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workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th International workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st workshop on Machine Learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and workshop on Multimodal Learning and fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

Workshop on Longitudinal Disease Tracking and Modeling with ...

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workshop on Longitudinal Disease Tracking and Modeling with Medical Images and data, LDTM 2024, 5th International workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st workshop on Machine Learning for Multimodal/-sensor Healthcare data, ML4MHD2024 and workshop on Multimodal Learning and fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031845246

The proceedings contain 21 papers. The special focus in this conference is on Longitudinal Disease Tracking and Modeling with Medical Images and data. The topics include: Disease Progression Modelling and Stratification for Detecting Sub-trajectories in the Natural History of Pathologies: Application to Parkinson’s Disease Trajectory Modelling;back to the Future: Challenges of Sparse and Irregular Medical Image Time Series;Individualized Multi-horizon MRI Trajectory Prediction for Alzheimer’s Disease;towards Longitudinal Characterization of Multiple Sclerosis Atrophy Employing SynthSeg Framework and Normative Modeling;segHeD: Segmentation of Heterogeneous data for Multiple Sclerosis Lesions with Anatomical Constraints;Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting;registration of Longitudinal Liver Examinations for Tumor Progress Assessment;Tracking Lesion Evolution Using a Boundary Enhanced Approach for MS Change Segmentation (BEAMS);a Radiological-Based Coordinate System for the Human Body: A Proof-of-Concept;language Models Meet Anomaly Detection for Better Interpretability and Generalizability;A Diffusion Model Embedded WCSAU-Net for 3D MRI Brain Tumor Segmentation;predicting Human Brain States with Transformer;Cross-Modality Image Quality Prediction for Time-Resolved CT from Breathing Signals;RATNUS: Rapid, Automatic Thalamic Nuclei Segmentation Using Multimodal MRI Inputs;HyperMM: Robust Multimodal Learning with Varying-Sized Inputs;HEMIT: H&E to Multiplex-Immunohistochemistry Image Translation with Dual-Branch Pix2pix Generator;Physics-Informed Latent Diffusion for Multimodal Brain MRI Synthesis;medPromptX: Grounded Multimodal Prompting for Chest X-Ray Diagnosis;predicting Stroke Through Retinal Graphs and Multimodal Self-supervised Learning;multimodality for Diagnosis of Asian Choroidal Vasculopathy: Results from a Novel dataset and Deep-Learning Experiments;multimodality Frequency Feature Customized Learning for Pediatric Ventricu

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Multi-sensor fusion based industrial action recognition method under the environment of intelligent manufacturing

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JOURNAL OF MANUFACTURING SYSTEMS 2024年 74卷 575-586页

作者： Wang, Zipeng Yan, Jihong Harbin Inst Technol Sch Mechatron Engn Harbin 150001 Peoples R China

In the context of intelligent manufacturing and Industry 4.0, the manufacturing industry is rapidly transitioning toward mass personalization production. Despite this trend, the assembly industry still relies on manual operations performed by workers, considering their cognitive ability and flexibility. Thereinto, studying operator action perception and recognition methods is a vital filed and of great significance for improving the production efficiency and ensuring product quality. In this paper, a multi-sensor fusion-based data acquisition system is constructed to address the challenge of achieving comprehensive and accurate perception of the assembly process with a single sensor. Then, an action recognition model architecture based on ResNet + LSTM + D-S evidence theory is proposed and established. By fully considering the characteristics of different data, the multisensor data values are maximized, data complementarity is achieved, and the recognition accuracy exceeds 97 %. This research is expected to provide guidance for increasing the degree of workshop automation and improving the efficiency and quality of the production process.

关键词： Intelligent manufacturing Industry 4.0 Multi -sensor fusion Action recognition D -S evidence theory

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2015 workshop on sensor data fusion: Trends, Solutions, Applications, SDF 2015

2015 Workshop on Sensor Data Fusion: Trends, Solutions, Appl...

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workshop on sensor data fusion, SDF 2015

ISBN: (纸本)9781467371759

The proceedings contain 21 papers. The topics discussed include: case study: efficient algorithms for air traffic management decision support tools;an integrated navigation system design for quadrotors;on the functional derivative with respect to the Dirac delta;a Kriging algorithm for location fingerprinting based on received signal strength;conceptual application of the MAPE-K feedback loop to opportunistic sensing;acceptance probability of IP-MCMC-PF: revisited;recursive Bayesian pose and shape estimation of 3D objects using transformed plane curves;fusion of Doppler radar and geometric attributes for motion estimation of extended objects;multiple video object tracking using variational inference;stochastic sampling of the hyperspherical von mises-fisher distribution without rejection methods;a shrinkage-based particle filter for tracking with correlated measurements;and compressive sensing approaches for autonomous object detection in video sequences.

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Enhancing Object Detection and Localization through Multi-sensor fusion for Smart City Infrastructure 4

Enhancing Object Detection and Localization through Multi-Se...

