The Linear Filter for a Single Time Series

2009 ◽  
Keyword(s):  
Time Series ◽  
Linear Filter ◽  
Single Time

scholarly journals Combined Multi-Time Series SAR Imagery and InSAR Technology for Rice Identification in Cloudy Regions

2021 ◽  
Vol 11 (15) ◽  
pp. 6923
Author(s):  
Rui Zhang ◽  
Zhanzhong Tang ◽  
Dong Luo ◽  
Hongxia Luo ◽  
Shucheng You ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Identification Accuracy ◽  
Optical Remote Sensing ◽  
Sar Image ◽  
Single Time ◽  
Growing Period ◽  
Sensing Technology ◽  
Sar Imagery ◽  
Cloudy Region

The use of remote sensing technology to monitor farmland is currently the mainstream method for crop research. However, in cloudy and misty regions, the use of optical remote sensing image is limited. Synthetic aperture radar (SAR) technology has many advantages, including high resolution, multi-mode, and multi-polarization. Moreover, it can penetrate clouds and mists, can be used for all-weather and all-time Earth observation, and is sensitive to the shape of ground objects. Therefore, it is widely used in agricultural monitoring. In this study, the polarization backscattering coefficient on time-series SAR images during the rice-growing period was analyzed. The rice identification results and accuracy of InSAR technology were compared with those of three schemes (single-time-phase SAR, multi-time-phase SAR, and combination of multi-time-phase SAR and InSAR). Results show that VV and VH polarization coherence coefficients can well distinguish artificial buildings. In particular, VV polarization coherence coefficients can well distinguish rice from water and vegetation in August and September, whereas VH polarization coherence coefficients can well distinguish rice from water and vegetation in August and October. The rice identification accuracy of single-time series Sentinel-1 SAR image (78%) is lower than that of multi-time series SAR image combined with InSAR technology (81%). In this study, Guanghan City, a cloudy region, was used as the study site, and a good verification result was obtained.

The DCT-based oscillation detection method for a single time series

2010 ◽  
Vol 20 (5) ◽  
pp. 609-617 ◽  
Author(s):  
Xinong Li ◽  
Jiandong Wang ◽  
Biao Huang ◽  
Sien Lu
Keyword(s):  
Time Series ◽  
Detection Method ◽  
Single Time ◽  
Oscillation Detection

Analysis Versus Synthesis for Trending of Gas-Path Measurement Time Series

2014 ◽  
Author(s):  
S. Borguet ◽  
O. Léonard ◽  
P. Dewallef
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Synthetic Data ◽  
Noise Removal ◽  
Health Condition ◽  
Fault Detection And Isolation ◽  
Linear Filter ◽  
Linear Filters ◽  
Detection Delay ◽  
Low Pass

Gas-path measurements used to assess the health condition of an engine are corrupted by noise. Generally, a data cleaning step occurs before proceeding with fault detection and isolation. Classical linear filters such as the exponentially weighted moving average filter are traditionally used for noise removal. Unfortunately, these low-pass filters distort trend shifts indicative of faults, which increases the detection delay. The present paper investigates two new approaches to non-linear filtering of time series. On one hand, the synthesis approach reconstructs the signal as a combination of elementary signals chosen from a pre-defined library. On the other hand, the analysis approach imposes a constraint on the shape of the signal (e.g., piecewise constant). Both approaches incorporate prior information about the signal in a different way, but they lead to trend filters that are very capable at noise removal while preserving at the same time sharp edges in the signal. This is highlighted through the comparison with a classical linear filter on a batch of synthetic data representative of typical engine fault profiles.

scholarly journals A Platform to Record Patient Events During Physiological Monitoring With Wearable Sensors: Proof-of-Concept Study (Preprint)

2018 ◽  
Author(s):  
Jonathan Duc Vinh Vo ◽  
Alexander M Gorbach
Keyword(s):  
Time Series ◽  
Clinical Data ◽  
Block Diagram ◽  
Wearable Sensors ◽  
Temperature Data ◽  
Full Potential ◽  
Proof Of Concept ◽  
Simple Method ◽  
Single Time ◽  
Patient Reports

BACKGROUND Patient journals have been used as valuable resources in clinical studies. However, the full potential value of such journals can be undermined by inefficiencies and ambiguities associated with handwritten patient reports. The increasing number of mobile phones and mobile-based health care approaches presents an opportunity to improve communications from patients to clinicians and clinical researchers through the use of digital patient journals. OBJECTIVE The objective of this project was to develop a smartphone-based platform that would enable patients to record events and symptoms on the same timeline as clinical data collected by wearable sensors. METHODS This platform consists of two major components: a smartphone for patients to record their journals and wireless sensors for clinical data collection. The clinical data and patient records are then exported to a clinical researcher interface, and the data and journal are processed and combined into a single time-series graph for analysis. This paper gives a block diagram of the platform’s principal components and compares its features to those of other methods but does not explicitly discuss the process of design or development of the system. RESULTS As a proof of concept, body temperature data were obtained in a 4-hour span from a 22-year-old male, during which the subject simultaneously recorded relevant activities and events using the iPhone platform. After export to a clinical researcher’s desktop, the digital records and temperature data were processed and fused into a single time-series graph. The events were filtered based on specific keywords to facilitate data analysis. CONCLUSIONS We have developed a user-friendly patient journal platform, based on widely available smartphone technology, that gives clinicians and researchers a simple method to track and analyze patient activities and record the activities on a shared timeline with clinical data from wearable devices.

