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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Marzano, Frank S. Mereu, Luigi Scollo, Simona Merucci, Luca Corradini, Stefano DIET Sapienza Univ Rome Rome Italy Univ Aquila Ctr Excellence CETEMPS Laquila Italy Ist Nazl Vulcanol & Geofis INGV Catania Italy Ist Nazl Vulcanol & Geofis INGV Rome Italy

ISBN: (纸本)9781665403696

Satellite-based detection of volcanic eruptions, using infrared radiometric data from Low Earth Orbit (LEO) spectroradiometers, may lead to an ambiguous detection in the proximity of the volcanic vent during sub-Plinian volcanic events. The thermal-infrared (TIR) brightness-temperature difference signatures saturate because of the large tephra particle within the expanding plume. In this respect, the use LEO spaceborne millimeter-wave (MMW) radiometric observations can help since plumes at millimeter wavelength are less optically opaque than at micron ones. To demonstrate this synergy, we show the analysis of the 2014 Kelud eruption case study considering LEO measurements and detection algorithms based on TIR and MMW.

关键词： Satellite volcanology thermal infrared radiometry millimeter wave systems detection algorithms

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Lognormal Background Modeling for Gaseous Plume detection 27

Lognormal Background Modeling for Gaseous Plume Detection

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Conference on algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXVII

作者： Schaum, Alan US Naval Res Lab Washington DC 20375 USA

ISBN: (数字)9781510642928

ISBN: (纸本)9781510642928

A uniformly most powerful algorithm is derived for the standoff spectral detection of absorptive gaseous plumes. No weak concentration approximation is invoked. Other methods then naturally follow for the detection and estimation of gas mixtures. The algorithms result from the modeling of spectral backgrounds with multivariate lognormal distributions.

关键词： Decision theory detection algorithms gaseous plumes hyperspectral imaging uniformly most powerful detector estimation

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Real-Time Adaptive Threshold Adjustment for Lane detection Application under Different Lighting Conditions using Model-Free Control

Real-Time Adaptive Threshold Adjustment for Lane Detection A...

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Modeling, Estimation and Control Conference (MECC)

作者： Zhou, Yujing Wang, Zejiang Wang, Junmin Univ Texas Austin Walker Dept Mech Engn Austin TX 78712 USA

Lane detection and tracking technologies are critical to automated vehicles and the lane departure warning (LDW) feature in advanced driver assistance systems (ADAS). The color difference between lane marks and their background is an important source for drivers to detect lanes. However, most existing lane detection techniques neglect the importance of color features contained within lane marks because the color is subject to lighting variations. The hue, saturation, and value (HSV) color space is commonly used for color identification, as it separates the color, saturation, and brightness information into the H, S, and V channels, respectively. Colors can be identified with specified thresholds in H, S, and V channels, but fixed thresholds can hardly treat the disturbances caused by variation of lighting conditions. This paper leverages the color information of lane marks to perform the lane detection task by adaptively changing the thresholds in the HSV space to handle lighting condition variations. In doing so, the vehicle is assumed to follow a straight lane with a reference steering angle of zero degree under varying lighting conditions. It is hard to model the relationship between the thresholds in the HSV space and the vehicle steering angle in automated driving, so we adopt the model-free control (MFC), which is a data-driven approach and does not require an explicit model for the system. Simulink-QUARC joint offline experiments demonstrate the effectiveness of the proposed real-time HSV threshold adjustment method for better lane detections. Copyright (C) 2021 The Authors.

关键词： Autonomous vehicles detection algorithms Threshold selection Adaptive systems Self-adjusting systems Model-free control

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Autonomous power line detection and tracking system using UAVs

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MICROPROCESSORS AND MICROSYSTEMS 2022年 94卷

作者： Schofield, Oscar Bowen Iversen, Nicolai Ebeid, Emad Univ Southern Denmark Maersk Mc Kinney Moller Inst SDU UAS Ctr Odense Denmark

A large portion of overhead power transmission infrastructure around the world is becoming aged, raising the demand for frequent grid inspections in an efficient, cost-effective manner in order to keep it functional. Drones are a promising solution for infrastructure inspection, however, they require further investigation and development to enable their autonomy for large scale inspection *** paper proposes a drone framework based on open-source platforms for autonomous inspection of electrical grid infrastructure. The framework incorporates (1) a cloud service for geo-location data of nearby power pylons;(2) a localisation system for pylon navigation and cable grasping;and (3) a cable following algorithm for cable inspection using onboard sensors. The software functionality of the proposed framework has been validated in a simulation power line environment. Custom drones have been developed for validating the framework and algorithms within an unenergised power line environment.

