Speech enhancement with an adaptive Wiener filter

This paper proposes an adaptive Wiener filtering method for speech enhancement. This method depends on the adaptation of the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. It is implemented in the time domain rather than in the frequency domain to accommodate for the time-varying nature of the speech signals. The proposed method is compared to the traditional frequency-domain Wiener filtering, spectral subtraction and wavelet denoising methods using different speech quality metrics. The simulation results reveal the superiority of the proposed Wiener filtering method in the case of Additive White Gaussian Noise (AWGN) as well as colored noise.     

Rheological and extrusion characteristics of glass fiber-reinforced polycarbonate

An experimental investigation of the rheological properties of glass fiber-reinforced polycarbonate melts and the extrusion of such compounds through capillary and slit dies is presented. The viscosity–shear rate function seems independent of instrument for cone-plate and capillary investigations. The presence of fibers increases the level of the viscosity. Normal stresses at fixed shear stress are also increased by the presence of fibers. The extrudate swell is decreased by the presence of fibers and surface roughness is increased. Fiber orientation increases and surface roughness decreases with increasing extrusion rate.     

NN-based Prediction Interval for Nonlinear Processes Controller

Neural networks (NNs) are extensively used in modelling, optimization, and control of nonlinear plants. NN-based inverse type point prediction models are commonly used for nonlinear process control. However, prediction errors (root mean square error (RMSE), mean absolute percentage error (MAPE) etc.) significantly increase in the presence of disturbances and uncertainties. In contrast to point forecast, prediction interval (PI)-based forecast bears extra information such as the prediction accuracy. The PI provides tighter upper and lower bounds with considering uncertainties due to the model mismatch and time dependent or time independent noises for a given confidence level. The use of PIs in the NN controller (NNC) as additional inputs can improve the controller performance. In the present work, the PIs are utilized in control applications, in particular PIs are integrated in the NN internal model-based control framework. A PI-based model that developed using lower upper bound estimation method (LUBE) is used as an online estimator of PIs for the proposed PI-based controller (PIC). PIs along with other inputs for a traditional NN are used to train the PIC to predict the control signal. The proposed controller is tested for two case studies. These include, a chemical reactor, which is a continuous stirred tank reactor (case 1) and a numerical nonlinear plant model (case 2). Simulation results reveal that the tracking performance of the proposed controller is superior to the traditional NNC in terms of setpoint tracking and disturbance rejections. More precisely, 36% and 15% improvements can be achieved using the proposed PIC over the NNC in terms of IAE for case 1 and case 2, respectively for setpoint tracking with step changes.

     

Single phase multi color emitting Ca2LuTaO6: Dy3+/Eu3+ double perovskite oxide phosphors

The trivalent rare-earth (RE³⁺) doped phosphors show tremendous achievement in narrow band multicolor line emission for various applications. However, the 4f–4f absorption transition of these ions is forbidden in UV and blue light excitation. Usually, a sensitizer having spin allowed transition was used as a co-dopant to excite these ions via the energy transfer phenomenon. Another approach promisingly using to excite these ions by efficient energy transfer from the intrinsic emission of the Ca₂LuTaO₆ double perovskite phosphors host lattice. Phosphors of Ca₂LuTaO₆ with double perovskite structure were synthesized by using a high-temperature solid-state reaction method. The produced Ca₂LuTaO₆ double perovskite phosphors show an intrinsic broad band emission centered at 424 nm under the excitation of 313 nm UV light. The origin of this broad band blue emission was deeply investigated by using computation and experimental approaches. The trivalent activator Dy³⁺ and Eu³⁺ were doped is a single and co-dopant in the produced Ca₂LuTaO₆ phosphors to check their excitation in UV and near-UV spectral region. X-ray diffraction and scanning electron microscopy were used to investigate the structure and phase analysis. Various characterizations such as photoluminescence excitation, emission, and CIE chromaticity coordinates were measured which illustrate the potential of Dy³⁺ and Eu³⁺ activated Ca₂LuTaO₆ double perovskite phosphors for narrow band multicolor line emission for various applications.     

Mechanical and viscoelastic properties of polyethylene-based microfibrillated composites from 100% recycled resources

In this study, recycled polyethylene (rPE) based microfibrillated composites (MFCs) were developed while incorporating recycled poly(ethylene terephthalate) (rPET) and recycled polyamide 6 (rPA) as the reinforcing fibrillar phases at a given weight ratio of 80 wt% (rPE)/20 wt% (rPET or rPA). The blends were first melt processed using a twin-screw extruder. The extrudates were then cold stretched at a drawing ratio of 2.5 to form rPET and rPA fibrillar structures. Next, the pelletized drawn samples were injection molded at the barrel temperatures below the melting temperatures of rPET and rPA. The tensile, three-point bending, impact strength, dynamic thermomechanical, and rheological properties of the fabricated MFCs were analyzed. The effects of injection molding barrel temperature (i.e., 150°C and 190°C) and extrusion melt processing temperature (i.e., 250°C and 275°C) on the generated fibrillar structure and the resultant properties were explored. A strong correlation between the fibrillar morphology and the mechanical properties with the extrusion and injection molding temperatures was observed. Moreover, the ethylene/n-butyl acrylate/glycidyl methacrylate (EnBAGMA) terpolymer and maleic anhydride grafted PE (MAH-g-PE) were, respectively, melt processed with rPE/rPET and rPE/rPA6 blends as compatibilizers. The compatibilizers refined the fibrillar structure and remarkably influenced mechanical properties, specifically the impact strength.     

