Layered titanate nanofibers as efficient adsorbents for removal of toxic radioactive and heavy metal ions from water

Titanate nanofibers with two formulas, Na2Ti3O7 and Na1.5H0.5Ti3O7, respectively, exhibit ideal properties for removal of radioactive and heavy metal ions in wastewater, such as Sr2+ , Ba2+ (as substitute of 226Ra2+), and Pb2+ ions. These nanofibers can be fabricated readily by a reaction between titania and caustic soda and have structures in which TiO6 octahedra join each other to form layers with negative charges; the sodium cations exist within the interlayer regions and are exchangeable. They can selectively adsorb the bivalent radioactive ions and heavy metal ions from water through ion exchange process. More importantly, such sorption finally induces considerable deformation of the layer structure, resulting in permanent entrapment of the toxic bivalent cations in the fibers so that the toxic ions can be safely deposited. This study highlights that nanoparticles of inorganic ion exchangers with layered structure are potential materials for efficient removal of the toxic ions from contaminated water.

Zernike radial slope polynomials for wavefront reconstruction and refraction

Ophthalmic wavefront sensors typically measure wavefront slope, from which wavefront phase is reconstructed. We show that ophthalmic prescriptions (in power-vector format) can be obtained directly from slope measurements without wavefront reconstruction. This is achieved by fitting the measurement data with a new set of orthonormal basis functions called Zernike radial slope polynomials. Coefficients of this expansion can be used to specify the ophthalmic power vector using explicit formulas derived by a variety of methods. Zernike coefficients for wavefront error can be recovered from the coefficients of radial slope polynomials, thereby offering an alternative way to perform wavefront reconstruction.

Experimental and theoretical studies of the redox potentials of cyclic nitroxides

The redox potentials of 25 cyclic nitroxides from four different structural classes (pyrrolidine, piperidine, isoindoline, and azaphenalene) were determined experimentally by cyclic voltammetry in acetonitrile, and also via high-level ab initio molecular orbital calculations. It is shown that the potentials are influenced by the type of ring system, ring substituents and/or groups surrounding the radical moiety. For the pyrrolidine, piperidine, and isoindolines there is excellent agreement (mean absolute deviation of 0.05 V) between the calculated and experimental oxidation potentials; for the azaphenalenes, however, there is an extraordinary discrepancy (mean absolute deviation of 0.60 V), implying that their one-electron oxidation might involve additional processes not considered in the theoretical calculations. This recently developed azaphenalene class of nitroxide represents a new variant of a nitroxide ring fused to an aromatic system and details of the synthesis of five derivatives involving differing aryl substitution are also presented.

Impact of cognitive load and frustration on drivers’ speech [Abstract]

Secondary tasks such as cell phone calls or interaction with automated speech dialog systems (SDSs) increase the driver’s cognitive load as well as the probability of driving errors. This study analyzes speech production variations due to cognitive load and emotional state of drivers in real driving conditions. Speech samples were acquired from 24 female and 17 male subjects (approximately 8.5 h of data) while talking to a co-driver and communicating with two automated call centers, with emotional states (neutral, negative) and the number of necessary SDS query repetitions also labeled. A consistent shift in a number of speech production parameters (pitch, first format center frequency, spectral center of gravity, spectral energy spread, and duration of voiced segments) was observed when comparing SDS interaction against co-driver interaction; further increases were observed when considering negative emotion segments and the number of requested SDS query repetitions. A mel frequency cepstral coefficient based Gaussian mixture classifier trained on 10 male and 10 female sessions provided 91% accuracy in the open test set task of distinguishing co-driver interactions from SDS interactions, suggesting—together with the acoustic analysis—that it is possible to monitor the level of driver distraction directly from their speech.

Characterisation of Ni carbonate-bearing minerals by UV-Vis-NIR spectroscopy

Four nickel carbonate-bearing minerals from Australia have been investigated to study the effect of Ni for Mg substitution. The spectra of nullaginite, zaratite, widgiemoolthalite and takovite show three main features in the range of 26,720–25,855 cm−1 (ν1-band), 15,230–14,740 cm−1 (ν2-band) and 9,200–9,145 cm−1 (ν3-band) which are characteristic of divalent nickel in six-fold coordination. The Crystal Field Stabilization Energy (CFSE) of Ni2+ in the four carbonates is calculated from the observed 3A2g(3F) → 3T2g(3F) transition. CFSE is dependent on mineralogy, crystallinity and chemical composition (Al/Mg-content). The splitting of the ν1- and ν3-bands and non-Gaussian shape of ν3-band in the minerals are the effects of Ni-site distortion from regular octahedral. The effect of structural cation substitutions (Mg2+, Ni2+, Fe2+ and trivalent cations, Al3+, Fe3+) in the carbonate minerals is noticed on band shifts. Thus, electronic bands in the UV–Vis–NIR spectra and the overtones and combination bands of OH and carbonate ion in NIR show shifts to higher wavenumbers, particularly for widgiemoolthalite and takovite.

