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Prediabetes detection in unconstrained conditions using wearable sensors

Clinical Nutrition Open Science 2024年 58卷 163-174页

作者： Tatli, Dimitra Papapanagiotou, Vasileios Liakos, Aris Tsapas, Apostolos Delopoulos, Anastasios Multimedia Understanding Group Dpt. of Electrical and Computer Engineering Faculty of Engineering AUTH Greece Embedded Systems Laboratory Institute of Electrical and Micro Engineering EPFL Lausanne Switzerland IMPACT Research Group Dpt. of Medicine Karolinska Institutet Huddinge Sweden Diabetes Centre Second Medical Department at Ippokratio General Hospital Thessaloniki Greece

Prediabetes is a common health condition that often goes undetected until it progresses to type 2 diabetes. Early identification of prediabetes is essential for timely intervention and prevention of complications. This research explores the feasibility of using wearable continuous glucose monitoring along with smartwatches with embedded inertial sensors to collect glucose measurements and acceleration signals respectively, for the early detection of prediabetes. We propose a methodology based on signal processing and machine learning techniques. Two feature sets are extracted from the collected signals, based both on a dynamic modeling of the human glucose-homeostasis system and on the Glucose curve, inspired by three major glucose related blood tests. Features are aggregated per individual using bootstrap. Support Vector Machines are used to classify normoglycemic vs. prediabetic individuals. We collected data from 22 participants for evaluation. The results are highly encouraging, demonstrating high sensitivity and precision. This work is a proof of concept, highlighting the potential of wearable devices in prediabetes assessment. Future directions involve expanding the study to a larger, more diverse population and exploring the integration of CGM and smartwatch functionalities into a unified device. Automated eating detecting algorithms can also be used. © 2024 The Authors

关键词： Continuous glucose monitors Machine learning Prediabetes Signal processing

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Multi-food source differential salp swarm algorithm 23

Multi-food source differential salp swarm algorithm

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2023 International Conference on computer, Vision and Intelligent Technology, ICCVIT 2023

作者： Lin, Anping Shi, Yaoting Zhang, Wenfen Liu, Dong Lan, Yulin School of Physics and Electronic Electrical Engineering Xiangnan University Chenzhou423000 China School of Computer and Artificial Intelligence Hunan Engineering Research Center of Advanced Embedded Computing and Intelligent Medical Systems Xiangnan University Chenzhou China

ISBN: (纸本)9798400708701

Salp swarm algorithm (SSA) divides the population into leaders and followers, and employs two different search methods to achieve steady convergence. However, the search methods adopted by SSA are relatively simple, hence SSA always trapped by local optima on complex multimodal problems. To improve the exploration of SSA, this paper proposes a multi-food source differential salp swarm algorithm (MFD-SSA) by transferring multi-leader mechanism and differential mutation strategy into SSA. MFD-SSA regards a portion of high fitness individuals as food sources and employ them for updating the position of leaders, the differential evolution mutation and the chaotic crossover are adopted to enhance swarm diversity. Then, the differential mutation and crossover operator are employed for enlarging search range. The MFD-SSA is tested on CEC2017 test suite and compared with five SSA variants and five other meta-heuristics. The test results indicate that MFD-SSA bears strong exploration, it outperforms five SSA variants and five meta-heuristics. On Planetary gear train design problem, MFD-SSA performs better than other five SSA variants too. © 2023 ACM.

关键词： Evolutionary algorithms

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Design and Characterization of the Output Network of a High-Current Buck Converter 46

Design and Characterization of the Output Network of a High-...

