Matrices representation of multi soft-sets and its application

In previous paper, we introduced a concept of multi-soft sets and used it for finding reducts. However, the comparison of the proposed reduct has not been presented yet, especially with rough-set based reduct. In this paper, we present matrices representation of multi-soft sets. We define AND and OR operations on a collection of such matrices and apply it for finding reducts and core of attributes in a multi-valued information system. Finally, we prove that our proposed technique for reduct is equivalent to Pawlak’s rough reduct.

Photoconductivity studies on some semiconducting thin films for photovoltaic applications

Photoconductivity (PC) processes may be the most suitable technique for obtaining information about the states in the gap. It finds applications in photovoItaics, photo detection and radiation measurements. The main task in the area of photovoltaics, is to increase the efficiency of the device and also to develop new materials with good optoelectronic properties useful for energy conversion, keeping the idea of cost effectiveness. Photoconduction includes generation and recombination of carriers and their transport to the electrodes. So thermal relaxation process, charge carrier statistics, effects of electrodes and several mechanisms of recombination are involved in photoconductivity.A major effect of trapping is to make the experimentally observed decay time of photocurrent, longer than carrier lifetime. If no trapping centers are present, then observed photocurrent will decay in the same way as the density of free carriers and the observed decay time will be equal to carrier lifetime. If the density of free carriers is much less than density of trapped carriers, the entire decay of photocurrent is effectively dominated by the rate of trap emptying rather than by the rate of recombination.In the present study, the decay time of carriers was measured using photoconductive decay (PCD) technique. For the measurements, the film was loaded in a liquid Helium cryostat and the temperature was controlled using Lakshore Auto tuning temperature controller (Model 321). White light was used to illuminate the required area of the sample. Heat radiation from the light source was avoided by passing the light beam through a water filter. The decay current. after switching off the illumination. was measured using a Kiethely 2000 multi meter. Sets of PCD measurements were taken varying sample temperature, sample preparation temperature, thickness of the film, partial pressure of Oxygen and concentration of a particular element in a compound. Decay times were calculated using the rate window technique, which is a decay sampling technique particularly suited to computerized analysis. For PCD curves with two well-defined regions, two windows were chosen, one at the fast decay region and the other at the slow decay region. The curves in a particular window were exponentially fitted using Microsoft Excel 2000 programme. These decay times were plotted against sample temperature and sample preparation temperature to study the effect of various defects in the film. These studies were done in order to optimize conditions of preparation technique so as to get good photosensitive samples. useful for photovoltaic applications.Materials selected for the study were CdS, In2Se3, CuIn2Se3 and CuInS2• Photoconductivity studies done on these samples are organised in six chapters including introduction and conclusion.

Determining operational measures from multi-camera surveillance systems using soft biometrics

CCTV and surveillance networks are increasingly being used for operational as well as security tasks. One emerging area of technology that lends itself to operational analytics is soft biometrics. Soft biometrics can be used to describe a person and detect them throughout a sparse multi-camera network. This enables them to be used to perform tasks such as determining the time taken to get from point to point, and the paths taken through an environment by detecting and matching people across disjoint views. However, in a busy environment where there are 100's if not 1000's of people such as an airport, attempting to monitor everyone is highly unrealistic. In this paper we propose an average soft biometric, that can be used to identity people who look distinct, and are thus suitable for monitoring through a large, sparse camera network. We demonstrate how an average soft biometric can be used to identify unique people to calculate operational measures such as the time taken to travel from point to point.

The Marulan data sets: multi-sensor perception in a natural environment with challenging conditions

In this paper we present large, accurately calibrated and time-synchronized data sets, gathered outdoors in controlled and variable environmental conditions, using an unmanned ground vehicle (UGV), equipped with a wide variety of sensors. These include four 2D laser scanners, a radar scanner, a color camera and an infrared camera. It provides a full description of the system used for data collection and the types of environments and conditions in which these data sets have been gathered, which include the presence of airborne dust, smoke and rain.

