Tree structure for efficient data mining using rough sets

In data mining, an important goal is to generate an abstraction of the data. Such an abstraction helps in reducing the space and search time requirements of the overall decision making process. Further, it is important that the abstraction is generated from the data with a small number of disk scans. We propose a novel data structure, pattern count tree (PC-tree), that can be built by scanning the database only once. PC-tree is a minimal size complete representation of the data and it can be used to represent dynamic databases with the help of knowledge that is either static or changing. We show that further compactness can be achieved by constructing the PC-tree on segmented patterns. We exploit the flexibility offered by rough sets to realize a rough PC-tree and use it for efficient and effective rough classification. To be consistent with the sizes of the branches of the PC-tree, we use upper and lower approximations of feature sets in a manner different from the conventional rough set theory. We conducted experiments using the proposed classification scheme on a large-scale hand-written digit data set. We use the experimental results to establish the efficacy of the proposed approach. (C) 2002 Elsevier Science B.V. All rights reserved.

Regimes of spray formation in gas-centered swirl coaxial atomizers

Spray formation in ambient atmosphere from gas-centered swirl coaxial atomizers is described by carrying out experiments in a spray test facility. The atomizer discharges a circular air jet and an axisymmetric swirling water sheet from its coaxially arranged inner and outer orifices. A high-speed digital imaging system along with a backlight illumination arrangement is employed to record the details of liquid sheet breakup and spray development. Spray regimes exhibiting different sheet breakup mechanisms are identified and their characteristic features presented. The identified spray regimes are wave-assisted sheet breakup, perforated sheet breakup, segmented sheet breakup, and pulsation spray regime. In the regime of wave-assisted sheet breakup, the sheet breakup shows features similar to the breakup of two-dimensional planar air-blasted liquid sheets. At high air-to-liquid momentum ratios, the interaction process between the axisymmetric swirling liquid sheet and the circular air jet develops spray processes which are more specific to the atomizer studied here. The spray exhibits a periodic ejection of liquid masses whose features are dominantly controlled by the central air jet.

A Real-time clustering system for spatio-temporal signals from network of neurons

Over past few years, the studies of cultured neuronal networks have opened up avenues for understanding the ion channels, receptor molecules, and synaptic plasticity that may form the basis of learning and memory. The hippocampal neurons from rats are dissociated and cultured on a surface containing a grid of 64 electrodes. The signals from these 64 electrodes are acquired using a fast data acquisition system MED64 (Alpha MED Sciences, Japan) at a sampling rate of 20 K samples with a precision of 16-bits per sample. A few minutes of acquired data runs in to a few hundreds of Mega Bytes. The data processing for the neural analysis is highly compute-intensive because the volume of data is huge. The major processing requirements are noise removal, pattern recovery, pattern matching, clustering and so on. In order to interface a neuronal colony to a physical world, these computations need to be performed in real-time. A single processor such as a desk top computer may not be adequate to meet this computational requirements. Parallel computing is a method used to satisfy the real-time computational requirements of a neuronal system that interacts with an external world while increasing the flexibility and scalability of the application. In this work, we developed a parallel neuronal system using a multi-node Digital Signal processing system. With 8 processors, the system is able to compute and map incoming signals segmented over a period of 200 ms in to an action in a trained cluster system in real time.

A Kinematic Theory for Planar Hoberman and Other Novel Foldable Mechanisms

In this paper, we present a kinematic theory for Hoberman and other similar foldable linkages. By recognizing that the building blocks of such linkages can be modeled as planar linkages, different classes of possible solutions are systematically obtained including some novel arrangements. Criteria for foldability are arrived by analyzing the algebraic locus of the coupler curve of a PRRP linkage. They help explain generalized Hoberman and other mechanisms reported in the literature. New properties of such mechanisms including the extent of foldability, shape-preservation of the inner and outer profiles, multi-segmented assemblies and heterogeneous circumferential arrangements are derived. The design equations derived here make the conception of even complex planar radially foldable mechanisms systematic and easy. Representative examples are presented to illustrate the usage of the design equations and the kinematic theory.