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46th ICT and Electronics Convention, MIPRO 2023

作者： Solaja, Kresimir Blecic, Raul Broznic, Andro Baric, Adrijan University of Zagreb Faculty of Electrical Engineering and Computing Department of Electronics Microelectronics Computer and Intelligent Systems Zagreb Croatia Rimac Technology d.o.o./Embedded Hardware Sveta Nedelja Croatia

ISBN: (纸本)9789532331042

A 10-A automotive buck converter is designed and realized on a four layer printed circuit board. The output network is designed in the electromagnetic solver to account for the trace and package parasitic elements. The output network of the converter is simulated in the SPICE solver. The converter is characterized in the time domain by the load transient measurements and in the frequency domain by the shunt measurement method. Three configurations of the output network are compared. The results in the frequency domain and the results of the SPICE simulations are correlated. © 2023 MIPRO Croatian Society.

关键词： Time domain analysis

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Lightweight Disaster Semantic Segmentation for UAV On-Device Intelligence

Lightweight Disaster Semantic Segmentation for UAV On-Device...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Hanbin Lee Gihwan Kim Sangho Ha Hyungshin Kim Department of Computer Science and Engineering Embedded Systems Laboratory (ESLAB) Chungnam National University Daejeon Republic of Korea

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Unmanned aerial vehicles (UAVs) are being used for monitoring natural disasters. To promptly assess the severity of the disaster, it is advantageous to analyze the disaster scenes with the on-board computer in real time. Various AI segmentation models with high accuracy are developed mainly for offline processing. They require significant memory capacity and computational power. However, on-device AI has the challenge of compressing high-precision models due to the limited memory size and the small computation power. In this research, we develop a lightweight disaster semantic segmentation model for UAV on-device intelligence. From a simple FANet as our baseline, we apply various optimization, such as compact backbone, lightweight attention block, quantization, knowledge distillation, and post processing. With our optimized model, we can reduce 84.2% of the inference time while achieving 0.5% increase in accuracy compared to the baseline model.

关键词： Accuracy Quantization (signal) Disasters Computational modeling Semantic segmentation Autonomous aerial vehicles Real-time systems

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Reliability analysis in ARM *** heterogeneous multi-core processor for IoT

Reliability analysis in ARM *** heterogeneous multi-core pro...

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International Conference on Internet of Things and Applications (IoT)

作者： Mahin Moradiyan Yaser Sedaghat Computer Engineering Department Dependable Distributed Embedded Systems (DDEmS) Laboratory Ferdowsi University of Mashhad Mashhad Iran

ISBN: (数字)9798350374674

ISBN: (纸本)9798350374681

This study navigates the landscape of the Internet of Things (IoT), spotlighting the escalating demand for robust processing solutions, thereby propelling the pervasive adoption of heterogeneous multi-core processors. Within this sphere, the ARM *** architecture takes center stage, adeptly harmonizing high performance with energy efficiency. The investigation zooms in on the pivotal role of failure-sensitive applications in the IoT realm, critical domains like healthcare. The paper underscores the indispensable need for failure analysis concerning ARM *** processors, unravel the complexities of data flow errors (DFE) and control flow errors (CFE). By providing insights into potential pitfalls and strategies for fortifying reliability, the study contributes to the evolving discourse on the resilience of ARM *** processors in the context of failure-sensitive IoT applications. Also concludes by emphasizing the need for continued exploration of optimal task mapping and fault tolerance strategies, paving the way for future research to refine and extend the reliability analysis of ARM *** platforms in evolving IoT landscapes.

关键词： Program processors Multicore processing Smart cities Navigation Fault tolerant systems Medical services Propulsion

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Self-Guided Autoencoders for Unsupervised Change Detection in Heterogeneous Remote Sensing Images

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2458-2471页

作者： Shi, Jiao Wu, Tiancheng Kai Qin, Alex Lei, Yu Jeon, Gwanggil Northwestern Polytechnical University School of Electronics and Information Xi'an710129 China Northwestern Polytechnical University in Shenzhen Research and Development Institute Shenzhen518057 China Chongqing Innovation Center of Northwestern Polytechnical University Chongqing401135 China Swinburne University of Technology Department of Computer Science and Software Engineering HawthornVIC3122 Australia Incheon National University Department of Embedded Systems Engineering Incheon22012 Korea Republic of

