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[IEEE 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Honolulu, HI, USA (2018.7.18-2018.7.21)] 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Reconstructed Dynamics of the Imaging Photoplethysmogram
摘要： Human photoplethysmogram (PPG) is one of the signals widely applied for health monitoring. Development of the new techniques made possible evolution of traditional contact PPG which was measured at red and near-infrared light (NIR) to the contactless, imaging PPG (iPPG) that can be recorded at various light wavelengths, including ambient visible light. However, despite the numerous advantages of iPPG its applications demonstrated so far are quite limited. The NIR PPG was previously found to be useful for various applications in the area of physiological and mental health monitoring by utilizing advanced methods of nonlinear time-series analysis applied on its reconstructed dynamics. The main purpose of this study is to demonstrate data-driven approach with time-delay-reconstructed attractor obtained from the iPPG. The results of this study demonstrated that the iPPG dynamics can be reconstructed with fine data resolution, and its time-delay-reconstructed trajectory is almost deterministic, though contains noise. The obtained results might be useful for further applied studies on the iPPG.
关键词： imaging PPG,iPPG,health monitoring,PPG,nonlinear time-series analysis,photoplethysmogram,NTSA
更新于2025-09-10 09:29:36
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Sentinel-2 Based Temporal Detection of Agricultural Land Use Anomalies in Support of Common Agricultural Policy Monitoring
摘要： The European Common Agricultural Policy (CAP) post-2020 timeframe reform will reshape the agriculture land use control procedures from a selected risk fields-based approach into an all-inclusive one. The reform fosters the use of Sentinel data with the objective of enabling greater transparency and comparability of CAP results in different Member States. In this paper, we investigate the analysis of a time series approach using Sentinel-2 images and the suitability of the BFAST (Breaks for Additive Season and Trend) Monitor method to detect changes that correspond to land use anomaly observations in the assessment of agricultural parcel management activities. We focus on identifying certain signs of ineligible (inconsistent) use in permanent meadows and crop fields in one growing season, and in particular those that can be associated with time-defined greenness (vegetation vigor). Depending on the requirements of the BFAST Monitor method and currently time-limited Sentinel-2 dataset for the reliable anomaly study, we introduce customized procedures to support and verify the BFAST Monitor anomaly detection results using the analysis of NDVI (Normalized Difference Vegetation Index) object-based temporal profiles and time-series standard deviation output, where geographical objects of interest are parcels of particular land use. The validation of land use candidate anomalies in view of land use ineligibilities was performed with the information on declared land annual use and field controls, as obtained in the framework of subsidy granting in Slovenia. The results confirm that the proposed combined approach proves efficient to deal with short time series and yields high accuracy rates in monitoring agricultural parcel greenness. As such it can already be introduced to help the process of agricultural land use control within certain CAP activities in the preparation and adaptation phase.
关键词： permanent meadows,change detection,crop monitoring,arable fields,NDVI object-based temporal profiles,GEOBIA,time series analysis
更新于2025-09-09 09:28:46
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Development of an Approach for Monitoring Sugarcane Harvested and Non-Harvested Conditions Using Time Series Sentinel-1 Data
摘要： With the recent launch of Sentinel-1 constellations and frequent availability of C-band synthetic aperture radar (SAR) data at no cost, there is an opportunity to monitor crops on regular basis, which is still not explored. Therefore, in this paper an approach has been proposed to monitor sugarcane harvest status using time series Sentinel-1 data. The proposed approach uses knowledge based classification and temporal profile for obtaining harvest status of crops. The approach is able to identify harvested and non-harvested sugarcane areas from other crops with an overall accuracy of 82.17%.
关键词： sugarcane,time series analysis,Sentinel-1,Synthetic Aperture Radar (SAR)
更新于2025-09-09 09:28:46
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Satellite-based forest monitoring: spatial and temporal forecast of growing index and short-wave infrared band
摘要： For detecting anomalies or interventions in the field of forest monitoring we propose an approach based on the spatial and temporal forecast of satellite time series data. For each pixel of the satellite image three different types of forecasts are provided, namely spatial, temporal and combined spatio-temporal forecast. Spatial forecast means that a clustering algorithm is used to group the time series data based on the features normalised difference vegetation index (NDVI) and the short-wave infrared band (SWIR). For estimation of the typical temporal trajectory of the NDVI and SWIR during the vegetation period of each spatial cluster, we apply several methods of functional data analysis including functional principal component analysis, and a novel form of random regression forests with online learning (streaming) capability. The temporal forecast is carried out by means of functional time series analysis and an autoregressive integrated moving average model. The combination of the temporal forecasts, which is based on the past of the considered pixel, and spatial forecasts, which is based on highly correlated pixels within one cluster and their past, is performed by functional data analysis, and a variant of random regression forests adapted to online learning capabilities. For evaluation of the methods, the approaches are applied to a study area in Germany for monitoring forest damages caused by wind-storm, and to a study area in Spain for monitoring forest fires.
关键词： Satellite images,Functional time series analysis,Forest monitoring,Online random regression forests,Autoregressive integrated moving average
更新于2025-09-04 15:30:14
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A center-median filtering method for detection of temporal variation in coronal images
摘要： Events in the solar corona are often widely separated in their timescales, which can allow them to be identified when they would otherwise be confused with emission from other sources in the corona. Methods for cleanly separating such events based on their timescales are thus desirable for research in the field. This paper develops a technique for identifying time-varying signals in solar coronal image sequences which is based on a per-pixel running median filter and an understanding of photon-counting statistics. Example applications to "EIT waves" (named after EIT, the EUV Imaging Telescope on the Solar and Heliospheric Observatory) and small-scale dynamics are shown, both using 193 ? data from the Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory. The technique is found to discriminate EIT waves more cleanly than the running and base difference techniques most commonly used. It is also demonstrated that there is more signal in the data than is commonly appreciated, finding that the waves can be traced to the edge of the AIA field of view when the data are rebinned to increase the signal-to-noise ratio.
关键词： Flares,Solar image processing,Coronal-Moreton waves,Time series analysis,Corona
更新于2025-09-04 15:30:14
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Integrating Grey Data Preprocessor and Deep Belief Network for Day-ahead PV Power Output Forecast
摘要： Generation output forecasting is a crucial task for planning and sizing of a photovoltaic (PV) power plant. The purpose of this paper is to present an effective model for day-ahead forecasting PV power output of a plant based on deep belief network (DBN) combined with grey theory-based data preprocessor (GT-DBN), where the DBN attempts to learn high-level abstractions in historical PV output data by utilizing hierarchical architectures. Test results obtained by the proposed model are compared with those obtained by other five forecasting methods including autoregressive integrated moving average model (ARIMA), back propagation neural network (BPNN), radial basis function neural network (RBFNN), support vector regression (SVR), and DBN alone. It shows that the proposed model is superior to other models in forecasting accuracy and is suitable for day-ahead PV power output prediction.
关键词： supervised learning,time series analysis,power generation planning,neural networks,Renewable energy
更新于2025-09-04 15:30:14