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19th IEEE International conference on Cognitive Informatics and Cognitive Computing (ICCI*CC)

作者： Sarivougioukas, John Vagelatos, Aristides G Gennimatas Gen Hosp Athens Greece Comp Technol Inst & Press Athens Greece

ISBN: (纸本)9781728195940

According to the ubiquitous computing paradigm, dispersed computers within the home environment can support the residents' health by being aware of all the developing and evolving situations. The context-awareness of the supporting computers stems from the data acquisition of the occurring events at home. in some cases, different sensors provide input of identical type, thereby raising conflict-related issues. Thus, for each type of input data, fusion methods must be applied on the raw data to obtain a dominant input value. Also, for diagnostic inference purpose, data fusion methods must be applied on the values of the available classes of multiple contextual data structures. Dempster-Shafer theory offers the algorithmic tools to efficiently fuse the data of each input type or class. However, the fusion manipulations of large data volumes within strict time limits impose significant computational overhead. in the present work, threading technology is employed to take advantage of the processing capabilities of modern computers for the data fusion of the contextual parameter sensor readings, along with the selection of appropriate computing architectures and matching algorithms. The advantages offered by the proposed approach are presented and analyzed.

关键词： Home UbiHealth Data fusion Ubiquitous computing Threading technology

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Machine Learning vs. Human Performance in the Real-time Acoustic Detection of Drones

Machine Learning vs. Human Performance in the Real-time Acou...

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IEEE Aerospace conference (AeroConf)

作者： Alaparthy, Vishwa Mandal, Sayan Cummings, Mary Duke Univ 304 Res Dr Durham NC 27708 USA

ISBN: (纸本)9781728174365

Usage of drones has increased substantially in both recreation and commercial applications and is projected to proliferate in the near future. As this demand rises, the threat they pose to both privacy and safety also increases. Delivering contraband and unauthorized surveillance are new risks that accompany the growth in this technology. Prisons and other commercial settings where venue managers are concerned about public safety need cost-effective detection solutions in light of their increasingly strained budgets. Hence, there arises a need to design a drone detection system that is low cost, easy to maintain, and without the need for expensive real-time human monitoring and supervision. To this end, this paper presents a low-cost drone detection system, which employs a Convolutional Neural Network (CNN) algorithm, making use of acoustic features. The Mel Frequency Cepstral Co-efficients (MFCC) derived from audio signatures are fed as features to the CNN, which then predicts the presence of a drone. We compare field test results with an earlier Support Vector Machine (SVM) detection algorithm. Using the CNN yielded a decrease in the false positives and an increase in the correct detection rate. Previous tests showed that the SVM was particularly susceptible to false alarms for lawn equipment and helicopters, which were significantly improved when using the CNN. Also, in order to determine how well such a system compared to human performance and also explore including the end-user in the detection loop, a human performance experiment was conducted. With a sample of 35 participants, the human classification accuracy was 92.47%. These preliminary results clearly indicate that humans are very good at identifying drone's acoustic signatures from other sounds and can augment the CNN's performance.

关键词： Support vector machines Privacy Surveillance sensor fusion Prediction algorithms Real-time systems Safety

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Verifying RADAR Data Using Two-Dimensional QIM-based Data Hiding

Verifying RADAR Data Using Two-Dimensional QIM-based Data Hi...

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作者： Fedoruk, Brandonlib. University of Michigan

Modern vehicles have evolved into supporting advanced internal networks and connecting System Based Chips (SBC), System in a Package (SiP) solutions or traditional micro controllers to foster an electronic ecosystem for high speed data transfers, precision and real-time control. The use of Controller Area Networks (CAN) is widely adopted as the backbone of internal vehicle communication infrastructure. Automotive applications such as ADAS, autonomous driving, battery management systems, power train systems, telematics and infotainment, all utilize CAN transmissions directly or through gateway management. The network transmissions lack robust integrity verification mechanisms to validate authentic data payloads, making it vulnerable to packet replay, spoofing, insertion, deletion and denial of service attacks. Additional methods exist to secure network data such as traditional cryptography. Utilizing this method will increase the computational complexity, processing latency and increase overall system cost. This thesis proposes a robust, light and adaptive solution to validate the authenticity of automotive sensor data using CAN network protocol. We propose using a two-dimensional Quantization Index Modulation (QIM) data hiding technique, to create a means of verification. Analysis of the proposed framework will be conducted in a sensor transmission scenario for RADAR sensors in an autonomous vehicle setting. The detection and effects of distortion on the application are tested through the implementation of sensor fusion algorithms and the results are observed and analyzed. The proposed framework offers a needed capability to maintain transmission integrity without the compromise of data quality and low design complexity. This framework could also be applied to different network architectures, as well as its operational scope could be modified to operate with more abstract types of data.

