Nonlinear dynamic state estimation in instrumented structures with conditionally linear Gaussian substructures

Many problems of state estimation in structural dynamics permit a partitioning of system states into nonlinear and conditionally linear substructures. This enables a part of the problem to be solved exactly, using the Kalman filter, and the remainder using Monte Carlo simulations. The present study develops an algorithm that combines sequential importance sampling based particle filtering with Kalman filtering to a fairly general form of process equations and demonstrates the application of a substructuring scheme to problems of hidden state estimation in structures with local nonlinearities, response sensitivity model updating in nonlinear systems, and characterization of residual displacements in instrumented inelastic structures. The paper also theoretically demonstrates that the sampling variance associated with the substructuring scheme used does not exceed the sampling variance corresponding to the Monte Carlo filtering without substructuring. (C) 2012 Elsevier Ltd. All rights reserved.

Mining sentiments from songs using latent dirichlet allocation

Song-selection and mood are interdependent. If we capture a song’s sentiment, we can determine the mood of the listener, which can serve as a basis for recommendation systems. Songs are generally classified according to genres, which don’t entirely reflect sentiments. Thus, we require an unsupervised scheme to mine them. Sentiments are classified into either two (positive/negative) or multiple (happy/angry/sad/...) classes, depending on the application. We are interested in analyzing the feelings invoked by a song, involving multi-class sentiments. To mine the hidden sentimental structure behind a song, in terms of “topics”, we consider its lyrics and use Latent Dirichlet Allocation (LDA). Each song is a mixture of moods. Topics mined by LDA can represent moods. Thus we get a scheme of collecting similar-mood songs. For validation, we use a dataset of songs containing 6 moods annotated by users of a particular website.

Fully complex-valued ELM classifiers for human action recognition

In this paper, we present a fast learning neural network classifier for human action recognition. The proposed classifier is a fully complex-valued neural network with a single hidden layer. The neurons in the hidden layer employ the fully complex-valued hyperbolic secant as an activation function. The parameters of the hidden layer are chosen randomly and the output weights are estimated analytically as a minimum norm least square solution to a set of linear equations. The fast leaning fully complex-valued neural classifier is used for recognizing human actions accurately. Optical flow-based features extracted from the video sequences are utilized to recognize 10 different human actions. The feature vectors are computationally simple first order statistics of the optical flow vectors, obtained from coarse to fine rectangular patches centered around the object. The results indicate the superior performance of the complex-valued neural classifier for action recognition. The superior performance of the complex neural network for action recognition stems from the fact that motion, by nature, consists of two components, one along each of the axes.

A new species of Duttaphrynus (Anura : Bufonidae) from Northeast India

A new species of montane toad Duttaphrynus is described from Nagaland state of Northeast India. The new species is diagnosable based on following combination of characters: absence of preorbital, postorbital and orbitotympanic ridges, elongated and broad parotid gland, first finger longer than second and presence of a mid-dorsal line. The tympanum is hidden under a skin fold (in male) or absent (in female). The species is compared with its congers from India and Indo-China. We propose to consider Duttaphrynus wokhaensis as junior synonym of Duttaphrynus melanostictus.

Memory effect in SrRu(1-x)O3 (0.01 < x < 0.07)

SrRuO3 is widely known to be an itinerant ferromagnet with a T-C similar to 160 K. It is well known that glassy materials exhibit time dependent phenomena such as memory effect due to their generic slow dynamics. However, for the first time, we have observed memory effect in SrRu(1-x)O3 (0.01hidden magnetic interaction responsible for bringing glassiness in a ferromagnet.

Improved LSB stegananalysis based on analysis of adjacent pixel pairs

We propose a simple, reliable method based on probability of transitions and distribution of adjacent pixel pairs for steganalysis on digital images in spatial domain subjected to Least Significant Bit replacement steganography. Our method is sensitive to the statistics of underlying cover image and is a variant of Sample Pair Method. We use the new method to estimate length of hidden message reliably. The novelty of our method is that it detects from the statistics of the underlying image, which is invariant with embedding, whether the results it calculate are reliable or not. To our knowledge, no steganalytic method so far predicts from the properties of the stego image, whether its results are accurate or not.

Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network

In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBLMcRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multimodal Human Action Database (MHAD). (C) 2013 Elsevier Ltd. All rights reserved.

Crevasses detection in Himalayan glaciers using ground-penetrating radar

Identification and mapping of crevasses in glaciated regions is important for safe movement. However, the remote and rugged glacial terrain in the Himalaya poses greater challenges for field data collection. In the present study crevasse signatures were collected from Siachen and Samudra Tapu glaciers in the Indian Himalaya using ground-penetrating radar (GPR). The surveys were conducted using the antennas of 250 MHz frequency in ground mode and 350 MHz in airborne mode. The identified signatures of open and hidden crevasses in GPR profiles collected in ground mode were validated by ground truthing. The crevasse zones and buried boulder areas in a glacier were identified using a combination of airborne GPR profiles and SAR data, and the same have been validated with the high-resolution optical satellite imagery (Cartosat-1) and Survey of India mapsheet. Using multi-sensor data, a crevasse map for Samudra Tapu glacier was prepared. The present methodology can also be used for mapping the crevasse zones in other glaciers in the Himalaya.

Performance analysis of beacon-less IEEE 802.15.4 multi-hop networks

We develop an approximate analytical technique for evaluating the performance of multi-hop networks based on beacon-less CSMA/CA as standardised in IEEE 802.15.4, a popular standard for wireless sensor networks. The network comprises sensor nodes, which generate measurement packets, and relay nodes which only forward packets. We consider a detailed stochastic process at each node, and analyse this process taking into account the interaction with neighbouring nodes via certain unknown variables (e.g., channel sensing rates, collision probabilities, etc.). By coupling these analyses of the various nodes, we obtain fixed point equations that can be solved numerically to obtain the unknown variables, thereby yielding approximations of time average performance measures, such as packet discard probabilities and average queueing delays. Different analyses arise for networks with no hidden nodes and networks with hidden nodes. We apply this approach to the performance analysis of tree networks rooted at a data sink. Finally, we provide a validation of our analysis technique against simulations.

RES-TOCSY: A facile approach for accurate determination of magnitudes, and relative signs of (n)J(HF)

The RES-TOCSY experiment for accurate determination of heteronuclear (n)J(HF) is reported. The main feature of the proposed technique is the accurate measurement of magnitudes of heteronuclear couplings from the displacement of cross sections of the 2D spectrum and their relative signs from the slopes of their displacement vectors. The experiment is highly advantageous as the couplings of smaller magnitudes hidden within line widths could also be accurately determined, and also in situations when the spectrum does not display any coupling fine structures. The efficient utility of the developed pulse sequence is unambiguously established on fluorine containing aromatic and aliphatic molecules. (C) 2014 Elsevier B.V. All rights reserved.

Surrogate Regret Bounds for Bipartite Ranking via Strongly Proper Losses

The problem of bipartite ranking, where instances are labeled positive or negative and the goal is to learn a scoring function that minimizes the probability of mis-ranking a pair of positive and negative instances (or equivalently, that maximizes the area under the ROC curve), has been widely studied in recent years. A dominant theoretical and algorithmic framework for the problem has been to reduce bipartite ranking to pairwise classification; in particular, it is well known that the bipartite ranking regret can be formulated as a pairwise classification regret, which in turn can be upper bounded using usual regret bounds for classification problems. Recently, Kotlowski et al. (2011) showed regret bounds for bipartite ranking in terms of the regret associated with balanced versions of the standard (non-pairwise) logistic and exponential losses. In this paper, we show that such (non-pairwise) surrogate regret bounds for bipartite ranking can be obtained in terms of a broad class of proper (composite) losses that we term as strongly proper. Our proof technique is much simpler than that of Kotlowski et al. (2011), and relies on properties of proper (composite) losses as elucidated recently by Reid and Williamson (2010, 2011) and others. Our result yields explicit surrogate bounds (with no hidden balancing terms) in terms of a variety of strongly proper losses, including for example logistic, exponential, squared and squared hinge losses as special cases. An important consequence is that standard algorithms minimizing a (non-pairwise) strongly proper loss, such as logistic regression and boosting algorithms (assuming a universal function class and appropriate regularization), are in fact consistent for bipartite ranking; moreover, our results allow us to quantify the bipartite ranking regret in terms of the corresponding surrogate regret. We also obtain tighter surrogate bounds under certain low-noise conditions via a recent result of Clemencon and Robbiano (2011).

