http://www.iiste.org/Journals/index.php/CTI/issue/feedControl Theory and Informatics2015-02-28T10:53:53+00:00Alexander Deckeradmin@iiste.orgOpen Journal Systems<p><span id="internal-source-marker_0.04939836589619517">Control Theory and Informatics <span id="internal-source-marker_0.04939836589619517">is a peer reviewed journal published by IISTE. The journal publishes original papers at the forefront of classic and modern control theory, informatics research and their applications. The journal is published in both printed and online versions. The online version is free access and download.</span></span></p><p><span><span>IISTE is member of <a href="http://www.crossref.org/01company/17crossref_members.html">CrossRef</a>.<br /></span></span></p>http://www.iiste.org/Journals/index.php/CTI/article/view/20058Journal Cover Page2015-02-28T10:53:52+00:00Journal Editord@d.comJournal Cover Pagehttp://www.iiste.org/Journals/index.php/CTI/article/view/20059Modernized IRNSS Broadcast Ephemeris Parameters2015-02-28T10:53:52+00:00M V Chandrasekhar D.Rajarajand@d.comG Satyanarayana Neetha Tirmald@d.comS C Rathnakara A S Ganeshand@d.com<p>India has successfully stepped into satellite Navigation system with the launch of its first three IRNSS satellites IRNSS 1A, 1B and 1C. IRNSS provides two types of services, Standard Posting Service (SPS), which is open for civilian use and the Restricted Service (RS), for authorized users. The system is set to change the facet of navigation, surveying, transportation, precision agriculture, disaster management and telecommunication in India. In any navigation system, broadcast navigation parameters are of paramount importance in arriving user position solution at user receiver end. IRNSS Navigation data is classified as primary and secondary Navigation parameters. Primary navigation data of a satellite principally represents its own orbit and onboard clock offset in the form of quasi-keplerian elements and clock coefficients (Bias, Drift and Drifts rate) respectively. Whereas secondary navigation parameters includes satellite almanac, ionosphere delay correction messages, differential corrections, Earth orientation parameters and IRNSS Time offset with respect to other GNSS. In existing IRNSS system satellite ephemeris of primary navigation parameters are broadcast in the form of 15 quasi-keplerian elements valid for a period of 2 hours or more. Spacecraft ephemeris which represents orbit in the form of 9 parameters, i.e., position, velocity and acceleration component of spacecraft in Cartesian coordinate system are chosen from Russian Global Navigation satellite system (Glonass) to improve Time to First Fix (TTFF) of IRNSS system with similar existing orbit accuracy. In addition, two models of user receiver orbit propagation algorithms with proposed ephemeris are briefed and their results are compared with standalone Glonass model. Generation of IRNSS ephemeris in Cartesian coordinate system and description of user receiver orbit propagation algorithms using new type of ephemeris to get user position solution is the scope of this paper..</p> <p><strong>Keywords: </strong>IRNSS, TTFF (Time to First Fix), Broadcast ephemeris</p>http://www.iiste.org/Journals/index.php/CTI/article/view/20060Modeling of IRNSS System Time-Offset with Respect to other GNSS2015-02-28T10:53:53+00:00Kalasagar Varma D.Rajarajand@d.comNeetha Tirmal Rathnakara S Cd@d.comGaneshan A Sd@d.com<p>The IRNSS System Time started at 00:00 UT on Sunday August 22nd 1999 (midnight between August 21st and 22nd). At the start epoch, IRNSS system time was ahead of UTC by 13 leap seconds. (i.e. IRNSS time, August 22nd 1999, 00:00:00 corresponds to UTC time August 21st 1999,23:59:47). IRNSS time is a continuous time without leap second corrections determined by the IRNSS System Precise Timing Facility (IRNPT) with an ensemble of Caesium and Hydrogen maser standard atomic clocks.Combining of multi GNSS satellites provides very significant advantages a) paves the way for computing the user position with increased number of satellites. b) Reduced horizontal and vertical Dilution of Precision (DOP) factors. And c) Decreased occupation time which means faster positioning results.This paper presents the 1.IRNSS time offset generation with respect to other GNSS timescales such as GPS, GLONASS system and traceability to UTC,UTC(NPLI)/UTC(K) 2.Validation of predicted time offsets with actual offsets.3.The IRNSS time offsets are derived from GNSS navigation message using UTC offsets to validate the predicted IRNSS time offsets. IRNSS times offset from GNSS are broadcasted in the form of coefficients in one of the IRNSS navigation messages. This broadcast message also allows the user to recover UTC and UTC (NPLI)/UTC(K) time for precise timings.