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Research on airborne data transmission technology based on 5G technology 9

Research on airborne data transmission technology based on 5...

引用

9th International symposium on Computer and Information Processing technology, ISCIPT 2024

作者： Sui, Jiahuan Luo, Qing Wu, Di Zhao, Xiaoou Shenyang Aircraft Design and Research Institute Shenyang China

ISBN: (纸本)9798350388404

The traditional airborne data transmission adopts data card as the transmission medium, which has the loss of personnel and time cost. In order to further improve combat effectiveness and take advantage of the technical characteristics of 5G technology with large bandwidth, high reliability and low delay, this paper carries out research on airborne data wireless transmission technology, improves airborne data acquisition system, designs and develops an air-to-ground transmission 5G system, and carries out airborne data wireless transmission communication test and verification under the real airborne structure model. The test results show that the airborne data peak rate reaches 1Gbps, and the research results provide technical support for 5G technology to adapt to complex airborne environment applications. © 2024 ieee.

关键词： 5G mobile communication systems

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real-time bioaerosol detecting via combination of cyclone based collecting system and SiNW biosensor

Real-time bioaerosol detecting via combination of cyclone ba...

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2023 ieee International symposium on Medical Measurements and applications, MeMeA 2023

作者： Woo, Gunhoo Lee, Do Hoon Lee, Byung Yang Kim, Taesung Sungkyunkwan Univ. Dept. of Nano Science and Technology Suwon Korea Republic of Korea University Dept. of Mechanic Engineering Seoul Korea Republic of Sungkyunkwan Univ. Dept. of Mechanic Engineering Suwon Korea Republic of

ISBN: (纸本)9781665493840

The recent outbreak of coronavirus has raised people's awareness of respiratory diseases by creating unprecedentedly high infection rates and deaths. Accordingly, the importance of indoor air quality management and the need for regulations have emerged, and various indoor air quality management activities such as ventilation and mask wearing have been carried out. However, unlike these efforts, real-time analysis of internal airborne bioaerosols still needs a lot of improvement. The naturally very low concentration of bioaerosols makes them unsuitable for existing biosensor research in development, which requires highly concentrated liquid samples. Therefore, it is necessary to collect high concentration of bioaerosol with liquid form in real time. In this study, by utilizing cyclone sampler, continuous sampling obtaining highly concentrated bioaerosol sample can be achieved rather than the existing discontinuous impactor method, and a study was conducted to implement real-time analysis based on silicon nanowire biosensor. Structural optimization was performed based on computational fluid dynamics analysis of the air flow inside the bioaerosol sampler, and the performance was investigated using Aspergillus niger fungus and standard particles, which show a low cut-off size of 0.5 um. Finally, possibility of real-time analysis was confirmed through continuous collection test. The proposed technology is expected to make a significant contribution to the systematic management of indoor air quality management and the prevention of respiratory diseases through linkage with internet of things technology. © 2023 ieee.

关键词： Nanowires

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Multi-agent Reinforcement Learning for Improving Supply Chain Visibility in Inventory Management 27

Multi-agent Reinforcement Learning for Improving Supply Chai...

引用

27th ieee/ACM International symposium on Distributed Simulation and real time applications, DS-RT 2023

作者： Zhang, Bo Tan, Wen Jun Cai, Wentong Zhang, Allan N School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Technology and Research Singapore Institute of Manufacturing Technology Agency for Science Singapore Singapore

ISBN: (纸本)9798350337846

This paper proposes a novel approach to enhance supply chain (SC) visibility, cooperation, and performance during inventory management while effectively mitigating the risk of information leakage by leveraging machine learning techniques. The SC inventory policies are optimized using multi-agent reinforcement learning (MaRL) and SC network topological information. Furthermore, we conduct a simulation-based evaluation that demonstrates the superior performance of our method compared to alternative optimization approaches. This research effectively addresses the dual objectives of ensuring information security and achieving cost reduction in SC inventory management. © 2023 ieee.