Estimating the volume of glaciers in the Himalayan-Karakoram region using different methods

Ice volume estimates are crucial for assessing water reserves stored in glaciers. Due to its large glacier coverage, such estimates are of particular interest for the Himalayan-Karakoram (HK) region. In this study, different existing methodologies are used to estimate the ice reserves: three area-volume relations, one slope-dependent volume estimation method, and two ice-thickness distribution models are applied to a recent, detailed, and complete glacier inventory of the HK region, spanning over the period 2000-2010 and revealing an ice coverage of 40 775 km(2). An uncertainty and sensitivity assessment is performed to investigate the influence of the observed glacier area and important model parameters on the resulting total ice volume. Results of the two ice-thickness distribution models are validated with local ice-thickness measurements at six glaciers. The resulting ice volumes for the entire HK region range from 2955 to 4737 km(3), depending on the approach. This range is lower than most previous estimates. Results from the ice thickness distribution models and the slope-dependent thickness estimations agree well with measured local ice thicknesses. However, total volume estimates from area-related relations are larger than those from other approaches. The study provides evidence on the significant effect of the selected method on results and underlines the importance of a careful and critical evaluation.

Recent advances in purely organic phosphorescent materials

Luminescent organic materials have attracted significant attention in recent times owing to their opportunities in various functional applications. Interestingly, unlike fluorescence, opportunities hidden within the phosphorescence properties of organic compounds have received considerably less attention even until last few years. It is only in the second decade of the 21st century, within a time span of less than last 5 years, that the concepts and prospects of organic compounds as phosphorescent materials have evolved rapidly. The previously perceived limitations of organic compounds as phosphorescent materials have been overcome and several molecules have been designed using old and new concepts, such as heavy atom effects, matrix assisted isolation, hydrogen bonding and halogen bonding, thereby gaining access to a significant number of materials with efficient phosphorescent features. In addition, significant improvements have been made in the development of RTP (room temperature phosphorescent) materials, which can be used under ambient conditions. In this review, we bring together the vastly different approaches developed by various researchers to understand and appreciate this recent revolution in organic luminescent materials.

An approximation to the QoS aware throughput region of a tree network under IEEE 802.15.4 CSMA/CA with application to wireless sensor network design

In the context of wireless sensor networks, we are motivated by the design of a tree network spanning a set of source nodes that generate packets, a set of additional relay nodes that only forward packets from the sources, and a data sink. We assume that the paths from the sources to the sink have bounded hop count, that the nodes use the IEEE 802.15.4 CSMA/CA for medium access control, and that there are no hidden terminals. In this setting, starting with a set of simple fixed point equations, we derive explicit conditions on the packet generation rates at the sources, so that the tree network approximately provides certain quality of service (QoS) such as end-to-end delivery probability and mean delay. The structures of our conditions provide insight on the dependence of the network performance on the arrival rate vector, and the topological properties of the tree network. Our numerical experiments suggest that our approximations are able to capture a significant part of the QoS aware throughput region (of a tree network), that is adequate for many sensor network applications. Furthermore, for the special case of equal arrival rates, default backoff parameters, and for a range of values of target QoS, we show that among all path-length-bounded trees (spanning a given set of sources and the data sink) that meet the conditions derived in the paper, a shortest path tree achieves the maximum throughput. (C) 2015 Elsevier B.V. All rights reserved.

Boron clusters in luminescent materials

In recent times, luminescent materials with tunable emission properties have found applications in almost all aspects of modern material sciences. Any discussion on the recent developments in luminescent materials would be incomplete if one does not account for the versatile photophysical features of boron containing compounds. Apart from triarylboranes and tetra-coordinate borate dyes, luminescent materials consisting of boron clusters have also found immense interest in recent times. Recent studies have unveiled the opportunities hidden within boranes, carboranes and metalloboranes, etc. as active constituents of luminescent materials. From simple illustrations of luminescence, to advanced applications in LASERs, OLEDs and bioimaging, etc., the unique features of such compounds and their promising versatility have already been established. In this review, recent revelations about the excellent photophysical properties of such materials are discussed.