Supervised learning of large perceptual organization: graphspectral partitioning and learning automata

Perceptual organization offers an elegant framework to group low-level features that are likely to come from a single object. We offer a novel strategy to adapt this grouping process to objects in a domain. Given a set of training images of objects in context, the associated learning process decides on the relative importance of the basic salient relationships such as proximity, parallelness, continuity, junctions, and common region toward segregating the objects from the background. The parameters of the grouping process are cast as probabilistic specifications of Bayesian networks that need to be learned. This learning is accomplished using a team of stochastic automata in an N-player cooperative game framework. The grouping process, which is based on graph partitioning is able to form large groups from relationships defined over a small set of primitives and is fast. We statistically demonstrate the robust performance of the grouping and the learning frameworks on a variety of real images. Among the interesting conclusions is the significant role of photometric attributes in grouping and the ability to form large salient groups from a set of local relations, each defined over a small number of primitives     

Ternary Logic Gates Using Quantum Dot Gate FETs (QDGFETs)

Quantum dot gate FETs (QDGFET) produce one intermediate state between two stable on and off states due to the change in the threshold voltage. A circuit model based on Berkeley Short-channel IGFET Model (BSIM) that accounts for this intermediate state is developed. Different ternary logics such as ternary logic inverter, MAX-MIN functions, multiplier, comparator, etc. can be implemented using QDGFETs. In this work the designs of ternary logic AND and OR gate based on QDGFET is introduced. Increased number of states in three state QDGFETs will increase the number of bit handling capability of this device and will help to handle more bits at a time with less circuit elements.     

Statistical optimization of desugarization process parameters of liquid whole egg using response surface methodology

The glucose is considered to affect the quality of the dried egg products; therefore the statistical optimization of desugarization process parameters of Liquid whole egg (LWE) was carried out in this study. The fermentation of Liquid whole egg (LWE) was done using compressed baker’s yeast. A polynomial model was regressed between reducing sugar & desugarization conditions. The coefficient of determination (R²) of 0.998 (P < 0.0001) for the regressed model indicated about the adequacy of the model, which implies the model was significant to predict reducing sugar content at different fermentation conditions. Optimum conditions for complete desugarization of Liquid whole egg (LWE) were found to be time 6.49 h, temperature 28 °C, pH 6.40 and yeast concentration 3.8 g/L and confirmed by conducting actual experiments at these conditions.     

A low cost design and development of a reluctance type pressure transducer

Transmission of Bourdon gauge reading to a remote location is very important in industry. This requires a conversion of Bourdon tube displacement into an electrical signal with the help of a suitable transducer. In the present paper, a simple low cost reluctance type pressure transducer has been developed. Its theoretical analysis has been presented in the paper. The transducer has been developed and tested in the laboratory and has been found to follow the theoretical equations. The experimental results of the observed characteristics of the transducer are reported in the paper. A very good linearity and repeatability of the data has been observed.     

Association between some planktonic species and physicochemical characteristics of the water of Loktak Lake,Manipur, India

Four‐hourly data were collected for 24 hour periods in the months of December (1977), April (1978), July (1978) and December (1978) in respect of some physicochemical characteristics and plankton population in a protected part of the Loktak Lake (Manipur) India. Highly significant correlations, positive as well as negative, were observed between the diurnal and seasonal variations of physicochemical characteristics with those of total phyto or zooplankton of certain species.     

A Robust Experimental Methodology for Polymer Bubble Inflation

In thermoforming technique thermoplastic sheets are heated up well above their glass transition temperature and formed to the required shape by using an appropriate mold. Characterization of thermoplastic materials for thermoforming can be accomplished by employing polymer bubble inflation and rheology tests instead of undertaking expensive biaxial tensile testing. Polymer bubble inflation technique is very sensitive to process condition variations, so a robust experimental methodology is essential. Design and development of one such experimental system was undertaken by carrying out a variety of preliminary tests. This paper presents the experimental methodology developed for polymer bubble inflation. The developed experimental system demonstrates highly repeatable polymer bubble inflations. Bubble inflations were conducted at different temperatures and different diameter circular clamping using acrylonitrile butadiene styrene (ABS) thermoplastic. Polymer sheet initial sag due to heating and its influence on bubble inflation have been captured by using the experimental system.     

Studies of microstructural imperfections of powdered Zirconium-based alloys

Different model based approaches of X-ray diffraction line profile analysis have been applied on the heavily deformed zirconium-based alloys in the powdered form to characterize the microstructural parameters like domain size, microstrain and dislocation density. In characterizing the microstructure of the material, these methods are complimentary to each other. Though the parameters obtained by different techniques are differently defined and thus not necessarily comparable, the values of domain size and microstrain obtained from the different techniques show similar trends.     

GPARS: a general-purpose activity recognition system

The fundamental problem of the existing Activity Recognition (AR) systems is that these are not general-purpose. An AR system trained in an environment would only be applicable to that environment. Such a system would not be able to recognize the new activities of interest. In this paper we propose a General-Purpose Activity Recognition System (GPARS) using simple and ubiquitous sensors. It would be applicable to almost any environment and would have the ability to handle growing amounts of activities and sensors in a graceful manner (Scalable). Given a set of activities to monitor, object names (with embedded sensors) and their corresponding locations, the GPARS first mines activity knowledge from the web, and then uses them as the basis of AR. The novelty of our system, compared to the existing general-purpose systems, lies in: (1) it uses more robust activity models, (2) it significantly reduces the mining time. We have tested our system with three real world datasets. It is observed that the accuracy of activity recognition using our system is more than 80%. Our proposed mechanism yields significant improvement (more than 30%) in comparison with its counterpart.