Improving automatic speaker verification using SVM techniques

Automatic recognition of people is an active field of research with important forensic and security applications. In these applications, it is not always possible for the subject to be in close proximity to the system. Voice represents a human behavioural trait which can be used to recognise people in such situations. Automatic Speaker Verification (ASV) is the process of verifying a persons identity through the analysis of their speech and enables recognition of a subject at a distance over a telephone channel { wired or wireless. A significant amount of research has focussed on the application of Gaussian mixture model (GMM) techniques to speaker verification systems providing state-of-the-art performance. GMM's are a type of generative classifier trained to model the probability distribution of the features used to represent a speaker. Recently introduced to the field of ASV research is the support vector machine (SVM). An SVM is a discriminative classifier requiring examples from both positive and negative classes to train a speaker model. The SVM is based on margin maximisation whereby a hyperplane attempts to separate classes in a high dimensional space. SVMs applied to the task of speaker verification have shown high potential, particularly when used to complement current GMM-based techniques in hybrid systems. This work aims to improve the performance of ASV systems using novel and innovative SVM-based techniques. Research was divided into three main themes: session variability compensation for SVMs; unsupervised model adaptation; and impostor dataset selection. The first theme investigated the differences between the GMM and SVM domains for the modelling of session variability | an aspect crucial for robust speaker verification. Techniques developed to improve the robustness of GMMbased classification were shown to bring about similar benefits to discriminative SVM classification through their integration in the hybrid GMM mean supervector SVM classifier. Further, the domains for the modelling of session variation were contrasted to find a number of common factors, however, the SVM-domain consistently provided marginally better session variation compensation. Minimal complementary information was found between the techniques due to the similarities in how they achieved their objectives. The second theme saw the proposal of a novel model for the purpose of session variation compensation in ASV systems. Continuous progressive model adaptation attempts to improve speaker models by retraining them after exploiting all encountered test utterances during normal use of the system. The introduction of the weight-based factor analysis model provided significant performance improvements of over 60% in an unsupervised scenario. SVM-based classification was then integrated into the progressive system providing further benefits in performance over the GMM counterpart. Analysis demonstrated that SVMs also hold several beneficial characteristics to the task of unsupervised model adaptation prompting further research in the area. In pursuing the final theme, an innovative background dataset selection technique was developed. This technique selects the most appropriate subset of examples from a large and diverse set of candidate impostor observations for use as the SVM background by exploiting the SVM training process. This selection was performed on a per-observation basis so as to overcome the shortcoming of the traditional heuristic-based approach to dataset selection. Results demonstrate the approach to provide performance improvements over both the use of the complete candidate dataset and the best heuristically-selected dataset whilst being only a fraction of the size. The refined dataset was also shown to generalise well to unseen corpora and be highly applicable to the selection of impostor cohorts required in alternate techniques for speaker verification.

Bootstrap approaches for estimation and confidence intervals of long term memory processes

In this work, we investigate an alternative bootstrap approach based on a result of Ramsey [F.L. Ramsey, Characterization of the partial autocorrelation function, Ann. Statist. 2 (1974), pp. 1296-1301] and on the Durbin-Levinson algorithm to obtain a surrogate series from linear Gaussian processes with long range dependence. We compare this bootstrap method with other existing procedures in a wide Monte Carlo experiment by estimating, parametrically and semi-parametrically, the memory parameter d. We consider Gaussian and non-Gaussian processes to prove the robustness of the method to deviations from normality. The approach is also useful to estimate confidence intervals for the memory parameter d by improving the coverage level of the interval.

The Impact of Globalization and Corruption on Extraordinary Wealth: An Empirical Analysis

The billionaires of the world attract significant attention from the media and the public. The popular press is full of books selling formulas on how to become rich. Surprisingly, only a limited number of studies have explored empirically the determinants of extraordinary wealth. Using a large data set we explore whether globalization and corruption affect extreme wealth accumulation. We find evidence that an increase in globalization increases super-richness. In addition, we also find that an increase in corruption leads to an increase in the creation of super fortune. This supports the argument that in kleptocracies large sums are transferred into the hands of a small group of individuals.