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4th IEEE International workshop on Metrology for Automotive (MetroAutomotive)

作者： Syamal, Soujanya Huang, Cheng Petrunin, Ivan Cranfield Univ SATM Ctr Autonomous & Cyber Phys Syst Bedford England

ISBN: (纸本)9798350384994;9798350384987

The rapid advancement in autonomous systems and smart city infrastructure demands sophisticated object detection and localization capabilities to ensure safety, efficiency, and reliability. Traditional single sensor approaches often fall short, especially under complex environmental conditions. This paper introduces the CLR-Localiser, a novel multi-sensor fusion framework that synergistically integrates data from cameras, LiDAR and radar sensors mounted on roadside infrastructure to enhance object detection and 3D localization. Leveraging the complementary strengths of each sensor type, the CLR-Localiser employs an early fusion approach and deep learning techniques, including convolutional neural networks for object detection and regression networks for precise localization. We rigorously validated the performance of the CLR-Localiser against the benchmark Kitti dataset, and a custom dataset specifically designed for this research, demonstrating significant improvements in detection accuracy, localization precision, and object-tracking capabilities under diverse conditions. Our findings highlight the CLR-Localiser's potential to overcome the limitations of conventional monocular and single-sensor methods, offering a robust solution for autonomous driving, robotics, surveillance, and industrial automation applications. The development and validation of the CLR-Localiser not only prove the technical feasibility of early sensor data fusion but also pave the way for future advancements in multi-sensor fusion technology for enhanced environmental perception in autonomous systems.

关键词： Multi-sensor fusion Object Detection 3D Localization Autonomous Systems Deep Learning LiDAR Radar Camera Environmental Perception Smart City Infrastructure

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9 in 10 cameras agree: Pedestrians in front possibly endangered 5

9 in 10 cameras agree: Pedestrians in front possibly endange...

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5th IEEE/ACM International Conference on Automation of Software Test (AST)

作者： Prikler, Liliana Marie Wotawa, Franz Graz Univ Technol Inst Software Technol Graz Styria Austria

ISBN: (纸本)9798400705885

Modern cyber-physical systems integrate data from many sensors as a regular part of their operations. Over the years, researchers have proposed methods ranging from statistical approaches to neural networks to achieve this sensor fusion, along with high-level paradigms such as early and late fusion. However, quality assurance of sensor fusion algorithms typically focuses on highlighting their accuracy or ability to reduce uncertainty under given conditions. This paper aims to establish a qualitative approach to testing sensor fusion. We formulate an answer set program based on desirable properties for sensor fusion and show how to apply it to test fusion algorithms. Our results indicate that our approach is effective at finding faults, but does not easily find minimal models for large inputs.

关键词： sensor fusion passive testing qualitative testing

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Semi-Supervised Multi-modal sensor fusion Framework for In-Vehicle Networks

Semi-Supervised Multi-modal Sensor Fusion Framework for In-V...

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19th workshop on Mobility in the Evolving Internet Architecture

作者： Zheng, Zijian Yi, Wenqiang Nallanathan, Arumugam Queen Mary Univ London London England Univ Essex Colchester Essex England

ISBN: (纸本)9798400712470

With the rapid development of technologies such as autonomous driving, vehicle-to-everything communication, and edge computing, an increasing number of vehicles are equipped with multiple sensors to perceive the surroundings. As a result, the amount of sensing data has exploded, and the communication pressure on the in-vehicle network becomes severe. In-sensor or near-sensor computation is considered an effective method to address these issues. However, current multi-modal fusion frameworks are challenging to be modularised and trained in a distributed manner across multiple devices. In this paper, we propose a variational autoencoder (VAE) based multi-modal fusion solution with its theoretical analysis framework. Notably, we design two auxiliary tasks to utilize data from a single modality to discover the joint distribution of multiple modalities. Compared to traditional algorithms, the proposed solution is able to use unlabeled data for self-supervised learning and has the added advantage of modularity, which helps to reduce the communication overhead in in-vehicle networks. Experiments show that, compared to single-modality algorithms, our multi-modal fusion framework increases average precision by over 10% on the KITTI dataset.

关键词： In-vehicle networks multi-modality learning variational autoencoder

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Distributed Sparse Subspace Clustering by K-Means Subspace fusion 13

Distributed Sparse Subspace Clustering by K-Means Subspace F...

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13rd IEEE sensor Array and Multichannel Signal Processing workshop (SAM)

作者： Huang, Liang-Chi Hong, Y. -W. Peter Wu, Jwo-Yuh Natl Yang Ming Chiao Tung Univ Inst Commun Engn Hsinchu Taiwan Natl Tsing Hua Univ Inst Commun Engn Hsinchu Taiwan

ISBN: (纸本)9798350344820;9798350344813

Sparse subspace clustering (SSC) achieves evidenced success in many areas but most related works focus on the centralized scenario. In many practical applications such as security surveillance, several distributed agents jointly collect a large dataset, while each one is however just able to observe a subset through which a local inference is to be made. This paper proposes a collaborative distributed SSC scheme targeted for this situation. In the proposed scheme, each agent first conducts a fast local data clustering, and transmits basis matrices of the estimated subspaces to the data center, which then conducts subspace information fusion using a k-means type method;the aggregated subspace information is fed back to local agents to update their data partitions. Computer simulations using real human face data are used to illustrate the effectiveness of the proposed method.