A stochastic dynamical system for image segmentation

Image segmentation is formulated as a stochastic process whose invariant distribution is concentrated at points of the desired region. By choosing multiple seed points, different regions can be segmented. The algorithm is based on the theory of time-homogeneous Markov chains and has been largely motivated by the technique of simulated annealing. The method proposed here has been found to perform well on real-world clean as well as noisy images while being computationally far less expensive than stochastic optimisation techniques

Periodically Grafted Amphiphilic Copolymers: Nonionic Analogues of Ionenes

We describe a novel class of periodically grafted amphiphilic copolymers (PGACs) that could serve as nonionic functional mimics of ionenes, the primary difference being that the periodically occurring charged units along the backbone of ionenes are replaced by hydrophilic oligoethylene glycol segments. The synthesis and properties of this new class of segmented polymers that carry a hydrophobic alkylene polyester backone with periodically placed hydrophilic oligo(oxyethylene) pendant units are presented. When the length of the intervening alkylene segment is adequately long, 22-carbons in this case, and the pendant unit is a hexaethylene glycol monomethyl ether (HEG) segment, the polymer chain appears to adopt a folded zigzag conformation, reminiscent of the accordion-type structure formed by cationic ionenes. This transformation is driven by the intrinsic immiscibility of the alkylene and HEG segments and is reinforced by the strong tendency for long chain alkylene segments to crystallize in a paraffinic lattice. Evidence of the formation of such structures comes from the AFM images, which reveal the formation of remarkably flat pancake-like aggregates that are formed by the lateral aggregation of individually collapsed polymer chains; importantly, the heights of these structures match well with the lamellar layer-spacing obtained from SAXS studies of bulk samples. DSC studies further confirm the crystallization of the intervening alkylene segments, especially when they are long (C22), suggesting the formation of the folded zigzag structures. In a suitably designed PGAC that carries diacetylene units symmetrically placed within the alkylene segment, attempts were made to cross-polymerize the diacetylene units and generate PEGylated nanoparticles. However, these attempts were unsuccessful demonstrating the very stringent geometric requirements for the topotactic polymerization of diacetylenes.

An approach to multi-temporal MODIS image analysis using image classification and segmentation

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time series analysis of satellite images utilizing pixel spectral information for image classification and region-based segmentation for extracting water-covered regions. Analysis of MODIS satellite images is applied in three stages: before flood, during flood and after flood. Water regions are extracted from the MODIS images using image classification (based on spectral information) and image segmentation (based on spatial information). Multi-temporal MODIS images from ``normal'' (non-flood) and flood time-periods are processed in two steps. In the first step, image classifiers such as Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs) separate the image pixels into water and non-water groups based on their spectral features. The classified image is then segmented using spatial features of the water pixels to remove the misclassified water. From the results obtained, we evaluate the performance of the method and conclude that the use of image classification (SVM and ANN) and region-based image segmentation is an accurate and reliable approach for the extraction of water-covered regions. (c) 2012 COSPAR. Published by Elsevier Ltd. All rights reserved.

Development of moving magnet type linear motor for dual piston compressor for pulse tube cryocooler

This paper describes the design, fabrication and testing of a moving magnet type linear motor of dual piston configuration for a pulse tube cryocooler for ground applications. Eight radially magnetized segmented magnets were used to form one set of a magnet ring. Four magnet rings of such type were constructed, in which one pair of rings has north-pole on its outer diameter and south-pole on inner diameter, while the other pair is it's complementary. The magnets were mounted with opposite poles together on the magnet holder with an axial moving shaft having a piston mounted on both ends of the shaft. The shaft movement was restricted to the axial direction by using C-clamp type flexures, mounted on both sides of the shaft. The force requirement for driving the compressor was calculated based on which the electrical circuit of motor is designed by proper selection of wire gauge and Ampere-turns. The flexure spring force estimation was done through simulation using ANSYS 11.0 and was verified experimentally; while the magnet spring force was determined experimentally. The motor with mounted piston was tested using a variable voltage and variable frequency power supply capable of driving 140 watts of load.