To address the problem of enormous differences in two heterogeneous images, the traditional unsupervised frameworks are most normally realized by converting two images into a common domain with various auxiliary strategies, such as transformation and alignment, which requires extensive calculation and has difficulty balancing the training tasks. To achieve a concise framework, this article proposes self-guided autoencoders (SGAE) for unsupervised change detection (CD) in heterogeneous remote sensing images (RSIs). Unlike traditional methods that aim to narrow the differences of heterogeneous images to highlight the changed information, SGAE forces the flow of identification in formation generated in unlabeled data through self-guided iterations. First, initial unsupervised networks output an elementary change map that will be screened to obtain reliable pseudolabels. The selected pseudolabeled samples will be used as the input of a supervised network to obtain another change map. Then, multiple change maps will be fused to refine the confidence of pseudolabels again, obtaining new fused pseudolabeled samples for the self-guided network, which will be trained with pseudolabeled samples and unlabeled samples. Finally, the above operations will be repeated to continuously optimize the net, which helps itself to extract the discriminative features for classification in self-guided iterations. Experiments compared with several algorithms on four datasets demonstrate the effectiveness and robustness of our method, which can help unsupervised models improve discriminative feature extraction and classification performance with a more flexible learning method. © 2020 IEEE.

关键词： Feature extraction

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Real-Time Implementation of Quadrotor UAV Control System Based on a Deep Reinforcement Learning Approach

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computers, Materials & Continua 2024年第12期81卷 4757-4786页

作者： Taha Yacine Trad Kheireddine Choutri Mohand Lagha Souham Meshoul Fouad Khenfri Raouf Fareh Hadil Shaiba Aeronautical Sciences Laboratory Aeronautical and Spatial Studies InstituteBlida 1 UniversityBlida0900Algeria Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Energy and Embedded Systems for Transportation Research Department ESTACA-LABMontigny-Le-Bretonneux78066France Department of Electrical Engineering University of SharjahSharjah27272United Arab Emirates Department of Computer Science College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

The popularity of quadrotor Unmanned Aerial Vehicles(UAVs)stems from their simple propulsion systems and structural ***,their complex and nonlinear dynamic behavior presents a significant challenge for control,necessitating sophisticated algorithms to ensure stability and accuracy in *** strategies have been explored by researchers and control engineers,with learning-based methods like reinforcement learning,deep learning,and neural networks showing promise in enhancing the robustness and adaptability of quadrotor control *** paper investigates a Reinforcement Learning(RL)approach for both high and low-level quadrotor control systems,focusing on attitude stabilization and position tracking tasks.A novel reward function and actor-critic network structures are designed to stimulate high-order observable states,improving the agent’s understanding of the quadrotor’s dynamics and environmental *** address the challenge of RL hyper-parameter tuning,a new framework is introduced that combines Simulated Annealing(SA)with a reinforcement learning algorithm,specifically Simulated Annealing-Twin Delayed Deep Deterministic Policy Gradient(SA-TD3).This approach is evaluated for path-following and stabilization tasks through comparative assessments with two commonly used control methods:Backstepping and Sliding Mode Control(SMC).While the implementation of the well-trained agents exhibited unexpected behavior during real-world testing,a reduced neural network used for altitude control was successfully implemented on a Parrot Mambo mini *** results showcase the potential of the proposed SA-TD3 framework for real-world applications,demonstrating improved stability and precision across various test scenarios and highlighting its feasibility for practical deployment.

关键词： Deep reinforcement learning hyper-parameters optimization path following quadrotor twin delayed deep deterministic policy gradient and simulated annealing

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Online Reduction of Exploration Space for Automated Underwater Modem Optimization

Online Reduction of Exploration Space for Automated Underwat...