关键词： Cloud services detection algorithms Autonomous missions Powerline inspection Drone framework

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A Novel Gait Phase detection Algorithm for Foot Drop Correction through Optimal Hybrid FES-Orthosis Assistance

A Novel Gait Phase Detection Algorithm for Foot Drop Correct...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jung, Pyeong-gook Huo, Weiguang Moon, Huiseok Amirat, Yacine Mohammed, Samer Univ Paris Est Creteil LISSI F-94400 Vitry Sur Seine France Imperial Coll London Dept Mech Engn London England

ISBN: (纸本)9781728190778

As a life-threatening disease, stroke can lead to long-term problems affecting the patients' daily living ability. A common problem facing post-stroke patients is foot drop. An emerging modality of interest for correcting the foot drop is to combine both actuated ankle-foot orthosis (AAFO) and functional electrical stimulation (FES). Such hybrid assistive system not only ensure effective assistance but also can avoid fast muscular fatigue due to excessive muscular stimulation. Due to the significant changes in the ankle joint's kinematics and kinetics with gait cycles, optimization control strategies for hybrid AAFO and FES systems are highly demanded. However, it is challenging to develop accurate gait phase detection algorithms to guide the control of AAFO and FES while ensuring robustness with respect to the diversity and variability of patients' gaits. In this paper, we present a novel swing sub-phase detection algorithm based on a moving average convergence divergence (MACD) indicator. The proposed detection algorithm uses only information collected from the affected leg by means of two inertia measurement units (IMU) and the AAFO. Moreover, a gait-phase based control strategy is developed to optimize the assistive effect of a hybrid AAFO and FES system. Experimental results with five healthy show the potential of the proposed approaches in ensuring both satisfactory ankle joint trajectory tracking and effective reduction in stimulation intensity, compared to the use of conventional FES assistance.

关键词： Legged locomotion Measurement units Trajectory tracking Iron Robustness Neuromuscular stimulation detection algorithms

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Optimality of Graph Scanning Statistic for Online Community detection

Optimality of Graph Scanning Statistic for Online Community ...

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IEEE International Symposium on Information Theory (ISIT)

作者： Xie, Liyan Xie, Yao Georgia Inst Technol H Milton Stewart Sch Ind & Syst Engn Atlanta GA 30332 USA

ISBN: (纸本)9781538682098

Sequential change detection for graphs is a fundamental problem for streaming network data and has wide applications in social networks and power systems. Given fixed vertices and a sequence of random graphs, the objective is to detect the change-point where the underlying distribution of the random graph changes. In particular, we focus on the local change that only affects a small subgraph. We adopt the classical Erdos-Renyi model and revisit the generalized likelihood ratio (GLR) procedure. The scan statistic is computed by sequentially estimating the most-likely subgraph where the change happens. We provide theoretical analysis for the asymptotic optimality of the proposed procedure and we comment on generalizations to other random graph models. We demonstrate the efficiency of our detection algorithm using simulations.

关键词： Analytical models Social networking (online) Computational modeling Power systems detection algorithms Information theory

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Activity-Based Model using GPS Data and Google APIs 25

Activity-Based Model using GPS Data and Google APIs

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IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

作者： Tran, Yen Hashimoto, Naohisa Ando, Takafumi Sato, Toshihisa Konishi, Naoki Takeda, Yuji Akamatsu, Motoyuki Natl Inst Adv Ind Sci & Technol Digital Architecture Res Ctr Tsukuba Ibaraki 3058568 Japan Natl Inst Adv Ind Sci & Technol Human Ctr Mobil Res Ctr Tsukuba Ibaraki 3058568 Japan

ISBN: (数字)9781665468800

ISBN: (纸本)9781665468800

This study explores the potentials of utilizing GPS data for Activity-Based Models, under the condition that no additional information, such as travel diaries, is required. To extract activity details, we first developed a Time-Spatial Centroid-based Clustering algorithm to identify activity locations and times. Then a Home detection algorithm was used in combination with two APIs from Google Maps, namely Nearby Search and Place Details, to label each identified activity as either `home' or a specific activity type. Next, a Markov Chain Multinomial Logit Choice model was developed for the extracted activities that models the sequential relationship between consecutive activities. The approach was applied to a GPS dataset collected in Japan in 2020. The estimated parameters revealed how background factors, such as activity time and the person's age, and the previous activity associate with the current activity. Thus, GPS data alone can provide certain knowledge about activity-travel, which potentially benefit practices of travel demands forecasting.

关键词： Demand forecasting Clustering algorithms Markov processes Data models Internet detection algorithms Intelligent transportation systems

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Evaluation of the Potential of Automatic Naming Latency detection for Different Initial Phonemes during Picture Naming Task 43

Evaluation of the Potential of Automatic Naming Latency Dete...

