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arXiv 2023年

作者： Rashid, Nafiul Mortlock, Trier Al Faruque, Mohammad Abdullah The Department of Electrical Engineering and Computer Science The Department of Mechanical and Aerospace Engineering Electrical Engineering and Computer Science and Mechanical and Aerospace Engineering The University of California IrvineCA92697 United States

Wearable medical technology has become increasingly popular in recent years. One function of wearable health devices is stress detection, which relies on sensor inputs to determine a patient’s mental state. This continuous, real-time monitoring can provide healthcare professionals with vital physiological data and enhance the quality of patient care. Current methods of stress detection lack: (i) robustness—wearable health sensors contain high levels of measurement noise that degrades performance, and (ii) adaptation—static architectures fail to adapt to changing contexts in sensing conditions. We propose to address these deficiencies with SELF-CARE, a generalized selective sensor fusion method of stress detection that employs novel techniques of context identification and ensemble machine learning. SELF-CARE uses a learning-based classifier to process sensor features and model the environmental variations in sensing conditions known as the noise context. SELF-CARE uses noise context to selectively fuse different sensor combinations across an ensemble of models to perform robust stress classification. Our findings suggest that for wrist-worn devices, sensors that measure motion are most suitable to understand noise context, while for chest-worn devices, the most suitable sensors are those that detect muscle contraction. We demonstrate SELF-CARE’s state-of-the-art performance on the WESAD dataset. Using wrist-based sensors, SELF-CARE achieves 86.34% and 94.12% accuracy for the 3-class and 2-class stress classification problems, respectively. For chest-based wearable sensors, SELF-CARE achieves 86.19% (3-class) and 93.68% (2-class) classification accuracy. This work demonstrates the benefits of utilizing selective, context-aware sensor fusion in mobile health sensing that can be applied broadly to Internet of Things applications. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning systems

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Whose Data are Reliable: sensor Declared Data Reliability

Whose Data are Reliable: Sensor Declared Data Reliability

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IEEE International Conference on Wireless and Mobile Computing, Networking And Communications (WiMob)

作者： Sakib Shahriar Shafin Gour Karmakar Iven Mareels Venki Balasubramanian Ramachandra Rao Kolluri Centre for Smart Analytics (CSA) Institute of Innovation Science and Sustainability Federation University Australia Ballarat VIC Australia IBM Consulting IBM Australia Pty Ltd

sensor data is susceptible to faults, noise, and malicious attacks, posing a significant operational and security threat. Therefore, ensuring reliability of sensor data is critical for real-time monitoring systems. Prior research on sensor data reliability relies on edge or upper-layer devices for data fusion from multiple sensors, employing architectures with major overheads and latency due to transmission and storage demands. An alternative approach is to have the sensor estimate and declare its own reliability. While some methods involve sensors computing data confidence and including it in payloads, limitations arise in the absence of neighboring sensor data, and communication overheads are incurred. To address this problem, this paper proposes an innovative approach to enhance the reliability of sensor data using an intelligent self-declaration process. Proposed reliability estimation is evaluate with three lightweight estimation algorithms, namely, Kalman Filter, Holt-Winters Method, and Mahalanobis Distance using sensor’s historical data. The reliability level is then added to the three reserved bits of a TCP packet header which results in zero additional overhead. Experiments conducted using real-world sensor data (from water quality monitoring systems) obtained from our IoT lab demonstrate the effectiveness of our proposal and the potential for application in real-world sensor-based applications.

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A Comprehensive Systematic Review of Deep Learning Techniques for Anomaly Detection in Urban Video Surveillance

A Comprehensive Systematic Review of Deep Learning Technique...

