Statistical Mechanics and Information-Theoretic Perspectives on Complexity in the Earth System

Peer reviewedPublisher PD

A Nobel Approach for Entropy Reduction of Wireless Sensor Networks (WSN)

In contrast to RF, optical devices are smaller and consume less power; reflection, diffraction, and scattering from aerosols help distribute signal over large areas; and optical wireless provides freedom from interference and eavesdropping within an opaque enclosure. For a densely deployed Wireless Multimedia Sensor Network (WMSN), an entropy-based analytical framework is developed to measure the amount of visual information provided by multiple cameras in the network. The limitations of limited energy, processing power and bandwidth capabilities of sensors networks become critical in the case of event-based sensor networks where multiple collocated nodes are likely to notify the sink about the same event, at almost the same time. Data aggregation is considered to be an effective technique. Selective use of informative sensors reduces the number of sensors needed to obtain information about the target state and therefore prolongs the system lifetime. In this paper the use of entropy in spectrum sensing is also described. This sensing gives knowledge about the usage of spectrum by primary user and based on that a secondary user can utilize the unused spectrum without interfere the primary user

Physics and Applications of Laser Diode Chaos

An overview of chaos in laser diodes is provided which surveys experimental achievements in the area and explains the theory behind the phenomenon. The fundamental physics underpinning this behaviour and also the opportunities for harnessing laser diode chaos for potential applications are discussed. The availability and ease of operation of laser diodes, in a wide range of configurations, make them a convenient test-bed for exploring basic aspects of nonlinear and chaotic dynamics. It also makes them attractive for practical tasks, such as chaos-based secure communications and random number generation. Avenues for future research and development of chaotic laser diodes are also identified.Comment: Published in Nature Photonic

Lossless compression of hyperspectral images

Band ordering and the prediction scheme are the two major aspects of hyperspectral imaging which have been studied to improve the performance of the compression system. In the prediction module, we propose spatio-spectral prediction methods. Two non-linear spectral prediction methods have been proposed in this thesis. NPHI (Non-linear Prediction for Hyperspectral Images) is based on a band look-ahead technique wherein a reference band is included in the prediction of pixels in the current band. The prediction technique estimates the variation between the contexts of the two bands to modify the weights computed in the reference band to predict the pixels in the current band. EPHI (Edge-based Prediction for Hyperspectral Images) is the modified NPHI technique wherein an edge-based analysis is used to classify the pixels into edges and non-edges in order to perform the prediction of the pixel in the current band. Three ordering methods have been proposed in this thesis. The first ordering method computes the local and global features in each band to group the bands. The bands in each group are ordered by estimating the compression ratios achieved between the entire band in the group and then ordering them using Kruskal\u27s algorithm. The other two methods of ordering compute the compression ratios between b-neighbors in performing the band ordering

EEG signals analysis using multiscale entropy for depth of anesthesia monitoring during surgery through artificial neural networks

In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sample entropy (SampEn), a commonly used and important tool to measure the regularity of data series. However, SampEn only estimates the complexity of signals on one time scale. In this study, a new approach is introduced using multiscale entropy (MSE) considering the structure information over different time scales. The entropy values over different time scales calculated through MSE are applied as the input data to train an artificial neural network (ANN) model using bispectral index (BIS) or expert assessment of conscious level (EACL) as the target. To test the performance of the new index's sensitivity to artifacts, we compared the results before and after filtration by multivariate empirical mode decomposition (MEMD). The new approach via ANN is utilized in real EEG signals collected from 26 patients before and after filtering by MEMD, respectively; the results show that is a higher correlation between index from the proposed approach and the gold standard compared with SampEn. Moreover, the proposed approach is more structurally robust to noise and artifacts which indicates that it can be used for monitoring the DOA more accurately.This research was financially supported by the Center for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is sponsored by Ministry of Science and Technology (Grant no. MOST103-2911-I-008-001). Also, it was supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302) and National Natural Science Foundation of China (Grant no. 51475342)

A new method for ecoacoustics? Toward the extraction and evaluation of ecologically-meaningful soundscape components using sparse coding methods

