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共 417 条记录，以下是281-290 订阅

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COLLABORATIVE VISUAL-INERTIAL LOCALIZATION OF TEAMS WITH FLOORPLAN EXTRACTION

COLLABORATIVE VISUAL-INERTIAL LOCALIZATION OF TEAMS WITH FLO...

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IEEE International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Gazdag, Sandor Pasztornicky, Daniel Janko, Zsolt Sziranyi, Tamas Majdik, Andras L. Inst Comp Sci & Control SZTAKI Machine Percept Res Lab Kende U 13-17 H-1111 Budapest Hungary Budapest Univ Technol & Econ Dept Mat Handling & Logist Syst Bertalan Lajos U 7 H-1111 Budapest Hungary

ISBN: (纸本)9798350302615

This paper showcases a real-world example of a system that achieves collaborative localization and mapping of multiple agents within a building. The proposed system processes the odometry and 3D point cloud data collected by the agents moving around the building to automatically generate the building's floorplan on which the agent trajectories are overlaid. The wearable hardware consists of a low-cost passive integrated sensor that includes both a camera and an IMU (Inertial Measurement Unit) and an embedded compute unit. The system's capabilities are shown through real-world experiments.

关键词： collaborative localization floorplan extraction visual-inertial localization human-interpretable map wearable hardware

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MetaSPICE: Metaprogramming SPICE Framework for the Design space Exploration of PUF Circuits 12

MetaSPICE: Metaprogramming SPICE Framework for the Design Sp...

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12th International conference on Modern Circuits and Systems Technologies, MOCAST 2023

作者： Xynos, Athanasios Tenentes, Vasileios University of Ioannina Dept. of Computer Science & Engineering Greece

ISBN: (纸本)9798350321074

Silicon Physical Unclonable Functions (PUFs) generate challenge-response pairs that are unique to each die, allowing for secure identification, authentication and cryptographic keys generation. However, designing and evaluating PUFs is a complex task that can require a vast amount of SPICE netlists simulation. In this paper, we present MetaSPICE, which is a novel metaprogramming SPICE framework for the design space exploration of PUFs. Metaprogramming refers to the process of writing programs that manipulate other programs, data structures or even themselves. The designer in MetaSPICE, instead of defining multiple SPICE netlists, has to define a single SPICE template that is used to generate multiple netlists. The MetaSPICE is complementary to classical SPICE simulators, and enables the design exploration impact on PUFs' performance. The framework consists of a template engine for generating SPICE models from SPICE templates, and software for evaluating the performance of PUF circuits in terms of uniqueness, uniformity and reliability from SPICE simulation results. We demonstrate the effectiveness of MetaSPICE by evaluating the performance and presenting improvements on a recently proposed PUF design. © 2023 IEEE.

关键词： SPICE

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Credal Marginal MAP 37

Credal Marginal MAP

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37th conference on Neural Information processing Systems (NeurIPS)

作者： Marinescu, Radu Bhattacharjya, Debarun Lee, Junkyu Gray, Alexander Cozman, Fabio IBM Res Dublin Ireland IBM Res Armonk NY USA Univ Sao Paulo Sao Paulo Brazil

ISBN: (纸本)9781713899921

Credal networks extend Bayesian networks to allow for imprecision in probability values. Marginal MAP is a widely applicable mixed inference task that identifies the most likely assignment for a subset of variables (called MAP variables). However, the task is extremely difficult to solve in credal networks particularly because the evaluation of each complete MAP assignment involves exact likelihood computations (combinatorial sums) over the vertices of a complex joint credal set representing the space of all possible marginal distributions of the MAP variables. In this paper, we explore Credal Marginal MAP inference and develop new exact methods based on variable elimination and depth-first search as well as several approximation schemes based on the mini-bucket partitioning and stochastic local search. An extensive empirical evaluation demonstrates the effectiveness of our new methods on random as well as real-world benchmark problems.

关键词： data handling

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Coherence Feature Modelling for Single-Baseline Pol-InSAR

Coherence Feature Modelling for Single-Baseline Pol-InSAR

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IET International Radar conference 2023, IRC 2023

作者： Sun, Xiaofan Jiang, Shuai Song, Chong Beijing Institute of Spacecraft System Engineering China Academy of Space Technology Beijing100094 China National Key Laboratory of Microwave Imaging Technology Aerospace Information Research Institute Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781839539954

In order to solve the problem that P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) observation is difficult to be modelled, this paper focuses on the analysis of Pol-InSAR coherence feature, in which polarimetric state is taken into consideration as a variable. To uniquely identify the Pol-InSAR coherence region, coherence feature modelling is carried out. Six parameters are used to determine the shape, direction and position of the coherence region in complex plane. Finally, the potential strategy of single-baseline based on the proposed coherence feature model for forest height inversion using P-band Pol-InSAR data is investigated. © The Institution of Engineering & Technology 2023.

关键词： Polarimeters

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Revisiting Rosebank OBN | Contemporary processing and imaging techniques enable new insights from heritage data 84

Revisiting Rosebank OBN | Contemporary processing and imagin...

