Efficient computation of cross-sections from human brain model by geometric processing

In this paper, an efficient method for computing the cross-sections of the internal structure from a 3D human brain model has been proposed. It can extract image slices from the brain model in sagittal, coronal, and axial views used for computed tomography and ultrasonography. A doubly connected edge list (DCEL) has been used for speeding up the computation during geometric processing, since the DCEL captures the topological relationship among vertices, edges, and faces of the triangulated surface. For a sectional plane, image slices are computed quite efficiently using the information of geometric coherence from the previous sectional plane with the help of DCEL. The optimal distance between two successive sectional planes is determined from the frequency distribution (Poisson distribution) of the edge lengths in the model. It reduces computational overhead without compromising on the quality of output, as demonstrated by experimental results.     

Runtime and language support for compiling adaptive irregular programs on distributed-memory machines

In many scientific applications, arrays containing data are indirectly indexed through indirection arrays. Such scientific applications are called irregular programs and are a distinct class of applications that require special techniques for parallelization. This paper presents a library called CHAOS, which helps users implement irregular programs on distributed-memory message-passing machines, such as the Paragon, Delta, CM-5 and SP-1. The CHAOS library provides efficient runtime primitives for distributing data and computation over processors; it supports efficient index translation mechanisms and provides users high-level mechanisms for optimizing communication. CHAOS subsumes the previous PARTI library and supports a larger class of applications. In particular, it provides efficient support for parallelization of adaptive irregular programs where indirection arrays are modified during the course of computation. To demonstrate the efficacy of CHAOS, two challenging real-life adaptive applications were parallelized using CHAOS primitives: a molecular dynamics code, CHARMM, and a particle-in-cell code, DSMC. Besides providing runtime support to users, CHAOS can also be used by compilers to automatically parallelize irregular applications. This paper demonstrates how CHAOS can be effectively used in such a framework. By embedding CHAOS primitives in the Syracuse Fortran 90D/HPF compiler, kernels taken from the CHARMM and DSMC codes have been automatically parallelized.     

Vulnerabilities in cognitive radio networks: A survey

Cognitive radio networks are envisioned to drive the next generation wireless networks that can dynamically optimize spectrum use. However, the deployment of such networks is hindered by the vulnerabilities that these networks are exposed to. Securing communications while exploiting the flexibilities offered by cognitive radios still remains a daunting challenge. In this survey, we put forward the security concerns and the vulnerabilities that threaten to plague the deployment of cognitive radio networks. We classify various types of vulnerabilities and provide an overview of the research challenges. We also discuss the various techniques that have been devised and analyze the research developments accomplished in this area. Finally, we discuss the open research challenges that must be addressed if cognitive radio networks were to become a commercially viable technology.     

An interactive evolutionary multi-objective optimization and decision making procedure

With the advent of efficient techniques for multi-objective evolutionary optimization (EMO), real-world search and optimization problems are being increasingly solved for multiple conflicting objectives. During the past decade of research and application, most emphasis has been spent on finding the complete Pareto-optimal set, although EMO researchers were always aware of the importance of procedures which would help choose one particular solution from the Pareto-optimal set for implementation. This is also one of the main issues on which the classical and EMO philosophies are divided on. In this paper, we address this long-standing issue and suggest an interactive EMO procedure which will involve a decision-maker in the evolutionary optimization process and help choose a single solution at the end. This study uses many year’s of research on EMO and would hopefully encourage both practitioners and researchers to pay more attention in viewing the multi-objective optimization as a aggregate task of optimization and decision-making.     

Batch and continuous (fixed-bed column) biosorption of Cu(II) by Tamarindus indica fruit shell

The feasibility of employing Tamarindus indica (tamarind) fruit shell (TFS) as low-cost biosorbent for removal of Cu(II) from aqueous solutions was investigated. Batch experiments were carried out as function of initial solution pH (2–7), contact time (10–240 min), initial Cu(II) concentration (20–100 mg L^?1), biosorbent dose (0.5–5 g) and temperature (293–313 K). Biosorption equilibrium data were well described by the Langmuir isotherm model with maximum biosorption capacity of 80.01 mg g^?1 at 313 K. Biosorption of Cu(II) followed pseudo-second-order kinetics. Gibbs free energy (ΔG⁰) was spontaneous for all interactions, and the biosorption process exhibited endothermic enthalpy values. To ascertain the practical applicability of the biosorbent, fixed-bed column studies were also performed. The breakthrough time increased with increasing bed height and decreased with increasing flow rate. The Thomas model as well as the Bed Depth Service Time (BDST) model was fitted to the dynamic flow experimental data to determine the column kinetic parameters useful for designing large-scale column studies. The Thomas model showed good agreement with the experimental results at all the process parameters studied. It could be concluded that TFS may be used as an inexpensive and effective biosorbent without any treatment or any other modification for the removal of Cu(II) ions from aqueous solutions.     