scholarly journals Research of Biogenic Substances Concentrations and their Connection with Runoff of the Siverskiy Donets River in the Borders of the Kharkiv Area

2019 ◽  
Keyword(s):  
Time Series ◽  
Water Content ◽  
Coefficient Of Variation ◽  
Water Concentration ◽  
Average Concentration ◽  
Linear Filter ◽  
Biogenic Substances ◽  
Annual Water ◽  
Filter Time Series ◽  
Observation Period

Purpose. To investigate the spatial-temporal change in the runoff of water, concentration of nutrients in water and establishment of communication between them, on the river Seversky Donets. Methods. Statistical analysis. Results. For the study of water flow and changes in average annual concentrations of nutrients in the Seversky Donets River, selected posts are located: on the border with the Russian Federation (Ogurtsovo village); Pechenezh reservoir; Chuguev city; city of Zmiev. To identify cyclical patterns in runoff fluctuations, chronological and smoothed, using a linear filter, time series for the periods 1923-2016 are used. To identify cyclical patterns in runoff fluctuations, chronological and smoothed, using a linear filter, time series for the periods 1923-2016 are used. The frequency of phases of water content is on average 3-5 years. The average perennial phosphate concentrations in posts are in the range of 0.65-1.96 mg/dm3, and the coefficient of variation is 0.2, that is, the variability of phosphates is negligible. The average concentration of nitrites by posts, for the entire observation period, varies in the range of 0.046-0.26 mg/dm3, and the coefficient of variation of nitrites varies in the range of 0.6-0.9, which indicates a significant variability of the indicator over time. The average annual concentrations of nitrates in the posts vary in the range of 0.71–4.96 mg/dm3, and the coefficient of variation of nitrates is 0.9. Conclusions. The average annual concentration of biogenic substances at the indicated positions has no relation to the cyclicity of the water content of the river, except for the Pechenezh reservoir, where the concentration of nitrites and phosphates changes in synchrony with the average annual water consumption.

Analysis Versus Synthesis for Trending of Gas-Path Measurement Time Series

2014 ◽  
Vol 137 (2) ◽  
Author(s):  
S. Borguet ◽  
O. Léonard ◽  
P. Dewallef
Keyword(s):  
Time Series ◽  
Nonlinear Filtering ◽  
Synthetic Data ◽  
Noise Removal ◽  
Health Condition ◽  
Fault Detection And Isolation ◽  
Linear Filter ◽  
Linear Filters ◽  
Low Pass ◽  
The One

Gas-path measurements used to assess the health condition of an engine are corrupted by noise. Generally, a data cleaning step occurs before proceeding with fault detection and isolation. Classical linear filters such as the EWMA filter are traditionally used for noise removal. Unfortunately, these low-pass filters distort trend shifts indicative of faults, which increases the detection delay. The present paper investigates two new approaches to nonlinear filtering of time series. On the one hand, the synthesis approach reconstructs the signal as a combination of elementary signals chosen from a predefined library. On the other hand, the analysis approach imposes a constraint on the shape of the signal (e.g., piecewise constant). Both approaches incorporate prior information about the signal in a different way, but they lead to trend filters that are very capable at noise removal while preserving at the same time sharp edges in the signal. This is highlighted through the comparison with a classical linear filter on a batch of synthetic data representative of typical engine fault profiles.

scholarly journals Prediction of Wind Speed Using Hybrid Techniques

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6284
Author(s):  
Luis Lopez ◽  
Ingrid Oliveros ◽  
Luis Torres ◽  
Lacides Ripoll ◽  
Jose Soto ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Least Squares ◽  
Support Vector ◽  
Single Time ◽  
Hybrid Techniques ◽  
Mode Decomposition ◽  
Vector Machines ◽  
Auto Regressive ◽  
Using Data

This paper presents a methodology to calculate day-ahead wind speed predictions based on historical measurements done by weather stations. The methodology was tested for three locations: Colombia, Ecuador, and Spain. The data is input into the process in two ways: (1) As a single time series containing all measurements, and (2) as twenty-four separate parallel sequences, corresponding to the values of wind speed at each of the 24 h in the day over several months. The methodology relies on the use of three non-parametric techniques: Least-squares support vector machines, empirical mode decomposition, and the wavelet transform. Moreover, the traditional and simple auto-regressive model is applied. The combination of the aforementioned techniques results in nine methods for performing wind prediction. Experiments using a matlab implementation showed that the least-squares support vector machine using data as a single time series outperformed the other combinations, obtaining the least root mean square error (RMSE).

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