scholarly journals An Evaluation of Entropy Measures for Microphone Identification

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1235
Author(s):  
Gianmarco Baldini ◽  
Irene Amerini
Keyword(s):  
A Priori ◽  
Approximate Entropy ◽  
Identification Accuracy ◽  
Permutation Entropy ◽  
Features Selection ◽  
Statistical Features ◽  
Audio Signals ◽  
Trade Off ◽  
Entropy Measures ◽  
Physical Features

Research findings have shown that microphones can be uniquely identified by audio recordings since physical features of the microphone components leave repeatable and distinguishable traces on the audio stream. This property can be exploited in security applications to perform the identification of a mobile phone through the built-in microphone. The problem is to determine an accurate but also efficient representation of the physical characteristics, which is not known a priori. Usually there is a trade-off between the identification accuracy and the time requested to perform the classification. Various approaches have been used in literature to deal with it, ranging from the application of handcrafted statistical features to the recent application of deep learning techniques. This paper evaluates the application of different entropy measures (Shannon Entropy, Permutation Entropy, Dispersion Entropy, Approximate Entropy, Sample Entropy, and Fuzzy Entropy) and their suitability for microphone classification. The analysis is validated against an experimental dataset of built-in microphones of 34 mobile phones, stimulated by three different audio signals. The findings show that selected entropy measures can provide a very high identification accuracy in comparison to other statistical features and that they can be robust against the presence of noise. This paper performs an extensive analysis based on filter features selection methods to identify the most discriminating entropy measures and the related hyper-parameters (e.g., embedding dimension). Results on the trade-off between accuracy and classification time are also presented.

scholarly journals A review of entropy measures for uncertainty quantification of stochastic processes

2019 ◽  
Vol 11 (6) ◽  
pp. 168781401985735 ◽  
Author(s):  
Alireza Namdari ◽  
Zhaojun (Steven) Li
Keyword(s):  
Stochastic Process ◽  
Stochastic Processes ◽  
Uncertainty Quantification ◽  
Lithium Ion ◽  
Second Law Of Thermodynamics ◽  
Approximate Entropy ◽  
Sample Entropy ◽  
Permutation Entropy ◽  
Battery Capacity ◽  
Entropy Measures

Entropy is originally introduced to explain the inclination of intensity of heat, pressure, and density to gradually disappear over time. Based on the concept of entropy, the Second Law of Thermodynamics, which states that the entropy of an isolated system is likely to increase until it attains its equilibrium state, is developed. More recently, the implication of entropy has been extended beyond the field of thermodynamics, and entropy has been applied in many subjects with probabilistic nature. The concept of entropy is applicable and useful in characterizing the behavior of stochastic processes since it represents the uncertainty, ambiguity, and disorder of the processes without being restricted to the forms of the theoretical probability distributions. In order to measure and quantify the entropy, the existing probability of every event in the stochastic process must be determined. Different entropy measures have been studied and presented including Shannon entropy, Renyi entropy, Tsallis entropy, Sample entropy, Permutation entropy, Approximate entropy, and Transfer entropy. This review surveys the general formulations of the uncertainty quantification based on entropy as well as their various applications. The results of the existing studies show that entropy measures are powerful predictors for stochastic processes with uncertainties. In addition, we examine the stochastic process of lithium-ion battery capacity data and attempt to determine the relation between the changes in battery capacity over different cycles and two entropy measures: Sample entropy and Approximate entropy.

scholarly journals Entropy Measures as Descriptors to Identify Apneas in Rheoencephalographic Signals

Entropy ◽  
2019 ◽  
Vol 21 (6) ◽  
pp. 605
Author(s):  
Carmen González ◽  
Erik Jensen ◽  
Pedro Gambús ◽  
Montserrat Vallverdú
Keyword(s):  
Healthy Subjects ◽  
A Priori ◽  
Conditional Entropy ◽  
Approximate Entropy ◽  
Sample Entropy ◽  
P Value ◽  
Embedding Dimension ◽  
P Values ◽  
Entropy Measures ◽  
Inexpensive Technique