Adsorption Thermodynamics and Kinetics of Malachite Green onto Ca(OH)2-Treated Fly Ash

Fly ash, an industrial by-product abundant in India, was treated with alkali and tested as a low-cost adsorbent for the removal of malachite green from an aqueous solution in a batch adsorption procedure. Effects of stirring rate, temperature, pH, initial dye concentration, contact time, and adsorbent dose were investigated. The adsorption was found to be strongly dependent on pH of the medium and the adsorption capacity decreased with an increase in temperature. The Langmuir isotherm model showed a good fit to the equilibrium adsorption data at all temperatures. The mean free energy (E) estimated from the Dubinin-Radushkevich model indicated that the adsorption mechanism was chemical ion exchange. The kinetic data were found to follow the pseudo second-order kinetic model. The rate constant decreased with the increase in temperature indicating the exothermic nature of adsorption. Intraparticle diffusion was not the sole rate-controlling factor. The Arrhenius and Eyring equations were used to evaluate the activation parameters. The activation energy (Ea) was estimated to be 56.08??kJ?mol-1. Gibbs free energy (ΔG0) was spontaneous for all interactions, and the adsorption process exhibited exothermic enthalpy values. Results suggest that alkali-treated fly ash is a potential low-cost adsorbent for removal of malachite green from an aqueous solution.     

Simplex and duplex PCR assays for species specific identification of cattle and buffalo milk and cheese

A polymerase chain reaction, amplifying a fragment of the mitochondrial DNA D loop region was developed for species specific detection of cattle and buffalo milk. The method was simultaneously extended for detection of HTST pasteurized milk samples and cheeses of bovine and buffalo origin. A common forward primer was used with two different species specific reverse primers that resulted amplification of a 126 bp and 226 bp products for cattle and buffalo, respectively, in simplex as well as in multiplex polymerase chain reaction. The primers successfully amplified DNA extracted by conventional protocol from minimal amount of raw milk, heat treated milk and cheese of either bovine or buffalo origin. The primers showed a high degree of specificity. The sensitivity of the assay was excellent with detection level of 0.1 percent adulteration of cow and buffalo milk or cheese (0.15 ng buffalo and 0.04 ng cattle DNA). The assay represents a sensitive and simple method for identification of adulteration in milk and cheese.     

Investigating the Effect of Process Parameter on the Properties of Rf Sputtered pSi Thin Film by Taguchi and ANOVA Analysis

Silicon - Boron doped pSi was deposited on Si substrate in the RF magnetron sputtering system by varying three process parameters, namely-sputtering power, working pressure, and Ar gas flow rate....     

A hierarchical method combining gait and phase of motion with spatiotemporal model for person re-identification

Re-identification refers to the problem of establishing correspondence among various observations of the same subject viewed at different time instances in different camera positions. We propose a hierarchical approach for re-identifying a subject by combining gait with phase of motion and a spatiotemporal model. The fundamental nature of the gait biometric of being amenable to capturing from a distance even at low resolution without active co-operation of subjects, has motivated us to use it for re-identification. We use two features related to a subject’s motion dynamics, one is his exit/entry phase of motion and the other is his gait signature. An additional third feature is obtained from the spatiotemporal model of the camera network which is learnt during the training phase in the form of a multivariate probability density of space–time variables (entry/exit location, exit velocity, and inter-camera travel time) using kernel density estimation. Once all these three features have been computed, correspondences are established by dynamic programing based maximum likelihood (ML) estimation. The performance of our method has been evaluated on a real data set featuring a two-camera and a three-camera network in a hallway monitoring situation. The proposed approach shows promising results on both the data sets.     

IoT & AI Enabled Three-Phase Secure and Non-Invasive COVID 19 Diagnosis System

Corona is a viral disease that has taken the form of an epidemic and is causing havoc worldwide after its first appearance in the Wuhan state of China in December 2019. Due to the similarity in initial symptoms with viral fever, it is challenging to identify this virus initially. Non-detection of this virus at the early stage results in the death of the patient. Developing and densely populated countries face a scarcity of resources like hospitals, ventilators, oxygen, and healthcare workers. Technologies like the Internet of Things (IoT) and artificial intelligence can play a vital role in diagnosing the COVID-19 virus at an early stage. To minimize the spread of the pandemic, IoT-enabled devices can be used to collect patient’s data remotely in a secure manner. Collected data can be analyzed through a deep learning model to detect the presence of the COVID-19 virus. In this work, the authors have proposed a three-phase model to diagnose covid-19 by incorporating a chatbot, IoT, and deep learning technology. In phase one, an artificially assisted chatbot can guide an individual by asking about some common symptoms. In case of detection of even a single sign, the second phase of diagnosis can be considered, consisting of using a thermal scanner and pulse oximeter. In case of high temperature and low oxygen saturation levels, the third phase of diagnosis will be recommended, where chest radiography images can be analyzed through an AI-based model to diagnose the presence of the COVID-19 virus in the human body. The proposed model reduces human intervention through chatbot-based initial screening, sensor-based IoT devices, and deep learning-based X-ray analysis. It also helps in reducing the mortality rate by detecting the presence of the COVID-19 virus at an early stage.