Matching image sets via adaptive multi convex hull

Traditional nearest points methods use all the samples in an image set to construct a single convex or affine hull model for classification. However, strong artificial features and noisy data may be generated from combinations of training samples when significant intra-class variations and/or noise occur in the image set. Existing multi-model approaches extract local models by clustering each image set individually only once, with fixed clusters used for matching with various image sets. This may not be optimal for discrimination, as undesirable environmental conditions (eg. illumination and pose variations) may result in the two closest clusters representing different characteristics of an object (eg. frontal face being compared to non-frontal face). To address the above problem, we propose a novel approach to enhance nearest points based methods by integrating affine/convex hull classification with an adapted multi-model approach. We first extract multiple local convex hulls from a query image set via maximum margin clustering to diminish the artificial variations and constrain the noise in local convex hulls. We then propose adaptive reference clustering (ARC) to constrain the clustering of each gallery image set by forcing the clusters to have resemblance to the clusters in the query image set. By applying ARC, noisy clusters in the query set can be discarded. Experiments on Honda, MoBo and ETH-80 datasets show that the proposed method outperforms single model approaches and other recent techniques, such as Sparse Approximated Nearest Points, Mutual Subspace Method and Manifold Discriminant Analysis.

Parametric study on cement-based soft-hard-soft (SHS) multi-layer composite pavement against blast load

An innovative cement-based soft-hard-soft (SHS) multi-layer composite has been developed for protective infrastructures. Such composite consists of three layers including asphalt concrete (AC), high strength concrete (HSC), and engineered cementitious composites (ECC). A three dimensional benchmark numerical model for this SHS composite as pavement under blast load was established using LSDYNA and validated by field blast test. Parametric studies were carried out to investigate the influence of a few key parameters including thickness and strength of HSC and ECC layers, interface properties, soil conditions on the blast resistance of the composite. The outcomes of this study also enabled the establishment of a damage pattern chart for protective pavement design and rapid repair after blast load. Efficient methods to further improve the blast resistance of the SHS multi-layer pavement system were also recommended.

Multi-component, Self-assembled, Functional Soft Materials

Supramolecular chemistry is an emerging tool for devising materials that can perform specified functions. The self-assembly of facially amphiphilic bile acid molecules has been extensively utilized for the development of functional soft materials. Supramolecular hydrogels derived from the bile acid backbone act as useful templates for the intercalation of multiple components. Based on this, synthesis of gel-nanoparticle hybrid materials, photoluminescent coating materials, development of a new enzyme assay technique, etc. were achieved in the author's laboratory. The present account highlights some of these achievements.

Understanding ground deformation mechanisms during multi-propped excavation in soft clay

To maximize the utility of high land cost in urban development, underground space is commonly exploited, both to reduce the load acting on the ground and to increase the space available. The execution of underground constructions requires the use of appropriate retaining wall and bracing systems. Inadequate support systems have always been a major concern, as any excessive ground movement induced during excavation could cause damage to neighboring structures, resulting in delays, disputes and cost overruns. Experimental findings on the effect of wall stiffness, depth of the stiff stratum away from the wall toe and wall toe fixity condition are presented and discussed. © 2012 Taylor & Francis Group.

Automatically Recovering Geometry and Texture from Large Sets of Calibrated Images

Three-dimensional models which contain both geometry and texture have numerous applications such as urban planning, physical simulation, and virtual environments. A major focus of computer vision (and recently graphics) research is the automatic recovery of three-dimensional models from two-dimensional images. After many years of research this goal is yet to be achieved. Most practical modeling systems require substantial human input and unlike automatic systems are not scalable. This thesis presents a novel method for automatically recovering dense surface patches using large sets (1000's) of calibrated images taken from arbitrary positions within the scene. Physical instruments, such as Global Positioning System (GPS), inertial sensors, and inclinometers, are used to estimate the position and orientation of each image. Essentially, the problem is to find corresponding points in each of the images. Once a correspondence has been established, calculating its three-dimensional position is simply a matter of geometry. Long baseline images improve the accuracy. Short baseline images and the large number of images greatly simplifies the correspondence problem. The initial stage of the algorithm is completely local and scales linearly with the number of images. Subsequent stages are global in nature, exploit geometric constraints, and scale quadratically with the complexity of the underlying scene. We describe techniques for: 1) detecting and localizing surface patches; 2) refining camera calibration estimates and rejecting false positive surfels; and 3) grouping surface patches into surfaces and growing the surface along a two-dimensional manifold. We also discuss a method for producing high quality, textured three-dimensional models from these surfaces. Some of the most important characteristics of this approach are that it: 1) uses and refines noisy calibration estimates; 2) compensates for large variations in illumination; 3) tolerates significant soft occlusion (e.g. tree branches); and 4) associates, at a fundamental level, an estimated normal (i.e. no frontal-planar assumption) and texture with each surface patch.