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DAIIP: distributed Artificial Intelligence-based IoMT Platform for Clinical Data Collection and Applications 5

DAIIP: Distributed Artificial Intelligence-based IoMT Platfo...

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5th international conference on Computer, Big Data and Artificial Intelligence, ICCBD+AI 2024

作者： Fang, Keyu Qin, Xiaoli Wang, Junjun Zhang, Xin Chen, Ping Systems Engineering Institute Academy of Military Sciences Pla Tianjin China

ISBN: (纸本)9798331533991

the massive onsite time-series clinical data produced by Internet of Medical things (IoMT) can bring valuable information and immense potentials, thus enpowering a new wave of applications such as clinical decision support. However, current IoMT platforms is limited to data collection, some of them can cause data silos, further leading to data loss. To address these challenges, we present a distributed Artificial Intelligence (DAI)-based IoMT Platform for Clinical Data Collection and Applications, integrating time-series clinical data streaming processing with deep learning (DL)-based applications. this study focuses on optimizing the OCR model to improve the accuracy and processing speed of medical equipment data, thereby enhancing data collection compatibility. Additionally, we have integrated the MEWS scoring system with five fundamental physiological parameters and optimized the Autoformer time series prediction model. these enhancements enable the platform to intuitively depict changes in patients' physiological states. We validated the feasibility and effectiveness of our proposed platform by constructing an experimental setup that simulates real hospital scenarios and conducting comprehensive tests. the experimental results demonstrate that our platform effectively processes clinical data and supports clinical decision-making. these findings underscore the platform's potential and value in the IoMT field. © 2024 ieee.

关键词： Physiological models

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A Non-Linear Voltage Controller for Grid Voltage sensor-Less Power Factor Correction Converters 5

A Non-Linear Voltage Controller for Grid Voltage Sensor-Less...

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5th ieee international Transportation Electrification conference, ITEC-India 2023

作者： Singh, Prateek Bhule, Deeksha Kaarthik, R. Sudharshan Indian Institute of Space Science and Technology Department of Avionics India

ISBN: (纸本)9798350337808

In this paper, a grid voltage sensor-less control scheme for an AC-DC power factor correction converter using a non-linear voltage controller is proposed. the conventional PFC converters, used in EV battery chargers, electric drive systems, and computer power supplies, require a grid voltage sensor in order to operate at unity power factor. By using the proposed non-linear control method, the grid voltage sensor, and Phase Locked Loop (PLL) are not required for unity power factor operation of the PFC converter. the proposed grid voltage sensor-less control scheme also provides an additional degree of freedom (a), for controlling the transient response of the converter and its effect on the same in terms of peak overshoot and settling time for DC-link voltage is reviewed and analyzed. the mathematical equation for the grid current drawn by a PFC converter using the proposed control scheme is also derived and using it, the effect of circuit parameters on the power factor of the converter can be predicted. With the proposed control scheme, the system's overall cost, size, and weight are reduced, making it more reliable, less complex, and cost-effective. the proposed control scheme is simulated in MATLAB-Simulink for steady-state and transient conditions, and some hardware experiment results are also included. © 2023 ieee.

关键词： Transient analysis

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Smarter Way to Monitor and Detect Intrusions in Cloud Infrastructure using sensor-Driven Edge computing 5

Smarter Way to Monitor and Detect Intrusions in Cloud Infras...

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5th ieee international conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Venkateswara Reddy, B. Khader Basha, S.K. Polanki, Kusuma Vellela, Sai Srinivas Dalavai, Lavanya Vuyyuru, Lakshma Reddy Kumar, K. Kiran Chalapathi Institute of Technology Dept. of CSE-Cyber Security AP Guntur India Chalapathi Institute of Technology Dept. of CSE-Data Science AP Guntur India Chalapathi Institute of Technology Dept. of Cse AP Guntur India Koneru Lakshmaiah Education Foundation Dept. of Cse AP Vaddeswaram India

ISBN: (纸本)9798350383522

In today's linked world, essential infrastructure must be protected from unauthorized access. Traditional security measures often depend on closed-circuit television (CCTV) systems for perimeter monitoring. However, developing technologies provide substantial opportunity for improving security standards. this study presents a novel technique for achieving enhanced real-time, long-range object monitoring capabilities by combining modern edge computing and a varied spectrum of sensors, including LiDAR (Light Detection and Ranging) and ultrasonic sensors. LiDAR and ultrasonic sensors outperform traditional video cameras by offering real-time, long-range object monitoring capabilities, making them suitable for critical infrastructure security. However, these sensors create complex 3D point cloud data, which presents issues for data transmission and storage. To overcome this problem, we suggest implementing an enhanced edge computing architecture. the suggested architecture optimizes data transmission by compressing and centralizing point cloud data at the network's edge. this strategy reduces the demand on network resources by transferring computing activities to edge devices, allowing for real-time monitoring. Furthermore, the optimized data is smoothly transported to cost-effective Cloud-based servers provide effective resource utilization while protecting the security system. this research not only introduces a novel security paradigm for critical infrastructure but also contributes to the wider discourse on edge computing applications. By combining advanced sensors and edge computing technology, our approach not only enhances security but also ensures scalability, cost-effectiveness, and real-time responsiveness. the findings of this study have significant. Implications for the design and implementation of future security systems, emphasizing the importance of leveraging cutting-edge technologies to safeguard vital assets and infrastructure. © 2024 ieee.