关键词： Cost reduction

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Integration of C2000 Microcontrollers with MATLAB Simulink embedded Coder: A real-time Control Application 7

Integration of C2000 Microcontrollers with MATLAB Simulink E...

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7th International Conference on Electrical Engineering and Green Energy, CEEGE 2024

作者： Rahman, Mohammad Abidur Abushaiba, Ali A. Elrajoubi, Akrem M. University of Arkansas School of Engineering & Engineering Technology Little RockAR United States University of Misurata College of Engineering Misurata Libya

ISBN: (纸本)9798350350623

C2000 TM 32-bit microcontrollers excel in real-time control, optimizing closed-loop performance for various applications such as industrial motor drives, solar inverters, digital power systems, and sensing. The lineup offers Premium and Entry-performance microcontroller units (MCUs). This paper explores the integration process of Texas Instruments text TM (TI) C2000 microcontroller units with MATLAB and Simulink using Code Composer Studio (CCS) and embedded Coder (EC) support package for C2000 microcontrollers. It shows how to set up Simulink to program the Digital Signal Processor (DSP) microcontroller unit and the communication between the host computer and the C2000 target hardware using the LAUNCHXLF28379D launchpad. Basic functions such as Pulse Width Modulation, Analog Digital Conversion, and Proportional-Integral controllers are explained. Finally, a closed-loop control model for a C2000 Digital Power Buck Converter BoosterPack is implemented, showcasing the effective integration of TI C2000 microcontroller units with MATLAB, Simulink, and related tools for real-time control in the power system context. © 2024 ieee.

关键词： Microcontrollers

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Analyzing the Performance of 5G Enabled embedded Sensor Networks: Proof-of-Function 9

Analyzing the Performance of 5G Enabled Embedded Sensor Netw...

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9th International Conference on Information technology and Digital applications, ICITDA 2024

作者： Jun, Chai Jia Yogarayan, Sumendra Razak, Siti Fatimah Abdul Abdullah, Mohd. Fikri Azli Faculty of Information Science and Technology Melaka Malaysia

ISBN: (纸本)9798331510732

The fifth generation of cellular technology, known as 5G, marks a substantial evolution beyond the fourth generation (4G) standards, offering advancements in speed, connectivity, and overall performance. This research addresses the subject of evaluating 5G's practical implementation in sensor networks, which is fundamental for understanding the real-world applicability. The study explores the practical implementation of a 5G embedded sensor network, focusing on understanding how the network operates, assessing its performance, and identifying potential limitations using hardware components such as Raspberry Pi 3 and DHT22 sensors. The study reveals that while 5G technology offers improvements, different accessibility to the frequency bands across various locations can influence network performance. This variability may affect the widespread deployment and acceptance of 5G sensor networks. Despite these challenges, the research demonstrates that 5G technology can maintain low latency and stable throughput, demonstrating the potential to improve real-time applications and provide reliable data transfer in different environments. © 2024 ieee.

关键词： 5G mobile communication systems

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SUMMARY OF THE 2024 LOW-POWER EFFICIENT AND ACCURATE FACIAL-LANDMARK DETECTION FOR embedded SYSTEMS

SUMMARY OF THE 2024 LOW-POWER EFFICIENT AND ACCURATE FACIAL-...

引用

ieee International Conference on Multimedia and Expo (ICME)

作者： Ni, Yu-Shu Chen, Han-Chun Tsai, Chia-Chi Chen, Chih-Cheng Chen, Po-Yu Kuo, Hsien-Kai Hunag, Jun-Ying Hui, Po-Chi Hwang, Jenq-Neng Guo, Jiun-In Natl Yang Ming Chiao Tung Univ Dept Elect Engn Hsinchu Taiwan Natl Yang Ming Chiao Tung Univ Inst Elect Hsinchu Taiwan Natl Cheng Kung Univ Dept Elect Engn Tainan Taiwan Pervas Artificial Intelligence Res Labs PAIR Labs Hsinchu Taiwan Univ Washington Dept Elect Engn Seattle WA 98195 USA MediaTek Inc Hsinchu Taiwan

