Cognitive Single Carrier Systems: Joint Impact of Multiple Licensed Transceivers

In this paper, the impact of interference from multiple licensed transceivers on cognitive underlay single carrier systems is examined. Specifically, the situation is considered in which the secondary network is limited by three key parameters: 1) maximum transmit power at the secondary transmitter, 2) peak interference power at the primary receivers, and 3) interference power from the primary transmitters. For this cognitive underlay single carrier system, the signal-to-interference ratio (SIR) of the secondary network is obtained for transmission over frequency selective fading channels. Based on this, a new closedform expression for the cumulative distribution function of the SIR is evaluated, from which the outage probability and the ergodic capacity are derived. Further insights are established by analyzing the asymptotic outage probability and the asymptotic ergodic capacity in the high transmission power regime. In particular, it is corroborated that the asymptotic outage diversity gain is equal to the multipath gain of the frequency selective channel in the secondary network. The asymptotic ergodic capacity also gives new insight into the additional power cost for different network parameters while maintaining a specified target ergodic capacity. Illustrative numerical examples are presented to validate the outage probability and ergodic capacity under different interference power profiles.

Advanced Modelling and Optimization of Infared Oven in Injection Stretch Blow-moulding for Energy Saving

In the production process of polyethylene terephthalate (PET) bottles, the initial temperature of preforms plays a central role on the final thickness, intensity and other structural properties of the bottles. Also, the difference between inside and outside temperature profiles could make a significant impact on the final product quality. The preforms are preheated by infrared heating oven system which is often an open loop system and relies heavily on trial and error approach to adjust the lamp power settings. In this paper, a radial basis function (RBF) neural network model, optimized by a two-stage selection (TSS) algorithm combined with partial swarm optimization (PSO), is developed to model the nonlinear relations between the lamp power settings and the output temperature profile of PET bottles. Then an improved PSO method for lamp setting adjustment using the above model is presented. Simulation results based on experimental data confirm the effectiveness of the modelling and optimization method.

Generalized Selection Combining for Cognitive Relay Networks over Nakagami-m Fading

We consider transmit antenna selection with receive generalized selection combining (TAS/GSC) for cognitive decodeand-forward (DF) relaying in Nakagami-m fading channels. In an effort to assess the performance, the probability density function and the cumulative distribution function of the endto-end SNR are derived using the moment generating function, from which new exact closed-form expressions for the outage probability and the symbol error rate are derived. We then derive a new closed-form expression for the ergodic capacity. More importantly, by deriving the asymptotic expressions for the outage probability and the symbol error rate, as well as the high SNR approximations of the ergodic capacity, we establish new design insights under the two distinct constraint scenarios: 1) proportional interference power constraint, and 2) fixed interference power constraint. Several pivotal conclusions are reached. For the first scenario, the full diversity order of the
outage probability and the symbol error rate is achieved, and the high SNR slope of the ergodic capacity is 1/2. For the second scenario, the diversity order of the outage probability and the symbol error rate is zero with error floors, and the high SNR slope of the ergodic capacity is zero with capacity ceiling.

Electron-acoustic solitons in an electron-beam plasma system with kappa-distributed electrons

Interaction of a stream of high-energy electrons with the background plasma plays an important role in the astrophysical phenomena such as interplanetary and stellar bow shock and Earth's foreshock emission. It is not yet fully understood how electrostatic solitary waves are produced at the bow shock. Interestingly, a population of energetic suprathermal electrons were also found to exist in those environments. Previously, we have studied the properties of negative electrostatic potential solitary structures exist in such a plasma with excess suprathermal electrons. In the present study, we investigate the existence conditions and propagation properties of electron-acoustic solitary waves in a plasma consisting of an electron beam fluid, a cold electron fluid, and hot suprathermal electrons modeled by a kappa-distribution function. The Sagdeev pseudopotential method was used to investigate the occurrence of stationary-profile solitary waves. We have determined how the electron-acoustic soliton characteristics depend on the electron beam parameters. It is found that the existence domain for solitons becomes narrower with an increase in the suprathermality of hot electrons, increasing the beam speed, decreasing the beam-to-cold electron population ratio. These results lead to a better understanding of the formation of electron-acoustic solitary waves observed in those space plasma systems characterized by kappa-distributed electrons and inertial drifting (beam) electrons.