Interleaved PC-OFDM to reduce peak-to-average power ratio

Parallel combinatory orthogonal frequency division multiplexing (PC-OFDM yields lower maximum peak-to-average power ratio (PAR), high bandwidth efficiency and lower bit error rate (BER) on Gaussian channels compared to OFDM systems. However, PC-OFDM does not improve the statistics of PAR significantly. In this chapter, the use of a set of fixed permutations to improve the statistics of the PAR of a PC-OFDM signal is presented. For this technique, interleavers are used to produce K-1 permuted sequences from the same information sequence. The sequence with the lowest PAR, among K sequences is chosen for the transmission. The PAR of a PC-OFDM signal can be further reduced by 3-4 dB by this technique. Mathematical expressions for the complementary cumulative density function (CCDF)of PAR of PC-OFDM signal and interleaved PC-OFDM signal are also presented.

Wide-angle visual feature matching for outdoor localization

Wide-angle images exhibit significant distortion for which existing scale-space detectors such as the scale-invariant feature transform (SIFT) are inappropriate. The required scale-space images for feature detection are correctly obtained through the convolution of the image, mapped to the sphere, with the spherical Gaussian. A new visual key-point detector, based on this principle, is developed and several computational approaches to the convolution are investigated in both the spatial and frequency domain. In particular, a close approximation is developed that has comparable computation time to conventional SIFT but with improved matching performance. Results are presented for monocular wide-angle outdoor image sequences obtained using fisheye and equiangular catadioptric cameras. We evaluate the overall matching performance (recall versus 1-precision) of these methods compared to conventional SIFT. We also demonstrate the use of the technique for variable frame-rate visual odometry and its application to place recognition.

Experimental evaluation of position control methods for hydraulic systems

Presents a unified and systematic assessment of ten position control strategies for a hydraulic servo system with single-ended cylinder driven by a proportional directional control valve. We aim at identifying those methods that achieve better tracking, have a low sensitivity to system uncertainties, and offer a good balance between development effort and end results. A formal approach for solving this problem relies on several practical metrics, which is introduced herein. Their choice is important, as the comparison results between controllers can vary significantly, depending on the selected criterion. Apart from the quantitative assessment, we also raise aspects which are difficult to quantify, but which must stay in attention when considering the position control problem for this class of hydraulic servo systems.

Avoiding full extension field arithmetic in pairing computations

The most costly operations encountered in pairing computations are those that take place in the full extension field Fpk . At high levels of security, the complexity of operations in Fpk dominates the complexity of the operations that occur in the lower degree subfields. Consequently, full extension field operations have the greatest effect on the runtime of Miller’s algorithm. Many recent optimizations in the literature have focussed on improving the overall operation count by presenting new explicit formulas that reduce the number of subfield operations encountered throughout an iteration of Miller’s algorithm. Unfortunately, almost all of these improvements tend to suffer for larger embedding degrees where the expensive extension field operations far outweigh the operations in the smaller subfields. In this paper, we propose a new way of carrying out Miller’s algorithm that involves new explicit formulas which reduce the number of full extension field operations that occur in an iteration of the Miller loop, resulting in significant speed ups in most practical situations of between 5 and 30 percent.

Faster pairing computations on curves with high-degree twists

Research on efficient pairing implementation has focussed on reducing the loop length and on using high-degree twists. Existence of twists of degree larger than 2 is a very restrictive criterion but luckily constructions for pairing-friendly elliptic curves with such twists exist. In fact, Freeman, Scott and Teske showed in their overview paper that often the best known methods of constructing pairing-friendly elliptic curves over fields of large prime characteristic produce curves that admit twists of degree 3, 4 or 6. A few papers have presented explicit formulas for the doubling and the addition step in Miller’s algorithm, but the optimizations were all done for the Tate pairing with degree-2 twists, so the main usage of the high- degree twists remained incompatible with more efficient formulas. In this paper we present efficient formulas for curves with twists of degree 2, 3, 4 or 6. These formulas are significantly faster than their predecessors. We show how these faster formulas can be applied to Tate and ate pairing variants, thereby speeding up all practical suggestions for efficient pairing implementations over fields of large characteristic.

Hollywood dominance : will it continue?

