An Efficient Parallel Algorithm for the Layered Planar Monotone Circuit Value Problem

A planar monotone circuit (PMC) is a Boolean circuit that can be embedded in the plane and that contains only AND and OR gates. A layered PMC is a PMC in which all input nodes are in the external face, and the gates can be assigned to layers in such a way that every wire goes between gates in successive layers. Goldschlager, Cook and Dymond, and others have developed NC ² algorithms to evaluate a layered PMC when the output node is in the same face as the input nodes. These algorithms require a large number of processors (Ω(n ⁶ ), where n is the size of the input circuit). In this paper we give an efficient parallel algorithm that evaluates a layered PMC of size n in time using only a linear number of processors on an EREW PRAM. Our parallel algorithm is the best possible to within a polylog factor, and is a substantial improvement over the earlier algorithms for the problem. Received April 18, 1994; revised April 7, 1995.     

A robust feature extraction with optimized DBN-SMO for facial expression recognition

Multimedia Tools and Applications - Facial expression is the most common technique is used to convey the expressions of human beings. Due to different ethnicity and age, faces differ from one...     

A Randomized Linear-Work EREW PRAM Algorithm to Find a Minimum Spanning Forest

We present a randomized EREW PRAM algorithm to find a minimum spanning forest in a weighted undirected graph. On an n -vertex graph the algorithm runs in o(( log n) ^{1+ ɛ} ) expected time for any ɛ >0 and performs linear expected work. This is the first linear-work, polylog-time algorithm on the EREW PRAM for this problem. This also gives parallel algorithms that perform expected linear work on two general-purpose models of parallel computation—the QSM and the BSP.     

Intelligent power management in micro grids with EV penetration

Large deployment of Electric Vehicles (EVs) adds new challenges in the operation of a microgrid. Assuming that a number of EV owners allow their batteries to charge when their cars are parked, this paper proposes an approach that aims to find suitable individual active power set-points corresponding to the hourly charging rate of each EV battery connected to the microgrid. A multi agent system based controller is designed to find these active power set points for optimal power management of EVs, distributed energy resources in the microgrid, and the loads.     

A technique for generating natural colour images from false colour composite images

Colour is widely used in remote sensing work. In many instances, the use of colour conveys additional information both visually and scientifically. Remote sensing satellites view the earth in different spectral bands, viz. near infrared (NIR), red, green, and blue bands, in a conventional multispectral imaging system. In the absence of a blue channel, colour images can be generated using near infrared, red, and green bands in what is known as a false colour composite (FCC) and does not look natural, like the image we see with the naked eye. For a trained interpreter, this does not pose any problems. However, when the intended use is a fly‐through of a draped terrain, visual interpretation, or a display, meant for the non‐remote sensing professional, this becomes a handicap. To overcome this, there is a requirement to generate natural colour composites (NCC) from the given false colour composite, which demands the simulation of a blue band to be combined with green and red bands. This paper describes a unique method of generating a blue band to form natural colour images from a given false colour image set. We use a spectral transformation method to establish a relationship between the false colour and true colour image pairs provided by a sensor with all the four bands, which has a broader spectral coverage. A transformation function is fitted by selecting radiometric control points along the line of geometric registration to find a set of coefficients to be used for simulating a blue band. This blue band, along with the green and red bands, provides a near true colour or ‘natural colour’ on the display. In this paper, we present a set of adjustable radiometric transformation coefficients to accommodate variation in spatial and dynamic range offered by sensors to generate natural colour. These coefficients seem to work on a large number of images of different seasons, provided similar spectral bands and terrain are used. The proposed ‘natural colour generator’ can be used in changing false colour images to natural colour images with the aim of ‘what you get is what you would have seen’.     

Screening of Soil Amendments for Efficient Water-Holding Capacity Based on a Rainfall-Infiltration Model in a Vertisol

A simulation study was conducted with five soil amendments, viz., goat manure, coir pith, phosphogypsum, polyacrylamide, and a control at two rainfall intensities of 60 and 120?mm?h?1 under dry and wet soil conditions in a clay loam vertisol with the objective of identifying a superior soil amendment for maximum infiltration and suitable soil aggregate stability. The F-test based on analysis of variance of infiltration data indicated that soil amendments, rainfall intensities, and soil conditions were significantly different from each other. Based on least significant difference test, polyacrylamide was found to be superior with a significantly higher infiltration, compared to all other amendments. An exponential model of infiltration over a time interval was calibrated for each soil amendment under dry and wet soil conditions. Based on the model, polyacrylamide and phosphogypsum were found to have a better soil aggregate stability compared to other soil amendments. The exponential model gave a significant predictability of instantaneous infiltration ranging from 0.75 to 0.99 under different situations. A grouping of treatments based on mean and coefficient of variation of infiltration in comparison with soil aggregate stability values indicated that polyacrylamide was superior under different situations in the study. Phosphogypsum was found to be the second best soil amendment with a relatively lower infiltration compared to polyacrylamide, but with a better soil aggregate stability compared to other soil amendments. Coir pith, goat manure, and control gave a significantly lower infiltration with a relatively higher variation compared to polyacrylamide and phosphogypsum, and also had a relatively lower soil aggregate stability under different situations examined in the study.     