Rheoencephalography (REG) is a simple and inexpensive technique that intends to monitor cerebral blood flow (CBF), but its ability to reflect CBF changes has not been extensively proved. Based on the hypothesis that alterations in CBF during apnea should be reflected in REG signals under the form of increased complexity, several entropy metrics were assessed for REG analysis during apnea and resting periods in 16 healthy subjects: approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), corrected conditional entropy (CCE) and Shannon entropy (SE). To compute these entropy metrics, a set of parameters must be defined a priori, such as, for example, the embedding dimension m, and the tolerance threshold r. A thorough analysis of the effects of parameter selection in the entropy metrics was performed, looking for the values optimizing differences between apnea and baseline signals. All entropy metrics, except SE, provided higher values for apnea periods (p-values < 0.025). FuzzyEn outperformed all other metrics, providing the lowest p-value (p = 0.0001), allowing to conclude that REG signals during apnea have higher complexity than in resting periods. Those findings suggest that REG signals reflect CBF changes provoked by apneas, even though further studies are needed to confirm this hypothesis.

scholarly journals Employing entropy measures to identify visitors in multi-occupancy environments

2020 ◽  
Author(s):  
Aadel Howedi ◽  
Ahmad Lotfi ◽  
Amir Pourabdollah
Keyword(s):  
Home Environment ◽  
Daily Living ◽  
Wearable Sensors ◽  
Permutation Entropy ◽  
Care Workers ◽  
Entropy Measures ◽  
Novel Method ◽  
High Degree ◽  
Motion Detectors ◽  
To Receive

AbstractHuman activity recognition (HAR) is used to support older adults to live independently in their own homes. Once activities of daily living (ADL) are recognised, gathered information will be used to identify abnormalities in comparison with the routine activities. Ambient sensors, including occupancy sensors and door entry sensors, are often used to monitor and identify different activities. Most of the current research in HAR focuses on a single-occupant environment when only one person is monitored, and their activities are categorised. The assumption that home environments are occupied by one person all the time is often not true. It is common for a resident to receive visits from family members or health care workers, representing a multi-occupancy environment. Entropy analysis is an established method for irregularity detection in many applications; however, it has been rarely applied in the context of ADL and HAR. In this paper, a novel method based on different entropy measures, including Shannon Entropy, Permutation Entropy, and Multiscale-Permutation Entropy, is employed to investigate the effectiveness of these entropy measures in identifying visitors in a home environment. This research aims to investigate whether entropy measures can be utilised to identify a visitor in a home environment, solely based on the information collected from motion detectors [e.g., passive infra-red] and door entry sensors. The entropy measures are tested and evaluated based on a dataset gathered from a real home environment. Experimental results are presented to show the effectiveness of entropy measures to identify visitors and the time of their visits without the need for employing extra wearable sensors to tag the visitors. The results obtained from the experiments show that the proposed entropy measures could be used to detect and identify a visitor in a home environment with a high degree of accuracy.

scholarly journals Analyzing EEG of Quasi-Brain-Death Based on Dynamic Sample Entropy Measures

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Li Ni ◽  
Jianting Cao ◽  
Rubin Wang
Keyword(s):  
Brain Death ◽  
Real Time ◽  
Time Window ◽  
Approximate Entropy ◽  
Sample Entropy ◽  
Well Performance ◽  
Entropy Measures ◽  
Brain Death Determination ◽  
Death Determination ◽  
The Brain

To give a more definite criterion using electroencephalograph (EEG) approach on brain death determination is vital for both reducing the risks and preventing medical misdiagnosis. This paper presents several novel adaptive computable entropy methods based on approximate entropy (ApEn) and sample entropy (SampEn) to monitor the varying symptoms of patients and to determine the brain death. The proposed method is a dynamic extension of the standard ApEn and SampEn by introducing a shifted time window. The main advantages of the developed dynamic approximate entropy (DApEn) and dynamic sample entropy (DSampEn) are for real-time computation and practical use. Results from the analysis of 35 patients (63 recordings) show that the proposed methods can illustrate effectiveness and well performance in evaluating the brain consciousness states.

Comparison of Blind Diagnostic Indicators for Condition Monitoring of Wind Turbine Gearbox Bearings

2021 ◽  
Author(s):  
Junyu Qi ◽  
Alexandre Mauricio ◽  
Konstantinos Gryllias
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Wind Turbines ◽  
Fossil Fuels ◽  
Conditional Entropy ◽  
Approximate Entropy ◽  
Permutation Entropy ◽  
Wind Turbine Gearbox ◽  
Data Set ◽  
Wind Turbine Gearbox Bearings