Establishment of an in vitro transcription system for Peste des petits ruminant virus

Background: Peste-des-petits ruminants virus (PPRV) is a non segmented negative strand RNA virus of the genus Morbillivirus within Paramyxoviridae family. Negative strand RNA viruses are known to carry nucleocapsid (N) protein, phospho (P) protein and RNA polymerase (L protein) packaged within the virion which possess all activities required for transcription, post-transcriptional modification of mRNA and replication. In order to understand the mechanism of transcription and replication of the virus, an in vitro transcription reconstitution system is required. In the present work, an in vitro transcription system has been developed with ribonucleoprotein (RNP) complex purified from virus infected cells as well as partially purified recombinant polymerase (L-P) complex from insect cells along with N-RNA (genomic RNA encapsidated by N protein) template isolated from virus infected cells. Results: RNP complex isolated from virus infected cells and recombinant L-P complex purified from insect cells was used to reconstitute transcription on N-RNA template. The requirement for this transcription reconstitution has been defined. Transcription of viral genes in the in vitro system was confirmed by PCR amplification of cDNAs corresponding to individual transcripts using gene specific primers. In order to measure the relative expression level of viral transcripts, real time PCR analysis was carried out. qPCR analysis of the transcription products made in vitro showed a gradient of polarity of transcription from 3' end to 5' end of the genome similar to that exhibited by the virus in infected cells. Conclusion: This report describes for the first time, the development of an in vitro transcription reconstitution system for PPRV with RNP complex purified from infected cells and recombinant L-P complex expressed in insect cells. Both the complexes were able to synthesize all the mRNA species in vitro, exhibiting a gradient of polarity in transcription.

Spectral-spatial MODIS image analysis using swarm intelligence algorithms and region based segmentation for flood assessment

This paper discusses an approach for river mapping and flood evaluation based on multi-temporal time-series analysis of satellite images utilizing pixel spectral information for image clustering and region based segmentation for extracting water covered regions. MODIS satellite images are analyzed at two stages: before flood and during flood. Multi-temporal MODIS images are processed in two steps. In the first step, clustering algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to distinguish the water regions from the non-water based on spectral information. These algorithms are chosen since they are quite efficient in solving multi-modal optimization problems. These classified images are then segmented using spatial features of the water region to extract the river. From the results obtained, we evaluate the performance of the methods and conclude that incorporating region based image segmentation along with clustering algorithms provides accurate and reliable approach for the extraction of water covered region.

MAST: multi-script annotation toolkit for scenic text

This paper describes a semi-automatic tool for annotation of multi-script text from natural scene images. To our knowledge, this is the maiden tool that deals with multi-script text or arbitrary orientation. The procedure involves manual seed selection followed by a region growing process to segment each word present in the image. The threshold for region growing can be varied by the user so as to ensure pixel-accurate character segmentation. The text present in the image is tagged word-by-word. A virtual keyboard interface has also been designed for entering the ground truth in ten Indic scripts, besides English. The keyboard interface can easily be generated for any script, thereby expanding the scope of the toolkit. Optionally, each segmented word can further be labeled into its constituent characters/symbols. Polygonal masks are used to split or merge the segmented words into valid characters/symbols. The ground truth is represented by a pixel-level segmented image and a '.txt' file that contains information about the number of words in the image, word bounding boxes, script and ground truth Unicode. The toolkit, developed using MATLAB, can be used to generate ground truth and annotation for any generic document image. Thus, it is useful for researchers in the document image processing community for evaluating the performance of document analysis and recognition techniques. The multi-script annotation toolokit (MAST) is available for free download.

MAPS: midline analysis and propagation of segmentation

Scenic word images undergo degradations due to motion blur, uneven illumination, shadows and defocussing, which lead to difficulty in segmentation. As a result, the recognition results reported on the scenic word image datasets of ICDAR have been low. We introduce a novel technique, where we choose the middle row of the image as a sub-image and segment it first. Then, the labels from this segmented sub-image are used to propagate labels to other pixels in the image. This approach, which is unique and distinct from the existing methods, results in improved segmentation. Bayesian classification and Max-flow methods have been independently used for label propagation. This midline based approach limits the impact of degradations that happens to the image. The segmented text image is recognized using the trial version of Omnipage OCR. We have tested our method on ICDAR 2003 and ICDAR 2011 datasets. Our word recognition results of 64.5% and 71.6% are better than those of methods in the literature and also methods that competed in the Robust reading competition. Our method makes an implicit assumption that degradation is not present in the middle row.

