Change Point Detection Techniques on Seismicity Models

2021 ◽  
pp. 271-298
Author(s):  
Rodi Lykou ◽  
George Tsaklidis
Keyword(s):  
Change Point ◽  
Change Point Detection ◽  
Detection Techniques ◽  
Seismicity Models ◽  
Point Detection

scholarly journals Easing Power Consumption of Wearable Activity Monitoring with Change Point Detection

Sensors ◽  
2020 ◽  
Vol 20 (1) ◽  
pp. 310 ◽  
Author(s):  
Cristian Culman ◽  
Samaneh Aminikhanghahi ◽  
Diane J. Cook
Keyword(s):  
Change Point ◽  
Recognition Performance ◽  
Sampling Rate ◽  
Activity Monitoring ◽  
Change Point Detection ◽  
Continuous Sampling ◽  
Detection Techniques ◽  
Transition Times ◽  
Point Detection ◽  
Complex Activities

Continuous monitoring of complex activities is valuable for understanding human behavior and providing activity-aware services. At the same time, recognizing these activities requires both movement and location information that can quickly drain batteries on wearable devices. In this paper, we introduce Change Point-based Activity Monitoring (CPAM), an energy-efficient strategy for recognizing and monitoring a range of simple and complex activities in real time. CPAM employs unsupervised change point detection to detect likely activity transition times. By adapting the sampling rate at each change point, CPAM reduces energy consumption by 74.64% while retaining the activity recognition performance of continuous sampling. We validate our approach using smartwatch data collected and labeled by 66 subjects. Results indicate that change point detection techniques can be effective for reducing the energy footprint of sensor-based mobile applications and that automated activity labels can be used to estimate sensor values between sampling periods.

scholarly journals Change Point Determination in Audio Data Using Auditory Features

2015 ◽  
Vol 61 (2) ◽  
pp. 185-190 ◽  
Author(s):  
Tomasz Maka
Keyword(s):  
Change Point ◽  
Feature Space ◽  
Change Point Detection ◽  
Detection Scheme ◽  
Detection Techniques ◽  
Detection Process ◽  
Feature Spaces ◽  
Audio Data ◽  
A New Technique ◽  
Point Detection

Abstract The study is aimed to investigate the properties of auditory-based features for audio change point detection process. In the performed analysis, two popular techniques have been used: a metric-based approach and the ΔBIC scheme. The efficiency of the change point detection process depends on the type and size of the feature space. Therefore, we have compared two auditory-based feature sets (MFCC and GTEAD) in both change point detection schemes. We have proposed a new technique based on multiscale analysis to determine the content change in the audio data. The comparison of the two typical change point detection techniques with two different feature spaces has been performed on the set of acoustical scenes with single change point. As the results show, the accuracy of the detected positions depends on the feature type, feature space dimensionality, detection technique and the type of audio data. In case of the ΔBIC approach, the better accuracy has been obtained for MFCC feature space in the most cases. However, the change point detection with this feature results in a lower detection ratio in comparison to the GTEAD feature. Using the same criteria as for ΔBIC, the proposed multiscale metric-based technique has been executed. In such case, the use of the GTEAD feature space has led to better accuracy. We have shown that the proposed multiscale change point detection scheme is competitive to the ΔBIC scheme with the MFCC feature space.

scholarly journals The Role of Mobility and Sanitary Measures on the Delay of Community Transmission of COVID-19 in Costa Rica

Epidemiologia ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 294-304
Author(s):  
Luis A. Barboza ◽  
Paola Vásquez ◽  
Gustavo Mery ◽  
Fabio Sanchez ◽  
Yury E. García ◽  
...  
Keyword(s):  
Costa Rica ◽  
Change Point ◽  
Human Mobility ◽  
Change Point Detection ◽  
Population Mobility ◽  
Detection Techniques ◽  
Community Transmission ◽  
Parametric Change ◽  
Point Detection

The aim of this paper is to infer the effects that change on human mobility had on the transmission dynamics during the first four months of the SARS-CoV-2 pandemic in Costa Rica, which could have played a role in delaying community transmission in the country. First, by using parametric and non-parametric change-point detection techniques, we were able to identify two different periods when the trend of daily new cases significantly changed. Second, we explored the association of these changes with data on population mobility. This also allowed us to estimate the lag between changes in human mobility and rates of daily new cases. The information was then used to establish an association between changes in population mobility and the sanitary measures adopted during the study period. Results showed that during the initial two months of the pandemic in Costa Rica, the implementation of sanitary measures and their impact on reducing human mobility translated to a mean reduction of 54% in the number of daily cases from the projected number, delaying community transmission.

Understanding the CoVID-19 pandemic Curve through statistical approach (Preprint)

2020 ◽  
Author(s):  
Ibrar Ul Hassan Akhtar
Keyword(s):  
Probability Density ◽  
Change Point ◽  
Statistical Approach ◽  
Sudden Change ◽  
Global Scale ◽  
Change Point Detection ◽  
Segment Length ◽  
Infected Population ◽  
The Mean ◽  
Point Detection

UNSTRUCTURED Current research is an attempt to understand the CoVID-19 pandemic curve through statistical approach of probability density function with associated skewness and kurtosis measures, change point detection and polynomial fitting to estimate infected population along with 30 days projection. The pandemic curve has been explored for above average affected countries, six regions and global scale during 64 days of 22nd January to 24th March, 2020. The global cases infection as well as recovery rate curves remained in the ranged of 0 ‒ 9.89 and 0 ‒ 8.89%, respectively. The confirmed cases probability density curve is high positive skewed and leptokurtic with mean global infected daily population of 6620. The recovered cases showed bimodal positive skewed curve of leptokurtic type with daily recovery of 1708. The change point detection helped to understand the CoVID-19 curve in term of sudden change in term of mean or mean with variance. This pointed out disease curve is consist of three phases and last segment that varies in term of day lengths. The mean with variance based change detection is better in differentiating phases and associated segment length as compared to mean. Global infected population might rise in the range of 0.750 to 4.680 million by 24th April 2020, depending upon the pandemic curve progress beyond 24th March, 2020. Expected most affected countries will be USA, Italy, China, Spain, Germany, France, Switzerland, Iran and UK with at least infected population of over 0.100 million. Infected population polynomial projection errors remained in the range of -78.8 to 49.0%.

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