A blockwise relaxation labeling scheme and its application to edge detection in cardiac MR image sequences

We present a segmentation scheme for magnetic resonance (MR) image sequences based on vector quantization of a block-partitioned image followed by a relaxation labeling procedure. By first searching a coarse segmentation, the algorithm yields very fast and effective performance on images that are inherently noisy, and can effectively use the correlation in a sequence of images for robust performance and efficient implementation. The algorithm defines feature vectors by the local histogram on a block-partioned image and approximates the local histograms by normal distributions. The relative entropy is chosen as the meaningful distance measure between the feature vectors and the templates. After initial computation of the normal distribution parameters, a blockwise classification maximization algorithm classifies blocks in the block-partitioned image by minimizing their relative entropy distance for a coarse-resolution segmentation; and finally, finer resolution is obtained by contextual Bayesian relaxation labeling in which label update is performed pixelwise by incorporating neighborhood information. Sequence processing is then performed to segment all images in the sequence. The scheme is applied to left ventricular boundary detection in short-axis MR image sequences, and results are presented to show that the algorithm successfully extracts the endocardial contours and that sequence processing significantly improves edge detection performance and can avoid local minima problems. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 340–350, 1998     

Plane Strain Deformation in Generalized Thermoelastic Diffusion

The present investigation is concerned with plane strain deformation in homogeneous isotropic generalized thermoelastic diffusion subjected to a normal force, thermal source, and chemical potential source. Laplace and Fourier transform techniques are employed to solve the problem. The integral transform have been inverted by using a numerical technique to obtain the displacements, stresses, temperature distribution, and chemical potential distribution. The numerical results of these quantities are illustrated graphically to depict the response of various sources in the theories of thermoelastic diffusion and thermoelasticity for a particular model. Some particular cases have been deduced from the present investigation.     

Magnetodielectric composite with ferrite inclusions as substrates for microstrip patch antennas at microwave frequencies

A study has been conducted to estimate the complex permittivity and permeability along with magnetic characterization of different volume fractions of magnetodielectric composites with cobalt ferrite nano inclusions. Using an in touch superstrate technique dielectric properties are estimated. Cavity perturbation technique is used to study the complex permeability of the samples. 4πM_s value and coercivity is measured using vibrating sample magnetometry. Structural and surface morphologies on the composite samples are conducted to determine the size and homogeneous distribution of nano inclusions. The average grain size of cobalt ferrite nanoparticles is found to be ～10 nm. The real part of permittivity and permeability of the samples varies from ～1–2.905 to ～1.01–1.05 with increase in inclusion content from 1% VF to 5% VF, respectively. The tan δ of permittivity and imaginary part of permeability is found to be of the order of ～10^?3 and ～10^?1 respectively. Verification of these composites as potential substrates for microstrip patch antenna is carried out by fabricating simple rectangular patch at 9.5 GHz using transmission line model. Rectangular patch is designed on 5% VF composite system. The return loss for the composite system was found to be ～?19.451 dB which is comparable with that designed on standard glass epoxy substrate (?_r = 4.5).     

Experimental investigations on barium titanate nanocomposite thin films as an opto-electronic humidity sensor

This article reports the synthesis and characterisation of Barium titanate (BaTiO₃) nanocomposite and its application as opto-electronic humidity sensor. Titanium tetrachloride and barium hydroxide were mixed in molar ratio 1?:?1 in deionised water under continuous stirring at room temperature. Later, sodium hydroxide solution was added to above solution with continuous stirring. Finally, BaTiO₃ gel was obtained. The synthesised nano-composite material was characterised using a scanning electron microscope, X-ray diffraction (XRD) and UV-Visible spectrophotometer. SEM image of the composite film shows that the film is porous having uniform grains. From XRD the minimum crystallite size of BaTiO₃ was found to be 8?nm using Debye–Scherer formula. UV-Visible absorption spectroscopy was used for optical characterisation of the film. It was found that the optical band gap of the composite material was 3.50?eV. Barium titanate thin film was deposited on the base of an equilateral prism using sol–gel spin coating process at 4000?rpm. The humidity sensing properties of the film was investigated at different angles of incidence. It was observed that the intensity of reflected light increased with an increase in relative humidity (%RH) in the range 5–95% at a particular angle of incidence. Sensing element has maximum sensitivity ～6?µW/%RH, which is quite significant for sensor fabrication purposes.     

Retrieving keyworded subgraphs with graph ranking score

Keyword queries have long been popular to search engines and to the information retrieval community and have recently gained momentum for its usage in the expert systems community. The conventional semantics for processing a user query is to find a set of top-k web pages such that each page contains all user keywords. Recently, this semantics has been extended to find a set of cohesively interconnected pages, each of which contains one of the query keywords scattered across these pages. The keyword query having the extended semantics (i.e., more than a list of keywords hyperlinked with each other) is referred to the graph query. In case of the graph query, all the query keywords may not be present on a single Web page. Thus, a set of Web pages with the corresponding hyperlinks need to be presented as the search result. The existing search systems reveal serious performance problem due to their failure to integrate information from multiple connected resources so that an efficient algorithm for keyword query over graph-structured data is proposed. It integrates information from multiple connected nodes of the graph and generates result trees with the occurrence of all the query keywords. We also investigate a ranking measure called graph ranking score (GRS) to evaluate the relevant graph results so that the score can generate a scalar value for keywords as well as for the topology.     