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Innovative Research in Applied Science, Engineering and Technology (IRASET),Conference on

作者： Asmae Baala Hanoune Mostafa Bentaib Mohssine Department of Mathematics and Computer Science Faculty of Science Ben M’Scik Hassan II University Casablanca Morocco

ISBN: (数字)9798331532970

ISBN: (纸本)9798331532987

The rapid evolution of urban surveillance systems has created an urgent need for advanced anomaly detection methods capable of interpreting complex public environments. This study employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to evaluate deep learning's (DL) role in video-based anomaly detection. It contrasts conventional approaches with cutting-edge architectures like spatiotemporal convolutional neural networks (CNNs), generative adversarial networks (GANs), and transformer-based models. Our analysis demonstrates DL's superior performance over traditional methods across multiple benchmarks while revealing significant implementation challenges in real-world deployment, including computational complexity, cross-domain generalization, and ethical constraints. The study provides a comprehensive taxonomy of anomaly types, examines key evaluation metrics for operational systems, and identifies emerging solutions like edge-compatible lightweight models and privacy-preserving federated learning. By synthesizing a decade of research progress and practical limitations, this work offers actionable insights for developing robust, efficient, and socially responsible surveillance systems. The study proposes future directions focused on self-supervised learning, multimodal sensor fusion, and explainable AI frameworks to address critical gaps in urban security applications.

关键词： Deep learning Ethics Adaptation models Computational modeling Scalability Benchmark testing Video surveillance Transformers Anomaly detection Systematic literature review

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Neural Architecture Search for Automotive Grid fusion Networks under Embedded Hardware Constraints 19

Neural Architecture Search for Automotive Grid Fusion Networ...

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19th IEEE International Conference on Machine Learning and applications, ICMLA 2020

作者： Balazs, Gabor Stechele, Walter Technische Universität München Munich Germany

ISBN: (纸本)9781728184708

The goal of automotive sensor fusion is to generate an environmental model with low-cost, mass-produced sensors - typically camera and radar. The central fusion controller needs sophisticated post-processing algorithms to achieve this goal despite extensive pre-processing on the sensor side. Recent advances show the capabilities of deep convolutional auto-encoders for sensor fusion but are still limited to high-performance hardware. Limited memory and processing capabilities of embedded devices lead to the multi-objective optimization problem addressed in this work. The proposed evolutionary neural architecture search is configured to find novel sensor fusion network architectures optimized for embedded hardware. The discovered encoding and decoding cells improve fusion performance compared to that of corresponding cells from literature. These lightweight network architectures allow the deployment on constrained embedded devices, thus leverage environmental perception of level 2/2+ automated cars. © 2020 IEEE.

关键词： Network architecture

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Improve Performances of Wireless sensor Networks for Data Transfer Based on Fuzzy Clustering and Huffman Compression

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JOURNAL OF sensorS 2022年第0期2022卷

作者： Molk, Ali Mohammad Norouzzadeh Gil Ghoreishi, Seyed Mohsen Ghasemi, Fatemeh Elyasi, Iraj Univ Guilan Dept Comp Engn Rasht Iran Isfahan Univ Technol Dept Math Sci Esfahan *** Iran Minist Sci Res & Technol Comp Engn Dept Inst Higher Educ MSC Software Engn Qazvin Iran Islamic Azad Univ Dept Comp Engn Andimeshk Branch Andimeshk Iran

In today's world, the main challenge is to save use energy optimally. The IoT devices generate a large amount of data for wide applications. Considering the application perspective in the IoT market, in one instance of the IoT technology, that is a wireless sensor network, factors like energy, storage capacity, computation power, and limitations of communication bandwidth resources are the reason for using data fusion. Data fusion and aggregation in wireless sensor networks (WSNs) such that minimum energy is consumed are an essential issue. In most clustering models, data aggregation is carried out by the cluster-head (CH). In the proposed algorithm, data aggregation in the cluster-head is carried out using the lossless cascode Huffman compression algorithm. Due to the correlation among data of nodes, the data sensed by each node is compared with the data of the cluster-head node;after removing redundancy, the coded data is transmitted to the main node. The CH node is selected by an algorithm based on fuzzy logic according to the residual energy of the node and the distance of the node from the sink node. Various fuzzy type-I systems of Mamdani and Takagi-Sugeno and type-ii systems are used. In this paper, the CH selection algorithms are evaluated using three scenarios in terms of the number of live nodes, received packets and CHs, proper distribution rate, and other parameters of the LEACH protocol, network lifetime, and network energy. In the following, to demonstrate the performance of this new algorithm, simulations are performed in MATLAB based on the proposed method. The results show that the proposed compression algorithm in environments with high data correlation improves the compression rate by 8% compared to the conventional Huffman compression, while in environments with low data correlation, these two algorithms perform almost the same. This compression helps reduce the energy consumption of the network.