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43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC)

作者： Park, Sunghea Altermatt, Sven Beierlein, Sandra Widmer Blechschmidt, Anja Reymond, Claire Degen, Markus Rickert, Eliane Wyss, Sandra Kuntner, Katrin P. Hemm, Simone Univ Appl Sci & Arts Northwestern Switzerland Sch Educ Inst Special Educ & Psychol Windisch Switzerland Univ Appl Sci & Arts Northwestern Switzerland Sch Life Sci Inst Med Engn & Med Informat Windisch Switzerland Univ Appl Sci & Arts Northwestern Switzerland Acad Art & Design Inst Visual Commun Windisch Switzerland

ISBN: (纸本)9781728111797

Naming latency (NL) represents the speech onset time after the presentation of an image. We recently developed an extended threshold-based algorithm for automatic NL (aNL) detection considering the envelope of the speech wave. The present study aims at exploring the influence of different manners (e.g., "m" and "p") and positions (e.g., "t" and "p") of articulation on the differences between manual NL (mNL) and aNL detection. Speech samples were collected from 123 healthy participants. They named 118 pictures in German, including different initial phonemes. NLs were manually (Praat, waveform and spectrogram) and automatically (developed algorithm) determined. To investigate the accuracy of automatic detections, correlations between mNLs and aNLs were analyzed for different initial phonemes. ANLs and mNLs showed a strong positive correlation and similar tendencies in initial phoneme groups. ANL mean values were shorter than the ones of mNLs. Nasal sounds (e.g., /m/) showed the largest and those for fricatives (e.g., /s/) the smallest difference. However, in fricatives, 39% of NLs were detected later by automatic detections than by manual detections, which led to a reduced mean difference with mNLs. The signal energy of the initial phonemes, i.e., if they are voiced or voiceless, influences the form of the speech envelope: initial high signal energy is often responsible for an early detection by the algorithm. Our study provides evidence of a similar tendency in mNL and aNL according to different positions of articulation in each initial phoneme group. ANLs are highly sensitive to detection of speech onsets across different initial phonemes. The dependency of the NL differences on the initial phonemes will lose importance during progress evaluations in aphasia patients if the relative changes for each picture are considered separately. Nevertheless, the algorithm will be further optimized by adapting its parameters for each initial phoneme group individually.

关键词： Correlation Prototypes Nose Manuals Biology Task analysis detection algorithms

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PixelDINO: Semi-Supervised Semantic Segmentation for Detecting Permafrost Disturbances in the Arctic

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Heidler, Konrad Nitze, Ingmar Grosse, Guido Zhu, Xiao Xiang Tech Univ Munich TUM Chair Data Sci Earth Observat SiPEO Sch Engn & Design Dept Aerosp & Geodesy D-80333 Munich Germany Helmholtz Ctr Polar & Marine Res Alfred Wegener Inst Permafrost Res Sect D-14473 Potsdam Germany Univ Potsdam Inst Geosci D-14469 Potsdam Germany Tech Univ Munich TUM Munich Ctr Machine Learning D-80333 Munich Germany

Arctic permafrost is facing significant changes due to global climate change. As these regions are largely inaccessible, remote sensing plays a crucial rule in better understanding the underlying processes across the Arctic. In this study, we focus on the remote detection of retrogressive thaw slumps (RTSs), a permafrost disturbance comparable to slow landslides. For such remote sensing tasks, deep learning has become an indispensable tool, but limited labeled training data remains a challenge for training accurate models. We present PixelDINO, a semi-supervised learning approach, to improve model generalization across the Arctic with a limited number of labels. PixelDINO leverages unlabeled data by training the model to define its own segmentation categories (pseudoclasses), promoting consistent structural learning across strong data augmentations. This allows the model to extract structural information from unlabeled data, supplementing the learning from labeled data. PixelDINO surpasses both supervised baselines and existing semi-supervised methods, achieving average intersection-over-union (IoU) of 30.2 and 39.5 on the two evaluation sets, representing significant improvements of 13% and 21%, respectively, over the strongest existing models. This highlights the potential for training robust models that generalize well to regions that were not included in the training data.

关键词： Permafrost Remote sensing Data models Training data Satellite images Arctic Semisupervised learning Semantic segmentation Climate change detection algorithms retrogressive thaw slumps (RTSs) self-distillation without labels semantic segmentation semi-supervised learning

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Clothoidal Mapping of Road Line Markings for Autonomous Driving High-Definition Maps

Clothoidal Mapping of Road Line Markings for Autonomous Driv...

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33rd IEEE Intelligent Vehicles Symposium (IEEE IV)

作者： Gallazzi, Barbara Cudrano, Paolo Frosi, Matteo Mentasti, Simone Matteucci, Matteo Politecn Milan Dept Elect Informat & Bioengn Pzza Leonardo da Vinci 32 Milan Italy

ISBN: (纸本)9781665488211

Lane-level HD maps are crucial for trajectory planning and control in current autonomous vehicles. For this reason, appropriate line models should be adopted to define them. Whereas mapping algorithms often rely on inaccurate representations, clothoid curves possess peculiar smoothness properties that make them desirable representations of road lines in control algorithms. We propose a multi-stage pipeline for the generation of lane-level HD maps from monocular vision relying on clothoidal spline models. We obtain measurements of the line positions using a line detection algorithm, and we exploit a graph-based optimization framework to reach an optimal fitting. An iterative greedy procedure reduces the model complexity removing unnecessary clothoids. We validate our system on a real-world dataset, which we make publicly available for further research at https://***. ***/datasets- and- tools/.

关键词： Trajectory planning Roads Pipelines Fitting Position measurement Splines (mathematics) detection algorithms

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