Objective evaluation of speech dysfluencies using wavelet packet transform with sample entropy

Dysfluency and stuttering are a break or interruption of normal speech such as repetition, prolongation, interjection of syllables, sounds, words or phrases and involuntary silent pauses or blocks in communication. Stuttering assessment through manual classification of speech dysfluencies is subjective, inconsistent, time consuming and prone to error. This paper proposes an objective evaluation of speech dysfluencies based on the wavelet packet transform with sample entropy features. Dysfluent speech signals are decomposed into six levels by using wavelet packet transform. Sample entropy (SampEn) features are extracted at every level of decomposition and they are used as features to characterize the speech dysfluencies (stuttered events). Three different classifiers such as k-nearest neighbor (kNN), linear discriminant analysis (LDA) based classifier and support vector machine (SVM) are used to investigate the performance of the sample entropy features for the classification of speech dysfluencies. 10-fold cross validation method is used for testing the reliability of the classifier results. The effect of different wavelet families on the classification performance is also performed. Experimental results demonstrate that the proposed features and classification algorithms give very promising classification accuracy of 96.67% with the standard deviation of 0.37 and also that the proposed method can be used to help speech language pathologist in classifying speech dysfluencies.     

A neuro-genetic based short-term forecasting framework for network intrusion prediction system

Information systems are one of the most rapidly changing and vulnerable systems, where security is a major issue. The number of security-breaking attempts originating inside organizations is increasing steadily. Attacks made in this way, usually done by ＂authorized＂ users of the system, cannot be immediately traced. Because the idea of filtering the traffic at the entrance door, by using firewalls and the like, is not completely successful, the use of intrusion detection systems should be considered to increase the defense capacity of an information system. An intrusion detection system （IDS） is usually working in a dynamically changing environment, which forces continuous tuning of the intrusion detection model, in order to maintain sufficient performance. The manual tuning process required by current IDS depends on the system operators in working out the tuning solution and in integrating it into the detection model. Furthermore, an extensive effort is required to tackle the newly evolving attacks and a deep study is necessary to categorize it into the respective classes. To reduce this dependence, an automatically evolving anomaly IDS using neuro-genetic algorithm is presented. The proposed system automatically tunes the detection model on the fly according to the feedback provided by the system operator when false predictions are encountered. The system has been evaluated using the Knowledge Discovery in Databases Conference （KDD 2009） intrusion detection dataset. Genetic paradigm is employed to choose the predominant features, which reveal the occurrence of intrusions. The neuro-genetic IDS （NGIDS） involves calculation of weightage value for each of the categorical attributes so that data of uniform representation can be processed by the neuro-genetic algorithm. In this system unauthorized invasion of a user are identified and newer types of attacks are sensed and classified respectively by the neuro-genetic algorithm. The experimental results obtained in this work show that the system achieves improvement in terms of misclassification cost when compared with conventional IDS. The results of the experiments show that this system can be deployed based on a real network or database environment for effective prediction of both normal attacks and new attacks.     

Adaptive RTRL based neurocontroller for damping subsynchronous oscillations using TCSC

Modern interconnected electrical power systems are complex and require perfect planning, design and operation. Hence the recent trends towards restructuring and deregulation of electric power supply has put great emphasis on the system operation and control. Flexible AC transmission system (FACTS) devices such as thyristor controlled series capacitor (TCSC) are capable of controlling power flow, improving transient stability and mitigating subsynchronous resonance (SSR). In this paper an adaptive neurocontroller is designed for controlling the firing angle of TCSC to damp subsynchronous oscillations. This control scheme is suitable for non-linear system control, where the exact linearised mathematical model of the system is not required. The proposed controller design is based on real time recurrent learning (RTRL) algorithm in which the neural network (NN) is trained in real time. This control scheme requires two sets of neural networks. The first set is a recurrent neural network (RNN) which is a fully connected dynamic neural network with all the system outputs fed back to the input through a delay. This neural network acts as a neuroidentifier to provide a dynamic model of the system to evaluate and update the weights connected to the neurons. The second set of neural network is the neurocontroller which is used to generate the required control signals to the thyristors in TCSC. This is a single layer neural network. Performance of the system with proposed neurocontroller is compared with two linearised controllers, a conventional controller and with a discrete linear quadratic Gaussian (DLQG) compensator which is an optimal controller. The linear controllers are designed based on a linearised model of the IEEE first benchmark system for SSR studies in which a modular high bandwidth (six-samples per cycle) linear time-invariant discrete model of TCSC is interfaced with the rest of the system. In the proposed controller, since the response time is highly dependent on the number of states of the system, it is often desirable to approximate the system by its reduced model. By using standard Hankels norm approximation technique, the system order is reduced from 27 to 11th order by retaining the dominant dynamic characteristics of the system. To validate the proposed controller, computer simulation using MATLAB is performed and the simulation studies show that this controller can provide simultaneous damping of swing mode as well as torsional mode oscillations, which is difficult with a conventional controller. Moreover the fast response of the system can be used for real-time applications. The performance of the controller is tested for different operating conditions.     