ISBN: (纸本)9798350379822;9798350379815

The 2024 ieee ICME Grand Challenge has been a pivotal event in advancing low-power efficient and accurate facial-landmark detection for embedded systems. It emphasized the development of models that are both energy-efficient and highly accurate. With participation from 165 teams, the competition provided an extensive dataset for model training, highlighting facial diversity to bolster model resilience. This competition unfolded in two phases: an initial online evaluation and a subsequent final round conducted on the MediaTek Dimensity 9300 Series platform, aimed at assessing the efficacy of real-time applications. A critical aspect of the competition was the models' ability to precisely detect 51 facial landmarks, a feature integral to applications such as autonomous driving, by capturing detailed facial expressions. The victors of the challenge were recognized based on their models' accuracy and minimal power consumption, marking a notable advancement in the field of facial-landmark detection and significantly advancing applications in embedded systems.

关键词： Facial-landmark detection autonomous driving and embedded deep learning

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Evaluation of Computational Cost and Result Accuracy in Design and Efficient Implementation of Log-Mel Spectrogram and MFCC Feature Extraction Using Fixed-Point Arithmetic on FPGA

Evaluation of Computational Cost and Result Accuracy in Desi...

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2024 International symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2024

作者： Zhao, Chenyu Yamamura, Naoya Tsutsui, Hiroshi Ohgane, Takeo Graduate School of Information Science and Technology Hokkaido University Japan Faculty of Information Science and Technology Hokkaido University Japan

ISBN: (纸本)9798350389210

Mel-frequency cepstral coefficients (MFCCs) are essential features in audio processing, aligning with human auditory perception and are used in applications such as speech recognition, speaker identification, and audio compression. Despite their benefits, the computational complexity of MFCCs poses challenges for real-time applications and embedded systems. Field-programmable gate arrays (FPGAs) offer a solution with their parallel processing capabilities and efficient handling of complex arithmetic operations. This paper presents an optimized implementation of the MFCC algorithm on an FPGA, focusing on computational cost and resulting accuracy. Our key strategies include using fixed-point arithmetic to reduce computational complexity and resource usage. We compare software and hardware implementations of the fast Fourier transform (FFT) to demonstrate the advantages of FPGA-based acceleration. The system is designed for high customization, allowing adaptation to various application requirements. Experimental results explore both the speed of fixed-point arithmetic and the processing results compared to the librosa library in Python, validating the data accuracy under fixed-point operations. These findings provide a foundation for integrating audio feature extraction in system-on-a-chips (SoCs) for various application scenarios. © 2024 ieee.

关键词： Fixed point arithmetic

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Framework for Quality Evaluation of Smart Roadside Infrastructure Sensors for Automated Driving applications 34

Framework for Quality Evaluation of Smart Roadside Infrastru...

引用

34th ieee Intelligent Vehicles symposium (IV)

作者： Kloeker, Laurent Liu, Chenghua Wei, Chao Eckstein, Lutz Rhein Westfal TH Aachen Inst Automot Engn Aachen Germany

ISBN: (纸本)9798350346916

The use of smart roadside infrastructure sensors is highly relevant for future applications of connected and automated vehicles. External sensor technology in the form of intelligent transportation system stations (ITS-Ss) can provide safety-critical real-time information about road users in the form of a digital twin. The choice of sensor setups has a major influence on the downstream function as well as the data quality. To date, there is insufficient research on which sensor setups result in which levels of ITS-S data quality. We present a novel approach to perform detailed quality assessment for smart roadside infrastructure sensors. Our framework is multimodal across different sensor types and is evaluated on the DAIR-V2X dataset. We analyze the composition of different lidar and camera sensors and assess them in terms of accuracy, latency, and reliability. The evaluations show that the framework can be used reliably for several future ITS-S applications.

