Dynamic mode decomposition: A feature extraction technique based hidden Markov model for detection of Mysticetes' vocalisations

2021 ◽  
pp. 101306
Author(s):  
O.O. Ogundile ◽  
A.M. Usman ◽  
O.P. Babalola ◽  
D.J.J. Versfeld
Keyword(s):  
Feature Extraction ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Dynamic Mode Decomposition ◽  
Extraction Technique ◽  
Dynamic Mode ◽  
Mode Decomposition ◽  
Feature Extraction Technique

scholarly journals WiFi Fingerprinting Indoor Localization Based on Dynamic Mode Decomposition Feature Selection with Hidden Markov Model

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6778
Author(s):  
Oluwaseyi Paul Babalola ◽  
Vipin Balyan
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Hidden Markov ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Feature Extraction Method ◽  
Mode Decomposition ◽  
Localization Performance

Over the years, WiFi received signal strength indicator (RSSI) measurements have been widely implemented for determining the location of a user’s position in an indoor environment, where the GPS signal might not be received. This method utilizes a huge RSSI dataset collected from numerous access points (APs). The WiFi RSSI measurements are nonlinear with distance and are largely influenced by interference in the indoor environment. Therefore, machine learning (ML) techniques such as a hidden Markov model (HMM) are generally utilized to efficiently identify a trend of RSSI values, which corresponds to locations around a region of interest. Similar to other ML tools, the performance and computing cost of the HMM are dependent on the feature dimension since a large quantity of RSSI measurements are required for the learning process. Hence, this article introduces a feature extraction method based on dynamic mode decomposition (DMD) for the HMM to effectively model WiFi fingerprint indoor localization. The DMD is adopted since it decomposes RSSIs to meaningful spatial and temporal forms over a given time. Here, the mode forms are analytically reconstructed to produce low-dimensional feature vectors, which are used with the HMM. The localization performance of the proposed HMM-DMD is compared with other well-known ML algorithms for WiFi fingerprinting localization using simulations. The results show that the HMM-DMD algorithm yields a significant localization performance improvement, accuracy, and reasonable processing time in comparison with the state-of-the-art algorithms.

scholarly journals A Novel Linear Spectrum Frequency Feature Extraction Technique for Warship Radio Noise Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Duffing Chaotic Oscillator, and Weighted-Permutation Entropy

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 507 ◽  
Author(s):  
Yuxing Li ◽  
Long Wang ◽  
Xueping Li ◽  
Xiaohui Yang
Keyword(s):  
Feature Extraction ◽  
Empirical Mode Decomposition ◽  
Low Frequency ◽  
Extraction Technique ◽  
Permutation Entropy ◽  
Radio Noise ◽  
Mode Decomposition ◽  
Linear Spectrum ◽  
Frequency Feature ◽  
Feature Extraction Technique

Warships play an important role in the modern sea battlefield. Research on the line spectrum features of warship radio noise signals is helpful to realize the classification and recognition of different types of warships, and provides critical information for sea battlefield. In this paper, we proposed a novel linear spectrum frequency feature extraction technique for warship radio noise based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), duffing chaotic oscillator (DCO), and weighted-permutation entropy (W-PE). The proposed linear spectrum frequency feature extraction technique, named CEEMDAN-DCO-W-PE has the following advantages in comparison with other linear spectrum frequency feature extraction techniques; (i) as an adaptive data-driven algorithm, CEEMDAN has more accurate and more reliable decomposition performance than empirical mode decomposition (EMD) and ensemble EMD (EEMD), and there is no need for presetting parameters, such as decomposition level and basis function; (ii) DCO can detect the linear spectrum of narrow band periodical warship signals by way of utilizing its properties of sensitivity for weak periodical signals and the immunity for noise; and (iii) W-PE is used in underwater acoustic signal feature extraction for the first time, and compared with traditional permutation entropy (PE), W-PE increases amplitude information to some extent. Firstly, warship radio noise signals are decomposed into some intrinsic mode functions (IMFs) from high frequency to low frequency by CEEMDAN. Then, DCO is used to detect linear spectrum of low-frequency IMFs. Finally, we can determine the linear spectrum frequency of low-frequency IMFs using W-PE. The experimental results show that the proposed technique can accurately extract the line spectrum frequency of the simulation signals, and has a higher classification and recognition rate than the traditional techniques for real warship radio noise signals.

scholarly journals Intelligent health monitoring system based on smart clothing

2018 ◽  
Vol 14 (8) ◽  
pp. 155014771879431 ◽  
Author(s):  
Chung-Chih Lin ◽  
Chih-Yu Yang ◽  
Zhuhuang Zhou ◽  
Shuicai Wu
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Empirical Mode Decomposition ◽  
Monitoring System ◽  
Health Monitoring ◽  
Hidden Markov ◽  
Fall Detection ◽  
Health Monitoring System ◽  
Smart Clothing ◽  
Mode Decomposition

In this study, we proposed an intelligent health monitoring system based on smart clothing. The system consisted of smart clothing and sensing component, care institution control platform, and mobile device. The smart clothing is a wearable device for electrocardiography signal collection and heart rate monitoring. The system integrated our proposed fast empirical mode decomposition algorithm for electrocardiography denoising and hidden Markov model–based algorithm for fall detection. Eight kinds of services were provided by the system, including surveillance of signs of life, tracking of physiological functions, monitoring of the activity field, anti-lost, fall detection, emergency call for help, device wearing detection, and device low battery warning. The performance of fast empirical mode decomposition and hidden Markov model were evaluated by experiment I (fast empirical mode decomposition evaluation) and experiment II (fall detection), respectively. The accuracy and sensitivity of R-peak detection using fast empirical mode decomposition were 96.46% and 98.75%, respectively. The accuracy, sensitivity, and specificity of fall detection using hidden Markov model were 97.92%, 90.00%, and 99.50%, respectively. The system was evaluated in an elderly long-term care institution in Taiwan. The results of the satisfaction survey showed that both the caregivers and the elders are willing to use the proposed intelligent health monitoring system. The proposed system may be used for long-term health monitoring.

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