关键词： 3D Object Detection Data Processing Edge computing Facility Security LiDAR Ultrasonic Technology

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Fabrication of Concave-Shaped Ultra-thinned FDSOI CMOS Circuit for Curved Image sensor 5

Fabrication of Concave-Shaped Ultra-Thinned FDSOI CMOS Circu...

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5th ieee international conference on Flexible and Printable sensors and systems, FLEPS 2023

作者： Imura, Shigeyuki Goto, Masahide Sakai, Toshikatsu Sato, Hiroto Nhk Science & Technology Research Laboratories Tokyo Japan

ISBN: (纸本)9781665457330

A high-performance flexible image sensor is useful for acquiring various types of image information around us. Particularly, if the image sensor is curved, lens aberrations can be directly corrected and high-quality images can be acquired. An ultra-thin complementary metal oxide semiconductor circuit and photoconversion film were transferred onto a flexible substrate to realize a curved image sensor with high performance and flexibility. Fully depleted silicon on insulator circuits were prepared to eliminate a thick silicon substrate using xenon difluoride etching and only 6-μm-thick thinned silicon circuits were bonded to a flexible substrate. In this study, we established a method for transferring a thin silicon circuit to a flexible substrate after removing a thick silicon substrate, and transistors operated properly in silicon circuits bent into a uniaxial cylindrical surface with a radius of curvature of 15 mm. Furthermore, we used a stretchable substrate to form a concave shape like a part of a sphere demonstrating that the thin silicon device can change shape flexibly. © 2023 ieee.

关键词： Gallium compounds

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distributed data-driven engine framework facing CPS brain-inspired intelligent transportation 23

Distributed data-driven engine framework facing CPS brain-in...

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23rd ieee international conference on High Performance computing and Communications, 7th ieee international conference on Data Science and systems, 19th ieee international conference on Smart City and 7th ieee international conference on Dependability in sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Yang, Zhengzhuang Xu, Mingyue Zhao, Kaidi Yang, Chao Zhi, Huiyu Zhang, Xihou Chen, Zhuoran Qiu, Song Han, Dingding School of Information Science and Technology Fudan University Shanghai China School of Communication and Electronic Engineering East China Normal University Shanghai China

ISBN: (纸本)9781665494571

Single data source, isolated data islands and low information utilization in intelligent transportation lead to the poor inversion of the cyber space to the physical space including Incomplete data integration, high interdependency of systems and poor anti-control ability. In this paper the data-driven method is adopted to optimize the W parameter in Webster method to reduce the waiting time of vehicles to realize the traffic congestion. In particular, a multi-source heterogeneous data processing method and a unified modeling of the multi-dimensional and multi-layer networks have been introduced. By data-driven engine, we have implemented both massive storage and fast processing system. We have also build a service system which can rapidly realize data integration and services delivery as well as be directly called by researchers from the database through network requests to reduce the data reprocessing. this paper also produces the data interaction process of the data engine framework in traffic management and control, and proposes a solution for the use of massive city data. © 2021 ieee.

关键词： Smart cities Soft sensors Transportation Data integration Process control distributed databases Data models

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A Reputation System for distributed Intelligent Blockchain Decision-Making 5

A Reputation System for Distributed Intelligent Blockchain D...

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5th international conference on Blockchain computing and Applications, BCCA 2023

作者： Rawlins, Charles Jagannathan, S. Nadendla, Venkata Sriram Siddhardh Electrical and Computer Engineering Missouri University of Science and Technology RollaMO United States Computer Science Missouri University of Science and Technology RollaMO United States

ISBN: (纸本)9798350339239

Discerning ground-truth amidst misinformation is a nontrivial task in decentralized networks. While trivial for traditional blockchain networks using Proof of Work (PoW) consensus, it requires immense computational resources at the cost of network scalability and therefore has compatibility challenges with limited devices like Internet of things (IoT) networks. Moving towards a lightweight alternative-a novel scheme is proposed for provable identification of reputable nodes for making intelligent decisions using distributed machine-learning optimization, called Weight-Based Reputation (WBR). A distributed learning task identifies nodes that are willing to cooperate as a means for securing ground-truth, and is provably resistant to data poisoning in theory and experimentation. A novel method combining WBR with a classifier creates lightweight and robust blockchain decision-making. the results of this two-step approach is a novel method for predicting various attacks that result transaction log, such as fraud or the double-spend. A Bayesian game against adversary manipulation formulation and experimental results show superior performance and robustness to the state-of-the-art. Resource consumption compared against blockchain protocols is also provided. Future work will explore WBR in distributed consensus as a complete alternative to blockchain PoW. © 2023 ieee.

关键词： Internet of things

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distributed Collaborative Control Technology in Multiple Unmanned Aerial Vehicle Operations 5

Distributed Collaborative Control Technology in Multiple Unm...

