Detection of a periodic structure embedded in surface roughness, for various correlation functions

This paper deals with surface profilometry, where we try to detect a periodic structure, hidden in randomness using the matched filter method of analysing the intensity of light, scattered from the surface. From the direct problem of light scattering from a composite rough surface of the above type, we find that the detectability of the periodic structure can be hindered by the randomness, being dependent on the correlation function of the random part. In our earlier works, we had concentrated mainly on the Cauchy-type correlation function for the rough part. In the present work, we show that this technique can determine the periodic structure of different kinds of correlation functions of the roughness, including Cauchy, Gaussian etc. We study the detection by the matched filter method as the nature of the correlation function is varied.

Federated Search Service for OAI-compliant, Open-Access Repositories in India

Many of the research institutions and universities across the world are facilitating open-access (OA) to their intellectual outputs through their respective OA institutional repositories (IRs) or through the centralized subject-based repositories. The registry of open access repositories (ROAR) lists more than 2850 such repositories across the world. The awareness about the benefits of OA to scholarly literature and OA publishing is picking up in India, too. As per the ROAR statistics, to date, there are more than 90 OA repositories in the country. India is doing particularly well in publishing open-access journals (OAJ). As per the directory of open-access journals (DOAJ), to date, India with 390 OAJs, is ranked 5th in the world in terms of numbers of OAJs being published. Much of the research done in India is reported in the journals published from India. These journals have limited readership and many of them are not being indexed by Web of Science, Scopus or other leading international abstracting and indexing databases. Consequently, research done in the country gets hidden not only from the fellow countrymen, but also from the international community. This situation can be easily overcome if all the researchers facilitate OA to their publications. One of the easiest ways to facilitate OA to scientific literature is through the institutional repositories. If every research institution and university in India set up an open-access IR and ensure that copies of the final accepted versions of all the research publications are uploaded in the IRs, then the research done in India will get far better visibility. The federation of metadata from all the distributed, interoperable OA repositories in the country will serve as a window to the research done across the country. Federation of metadata from the distributed OAI-compliant repositories can be easily achieved by setting up harvesting software like the PKP Harvester. In this paper, we share our experience in setting up a prototype metadata harvesting service using the PKP harvesting software for the OAI-compliant repositories in India.

Human action recognition using a fast learning fully complex-valued classifier

In this paper, we use optical flow based complex-valued features extracted from video sequences to recognize human actions. The optical flow features between two image planes can be appropriately represented in the Complex plane. Therefore, we argue that motion information that is used to model the human actions should be represented as complex-valued features and propose a fast learning fully complex-valued neural classifier to solve the action recognition task. The classifier, termed as, ``fast learning fully complex-valued neural (FLFCN) classifier'' is a single hidden layer fully complex-valued neural network. The neurons in the hidden layer employ the fully complex-valued activation function of the type of a hyperbolic secant function. The parameters of the hidden layer are chosen randomly and the output weights are estimated as the minimum norm least square solution to a set of linear equations. The results indicate the superior performance of FLFCN classifier in recognizing the actions compared to real-valued support vector machines and other existing results in the literature. Complex valued representation of 2D motion and orthogonal decision boundaries boost the classification performance of FLFCN classifier. (c) 2012 Elsevier B.V. All rights reserved.

Cross-Guided Clustering: Transfer of Relevant Supervision across Tasks

Lack of supervision in clustering algorithms often leads to clusters that are not useful or interesting to human reviewers. We investigate if supervision can be automatically transferred for clustering a target task, by providing a relevant supervised partitioning of a dataset from a different source task. The target clustering is made more meaningful for the human user by trading-off intrinsic clustering goodness on the target task for alignment with relevant supervised partitions in the source task, wherever possible. We propose a cross-guided clustering algorithm that builds on traditional k-means by aligning the target clusters with source partitions. The alignment process makes use of a cross-task similarity measure that discovers hidden relationships across tasks. When the source and target tasks correspond to different domains with potentially different vocabularies, we propose a projection approach using pivot vocabularies for the cross-domain similarity measure. Using multiple real-world and synthetic datasets, we show that our approach improves clustering accuracy significantly over traditional k-means and state-of-the-art semi-supervised clustering baselines, over a wide range of data characteristics and parameter settings.

Averting biodiversity collapse in tropical forest protected areas

The rapid disruption of tropical forests probably imperils global biodiversity more than any other contemporary phenomenon(1-3). With deforestation advancing quickly, protected areas are increasingly becoming final refuges for threatened species and natural ecosystem processes. However, many protected areas in the tropics are themselves vulnerable to human encroachment and other environmental stresses(4-9). As pressures mount, it is vital to know whether existing reserves can sustain their biodiversity. A critical constraint in addressing this question has been that data describing a broad array of biodiversity groups have been unavailable for a sufficiently large and representative sample of reserves. Here we present a uniquely comprehensive data set on changes over the past 20 to 30 years in 31 functional groups of species and 21 potential drivers of environmental change, for 60 protected areas stratified across the world's major tropical regions. Our analysis reveals great variation in reserve `health': about half of all reserves have been effective or performed passably, but the rest are experiencing an erosion of biodiversity that is often alarmingly widespread taxonomically and functionally. Habitat disruption, hunting and forest-product exploitation were the strongest predictors of declining reserve health. Crucially, environmental changes immediately outside reserves seemed nearly as important as those inside in determining their ecological fate, with changes inside reserves strongly mirroring those occurring around them. These findings suggest that tropical protected areas are often intimately linked ecologically to their surrounding habitats, and that a failure to stem broad-scale loss and degradation of such habitats could sharply increase the likelihood of serious biodiversity declines.

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.