关键词： Data transfer

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An AI-Based Deep Learning with K-Mean Approach for Enhancing Altitude Estimation Accuracy in Unmanned Aerial Vehicles

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DRONES 2024年第12期8卷 718-718页

作者： Piyakawanich, Prot Phasukkit, Pattarapong King Mongkuts Inst Technol Ladkrabang Sch Engn Bangkok 10520 Thailand King Mongkut Chaokhun Thahan Hosp King Mongkuts Inst Technol Ladkrabang Bangkok 10520 Thailand

In the rapidly evolving domain of Unmanned Aerial Vehicles (UAVs), precise altitude estimation remains a significant challenge, particularly for lightweight UAVs. This research presents an innovative approach to enhance altitude estimation accuracy for UAVs weighing under 2 kg without cameras, utilizing advanced AI Deep Learning algorithms. The primary novelty of this study lies in its unique integration of unsupervised and supervised learning techniques. By synergistically combining K-Means Clustering with a multiple-input deep learning regression-based model (DL-KMA), we have achieved substantial improvements in altitude estimation accuracy. This methodology represents a significant advancement over conventional approaches in UAV technology. Our experimental design involved comprehensive field data collection across two distinct altitude environments, employing a high-precision Digital Laser Distance Meter as the reference standard (Class ii). This rigorous approach facilitated a thorough evaluation of our model's performance across varied terrains, ensuring robust and reliable results. The outcomes of our study are particularly noteworthy, with the model demonstrating remarkably low Mean Squared Error (MSE) values across all data clusters, ranging from 0.011 to 0.072. These results not only indicate significant improvements over traditional methods, but also establish a new benchmark in UAVs altitude estimation accuracy. A key innovation in our approach is the elimination of costly additional hardware such as Light Detection and Ranging (LiDAR), offering a cost-effective, software-based solution. This advancement has broad implications, enhancing the accessibility of advanced UAVs technology and expanding its potential applications across diverse sectors including precision agriculture, urban planning, and emergency response. This research represents a significant contribution to the integration of AI and UAVs technology, potentially unlocking new possibilities i

关键词： Unmanned Aerial Vehicles (UAVs) drones altitude estimation deep learning sensor fusion K-means clustering lightweight UAVs lightweight drones

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An Optimization of Deep sensor fusion Based on Generalized Intersection over Union 20th

An Optimization of Deep Sensor Fusion Based on Generalized I...

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20th International Conference on algorithms and architectures for Parallel Processing (ICA3PP)

作者： Meng, Lianxiao Yang, Lin Tang, Gaigai Ren, Shuangyin Yang, Wu Harbin Engn Univ Informat Secur Res Ctr Harbin Peoples R China Chinese Acad Mil Sci Inst Syst Engn Natl Key Lab Sci & Technol Informat Syst Secur Beijing Peoples R China

ISBN: (纸本)9783030602390;9783030602383

3D object detection is a major topic in unmanned driving and robotics, which is suffering from the low accuracy recently. We found that the loss function of 3D object detection network is the main cause to the low accuracy. For this, we proposed an optimized realization of deep sensor fusion network model (DSFN) based on Generalized Intersection over Union (GIoU). In DSFN, the designed backbone network is used to fuse point cloud features and image features, making full use of heterogeneous sensor information. Specifically, we introduced the GIoU as the loss function of the backbone network. We evaluated our model on KITTI dataset which is resulted from a LIDAR-camera setup. Compared with similar models, our model shows a higher accuracy.

关键词： Deep sensor fusion Network (DSFN) 3D bounding box estimation Loss function Deep learning Camera and Lidar

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Multi-sensor aided deep pose tracking

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IFAC-PapersOnLine 2022年第37期55卷 326-332页

作者： Hojun Lee Tyler Toner Dawn Tilbury Kira Barton Department of Robotics University of Michigan Ann Arbor MI 48109 USA Department of Mechanical Engineering University of Michigan Ann Arbor MI 48109 USA

