Geographic Resources Decision Support System – an Open Source GIS

Fully structured and matured open source spatial and temporal analysis technology seems to be the official carrier of the future for planning of the natural resources especially in the developing nations. This technology has gained enormous momentum because of technical superiority, affordability and ability to join expertise from all sections of the society. Sustainable development of a region depends on the integrated planning approaches adopted in decision making which requires timely and accurate spatial data. With the increased developmental programmes, the need for appropriate decision support system has increased in order to analyse and visualise the decisions associated with spatial and temporal aspects of natural resources. In this regard Geographic Information System (GIS) along with remote sensing data support the applications that involve spatial and temporal analysis on digital thematic maps and the remotely sensed images. Open source GIS would help in wide scale applications involving decisions at various hierarchical levels (for example from village panchayat to planning commission) on economic viability, social acceptance apart from technical feasibility. GRASS (Geographic Resources Analysis Support System, http://wgbis.ces.iisc.ernet.in/grass) is an open source GIS that works on Linux platform (freeware), but most of the applications are in command line argument, necessitating a user friendly and cost effective graphical user interface (GUI). Keeping these aspects in mind, Geographic Resources Decision Support System (GRDSS) has been developed with functionality such as raster, topological vector, image processing, statistical analysis, geographical analysis, graphics production, etc. This operates through a GUI developed in Tcltk (Tool command language / Tool kit) under Linux as well as with a shell in X-Windows. GRDSS include options such as Import /Export of different data formats, Display, Digital Image processing, Map editing, Raster Analysis, Vector Analysis, Point Analysis, Spatial Query, which are required for regional planning such as watershed Analysis, Landscape Analysis etc. This is customised to Indian context with an option to extract individual band from the IRS (Indian Remote Sensing Satellites) data, which is in BIL (Band Interleaved by Lines) format. The integration of PostgreSQL (a freeware) in GRDSS aids as an efficient database management system.

A Scalable Low Power Issue Queue for Large Instruction Window Processors

Large instruction windows and issue queues are key to exploiting greater instruction level parallelism in out-of-order superscalar processors. However, the cycle time and energy consumption of conventional large monolithic issue queues are high. Previous efforts to reduce cycle time segment the issue queue and pipeline wakeup. Unfortunately, this results in significant IPC loss. Other proposals which address energy efficiency issues by avoiding only the unnecessary tag-comparisons do not reduce broadcasts. These schemes also increase the issue latency.To address both these issues comprehensively, we propose the Scalable Lowpower Issue Queue (SLIQ). SLIQ augments a pipelined issue queue with direct indexing to mitigate the problem of delayed wakeups while reducing the cycle time. Also, the SLIQ design naturally leads to significant energy savings by reducing both the number of tag broadcasts and comparisons required.A 2 segment SLIQ incurs an average IPC loss of 0.2% over the entire SPEC CPU2000 suite, while achieving a 25.2% reduction in issue latency when compared to a monolithic 128-entry issue queue for an 8-wide superscalar processor. An 8 segment SLIQ improves scalability by reducing the issue latency by 38.3% while incurring an IPC loss of only 2.3%. Further, the 8 segment SLIQ significantly reduces the energy consumption and energy-delay product by 48.3% and 67.4% respectively on average.

Advances in Light-Emitting Doped Semiconductor Nanocrystals

Insertion of just a few impurity atoms in a host semiconductor nanocrystal can drastically alter its phase, shape, and physical properties. Such doped nanomaterials now constitute an important class of optical materials that can provide efficient, stable, and tunable dopant emission in visible and NIR spectral windows. Selecting proper dopants and inserting them in appropriate hosts can generate many new series of such doped nanocrystals with several unique and attractive properties in order to meet current challenges in the versatile field of luminescent materials. However, the synthesis of such doped nanomaterials with a specific dopant in a predetermined host at a desired site leading to targeted optical properties requires fundamental understanding of both the doping process as well as the resulting photophysical properties. Summarizing up to date literature reports, in this Perspective we discuss important advances in synthesis methods and in-depth understanding of the optical properties, with an emphasis on the most widely investigated Mn-doped semiconductor nanocrystals.

mRegistry: a registry representation for fault diagnosis

Microsoft Windows uses the notion of registry to store all configuration information. The registry entries have associations and dependencies. For example, the paths to executables may be relative to some home directories. The registry being designed with faster access as one of the objectives does not explicitly capture these relations. In this paper, we explore a representation that captures the dependencies more explicitly using shared and unifying variables. This representation, called mRegistry exploits the tree-structured hierarchical nature of the registry, is concept-based and obtained in multiple stages. mRegistry captures intra-block, inter-block and ancestor-children dependencies (all leaf entries of a parent key in a registry put together as an entity constitute a block thereby making the block as the only child of the parent). In addition, it learns the generalized concepts of dependencies in the form of rules. We show that mRegistry has several applications: fault diagnosis, prediction, comparison, compression etc.