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5th international conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2023

作者： Zhao, Xin Zhou, Huan Aviation Engineering School Air Force Engineering University Xi'an China Aviation Engineering School Air Force Engineering University China Research Center of Unmanned System Strategy Development Northwestern Poly Technical University Xi'an China

ISBN: (纸本)9798350357950

Compared with single aircraft, multiple unmanned aerial vehicle (UAV) collaborative combat has many advantages and has been widely studied and applied. this article reviews and studies the distributed collaborative control technology in multi-UAV combat. Firstly, the background and problems to be solved in the collaborative operation of multiple unmanned aerial vehicles are elaborated. then the advantages of distributed collaborative control over centralized collaborative control are analyzed, commonly used artificial potential field methods, consistency theory methods, behavior-based methods, and their applications are summarized, and collaborative flight strategies and implementation methods applicable to different formation systems are explored. Finally, the challenges faced by the collaborative control of UA V formations are elaborated, and prospects for future issues worth further research are presented. Exploring collaborative control methods for unmanned aerial vehicle formation flight will contribute to innovative research in this field, thereby promoting the rapid development of multi-agent collaborative control technology. © 2023 ieee.

关键词： artificial potential field Collaborative control cooperative coperation distributed control unmanned aerial vehicle

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Efficiently Managing Large-Scale Keys in HDFS 23

Efficiently Managing Large-Scale Keys in HDFS

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作者： Jin, Wei Geng, Kui Yu, Mingjie Guo, Yunchuan Li, Fenghua Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China School of Cyber Security University of Science and Technology of China Hefei230027 China

ISBN: (纸本)9781665494571

To transparently encrypt/decrypt data stored in Hadoop distributed File System (HDFS), the Key Management Server (KMS) has been designed to automatically assign encryption zone keys (EZK) to encryption zones. However, the current key-management scheme suffers from performance bottleneck. In this paper, dividing key files for storing key data into metadata file and material file, we propose a memory-disk coordinated key management architecture for Hadoop EZK to manage massive keys, where a tree structure is designed to index key metadata stored in memory, and the inverted index structure is used to record materials stored on hard disk. Further balance-tree-based management algorithms without Java serialization is designed to efficiently query keys (including metadata and materials). A series of theoretical analysis are conducted to show the upper and lower boundary of metadata query. Finally, we implement the above scheme in the Hadoop KMS without modifying the original key-management interfaces. A series of experiments are carried out on the simulated data. Experimental results show that, our scheme can efficiently manage ten billion keys and perform much better than the existing KMS in Hadoop. © 2021 ieee.

关键词： Java Smart cities File systems Memory management distributed databases Metadata Hard disks

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FLEPS 2023 - ieee international conference on Flexible and Printable sensors and systems, Proceedings

FLEPS 2023 - IEEE International Conference on Flexible and P...

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5th ieee international conference on Flexible and Printable sensors and systems, FLEPS 2023

ISBN: (纸本)9781665457330

the proceedings contain 82 papers. the topics discussed include: exploration of lossy posture classification model using in-bed flexible pressure sensors;fully inkjet-printed soft wearable strain sensors based on metal/polymer composite sensing films;flexible stencil-printed piezoelectric transducer arrays for catheter based ultrasound applications;printed memristors using hydrothermally grown zinc oxide nanowires;a 3D printed wearable electromyography wristband;plasma printed antenna for flexible battery-less smart mask for lung health monitoring;powering wire-mesh circuits through MEMS fiber-gripper;development of a printed impedance based electrochemical sensor for e. coli detection;electrospun PANi nanofibers for biodegradable sensors;and full-lig wireless batteryless sensor for the detection of amines.

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distributed Training Decentralized Execution Framework of Multi-agent Learning for Cooperative Edge Caching 5

Distributed Training Decentralized Execution Framework of Mu...

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5th international conference on Frontiers Technology of Information and Computer, ICFTIC 2023

作者： Tang, Wenyuan Gao, Minghan Gao, Qiang Peng, Xiaohong Beihang University School of Electronic and Information Engineering Beijing China Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing China Birmingham City University Faculty of Computing Engineering and the Built Environment Birmingham United Kingdom

ISBN: (纸本)9798350309034

the edge caching technology has a great potential to improve the performance of modern wireless networks. the transmission bottleneck at the cloud server will occur when the centralized training decentralized execution (CTDE) framework is applied to find an effective caching policy running on each base station (BS) in a multi-BS cooperative caching scenario. In this paper, we propose a cooperative edge caching method based on the distributed training decentralized execution (DTDE) framework to address this issue. We further propose two DTDE based caching models, namely Single Neighbor DTDE (SN-DTDE) and Local Information Only Executing SN-DTDE (LE-SN-DTDE), to deal with the transmission bottleneck problem at BSs in DTDE. Our simulation results indicate that the models proposed can effectively address the transmission bottleneck problem and they all outperform CTDE on average user access delay in networks with training information loss. Among these new caching models, LESN-DTDE has the best performance when the network experience heavy training information loss. © 2023 ieee.

关键词： centralized training decentralized execution cooperative edge caching distributed training decentralized execution multi-agent learning

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