X-ray Polarization Measurements of Dense Plasmas Heated by Fast Electrons

The detailed knowledge of fast electron energy transport following interaction with high-intensity, ultra-short laser pulses is a key area for secondary source generation for ELI. We demonstrate polarization spectroscopy at laser intensities up to 10(21) Wcm(-2). This is significant as it suggests that in situ emission spectroscopy may be used as an effective probe of fast electron velocity distributions in regimes relevant to electron transport in solid targets. Ly-alpha doublet emission of nickel (Z = 28) and sulphur (Z = 16) is observed to measure the degree of polarization from the Ly-alpha(1) emission. Ly-alpha(2) emission is unpolarized, and as such acts as a calibration source between spectrometers. The measured ratio of the X-ray sigma- and pi-polarization allows the possibility to infer the velocity distribution function of the fast electron beam.

Electron-acoustic solitons in an electron-beam plasma system with kappa-distributed electrons

We investigate the existence conditions and propagation properties of electron-acoustic solitary waves in a plasma consisting of an electron beam fluid, a cold electron fluid, and a hot suprathermal electron component modeled by a k-distribution function. The Sagdeev pseudopotential method was used to investigate the occurrence of stationary-profile solitary waves. We have determined how the soliton characteristics depend on the electron beam parameters. It is found that the existence domain for solitons becomes narrower with an increase in the suprathermality of hot electrons, increasing the beam speed, and decreasing the beam-to-cold electron population ratio.

X-ray Polarization Spectroscopy from Ultra-Intense Interactions

Detailed knowledge of fast electron energy transport following the interaction of ultrashort intense laser pulses is a key subject for fast ignition. This is a problem relevant to many areas of laser-plasma physics with particular importance to fast ignition and X-ray secondary source development, necessary for the development of large-scale facilities such as HiPER and ELI. Operating two orthogonal crystal spectrometers set at Bragg angles close to 45 degrees determines the X-ray s- and p-polarization ratio. From this ratio, it is possible to infer the velocity distribution function of the fast electron beam within the dense plasma. We report on results of polarization measurements at high density for sulphur and nickel buried layer targets in the high intensity range of 10(19) - 10(21) Wcm(-2). We observe at 45 degrees the Ly-alpha doublet using two sets of orthogonal highly-orientated pyrolytic graphite (HOPG) crystals set in 1(st) order for sulphur and 3(rd) order for nickel.

Predicting forest attributes In southeast Alaska using artificial neural networks

Artificial neural network (ANN) methods are used to predict forest characteristics. The data source is the Southeast Alaska (SEAK) Grid Inventory, a ground survey compiled by the USDA Forest Service at several thousand sites. The main objective of this article is to predict characteristics at unsurveyed locations between grid sites. A secondary objective is to evaluate the relative performance of different ANNs. Data from the grid sites are used to train six ANNs: multilayer perceptron, fuzzy ARTMAP, probabilistic, generalized regression, radial basis function, and learning vector quantization. A classification and regression tree method is used for comparison. Topographic variables are used to construct models: latitude and longitude coordinates, elevation, slope, and aspect. The models classify three forest characteristics: crown closure, species land cover, and tree size/structure. Models are constructed using n-fold cross-validation. Predictive accuracy is calculated using a method that accounts for the influence of misclassification as well as measuring correct classifications. The probabilistic and generalized regression networks are found to be the most accurate. The predictions of the ANN models are compared with a classification of the Tongass national forest in southeast Alaska based on the interpretation of satellite imagery and are found to be of similar accuracy.