Abstract Under the pressure of climate change, renewable energy gradually replaces fossil fuels and plays nowadays a significant role in energy production. The O&M costs of wind turbines may easily reach up to 25% of the total leverised cost per kWh produced over the lifetime of the turbine for a new unit. Manufacturers and operators try to reduce O&M by developing new turbine designs and by adopting condition monitoring approaches. One of the most critical assembly of wind turbines is the gearbox. Gearboxes are designed to last till the end of asset's lifetime, according to the IEC 61400-4 standards but a recent study indicated that gearboxes might have to be replaced as early as 6.5 years. A plethora of sensor types and signal processing methodologies have been proposed in order to accurately detect and diagnose the presence of a fault but often the gearbox is equipped with a limited number of sensors and a simple global diagnostic indicator is demanded, being capable to detect globally various faults of different components. The scope of this paper is the application and comparison of a number of blind global diagnostic indicators which are based on Entropy, on Negentropy, on Sparsity and on Statistics. The performance of the indicators is evaluated on a wind turbine data set with two different bearing faults. Among the different diagnostic indicators Permutation entropy, Approximate entropy, Samples entropy, Fuzzy entropy, Conditional entropy and Wiener entropy achieve the best results detecting blindly the two failure events.

scholarly journals Adaptive Multiscale Symbolic-Dynamics Entropy for Condition Monitoring of Rotating Machinery

Entropy ◽  
2019 ◽  
Vol 21 (12) ◽  
pp. 1138
Author(s):  
Chunhong Dou ◽  
Jinshan Lin
Keyword(s):  
Symbolic Dynamics ◽  
Spatial Scales ◽  
Approximate Entropy ◽  
Rotating Machinery ◽  
Temporal Scale ◽  
Permutation Entropy ◽  
Temporal Scales ◽  
Spatial And Temporal Scales ◽  
Vibration Data ◽  
Temporal Statistics

Vibration data from rotating machinery working in different conditions display different properties in spatial and temporal scales. As a result, insights into spatial- and temporal-scale structures of vibration data of rotating machinery are fundamental for describing running conditions of rotating machinery. However, common temporal statistics and typical nonlinear measures have difficulties in describing spatial and temporal scales of data. Recently, statistical linguistic analysis (SLA) has been pioneered in analyzing complex vibration data from rotating machinery. Nonetheless, SLA can examine data in spatial scales but not in temporal scales. To improve SLA, this paper develops symbolic-dynamics entropy for quantifying word-frequency series obtained by SLA. By introducing multiscale analysis to SLA, this paper proposes adaptive multiscale symbolic-dynamics entropy (AMSDE). By AMSDE, spatial and temporal properties of data can be characterized by a set of symbolic-dynamics entropy, each of which corresponds to a specific temporal scale. Afterward, AMSDE is employed to deal with vibration data from defective gears and rolling bearings. Moreover, the performance of AMSDE is benchmarked against five common temporal statistics (mean, standard deviation, root mean square, skewness and kurtosis) and three typical nonlinear measures (approximate entropy, sample entropy and permutation entropy). The results suggest that AMSDE performs better than these benchmark methods in characterizing running conditions of rotating machinery.

scholarly journals Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins

2019 ◽  
Vol 31 (8) ◽  
pp. 1671-1717 ◽  
Author(s):  
Jérôme Tubiana ◽  
Simona Cocco ◽  
Rémi Monasson
Keyword(s):  
Graphical Model ◽  
A Priori ◽  
Protein Sequences ◽  
Ground Truth ◽  
Representation Learning ◽  
Statistical Features ◽  
Restricted Boltzmann Machines ◽  
Interacting Systems ◽  
Hidden Layer ◽  
Stochastic Mapping

A restricted Boltzmann machine (RBM) is an unsupervised machine learning bipartite graphical model that jointly learns a probability distribution over data and extracts their relevant statistical features. RBMs were recently proposed for characterizing the patterns of coevolution between amino acids in protein sequences and for designing new sequences. Here, we study how the nature of the features learned by RBM changes with its defining parameters, such as the dimensionality of the representations (size of the hidden layer) and the sparsity of the features. We show that for adequate values of these parameters, RBMs operate in a so-called compositional phase in which visible configurations sampled from the RBM are obtained by recombining these features. We then compare the performance of RBM with other standard representation learning algorithms, including principal or independent component analysis (PCA, ICA), autoencoders (AE), variational autoencoders (VAE), and their sparse variants. We show that RBMs, due to the stochastic mapping between data configurations and representations, better capture the underlying interactions in the system and are significantly more robust with respect to sample size than deterministic methods such as PCA or ICA. In addition, this stochastic mapping is not prescribed a priori as in VAE, but learned from data, which allows RBMs to show good performance even with shallow architectures. All numerical results are illustrated on synthetic lattice protein data that share similar statistical features with real protein sequences and for which ground-truth interactions are known.

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