</p> <p><strong>Keywords: </strong>IRNSS,<em> </em>IRNSS Time offsets, IRNWT, UTC, UTC (NPLI) and GNSS</p>http://www.iiste.org/Journals/index.php/CTI/article/view/20061A Review of the Effectiveness of Malware Signature Databases against Metamorphic Malwares2015-02-28T10:53:53+00:00Adesegun Oreoluwa Ad@d.com<p>Known obfuscation techniques and other methods discovered by other researches such as Desai and Stamp (2010), Mohan & Hamlem(2012) have made detection of malware more difficult. This research is positioned to reviewing the current practices in the antivirus industry and determining if malware signature databases are adequate in detecting metamorphic malwares.</p>http://www.iiste.org/Journals/index.php/CTI/article/view/20062Rational Model and Probability of Infection2015-02-28T10:53:53+00:00Are S.O Olaiju O.Ad@d.comEzikiel I.D Afolayan Od@d.com<p>We generalized the solution to the problem of probability of infection from, (<cite>mathforum.org/dr.math</cite>?). We derived a model for n hermits. The model was used to generate expectation for some number of hermits and the result tabulated and graphed. Due to computational limitation of the software used to evaluate factorial n where n is greater than 170, the model could be evaluated up to n=170 populations, we then used the curve fitting tool to fit some curves using the data generated by our model. Among all, the Rational function model(R4) performed best with SSE: 0.01654, R-square: 0.9999, Adjusted R-square: 0.9999 and RMSE: 0.0108.</p> <p><strong>Keywords: </strong>Probability, Mathematical model , Series , Infection, Population.</p> <p> </p>http://www.iiste.org/Journals/index.php/CTI/article/view/20063A Mathematical Model for the Release of Vasopressin using Fuzzy Step-Stress Approach2015-02-28T10:53:53+00:00S. Mohankumard@d.comA. Venkateshd@d.com<p>The theoretical study was to investigate the release of the hormones Vasopressin and Oxytocin from explants of the hypothalamoneurohypophysial system (HNS). A mathematical model using fuzzy constant step –stress approach was developed and used this model to calculate the mean values of the release of Vasopressin and Oxytocin. The result shows that a synergistic effect of metabotropic glutamate receptor activation of Vasopressin and Oxytocin.</p> <p><strong>Keywords: </strong> Fuzzy step-stress mean value, Vasopressin, oxytocin</p> <p><strong>2010 Mathematics Subject Classification:</strong> 97Mxx, 93A30, 60A86<strong></strong></p>http://www.iiste.org/Journals/index.php/CTI/article/view/20064Performance of Estimates of Reliability Parameters for Compound Rayleigh Progressive Type II Censored Data2015-02-28T10:53:53+00:00D. R. Barotd@d.comM. N. Pateld@d.com<p>This paper develops Bayesian analysis in the context of progressively Type II censored data from the two-parameter compound Rayleigh distribution. The maximum likelihood and Bayes estimates along with the associated posterior risks are derived for unknown reliability parameters under the balanced logarithmic loss and balanced general entropy loss functions. A practical example and simulation study have been considered to illustrate the proposed estimation methods and compare the performance of derived estimates based on maximum likelihood and Bayesian frameworks. The study indicates that Bayesian approach is more preferable over the maximum likelihood approach for estimation of the reliability parameters, while in Bayesian approach, a balance general entropy loss function can effectively be employed.</p> <p><strong>Keywords: </strong>Maximum likelihood estimation, Bayes estimation, balanced logarithmic loss function, balanced general entropy loss function, posterior risk, Monte Carlo simulation.</p>http://www.iiste.org/Journals/index.php/CTI/article/view/20065A String of Disjoint Job Blocks on Two Stage Open Shop Scheduling with Transportation Time2015-02-28T10:53:53+00:00Deepak Guptad@d.comRenuka .d@d.comHarminder Singhd@d.com<p class="Default">This paper provides a heuristic algorithm for n jobs, 2-machine Open-shop scheduling problem in which Processing times are associated with their respective probabilities. The concepts of disjoint job block in a string and transportation time from one machine to another are also taken into consideration. The specific goal of the study is to obtain an optimal or near optimal String of jobs to minimize the makespan. The heuristic algorithm developed in this paper is very simple and easy to understand. A numerical illustration is provided to demonstrate the computational efficiency of proposed algorithm.</p> <p class="Default"><strong>Key</strong><strong>w</strong><strong>ords: </strong>Open Shop Scheduling, Equivalent job, Disjoint job block, Transportation Time.<em> </em></p> <p><strong> </strong></p>