Integration of speckle de-noising and image segmentation using synthetic aperture radar image for flood extent extraction

Flood is one of the detrimental hydro-meteorological threats to mankind. This compels very efficient flood assessment models. In this paper, we propose remote sensing based flood assessment using Synthetic Aperture Radar (SAR) image because of its imperviousness to unfavourable weather conditions. However, they suffer from the speckle noise. Hence, the processing of SAR image is applied in two stages: speckle removal filters and image segmentation methods for flood mapping. The speckle noise has been reduced with the help of Lee, Frost and Gamma MAP filters. A performance comparison of these speckle removal filters is presented. From the results obtained, we deduce that the Gamma MAP is reliable. The selected Gamma MAP filtered image is segmented using Gray Level Co-occurrence Matrix (GLCM) and Mean Shift Segmentation (MSS). The GLCM is a texture analysis method that separates the image pixels into water and non-water groups based on their spectral feature whereas MSS is a gradient ascent method, here segmentation is carried out using spectral and spatial information. As test case, Kosi river flood is considered in our study. From the segmentation result of both these methods are comprehensively analysed and concluded that the MSS is efficient for flood mapping.

Evaluation of document binarization using eigen value decomposition

A necessary step for the recognition of scanned documents is binarization, which is essentially the segmentation of the document. In order to binarize a scanned document, we can find several algorithms in the literature. What is the best binarization result for a given document image? To answer this question, a user needs to check different binarization algorithms for suitability, since different algorithms may work better for different type of documents. Manually choosing the best from a set of binarized documents is time consuming. To automate the selection of the best segmented document, either we need to use ground-truth of the document or propose an evaluation metric. If ground-truth is available, then precision and recall can be used to choose the best binarized document. What is the case, when ground-truth is not available? Can we come up with a metric which evaluates these binarized documents? Hence, we propose a metric to evaluate binarized document images using eigen value decomposition. We have evaluated this measure on DIBCO and H-DIBCO datasets. The proposed method chooses the best binarized document that is close to the ground-truth of the document.

Toward Real-Time Availability of 3D Temperature Maps Created with Temporally Constrained Reconstruction

PurposeTo extend the previously developed temporally constrained reconstruction (TCR) algorithm to allow for real-time availability of three-dimensional (3D) temperature maps capable of monitoring MR-guided high intensity focused ultrasound applications. MethodsA real-time TCR (RT-TCR) algorithm is developed that only uses current and previously acquired undersampled k-space data from a 3D segmented EPI pulse sequence, with the image reconstruction done in a graphics processing unit implementation to overcome computation burden. Simulated and experimental data sets of HIFU heating are used to evaluate the performance of the RT-TCR algorithm. ResultsThe simulation studies demonstrate that the RT-TCR algorithm has subsecond reconstruction time and can accurately measure HIFU-induced temperature rises of 20 degrees C in 15 s for 3D volumes of 16 slices (RMSE = 0.1 degrees C), 24 slices (RMSE = 0.2 degrees C), and 32 slices (RMSE = 0.3 degrees C). Experimental results in ex vivo porcine muscle demonstrate that the RT-TCR approach can reconstruct temperature maps with 192 x 162 x 66 mm 3D volume coverage, 1.5 x 1.5 x 3.0 mm resolution, and 1.2-s scan time with an accuracy of 0.5 degrees C. ConclusionThe RT-TCR algorithm offers an approach to obtaining large coverage 3D temperature maps in real-time for monitoring MR-guided high intensity focused ultrasound treatments. Magn Reson Med 71:1394-1404, 2014. (c) 2013 Wiley Periodicals, Inc.