关键词： Smart roadside infrastructure sensors framework quality assessment digital twin automated driving

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REMONI: An Autonomous System Integrating Wearables and Multimodal Large Language Models for Enhanced Remote Health Monitoring

REMONI: An Autonomous System Integrating Wearables and Multi...

引用

ieee International symposium on Medical Measurements and applications (MeMeA)

作者： Ho, Thanh Cong Kharrat, Farah Abid, Ahderrazek Karray, Fakhri Koubaa, Anis Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Univ Waterloo Dept Elect & Comp Engn Waterloo ON N2L 3G1 Canada Prince Sultan Univ Coll Comp & Informat Sci Riyadh Saudi Arabia

ISBN: (纸本)9798350308006;9798350307993

With the widespread adoption of wearable devices in our daily lives, the demand and appeal for remote patient monitoring have significantly increased. Most research in this field has concentrated on collecting sensor data, visualizing it, and analyzing it to detect anomalies in specific diseases such as diabetes, heart disease and depression. However, this domain has a notable gap in the aspect of human-machine interaction. This paper proposes REMONI, an autonomous REmote health MONItoring system that integrates multimodal large language models (MLLMs), the Internet of Things (IoT), and wearable devices. The system automatically and continuously collects vital signs, accelerometer data from a special wearable (such as a smartwatch), and visual data in patient video clips collected from cameras. This data is processed by an anomaly detection module, which includes a fall detection model and algorithms to identify and alert caregivers of the patient's emergency conditions. A distinctive feature of our proposed system is the natural language processing component, developed with MLLMs capable of detecting and recognizing a patient's activity and emotion while responding to healthcare worker's inquiries. Additionally, prompt engineering is employed to integrate all patient information seamlessly. As a result, doctors and nurses can access real-time vital signs and the patient's current state and mood by interacting with an intelligent agent through a user-friendly web application. Our experiments demonstrate that our system is implementable and scalable for real-life scenarios, potentially reducing the workload of medical professionals and healthcare costs. A full-fledged prototype illustrating the functionalities of the system has been developed and being tested to demonstrate the robustness of its various capabilities.

关键词： Remote Health Monitoring Wearable technology Multimodal Large Language Models Healthcare

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Fast Adapting Few-Shot Federated Learning System for Human Activity Recognition 22

Fast Adapting Few-Shot Federated Learning System for Human A...

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22nd ieee International symposium on Parallel and Distributed Processing with applications, ISPA 2024

作者： Zhang, Ziyan Zhao, Yanchao College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (纸本)9798331509712

The advent of smart devices has revolutionized our daily lives, especially through mobile sensing, with Human Activity Recognition (HAR) emerging as a key area of interest. Its applications span healthcare, indoor localization, and smart environments. Despite its promise, HAR's deployment faces numerous obstacles, such as personalization loss, scarce labeling, long training time, and poor edge device adaptation. Current research often addresses only a subset of these challenges, leaving a gap for a holistic solution. In response, we introduce Meta-Sense-Federated-Learning (MSFL), an innovative federated sensing system designed to personalize deep sensing models to individual users in a shorter time. MSFL leverages meta-learning to navigate data diversity, integrating physical principles to mitigate labeling biases and support real-world applications with minimal user input. Our framework synergizes a server-side model with mobile user engagement, where meta-learning crafts tasks that refine the base model and recalibrate it for novel scenarios. MSFL stands out by extracting uniform activity insights from varied Inertial Measurement Unit (IMU) data, employing data augmentation aligned with physical laws. This approach requires minimal calibration, addressing mobile sensing data's heterogeneity effectively. Users get their personalized model in a short time without having to spend extra time on the federal process. Our empirical findings underscore MSFL's superiority, showcasing a significant boost in model accuracy - over 10% higher than traditional techniques under conditions of limited labels, and 11% higher than MAML in the cross-dataset test. Furthermore, MSFL achieves local adaptation more rapidly, cutting down time expenditure by approximately 30%. © 2024 ieee.

关键词： Federated learning

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