Examining the Effectiveness of Discriminant Function Analysis and Cluster Analysis in Species Identification of Male Field Crickets Based on Their Calling Songs

Traditional taxonomy based on morphology has often failed in accurate species identification owing to the occurrence of cryptic species, which are reproductively isolated but morphologically identical. Molecular data have thus been used to complement morphology in species identification. The sexual advertisement calls in several groups of acoustically communicating animals are species-specific and can thus complement molecular data as non-invasive tools for identification. Several statistical tools and automated identifier algorithms have been used to investigate the efficiency of acoustic signals in species identification. Despite a plethora of such methods, there is a general lack of knowledge regarding the appropriate usage of these methods in specific taxa. In this study, we investigated the performance of two commonly used statistical methods, discriminant function analysis (DFA) and cluster analysis, in identification and classification based on acoustic signals of field cricket species belonging to the subfamily Gryllinae. Using a comparative approach we evaluated the optimal number of species and calling song characteristics for both the methods that lead to most accurate classification and identification. The accuracy of classification using DFA was high and was not affected by the number of taxa used. However, a constraint in using discriminant function analysis is the need for a priori classification of songs. Accuracy of classification using cluster analysis, which does not require a priori knowledge, was maximum for 6-7 taxa and decreased significantly when more than ten taxa were analysed together. We also investigated the efficacy of two novel derived acoustic features in improving the accuracy of identification. Our results show that DFA is a reliable statistical tool for species identification using acoustic signals. Our results also show that cluster analysis of acoustic signals in crickets works effectively for species classification and identification.

Assimilation of endmember variability in spectral for urban land cover extraction mixture analysis

Variable Endmember Constrained Least Square (VECLS) technique is proposed to account endmember variability in the linear mixture model by incorporating the variance for each class, the signals of which varies from pixel to pixel due to change in urban land cover (LC) structures. VECLS is first tested with a computer simulated three class endmember considering four bands having small, medium and large variability with three different spatial resolutions. The technique is next validated with real datasets of IKONOS, Landsat ETM+ and MODIS. The results show that correlation between actual and estimated proportion is higher by an average of 0.25 for the artificial datasets compared to a situation where variability is not considered. With IKONOS, Landsat ETM+ and MODIS data, the average correlation increased by 0.15 for 2 and 3 classes and by 0.19 for 4 classes, when compared to single endmember per class. (C) 2013 COSPAR. Published by Elsevier Ltd. All rights reserved.

Analysis of the modulation process in advanced bus-clamping PWM techniques

Advanced bus-clamping pulse width modulation (ABCPWM) techniques are advantageous in terms of line current distortion and inverter switching loss in voltage source inverter-fed applications. However, the PWM waveforms corresponding to these techniques are not amenable to carrier-based generation. The modulation process in ABCPWM methods is analyzed here from a “per-phase” perspective. It is shown that three sets of descendant modulating functions (or modified modulating functions) can be generated from the three-phase sinusoidal signals. Each set of the modified modulating functions can be used to produce the PWM waveform of a given phase in a computationally efficient manner. Theoretical results and experimental investigations on a 5hp motor drive are presented

Ontology analysis of global gene expression differences of human bone marrow stromal cells cultured on 3D scaffolds or 2D films

Differences in gene expression of human bone marrow stromal cells (hBMSCs) during culture in three-dimensional (3D) nanofiber scaffolds or on two-dimensional (2D) films were investigated via pathway analysis of microarray mRNA expression profiles. Previous work has shown that hBMSC culture in nanofiber scaffolds can induce osteogenic differentiation in the absence of osteogenic supplements (OS). Analysis using ontology databases revealed that nanofibers and OS regulated similar pathways and that both were enriched for TGF-beta and cell-adhesion/ECM-receptor pathways. The most notable difference between the two was that nanofibers had stronger enrichment for cell-adhesion/ECM-receptor pathways. Comparison of nanofibers scaffolds with flat films yielded stronger differences in gene expression than comparison of nanofibers made from different polymers, suggesting that substrate structure had stronger effects on cell function than substrate polymer composition. These results demonstrate that physical (nanofibers) and biochemical (OS) signals regulate similar ontological pathways, suggesting that these cues use similar molecular mechanisms to control hBMSC differentiation. Published by Elsevier Ltd.

Exact Phase Retrieval for a Class of 2-D Parametric Signals

We address the problem of two-dimensional (2-D) phase retrieval from magnitude of the Fourier spectrum. We consider 2-D signals that are characterized by first-order difference equations, which have a parametric representation in the Fourier domain. We show that, under appropriate stability conditions, such signals can be reconstructed uniquely from the Fourier transform magnitude. We formulate the phase retrieval problem as one of computing the parameters that uniquely determine the signal. We show that the problem can be solved by employing the annihilating filter method, particularly for the case when the parameters are distinct. For the more general case of the repeating parameters, the annihilating filter method is not applicable. We circumvent the problem by employing the algebraically coupled matrix pencil (ACMP) method. In the noiseless measurement setup, exact phase retrieval is possible. We also establish a link between the proposed analysis and 2-D cepstrum. In the noisy case, we derive Cramer-Rao lower bounds (CRLBs) on the estimates of the parameters and present Monte Carlo performance analysis as a function of the noise level. Comparisons with state-of-the-art techniques in terms of signal reconstruction accuracy show that the proposed technique outperforms the Fienup and relaxed averaged alternating reflections (RAAR) algorithms in the presence of noise.

Study of wrist pulse signals using time domain spatial features

Blood travels throughout the body and thus its flow is modulated by changes in body condition. As a consequence, the wrist pulse signal contains important information about the status of the human body. In this work we have employed signal processing techniques to extract important information from these signals. Radial artery pulse pressure signals are acquired at wrist position noninvasively for several subjects for two cases of interest, viz. before and after exercise, and before and after lunch. Further analysis is performed by fitting a bi-modal Gaussian model to the data and extracting spatial features from the fit. The spatial features show statistically significant (p < 0.001) changes between the groups for both the cases, which indicates that they are effective in distinguishing the changes taking place due to exercise or food intake. Recursive cluster elimination based support vector machine classifier is used to classify between the groups. A high classification accuracy of 99.71% is achieved for the exercise case and 99.94% is achieved for the lunch case. This paper demonstrates the utility of certain spatial features in studying wrist pulse signals obtained under various experimental conditions. The ability of the spatial features in distinguishing changing body conditions can be potentially used for various healthcare applications. (C) 2015 Elsevier Ltd. All rights reserved.

Analysis of sine-triangle and zero-sequence injection modulation schemes for split-phase induction motor drive

A split-phase induction motor is fed from two three-phase voltage source inverters for speed control. This study analyses carrier-comparison based pulse width modulation (PWM) schemes for a split-phase motor drive, from a space-vector perspective. Sine-triangle PWM, one zero-sequence injection PWM where the same zero-sequence signal is used for both the inverters, and another zero-sequence injection PWM where different zero-sequence signals are employed for the two inverters are considered. The set of voltage vectors applied, the sequence in which the voltage vectors are applied, and the resulting current ripple vector are analysed for all the PWM methods. Besides all the PWM methods are compared in terms of dc bus utilisation. For the same three-phase sine reference, the PWM method with different zero-sequence signals for the two inverters is found to employ a set of vectors different from the other methods. Both analysis and experimental results show that this method results in lower total harmonic distortion and higher dc bus utilisation than the other two PWM methods.