Polyaniline and polypyrrole modified conductive nanocomposites of polyfuran and polythiophene with montmorillonite clay

Nanocomposites of polyfuran (PF) and polythiophene (PTP) with montmorillonite clay (MMT) were prepared and modified by loading of polyaniline (PANI) and polypyrrole (PPY) moieties via polymerization of aniline (ANI) and pyrrole (PY) in aqueous dispersions of PF-MMT and PTP-MMT nanocomposites. Formation of PANI and PPY and their subsequent incorporation in the PF-MMT and PTP-MMT composites was confirmed by FTIR absorption studies. X-ray diffraction (XRD) patterns of PANI and PPY modified PF-MMT and PTP-MMT composites showed that PF-MMT and PTP-MMT intercalates were still present in the modified composites. Scanning electron microscopic analysis revealed distinctive morphological patterns of the various composite particles. The dc conductivity values of PANI and PPY modified PF-MMT and PTP-MMT composites were in the order of 10⁻² S/cm in either system – a value much improved compared to the same for both of the unmodified PF-MMT (10⁻⁷ S/cm) and PTP-MMT (10⁻⁵ S/cm) nanocomposites respectively.     

A simulation‐based algorithm for optimal pricing policy under demand uncertainty

We propose a simulation‐based algorithm for computing the optimal pricing policy for a product under uncertain demand dynamics. We consider a parameterized stochastic differential equation (SDE) model for the uncertain demand dynamics of the product over the planning horizon. In particular, we consider a dynamic model that is an extension of the Bass model. The performance of our algorithm is compared to that of a myopic pricing policy and is shown to give better results. Two significant advantages with our algorithm are as follows: (a) it does not require information on the system model parameters if the SDE system state is known via either a simulation device or real data, and (b) as it works efficiently even for high‐dimensional parameters, it uses the efficient smoothed functional gradient estimator.     

Facile synthesis of reduced graphene oxide–gold nanohybrid for potential use in industrial waste-water treatment

Here, we report a facile approach, by the photochemical reduction technique, for in situ synthesis of Au-reduced graphene oxide (Au-RGO) nanohybrids, which demonstrate excellent adsorption capacities and recyclability for a broad range of dyes. High-resolution transmission electron microscopy (HRTEM), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) data confirm the successful synthesis of Au-RGO nanohybrids. The effect of several experimental parameters (temperature and pH) variation can effectively control the dye adsorption capability. Furthermore, kinetic adsorption data reveal that the adsorption process follows a pseudo second-order model. The negative value of Gibbs free energy (ΔG⁰) confirms spontaneity while the positive enthalpy (ΔH⁰) indicates the endothermic nature of the adsorption process. Picosecond resolved fluorescence technique unravels the excited state dynamical processes of dye molecules adsorbed on the Au-RGO surface. Time resolved fluorescence quenching of Rh123 after adsorption on Au-RGO nanohybrids indicates efficient energy transfer from Rh123 to Au nanoparticles. A prototype device has been fabricated using Au-RGO nanohybrids on a syringe filter (pore size: 0.220 μm) and the experimental data indicate efficient removal of dyes from waste water with high recyclability. The application of this nanohybrid may lead to the development of an efficient reusable adsorbent in portable water purification.     

Efficiency enhancement in DSSC using metal nanoparticles: A size dependent study

The photoelectrode of Eosin-Y sensitised DSSC was modified by incorporating Au-nanoparticles to enhance the power conversion efficiency via scattering from surface plasmon polaritons. Size dependence of Au nanoparticle on conversion efficiency was performed in DSSC for the first time by varying the particle size from 20 to 94 nm. It was found that, the conversion efficiency is highly dependent on the size of the Au nanoparticles. For larger particles (>50 nm), the efficiency was found to be increased due to constructive interference between the transmitted and scattered waves from the Au nanoparticle while for smaller particles, the efficiency decreases due to destructive interference. Also a reduction in the V_oc was observed in general, due to the negative shifting of the TiO₂ Fermi level on the adsorption of Au nanoparticle. This shift was negligible for larger particles. When 94 nm size particles were employed the conversion efficiency was doubled from 0.74% to 1.52%. This study points towards the application of the scattering effect of metal nanoparticle to enhance the conversion efficiency in DSSCs.     

Reflective light modulator based on epsilon-GaSe crystal

We report the results of investigating a low-voltage, polarization-insensitive, reflective-type modulator based on an epsilon-GaSe crystal and operated at the 1.960-eV line of a He-Ne laser. We demonstrate that the modulation in an Al-epsilon-GaSe-Cu device results mainly from the Franz-Keldysh effect. Relatively high speed and low operating voltage could make these modulators with Schottky-barrier contacts attractive devices in the red range of the spectrum.     

Tailoring magnetic and dielectric properties of rubber ferrite composites containing mixed ferrites

Rubber ferrite composites containing various mixed ferrites were prepared for different compositions and various loadings. The magnetic and dielectric properties of the fillers as well as the ferrite filled matrixes were evaluated separately. The results are correlated. Simple equations are proposed to predetermine the magnetic and dielectric properties. The validity of these equations is verified and they are found to be in good agreement. These equations are useful in tailoring the magnetic and dielectric properties of these composites with predetermined properties.