关键词： sensor networks RADAR Data integrity Data security ADAS Digital watermarking Connected vehicle Thesis

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Low Cost Embedded Multimodal Opto-Inertial Human Motion Tracking System 31

Low Cost Embedded Multimodal Opto-Inertial Human Motion Trac...

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31st Irish Signals and Systems conference (ISSC)

作者： Wilk, Mariusz P. Walsh, Michael O'Flynn, Brendan Univ Coll Cork Tyndall Natl Inst Cork Ireland

ISBN: (纸本)9781728194189

Human motion tracking systems are widely used in various application spaces, such as motion capture, rehabilitation, or sports. There exists a number of such systems in the State-Of-The-Art (SOA) that vary in price, complexity, accuracy and the target applications. With the continued advances in system integration and miniaturization, wearable motion trackers gain in popularity in the research community. The opto-inertial trackers with multimodal sensor fusion algorithms are some of the common approaches found in SOA. However, these trackers tend to be expensive and have high computational requirements. In this work, we present a prototype version of our opto-inertial, motion tracking system that offers a low-cost alternative. The 3D position and orientation are determined by fusing optical and inertial sensor data together with knowledge about two external reference points using a purpose-designed data fusion algorithm. An experimental validation was carried out on one of the use cases that this system is intended for, i.e. barbell squat in strength training. The results showed that the total RMSE in position and orientation was 32.8 mm and 0.89 degree, respectively. It operated in real-time at 20 frames per second.

关键词： Internet of Things Opto-inertial Motion Tracking Embedded Low-Cost sensor fusion

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On the Adaptivity of Unscented Particle Filter for GNSS/INS Tightly-Integrated Navigation Unit in Urban Environment

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IEEE ACCESS 2021年 9卷 144157-144170页

作者： Vouch, Oliviero Minetto, Alex Falco, Gianluca Dovis, Fabio Politecn Torino Dept Elect & Telecommun DET I-10129 Turin Italy LINKS Fdn Space & Nav Technol Area I-10138 Turin Italy

Tight integration algorithms fusing Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) have become popular in many high-accuracy positioning and navigation applications. Despite their reliability, common integration architectures can still run into accuracy drops under challenging navigation settings. The growing computational power of low-cost, embedded systems has allowed for the exploitation of several advanced Bayesian state estimation algorithms, such as the Particle Filter (PF) and its hybrid variants, e.g. Unscented Particle Filter (UPF). Although sophisticated, these architectures are not immune from multipath scattering and Non-Line-of-Sight (NLOS) signal receptions, which frequently corrupt satellite measurements and jeopardise GNSS/INS solutions. Hence, a certain level of modelling adaptivity should be granted to avoid severe drifts in the estimated states. Given these premises, the paper presents a novel Adaptive Unscented Particle Filter (AUPF) architecture leveraging two cascading stages to cope with disruptive, biased GNSS input observables in harsh conditions. A INS-based signal processing block is implemented upstream of a Redundant Measurement Noise Covariance Estimation (RMNCE) stage to strengthen the adaptation of observables' statistics and improve the state estimation. An experimental assessment is provided for the proposed robust AUPF that demonstrates a 10 % average reduction of the horizontal position error above the 75-th percentile. In addition, a comparative analysis both with previous adaptive architectures and a plain UPF is carried out to highlight the improved performance of the proposed methodology.

关键词： Global navigation satellite system Navigation Measurement uncertainty Noise measurement Adaptation models Satellites Position measurement Bayes methods sensor fusion inertial navigation satellite navigation systems particle filters adaptive estimation position accuracy

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Curve Trajectory Prediction through Vehicle Infrastructure Cooperation

Curve Trajectory Prediction through Vehicle Infrastructure C...