引用

84th EAGE Annual conference and Exhibition

作者： Sokolowski, F. Ahmed, H. Cooke, A. Sundoy, K. Wallis, D. Buker, F. SLB United States Equinor Norway

ISBN: (纸本)9781713884156

Deep-water sparse ocean-bottom node acquisition geometries have well-documented benefits, in terms of rich sampling and broadband signal content, and provide the ideal input to modern data processing and imaging workflows. However, they also have some intrinsic challenges. These include uncertainties in positioning and timing perturbations associated with water-column variations and clock drift. The sparse receiver sampling can also present a challenge for free-surface multiple attenuation and introduce acquisition footprint. The Rosebank field lies to the West of the Shetland Islands and consists of sand reservoirs that lie between and beneath layers of basalt with strong impedance contrasts. In this work we show how we used an inversion approach to address acquisition-related perturbations and uncertainties. We describe a method to apply deghosting and demultiple to both upgoing and downgoing wavefields and describe a multi-survey approach to the velocity model-building workflow that included full-waveform inversion and common image point tomography. The results show that the new processing was able to provide an uplift to existing legacy processing and enabled improvements in image quality in the area, improving resolution, focusing, enhancing, structural definition consistent with geological features in the area, reducing multiples and footprint, and minimizing seismic-well tie mismatches. © (2023) by the european Association of Geoscientists & Engineers (EAGE). All rights reserved.

关键词： data handling

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Ranking on Heterogeneous Manifold for Multimodal Information Retrieval 21

Ranking on Heterogeneous Manifold for Multimodal Information...

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21st IEEE International Symposium on Parallel and Distributed processing with Applications, 13th IEEE International conference on Big data and Cloud Computing, 16th IEEE International conference on Social Computing and Networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Jin, Zhenkun Wan, Xingshi Nie, Xin Zhou, Xinlei Yi, Yuanyuan Zhou, Gefei Wuhan Business University Wuhan Research Institute of Posts and Telecommunications Wuhan China Wuhan Research Institute of Posts and Telecommunications Wuhan China Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Wuhan China Huazhong University of Science and Technology School of Journalism and Information Communication Wuhan China Huazhong University of Science and Technology School of Computer Science & Technology Wuhan China

ISBN: (纸本)9798350329223

In light of recent advancements in Internet of Multimedia Things (IoMT) and 5G technology, both the variety and quantity of data have been rapidly increasing. Consequently, handling zero-shot cross-modal retrieval (ZSCMR) of unlabeled multi-modal data has emerged as a prominent research topic. Early models utilized generative adversarial networks and class embeddings to assist in building a common feature space. However, this approach resulted in a loss of reliability in the feature distributions of individual modalities. In this paper, we propose a method called Ranking on Heterogeneous Manifold (RHM) to address the aforementioned issue. Our model comprises three feature extractors, each dedicated to extracting features from text, images and multimodal data. Subsequently, the relationship structures derived from these feature extractors are employed to construct a heterogeneous manifold. A diffusion process is then conducted on this heterogeneous manifold, ultimately yielding the final ranking scores. Comprehensive experiments demonstrate that the proposed modal has the capability to acquire more effective knowledge representation, thus enhancing the effectiveness of ZSCMR. © 2023 IEEE.

关键词： Knowledge representation

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data pipeline of a multi-spectral satellite experiment for object detection and artificial intelligence-based processing 33

Data pipeline of a multi-spectral satellite experiment for o...

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conference on Automatic Target Recognition XXXIII

作者： Mueller, Markus C. Swami, Sanjay Haser, Benjamin Bilal, Mohd Kinzel, Artur Foerstner, Roger Mundt, Christian Univ Bundeswehr Inst Thermodynam Munich Germany Univ Bundeswehr Munchen Inst Space Technol & Space Applicat Werner Heisenberg Weg 39 D-85579 Neubiberg Germany

ISBN: (纸本)9781510661561;9781510661578

Recent developments in the military domain introduce the need to detect and track hypersonic glide vehicles in Earth's atmosphere. The Multispectral Object Sensing by Artificial Intelligence-processed Cameras (MOSAIC) experiment is part of the small-satellite ATHENE-1 of the Universitat der Bundeswehr Munchen. The primary scientific objective of MOSAIC is to demonstrate reliable detection, identification and tracking of hypersonic glide vehicles using primarily a cooled infrared camera and complementary a visual camera. To cope with a large amount of data from both high-resolution cameras in real-time, state-of-the-art computer vision on-board processing methods are used for detection and tracking. The secondary scientific objective is to investigate the efficiency and reliability of Artificial Intelligence (AI) based image processing algorithms and data compression for space applications. This is of particular importance given the high volumes and rates of data. The application of such algorithms requires a reliable and resource-efficient On-Board Computer (OBC) that can withstand the harsh space environment. The approach outlined in this paper envisions a dedicated OBC to manage the AI-based experiments of the satellite, called the Artificial Intelligence capable On-Board Computer (AI-OBC). The AI-OBC includes multiple hardware-based AI accelerators to meet the computational requirements and ensure real-time processing for object detection and tracking. This paper describes the structure of the data processing pipeline and includes the AI-OBC architecture with its connections to both the cameras and the platform's OBC. Further, the study discusses the training and validation steps of the intended use-cases.