Comparative statistical aging studies on an oil-pressboard insulation model

Accelerated aging experiments have been conducted on a representative oil-pressboard insulation model to investigate the effect of constant and sequential stresses on the PD behavior using a built-in phase resolved partial discharge analyzer. A cycle of the applied voltage starting from the zero of the positive half cycle was divided into 16 equal phase windows (Φ1 to Φ16) and partial discharge (PD) magnitude distribution in each phase was determined. Based on the experimental results, three stages of aging mechanism were identified. Gumbel's extreme value distribution of the largest element was used to model the first stage of aging process. Second and subsequent stages were modeled using two-parameter Weibull distribution. Spearman's non-parametric rank correlation test statistic and Kolmogrov-Smirnov two sample test were used to relate the aging process of each phase with the corresponding process of the full cycle. To bring out clearly the effect of stress level, its duration and test procedure on the distribution parameters and hence of the aging process, non-parametric ANOVA techniques like Kruskal-Wallis and Fisher's LSD multiple comparison tests were used. Results of the analysis show that two phases (Φ13 and Φ14) near the vicinity of the negative voltage peak were found to contribute significantly to the aging process and their aging mechanism also correlated well with that of the corresponding full cycle mechanism. Attempts have been made to relate these results with the published work of other workers

Fast algorithms for frequent episode discovery in event sequences

In this paper we consider the process of discovering frequent episodes in event sequences. The most computationally intensive part of this process is that of counting the frequencies of a set of candidate episodes. We present two new frequency counting algorithms for speeding up this part. These, referred to as non-overlapping and non-inteleaved frequency counts, are based on directly counting suitable subsets of the occurrences of an episode. Hence they are different from the frequency counts of Mannila et al [1], where they count the number of windows in which the episode occurs. Our new frequency counts offer a speed-up factor of 7 or more on real and synthetic datasets. We also show how the new frequency counts can be used when the events in episodes have time-durations as well.

Unusual photoresponse of indium doped ZnO/organic thin film heterojunction

Photoresponse of n-type indium-doped ZnO and a p-type polymer (PEDOT:PSS) heterojunction devices are studied, juxtaposed with the photoluminescence of the In-ZnO samples. In addition to the expected photoresponse in the ultraviolet, the heterojunctions exhibit significant photoresponse to the visible (532 nm). However, neither the doped ZnO nor PEDOT: PSS individually show any photoresponse to visible light. The sub-bandgap photoresponse of the heterojunction originates from visible photon mediated e-h generation between the In-ZnO valence band and localized states lying within the band gap. Though increased doping of In-ZnO has limited effect on the photoluminescence, it significantly diminishes the photoresponse. The study indicates that optimally doped devices are promising for the detection of wavelengths in selected windows in the visible. (C) 2012 American Institute of Physics. http://dx.doi.org/10.1063/1.4704655]

Suite of tools for statistical N-gram language modeling for pattern mining in whole genome sequences

Genome sequences contain a number of patterns that have biomedical significance. Repetitive sequences of various kinds are a primary component of most of the genomic sequence patterns. We extended the suffix-array based Biological Language Modeling Toolkit to compute n-gram frequencies as well as n-gram language-model based perplexity in windows over the whole genome sequence to find biologically relevant patterns. We present the suite of tools and their application for analysis on whole human genome sequence.

Reactivities of Modified and Unmodified Exfoliated Graphite Electrodes in Selected Redox Systems

The electrochemical profiles of exfoliated graphite electrodes (EG) and glassy carbon electrodes (GCE) were recorded using cyclic voltammetry and square wave voltammetry in the presence of various supporting electrolytes and Fe(CN)(6)](3-/4-), Ru(NH3)(6)](2+/3+), ferrocene redox probes. In the supporting electrolytes (KCl, H2SO4, NaOH, tetrabutylammoniumtetraflouroborate, phosphate buffers), the potential windows of EG were found in some cases to be about 200 mV larger than that of GCE. The electroactive surface area of EG was estimated to be 19.5 % larger than the GCE which resulted in higher peak currents on the EG electrode. Furthermore, EG was modified with various nanomaterials such as poly (propylene imine) dendrimer, gold nanoparticles, and dendrimer-gold nanoparticles composite. The morphologies of the modified electrodes were studied using scanning electron microscopy and their electrochemical reactivities in the three redox probes were investigated. The current and the reversibility of redox probes were enhanced with the presence of modifiers in different degrees with dendrimer and gold nanoparticles having a favorable edge.

Large scale features and assessment of spatial scale correspondence between TMPA and IMD rainfall datasets over Indian landmass

