Context-aware Distance Measures for Dynamic Networks

Dynamic networks are widely used in the social, physical, and biological sciences as a concise mathematical representation of the evolving interactions in dynamic complex systems. Measuring distances between network snapshots is important for analyzing and understanding evolution processes of dynamic systems. To the best of our knowledge, however, existing network distance measures are designed for static networks. Therefore, when measuring the distance between any two snapshots in dynamic networks, valuable context structure information existing in other snapshots is ignored. To guide the construction of context-aware distance measures, we propose a context-aware distance paradigm, which introduces context information to enrich the connotation of the general definition of network distance measures. A Context-aware Spectral Distance (CSD) is then given as an instance of the paradigm by constructing a context-aware spectral representation to replace the core component of traditional Spectral Distance (SD). In a node-aligned dynamic network, the context effectively helps CSD gain mainly advantages over SD as follows: (1) CSD is not affected by isospectral problems; (2) CSD satisfies all the requirements of a metric, while SD cannot; and (3) CSD is computationally efficient. In order to process large-scale networks, we develop a kCSD that computes top- k eigenvalues to further reduce the computational complexity of CSD. Although kCSD is a pseudo-metric, it retains most of the advantages of CSD. Experimental results in two practical applications, i.e., event detection and network clustering in dynamic networks, show that our context-aware spectral distance performs better than traditional spectral distance in terms of accuracy, stability, and computational efficiency. In addition, context-aware spectral distance outperforms other baseline methods.

Deep Learning-Based Estimation of Reverberant Environment for Audio Data Augmentation

This paper proposes an audio data augmentation method based on deep learning in order to improve the performance of dereverberation. Conventionally, audio data are augmented using a room impulse response, which is artificially generated by some methods, such as the image method. The proposed method estimates a reverberation environment model based on a deep neural network that is trained by using clean and recorded audio data as inputs and outputs, respectively. Then, a large amount of a real augmented database is constructed by using the trained reverberation model, and the dereverberation model is trained with the augmented database. The performance of the augmentation model was verified by a log spectral distance and mean square error between the real augmented data and the recorded data. In addition, according to dereverberation experiments, the proposed method showed improved performance compared with the conventional method.

Quality Assessment of Heterogeneous Training Data Sets for Classification of Urban Area with Landsat Imagery

Quality assessment of training samples collected from heterogeneous sources has received little attention in the existing literature. Inspired by Euclidean spectral distance metrics, this article derives three quality measures for modeling uncertainty in spectral information of open-source heterogeneous training samples for classification with Landsat imagery. We prepared eight test case data sets from volunteered geographic information and open government data sources to assess the proposed measures. The data sets have significant variations in quality, quantity, and data type. A correlation analysis verifies that the proposed measures can successfully rank the quality of heterogeneous training data sets prior to the image classification task. In this era of big data, pre-classification quality assessment measures empower research scientists to select suitable data sets for classification tasks from available open data sources. Research findings prove the versatility of the Euclidean spectral distance function to develop quality metrics for assessing open-source training data sets with varying characteristics for urban area classification.

An FT-IR based investigation of trehalose mediated thermal stabilisation of Bacillus clausii

Background: Fourier Transform InfraRed spectroscopy analysis on Bacillus clausii and Bacillus clausii in the presence of trehalose are reported. Objective: in order to characterize the thermal response of such systems the InfraRed technique was employed to collect the spectra from 25.0°C to 80.0°C in the 4000 cm-1 - 400 cm-1 spectral range. Methods: The data analysis was performed focusing the attention to the intramolecular OH stretching vibrational region by means evaluating the spectral distance as a function of temperature. Results: From this analysis it emerges that the thermal restraint of Bacillus clausii in the presence of trehalose is higher in respect to Bacillus clausii alone. Conclusion: Such a result, which confirms the bioprotective role of trehalose against external temperature changes, provides useful information for the applications of the disaccharide in food industry.

Thermal Investigations on Carbon Nanotubes by Spectroscopic Techniques

Carbon nanotubes (CNTs) thanks to their unique physical properties have been employed in several innovative applications particularly for energy storage applications. Certain technical features of carbon nanotubes, such as their remarkable specific surface, mechanical strength, as well as their electron and thermal conductivity are suitable for these applications. Furthermore, in order to produce a device, thermal treatment is needed and for this reason the trend of thermal decomposition of the tubes plays a key role in the integration process. The main purpose of this work was to characterize the thermal behavior of CNTs. In particular, we show the findings of an experimental study on CNTs performed by means of Fourier Transform InfraRed and Raman spectroscopy investigations. The collected FTIR and Raman spectra were analyzed by using two innovative procedures: spectral distance (SD) and wavelet cross correlation (XWT). From both analyses, a relaxation temperature value emerged of T = 206 °C, corresponding to a relaxation inflection point. Such a system relaxation phenomenon, occurring in the fiber CNTs, could be connected with the decay of the mechanical properties due to a decrease in the alignment and compaction of the fibers.

Machinery signal separation using non-negative matrix factorization with real mixing

A big challenge in detecting damage occurs when the sound of a machine mixes with the sound of another machine. This paper proposes the separation of mixed acoustic signals using Non-negative Matrix Factorization (NMF) method for fault diagnosis. The NMF method is an effective solution for finding hidden parameters when the number of observations obtained by the sensor is less than the number of sources. The real mixing process is done by placing two microphones in front of the machine. Two microphones will be used as sensors to capture a mixture of four machinery signals. Performance testing of signal separation is done by comparing baseline signals with estimated signals through the mean log spectral distance (LSD) and the mean square error (MSE). The smallest spectral distance between the estimated signal and the baseline signal is found in Ŝ2 with an average LSD of 1.26. The estimated signal Ŝ2 is the closest to the baseline signal with MSE of 1.15 x 10-2. The pattern of bearing damage in the male screw compressor can be identified from the spectrum of estimated signal through harmonic frequencies as in the estimated signal Ŝ3 which is seen at 11x fundamental frequency, 12x fundamental frequency, 15x fundamental frequency, and 16x fundamental frequency.