关键词： Subspace clustering sparse subspace clustering distributed learning k-means algorithm Chordal distance

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Multi-sensor Multi-Stream data fusion Using Deep Learning for Hyperspectral Point Cloud Segmentation 14

Multi-Sensor Multi-Stream Data Fusion Using Deep Learning fo...

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14th workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： Afifi, Ahmed Jamal Rizaldy, Aldino Arbash, Elias Lorenz, Sandra Gloaguen, Richard Heizmann, Michael Freiberg Germany Freie Universität Berlin Department of Earth Science Berlin Germany University of Antwerp Imec-Visionlab Department of Physics Antwerp Belgium Karlsruhe Germany

ISBN: (纸本)9798331513139

In this paper, we explore various sensor fusion techniques for the deep learning segmentation of hyperspectral point clouds. Specifically, we trained a Residual Multi-Layer Perceptron (Res-MLP) model using early, intermediate, and late fusion strategies to enhance segmentation performance. By leveraging the rich spectral information in hyperspectral data acquired using multiple sensors, sensor fusion at different levels improves feature learning and produces more precise segmentation maps. Utilizing these fusion mechanisms, our model effectively segments mineralogical structures, demonstrating superior accuracy and computational efficiency compared to other models that rely on a single data source. Extensive experiments on the Tinto benchmark dataset validate the efficacy of sensor fusion in deep learning, highlighting its potential to significantly advance geological mapping and mineral exploration. © 2024 IEEE.

关键词： Deep learning

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Optimal Fault-Tolerant data fusion in sensor Networks: Fundamental Limits and Efficient Algorithms

Optimal Fault-Tolerant Data Fusion in Sensor Networks: Funda...

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IEEE Information Theory workshop (ITW)

作者： Alonso, Marian Temprana Shirani, Farhad Iyengar, S. Sitharama Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL 33199 USA

ISBN: (纸本)9798350301496

Distributed estimation in the context of sensor networks is considered, where distributed agents are given a set of sensor measurements, and are tasked with estimating a target variable. A subset of sensors are assumed to be faulty. The objective is to minimize i) the mean squared estimation error at each node (accuracy objective), and ii) the mean squared distance between the estimates at each pair of nodes (consensus objective). It is shown that there is an inherent tradeoff between the former and latter objectives. Assuming a general stochastic model, the sensor fusion algorithm optimizing this tradeoff is characterized through a computable optimization problem, and a Cramer-Rao type lower bound for the achievable accuracy-consensus loss is obtained. Finding the optimal sensor fusion algorithm is computationally complex. To address this, a general class of low-complexity Brooks-Iyengar Algorithms are introduced, and their performance, in terms of accuracy and consensus objectives, is compared to that of optimal linear estimators through case study simulations of various scenarios.

关键词： sensor networks

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Enhanced UAV Tracking through Multi-sensor fusion and Extended Kalman Filtering 6

Enhanced UAV Tracking through Multi-Sensor Fusion and Extend...

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6th Doctoral Symposium on Intelligence Enabled Research, DoSIER 2024

作者： Baidya, Bodhisattwa Mondal, Atanu Manna, Sarbajit Das, Gourab Santra, Anirban Chakraborty, Arkaprava Department of Computer Science and Electronics Ramakrishna Mission Vidyamandira Belur Math West Bengal Howrah711202 India

This research introduces a sophisticated method for UAV tracking utilizing multi-sensor fusion and Extended Kalman Filter (EKF) techniques. We model a sophisticated 3D UAV trajectory alongside diverse sensor information, encompassing RF signal strength, radar range and velocity, audio signals, and optical location data. The EKF technique is employed to estimate the drone's position and velocity, exhibiting enhanced tracking precision relative to individual sensor observations. The simulation integrates variable noise levels and adaptive estimation of measurement noise covariance to improve the resilience of the EKF. Real-time visualization of sensor data and EKF estimates facilitates instant evaluation of the algorithm's efficacy. The results indicate that the EKF efficiently eliminates sensor noise, yielding a more refined and precise trajectory estimation. The analysis of sensor data indicates the average signal strengths and standard deviations for each sensor category. The EKF exhibits a 12.5% enhancement in tracking accuracy relative to unprocessed optical sensor data, with mean tracking errors of 0.07 m for the EKF compared to 0.08 m for the optical sensor independently. This study emphasizes the efficacy of multi-sensor fusion and EKF implementation in improving drone tracking capabilities across diverse applications, such as surveillance, search and rescue, and air traffic management. © 2025 Copyright for this paper by its authors.

关键词： Drones

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