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IEEE conference on Industrial Electronics and applications (ICIEA)

作者： Zhaoyang Li Tianhao Liang Qinyuan Ren Collage of Control Science and Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9781665409858

The concept of vehicle infrastructure cooperative technology has been widely applied in the field of intelligent transportation. Through the technology, a novel vehicle trajectory prediction method is proposed in this paper, which can be used to provide a corresponding reference for risk prevention and control on the curve road. By integrating the multi-sensor data of the onboard and roadside sensors, the perception dimension is broadened and detailed vehicle information has been obtained. For the curve, the coordinates of the vehicles are transformed from the global coordinates to the Frenet coordinates based on the tangential and normal displacement of lane centre lines. A Bidirectional Long-Short Term Memory (B-LSTM) neural network is used to enhance the ability to process historical vehicle status information. Meanwhile, an encoder-decoder structure with the attention mechanism is applied to generate prediction trajectory points. A Mixture Density network gives a probability distribution for the final prediction output. In this paper, a curve scene is built by Unity to test the performance of the algorithm. Driving data is gathered by semi-physical simulation experiments from different drivers, after which a driver data set under the curve scene has been constructed. Utilizing vehicle infrastructure cooperative technology to expand the perception dimension, the trajectory obtained by the prediction algorithm has a smaller mean square error and higher accuracy in long-term prediction than relying on single on-vehicle sensors or on-road sensors. Through optimization of the curve, the prediction error has been reduced on longitudinal and lateral directions.

关键词： Roads Prediction methods sensor fusion Prediction algorithms Hardware Data models Probability distribution

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13th International Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020

13th International Joint Conference on Biomedical Engineerin...

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13th International Joint conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020

ISBN: (纸本)9783030723781

The proceedings contain 29 papers. The special focus in this conference is on Biomedical Engineering Systems and Technologies. The topics include: Classification of Biochemical Pathway Robustness with Neural Networks for Graphs;The Extended i-NSS: An Intelligent EEG Tool for Diagnosing and Managing Epilepsy;Idle State Detection with an Autoregressive Multiple Model Probabilistic Framework in SSVEP-Based Brain-Computer Interfaces;exploring Inertial sensor fusion Methods for Direct Ergonomic Assessments;PDC-MI Method for EEG Functional Conectivity Analysis;comparison of Medical Image Evaluations in Magnetic Resonance Cholangiopancreatography Through Image J;evaluating a Comparing Deep Learning architectures for Blood Glucose Prediction;cognitive Internet of Medical Things Architecture for Decision Support Tool to Detect Early Sepsis Using Deep Learning;interoperability Evaluation in Building Automation and Health Smart Homes Using Tag-Based Semantic Frameworks;novel Compact Robotic Flow Control Valve for Bioinspired Exosuit and Other applications;pathobox: The Collaborative Tele-pathology Platform with Access Management;Combining Rhythmic and Morphological ECG Features for Automatic Detection of Atrial Fibrillation: Local and Global Prediction Models;privacy by Design for Neuropsychological Studies Based on an mHealth App;food Data Normalization Using Lexical and Semantic Similarities Heuristics;muscle Deformation Using Position Based Dynamics;an Assessment of National Health Information Systems in Ireland Using Nationally Agreed Standards;leveraging Clinical Notes for Enhancing Decision-Making Systems with Relevant Patient Information;mobile Marketing in Health: User Experience Guiding the Implementation of a Medical Booking Application;stochastic Workflow Modeling in a Surgical Ward: Towards Simulating and Predicting Patient Flow;classification of Eating Behaviors in Unconstrained Environments.

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fusion of Data from Radar and Depth sensors Applied for Healthcare-Oriented Characterization of Persons' Gait 23

Fusion of Data from Radar and Depth Sensors Applied for Heal...