Passive acoustic monitoring is emerging as a promising non-invasive proxy for ecological complexity with potential as a tool for remote assessment and monitoring (Sueur and Farina, 2015). Rather than attempting to recognise species-specific calls, either manually or automatically, there is a growing interest in evaluating the global acoustic environment. Positioned within the conceptual framework of ecoacoustics, a growing number of indices have been proposed which aim to capture community-level dynamics by (e.g. Pieretti et al., 2011; Farina, 2014; Sueur et al., 2008b) by providing statistical summaries of the frequency or time domain signal. Although promising, the ecological relevance and efficacy as a monitoring tool of these indices is still unclear. In this paper we suggest that by virtue of operating in the time or frequency domain, existing indices are limited in their ability to access key structural information in the spectro-temporal domain. Alternative methods in which time-frequency dynamics are preserved are considered. Sparse-coding and source separation algorithms (specifically, shift-invariant probabilistic latent component analysis in 2D) are proposed as a means to access and summarise time- frequency dynamics which may be more ecologically-meaningful

Evaluation of Temporal Damage Progression in Concrete Structures Affected by ASR Using Data-driven Methods

Alkali-silica reaction (ASR) is a chemical reaction, which causes damage in concrete structures such as bridges, dams, and nuclear containments and powerplant structures. The ASR-induced damage may endanger the integrity and serviceability of structures. Several methods such as visual inspection, petrographic analysis, demountable mechanical strain gauges, and cracking index have been utilized for study the effect of ASR on structures, which are not always efficient in early damage detection and some are destructive and prohibited in nuclear structures. Nondestructive methods and structural health monitoring techniques can be alternatives for the condition assessment of structures. Among the nondestructive methods, acoustic emission (AE) is preferable due to high sensitivity of AE sensors, source localization ability, and sensing capability in one-side-access structures. The goal is the condition assessment of structures affected by ASR using AE. Therefore, in the current research, data-driven methods in combination with signal processing techniques are employed to find a potential temporal trend in the AE data and relate the trend to the damage progression caused by ASR. In addition, the effect of stress boundary condition on the ASR-induced damage distribution and its reflection on the AE data is investigated. Damage contours based on AE data are developed and utilized to compare event distributions though the medium-scale specimens with different confinements and investigate the temporal evolution of the distributions. Furthermore, the efficacy of differing information entropy calculation approaches for concrete structures undergoing Alkali-Silica Reaction (ASR) induced damage is investigated. The results of the studies indicate that confinement affects the distribution of AE events. In the confined specimen, the distribution of AE events in the mid-width region of the specimen is concentrated and has a sharp peak. However, in the unconfined specimen, the distribution of AE events is more uniform, and cracks are randomly distributed. The entropy results show that the randomness of events increases at the earlier stage of ASR, which is expected due to the microcrack formation and decreases at the later stage due to the formation of macrocracks. The overall outcome in this dissertation demonstrates the potential of using AE for condition assessment of concrete structures affected by ASR degradation. However, more research is required to standardize the method for the field application

Fault Classification of Nonlinear Small Sample Data through Feature Sub-Space Neighbor Vote

The fault classification of a small sample of high dimension is challenging, especially for a nonlinear and non-Gaussian manufacturing process. In this paper, a similarity-based feature selection and sub-space neighbor vote method is proposed to solve this problem. To capture the dynamics, nonlinearity, and non-Gaussianity in the irregular time series data, high order spectral features, and fractal dimension features are extracted, selected, and stacked in a regular matrix. To address the problem of a small sample, all labeled fault data are used for similarity decisions for a specific fault type. The distances between the new data and all fault types are calculated in their feature subspaces. The new data are classified to the nearest fault type by majority probability voting of the distances. Meanwhile, the selected features, from respective measured variables, indicate the cause of the fault. The proposed method is evaluated on a publicly available benchmark of a real semiconductor etching dataset. It is demonstrated that by using the high order spectral features and fractal dimensionality features, the proposed method can achieve more than 84% fault recognition accuracy. The resulting feature subspace can be used to match any new fault data to the fingerprint feature subspace of each fault type, and hence can pinpoint the root cause of a fault in a manufacturing process