Hard Decision Cooperative Spectrum Sensing Based on Estimating the Noise Uncertainty Factor

Spectrum Sensing (SS) is one of the most challenging issues in Cognitive Radio (CR) systems. Cooperative Spectrum Sensing (CSS) is proposed to enhance the detection reliability of a Primary User (PU) in fading environments. In this paper, we propose a hard decision based CSS algorithm using energy detection with taking into account the noise uncertainty effect. In the proposed algorithm, two dynamic thresholds are toggled based on predicting the current PU activity, which can be successfully expected using a simple successive averaging process with time. Also, their values are evaluated using an estimated value of the noise uncertainty factor. These dynamic thresholds are used to compensate the noise uncertainty effect and increase (decrease) the probability of detection (false alarm), respectively. Theoretical analysis is performed on the proposed algorithm to deduce its enhanced false alarm and detection probabilities compared to the conventional hard decision CSS. Moreover, simulation analysis is used to confirm the theoretical claims and prove the high performance of the proposed scheme compared to the conventional CSS using different fusion rules.Comment: 5 pages, 4 figures, IEEE International Conference on Computer Engineering and Systems (ICCES 2015). arXiv admin note: text overlap with arXiv:1505.0558

Platforms for big data business models in the healthcare context

Abstract. The profitability of the business opportunity is defined by the level of owned data and its insights to the business organization. However, the existing literature has not identified how to link between different business models in the data-oriented systems. The previous research efforts focused on the technical aspects of data including data monetization, clustering, and data lifecycle. The purpose of this research is to understand how to link big data and business model thinking in the healthcare context. The main argument of this study provides a novel way to the modularity in the big data business models, which enables the system customers to control the system Studies show if there is a kind of data-oriented platform that remind patients to do certain tasks (ex. nutrition and medicine reminders) before going to doctors and nurses; the patients would like to use it. In addition, around 90% of the platform users will recommend it to other patients and so on. This pushes the operators in the healthcare industry to transform their traditional human-based data systems into a computer-to-computer system. In the data-intensive systems like the healthcare industry, the value creation is done by monetizing data between system actors to analyze the data and develop extensive knowledge about the end customer. For example, the hospitals have the right to own and anonymize the patient data to ensure the privacy and security of patient information. Then hospitals monetize the patient data with their business partner who has the technical and analytical capability to analyze data. Later, they provide the system with useful insights gained from data analytics. This is an exploratory phase of research where the qualitative case study approach is applied to examine the possibility of having a common platform for the integrated solutions in the data-oriented systems. To approach these platforms, an empirical study has been conducted over three case companies working in the healthcare context. The data were collected using semi-structured interview discussion. Similar qualitative approaches have been used in some prior studies to examine the value creation in the data-oriented systems and identify the future business models for the digital environments and IoT. This research contributes to the existing literature by identifying four main platforms for big data business models. The modular platform is done due to the lack of knowledge about the end-customer, it grants system partners the right to control over their platforms. The partnership platform guarantees the continuity of the business process, the Ecosystemic platform gives the end customer the possibility to select what they need from the overall ecosystem. The ownership platform is related to the centralized control over the data source, enabling consistency of the business process

Flow boiling of R134a in vertical mini-diameter tubes

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 21/03/2011.The current study is a part of a long term experimental project devoted to investigate flow boiling heat transfer, pressure drop and flow visualization of R134a in small to mini/micro-diameter tubes. The experimental facility was first designed and constructed by X. Huo (2005) with the contribution of L. Chen (2006). In the present study, the experimental facility was upgraded by changing the heating system from AC to DC heating and also upgrading the logging system through using a faster data logger and developing a new Labview program. The objectives of the current study include (i) contribute in identifying the reasons behind the wide scatter in the published flow boiling heat transfer results, (ii) contribute in understanding the fundamentals of flow boiling heat transfer in mini/micro-diameter tubes and (iii) evaluation of the existing heat transfer and pressure drop prediction methods. Two sizes of stainless steel tubes were investigated in the current study; 0.52 mm and 1.1 mm diameter. In the current study, the 0.52 mm tube was roughly called a “micro-tube” whilst the 1.1 mm tubes were called “mini-tubes”. The present study proposes two possible reasons for the scatter in the published heat transfer results. The first reason is the variations in the heated length from one study to another–there is no criterion for choosing the heated length. The second reason is the variations in the inner surface characteristics of the channels from one study to another. These two important parameters were not taken into consideration by researchers in the past studies. Accordingly, the effect of the heated length was investigated in the current study using a seamless cold drawn tube with diameter of 1.1 mm and heated length ranging from 150 to 450 mm. The effect of the tube inner surface was also tested here by conducting the test in two stainless steel tubes with diameter of 1.1 mm and manufactured by two different processes. The first tube was manufactured by welding technique whilst the second tube was a seamless cold drawn tube. Both tubes were identical in design and dimensions. The inner surface of each tube was examined first using SEM analysis and demonstrated that, the surface morphology is completely different. The local heat transfer coefficient was determined through measuring the local wall temperature using 14 K-type thermocouples attached to the wall using thermally conducting but electrically insulating epoxy supplied by Omega. Pressure drop was measured directly across the heated section and a high speed camera was used for the flow visualization at 1000 frames/s. All measurements were recorded after the system attained steady state. The experimental conditions include mass flux range of 100 – 500 kg/m2 s, system pressure range of 6 – 10 bar, inlet sub-cooling of about 5K and exit quality up to about 0.9. The most frequently observed flow regimes in the 0.52 mm tube were found to be slug (elongated bubble), transition to annular and annular flow regimes. In the 1.1 mm tube, the observed regimes were found to be slug, churn and annular. The transition from slug flow to annular flow in the 0.52 mm tube occurred smoothly with little disturbances at the liquid vapour interface compared to the 1.1 mm tube. Additionally, increasing the heated length of the 1.1 mm tube was found to shift the transition to annular flow to occur at lower vapour quality. The heat transfer results demonstrated that the behaviour of the local heat transfer coefficient in the 0.52 mm diameter tube is different compared to that in the 1.1 mm tubes. Also, the tube inner surface characteristics and the heated length were found to strongly influence the local behaviour of the heat transfer coefficient. Flow boiling hysteresis was investigated and the results indicated that hysteresis exists only at very low heat fluxes near the boiling incipience. Existing heat transfer and pressure drop correlations were examined using the results of the 0.52 and 1.1 mm seamless cold drawn tubes. The pressure drop data were predicted very well using the Muller-Stienhagen and Heck (1986) correlation, the homogeneous flow model and the correlation of Mishima and Hibiki (1996). On the contrary, all macro and microscale heat correlations failed to predict the current experimental data. The mechanistic models failed to predict the data of all tubes with the same accuracy. Accordingly, two heat transfer correlations were proposed in the current study. The first correlation is based on dimensionless groups whilst the second is based on the superposition model of Chen (1966). Both correlations predicted the current experimental data and the data of Huo (2005) and Shiferaw (2008) very well.Egyptian Ministry of Higher Educatio

