On Spectrum Sharing Between Energy Harvesting Cognitive Radio Users and Primary Users

This paper investigates the maximum secondary throughput for a rechargeable secondary user (SU) sharing the spectrum with a primary user (PU) plugged to a reliable power supply. The SU maintains a finite energy queue and harvests energy from natural resources and primary radio frequency (RF) transmissions. We propose a power allocation policy at the PU and analyze its effect on the throughput of both the PU and SU. Furthermore, we study the impact of the bursty arrivals at the PU on the energy harvested by the SU from RF transmissions. Moreover, we investigate the impact of the rate of energy harvesting from natural resources on the SU throughput. We assume fading channels and compute exact closed-form expressions for the energy harvested by the SU under fading. Results reveal that the proposed power allocation policy along with the implemented RF energy harvesting at the SU enhance the throughput of both primary and secondary links

Power-Optimal Feedback-Based Random Spectrum Access for an Energy Harvesting Cognitive User

In this paper, we study and analyze cognitive radio networks in which secondary users (SUs) are equipped with Energy Harvesting (EH) capability. We design a random spectrum sensing and access protocol for the SU that exploits the primary link's feedback and requires less average sensing time. Unlike previous works proposed earlier in literature, we do not assume perfect feedback. Instead, we take into account the more practical possibilities of overhearing unreliable feedback signals and accommodate spectrum sensing errors. Moreover, we assume an interference-based channel model where the receivers are equipped with multi-packet reception (MPR) capability. Furthermore, we perform power allocation at the SU with the objective of maximizing the secondary throughput under constraints that maintain certain quality-of-service (QoS) measures for the primary user (PU)

Optimal Spectrum Access for a Rechargeable Cognitive Radio User Based on Energy Buffer State

This paper investigates the maximum throughput for a rechargeable secondary user (SU) sharing the spectrum with a primary user (PU) plugged to a reliable power supply. The SU maintains a finite energy queue and harvests energy from natural resources, e.g., solar, wind and acoustic noise. We propose a probabilistic access strategy by the SU based on the number of packets at its energy queue. We investigate the effect of the energy arrival rate, the amount of energy per energy packet, and the capacity of the energy queue on the SU throughput under fading channels. Results reveal that the proposed access strategy can enhance the performance of the SU.Comment: arXiv admin note: text overlap with arXiv:1407.726

Microarrays and breast cancer clinical studies: forgetting what we have not yet learnt

This review takes a sceptical view of the impact of breast cancer studies that have used microarrays to identify predictors of clinical outcome. In addition to discussing general pitfalls of microarray experiments, we also critically review the key breast cancer studies to highlight methodological problems in cohort selection, statistical analysis, validation of results and reporting of raw data. We conclude that the optimum use of microarrays in clinical studies requires further optimisation and standardisation of methodology and reporting, together with improvements in clinical study design

Binding of chlorinated environmentally active chemicals to soil surfaces: Chromatographic measurements and quantum chemicalSimulations

Adsorption studies of hexachlorobenzene (HCB) on the different well-characterized soil samples were performed. A new soil organic matter (SOM) model has been developed. Interaction of this model with HCB has been studied using different quantum-mechanical methods and molecular dynamics simulations. It has been explored that the alkylated aromatic, phenol, and lignin monomer compounds dominate the adsorption process. Moreover it was found that the most vital physical properties controlling this interaction are polarizability, molar volume, and charges of C atoms of the soil constituents

Bayesian and Non-Bayesian Estimation for Weibull Parameters Based on Generalized Type-II Progressive Hybrid Censoring Scheme

Bayesian and non-Bayesian estimators are obtained for the unknown parameters of Weibull distribution based on the generalized Type-II progressive hybrid censoring scheme and different special cases are obtained. The asymptotic variance covariance matrix and approximate confidence intervals based on the asymptotic normality of the maximum likelihood estimators are obtained. Bayes estimates and Bayes risks have been developed under a squared error loss function using informative and non-informative priors for the unknown Weibull parameters. It is observed that the estimators obtained are not available in closed forms, although they can be easily evaluated for a given sample by using suitable numerical methods. Therefore, a numerical example is considered to illustrate the proposed estimators

Clustering large-scale data based on modified affinity propagation algorithm

Traditional clustering algorithms are no longer suitable for use in data mining applications that make use of large-scale data. There have been many large-scale data clustering algorithms proposed in recent years, but most of them do not achieve clustering with high quality. Despite that Affinity Propagation (AP) is effective and accurate in normal data clustering, but it is not effective for large-scale data. This paper proposes two methods for large-scale data clustering that depend on a modified version of AP algorithm. The proposed methods are set to ensure both low time complexity and good accuracy of the clustering method. Firstly, a data set is divided into several subsets using one of two methods random fragmentation or K-means. Secondly, subsets are clustered into K clusters using K-Affinity Propagation (KAP) algorithm to select local cluster exemplars in each subset. Thirdly, the inverse weighted clustering

Maximum likelihood estimation of the generalised Gompertz distribution under progressively first-failure censored sampling

In this paper, the maximum likelihood estimators of the unknown parameters, as well as some lifetime parameters survival and hazard rate functions, of a three-parameter generalised Gompertz lifetime model based on progressively first-failure censored sampling are obtained. Approximate confidence intervals for the unknown parameters and the reliability characteristics are constructed based on the s-normal approximation to the asymptotic distribution of maximum likelihood estimators. Although the proposed estimators cannot be expressed in explicit forms, these can be easily obtained through the use of appropriate numerical techniques. Finally, a real data set has been analysed for illustrative purposes