Rheological characterization of hydroxypropylcellulose gels

The present paper describes the rheological properties of hydroxypropylcellulose (HPC) gels formulated in propylene glycol (PG), water, ethanol, and mixtures of these components. The effects of molecular weight, polymer concentration, and solvent composition on the apparent viscosity and flow characteristics have been studied by continuous shear rheometry. The HPC gels are shear thinning and do not exhibit significant yield or hysteresis in their rheograms. The apparent viscosity increases with increasing molecular weight and concentration of the polymer, as expected. Although not so pronounced at lower concentrations (≤ 1.5%), HPC gels tend to become increasingly non-Newtonian with increasing molecular weight at higher polymer concentrations (3%). A mathematical model has been proposed for the prediction of viscosities of HPC gels. There exists a high degree of dependence on molecular interactions between various solvent molecules in the prediction of mixture viscosities in ternary systems. The effects of solvent composition on the viscoelastic behavior of these gels have also been examined by dynamic mechanical analysis. The HPC gels are highly viscoelastic and exhibit greater degrees of elasticity with increased PG content in ternary solvent mixtures with water and ethanol. The study also suggests that dynamic mechanical analysis could prove to be a useful tool in the determination of zero-shear viscosities, viscosities that are representative of most realistic situations.     

ArRaNER: A novel named entity recognition model for biomedical literature documents

Developments in advanced innovations have prompted the generation of an immense amount of digital information. The data deluge contains hidden information that is difficult to extract. In the biomedical domain, the development of technology has caused the production of voluminous data. Processing these voluminous textual data is referred to as ‘biomedical content mining’. Emerging artificial intelligence (AI) models play a major role in the automation of Pharma 4.0. In AI, natural language processing (NLP) plays a dynamic role in extracting knowledge from biomedical documents. Research articles published by scientists and researchers contain an enormous amount of hidden information. Most of the original and peer-reviewed articles are indexed in PubMed. Extracting meaningful information from a large number of literature documents is very difficult for human beings. This research aims to extract the named entities of literature documents available in the life science domain. A high-level architecture is proposed along with a novel named entity recognition (NER) model. The model is built using rule-based machine learning (ML). The proposed ArRaNER model produced better accuracy and was also able to identify more entities. The NER model was tested on two different datasets: a PubMed dataset and a Wikipedia talk dataset. The ArRaNER model obtains an accuracy of 83.42% on the PubMed articles and 77.65% on the Wikipedia articles.

     

Hybrid Metaheuristics Web Service Composition Model for QoS Aware Services

Recent advancements in cloud computing (CC) technologies signified that several distinct web services are presently developed and exist at the cloud data centre. Currently, web service composition gains maximum attention among researchers due to its significance in real-time applications. Quality of Service (QoS) aware service composition concerned regarding the election of candidate services with the maximization of the whole QoS. But these models have failed to handle the uncertainties of QoS. The resulting QoS of composite service identified by the clients become unstable and subject to risks of failing composition by end-users. On the other hand, trip planning is an essential technique in supporting digital map services. It aims to determine a set of location based services (LBS) which cover all client intended activities quantified in the query. But the available web service composition solutions do not consider the complicated spatio-temporal features. For resolving this issue, this study develops a new hybridization of the firefly optimization algorithm with fuzzy logic based web service composition model (F3L-WSCM) in a cloud environment for location awareness. The presented F3L-WSCM model involves a discovery module which enables the client to provide a query related to trip planning such as flight booking, hotels, car rentals, etc. At the next stage, the firefly algorithm is applied to generate composition plans to minimize the number of composition plans. Followed by, the fuzzy subtractive clustering (FSC) will select the best composition plan from the available composite plans. Besides, the presented F3L-WSCM model involves four input QoS parameters namely service cost, service availability, service response time, and user rating. An extensive experimental analysis takes place on CloudSim tool and exhibit the superior performance of the presented F3L-WSCM model in terms of accuracy, execution time, and efficiency.