenzhen518055 China School of Electronics and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom

The Internet of underwater things (IoUT) is envisioned to be an essential part of maritime activities. Given the IoUT devices’ wide-area distribution and constrained transmit power, autonomous underwater vehicles (AUVs) have been widely adopted for collecting and forwarding the data sensed by IoUT devices to the surface-stations. In order to accommodate the diverse requirements of IoUT applications, it is imperative to conceive a multi-tier underwater computing (MTUC) framework by carefully harnessing both the computing and the communications as well as the storage resources of both the surface-station and of the AUVs as well as of the IoUT devices. Furthermore, to meet the stringent energy constraints of the IoUT devices and to reduce the operating cost of the MTUC framework, a joint environment-aware AUV trajectory design and resource management problem is formulated, which is a high-dimensional NP-hard problem. To tackle this challenge, we first transform the problem into a Markov decision process (MDP) and solve it with the aid of the asynchronous advantage actor-critic (A3C) algorithm. Our simulation results demonstrate the superiority of our scheme. Copyright © 2022, The Authors. All rights reserved.

关键词： Resource allocation

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Counting Monkeypox Lesions in Patient Photographs: Limits of Agreement of Manual Counts and Artificial Intelligence

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Journal of Investigative Dermatology 2023年第2期143卷 347-350.e4页

作者： McNeil, Andrew J. House, David W. Mbala-Kingebeni, Placide Mbaya, Olivier Tshiani Dodd, Lori E. Cowen, Edward W. Nussenblatt, Véronique Bonnett, Tyler Chen, Ziche Saknite, Inga Dawant, Benoit M. Tkaczyk, Eric R. Dermatology Service and Research Service Department of Veterans Affairs Tennessee Valley Healthcare System Nashville TN United States Department of Dermatology Vanderbilt University Medical Center Nashville TN United States Department of Electrical and Computer Engineering School of Engineering Vanderbilt University Nashville TN United States Institut National de Recherche Biomédicale Kinshasa Democratic Republic Congo Clinical Monitoring Research Program Directorate Frederick National Laboratory for Cancer Research Frederick MD United States Clinical Trials Research Section Division of Clinical Research National Institute of Allergy and Infectious Disease Bethesda MD United States Dermatology Branch National Institute of Arthritis and Musculoskeletal and Skin Diseases Bethesda MD United States Laboratory of Clinical Immunology and Microbiology National Institute of Allergy and Infectious Diseases Bethesda MD United States Biophotonics Laboratory Institute of Atomic Physics and Spectroscopy University of Latvia Riga Latvia Department of Biomedical Engineering School of Engineering Vanderbilt University Nashville TN United States

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Revealing intra-urban spatial structure through an exploratory analysis by combining road network abstraction model and taxi trajectory data

arXiv

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arXiv 2022年

作者： Hu, Sheng Gao, Song Luo, Wei Wu, Liang Li, Tianqi Xu, Yongyang Zhang, Ziwei School of Geography and Information Engineering China University of Geosciences Wuhan430074 China Guangdong-Hong Kong-Macau Joint Laboratory for Smart Cities Shenzhen518061 China GeoDS Lab Department of Geography University of Wisconsin‐Madison MadisonWI United States Geography Department National University of Singapore Singapore Singapore School of Computer Science China University of Geosciences Wuhan430074 China National Engineering Research Center of Geographic Information System Wuhan430074 China

The unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily focuses on the topology of the road network without considering individual travel flows along with the road networks. Individual travel flows reflect the urban dynamics, which can further help understand the underlying spatial structure. This study therefore aims to reveal the intra-urban spatial structure by integrating the road network abstraction model and individual travel flows. To achieve this goal, we 1) quantify the spatial interaction relatedness of road segments based on the Word2Vec model using large volumes of taxi trip data, then 2) characterize the road abstraction network model according to the identified spatial interaction relatedness, and 3) implement a community detection algorithm to reveal sub-regions of a city. Our results reveal three levels of hierarchical spatial structures in the Wuhan metropolitan area. This study provides a data-driven approach to the investigation of urban spatial structure via identifying traffic interaction patterns on the road network, offering insights to urban planning practice and transportation management. Copyright © 2022, The Authors. All rights reserved.