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MILCOM, Military Communications Conference

作者： Marcel Rieß Steffen Moser Frank Slomka Institute of Embedded Systems/Real-Time Systems Faculty of Engineering Computer Science and Psychology Ulm University Germany

Developing efficient underwater communication systems is complex, as multiple factors impact communication quality. While rapid prototyping has been a common approach, it carries a high risk of failure. To address this challenge, model-based development and implementation have gained popularity. However, selecting the optimal combination of modulation type, correction properties, synchronization properties, and other parameters remains time-consuming and intricate. This paper presents an innovative approach to streamline the development process by reducing the exploration space. The proposed method evaluates potential solutions based on scenario-specific, application-specific, and performance-related upper and lower bounds for the selectable communication system. These bounds consider common underwater challenges, such as SNR degradation and multiple bounce loss, and distinguish between dynamic and static system analyses. By adopting this approach, existing analysis and development workflow achieve more optimized communication systems and afford more efficient variant selection using narrowing down the solution space and considering the specific requirements of each scenario. The approach’s effectiveness is demonstrated through case studies and evaluations, highlighting the benefits of reducing the exploration space and utilizing scenario-, application-, and performance-based bounds. This enables the development of robust and tailored underwater communication systems, improving communication quality and overall system performance.

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Frequency-Spatial Feature Fusion Network for Infrared and Visible Image Fusion 13th

Frequency-Spatial Feature Fusion Network for Infrared and Vi...

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13th International Conference on Information Technology and its Applications, CITA 2024

作者： Song, Wenhao Gao, Mingliang Li, Qilei Jeon, Gwanggil Camacho, David School of Electrical and Electronic Engineering Shandong University of Technology Zibo255000 China School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Embedded Systems Engineering Incheon National University Incheon22012 Korea Republic of Madrid Spain

ISBN: (纸本)9783031741265

Infrared and visible image fusion seeks to retain complementary information from source images and generate a comprehensive image. Most fusion methods ignore the detailed information in the frequency domain. To address this problem, we propose a Frequency-spatial Feature Fusion Network (F3Net) in this work. The F3Net consists of three modules, namely Frequency-Spatial Feature Extraction Module (FSFEM), Feature Fusion Module (FFM), and Image Reconstruction Module (IRM). First, the FSFEM is built to extract complementary information from the source image separately in the frequency and spatial domains. Then, the FMM is introduced to fuse the features of the frequency and spatial domains. Finally, the fused image is reconstructed by IRM. Comprehensive experiments demonstrate that the F3Net outperforms the state-of-the-art (SOTA) methods subjectively and objectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Image fusion

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Artificial Intelligence for securing SDIoT: Challenges and countermeasures 20

Artificial Intelligence for securing SDIoT: Challenges and c...

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20th International Multi-Conference on systems, Signals and Devices, SSD 2023

作者： Zaidoun, Ameur Salem Baccouch, Hajer Guermazi, Abderrahmen Lachiri, Zied National Engineering School of Tunis Signal Images and Information Technologies Lab Tunisia Higher Institute of Technological Studies of Siliana Tunisia National Engineering School of Sfax Computer and Embedded Systems Lab Tunisia Higher Institute of Technological Studies of Sfax Tunisia

ISBN: (纸本)9798350332568

SDIoT (Software Defined IoT) is an emerging IoT technology, deployed using SDN architecture;nevertheless, it presents several weaknesses, which can be raised in different layers. To mitigate SDIoT intrusions and attacks, security as used in a common network cannot provide a complete solution and must be enhanced with AI (Artificial Intelligence) based countermeasures tools, mainly machine and deep learning. The paper identifies the salient features of SDIoT technology. Afterwards, it describes the most common attacks against SDIoT networks and possible countermeasures using several deep learning algorithms. In addition, the paper describes datasets used for learning steps. At the end, it discusses the suitability of AI based security tools to some attacks. © 2023 IEEE.

关键词： Deep learning

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