Hollywood has dominated the global film business since the First World War. Economic formulas used by governments to assess levels of industry dominance typically measure market share to establish the degree of industry concentration. The business literature reveals that a marketing orientation strongly correlates with superior market performance and that market leaders that possess a set of six superior marketing capabilities are able to continually outperform rival firms. This paper argues that the historical evidence shows that the Hollywood Majors have consistently outperformed rival firms and rival film industries in each of those six marketing capabilities and that unless rivals develop a similarly integrated and cohesive strategic marketing management approach to the movie business and match the Major studios’ superior capabilities, then Hollywood’s dominance will continue. This paper also proposes that in cyberspace, whilst the Internet does provide a channel that democratises film distribution, the flat landscape of the world wide web means that in order to stand out from the clutter of millions of cyber-voices seeking attention, independent film companies need to possess superior strategic marketing management capabilities and develop effective e-marketing strategies to find a niche, attract a loyal online audience and prosper. However, mirroring a recent CIA report forecasting a multi-polar world economy, this paper also argues that potentially serious longer-term rivals are emerging and will increasingly take a larger slice of an expanding global box office as India, China and other major developing economies and their respective cultural channels grow and achieve economic parity with or surpass the advanced western economies. Thus, in terms of global market share over time, Hollywood’s slice of the pie will comparatively diminish in an emerging multi-polar movie business.

Performance analysis of adaptive lattice filters for FM signals and alpha-stable processes

The performance of an adaptive filter may be studied through the behaviour of the optimal and adaptive coefficients in a given environment. This thesis investigates the performance of finite impulse response adaptive lattice filters for two classes of input signals: (a) frequency modulated signals with polynomial phases of order p in complex Gaussian white noise (as nonstationary signals), and (b) the impulsive autoregressive processes with alpha-stable distributions (as non-Gaussian signals). Initially, an overview is given for linear prediction and adaptive filtering. The convergence and tracking properties of the stochastic gradient algorithms are discussed for stationary and nonstationary input signals. It is explained that the stochastic gradient lattice algorithm has many advantages over the least-mean square algorithm. Some of these advantages are having a modular structure, easy-guaranteed stability, less sensitivity to the eigenvalue spread of the input autocorrelation matrix, and easy quantization of filter coefficients (normally called reflection coefficients). We then characterize the performance of the stochastic gradient lattice algorithm for the frequency modulated signals through the optimal and adaptive lattice reflection coefficients. This is a difficult task due to the nonlinear dependence of the adaptive reflection coefficients on the preceding stages and the input signal. To ease the derivations, we assume that reflection coefficients of each stage are independent of the inputs to that stage. Then the optimal lattice filter is derived for the frequency modulated signals. This is performed by computing the optimal values of residual errors, reflection coefficients, and recovery errors. Next, we show the tracking behaviour of adaptive reflection coefficients for frequency modulated signals. This is carried out by computing the tracking model of these coefficients for the stochastic gradient lattice algorithm in average. The second-order convergence of the adaptive coefficients is investigated by modeling the theoretical asymptotic variance of the gradient noise at each stage. The accuracy of the analytical results is verified by computer simulations. Using the previous analytical results, we show a new property, the polynomial order reducing property of adaptive lattice filters. This property may be used to reduce the order of the polynomial phase of input frequency modulated signals. Considering two examples, we show how this property may be used in processing frequency modulated signals. In the first example, a detection procedure in carried out on a frequency modulated signal with a second-order polynomial phase in complex Gaussian white noise. We showed that using this technique a better probability of detection is obtained for the reduced-order phase signals compared to that of the traditional energy detector. Also, it is empirically shown that the distribution of the gradient noise in the first adaptive reflection coefficients approximates the Gaussian law. In the second example, the instantaneous frequency of the same observed signal is estimated. We show that by using this technique a lower mean square error is achieved for the estimated frequencies at high signal-to-noise ratios in comparison to that of the adaptive line enhancer. The performance of adaptive lattice filters is then investigated for the second type of input signals, i.e., impulsive autoregressive processes with alpha-stable distributions . The concept of alpha-stable distributions is first introduced. We discuss that the stochastic gradient algorithm which performs desirable results for finite variance input signals (like frequency modulated signals in noise) does not perform a fast convergence for infinite variance stable processes (due to using the minimum mean-square error criterion). To deal with such problems, the concept of minimum dispersion criterion, fractional lower order moments, and recently-developed algorithms for stable processes are introduced. We then study the possibility of using the lattice structure for impulsive stable processes. Accordingly, two new algorithms including the least-mean P-norm lattice algorithm and its normalized version are proposed for lattice filters based on the fractional lower order moments. Simulation results show that using the proposed algorithms, faster convergence speeds are achieved for parameters estimation of autoregressive stable processes with low to moderate degrees of impulsiveness in comparison to many other algorithms. Also, we discuss the effect of impulsiveness of stable processes on generating some misalignment between the estimated parameters and the true values. Due to the infinite variance of stable processes, the performance of the proposed algorithms is only investigated using extensive computer simulations.