A new self-learning TLBO algorithm for RBF neural modelling of batteries in electric vehicles

One of the main purposes of building a battery model is for monitoring and control during battery charging/discharging as well as for estimating key factors of batteries such as the state of charge for electric vehicles. However, the model based on the electrochemical reactions within the batteries is highly complex and difficult to compute using conventional approaches. Radial basis function (RBF) neural networks have been widely used to model complex systems for estimation and control purpose, while the optimization of both the linear and non-linear parameters in the RBF model remains a key issue. A recently proposed meta-heuristic algorithm named Teaching-Learning-Based Optimization (TLBO) is free of presetting algorithm parameters and performs well in non-linear optimization. In this paper, a novel self-learning TLBO based RBF model is proposed for modelling electric vehicle batteries using RBF neural networks. The modelling approach has been applied to two battery testing data sets and compared with some other RBF based battery models, the training and validation results confirm the efficacy of the proposed method.

Forward and backward least angle regression for nonlinear system identification

A forward and backward least angle regression (LAR) algorithm is proposed to construct the nonlinear autoregressive model with exogenous inputs (NARX) that is widely used to describe a large class of nonlinear dynamic systems. The main objective of this paper is to improve model sparsity and generalization performance of the original forward LAR algorithm. This is achieved by introducing a replacement scheme using an additional backward LAR stage. The backward stage replaces insignificant model terms selected by forward LAR with more significant ones, leading to an improved model in terms of the model compactness and performance. A numerical example to construct four types of NARX models, namely polynomials, radial basis function (RBF) networks, neuro fuzzy and wavelet networks, is presented to illustrate the effectiveness of the proposed technique in comparison with some popular methods.

Identification and output tracking control of Hammerstein systems through wireless networks

his paper investigates the identification and output tracking control of a class of Hammerstein systems through a wireless network within an integrated framework and the statistic characteristics of the wireless network are modelled using the inverse Gaussian cumulative distribution function. In the proposed framework, a new networked identification algorithm is proposed to compensate for the influence of the wireless network delays so as to acquire the more precise Hammerstein system model. Then, the identified model together with the model-based approach is used to design an output tracking controller. Mean square stability conditions are given using linear matrix inequalities (LMIs) and the optimal controller gains can be obtained by solving the corresponding optimization problem expressed using LMIs. Illustrative numerical simulation examples are given to demonstrate the effectiveness of our proposed method.

Locations of peculiar supernovae as a diagnostic of their origins

We put constraints on the properties of the progenitors of peculiar calcium-rich transients using the distribution of locations within their host galaxies. We confirm that this class of transients do not follow the galaxy stellar mass profile and are more likely to be found in remote locations of their apparent hosts. We test the hypothesis that these transients are from low-metallicity progenitors by comparing their spatial distributions with the predictions of self-consistent cosmological simulations that include star formation and chemical enrichment. We find that while metal-poor stars and our transient sample show a consistent preference for large offsets, metallicity alone cannot explain the extreme cases. Invoking a lower age limit on the progenitor helps to improve the match, indicating these events may result from a very old metal-poor population. We also investigate the radial distribution of globular cluster systems, and show that they too are consistent with the class of calcium-rich transients. Because photometric upper limits exist for globular clusters for some members of the class, a production mechanism related to the dense environment of globular clusters is not favoured for the calcium-rich events. However, the methods developed in this paper may be used in the future to constrain the effects of low metallicity on radially distant core-collapse events or help establish a correlation with globular clusters for other classes of peculiar explosions.