Accurate pose estimation of nearby objects is critical for robots to dynamically interact with their surroundings. The complexity of this task has led researchers to explore deep learning methods. Nowadays, many works have solely focused on developing complicated neural network architectures to estimate pose from a simple monocular camera. However, most of these methods struggle with inherent limitations of a single sensor system, like occlusion, which are commonly encountered in mobile robotics applications. Online, occlusion-robust pose estimation is extremely important in such cases, as mobility of a robot introduces major uncertainty that complicates manipulation. Hence, we present Multi-sensor aided Deep Pose Tracking (MS-DPT), a framework for online object pose estimation to enable robust mobile manipulation. A Convolutional Neural Network (CNN) identifies key objects in an RGB-D image, from which object pose estimates are generated using a variant of the Iterative Closest Point (ICP) algorithm. An extended Kalman filter is used to fuse this pose estimate with onboard motion sensors to compensate for occlusion and robot motion during manipulation. This three stage method focuses on cohering different modalities and algorithms to improve the pose tracking stability and continuity in cases where the target object becomes heavily occluded by an obstacle or a mobile robot itself. The proposed approach accurately tracks textureless objects with high symmetries while operating at 10 FPS during experiments.

关键词： Object pose estimation Neural-network model Robot vision Data fusion Tracking

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Streamlining River Flood Prevention with an Integrated AIoT Framework

Streamlining River Flood Prevention with an Integrated AIoT ...

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ACS/IEEE International Conference on Computer Systems and applications

作者： Zakaria Boulouard Mariya Ouaissa Mariyam Ouaissa Moez Krichen Mutiq Almutiq Mohammad Algarni LIM Hassan II University Casablanca Morocco Computer Systems Engineering Laboratory Cadi Ayyad University Marrakech Morocco Laboratory of Information Technologies Chouaib Doukkali University El Jadida Morocco Department of Information Technology Al-Baha University Al-Baha Saudi Arabia Department of Management Information Systems and Production Management College of Business and Economics Qassim University Buraidah Saudi Arabia Computer Science Department Al-Baha University Al-Baha Saudi Arabia

River floods stand among the natural disasters with far-reaching impacts, affecting human lives, the economy, infrastructure, agriculture, and more. Substantial investments by organizations are directed toward innovative strategies for flood prevention. The concept of Artificial Intelligence of Things (AIoT), a fusion of Artificial Intelligence and Internet of Things technologies, has showcased its prowess across various domains. In this paper, we introduce an AIoT framework where river flood sensors, located in every region, transmit their data using LoRaWAN technology to local broadcast centers in proximity. These broadcast centers subsequently forward the data via 4G/5G networks to a centralized cloud server. The server employs efficient AI algorithms to analyze the data and predict river conditions nationwide, contributing to proactive flood prevention. This approach has demonstrated effectiveness on multiple fronts. LoRaWAN-based communication between sensor nodes and broadcast centers offers reduced energy consumption and expanded coverage, while AI-driven data analysis enhances the accuracy of river flood predictions.

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Soft body sensor networks: architectures and applications

Soft body sensor networks: Architectures and Applications

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2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

作者： Dong, Wentao Cheng, Xiao Yao, Daojin School of Electrical and Automation Engineering East China Jiaotong University Nanchang China Rail Transportation Technology Innovation Center East China Jiaotong University Nanchang China

ISBN: (纸本)9781728168531

Body sensor networks (BSNs) are designed to collect, process, and transmit the biological data from different parts of human body from wearable electronics with limited resources. Conventional BSN node is also designed and fabricated by the rigid sensors, printed circuit board (PCB), and integrated chip (IC), which are not compatible with the soft human skin. The concepts of soft body sensor networks (SBSNs) are given in the manuscript based on wireless sensor networks and flexible electronics. SBSN node is composed of soft sensors, flexible data modulation and process unit and flexible wireless communication unit, and all the components of SBSN node are designed to be bended and be compatible with soft skin surface by flexible electronic. The architecture of SBSNs are built with several layers: soft sensors, data collection, wireless communication, body could, data fusion algorithm and monitoring interface for further online healthy monitoring. Multisensory decision fusion algorithm has been presented with biometric and medical monitoring applications which is subsequently analyzed for medical diagnosis. Emotion recognitions are realized to predict healthy state based on SBSNs using the intelligent data fusion algorithms. © 2020 IEEE.

关键词： sensor nodes

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