关键词： Abstracting

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Secure and Privacy-preserving Data-sharing Framework based on Blockchain Technology for Al-Najaf/Iraq Oil Refinery

Secure and Privacy-preserving Data-sharing Framework based o...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Samir M. Umran SongFeng Lu Zaid Ameen Abduljabbar Zhi Lu Bingyan Feng Lu Zheng Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Ministry of Industry and Minerals Iraqi Cement State Company Baghdad Iraq Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China College of Education for Pure Sciences University of Basrah Technical Computer Engineering Department Al-Kunooze University College Basrah Iraq School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Wuhan Huazhong Numerical Control Co. Ltd Industrial Internet Research Institute Wuhan China College of Computer Science South-Central University for Nationalities Wuhan China

The Industrial Internet of Things or Industry 4.0 efficiently enhances the manufacturing process in terms of raising productivity, system performance, cost reduction, and building large-scale systems. It enables the connection of numerous heterogeneous devices and sensors into the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional industrial internet of things adopts centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and lead to data leakage, privacy disclosure, and high computational and communicational costs. In our work, we proposed a decentralized, efficient, low-power, scalable, secure, privacy-preserving, and trusting architecture based on a private blockchain network/smart contract and interplanetary file system technology for the Al-NajafyIraq oil refinery factory. That enables autonomous working and provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability and information backtracking. Traditional blockchain technology has storage limitation issues, most recent works adopted external servers or the cloud for storage purposes. Our proposed scheme addresses the limitations of blockchain data storage by adopting an interplanetary file system (IPFS) network; provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm, high-performance, and ultra-low-power consumption ARM Cortex-M microcontroller. In addition, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the oil refinery as an industrial application area. Moreover, it provides an efficient framework that resists common cyber-security attacks and realizes cyber-security requirements represented by data availability, integrity, and confidentiality.

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An Efficient Evolutionary Algorithm for High-Speed Train Rescheduling Under a Partial Station Blockage

SSRN

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SSRN 2023年

作者： Wang, Rongsheng Zhang, Qi Dai, Xuewu Yuan, Zhiming Zhang, Tao Ding, Shuxin Jin, Yaochu China Academy of Railway Sciences Corporation Limited Beijing100081 China Signal and Communication Research Institute China Academy of Railway Sciences Corporation Limited Beijing100081 China Train Operation Control Laboratory for High-Speed Railway National Engineering Research Center of System Technology for High-Speed Railway and Urban Rail Transit Beijing100081 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Faculty of Technology Bielefeld University BielefeldD-33615 Germany Department of Computer Science University of Surrey GuildfordGU2 7XH United Kingdom

This paper investigates the high-speed train rescheduling (HSTR) problem under a partial station blockage and proposes an efficient problem-specific strengthen elitist genetic algorithm (PS-SEGA) for HSTR. An HSTR model that is subject to train operation constraints is established to minimize the total train delay. The PS-SEGA framework consists of the following main steps. Firstly, a permutation-based encoding method is developed to define an efficient search space based on a train departure sequence. A decoding method is employed to output the rescheduled timetable and objective value based on the departure sequence. Moreover, a hybrid initialization method involving an efficient heuristic strategy (EHS) is put forward to accelerate the convergence speed of Evolutionary algorithms (EAs). Using problem-specific knowledge, EHS generates an efficient and feasible solution for the initial population. Finally, a restart strategy is presented to maintain genetic diversity. Compared with other advanced EAs and their improved variants also using the PS-SEGA framework, experimental results demonstrate the effectiveness of the proposed PS-SEGA for addressing HSTR scenarios under the partial station blockage. As for the scenarios that CPLEX can not obtain optimal solutions within 10 minutes, PS-SEGA can all provide quasi-optimal solutions in real-time. Furthermore, compared with the other two strategies (i.e., First-Scheduled-First-Served and EHS), PS-SEGA can find the best departure sequence that can output the timetable with a quasi-optimal solution. © 2023, The Authors. All rights reserved.

关键词： Genetic algorithms

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An open-source project for real-time image semantic segmentation

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Science China(Information Sciences) 2019年第12期62卷 246-247页

作者： Quan ZHOU Yu WANG Jia LIU Xin JIN Longin Jan LATECKI National Engineering Research Center of Communication and Network Technology Nanjing University of Posts and Telecommunications Key Laboratory of Ministry of Education for Broadband Wireless Communication and Sensor Network Technology Nanjing University of Posts and Telecommunications Beijing Electronic Science and Technology Institute State Key Laboratory of Virtual Reality Technology and Systems Beihang University Department of Computer and Information Sciences Temple University

Recent years have witnessed great progress of deep convolutional neural networks (DCNNs) for solving scene understanding tasks [1–3]. These advances prefer to construct deeper and larger net-

关键词： image An open-source project for real-time image semantic segmentation

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