Adaptive Network-Based Fuzzy Inference Systems Coupled with Genetic Algorithms for Predicting Soil Permeability Coefficient

This paper extends hybrid-type optimization models of genetic algorithm adaptive network-based fuzzy inference system (GA-ANFIS) for predicting the soil permeability coefficient (SPC) of different types of soil. In these models, GA optimizes parameters of a subtractive clustering technique that controls the structure of the ANFIS model’s fuzzy rule base. Simultaneously, a hybrid leaning algorithm is employed in the ANFIS, as a trained fuzzy inference system (FIS), which optimally determines the parameter sets of the examined FISs in ANFIS. Using an updated large database of SPCs consisting of 338 fine-grained, 178 mixed and 94 granular soil samples, GA-ANFIS framework constructs different models of predicting the permeability coefficient of respectively fine-grained, mixed and granular soils. A fuzzy C-mean technique has been used to cluster the entire data samples of each type of soil and divide them uniformly into training and testing data sets. Different prediction models of SPC have been trained and tested for each of the three soil types, and the appropriate models have been selected. The selected models have been compared with ANN and modified-by-GA empirical prediction models. Results show that the constructed GA-ANFIS models outperform the other models in terms of the prediction accuracy and the generalization capability.     

An efficient and noise resistive selective image encryption scheme for gray images based on chaotic maps and DNA complementary rules

A novel image encryption algorithm in streaming mode is proposed which exhaustively employs an entire set of DNA complementary rules alongwith one dimensional chaotic maps. The proposed algorithm is highly efficient due to encrypting the subset of digital image which contains 92.125 % of information. DNA addition operation is carried out on this MSB part. The core idea of the proposed scheme is to scramble the whole image by means of piecewise linear chaotic map (PWLCM) followed by decomposition of image into most significant bits (MSB) and least significant bits (LSB). The logistic sequence is XORed with the decoded MSB and LSB parts separately and finally these two parts are combined to get the ciphered image. The parameters for PWLCM, logistic map and selection of different DNA rules for encoding and decoding of both parts of an image are derived from 128-bit MD5 hash of the plain image. Simulated experimental results in terms of quantitative and qualitative ways prove the encryption quality. Efficiency and robustness against different noises make the proposed cipher a good candidate for real time applications.     

Solving the Problem of Forbidden States in Discrete Event Systems: A Novel Systematic Method for Reducing the Number of Control Places

This paper deals with the problem of forbidden states in Discrete Event Systems modelled by non‐safe Petri Nets. To avoid these states, some Generalized Mutual Exclusion Constraints can be assigned to them. These constraints limit the weight sum of tokens in some places and can be enforced on the system using control places. When the number of these constraints is large, a large number of control places should be added to the system. In this paper, a method is presented to assign the small number of constraints to forbidden states using some states which cover the forbidden states. So, a small number of control places are added to the system leading to obtaining a maximally permissive controller.     

A Saturating Extension of an Output Feedback Controller for Internally Damped Euler‐Lagrange Systems

In this research, a novel extension of the passivity‐based output feedback trajectory tracking controller is developed for internally damped Euler‐Lagrange systems with input saturation. Compared with the previous output feedback controllers, this new design of a combined adaptive controller‐observer system will reduce the risk of actuator saturation effectively via generalized saturation functions. Semi‐global uniform ultimate boundedness stability of the tracking errors and state estimation errors is guaranteed by Lyapunov stability analysis. An application of the proposed saturated output feedback controller is the stabilization of a nonholonomic wheeled mobile robot with saturated actuators towards desired trajectories. Simulation results are provided to illustrate the efficiency of the proposed controller in dealing with the actuator saturation.     

Controller Synthesis with Highly Simplified Linear Constraints

This paper deals with the problem of forbidden states in safe Petri nets to obtain a maximally permissive controller. To prevent the system from entering the forbidden states, assigning some constraints to them is possible. The constraints can be enforced on the system using control places. When the number of forbidden states is large, a large number of constraints should be assigned to them. This results in a large number of control places being added to the model of the system, which causes a complicated model. Some methods have been proposed to reduce the number of constraints. Nevertheless, they do not always give the best results. In this paper, two ideas are offered to reduce the number of constraints, giving a more simplified controller.