The dust environment of comet 67P/Churyumov-Gerasimenko from Rosetta OSIRIS and VLT observations in the 4.5 to 2.9 au heliocentric distance range inbound

Context. The ESA Rosetta spacecraft, currently orbiting around comet 67P/Churyumov-Gerasimenko, has already provided in situ measurements of the dust grain properties from several instruments,particularly OSIRIS and GIADA. We propose adding value to those measurements by combining them with ground-based observations of the dust tail to monitor the overall, time-dependent dust-production rate and size distribution.
Aims. To constrain the dust grain properties, we take Rosetta OSIRIS and GIADA results into account, and combine OSIRIS data during the approach phase (from late April to early June 2014) with a large data set of ground-based images that were acquired with the ESO Very Large Telescope (VLT) from February to November 2014.
Methods. A Monte Carlo dust tail code, which has already been used to characterise the dust environments of several comets and active asteroids, has been applied to retrieve the dust parameters. Key properties of the grains (density, velocity, and size distribution) were obtained from Rosetta observations: these parameters were used as input of the code to considerably reduce the number of free parameters. In this way, the overall dust mass-loss rate and its dependence on the heliocentric distance could be obtained accurately.
Results. The dust parameters derived from the inner coma measurements by OSIRIS and GIADA and from distant imaging using VLT data are consistent, except for the power index of the size-distribution function, which is α = −3, instead of α = −2, for grains smaller than 1 mm. This is possibly linked to the presence of fluffy aggregates in the coma. The onset of cometary activity occurs at approximately 4.3 AU, with a dust production rate of 0.5 kg/s, increasing up to 15 kg/s at 2.9 AU. This implies a dust-to-gas mass ratio varying between 3.8 and 6.5 for the best-fit model when combined with water-production rates from the MIRO experiment.

Modelling the fresh properties of self-compacting concrete using support vector machine approach

The main objective of the study presented in this paper was to investigate the feasibility using support vector machines (SVM) for the prediction of the fresh properties of self-compacting concrete. The radial basis function (RBF) and polynomial kernels were used to predict these properties as a function of the content of mix components. The fresh properties were assessed with the slump flow, T50, T60, V-funnel time, Orimet time, and blocking ratio (L-box). The retention of these tests was also measured at 30 and 60 min after adding the first water. The water dosage varied from 188 to 208 L/m3, the dosage of superplasticiser (SP) from 3.8 to 5.8 kg/m3, and the volume of coarse aggregates from 220 to 360 L/m3. In total, twenty mixes were used to measure the fresh state properties with different mixture compositions. RBF kernel was more accurate compared to polynomial kernel based support vector machines with a root mean square error (RMSE) of 26.9 (correlation coefficient of R2 = 0.974) for slump flow prediction, a RMSE of 0.55 (R2 = 0.910) for T50 (s) prediction, a RMSE of 1.71 (R2 = 0.812) for T60 (s) prediction, a RMSE of 0.1517 (R2 = 0.990) for V-funnel time prediction, a RMSE of 3.99 (R2 = 0.976) for Orimet time prediction, and a RMSE of 0.042 (R2 = 0.988) for L-box ratio prediction, respectively. A sensitivity analysis was performed to evaluate the effects of the dosage of cement and limestone powder, the water content, the volumes of coarse aggregate and sand, the dosage of SP and the testing time on the predicted test responses. The analysis indicates that the proposed SVM RBF model can gain a high precision, which provides an alternative method for predicting the fresh properties of SCC.

Neutron Scattering of Aromatic and Aliphatic Liquids

Organic solvents, such as cyclohexane, cyclohexene, methylcyclohexane, benzene and toluene, are widely used as both reagents and solvents in industrial processes. Despite the ubiquity of these liquids, the local structures that govern the chemical properties have not been studied extensively. Herein, we report neutron diffraction measurements on liquid cyclohexane, cyclohexene, methylcyclohexane, benzene and toluene at 298 K to obtain a detailed description of the local structure in these compounds. The radial distribution functions of the centres of the molecules, as well as the partial distribution functions for the double bond for cyclohexene and methyl group for methylcyclohexane and toluene have been calculated. Additionally, probability density functions and angular radial distribution functions were extracted to provide a full description of the local structure within the chosen liquids. Structural motifs are discussed and compared for all liquids, referring specifically to the functional group and aromaticity present in the different liquids.