Preparation of proton exchange membrane by radiation-induced grafting method : Grafting of styrene onto poly(ethylene tetrafluoroethylene) copolymer films

Radiation induced grafting of styrene onto poly(ethylene-tetrafluoroethylene) (ETFE) copolymer film was carried out to prepare graft copolymer (ETFE-g-polystyrene) that can host sulfonic acid groups and form proton exchange membrane for polymer electrolyte fuel cell (PEFC). The effect of monomer concentration and type of solvent on the degree of grafting was investigated. The formation of graft copolymer film was confirmed by FTIR spectrum analysis

Recurrence Relations for Moments of Dual Generalized Order Statistics from Weibull Gamma Distribution and Its Characterizations

In this paper, we establish explicit forms and new recurrence relations satisfied by the single and product moments of dual generalized order statistics from Weibull gamma distribution (WGD). The results include as particular cases the relations for moments of reversed order statistics and lower records.We present characterizations ofWGD based on (i) recurrence relation for single moments, (ii) truncated moments of certain function of the variable and (iii) hazrad function

New Mathematical Properties For Rayleigh distribution

Regression analysis is one of the most commonly statistical techniques used for analyzing data in different fields. And used to fit the relation between the dependent variable and the independent variables require strong assumption to be met in the model. Generalized linear models (GLMs) allow the extension of linear modeling ideas to a wider class of response types, such as count data or binary responses. Many statistical methods exist for such data types, but the advantage of the GLM approach is that it unites a seemingly disparate collection of response types under a common modeling methodology. So, the problem of the current research is to try to provide a new mathematical property for Exponentiated Rayleigh distribution, and it was one of the most important properties that was studied is to determine Harmonic Mean, as well as calculating the Quantile function, Moments of Residual life (MRL), Reversed Residual Life, Mean of Residual life. The study also presented the probability density function (pdf) and cumulative distribution function according to linear representations.

Soft Decision Cooperative Spectrum Sensing Based Upon Noise Uncertainty Estimation

Spectrum Sensing (SS) constitutes the most critical task i n Cognitive Radio (CR) systems for Primary User (PU) detection. Cooperative Spectrum Sensing (CSS) is introduced to enhance the detection reliability of the PU in fading environments. In this paper, we propose a soft decision based CSS algorithm using energy detection by taking into account the noise uncertainty effect. In the proposed algorithm, two threshold levels are utilized based on predicting the current PU activity, which can be successfully expected using a simple successive averaging process with time. The two threshold levels are evaluated based on estimating the noise uncertainty factor. In addition, they are toggled in a dynamic manner to compensate the noise uncertainty effect and to increase the probability of detection and decrease the probability of false alarm. Theoretical analysis is performed on the proposed algorithm to evaluate its enhanced false alarm and detection probabilities over the conventional soft decision CSS using different combining schemes. In addition, simulation results show the high efficiency of the proposed scheme compared to the conventional soft decision CSS, with high computational complexity enhancements.Comment: 6 Pages, 5 Figures, ICC workshops 201

Spectral tau-Jacobi algorithm for space fractional advection-dispersion problem

In this paper, we use the shifted Jacobi polynomials to approximate the solution of the space fractional advection-dispersion. The method is based on the Jacobi operational matrices of fractional derivative and integration. A double shifted Jacobi expansion is used as an approximating polynomial. We apply this method to solve linear and nonlinear term FDEs by using initial and boundary conditions