Graph based Ranked Answers for Keyword Graph Structure

Keyword query processing over graph structured data is beneficial across various real world applications. The basic unit, of search and retrieval, in keyword search over graph, is a structure (interconnection of nodes) that connects all the query keywords. This new answering paradigm, in contrast to single web page results given by search engines, brings forth new challenges for ranking. In this paper, we propose a simple but effective Fuzzy set theory based Ranking measure, called FRank. Fuzzy sets acknowledge the contribution of each individual query keyword, discretely, to enumerate node relevance. A novel aggregation operator is defined, to combine the content relevance based fuzzy sets and, compute query dependent edge weights. The final rank, of an answer, is computed by non-monotonic addition of edge weights, as per their relevance to keyword query. FRank evaluates each answer based on the distribution of query keywords and structural connectivity between those keywords. An extensive empirical analysis shows superior performance by our proposed ranking measure as compared to the ranking measures adopted by current approaches in the literature.     

Joint watermarking and encryption for still visual data

Joint watermarking and encryption is an upcoming security solution that combines leading but complementary techniques to achieve an enhanced security level. Real time applications using joint watermarking and encryption framework has three requirements: data to be efficiently compressed, watermarking technique to sustain compression, and encryption technique to be developed in a way so as not to disturb the compression efficiency. Finding an optimal solution that combines the three techniques while fulfilling these requirements is a challenging problem. This paper thus, proposes a wavelet domain based joint watermarking and encryption framework that employs singular value decomposition based watermark embedding and sign bit encryption prior to compression. The varying significance of different subbands has been considered to encrypt the data without adversely effecting the compression ratio. Experimental analysis using various evaluation parameters and attack scenarios has revealed the ability of the proposed framework to prove content-ownership, even from the encrypted data. Comparative analysis with the existing techniques reflect its ability to provide better security with less computational resources. This makes it a preferable solution for data security at all stages of data archival, transmission or distribution.     

Modelling majority and expert influences on opinion formation in online social networks

Two most important social influences that shape the opinion formation process are: (i) the majority influence caused by the existence of a large group of people sharing similar opinions and (ii) the expert influence originated from the presence of experts in a social group. When these two effects contradict each other in real life, they may pull the public opinions towards their respective directions. Existing models on opinion formation utilised the idea of expertise levels in conjunction with the expressed opinions of the agents to encapsulate the expert effect. However, they have disregarded the explicit consideration of the majority effect, and thereby failed to capture the concurrent and combined impact of these two influences on opinion evolution. To represent the majority and expert impacts, we explicitly use the concept of opinion consistency and expertise level consistency respectively in an innovative way by capitalizing the notion of entropy in measuring the homogeneity of a group. Consequently, our model successfully captures the opinion dynamics under the concomitant influence of majority and expert. We validate the efficacy of our model in capturing opinion dynamics in a real world scenario using the opinion evolution traces collected from a widely used online social network (OSN) platform. Moreover, simulation results reveal the impact of the aforementioned effects, and confirm that our model can properly capture the consensus, polarization and fragmentation properties of public opinion. Our model is also compared with some recent models to evaluate its performance in both real world and simulated environments.     

Constrained MIMO dynamic discrete-time modeling exploiting optimal experimental design

This article presents a new multiple input, multiple output (MIMO) constrained discrete-time modeling (DTM) approach for dynamic block-oriented processes that does not require the nonlinear steady state characteristics to be known prior to model development. This approach uses an efficient statistical experimental design to provide design points for sequential step tests. The DTM is developed from this data in two stages. In the first stage, the ultimate response (steady state) model is determined from just the ultimate response data of the sequential step tests. In the second stage, the dynamic parameters are estimated under the constraint of the fitted ultimate response model obtained in the first stage. The constrained formulation is given for MIMO Hammerstein and Wiener block-oriented systems. Comparison of the proposed constrained DTM method is made with unconstrained DTM and constrained continuous-time modeling (CTM). Prediction accuracy of the proposed method is significantly better than unconstrained DTM and comparable to constrained CTM for the process studied.     

Incremental reconstruction of generalized Voronoi diagrams on grids

We present an incremental algorithm for constructing and reconstructing Generalized Voronoi Diagrams (GVDs) on grids. Our algorithm, Dynamic Brushfire, uses techniques from the path planning community to efficiently update GVDs when the underlying environment changes or when new information concerning the environment is received. Dynamic Brushfire is an order of magnitude more efficient than current approaches. In this paper we present the algorithm, compare it to current approaches on several experimental domains involving both simulated and real data, and demonstrate its usefulness for multirobot path planning.