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23rd IEEE conference on Signal Processing algorithms, architectures, Arrangements, and applications (SPA)

作者： Mazurek, Pawel Wagner, Jakub Morawski, Roman Z. Warsaw Univ Technol Fac Elect & Informat Technol Inst Radioelect & Multimedia Technol Nowowiejska 15-19 PL-00665 Warsaw Poland

ISBN: (纸本)9788362065363

The research reported in this paper is related to the fusion of measurement data from the impulse-radar sensors and infrared depth sensors applied in a system for unobtrusive monitoring of elderly persons. The performance of monitoring systems, based on both types of sensors - when used separately and when used in combination - has been compared with respect to their potential for decreasing the uncertainty of estimation of several quantities, carrying information important for medical and healthcare services, viz. the state of being in motion, the direction of the movement, the travelled distance and the mean walking speed. The results of the numerical experimentation performed on real-world data - show that the fusion of data enables more robust and accurate estimation of those parameters, and therefore more reliable monitoring of elderly persons.

关键词： measurement data fusion impulse-radar sensor depth sensor healthcare

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Efficient fusion network with label generation and branch transformations for visible and infrared images fusion

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Infrared Physics & Technology 2025年 150卷

作者： Dongdong Sun Chuanyun Wang Tian Wang Qian Gao Zhaokui Li College of Artificial Intelligence Shenyang Aerospace University Shenyang 110136 China Institute of Artificial Intelligence Beihang University Beijing 100191 China School of Computer Science Shenyang Aerospace University Shenyang 110136 China

The information obtained from a single sensor is often insufficient for accurate environmental perception. In contrast, multi-sensor fusion significantly enhances perceptual accuracy, with the integration of infrared and visible light sensors being one of the most common approaches in multi-sensor fusion. Infrared and visible image fusion aims to combine the texture details of visible images with the thermal radiation information of infrared images to produce fused images with enhanced visual quality, which find broad applications in night vision, surveillance, and target detection. Existing fusion methods primarily focus on improving the quality of fused images, often at the expense of computational efficiency due to the reliance on complex, multi-layered architectures. This limitation makes them unsuitable for deployment on low-power terminal devices. To address these challenges, this paper proposes a novel image fusion framework, termed TSfusion, which leverages teacher-student learning to achieve both high-quality fusion and high computational efficiency. A teacher network, equipped with sequence-model-based feature extraction blocks and progressive integration modules, generates high-quality fusion labels by overcoming the limited receptive field of conventional convolutional networks. The lightweight student network adopts a reparameterisation-based Fast Inception Module, ensuring robust fitting capabilities while maintaining high-speed operation. Additionally, a novel multi-scale loss function is introduced to guide the student network in learning from the teacher at both semantic and pixel levels. Experimental results on three benchmark datasets demonstrate that the proposed TSfusion framework achieves superior visual and quantitative performance compared to state-of-the-art methods. Furthermore, TSfusion exhibits exceptional inference speed, outperforming all existing fusion algorithms, making it highly suitable for real-time applications. The implementatio

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Improved Cubature Kalman Filter for Target Tracking in Underwater Wireless sensor Networks 23

Improved Cubature Kalman Filter for Target Tracking in Under...

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23rd International conference on Information fusion (fusion)

作者： Luo, Junhai Chen, Yanping Wang, Zhiyan Wu, Man Yang, Yang Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu Sichuan Peoples R China

ISBN: (纸本)9780578647098

The underwater sensor network is currently a hot research field in academia and industry with many underwater applications, such as ocean monitoring, seismic monitoring, environment monitoring, and seabed exploration. Underwater target tracking is a critical component of ocean development. This paper studies the underwater target tracking problem of the wireless sensor network. The core technology of the target tracking algorithm is the filtering algorithm, which identifies the accuracy of the target tracking system. Nonlinear filtering is a hot issue in target tracking because feasible projects are mostly non-linear systems. The linearization method used in traditional Kalman filtering has serious shortcomings. Therefore, this paper presents the improved cubature Kalman filtering (ICKF) algorithm for underwater target tracking. There is uncertainty in the target movement, an adaptive forgetting factor is given into the cubature Kalman filtering algorithm to directly modify the error covariance to reduce the impact of uncertainties. Then, interactive multi-model technology is introduced to establish the IMMICKF algorithm with multiple states. Compared with other filtering algorithms, the new algorithm can effectively deal with non-linear target tracking problems and obtain better estimation accuracy. The numerical simulation is given to demonstrate the effectiveness of the IMMICKF algorithm.

关键词： underwater sensor network target tracking cubature Kalman filtering adaptive forgetting factor interacting multiple model

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