关键词： data processing Artificial Intelligence Deep Learning Object Detection Object Tracking Hypersonic Glide Vehicle Earth Observation Small-Satellite

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spacecraft Pose Estimation Based on Unsupervised Domain Adaptation and on a 3D-Guided Loss Combination 17th

Spacecraft Pose Estimation Based on Unsupervised Domain Ada...

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17th european conference on Computer Vision, ECCV 2022

作者： Pérez-Villar, Juan Ignacio Bravo García-Martín, Álvaro Bescós, Jesús Deimos Space Madrid28760 Spain Video Processing and Understanding Lab Univ. Autónoma de Madrid Madrid28049 Spain

ISBN: (纸本)9783031250552

spacecraft pose estimation is a key task to enable space missions in which two spacecrafts must navigate around each other. Current state-of-the-art algorithms for pose estimation employ data-driven techniques. However, there is an absence of real training data for spacecraft imaged in space conditions due to the costs and difficulties associated with the space environment. This has motivated the introduction of 3D data simulators, solving the issue of data availability but introducing a large gap between the training (source) and test (target) domains. We explore a method that incorporates 3D structure into the spacecraft pose estimation pipeline to provide robustness to intensity domain shift and we present an algorithm for unsupervised domain adaptation with robust pseudo-labelling. Our solution has ranked second in the two categories of the 2021 Pose Estimation Challenge organised by the european space Agency and the Stanford University, achieving the lowest average error over the two categories (Code is available at: https://***/JotaBravo/spacecraft-uda ). © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： spacecraft

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Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean 37

Mesogeos: A multi-purpose dataset for data-driven wildfire m...

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37th conference on Neural Information processing Systems (NeurIPS)

作者： Kondylatos, Spyros Prapas, Ioannis Camps-Valls, Gustau Papoutsis, Ioannis Natl Observ Athens Orion Lab Inst Astron Astrophys Space Applicat & Remote Sen Athens Greece Univ Valencia Image Proc Lab IPL Valencia Spain

ISBN: (纸本)9781713899921

We introduce Mesogeos(1), a large-scale multi-purpose dataset for wildfire modeling in the Mediterranean. Mesogeos integrates variables representing wildfire drivers (meteorology, vegetation, human activity) and historical records of wildfire ignitions and burned areas for 17 years (2006-2022). It is designed as a cloud-friendly spatio-temporal dataset, namely a datacube, harmonizing all variables in a grid of 1km x 1km x 1-day resolution. The datacube structure offers opportunities to assess machine learning (ML) usage in various wildfire modeling tasks. We extract two ML-ready datasets that establish distinct tracks to demonstrate this potential: (1) short-term wildfire danger forecasting and (2) final burned area estimation given the point of ignition. We define appropriate metrics and baselines to evaluate the performance of models in each track. By publishing the datacube, along with the code to create the ML datasets and models, we encourage the community to foster the implementation of additional tracks for mitigating the increasing threat of wildfires in the Mediterranean.

关键词： Spatio-temporal data

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Early Diagnosis of Carotid Artery Disease based on non-imaging data

Early Diagnosis of Carotid Artery Disease based on non-imagi...

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2023 IEEE EMBS Special Topic conference on data Science and Engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Kigka, Vassiliki I. Sakellarios, Antonis I. Tsakanikas, Vassilis D. Potsika, Vassiliki T. Koncar, Igor Fotiadis, Dimitrios I. University of Ioannina Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering Ioannina Greece Clinic Center of Serbia Department of Vascular and Endovascular Surgery Belgrade11000 Serbia University Campus of Ioannina Institute of Molecular Biology and Biotechnology-FORTH Department of Biomedical Research Ioannina45110 Greece

ISBN: (纸本)9798350383386

Carotid artery disease refers to the build up of atherosclerotic plaques into the carotid arteries. This pathological condition is responsible for cerebral events and stroke. The diagnosis of the disease is based on imaging techniques (carotid ultrasound, computed tomography angiography and magnetic resonance angiography) and the disease is either symptomatic or asymptomatic. The aim of the presented study is to early identify individuals of high risk for carotid artery disease through utilizing data driven techniques. More specifically, we utilize a dataset of 135 individuals with carotid artery disease and healthy ones, as it is defined with the use of carotid ultrasound. The dataset includes both typical medical records (demographics, risk factors) and biomarkers (biochemical, hematological) and is trained into a gradient boosting, aiming to detect the healthy participants and the individuals of high risk. The pipeline includes the pre-processing of the dataset, the handling of the class imbalance, the implementation of feature ranking techniques and finally the evaluation of the proposed pipeline. The model achieved an accuracy of 0.82 and a sensitivity and specificity of 0.68 and 0.92, respectively. © 2023 IEEE.

关键词： Pipelines

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