Cooperation and Underlay Mode Selection in Cognitive Radio Network

In this research, we proposes a new method for cooperation and underlay mode selection in cognitive radio networks. We characterize the maximum achievable throughput of our proposed method of hybrid spectrum sharing. Hybrid spectrum sharing is assumed where the Secondary User (SU) can access the Primary User (PU) channel in two modes, underlay mode or cooperative mode with admission control. In addition to access the channel in the overlay mode, secondary user is allowed to occupy the channel currently occupied by the primary user but with small transmission power. Adding the underlay access modes attains more opportunities to the secondary user to transmit data. It is proposed that the secondary user can only exploits the underlay access when the channel of the primary user direct link is good or predicted to be in non-outage state. Therefore, the secondary user could switch between underlay spectrum sharing and cooperation with the primary user. Hybrid access is regulated through monitoring the state of the primary link. By observing the simulation results, the proposed model attains noticeable improvement in the system performance in terms of maximum secondary user throughput than the conventional cooperation and non-cooperation schemes

Novel Dynamic Partial Reconfiguration Implementation of K-Means Clustering on FPGAs: Comparative Results with GPPs and GPUs

K-means clustering has been widely used in processing large datasets in many fields of studies. Advancement in many data collection techniques has been generating enormous amounts of data, leaving scientists with the challenging task of processing them. Using General Purpose Processors (GPPs) to process large datasets may take a long time; therefore many acceleration methods have been proposed in the literature to speed up the processing of such large datasets. In this work, a parameterized implementation of the K-means clustering algorithm in Field Programmable Gate Array (FPGA) is presented and compared with previous FPGA implementation as well as recent implementations on Graphics Processing Units (GPUs) and GPPs. The proposed FPGA has higher performance in terms of speedup over previous GPP and GPU implementations (two orders and one order of magnitude, resp.). In addition, the FPGA implementation is more energy efficient than GPP and GPU (615x and 31x, resp.). Furthermore, three novel implementations of the K-means clustering based on dynamic partial reconfiguration (DPR) are presented offering high degree of flexibility to dynamically reconfigure the FPGA. The DPR implementations achieved speedups in reconfiguration time between 4x to 15x

High-sensitivity troponin T in preterm infants with a hemodynamically significant patent ductus arteriosus

Background: The incidence of patent ductus arteriosus (PDA) is inversely related to gestational age. Cardiac troponin T (cTnT) is a specific marker for myocardial ischemic injury. The aim of this work was to investigate if a hemodynamically significant patent ductus arteriosus leads to elevated   high- sensitivity troponin T (hsTnT) levels in serum. Methods: This prospective observational research included 60 preterm infants <34 weeks’ gestation, weighing <1500 g with PDA. Patients were classified into two groups according to the ECHO findings: Group A: preterm infants who had hs.PDA (PDA >1.5 mm and LA: Ao >1.2 and group B: preterm infants without hs.PDA. All cases were subjected to clinical examination, echocardiogram and hsTnT assay. Results: Serum hsTnT levels was significantly elevated in patients with hs PDA compared to patients without hs PDA. High sensitive T-troponin can significantly detect hsPDA with AUC of 0.986, P value <0.001. At cut off >100 pg/ml, it’s a significant detector with sensitivity of 93.33%, specificity of 90%, PPV of 90.3, NPV of 93.1 and diagnostic accuracy of 91.5%. PDA had a significant positive correlation with hsTnT (r=0.803, P<0.001). Conclusions: HsTnT can significantly detect hemodynamically  significant PDA in preterm infants with high sensitivity and specificity

Pure and doped carbon quantum dots as fluorescent probes for the detection of phenol compounds and antibiotics in aquariums

Abstract The resulting antibiotic residue and organic chemicals from continuous climatic change, urbanization and increasing food demand have a detrimental impact on environmental and human health protection. So, we created a unique B, N-CQDs (Boron, Nitrogen doping carbon quantum dots) based fluorescent nanosensor to investigate novel sensing methodologies for the precise and concentrated identification of antibiotics and phenol derivatives substances to ensure that they are included in the permitted percentages. The as-prepared highly fluorescent B, N-CQDs had a limited range of sizes between 1 and 6 nm and average sizes of 2.5 nm in our study. The novel B, N-CQDs showed high sensitivity and selectivity for phenolic derivatives such as hydroquinone, resorcinol, and para aminophenol, as well as organic solvents such as hexane, with low detection limits of 0.05, 0.024, 0.032 and 0.013 µM respectively in an aqueous medium. The high fluorescence B, N-CQDs probes were examined using transmission electron microscopy (TEM), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), and UV/VIS spectroscopy. The outcomes were compared to carbon quantum dots (CQDs) previously generated from Urea