Daily rainfall datasets of 10 years (1998-2007) of Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) version 6 and India Meteorological Department (IMD) gridded rain gauge have been compared over the Indian landmass, both in large and small spatial scales. On the larger spatial scale, the pattern correlation between the two datasets on daily scales during individual years of the study period is ranging from 0.4 to 0.7. The correlation improved significantly (similar to 0.9) when the study was confined to specific wet and dry spells each of about 5-8 days. Wavelet analysis of intraseasonal oscillations (ISO) of the southwest monsoon rainfall show the percentage contribution of the major two modes (30-50 days and 10-20 days), to be ranging respectively between similar to 30-40% and 5-10% for the various years. Analysis of inter-annual variability shows the satellite data to be underestimating seasonal rainfall by similar to 110 mm during southwest monsoon and overestimating by similar to 150 mm during northeast monsoon season. At high spatio-temporal scales, viz., 1 degrees x1 degrees grid, TMPA data do not correspond to ground truth. We have proposed here a new analysis procedure to assess the minimum spatial scale at which the two datasets are compatible with each other. This has been done by studying the contribution to total seasonal rainfall from different rainfall rate windows (at 1 mm intervals) on different spatial scales (at daily time scale). The compatibility spatial scale is seen to be beyond 5 degrees x5 degrees average spatial scale over the Indian landmass. This will help to decide the usability of TMPA products, if averaged at appropriate spatial scales, for specific process studies, e.g., cloud scale, meso scale or synoptic scale.

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.

A Low-Cost System for Measurement and Spectral Analysis of Motor Acoustic Noise

Workplace noise has become one of the major issues in industry not only because of workers’ health but also due to safety. Electric motors, particularly, inverter fed induction motors emit objectionably high levels of noise. This has led to the emergence of a research area, concerned with measurement and mitigation of the acoustic noise. This paper presents a lowcost option for measurement and spectral analysis of acoustic noise emitted by electric motors. The system consists of an electret microphone, amplifier and filter. It makes use of the windows sound card and associated software for data acquisition and analysis. The measurement system is calibrated using a professional sound level meter. Acoustic noise measurements are made on an induction motor drive using the proposed system as per relevant international standards. These measurements are seen to match closely with those of a professional meter.

Optical properties of an inhomogeneously broadened Lambda V-system with multiple excited states

We present a theoretical model using a density matrix approach to show the influence of multiple excited states on the optical properties of an inhomogeneously broadened Lambda V-system of the Rb-87 D2 line. These closely spaced multiple excited states cause asymmetry in absorption and dispersion profiles. We observe the reduced absorption profiles, due to dressed state interactions of the applied electromagnetic fields, which results the Mollow sideband-like transparency windows. In a room temperature vapor, we obtain a narrow enhanced absorption and steep positive dispersion at the line center when the strengths of control and pump fields are equal. Here, we show how the probe transmittance varies when it passes through the atomic medium. We also discuss the transient behavior of our system which agrees well with the corresponding absorption and dispersion profiles. This study has potential applications in controllability of group velocity, and for optical and quantum information processing.

Strontium eluting graphene hybrid nanoparticles augment osteogenesis in a 3D tissue scaffold

The objective of this work was to prepare hybrid nanoparticles of graphene sheets decorated with strontium metallic nanoparticles and demonstrate their advantages in bone tissue engineering. Strontium-decorated reduced graphene oxide (RGO_Sr) hybrid nanoparticles were synthesized by the facile reduction of graphene oxide and strontium nitrate. X-ray diffraction, transmission electron microscopy, and atomic force microscopy revealed that the hybrid particles were composed of RGO sheets decorated with 200-300 nm metallic strontium particles. Thermal gravimetric analysis further confirmed the composition of the hybrid particles as 22 wt% of strontium. Macroporous tissue scaffolds were prepared by incorporating RGO_Sr particles in poly(epsilon-caprolactone) (PCL). The PCL/RGO_Sr scaffolds were found to elute strontium ions in aqueous medium. Osteoblast proliferation and differentiation was significantly higher in the PCL scaffolds containing the RGO_Sr particles in contrast to neat PCL and PCL/RGO scaffolds. The increased biological activity can be attributed to the release of strontium ions from the hybrid nanoparticles. This study demonstrates that composites prepared using hybrid nanoparticles that elute strontium ions can be used to prepare multifunctional scaffolds with good mechanical and osteoinductive properties. These findings have important implications for designing the next generation of biomaterials for use in tissue regeneration.

Identification of local variations within secondary structures of proteins

Secondary-structure elements (SSEs) play an important role in the folding of proteins. Identification of SSEs in proteins is a common problem in structural biology. A new method, ASSP (Assignment of Secondary Structure in Proteins), using only the path traversed by the C atoms has been developed. The algorithm is based on the premise that the protein structure can be divided into continuous or uniform stretches, which can be defined in terms of helical parameters, and depending on their values the stretches can be classified into different SSEs, namely -helices, 3(10)-helices, -helices, extended -strands and polyproline II (PPII) and other left-handed helices. The methodology was validated using an unbiased clustering of these parameters for a protein data set consisting of 1008 protein chains, which suggested that there are seven well defined clusters associated with different SSEs. Apart from -helices and extended -strands, 3(10)-helices and -helices were also found to occur in substantial numbers. ASSP was able to discriminate non--helical segments from flanking -helices, which were often identified as part of -helices by other algorithms. ASSP can also lead to the identification of novel SSEs. It is believed that ASSP could provide a better understanding of the finer nuances of protein secondary structure and could make an important contribution to the better understanding of comparatively less frequently occurring structural motifs. At the same time, it can contribute to the identification of novel SSEs. A standalone version of the program for the Linux as well as the Windows operating systems is freely downloadable and a web-server version is also available at .